Module 11: Helping Others

Module Overview

In Module 11 we move away from discussions of aggressive behavior, prejudice and discrimination covered in preceding modules, and talk about a more positive topic – prosocial behavior. We start by contrasting prosocial, altruistic, and egotistical behavior and then move to an evolutionary explanation for prosocial behavior. From this we cover dispositional or personal reasons why someone may help (or not) to include personal responsibility, time pressures, personality, self-conscious emotions, religiosity, feeling good, gender, empathy, and egotism. Next up are situational reasons to include the bystander effect, the decision-making process related to helping, and social norms. We end with ways to increase helping behavior.

Module Outline

11.1. Defining Prosocial Behavior

11.2. why we help – dispositional factors, 11.3. why we help – situational factors, 11.4. increasing helping behavior.

Module Learning Outcomes

  • Differentiate prosocial, altruistic, and egotistical behavior.
  • Clarify if there is an evolutionary precedent for helping behavior.
  • Outline dispositional reasons for why people help or do not.
  • Outline situational reasons for why people help or do not.
  • Strategize ways to increase helping behavior.

Section Learning Objectives

  • Define prosocial behavior.
  • Clarify the difference with altruistic behavior.
  • Contrast prosocial and egotistical behavior.
  • Explain how evolutionary psychology might approach the development of helping behavior.
  • Differentiate kin selection and reciprocal altruism.

11.1.1. Defining Terms

As a child, most of us learn to help an old lady across the street. First responders feverishly work to free trapped miners. Soldiers risk their own safety to pull a wounded comrade off the battlefield. Firefighters and police officers rush inside a burning building to help rescue trapped residents all while cognizant of the building’s likelihood to collapse on them. People pull over to help a stranded motorist or one involved in a car accident. And normal everyday people make tough decisions to take a little less of a valued commodity or give a little more so a public good can be provisioned. These are all examples of what is called prosocial behavior. Simply put, prosocial behavior is any act we willingly take that is meant to help others, whether the ‘others’ are a group of people or just one person. The key is that these acts are voluntary and not forced upon the helper. The motive for the behavior is not important. This is different from altruistic behavior, in which we choose to help another person voluntarily and with no expectation of reward or acknowledgement. If we make a life saving organ or blood donation and ask never to be identified, the act is altruistic. Whereas if we do not mind if the person knows, the act would be considered prosocial. The intention of the helping behavior is what is key.

Likely, the opposite of prosocial behavior is what is called egotistical behavior , or behavior focused on the self. According to dictionary.com, egotistic refers to behaviors that are vain, boastful, and selfish. Individuals like to talk about themselves and are indifferent to the well-being of others. The Merriam-Webster dictionary online adds that egotistical individuals are overly concerned with their own needs, desires, and interests.

11.1.2. An Evolutionary Precedent for Prosocial Behavior?

So, is the desire to help others an inborn tendency, or is it learned through socialization by caregivers and our culture? We will first discuss whether helping behavior could be the product of nature, not nurture. Evolutionary psychology is the subfield of psychology which uses changes in genetic factors over time due to the principle of natural selection to explain helping behavior. Charles Darwin noted that behaving in an altruistic way can prevent an organism from passing on its genes and so surviving. Being selfish pays while altruism does not, so then why has altruistic/prosocial behavior evolved? In the Descent of Man (1874, 2nd edition), Darwin writes:

“It has often been assumed that animals were in the first place rendered social, and that they feel as a consequence uncomfortable when separated from each other, and comfortable whilst together; but it is a more probable view that these sensations were first developed, in order that those animals which would profit by living in society, should be induced to live together, in the same manner as the sense of hunger and the pleasure of eating were, no doubt, first acquired in order to induce animals to eat. The feeling of pleasure from society is probably an extension of the parental or filial affections, since the social instinct seems to be developed by the young remaining for a long time with their parents; and this extension may be attributed in part to habit, but chiefly to natural selection. With those animals which were benefited by living in close association, the individuals which took the greatest pleasure in society would best escape various dangers, whilst those that cared least for their comrades, and lived solitary, would perish in greater numbers.”

Source: https://psychclassics.yorku.ca/Darwin/Descent/descent4.htm

According to ethologists and behavioral ecologists, altruism takes on two forms. First, kin selection , also known as inclusive fitness theory , states that any behavior aiding a genetic relative will be favored by natural selection (Wilson, 2005). Why is that? Though our own ability to pass our genes to offspring may be compromised, our relative shares those same genes and so indirectly we are passing on our genes. An example is putting the welfare of our children ahead of our own. Most would have no issue with this and I always find it interesting how on an airplane we are reminded that in the event of an emergency, we should put our own oxygen mask on first before helping others. This especially relates to our wanting to help our kids but if we are able to get their mask on before our own, and then we pass out, we really are not helping them at all. It’s best then to make sure we are conscious and then help them out so that we can be with them in the event of a crash. Still, it seems selfish to do this in light of kin selection.

Next is reciprocal altruism (Trivers, 1971) and is the basis for long-term cooperative interactions. According to it, an organism acts in a way that benefits others at expense to itself. It does so because it expects that in the future, the recipient of the altruistic act, who does not have to be related to the altruist, will reciprocate assistance. An example of this would be a firefighter. They run into burning buildings to save people at a risk to their own life. They do this with the belief that someone will save them or their family if they are in the same situation. Another possible example would be anytime you help someone in need. The belief is that if you are in need someone will help you. As Ashton et al. (1998) writes, “If the benefits to the recipient of this assistance outweigh the costs to the benefactor, then interactions of this kind, when reciprocated, result in a long-run net gain in chances for survival and reproduction for both individuals.” The authors looked for correlates of kin altruism (selection) and reciprocal altruism and found that for the former empathy and attachment were important, while for the latter forgiveness and non-retaliation mattered most. Kin selection was further related to high agreeableness and low emotional stability while reciprocal altruism (not kin related) was related to high agreeableness and high emotional stability (Ashton et al., 1998).

  • Clarify how a sense of personal responsibility can lead to helping behavior.
  • Clarify why being in a rush may reduce helping behavior.
  • Provide evidence for or against an altruistic personality.
  • Describe how the self-conscious emotions of embarrassment and guilt may affect helping behavior.
  • Clarify whether religiosity is an accurate predictor of helping behavior.
  • Describe the effect of mood on helping.
  • Clarify whether males or females are more likely to help.
  • Explain the role of empathy in helping.
  • Clarify whether egotism can lead to helping behavior.

11.2.1. Personal Responsibility

If we sense greater personal responsibility, we will be more likely to help, such as there being no one else around but us. If we see a motorist stranded on the side of the road on an isolated country road, and we know no other vehicle is behind us or approaching, responsibility solely falls on us, and we will be more likely to help. Keep this in mind for when we talk about diffusion of responsibility in a bit.

11.2.2. Time Pressure – The Costs of Motivated Behavior

Stopping to help someone in need takes time and represents a cost of motivated behavior. But what if we are in a rush to get to work or an appointment…or to class. Will we stop? Research by Batson et al. (1978) says that we will not. In a study utilizing 40 students at a large midwestern university, participants showed up at one location but were told they had to proceed to a different building for the study. Half were told they were late and half were told they were on time. Also, half were told their participation was vital while the other half were told it was not essential. As you might expect those in the unimportant condition stopped to help a confederate slumped in a doorway with his head down and coughing and groaning (Darley and Batson, 1973; Good Samaritan paradigm). Most who were late for their appointment did not stop to help.

11.2.3. An Altruistic Personality?

It would seem logical to assume that personality affects the decision to engage in helping behavior and we might hypothesize that moral behavior might be related to altruistic behavior. We would be wrong. In a classic study, Hartshorne and May (1929) found that the correlation of types of helping behavior and moral behavior was only 0.23 in a sample of 10,000 elementary and high school children. Subsequent research has also questioned whether such a construct is viable (Bierhoff & Rohmann, 2004) and Batson (1987) argued that prosocial motivation is actually egotistical when the goal is to increase one’s own welfare but altruistic when the goal is to increase the welfare of another person. Kerber (1984) found that those who could be classified as altruistic did examine the costs-benefits of engaging in helping behavior, though they viewed these situations as more rewarding and less costly than those low in altruism.

More recently, Dovidio et al. (2006) concluded that there truly is a ‘prosocial personality’ and that differences in the trait vary with the action a specific situation calls for such as rescuing people who are in danger, to serving as a volunteer, and to helping an individual in distress. Carlo et al. (2009) point out that gaps in the study of altruism exist and need to be studied to include changes in altruistic traits and behaviors over time, how altruism develops in childhood and adolescence, the biological basis of altruism, and cross-cultural and broader social contextual factors beyond proximal socializing agents of altruism. They conclude, “A focus on the positive aspects of human functioning will facilitate the development of more balanced, comprehensive solutions designed to enhance the personal and environmental factors that promote and foster a more caring, beneficent, and thriving society” (pg. 289).

11.2.4. Self-Conscious Emotions

We will be more likely to help if we do not expect to experience any type of embarrassment when helping. Let’s say you stop to help a fellow motorist with a flat tire. If you are highly competent at changing tires, then you will not worry about being embarrassed. But if you know nothing about tires, but are highly interpersonally attracted to the stranger on the side of the road holding a tire iron with a dumbstruck look on their face, you likely will look foolish if you try to change the tire and demonstrate your ignorance of how to do it (your solution is usually to call your auto club or AAA when faced with the same stressor).

Guilt can be used to induce helping behavior too. In one study, 90 adults received either a positive mood induction or no stimulus followed by a guilt induction, a distraction control, or no stimulus at all. Helping increase in relation to being in a positive mood but also being made to feel guilty. When the guilt induction followed the positive mood induction, there was no increase in helping behavior. In a second experiment, guilt was shown to increase helping only when an obligation to help was stressed (Cunningham, Steinberg, & Grev, 1980).

11.2.5. Religiosity

Does religious orientation affect prosocial behavior? According to Hansen, Vandenberg, & Patterson (1995) it does and of the three orientations – intrinsic, extrinsic, and quest – intrinsically oriented individuals prefer nonspontaneous helping opportunities while quest prefer spontaneous helping behaviors. Another study found that higher reports of subjective spirituality were linked to increased prosocial behavior (Bonner, Koven, & Patrick, 2003), though yet another study found evidence of altruistic hypocrisy such that intrinsic and orthodox religion were shown to be related to positive views toward helping others but were inversely related to actual altruistic behavior (Ji, Pendergraft, & Perry, 2006).

Before moving on, it is important to share an interesting article published by NPR in 2016. The article reported the results of a paper by Decety et al. (2015) which showed that in a sample of 1,151 children aged 5 to 12 and from cities in six different countries (i.e. Chicago, Toronto, Cape Town, Istanbul, Izmir, Amman, and Guangzhou) children from non-religious homes were more altruistic than children from Christian and Muslim households. In terms of religions affiliation, 23.9% of the sample were Christian, 43% were Muslim, and 27.6% were not religious. Here’s the issue. A re-analysis of the data by Azim Shariff of the University of California, Irvine, found that the original authors failed to consider variation in altruistic behavior that was actually accounted for by country and not religious affiliation. He updated the conclusions and found that country (likely culture) made a difference in altruistic behavior and not religion. Shariff concluded that religion does make people more generous but it is not the only factor, or even the best one. Even non-religious people can be motivated to engage in prosocial behavior.

To read the article for yourself, please visit: https://www.npr.org/sections/13.7/2016/08/15/490031512/does-religion-matter-in-determining-altruism

11.2.6. Feeling Good

It is not surprising to surmise that people in a good mood are more willing to help than those in a bad mood. Maybe we did well on a test, found $20 on the street, or were listening to uplifting or prosocial music (Greitmeyer, 2009; North, Tarrant, & Hargreaves, 2004). Though more of a situational factor, it should be noted that pleasant ambient odors such as the smell of baking cookies or roasting coffee lead to greater levels of positive affect and subsequent helping behavior (Baron, 1997).

We might also help because we have a need for approval such as we realize by helping save the old lady from the burning building, we could get our name in the paper. This of course could make us feel good about ourselves. Deutsch and Lamberti (1986) found that subjects high in a need for approval were more likely to help a confederate who dropped books if they had been socially rewarded and not punished while those low in the need for approval were unaffected by social reinforcement.

Might a person in a bad mood engage in helping behavior?  According to the negative-state relief model a person might alleviate their own bad mood and feel better. This relieves their discomfort and improves their mood (Cialdini, Darby, & Vincent, 1973).

11.2.7. Gender

Would you like to make a hypothesis about which gender is more likely to help? If you guessed males, you are correct. If you guessed females, you are correct. It all depends on what the prosocial behavior is. When it comes to being heroic or chivalrous, men are more likely to help, while nurturant expressions of aid are generally engaged in by women (Eagly & Crowley, 1986). In a 2009 study, Eagly found further evidence for gender differences in relation to classes of prosocial behaviors. Women specialize in prosocial behaviors that are communal and relational while men engage in behaviors that are collectively oriented and agentic. The author proposes that these differences are linked to the division of labor and hormones, individual traits, and social expectations mediate how these gender roles influence behavior.

11.2.8. Empathy

Before we can understand empathy, we need to distinguish it from sympathy. Sympathy is when we feel compassion, pity, or sorry for another due to the hardships they have experienced. Empathy is when we put ourselves in another person’s shoes and vicariously experience their perspective. In doing so, we can feel sympathy and compassion for them.

Batson proposed the empathy-altruism hypothesis (Batson et al., 1991) which states that when we feel empathy for a person, we will help them for purely altruistic reasons with no concern about personal gain. If we do not feel empathy for them, then we need to decide whether the benefits of helping outweigh the costs. In one study, 84 female participants were exposed to a person in distress and asked to either observe the victim’s reactions (the low empathy condition) or imagine the victim’s feelings (the high empathy condition). They also assessed how easy it was for the participant to escape without helping (2 levels – easy or hard). Results showed, and in keeping with the empathy-altruism hypothesis, that participants low in empathy helped less when escape was easy which led the authors to speculate that they were only trying to reduce their own distress in an egotistical way. Those high in empathy helped no matter how easy escape was. Analysis of the participants self-reported emotional response showed that feeling empathy, not distress, evoked altruistic behavior (Toi & Batson, 1982). The link between personal distress and an egotistic motivation has been found in subsequent research as well (Batson, Early, & Salvarani, 1997).

11.2.9. An Egotistical Reason to Help?

Another important strategy is called social exchange theory and arose out of the work of George Homans, John Thibaut, Harold Kelly, and Peter Blau from the late 1950s to the mid-1960s, though it has undergone revisions since (Cook et al., 2013) to include the addition of emotion (Lawler, 2001; Lawler & Thye, 1999). It is the idea that we utilize a minimax strategy whereby we seek to maximize our rewards all while minimizing our cost. Helping can be costly and so we help only when the gain to us is greater. In social exchange theory, there are no truly altruistic acts. Consider your decision to donate your time to a charity such as at Thanksgiving. Maybe you are considering volunteering at a homeless shelter and giving out food to those in need. You of course will consider the costs of such motivated helping behavior which includes less time with family, less time grazing at the dinner table, being unable to play or watch football, and possibly not having the time to do some shopping and get Black Friday deals. Then there are the benefits of helping which include feeling good about oneself, making a difference in someone else’s life, giving something back to your community, and possibly logging community service hours for your university or fraternity/sorority. If the benefits outweigh the costs, you volunteer. If not, you don’t.

Or we might help with an expectation of a specific form of repayment, called perceived self-interest . We offer our boss a ride home because we believe he will give us a higher raise when our annual review comes up. Maybe we engage in helping behavior to increase our self-worth. In a way, we have to wonder if it even matters. The recipient of the help is grateful and without it, may have been much worse off. If I am stranded on the side of the road with a flat tire and a stranger stops to help me change it, I really don’t care if they are there because they genuinely want to help or because they want to feel better about themselves.

  • Clarify whether the presence of others either facilitates or hinders helping behavior.
  • Outline the five-step process for how we decide whether to help or not.
  • Describe the effect of social norms on helping behavior.

11.3.1. Bystander Effect

As we saw in Section 11.2.1, if we are the only one on the scene (or at least one of a very small few) we will feel personal responsibility and help. But what if we are among a large group of people who could help. Will you step up then? You still might, but the bystander effect (Latane & Darley, 1970) says likely not. Essentially, the chances that we will aid someone needing help decreases as the number of bystanders increases. The phenomenon draws its name from the murder of Ms. Kitty Genovese in March 1964. Thirty-eight residents of New York City failed to aid the 28-year-old woman who was attacked and stabbed twice by Winston Moseley as she walked to her building from her car. Not surprisingly, she called for help which did successfully scare Winston away, but when no one came out to help her, despite turning on lights in their apartments and looking outside, he returned to finish what he started. Ms. Genovese later died from her wounds. Very sad but ask yourself, what would you do? Of course, we would say we would help….or we hope that we would but history and research say otherwise.

11.3.2. A Step-by-Step Guide to Helping???

Latane and Darley (1970) proposed that there are a series of five steps we follow when deciding whether to render assistance or not. These include noticing an event, interpreting an event as an emergency, assuming responsibility, knowing how to help, and deciding to help.

First, we have to notice that an emergency situation is occurring. This seems simple enough but is an important first step. Consider Milgram’s (1970) urban overload hypothesis which says that high levels of urban stimulation can overload people and produce negative effects on their perception of the city and other residents such that they tune them out. Hence, we may not notice emergency situations when they are occurring.

Second, we need to interpret the event as an emergency. According to Shotland and Huston (1979) an emergency is characterized by something happening suddenly such as an accident, there being a clear threat of harm to a victim, the harm or threat of harm will increase if no one intervenes, the victim cannot defend or help him/herself, and there is not an easy solution to the problem for the victim. Ambiguity can make interpretation difficult. Let’s say you are driving down the road and see someone pulled on the side. You can see them in the front seat but cannot tell what they are doing. If the situation does not clearly suggest an emergency, you will likely keep driving. Maybe the person was acting responsibly and pulled over to send a text or take a call and is not in need of any assistance at all. Latane and Darley (1968) conducted a study to examine the effects of an ambiguous event on the decision to intervene in an emergency. They predicted, and found, that the sight of nonresponsive others would lead a participant to perceive the event as not serious and bring about no action as compared to when there was a solitary participant in the room.

Third, when others are around, we experience a diffusion of responsibility (Darley & Latane, 1968), meaning that we are less likely to assume responsibility. Consider this. If 10 people witness an accident, each person has just 10% responsibility to act. If there are 5 people present, our responsibility is 20%. If 2, 50% and if we are the only person present, 100%. What if 100 people witnessed the accident? We have a 1% responsibility. So in keeping with the bystander effect as the number of people present increase, we will be less likely to act possibly because we assume less responsibility. To act, we have to feel personally responsible.

The final steps in the Latane and Darley (1970) model involve weighing the costs and benefits to engaging in helping behavior.  We might decide that helping is risky as we could look foolish in front of other witnesses called audience inhibition (Latane and Nida, 1981) or we might feel pressured by peers to engage in altruistic behavior such as donating blood or donating money to charity called reluctant altruism (Reyniers & Bhalla, 2013; Ferguson, Atsma, de Kort, & Veldhuizen, 2012). Once we have decided to help, we need to figure out what type of assistance will be most useful.

11.3.3. Social Norms and Culture

Consider the idea of the reciprocity norm (Gouldner, 1960) which states that we are more likely to survive if we enter into an understanding with our neighbor to help in times of need. If we help a friend move into their new apartment, we expect help from this individual when we move our next time. The norm is strongest when we are interacting with another person of equal status.

The norm of social responsibility , in contrast, states that we should help another person without any concern about future exchange. For instance, a parent cares for a child and a teacher instructs students. We might wonder if there are cultural differences in regards to this norm, particularly as it relates to collectivist and individualist cultures. Consider that collectivistic cultures have an interdependent view of the self while individualistic cultures have an independent view, and so we expect the former to engage in helping behavior more than the latter. Its not that simple though. Our discussion of in and out groups in Module 4 and again in Module 9 show that we will be more likely to help an ingroup member than an outgroup member. How strongly we draw a distinction between these groups can affect helping behavior. Collective cultures may make a firmer distinction between in and out groups and so help ingroup members more compared to individualistic cultures.

  • Describe how modeling could be used to increase helping behavior.
  • Outline reasons to volunteer.

11.4.1. Modeling Helping Behavior

One way to increase prosocial behavior comes from observational learning and the idea of copying a prosocial model. According to research by Schuhmacher, Koster, and Kartner (2018) when infants observed a prosocial model, they engaged in more helping behavior than if they had no model. Schuhmacher states, “These findings tell us that children’s prosocial development may be affected not only by direct and active structuring of helping situations by others, as when parents offer suggestions to babies to help someone, but also through learning by observing people who help others” (See Science Daily for more information on this article – https://www.sciencedaily.com/releases/2018/04/180417130053.htm .

11.4.2. Reasons to Volunteer

Clary and Snyder (1999) proposed five motivations for volunteerism. First, they suggest that people volunteer due to values and a desire to express or act on values such as humanitarianism. Second, understanding is critical and people volunteer so that they can exercise underused skills or learn about the world. Third, enhancement leads us to engage in volunteer activities so that we can grow and develop psychologically. Fourth, our career may lead us to volunteer so we gain career-related experience. Fifth is social or volunteering so that we can strengthen our social relationships. Finally, we volunteer to reduce feelings of guilt or to escape personal problems as a protective function. The authors used these functions to create the Volunteer Functions Inventory (VFI).

For additional reasons to volunteer, please read the Psychology Today article. Additional reasons include living longer, benefiting society, and giving a sense of purpose or meaning in life (Klein, 2016).

https://www.psychologytoday.com/us/blog/the-third-age/201403/5-reasons-why-you-should-volunteer

Module Recap

Module 11 covered the important, and more positive topic, of helping behavior. Of course, though prosocial behavior is generally a good thing, understanding reasons why someone may willingly choose not to help can be hard to process. We focused on a series of dispositional and situational factors and then proposed ways to increase helping. With this module now finished, we end the class on an equally important, and definitely more positive, topic of attraction.

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Chapter 13. Psychology in Our Social Lives

13.5 Helping and Prosocial Behavior

Dennis L. Poepsel and David A. Schroeder

People often act to benefit other people, and these acts are examples of prosocial behavior. Such behaviors may come in many guises: helping an individual in need; sharing personal resources; volunteering time, effort, and expertise; cooperating with others to achieve some common goals. The focus of this module is on helping—prosocial acts in dyadic situations in which one person is in need and another provides the necessary assistance to eliminate the other’s need. Although people are often in need, help is not always given. Why not? The decision of whether or not to help is not as simple and straightforward as it might seem, and many factors need to be considered by those who might help. In this module, we will try to understand how the decision to help is made by answering the question: Who helps when and why?

Learning Objectives

  • Learn which situational and social factors affect when a bystander will help another in need.
  • Understand which personality and individual difference factors make some people more likely to help than others.
  • Discover whether we help others out of a sense of altruistic concern for the victim, for more self-centered and egoistic motives, or both.

Introduction

A younger man and woman helping an elderly gentleman down the street.

Go to YouTube and search for episodes of “Primetime: What Would You Do?” You will find video segments in which apparently innocent individuals are victimized, while onlookers typically fail to intervene. The events are all staged, but they are very real to the bystanders on the scene. The entertainment offered is the nature of the bystanders’ responses, and viewers are outraged when bystanders fail to intervene. They are convinced that they would have helped. But would they? Viewers are overly optimistic in their beliefs that they would play the hero. Helping may occur frequently, but help is not always given to those in need. So  when  do people help, and when do they not? All people are not equally helpful— who  helps?  Why  would a person help another in the first place? Many factors go into a person’s decision to help—a fact that the viewers do not fully appreciate. This module will answer the question: Who helps when and why?

When Do People Help?

Social psychologists began trying to answer this question following the unfortunate murder of Kitty Genovese in 1964 (Dovidio, Piliavin, Schroeder, & Penner, 2006; Penner, Dovidio, Piliavin, & Schroeder, 2005). A knife-wielding assailant attacked Kitty repeatedly as she was returning to her apartment early one morning. At least 38 people may have been aware of the attack, but no one came to save her. More recently, in 2010, Hugo Alfredo Tale-Yax was stabbed when he apparently tried to intervene in an argument between a man and woman. As he lay dying in the street, only one man checked his status, but many others simply glanced at the scene and continued on their way. (One passerby did stop to take a cellphone photo, however.) Unfortunately, failures to come to the aid of someone in need are not unique, as the segments on “What Would You Do?” show. Help is not always forthcoming for those who may need it the most. Trying to understand why people do not always help became the focus of  bystander intervention  research (e.g., Latané & Darley, 1970).

To answer the question regarding when people help, researchers have focused on

  • how bystanders come to define emergencies,
  • when they decide to take responsibility for  helping , and
  • how the costs and benefits of intervening affect their decisions of whether to help.

Defining the situation: The role of pluralistic ignorance

The decision to help is not a simple yes/no proposition. In fact, a series of questions must be addressed before help is given—even in emergencies in which time may be of the essence. Sometimes help comes quickly; an onlooker recently jumped from a Philadelphia subway platform to help a stranger who had fallen on the track. Help was clearly needed and was quickly given. But some situations are ambiguous, and potential helpers may have to decide whether a situation is one in which help, in fact,  needs  to be given.

To define ambiguous situations (including many emergencies), potential helpers may look to the action of others to decide what should be done. But those others are looking around too, also trying to figure out what to do. Everyone is looking, but no one is acting! Relying on others to define the situation and to then erroneously conclude that no intervention is necessary when help is actually needed is called  pluralistic ignorance  (Latané & Darley, 1970). When people use the  inactions  of others to define their own course of action, the resulting pluralistic ignorance leads to less help being given.

Do I have to be the one to help?: Diffusion of responsibility

A huge crowd of people stand shoulder to shoulder during the World Cup in 2010.

Simply being with others may facilitate or inhibit whether we get involved in other ways as well. In situations in which help is needed, the presence or absence of others may affect whether a bystander will assume personal responsibility to give the assistance. If the bystander is alone, personal responsibility to help falls solely on the shoulders of that person. But what if others are present? Although it might seem that having more potential helpers around would increase the chances of the victim getting help, the opposite is often the case. Knowing that someone else  could  help seems to relieve bystanders of personal responsibility, so bystanders do not intervene. This phenomenon is known as  diffusion of responsibility  (Darley & Latané, 1968).

On the other hand, watch the video of the race officials following the 2013 Boston Marathon after two bombs exploded as runners crossed the finish line. Despite the presence of many spectators, the yellow-jacketed race officials immediately rushed to give aid and comfort to the victims of the blast. Each one no doubt felt a personal responsibility to help by virtue of their official capacity in the event; fulfilling the obligations of their roles overrode the influence of the diffusion of responsibility effect.

There is an extensive body of research showing the negative impact of pluralistic ignorance and diffusion of responsibility on helping (Fisher et al., 2011), in both emergencies and everyday need situations. These studies show the tremendous importance potential helpers place on the social situation in which unfortunate events occur, especially when it is not clear what should be done and who should do it. Other people provide important social information about how we should act and what our personal obligations might be. But does knowing a person needs help and accepting responsibility to provide that help mean the person will get assistance? Not necessarily.

The costs and rewards of helping

The nature of the help needed plays a crucial role in determining what happens next. Specifically, potential helpers engage in a  cost–benefit analysis  before getting involved (Dovidio et al., 2006). If the needed help is of relatively low cost in terms of time, money, resources, or risk, then help is more likely to be given. Lending a classmate a pencil is easy; confronting the knife-wielding assailant who attacked Kitty Genovese is an entirely different matter. As the unfortunate case of Hugo Alfredo Tale-Yax demonstrates, intervening may cost the life of the helper.

The potential rewards of helping someone will also enter into the equation, perhaps offsetting the cost of helping. Thanks from the recipient of help may be a sufficient reward. If helpful acts are recognized by others, helpers may receive social rewards of praise or monetary rewards. Even avoiding feelings of guilt if one does not help may be considered a benefit. Potential helpers consider how much helping will cost and compare those costs to the rewards that might be realized; it is the economics of helping. If costs outweigh the rewards, helping is less likely. If rewards are greater than cost, helping is more likely.

Do you know someone who always seems to be ready, willing, and able to help? Do you know someone who never helps out? It seems there are personality and individual differences in the helpfulness of others. To answer the question of who chooses to help, researchers have examined 1) the role that sex and gender play in helping, 2) what personality traits are associated with helping, and 3) the characteristics of the “prosocial personality.”

Who are more helpful—men or women?

A group of men and women stand together in a muddy field with shovels and wheelbarrows as they participate in an outdoor volunteer project.

In terms of individual differences that might matter, one obvious question is whether men or women are more likely to help. In one of the “What Would You Do?” segments, a man takes a woman’s purse from the back of her chair and then leaves the restaurant. Initially, no one responds, but as soon as the woman asks about her missing purse, a group of men immediately rush out the door to catch the thief. So, are men more helpful than women? The quick answer is “not necessarily.” It all depends on the type of help needed. To be very clear, the general level of helpfulness may be pretty much equivalent between the sexes, but men and women help in different ways (Becker & Eagly, 2004; Eagly & Crowley, 1986). What accounts for these differences?

Two factors help to explain sex and gender differences in helping. The first is related to the cost–benefit analysis process discussed previously. Physical differences between men and women may come into play (e.g., Wood & Eagly, 2002); the fact that men tend to have greater upper body strength than women makes the cost of intervening in some situations less for a man. Confronting a thief is a risky proposition, and some strength may be needed in case the perpetrator decides to fight. A bigger, stronger bystander is less likely to be injured and more likely to be successful.

The second explanation is simple socialization. Men and women have traditionally been raised to play different social roles that prepare them to respond differently to the needs of others, and people tend to help in ways that are most consistent with their gender roles. Female gender roles encourage women to be compassionate, caring, and nurturing; male gender roles encourage men to take physical risks, to be heroic and chivalrous, and to be protective of those less powerful. As a consequence of social training and the gender roles that people have assumed, men may be more likely to jump onto subway tracks to save a fallen passenger, but women are more likely to give comfort to a friend with personal problems (Diekman & Eagly, 2000; Eagly & Crowley, 1986). There may be some specialization in the types of help given by the two sexes, but it is nice to know that there is someone out there—man or woman—who is able to give you the help that you need, regardless of what kind of help it might be.

A trait for being helpful: Agreeableness

Graziano and his colleagues (e.g., Graziano & Tobin, 2009; Graziano, Habishi, Sheese, & Tobin, 2007) have explored how  agreeableness —one of the Big Five personality dimensions (e.g., Costa & McCrae, 1988)—plays an important role in  prosocial behavior . Agreeableness is a core trait that includes such dispositional characteristics as being sympathetic, generous, forgiving, and helpful, and behavioral tendencies toward harmonious social relations and likeability. At the conceptual level, a positive relationship between agreeableness and helping may be expected, and research by Graziano et al. (2007) has found that those higher on the agreeableness dimension are, in fact, more likely than those low on agreeableness to help siblings, friends, strangers, or members of some other group. Agreeable people seem to expect that others will be similarly cooperative and generous in interpersonal relations, and they, therefore, act in helpful ways that are likely to elicit positive social interactions.

Searching for the prosocial personality

Rather than focusing on a single trait, Penner and his colleagues (Penner, Fritzsche, Craiger, & Freifeld, 1995; Penner & Orom, 2010) have taken a somewhat broader perspective and identified what they call the  prosocial personality orientation . Their research indicates that two major characteristics are related to the prosocial personality and prosocial behavior. The first characteristic is called  other-oriented empathy : People high on this dimension have a strong sense of social responsibility, empathize with and feel emotionally tied to those in need, understand the problems the victim is experiencing, and have a heightened sense of moral obligation to be helpful. This factor has been shown to be highly correlated with the trait of agreeableness discussed previously. The second characteristic,  helpfulness , is more behaviorally oriented. Those high on the helpfulness factor have been helpful in the past, and because they believe they can be effective with the help they give, they are more likely to be helpful in the future.

Finally, the question of  why  a person would help needs to be asked. What motivation is there for that behavior? Psychologists have suggested that 1) evolutionary forces may serve to predispose humans to help others, 2) egoistic concerns may determine if and when help will be given, and 3) selfless, altruistic motives may also promote helping in some cases.

Evolutionary roots for prosocial behavior

Cave paintings from Western Australia appear to show an ancient family dressed in traditional clothes.

Our evolutionary past may provide keys about why we help (Buss, 2004). Our very survival was no doubt promoted by the prosocial relations with clan and family members, and, as a hereditary consequence, we may now be especially likely to help those closest to us—blood-related relatives with whom we share a genetic heritage. According to evolutionary psychology, we are helpful in ways that increase the chances that our DNA will be passed along to future generations (Burnstein, Crandall, & Kitayama, 1994)—the goal of the “selfish gene” (Dawkins, 1976). Our personal DNA may not always move on, but we can still be successful in getting some portion of our DNA transmitted if our daughters, sons, nephews, nieces, and cousins survive to produce offspring. The favoritism shown for helping our blood relatives is called  kin selection (Hamilton, 1964).

But, we do not restrict our relationships just to our own family members. We live in groups that include individuals who are unrelated to us, and we often help them too. Why?  Reciprocal altruism  (Trivers, 1971) provides the answer. Because of reciprocal altruism, we are all better off in the long run if we help one another. If helping someone now increases the chances that you will be helped later, then your overall chances of survival are increased. There is the chance that someone will take advantage of your help and not return your favors. But people seem predisposed to identify those who fail to reciprocate, and punishments including social exclusion may result (Buss, 2004). Cheaters will not enjoy the benefit of help from others, reducing the likelihood of the survival of themselves and their kin.

Evolutionary forces may provide a general inclination for being helpful, but they may not be as good an explanation for why we help in the here and now. What factors serve as proximal influences for decisions to help?

Egoistic motivation for helping

Most people would like to think that they help others because they are concerned about the other person’s plight. In truth, the reasons why we help may be more about ourselves than others: Egoistic or selfish motivations may make us help. Implicitly, we may ask, “What’s in it  for me ?” There are two major theories that explain what types of reinforcement helpers may be seeking. The  negative state relief model  (e.g., Cialdini, Darby, & Vincent, 1973; Cialdini, Kenrick, & Baumann, 1982) suggests that people sometimes help in order to make themselves feel better. Whenever we are feeling sad, we can use helping someone else as a positive mood boost to feel happier. Through socialization, we have learned that helping can serve as a secondary reinforcement that will relieve negative moods (Cialdini & Kenrick, 1976).

The  arousal: cost–reward model  provides an additional way to understand why people help (e.g., Piliavin, Dovidio, Gaertner, & Clark, 1981). This model focuses on the aversive feelings aroused by seeing another in need. If you have ever heard an injured puppy yelping in pain, you know that feeling, and you know that the best way to relieve that feeling is to help and to comfort the puppy. Similarly, when we see someone who is suffering in some way (e.g., injured, homeless, hungry), we vicariously experience a sympathetic arousal that is unpleasant, and we are motivated to eliminate that aversive state. One way to do that is to help the person in need. By eliminating the victim’s pain, we eliminate our own aversive arousal. Helping is an effective way to alleviate our own discomfort.

As an egoistic model, the arousal: cost–reward model explicitly includes the cost/reward considerations that come into play. Potential helpers will find ways to cope with the aversive arousal that will minimize their costs—maybe by means other than direct involvement. For example, the costs of directly confronting a knife-wielding assailant might stop a bystander from getting involved, but the cost of some  indirect  help (e.g., calling the police) may be acceptable. In either case, the victim’s need is addressed. Unfortunately, if the costs of helping are too high, bystanders may reinterpret the situation to justify not helping at all. We now know that the attack of Kitty Genovese was a murderous assault, but it may have been misperceived as a lover’s spat by someone who just wanted to go back to sleep. For some, fleeing the situation causing their distress may do the trick (Piliavin et al., 1981).

The egoistically based negative state relief model and the arousal: cost–reward model see the primary motivation for helping as being the helper’s own outcome. Recognize that the victim’s outcome is of relatively little concern to the helper—benefits to the victim are incidental byproducts of the exchange (Dovidio et al., 2006). The victim may be helped, but the helper’s real motivation according to these two explanations is egoistic: Helpers help to the extent that it makes them feel better.

Altruistic help

A woman stops on the sidewalk to offer food to a man holding a sign reading 'Homeless, please help Thank you.'

Although many researchers believe that  egoism  is the only motivation for helping, others suggest that  altruism —helping that has as its ultimate goal the improvement of another’s welfare—may also be a motivation for helping under the right circumstances. Batson (2011) has offered the  empathy–altruism model  to explain altruistically motivated helping for which the helper expects no benefits. According to this model, the key for altruism is empathizing with the victim, that is, putting oneself in the shoes of the victim and imagining how the victim must feel. When taking this perspective and having  empathic concern , potential helpers become primarily interested in increasing the well-being of the victim, even if the helper must incur some costs that might otherwise be easily avoided. The empathy–altruism model does not dismiss egoistic motivations; helpers not empathizing with a victim may experience  personal distress  and have an egoistic motivation, not unlike the feelings and motivations explained by the arousal: cost–reward model. Because egoistically motivated individuals are primarily concerned with their own cost–benefit outcomes, they are less likely to help if they think they can escape the situation with no costs to themselves. In contrast, altruistically motivated helpers are willing to accept the cost of helping to benefit a person with whom they have empathized—this “self-sacrificial” approach to helping is the hallmark of altruism (Batson, 2011).

Although there is still some controversy about whether people can ever act for purely altruistic motives, it is important to recognize that, while helpers may derive some personal rewards by helping another, the help that has been given is also benefitting someone who was in need. The residents who offered food, blankets, and shelter to stranded runners who were unable to get back to their hotel rooms because of the Boston Marathon bombing undoubtedly received positive rewards because of the help they gave, but those stranded runners who were helped got what they needed badly as well. “In fact, it is quite remarkable how the fates of people who have never met can be so intertwined and complementary. Your benefit is mine; and mine is yours” (Dovidio et al., 2006, p. 143).

A Red Cross volunteer assists an elderly woman from Mozambique, where a food distribution was taking place.

We started this module by asking the question, “Who helps when and why?” As we have shown, the question of when help will be given is not quite as simple as the viewers of “What Would You Do?” believe. The power of the situation that operates on potential helpers in real time is not fully considered. What might appear to be a split-second decision to help is actually the result of consideration of multiple situational factors (e.g., the helper’s interpretation of the situation, the presence and ability of others to provide the help, the results of a cost–benefit analysis) (Dovidio et al., 2006). We have found that men and women tend to help in different ways—men are more impulsive and physically active, while women are more nurturing and supportive. Personality characteristics such as agreeableness and the prosocial personality orientation also affect people’s likelihood of giving assistance to others. And, why would people help in the first place? In addition to evolutionary forces (e.g., kin selection, reciprocal altruism), there is extensive evidence to show that helping and prosocial acts may be motivated by selfish, egoistic desires; by selfless, altruistic goals; or by some combination of egoistic and altruistic motives. (For a fuller consideration of the field of prosocial behavior, we refer you to Dovidio et al. [2006].)

Outside Resources

Article: Alden, L. E., & Trew, J. L. (2013). If it makes you happy: Engaging in kind acts increases positive affect in socially anxious individuals. Emotion, 13, 64-75. doi:10.1037/a0027761 Review available at: http://nymag.com/scienceofus/2015/07/one-way-to-get-over-your-social-anxiety-be-nice.html

Book: Batson, C.D. (2009).  Altruism in humans . New York, NY: Oxford University Press.Book: Dovidio, J. F., Piliavin, J. A., Schroeder, D. A., & Penner, L. A. (2006).  The social psychology of prosocial behavior . Mahwah, NJ: Erlbaum.

Book: Mikuliner, M., & Shaver, P. R. (2010).  Prosocial motives, emotions, and behavior: The better angels of our nature . Washington, DC: American Psychological Association.

Book: Schroeder, D. A. & Graziano, W. G. (forthcoming).  The Oxford handbook of prosocial behavior . New York, NY: Oxford University Press.Institution: Center for Generosity, University of Notre Dame, 936 Flanner Hall, Notre Dame, IN 46556. http://www.generosityresearch.nd.edu

Institution: The Greater Good Science Center, University of California, Berkeley.  http://www.greatergood.berkeley.edu

News Article: Bystanders Stop Suicide Attempt http://jfmueller.faculty.noctrl.edu/crow/bystander.pdf

Social Psychology Network (SPN)  http://www.socialpsychology.org/social.htm#prosocial

Video: Episodes (individual) of “Primetime: What Would You Do?” http://www.YouTube.com

Video: Episodes of “Primetime: What Would You Do?” that often include some commentary from experts in the field may be available at http://www.abc.com

Video: From The Inquisitive Mind website, a great overview of different aspects of helping and pro-social behavior including – pluralistic ignorance, diffusion of responsibility, the bystander effect, and empathy.

Discussion Questions

  • Pluralistic ignorance suggests that inactions by other observers of an emergency will decrease the likelihood that help will be given. What do you think will happen if even one other observer begins to offer assistance to a victim?
  • In addition to those mentioned in the module, what other costs and rewards might affect a potential helper’s decision of whether to help? Receiving help to solve some problem is an obvious benefit for someone in need; are there any costs that a person might have to bear as a result of receiving help from someone?
  • What are the characteristics possessed by your friends who are most helpful? By your friends who are least helpful? What has made your helpful friends and your unhelpful friends so different? What kinds of help have they given to you, and what kind of help have you given to them? Are you a helpful person?
  • Do you think that sex and gender differences in the frequency of helping and the kinds of helping have changed over time? Why? Do you think that we might expect more changes in the future?
  • What do you think is the primary motive for helping behavior: egoism or altruism? Are there any professions in which people are being “pure” altruists, or are some egoistic motivations always playing a role?
  • There are other prosocial behaviors in addition to the kind of helping discussed here. People volunteer to serve many different causes and organizations. People come together to cooperate with one another to achieve goals that no one individual could reach alone. How do you think the factors that affect helping might affect prosocial actions such as volunteering and cooperating? Do you think that there might be other factors that make people more or less likely to volunteer their time and energy or to cooperate in a group?

Image Attribution

Figure 13.27: Ed Yourdon, https://goo.gl/BYFmcu, CC BY-NC-SA 2.0, https://goo.gl/Toc0ZF

Figure 13.28: flowcomm, https://goo.gl/tiRPch, CC BY 2.0, https://goo.gl/BRvSA7

Figure 13.29: Daniel Thornton, https://goo.gl/Rn7yL0, CC BY 2.0, https://goo.gl/BRvSA7

Figure 13.30: TimJN1, https://goo.gl/iTQfWk, CC BY-SA 2.0, https://goo.gl/eH69he

Figure 13.31: Ed Yourdon, https://goo.gl/MWCLk1, CC BY-NC-SA 2.0, https://goo.gl/Toc0ZF

Figure 13.32: International of Red Cross and Red Crescent Societies, https://goo.gl/0DXo8S, CC BY-NC-SA 2.0, https://goo.gl/Toc0ZF

Batson, C. D. (2011).  Altruism in humans . New York, NY: Oxford University Press.

Becker, S. W., & Eagly, A. H. (2004). The heroism of women and men.  American Psychologist, 59 , 163–178.

Burnstein, E., Crandall, C., & Kitayama, S. (1994). Some neo-Darwinian decision rules for altruism: Weighing cues for inclusive fitness as a function of the biological importance of the decision.  Journal of Personality and Social Psychology, 67 , 773–789.

Buss, D. M. (2004).  Evolutionary psychology: The new science of the mind . Boston, MA: Allyn Bacon.

Cialdini, R. B., & Kenrick, D. T. (1976). Altruism as hedonism: A social developmental perspective on the relationship of negative mood state and helping.  Journal of Personality and Social Psychology, 34 , 907–914.

Cialdini, R. B., Darby, B. K. & Vincent, J. E. (1973). Transgression and altruism: A case for hedonism.  Journal of Experimental Social Psychology, 9 , 502–516.

Cialdini, R. B., Kenrick, D. T., & Baumann, D. J. (1982). Effects of mood on prosocial behavior in children and adults. In N. Eisenberg (Ed.),  The development of prosocial behavior  (pp. 339–359). New York, NY: Academic Press.

Costa, P. T., & McCrae, R. R. (1998). Trait theories in personality. In D. F. Barone, M. Hersen, & V. B. Van Hasselt (Eds.),  Advanced Personality  (pp. 103–121). New York, NY: Plenum.

Darley, J. M. & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility.  Journal of Personality and Social Psychology, 8 , 377–383.

Dawkins, R. (1976).  The selfish gene . Oxford, U.K.: Oxford University Press.

Diekman, A. B., & Eagly, A. H. (2000). Stereotypes as dynamic structures: Women and men of the past, present, and future.  Personality and Social Psychology Bulletin, 26 , 1171–1188.

Dovidio, J. F., Piliavin, J. A., Schroeder, D. A., & Penner, L. A. (2006).  The social psychology of prosocial behavior . Mahwah, NJ: Erlbaum.

Eagly, A. H., & Crowley, M. (1986). Gender and helping behavior: A meta-analytic review of the social psychological literature.  Psychological Review, 66 , 183–201.

Fisher, P., Krueger, J. I., Greitemeyer, T., Vogrincie, C., Kastenmiller, A., Frey, D., Henne, M., Wicher, M., & Kainbacher, M. (2011). The bystander-effect: A meta-analytic review of bystander intervention in dangerous and non-dangerous emergencies.  Psychological Bulletin, 137 , 517–537.

Graziano, W. G., & Tobin, R. (2009). Agreeableness. In M. R. Leary & R. H. Hoyle (Eds.),  Handbook of Individual Differences in Social Behavior . New York, NY: Guilford Press.

Graziano, W. G., Habashi, M. M., Sheese, B. E., & Tobin, R. M. (2007). Agreeableness, empathy, and helping: A person x situation perspective.  Journal of Personality and Social Psychology, 93 , 583–599.

Hamilton, W. D. (1964). The genetic evolution of social behavior.  Journal of Theoretical Biology, 7 , 1–52.

Latané, B., & Darley, J. M. (1970).  The unresponsive bystander: Why doesn’t he help?  New York, NY: Appleton-Century-Crofts.

Penner, L. A., & Orom, H. (2010). Enduring goodness: A Person X Situation perspective on prosocial behavior. In M. Mikuliner & P.R. Shaver, P.R. (Eds.),  Prosocial motives, emotions, and behavior: The better angels of our nature  (pp. 55–72). Washington, DC: American Psychological Association.

Penner, L. A., Dovidio, J. F., Piliavin, J. A., & Schroeder, D. A. (2005). Prosocial behavior: Multilevel perspectives.  Annual Review of Psychology, 56 , 365–392.

Penner, L. A., Fritzsche, B. A., Craiger, J. P., & Freifeld, T. R. (1995). Measuring the prosocial personality. In J. Butcher & C.D. Spielberger (Eds.),  Advances in personality assessment  (Vol. 10, pp. 147–163). Hillsdale, NJ: Erlbaum.

Piliavin, J. A., Dovidio, J. F., Gaertner, S. L., & Clark, R. D., III (1981).  Emergency intervention . New York, NY: Academic Press.

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Introduction to Psychology Copyright © 2019 by Dennis L. Poepsel and David A. Schroeder is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Helping Behavior

Helping behavior definition.

Helping behavior is providing aid or benefit to another person. It does not matter what the motivation of the helper is, only that the recipient is assisted. This is distinguished from the more general term prosocial behavior, which can include any cooperative or friendly behavior. It is also distinguished from the more specific term altruistic behavior, which requires that the motivation for assisting others be primarily for the well-being of the other person or even at a cost to oneself.

History and Background of Helping Behavior

The value of one person helping another is an ancient virtue discussed by the Greeks, evident across cultures and civilizations, and pervasive in world religions. One ancient Greek philosopher, Plato, suggested that groups of people needed to form social contracts to ensure that individuals would restrain their own selfish behavior for the good of others. Aristotle saw human nature as more innately good. He also described the relative positive feelings of the giver and receiver for one another. According to Aristotle, these feelings are greater for the person giving help than the help recipient. The ancient Chinese Confucian value “Jen” is a benevolence or charity toward others and is regarded as the highest of Confucian values.

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The ancient Greeks and Chinese are not the only ones concerned with helping behavior. Almost all world religions have some version of the Golden Rule—people should treat others as they would like to be treated. The Christian Bible promotes care for each other, the poor, and the needy. It also tells the parable of the Good Samaritan, who helped a stranger in distress along the roadway. This parable has become the modern ideal model of positive helping behavior. Maimonides, the Jewish Rabbi and philosopher, described the Golden Ladder of Charity, or eight degrees of goodness in helping others. Charity toward others is the third Pillar of Islam (Zakat) and involves an annual obligation to give to those in need. Buddhism’s Noble Eight Fold Path encourages helping others through right speech, action, and livelihood. In Hinduism, kindness to all creatures is important because all creatures are manifestations of God. Furthermore, helping to reduce others’ suffering is good karma, or a positive effect that a person’s behavior has on subsequent incarnations.

In modern, scientific approaches, social psychologists have been at the forefront of understanding how and why people help others. However, very little was written on helping behavior until a key historical event: the murder of Catherine “Kitty” Genovese on March 13, 1964. The failure of people in the area to help during the attack made newspaper headlines and spurred a great deal of commentary. Social psychologists Bibb Latane and John Darley were inspired to study what decision-making processes were involved in deciding whether to help in an emergency situation. Latane and Darley’s work was among the first of thousands of professional journal articles and books on the topic.

Types of Helping Behavior

In people’s everyday lives there are innumerable small acts of helping, like lending a pen to a fellow student. There are also very large acts of helping that include donating large sums of money or rescuing someone from a burning building. P. L. Pearce and P. R. Amato classified the kinds of helping as falling along three dimensions: level of planning and formality, directness of the help, and seriousness of the need. Level of planning and formality can range from very formal and planned, like working as a hospital volunteer each week, to very spontaneous and informal, like helping someone who has dropped some papers in the hallway. Directness of help refers to level of contact with the recipient of help from very direct, like helping a young girl tie her shoes, to very indirect, like mailing off a charity donation to help hurricane victims. Finally, the seriousness of the need should be taken into account. There is a big difference in lending someone a few pennies when he or she is short at the grocery store and doing CPR and rescue breathing on someone who has had a heart attack. The consequences of the former are very small, whereas the consequences of the latter could mean the difference between life and death.

McGuire described four different types of helping behavior. Casual helping involves doing small favors for casual acquaintances, such as letting someone borrow your cell phone for a quick call. If you have ever helped a friend or family member to move, you’ve engaged in substantial personal helping. This helping involves putting out a lot of effort to help someone over an extended time, so that the recipient can have a benefit. Emotional helping means providing care and personalized emotional support to another, like listening to a friend who has had a bad day or giving knowledge and advice to someone who requests it. Finally, emergency helping is assisting someone who has an acute problem. This would be like calling 911 when you witness a car accident. A concept related to McGuire’s classifications of helping behavior is social support, which can involve providing both resources to help a person solve a problem and the emotional or psychological support required to endure the stresses of life’s problems.

Helping Behavior Importance

The importance of this topic is evident. It is the rare individual who can go through life never needing help from another person. Most people experience some sickness, a car break-down, or other problem in which they need at least the temporary assistance of others, and many people will experience an emergency or personal tragedy for which they will need much greater assistance. Understanding emergency helping behavior can help researchers better predict who will help under what circumstances. Then resources can be focused on getting help where it is most needed at the time it is needed. Community education efforts can increase the timeliness and usefulness of help provided and can direct those in need to appropriate services. Promoting helpfulness is a benefit to individuals, families, and communities. If the community is prepared to be helpful, then the help will be there when each community member needs it. Better understanding helping processes may even lead to ways to prepare those who need help to ask effectively.

Theoretical Explanations of Helping Behavior

One of the greatest unanswered questions in social psychology is why people help others, particularly if that helping comes at a cost to themselves. Three broad theoretical approaches seek to explain the origins of helping behavior: natural explanations (including evolutionary and genetic explanations), cultural approaches (including sociocultural and social learning explanations), and psychological or individual-level explanations.

Scientists who study evolutionary psychology or sociobiology explore the evolutionary origins of human behavior. They may examine human groups or animal behavior to help learn about the way in which the human species developed and maintained the ability to act prosocially. They believe that evolutionary pressures make people naturally inclined to help others. However, they qualify that people are most likely to help those who will help them pass on their own genes or to pass along similar genes. So, people are more likely to help relatives than nonrelatives. People may be more willing to help their own children than neighbors’ children, because one’s own child has more related genetic material. Similarly, people are more likely to help others with similar physical, attitudinal, and demographic characteristics because they are more likely to pass along similar genetic characteristics to the next generation. So, people are more likely to help their friends, who are like them, than they are to help strangers, who are not like them. Attractive group members may receive more help, because they are more likely to pass along high-quality genetic traits to the next generation. So, in the evolutionary past, people with helpful characteristics may have been more likely to pass their genes to the next generation, promoting the good of the group and making those characteristics more visible in subsequent generations.

Other scientists argue that it is not genetics and evolution but culture and learning processes that produce helpful people. These scientists use society’s rules, called social norms, and society’s child-rearing processes, called socialization, to explain how people become helpful. Perhaps the most universal norm in the world is the norm of reciprocity. This norm suggests that if someone does something for you, you are obligated to do something in return. This social pressure comes with exchange of goods, like birthday presents, and exchange of services, like giving friends a ride in expectation that they’ll drive next time. So, to repay their social debt, people are most likely to help those who have helped them in the past. People are also more likely to help those they think might help them in the future, reciprocating their own good deed. Another social norm that relates to helping is the norm of equity. If people perceive themselves to be overbenefited (getting more than their fair share in life) or others to be underbenefited (getting less than their fair share in life), they’ll act to fix the inequity. If they can’t fix the inequity, however, they may blame the victim for his or her own misfortune, keeping their perception of a just and fair world in balance. The third major social norm related to helping behavior is the norm of social responsibility. In general, people believe they are responsible for helping those in their society who need help or are dependent on them. For example, people may feel that it is their responsibility to be helpful to children, the infirm elderly, people with physical disabilities, and other groups. This norm of social responsibility is stronger among women than men, and it is stronger among people with a collectivist orientation than among people with an individualist orientation. Also, while people will follow the norm of social responsibility in most cases, they will not follow it if they believe the person to be helped was to blame for his or her own need. For example, a male student may not help a female friend with lunch money if he knows that she spent what should have been her lunch money on video games earlier in the day.

Social psychologists have also explored individual-level explanations for why people help. These explanations concern the rewards received and costs paid for helping and the emotions around helping. People may receive rewards for helping others. These rewards can be physical rewards, like receiving a monetary award for returning a lost wallet; social rewards, like having public recognition of a good deed; or emotional, like feeling good after carrying groceries for an elderly neighbor. Costs associated with not helping are also motivating. People may help others specifically to avoid the guilt and shame associated with not fulfilling social obligations. People may also fear the disapproval they would receive from others for not helping. It would look bad if you stood passively aside while someone struggled to get through a door with an armload of boxes, when you could easily have helped them. Social learning theory suggests that to the extent people experience these rewards for helping or costs for not helping, they are more likely to help others in the future, expecting the next situation to have similar rewards and costs. So, rewards and costs do not need to be immediate to influence motivation. Sometimes people help others because it will aid their long-term goals of social recognition, fulfill career aspirations, or increase the social reputation, goods, money, and services they may receive in the future. People learn which behaviors produce rewards and which bring costs, beginning with parental teaching and modeling of helpful behaviors and continuing through life as friends, coworkers, and families praise or criticize people for enacting behaviors. For example, children who are taught to give to the poor through food drives and receive praise for doing so are more likely to continue these behaviors through their life.

Research teams headed by Robert B. Cialdini and C. Daniel Batson have spurred an ongoing debate concerning the role of empathy in motivating helping behavior. Cialdini contends that feelings of empathy produce a merging with the other and experience of that person’s emotional pain, so the person helps others to relieve his or her own emotional pain. Batson describes the desire to help another out of empathic concern for the other’s well-being as more genuinely altruistic. Altruism is defined as helping another purely for the good of the other person, with no external or internal rewards for the self, and possibly at great cost to one’s self. Heroes who rescue people from burning buildings and saintly figures, like Mother Teresa, are often described as altruistic.

Deciding When to Help

Whatever the motivation to help, decisions must ultimately be made to help or not help. Latane and Darley describe a decision model of helping for explaining when people will or will not help. This model takes into consideration individual experiences and social situations that make a person less inclined to help. For example, if a person never notices that someone nearby in a noisy restaurant is choking, the person won’t be able to help. An example of a situational factor that influences helping behavior is diffusion of responsibility. If the same noisy restaurant is crowded with other people who could potentially help the choking victim, any one person is less likely to actually administer assistance, the responsibility for helping is diffused among the group.

In deciding whether to help, the person also takes into consideration the current rewards and costs of helping. Jane A. Piliavin’s arousal: cost-reward model explains this process. When a person sees another in distress, such as in an illness or emergency situation, the person may feel empathy and arousal. Piliavin states that this empathic arousal motivates helping a person in need. What the helper actually does to reduce the victim’s distress depends on the cost to the helper of acting and the costs for the victim if he or she doesn’t receive help. Personal costs for helping include injury, the effort put forth, and potential embarrassment. Costs for the victim not receiving help are the victim’s continued distress and the shame, guilt, and social criticism directed at the person who does not help. When the costs to the victim of not getting help are high but the costs for helping are low, like a child running out into a busy street, people are likely to directly intervene (such as catching the child before the child reaches the street). The more dangerous or costly it becomes to the self, the less direct help will be offered. For example, people are less likely to come between two people having a fistfight at an athletic event because of the danger of being hurt themselves. In these cases, people will be more likely to use indirect helping tactics, such as alerting security staff about the fight. Other people reinterpret the event so that they won’t have to feel responsible for helping. For example, thinking, “Those unruly drunk guys probably deserve the beating they’re getting from each other.” When the cost of helping is high and the cost for not helping is low, people often leave the scene or deny that there was ever a need for help. In the ambiguous situation of having a low cost of helping and a low cost to the victim of not getting help, social norms govern whether people will provide assistance.

Gender and Other Individual Differences in Helping Behavior

There is wide popular perception that women are more helpful than men, more generally kind and nurturing. Yet, awards for heroism are much more likely to go to men than to women. Laboratory studies in social psychology tend to show either that men are more helpful or that both genders are equally helpful. Men play the social role of heroes and protectors in Western society, encouraging helping behavior. Men are typically physically larger and stronger than women, so they may perceive or experience less danger of being hurt themselves in engaging in heroic acts. Therefore, we cannot attribute all of heroism to being biologically wired for helping in emergencies. Some research suggests that women may be more likely to help in the context of ongoing family and friendship relationships. They may also be more likely to help when the task involves doing things related to stereotypical gender roles for women, such as helping a lost child find her parent or delivering meals to someone who has been sick.

Samuel P. Oliner and Pearl M. Oliner studied the personality characteristics of some of the heroes of the Holocaust. These individuals rescued or aided Jewish people, Polish people, and others who were escaping the Nazi cruelties. The characteristics they identified as important in distinguishing helpers from nonhelpers have been supported in additional controlled research studies. These characteristics include having empathy for victims, that is, understanding the feelings of others and responding to them emotionally. An example would be feeling teary or sad when you see someone crying. In helpers this empathy is other-oriented. That is, it is concern for the welfare of others and a desire to help them. The Oliners also found that helpers had a strong sense of personal responsibility for the welfare of others, a characteristic that comes from high moral reasoning. During the Holocaust, some supervisors and teachers hid their loyal Jewish employees or students until they could escape. Finally, these helpers displayed a high sense of self-efficacy. They believed that they were likely to be helpful as they assisted others. In a natural disaster, the devastation can be so widespread and so many people can be affected that a person might feel overwhelmed and ineffective in what help he or she could offer. However, a person with high self-efficacy might feel that while he or she could not solve the enormity of the problems the natural disaster brought, he or she might be able to help one person or one family with a donation or by volunteering time in the clean-up efforts.

Helping Behavior Implications

Research in helping behavior has vast benefits for understanding human behavior, for increasing good outcomes for individuals, and for the overall good of society. To the extent that people understand the behavior, motivations, and personality characteristics of, and situational influences on, helpers, they may be able to increase helpfulness toward those who most need help in their society, benefit from ongoing personal relationships with others, and generally make the world a better place to live. Those who have done research on increasing helpfulness in others have found that explanations of need, and making kind attributions (internal explanations) for those needs, increase helping behavior. Reminding people of their moral responsibilities to help those in need, telling people how to help, and making the victims more human also increase helping behavior. Much research is currently in progress on linking helping to other positive psychological characteristics like gratitude and forgiveness.

References:

  • Batson, C. D. (1997). Self-other merging and the empathy-altruism hypothesis: Reply to Neuberg et al. (1997). Journal of Personality and Social Psychology, 73, 517-522.
  • Batson, C. D., Sager, K., Garst, E., Kang, M., Rubchinsky, K., & Dawson, K. (1997). Is empathy-induced helping due to self-other merging? Journal of Personality and Social Psychology, 73, 495-509.
  • Berkowitz, W. (1987). Local heroes. Lexington, MA: Lexington Books.
  • Cialdini, R. B., Brown, S. L., Lewis, B. P., Luce, C., & Neuberg, S. L. (1997). Reinterpreting the empathy-altruism relationship: When one into one equals oneness. Journal of Personality and Social Psychology, 73, 481-494.
  • Neuberg, S. L., Cialdini, R. B., Brown, S. L., Luce, C., & Sagarin, B. J. (1997). Does empathy lead to anything more than superficial helping? Comment on Batson et al. (1997). Journal of Personality and Social Psychology, 73, 510-516.
  • Oliner, S. P., & Oliner, P. M. (1992). Altruistic personality: Rescuers of Jews in Nazi Europe. New York: Free Press.
  • Schroeder, D. A., Penner, L. A., Dovidio, J. F., & Piliavin, J. A. (1995). The psychology of helping and altruism: Problems and puzzles. New York: McGraw-Hill.
  • Wilson, D. S., & Kniffin, K. M. (2003). Altruism from an evolutionary perspective. In S. G. Post, B. Johnson, M. E. McCullough, & J. P. Schloss (Eds.), Research on altruism and love: An annotated bibliography of major studies in psychology, sociology, evolutionary biology, and theology (pp. 117-136). Philadelphia: Templeton Foundation Press.

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Research Article

Real-life helping behaviours in North America: A genome-wide association approach

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Anthropology, University of Vienna, Vienna, Austria

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Roles Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

  • Georg Primes, 
  • Martin Fieder

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  • Published: January 11, 2018
  • https://doi.org/10.1371/journal.pone.0190950
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Table 1

In humans, prosocial behaviour is essential for social functioning. Twin studies suggest this distinct human trait to be partly hardwired. In the last decade research on the genetics of prosocial behaviour focused on neurotransmitters and neuropeptides, such as oxytocin, dopamine, and their respective pathways. Recent trends towards large scale medical studies targeting the genetic basis of complex diseases such as Alzheimer’s disease and schizophrenia pave the way for new directions also in behavioural genetics.

Based on data from 10,713 participants of the American Health and Retirement Study we estimated heritability of helping behaviour–its total variance explained by 1.2 million single nucleotide polymorphisms–to be 11%. Both, fixed models and mixed linear models identified rs11697300, an intergene variant on chromosome 20, as a candidate variant moderating this particular helping behaviour. We assume that this so far undescribed area is worth further investigation in association with human prosocial behaviour.

Citation: Primes G, Fieder M (2018) Real-life helping behaviours in North America: A genome-wide association approach. PLoS ONE 13(1): e0190950. https://doi.org/10.1371/journal.pone.0190950

Editor: Giuseppe Novelli, Universita degli Studi di Roma Tor Vergata, ITALY

Received: May 25, 2017; Accepted: December 24, 2017; Published: January 11, 2018

Copyright: © 2018 Primes, Fieder. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The survey data is provided by the health and retirement study (HRS) for free, after registration. The genomic data is provided by the health and retirement study after IRB approval and approval NCBI-dbGaP for free. HRS provides different sets of data gathered in its long-term study. While “phenotypic” data (such as demographics, basic health indicators, values, behaviours, etc. gathered via interviews and questionnaires) are mostly publicly available (with restrictions on some sensitive data such as financial data), many data sets (genetic data, biomarkers, identifying data such as zip-codes etc.) are only available through an application system (Health and Retirement Study, DUA Review Committee, 426 Thompson Street, Ann Arbor, Michigan 48104-2321, [email protected] ). Furthermore, in order to reproduce the study also cross-reference information between public data and restricted data is necessary - again subject to a separate review process. By requesting access to the genetic data the authors agreed that the data sets can only be shared among the working group permitted to work with the data (Martin Fieder as PI and Georg Primes) ( https://hrs.isr.umich.edu/sites/default/files/HRS-Genetic-Data-Access-Agreement.pdf ). The data can only be accessed directly from the health and retirement study and NCBI-dbGaP, authors are not allowed to provide any raw data (only summary data of the analyses) together with the article. The application process is described here: https://hrs.isr.umich.edu/data-products/geneticdata/products#apply .

Funding: The authors gratefully acknowledge funding from the IP Projekt (IP547011, molekulares Kompetenzzentrum UW/VU). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Prosocial behaviour–voluntary behaviour intended to benefit others [ 1 ]–is essential for social functioning in humans, who, next to eusocial insects, form the largest cooperative living groups on Earth. Extensive research has been conducted focusing on individual differences in this multifaceted trait that covers concepts such as helping, cooperation, altruism, and empathy [ 2 – 4 ]

Ever since Hamilton [ 5 ] the evolution of social behaviour on a species level has been discussed in terms of genetics. Unsurprisingly, the traditional twin study approach suggests a partial hardwiring of human prosocial behaviour. Its heritability is typically estimated to be between 10 and 60%, increasing with age and varying with the respective concept of prosocial behaviour under investigation [ 6 – 9 ].

On the individual level, however, we are only just beginning to understand the genetic influences on human (pro)social behaviour. Research on the regulatory effects of neuropeptides such as oxytocin and vasopressin on social cognition and behaviour [ 10 , 11 ] and the search for their genetic basis have produced several candidate genes. These include the oxytocin receptor gene (OXTR), the argenine vasopressine receptor 1A (AVPR1A) as well as others involved in the dopamine and serotonin pathway of receptors (DRD4, 5-HTR), in synthesis and degradation (COMT, MAOA), and in transportation (DAT, SERT). Studies focusing on these candidate genes found associations with social cognitive functioning, complex medical conditions, as well as social behaviour [ 12 – 17 ].

The predominant method in investigating the genetic basis of prosocial behaviour and decision-making is the application of incentivized laboratory-based experiments derived from the field of experimental economics. These complement behavioural genetics approaches [ 18 – 22 ]. All the commonly employed games in behavioural economics experiments (e.g. Dictator Game, Ultimatum Game, Trust Game, Public Goods Game) are easily adaptable and are increasingly being combined with brain imaging techniques to generate insights into the neurobiological structure of economic decision making [ 23 ], for example. Beyond this modularity, the approach provides researchers with experimental control by allowing for controlled variation of a variable while keeping all other conditions constant. This both facilitates interpretation of results and simplifies study replication.

Nonetheless, there are several drawbacks to this approach, varying in their severity with the field of application. The sample size of laboratory-based experiments is often small, limiting the generalizability of the results [ 24 ]. The trade-off between internal validity in the laboratory and external validity is a genuine, broadly discussed problem [ 25 ]. Increasing the sample size creates costly and time-consuming logistics to set up the study. This is especially true when researchers combine standard games with brain imaging techniques and behavioural genetics approaches. Consequently, the latter commonly employ a target gene approach that allows only a small number of variations to be analysed.

Today, the increasing number of predominantly medical studies provides a vast collection of genetic data of large study samples. Their aim is to reveal genetic influences on complex diseases such as Alzheimer’s disease, breast cancer, and schizophrenia using genome-wide association approaches [ 26 – 28 ].

These studies are often designed as longitudinal studies to keep track of their participants over a longer period of time (Wisconsin Longitudinal Study http://www.ssc.wisc.edu/wlsresearch/ , Health and Retirement Study http://hrsonline.isr.umich.edu/index.php , Avon Longitudinal Study of Parents and Children http://www.bristol.ac.uk/alspac/ ). The study teams also collect comprehensive phenotypic data beyond basic demographic information and medical condition. Therefore, these data sets provide an excellent opportunity to investigate genetic influences on 'every day' prosocial behaviour beyond strictly controlled laboratory-based experiments and on a much larger sample base. Simultaneously, recent progress in estimating heritability from whole genome sequence data [ 29 ] enable heritability research beyond the traditional twin study design.

To date, genome-wide association studies (GWAS) have not been used very frequently to identify the genomic basis of behavioural traits, besides the GWAS used in mental diseases research. Although GWAS have historically only explained a small proportion of the variance in a variety of complex traits being studied, they are well suited to detect unknown causal variants associated with a trait as in contrast to candidate gene tests GWAS are hypothesis free. They therefore offer the opportunity to gain completely new insights into the genetic basis of behaviour. In addition, large study data sets of unrelated individuals allow for an estimation of genome-wide variance explained which due to the availability of common causal variants usually present underestimates. A typical problem of GWAS is their limited potential to describe biological mechanisms on basis of GWAS results. Gene set analysis addresses this issue and uses GWAS results which describe a limited number of significantly associated SNP’s with a trait to estimate associations between the trait and entire gene sets known for their specific biological functions [ 30 ]. GWAS results also constitute the basis of the estimation of genetic correlations. This investigation of association between complex traits and diseases is especially relevant in gathering etiological insights in causal relationships [ 31 ].

All these points taken together, large study data sets provide a promising basis to explore new directions in behavioural genetics.

The goal of this study is to demonstrate new ways of exploring and investigating the genetic basis of (pro)social behaviour and decision making using established methods from medical/complex disease research. Not unlike complex diseases the genetic basis of a certain human behaviour is complex and heavily interdependent on various influence factors. However, unlike at least some complex diseases human prosocial behaviour is much more difficult to measure, quantify and describe compared to diseases and conditions with specified measurable symptoms.

This leads to the probably single most important limitation of the study presented here: the phenotypic representation of human prosocial behaviour by self-reported helping behaviour. The amount of time a person spends in order to help out his/her family, friends and neighbours without getting paid covers by no means the entire spectrum of prosocial behaviour. However, we feel that it constitutes a valid real-life approximation of a well-defined characteristic of prosocial behaviour. Observations on real-life human helping behaviour with friends and family basically approximates the degree of helpfulness a person exhibits in its everyday life. Unlike in standardized laboratory experiments we can only speculate on the reasons for these observations based on the information we have at hand (the questionnaire). Generally, helping behaviour towards friends and family may be accounted for by Hamilton’s rule of kin selection (family) or the basic principle of direct reciprocity [ 32 ]. The latter has often been targeted in well-constructed laboratory designs using (behavioural economic) settings in which participants interact–commonly under cover of anonymity–together in financially relevant interactions based on decisions on uncertainty. Trying to create an environment that resembles real-life interactions among fellow humans, interactions are being repeated over and over again, so that reputation and a history of (dis)trust can be established. From these studies we learned about facilitators and obstacles for the development of pro- and antisocial behaviour.

Using the data from the Health and Retirement Study we are able to go beyond this question. We can actually assess a degree of helpfulness in real-life. This comes of course with the cost of not being able to reproduce the motivations underlying these decisions.

The study at hand is limited to investigate a very narrow spectrum of human prosocial behaviour–namely individual differences in helping behaviour towards family and friends. And although it is not able to give answers similar to standardized (laboratory) studies, its exploratory approach might very well show new directions in investigating human prosocial behaviour.

Based on the University of Michigan's Health and Retirement Study (HRS), an on-going longitudinal panel study that collects survey data, anthropometric measurements, and physical performance tests, where more than 10,000 Americans have been genotyped, we used self-reported helping behaviour (SHB) to run a genetic association analysis on 1.2 Million SNPs.

One locus–rs11697300 –exceeded genome-wide significance in association with self-reported helping behaviour. Rs11697300 is an intergenic variant located between solute carrier family 52 (riboflavin transporter), member 3 (SLC52A3), and scratch family zinc finger 2 (SCRT2) on chromosome 20 (SNP = rs11697300, chromosome 20:718542, minor allele frequency (MAF) = 30.7%, P = 6.96 × 10 −10 ). Table 1 lists the 10 SNPs with the lowest P -values.

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https://doi.org/10.1371/journal.pone.0190950.t001

Fig 1 shows Manhattan and Q-Q plots for association results.

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(a) Manhattan plot of genome-wide association for self-reported helping behaviour. (b) Quantile-quantile plot of GWAS for self-reported helping behaviour.

https://doi.org/10.1371/journal.pone.0190950.g001

Rs11697300 is located in a conserved region in the Hominidae. This is based on data from the UCSC Genome Browser ( https://genome-euro.ucsc.edu/cgi-bin/hgTracks?db=hg38&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr20%3A737890%2D737906&hgsid=225572689_6w3VFIL5xieR9y6lUUDNVbK6pABP ). The Chimpanzee, the Orang—there is no data available for the Gorilla—as well as the phylogenetically closely related Gibbon show no differences in the region of interest.

Hence, rs11697300 seems to represent a phylogentic "old" variant in the Hominidae. However, drawing any further evolutionary conclusions on the basis on the available information must, at the moment, remain purely speculative.

Although only one locus reached genome-wide significance, association analysis revealed a striking pattern regarding a specific region on chromosome 4. The vast majority of SNPs approaching genome-wide significance (17 of 24 SNPs with P < 5 × 10 −6 ) is located in a narrow region, spanning 215,932 base pairs, on chromosome 4 covering transmembrane protein 33 (TMEM33), DDB1 and CUL4 associated factor 4-like 1 (DCAF4L1), solute carrier family 30 (zinc transporter), member 9 (SLC30A9), ATPase, Na+/K+ transporting, beta 1 polypeptide pseudogene 1 (ATP1B1P1), and BEN domain containing 4 (BEND4) ( Fig 2 ). S1 Table lists all SNPs with P values of association < 5 × 10 −6 .

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Within 215,932 base pairs, 17 single nucleotide polymorphisms (SNP) nearly reach genome-wide significance in association with self-reported helping behaviour. This area covers transmembrane protein 33 (TMEM33), DDB1 and CUL4 associated factor 4-like 1 (DCAF4L1), solute carrier family 30 (zinc transporter), member 9 (SLC30A9), ATPase, Na+/K+ transporting, beta 1 polypeptide pseudogene 1 (ATP1B1P1), and BEN domain containing 4 (BEND4). Black boxes depict exons, grey boxes are 5' and 3' untranslated regions.

https://doi.org/10.1371/journal.pone.0190950.g002

We confirmed the robustness of the results of the genetic association analysis with a linear model including six covariates from principal component analysis (Methods, PLINK). Again, only one locus exceeded genome-wide significance in association with SHB (SNP = rs11697300, P = 2.52 × 10 −9 ). And again, the area around SCL30A9 was revealed to be heavily populated with SNPs approaching genome-wide significance. Table 1 summarizes Top 10 SNPs for both genetic association analyses. S1 Fig shows Manhattan and Q-Q plots for PLINK results. Genomic inflation was estimated using the LD Score regression intercept to be 1.0318 (compare: λ gc = 1.0466).

Genetic variance estimation was conducted following Yang et al. [ 33 ]). Using the GREML-LDMS method, we estimated from 10,713 unrelated individuals that 1,244,134 SNPs (MAF > 5%) explain 11% (standard error (s.e.) = 2.9%) of variance for self-reported helping behaviour ( S2 Table ).

Applying LDHub we found significant genetic correlations to the following GWAS: a) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk , by Dupuis et al. 2010 [ 34 ] ( P = 0.0435); b) Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA , by Kettunen et al. 2016 [ 35 ] ( P = 0.0055); and c) Genome-wide Association Studies Identify Genetic Loci Associated With Albuminuria in Diabetes , by Teumer et al. 2016 [ 36 ] ( P = 0.0313; P = 0.0434). Studies a) and b) are flagged as “Caution” by LDHub because “ using this data may yield results outside bounds due to relative low Z score ”. However, there seems to be a genetic correlation between the presented GWAS on SHB and GWAS on metabolism and diabetes (for a summary of the genetic correlations see Table 2 ).

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https://doi.org/10.1371/journal.pone.0190950.t002

Gene set analysis revealed a total of 343 gene sets significantly associated ( P < 0.05) with SNPs from the present SHB GWAS ( S3 Table , S1 File 'gene-set-analysis.csv'), 26 of which with P < 10 −4 . Some of the gene sets found make biological sense, for instance gene sets involved in the synaptic membrane ( P = 0.00004), dendritic ( P = 0.00004) and neuron spine ( P = 0.00004) and hormone receptor activity ( P = 0.0007). Interestingly, some genes previously highlighted to influence prosocial behaviour are part of gene-sets significantly associated at P < 10 −4 : OXTR (adherents junction, telencephalon development), AVPR1a (telencephalon development), DRD4 (dendritic spine, neuron spine). Other interesting associated gene sets are negative regulation of behaviour (including DRD2, P = 0.004), learning (including COMT, DRD2, DRD3, DRD4, DRD5, P = 0.008), associative learning (including DRD1, DRD2, DRD3, DRD4, and DRD5, P = 0.041), and regulation of behaviour (including DRD1 and DRD2, P = 0.047).

Prosocial behaviour is a distinct human trait that is strongly influenced by genetic factors [ 6 – 8 ]. Our genome-wide association analysis was based on data collected by the Health and Retirement Study covering over 10,000 individuals and more than 1.2 million SNPs.

Our results indicate that one locus, rs11697300, an intergenic variant located between solute carrier family 52 (riboflavin transporter), member 3 (SLC52A3), and scratch family zinc finger 2 (SCRT2) on chromosome 20, is associated with self-reported helping behaviour. To date, no literature is available on the function of this variant or variants in strong linkage disequilibrium (LD) with rs11697300 ( S4 Table , based on data provided by the 1000 Genomes Project [ 37 ], S5 Table , based on the HRS dataset providing P-values and effect sizes for all SNPs in high LD with rs11697300).

On chromosome 4, a pattern emerged revealing 17 variants approaching but not reaching genome-wide significance ( S1 Table ). All variants are located within 215,932 base pairs, an area containing the transmembrane protein 33 (TMEM33), DDB1 and CUL4 associated factor 4-like 1 (DCAF4L1), solute carrier family 30 (zinc transporter), member 9 (SLC30A9), ATPase, Na+/K+ transporting, beta 1 polypeptide pseudogene 1 (ATP1B1P1), and BEN domain containing 4 (BEND4) ( Fig 2 ). None of these variants, however, have previously been described in the literature concerning functionality.

For the last decade, research concerned with genetic influences on prosocial behaviour focused on neuropeptides such as oxytocin and their pathway genes [ 38 , 39 ]. Our results suggest hint towards certain yet undescribed areas in the human genome to influence human helping behaviour. Note that, although we used two different methods to calculate the GWAS (GCTA and PLINK), we, due to the lack of comparable studies at hand, still miss the opportunity to replicate these results using a different data set to get more insights on the validity of the results provided by HRS data. Unfortunately, to our knowledge there is no other study available today that would qualify (either in scope or range of the study regarding the investigated behaviours) as a replication sample. Apart from that, this study is still subject to the general limitations common to all GWAS [ 40 ]: GWAS mainly report correlations between genetic loci and certain phenotypes. As a “correlational method“, a GWAS is unable to prove causality, as this is usually the case with correlational studies. A potential hint to the underlying biological mechanisms may be given by the genetic correlation and the gene set analysis we applied (discussed later). However, it will be necessary in future studies to investigate our results on a functional/physiological level, potentially clarifying the pathway from the genotype to the phenotype.

Moreover, due to the LD structure of the genome, GWAS are mainly designed to detect associations with relatively common variants in a population. Importantly, typical for GWA studies, the SNPs found to be significantly associated with a trait usually explain only a small proportion of the total variance. Accordingly, we applied the method of Yang et al. [ 29 ]–the estimation of the variance of a trait explained by all SNPs of a genome–to calculate the heritability due to additive effects of the trait “helping behaviour”. Due to the sample size of over 10k unrelated individuals this method yielded a robust estimate of heritability even for a substantially skewed measure of the trait “helping behaviour” (Table II)[ 41 ]. Existing studies on the heritability of prosocial behaviour report estimates between 10 and 60%. Estimates from 10 to 20% were found using a twin study design and cooperative behaviour in the trust game as a measure of behaviour [ 8 ]. 61% were found a twin study design by Knafo and Plomin 2006 [ 7 ] using parents and teacher ratings based on a validated behaviour questionnaire. While lower estimates are being achieved with measures of single behaviours (cooperative behaviour in the trust game), measures that combine observations of different behaviours [ 8 ] obtain a higher estimate. SHB presented in this study, yielding an estimate of 11%, however, only enabled measuring one dimension of human prosocial behaviour, namely “hours spent helping friends and family”. Therefore it is more comparable to the former method of measuring a single behaviour. We assume that additional data on prosocial behaviour which could be integrated into a more comprehensive variable on “prosocial behaviour” will become available in the future. Thus, bolstering the robustness of the measure might increase the “heritability coefficient” (the total variance explained by genome-wide data) according to the comprehensiveness of the measure in use.

However, our approach of heritability estimation is of course different from “classical” twin study designs to calculate heritability in prosocial behaviour (e.g. [ 7 ]) as the estimation of the variance of a trait is explained by all SNPs of a genome which are used to calculate the heritability due to additive effects of the trait s elf-reported helping behaviour .

Interestingly, albeit intuitively there no association between urinary albumin-to-creatinine ratio (microalbuminuria) would be expected, the genetic correlation between SHB and Albuminuria may make sense as Albuminuria is known of being associated with lower cognitive functioning particularly in elderly individuals [ 42 , 43 ]. If cognition in general is affected it could be speculated that prosocial behaviour may be affected as well. This may work directly by mutagenic or pleiotropic effects or indirectly via confounding effects of diseases. Comparable mechanisms may also hold true for the correlation of prosocial behaviour and lipoprotein blood levels, as there seems to be an association between cognition and lipoprotein blood levels [ 44 ]. However, at this stage such potential explanations for the genetic correlations must remain speculative, future studies far beyond the scope of this paper are needed.

Also the gene set analysis did find significant associations of the results to some gene sets that make biological sense including the dopamine receptor genes (DRD1 to DRD5), OXTR, and AVPR1a, all well known in the research of social behaviour. Especially associations with (associative) learning and (negative) regulation of behaviour appear intuitive and supportive of the results of the GWAS. However, as a “correlative approach” a GWAS is not able to transfer the vague concept of “genetic influence” in causality and determination. Accordingly, the relevance of the gene sets found to be associated with the results of the present GWAS may not be over-interpreted, but may provide a starting point for future analysis and deliver ideas where to start looking for causality and determination.

Based on our results we suggest that i) the potential function of rs11697300 and its surrounding area, as well as the other nearly genome-wide significant SNPs on and around SLC30A9, should be investigated in more detail; ii) rs11697300 and the other nearly genome-wide significant SNPs should be investigated in candidate-gene approaches, particularly in studies involving both laboratory-based experimental studies and studies on “every day” prosocial behaviour; iii) on the phenotypic level the accordance between lab and field data (laboratory-based experiments vs. “every day”prosocial behaviour) should be investigated in more detail because this issue is still under debate [ 25 , 45 ]; and iv) as mentioned above, additional GWA studies that sample a more comprehensive variety of “prosocial phenotypes” should be conducted in the future.

In conclusion, this study points towards new possible directions for research in behavioural genetics. We present results suggesting an association between yet undescribed genetic variants and human prosocial behaviour.

We encourage other studies to replicate and expand upon our findings. This would be an important step forward in clarifying the biological functioning of loci detected and supporting the notion that these areas are associated with prosocial behaviour.

Material and methods

Study description.

The University of Michigan Health and Retirement Study (HRS) is an on-going longitudinal panel study designed to monitor changes in labour force participation and health transition of individuals toward the end of work life and beyond. The current sample population consists of 22,037 Americans over age 50. The sampling mechanism is based on a national probability sample to represent the entire American population. HRS collects survey data (demographic variables, physical and psychological well-being, life and job history, assets and financials, etc.), anthropometric measurements, and physical performance tests (e.g. body height, body weight, blood pressure, grip strength), as well as blood and saliva samples.

The Health and Retirement Study (Project #6192) genetic data is sponsored by the National Institute on Aging (grant numbers U01AG009740, RC2AG036495, and RC4AG039029) and was conducted by the University of Michigan [ 46 ]. Collection and production of HRS data comply with the requirements of the University of Michigan’s Institutional Review Board (IRB). For a detailed description of the study, see http://hrsonline.isr.umich.edu/index.php . This individual research project was approved by the Ethics Committee of the University of Vienna (Reference number 00077), data use was approved by the National Center for Biotechnology Information Genotypes and Phenotypes Database (NCBI dbGaP) Data Access Request system at the National Institutes of Health (Project ID 6192).

Genotypic data

Based on voluntary participation, genotyping was performed on saliva samples. In total, 12,507 individuals have been genotyped since 2006. Genotyping was performed at the Center of Inherited Disease Research (CIDR) using the Illumina HumanOmni2.5-4v1 array and using the calling algorithm GenomeStudio version 2011.2, Genotyping Module 1.9.4 and GenTrain version 1.9. The medium call rate is 99.7% and the error estimated from 336 pairs of the study sample duplicates is 6 × 10 −5 . Further quality control steps were taken by teams at the University of Washington (UWGCC), the Health and Retirement Study investigator's team, and dbGaP. In total, 2,443,179 SNPs were genotyped. After several steps of stringent quality control measures, 1,244,134 SNPs were left for each participant Quality control steps included dropping dublicate SNPs and SNPs with a missing call rate > = 2%, Hardy-Weinberg-Equilibrium (HWE) P-value < 10 −4 in either European or African samples, and a MAF < 0.05. Table 3 presents a detailed QC summary pipeline with the numbers of SNPs lost after each step (for more details on the process of quality control, see http://hrsonline.isr.umich.edu/sitedocs/genetics/HRS_QC_REPORT_MAR2012.pdf ).

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https://doi.org/10.1371/journal.pone.0190950.t003

After removing 172 related individuals (80 families of two and four families of three individuals) of the initial pool of 12,507 study participants 12,235 individuals were left in the subject pool. Families were defined as individuals being connected by a kinship coefficient (KC) > 0.1. The threshold corresponds to the expected KC of half-siblings minus two standard deviations.

Self-reported helping behaviour (SHB) is coded in four questions (MG198, MG199, MG200, MG201) in section G (Functional Limitations and Helpers) of the Core questionnaire catalogue (The HRS 2010 Core Final Release (Version 5.0), public use dataset). The questions read as follows:

MG198, Have you spent any time in the past 12 months helping friends, neighbors, or relatives who did not live with you and did not pay you for the help? (1 = Yes, 5 = No) MG199, Altogether, would you say the time amounted to less than 100 hours, more than 100 hours, or what? (1 = Less than 100, 3 = about 100, 5 = more than 100) MG200, Would it be less than 200 hours, more than 200 hours, or what? (1 = Less than 200, 3 = about 200, 5 = more than 200) MG201, Would it be less than 50 hours, more than 50 hours, or what? (1 = Less than 50, 3 = about 50, 5 = more than 50)

Based on these questions, we merged the eight possible combinations of answers into five categories of hours spent helping others: 0, 1 to 50, 51 to 100, 101 to 200, and 200+. Table 4 summarizes the possible combinations and gives the distribution of participants for each category.

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https://doi.org/10.1371/journal.pone.0190950.t004

Genetic association analysis

10,713 individuals with non-missing answers to SHB were matched to 1,244,134 SNPs. Genetic association analysis was carried out with i) a linear mixed model with a genetic relatedness matrix (GRM) and the effects of SNPs treated as random and ii) a standard linear regression approach with six principal component analysis eigenvectors as covariates.

GCTA (version 1.25) provides options to perform mixed linear model (MLM)-based association analyses [ 47 ]. The MLM association technique is a widely recognized method of choice for association mapping when sample structure is present. It is based on constructing a GRM modelling the genome-wide sample structure. A random-effects model then estimates the contribution of the GRM to phenotypic variance, and association statistics are calculated to account for this phenotypic variance [ 48 ].

We implemented the GCTA-LOCO approach, which evaluates markers on a given chromosome using a GRM calculated from the remaining chromosomes. This 'leaving-one-chromosome-out' (LOCO) method avoids double-fitting the candidate marker and increases power of the analysis compared to regular MLM approaches as well as linear regression [ 48 , 49 ].

In PLINK (version 1.07) we used the implemented standard linear regression for quantitative trait data [ 50 ] to find potential associations of the genotype and self-reported helping behaviour, after including the eigenvectors of the PCA as covariates as recommended by the Health and Retirement Study for population stratification based on Patterson et al. [ 51 ]. PCA results are provided by the Health and Retirement Study. After LD pruning based on the set of autosomal SNPs with a missing call rate < 5%, MAF > 5%, and excluding the regions LCT, HLA, 8p23, and 17q21.31, 154,644 SNPs were selected for PCA. For details, see http://hrsonline.isr.umich.edu/sitedocs/genetics/HRS_QC_REPORT_MAR2012.pdf .

Genomic inflation

We used the python tool LDSC to estimate genomic inflation ( https://github.com/bulik/ldsc/wiki/Heritability-and-Genetic-Correlation ). LDSC calculates genomic inflation as the proportion of the inflation in the mean χ2 that the LD Score regression attributes to causes other than polygenic heritability [ 52 ]. Using the LD Score regression intercept as an estimate of inflation, the estimate is, other than λ gc , not biased by sample size in the presence of polygenicity [ 53 ].

Genetic variance estimation

We estimated genetic variance based on GCTA's GREML-LDMS method [ 33 ] using whole genome sequence data. As this method cannot account for variance attributable to extremely rare causal variant or variants that are not polymorphic in the dataset, we calculated a slight underestimate of the genetic variance. The analysis is conducted in four steps using GCTA [ 47 ] (steps i, iii, and iv) and R statistical programming software [ 54 ] (step ii). The first step is to calculate the segment-based LD score (i). Subsequently, SNP stratification (ii) is done based on (i) and MAF. Stratified SNPs are used to calculate four GRMs based on the quartiles of the ld score (iii), which are then used as multiple GRMs in performing a REML analysis (iv) [ 33 ].

Genetic correlation

We used the online tool LDHub ( http://ldsc.broadinstitute.org ) to estimate potential genetic correlations among SHB and 177 diseases and traits gathered from publicly available resources and consortia. Estimation is done on the basis of the summary level results of the present GWAS on SHB and the summary results of those 177 GWAS [ 55 ].

LDhub has been implemented on basis of Bulik-Sullivan et al. 2015a [ 52 ], Bulik-Sullivan et al. 2015b [ 31 ]. This method regresses the summary results statistics of GWAS including the genetic variants across the genome measuring each variant’s ability to tag other variants locally (detailed explanation can be found in Bulik-Sullivan et al. 2015a [ 51 ]).

Gene set analysis

We applied the gene set analysis (GSA) approach developed by Nam et al [ 30 ] implementing in the Java application “GSA SNP” ( https://sourceforge.net/projects/gsa-snp/files/?source=navbar ) on the present GWAS results (SNP with its P value from the GWAS). GSA assigns SNPs to a gene that encompasses the SNP with some padding. Genes are clustered in gene sets of known function. As gene set we used the set “Gene Ontology” (default) with a padding size of +/- 20,000 and k-th best P value (default 2). P values are corrected according to Benjamini and Hochberg [ 56 ]. The GSA-SNP analysis uses the PAGE method [ 57 ]. Details to the method can be found in Nam et al. 2010 [ 30 ] and Kim et al. 2005 [ 56 ].

Supporting information

S1 fig. one locus on chromosome 20 reaches genome-wide significance in the plink association analysis..

https://doi.org/10.1371/journal.pone.0190950.s001

S1 File. Gene-set analysis.

Gene set analysis revealed a total of 343 gene sets significantly associated ( P < 0.05) with SNPs. Information includes set name, gene count, set size, z-score, p-value, corrected p-value, FDR, and gene symbols.

https://doi.org/10.1371/journal.pone.0190950.s002

S1 Table. Summary results of genetic association analyses for SNPs with P values < 5 x 10 −6 (GCTA-LOCO).

SNP: Single nucleotide polymorphism, Chr: chromosome, Pos: base pair position, ID: SNP name, Ref: reference allele, Alt: alternative allele, Freq: reference allele frequency. GCTA-LOCO: mixed-linear model implemented with GCTA's leaving-one-chromosome-out method with regression coefficient ( b ), standard error ( se ), and p-value ( p ). PLINK: linear regression implemented with PLINK association analysis and PCA eigenvectors as covariates with regression coefficient ( b ), t-statistic ( stat ), and p-value ( p ). 17 SNPs located within 215,932 base pairs on chromosome 4 are highlighted in bold.

https://doi.org/10.1371/journal.pone.0190950.s003

S2 Table. Estimates of variance explained from GREML-LDMS analysis for self-reported helping behaviour.

GREML-LDMS (Yang et al. 2015): Linkage disequilibrium and minor allele frequency stratified GREML analysis with estimates ( Est ) and standard errors ( s . e .); for details see main text.

https://doi.org/10.1371/journal.pone.0190950.s004

S3 Table. A selection of gene sets strongly associated with SHB.

SHB: self-reported helping behaviour. The list of gene sets is grouped in to the top results (Top) and interesting results (Misc).

https://doi.org/10.1371/journal.pone.0190950.s005

S4 Table. SNPs in strong linkage disequilibrium (LD) with rs11697300 based on data provided by the 1000 Genomes Project (Consortium 2012).

SNP: single nucleotide polymorphism. Data available on http://www.ensembl.org/Homo_sapiens/Variation/Explore?db=core;r=20:737398-738398;v=rs11697300;vdb=variation;vf=107862839 . ASW: African Ancestry in Southwest US, CEU: Utah residents with Northern and Western European Ancestry, MXL: Mexican Ancestry in Los Angeles, California. Populations were chosen to represent the Health and Retirement Study sample population.

https://doi.org/10.1371/journal.pone.0190950.s006

S5 Table. SNPs in strong linkage disequilibrium (LD) with rs11697300 based on the HRS dataset.

SNP: single nucleotide polymorphism, Chr: chromosome, Pos: genomic position, ID: SNP name, Ref: reference allele, Alt: alternative allele, Freq: reference allele frequency, r 2 : LD score with rs11697300. GCTA-LOCO: mixed-linear model implemented with GCTA's leaving-one-chromosome-out method with regression coefficient ( b ), standard error ( s . e .), and p-value ( p ).

https://doi.org/10.1371/journal.pone.0190950.s007

Acknowledgments

The authors would like to thank the Health and Retirement Study team from the University of Michigan, the Center for Inherited Disease Research (CIDR), the Genetics Coordinating Center of the University of Washington (UWGCC), the Database of Genotypes and Phenotypes (dbGaP) for providing the data, and the 1000 Genomes Project. The authors' special thanks goes to Martin Dockner who set up and maintained the technical infrastructure enabling the work on this project.

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Book cover

Lifespan Perspectives on Natural Disasters pp 219–240 Cite as

The Psychology Behind Helping and Prosocial Behaviors: An Examination from Intention to Action

  • Jennifer L. Silva 2 ,
  • Loren D. Marks Ph.D. &
  • Katie E. Cherry 3  
  • First Online: 01 January 2009

1711 Accesses

16 Citations

6 Altmetric

When disasters strike, many people rise to the challenge of providing immediate assistance to those whose lives are in peril. The spectrum of helping behaviors to counter the devastating effects of a natural disaster is vast and can be seen on many levels, from concerned individuals and community groups to volunteer organizations and larger civic entities. In this chapter, we examine the psychology of helping in relation to natural disasters. Definitions of helping behaviors, why we help, and risks of helping others are discussed first. Next, we discuss issues specific to natural disasters and life span considerations, noting the developmental progression of age-related, altruistic motivations. We present a qualitative analysis of helping behaviors based on interviews with participants in the Louisiana Healthy Aging Study (LHAS; see Cherry, Silva, & Galea, Chapter 9). These data show that some people directly engaged in helping behaviors to further the relief effort after Hurricanes Katrina and Rita, while others spoke of helping indirectly through their associations with local churches and faith-based organizations that provided storm relief. Implications for helping behaviors and intentions to help in a post-disaster situation are considered.

  • Prosocial Behavior
  • Altruistic Behavior
  • Relief Effort
  • Faith Community
  • Reciprocal Altruism

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Acknowledgment

We thank Tracey Frias, Miranda Melancon, and Zia McWilliams for their assistance with data summary and qualitative analyses. We also thank Erin C. Goforth for her helpful comments on an earlier version of this manuscript.

This research was supported by grants from the Louisiana Board of Regents through the Millennium Trust Health Excellence Fund (HEF[2001-06]-02) and the National Institute on Aging P01 AG022064. This support is gratefully acknowledged.

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Silva, J.L., Marks, L.D., Cherry, K.E. (2009). The Psychology Behind Helping and Prosocial Behaviors: An Examination from Intention to Action. In: Cherry, K. (eds) Lifespan Perspectives on Natural Disasters. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0393-8_11

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Helping Behavior

The following is an excerpt from “More about Generosity: An Addendum to the Generosity, Social Psychology and Philanthropy Literature Reviews,” (University of Notre Dame, July 7, 2009).

Banyard, Victoria L. 2008. Measurement and correlates of prosocial bystander behavior: the case of interpersonal violence. Violence and Victims 23:83-97.

The field of social psychology has long investigated the role of prosocial bystanders in assisting crime victims and helping in emergency situations. This research has usually been experimental and has established important principles about the conditions under which individuals will choose to engage in prosocial bystander behaviors. More recently, interest has grown in applying this work to the important practical problem of preventing interpersonal violence in communities. Yet, to date, there has been little research on the role of bystanders in cases of interpersonal violence. The current study is thus exploratory. Using a sample of 389 undergraduates, the study discusses key issues in the development of measures to investigate these questions and presents preliminary analyses of correlates of bystander behavior in the context of sexual and intimate partner violence.

Batson, C. Daniel, Jakob Hakansson Eklund, Valerie L. Chermok, Jennifer L. Hoyt, and Biaggio G. Ortiz. 2007.“An additional antecedent of empathic concern: Valuing the welfare of the person in need.” Journal of Personality and Social Psychology 93:65-74.

Two experiments examined the role of valuing the welfare of a person in need as an antecedent of empathic concern. Specifically, these experiments explored the relation of such valuing to a well-known antecedent-perspective taking. In Experiment 1, both perspective taking and valuing were manipulated, and each independently increased empathic concern, which, in turn, increased helping behavior. In Experiment 2, only valuing was manipulated. Manipulated valuing increased measured perspective taking and, in part as a result, increased empathic concern, which, in turn, increased helping. Valuing appears to be an important, largely overlooked, situational antecedent of feeling empathy for a person in need.

Bshary, Redouan., and R. Bergmüller. 2008. “Distinguishing four fundamental approaches to the evolution of helping.” Journal of Evolutionary Biology 21:405-420.

The evolution and stability of helping behaviour has attracted great research efforts across disciplines. However, the field is also characterized by a great confusion over terminology and a number of disagreements, often between disciplines but also along taxonomic boundaries. In an attempt to clarify several issues, we identify four distinct research fields concerning the evolution of helping: (1) basic social evolution theory that studies helping within the framework of Hamilton’s inclusive fitness concept, i.e. direct and indirect benefits, (2) an ecological approach that identifies settings that promote life histories or interaction patterns that favour unconditional cooperative and altruistic behaviour, e.g. conditions that lead to interdependency or interactions among kin, (3) the game theoretic approach that identifies strategies that provide feedback and control mechanisms (protecting from cheaters) favouring cooperative behaviour (e.g. pseudo-reciprocity, reciprocity), and (4) the social scientists’ approach that particularly emphasizes the special cognitive requirements necessary for human cooperative strategies. The four fields differ with respect to the ‘mechanisms’ and the ‘conditions’ favouring helping they investigate. Other major differences concern a focus on either the life-time fitness consequences or the immediate payoff consequences of behaviour, and whether the behaviour of an individual or a whole interaction is considered. We suggest that distinguishing between these four separate fields and their complementary approaches will reduce misunderstandings, facilitating further integration of concepts within and across disciplines.

Flynn, Francis J., and Vanessa Lake. 2008. “If you need help, just ask: underestimating compliance with direct requests for help.” Journal of Personality and Social Psychology 95:128-143.

A series of studies tested whether people underestimate the likelihood that others will comply with their direct requests for help. In the first 3 studies, people underestimated by as much as 50% the likelihood that others would agree to a direct request for help, across a range of requests occurring in both experimental and natural field settings. Studies 4 and 5 demonstrated that experimentally manipulating a person’s perspective (as help seeker or potential helper) could elicit this underestimation effect. Finally, in Study 6, the authors explored the source of the bias, finding that help seekers were less willing than potential helpers were to appreciate the social costs of refusing a direct request for help (the costs of saying “no”), attending instead to the instrumental costs of helping (the costs of saying “yes”).

Hendriks, Michelle, Marcel A. Croon, and Ad Vingerhoets. 2008. “Social reactions to adult crying: The help-soliciting function of tears.” Journal of Social Psychology 148:22-41.

The authors investigated how people believe they respond to crying individuals. Participants (N = 530) read 6 vignettes describing situations in which they encountered a person who either cried or did not cry. Participants reported they would give more emotional support to and express less negative affect toward a crying person than a noncrying person. However, regression analyses revealed that participants judged a crying person less positively than a non-crying person and felt more negative feelings in the presence of a crying person than a non-crying person. The valence of the situation strongly moderated these reactions. Overall, results support the theory that crying is an attachment behavior designed to elicit help from others.

Kunstman, Jonathan W., and E. Ashby Plant. 2008. “Racing to help: Racial bias in high emergency helping situations.” Journal of Personality and Social Psychology 95:1499-1510.

The present work explored the influence of emergency severity on racial bias in helping behavior. Three studies placed participants in staged emergencies and measured differences in the speed and quantity of help offered to Black and White victims. Consistent with predictions, as the level of emergency increased, the speed and quality of help White participants offered to Black victims relative to White victims decreased. In line with the authors’ predictions based on an integration of aversive racism theory and the arousal: cost-reward perspective on prosocial behavior, severe emergencies with Black victims elicited high levels of aversion from White helpers, and these high levels of aversion were directly related to the slower help offered to Black victims but not to White victims (Study 1). In addition, the bias was related to White individuals’ interpretation of the emergency as less severe and themselves as less responsible to help Black victims rather than White victims (Studies 2 and 3). Study 3 also illustrated that emergency racial bias is unique to White individuals’ responses to Black victims and not evinced by Black helpers. ( APA )

Lindsey, Lisa L. Massi, Kimo Ah Yun, and Jennifer B. Hill. 2007. “Anticipated guilt as motivation to help unknown others: An examination of empathy as a moderator.” Communication Research 34:468-480.

Previous research finds that messages that induce substantial perceptions of (a) an unknown-other directed threat, (b) response-efficacy, and © self-efficacy result in feelings of anticipated guilt that subsequently motivate behavioral intent, and ultimately, behaviors to avert the threat to unknown others. It is not clear, however, if certain individual differences make people more or less likely to experience anticipatory guilt. To this end, this study asks whether empathic concern and perspective taking moderates the relationship between exposure to such a message and anticipated guilt. This question is tested by focusing on the topic of bone marrow donation. Participants are assigned randomly to 1 of 3 message conditions and complete a questionnaire designed to assess perspective taking, empathic concern, and anticipated guilt. The data indicate that the message has a substantial direct effect on guilt anticipation, and neither a direct effect for the empathy dimensions nor an interaction effect between empathy and anticipated guilt are present.

Levine, Robert V., Stephen Reysen, and Ellen Ganz. 2008 .”The kindness of strangers revisited: A comparison of 24 U.S. cities.” Social Indicators Research 85:461-481.

Three field studies compared helping behavior across a sample of 24 small, medium and large cities across the United States. The relationship of helping to statistics reflecting the demographic, social, and economic characteristics of these communities was then examined. The strongest predictors of city differences in helping were population size, population density, economic purchasing power and, to a somewhat lesser extent, walking speed. Changes in several community variables over the past decade were also associated with helping: population size, economic well-being as measured by both purchasing power and poverty rates, and crime rates. These data were compared to similar data collected 13-15 years ago. (SocAbs)

Miller, Christian B. 2009. “Empathy, social psychology, and global helping traits.” Philosophical Studies 142:247-275.

The central virtue at issue in recent philosophical discussions of the empirical adequacy of virtue ethics has been the virtue of compassion. Opponents of virtue ethics such as Gilbert Harman and John Doris argue that experimental results from social psychology concerning helping behavior are best explained not by appealing to so-called ‘global’ character traits like compassion, but rather by appealing to external situational forces or, at best, to highly individualized ‘local’ character traits. In response, a number of philosophers have argued that virtue ethics can accommodate the empirical results in question. My own view is that neither side of this debate is looking in the right direction. For there is an impressive array of evidence from the social psychology literature which suggests that many people do possess one or more robust global character traits pertaining to helping others in need. But at the same time, such traits are noticeably different from a traditional virtue like compassion.

Nakao, Hisashi, and Shoji Itakura. 2009. “An integrated view of empathy: Psychology, philosophy, and neuroscience.” Integrative Psychological & Behavioral Science 43:42-52.

In this paper, we will examine and untangle a conflict mainly between a developmental psychologist, Martin Hoffman and a social psychologist, Daniel Batson. According to Hoffman, empathic distress, a vicarious feeling through empathy, is transformed into an altruistic motivation. Batson and others on the other hand, criticize Hoffman, claiming that empathic altruism has no relation with empathic distress. We will point out some problems with Batson’s position by referring to the results of fMRI experiments that suggest empathic distress and empathic altruism share a common basis, and defend Hoffman’s argument. This will also offer new insights into the evolution of empathy.

Sprecher, Susan, Beverly Fehr and Corinne Zimmerman. 2007. “Expectation for mood enhancement as a result of helping: The effects of gender and compassionate love.” Sex Roles 56:43-549.

Several theoretical perspectives in the social psychology literature on helping suggest that people forecast the benefit that they will receive as a result of helping others, and help only if they determine that it is rewarding to do so. One type of self-benefit that can be received from helping is an enhancement of positive mood. The major hypotheses of the present study were: (1) women, to a greater degree than men, would expect to experience enhanced positive mood as a consequence of both helping and receiving help in a relational context; and (2) those who are high in compassionate love for others would expect to experience enhanced positive mood from giving and receiving help relative to those who are lower on compassionate love. Support was found for both hypotheses. In addition, women were more likely than men to rate certain helping behaviors in a relational context (e.g., providing verbal support) as good examples of “compassionate love acts.” The meaning of the results with respect to altruism and for gender differences in

Shaw, Eric K. 2008. “Fictive kin and helping behavior: A social psychosocial exploration among Haitian immigrants, Christian fundamentalists, and gang members.” Sociation Today 6. (http://www.ncsociology.org/sociationtoday/v62/fictive.htm).

This paper is primarily about why individuals choose to help others. Kinship is an important concept in research on helping behavior with common distinctions made between kin, non-kin, and fictive kin. Unrelated individuals become ‘adopted’ family members who accept the affection, obligations and duties of ‘real’ kin. Understanding more about the subjective nature of fictive kin relations is important for understanding individual motivations for engaging in various helping behaviors. Gang members are found to use fictive kin terminology and gangs are a substitute family for members. Adapted from the source document. (SocAbs)

Tang, Thomas Li-Ping, et al. 2008. “To help or not to help? The Good Samaritan Effect and the Love of Money on helping behavior.” Journal of Business Ethics 82:865-887.

This research tests a model of employee helping behavior (a component of Organizational Citizenship Behavior, OCB ) that involves a direct path (Intrinsic Motives → Helping Behavior, the Good Samaritan Effect) and an indirect path (the Love of Money → Extrinsic Motives → Helping Behavior). Results for the full sample supported the Good Samaritan Effect. Further, the love of money was positively related to extrinsic motives that were negatively related with helping behavior. We tested the model across four cultures (the USA ., Taiwan, Poland, and Egypt). The Good Samaritan Effect was significant for all four countries. For the indirect path, the first part was significant for all countries, except Egypt, whereas the second part was significant for Poland only. For Poland, the indirect path was significant and positive. The love of money may cause one to help in one culture (Poland) but not to help in others. Results were discussed in the light of ethical decision making.

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12: Altruism

Helping and Prosocial Behavior

By Dennis L. Poepsel and David A. Schroeder Truman State University, University of Arkansas

People often act to benefit other people, and these acts are examples of prosocial behavior. Such behaviors may come in many guises: helping an individual in need; sharing personal resources; volunteering time, effort, and expertise; cooperating with others to achieve some common goals. The focus of this module is on helping—prosocial acts in dyadic situations in which one person is in need and another provides the necessary assistance to eliminate the other’s need. Although people are often in need, help is not always given. Why not? The decision of whether or not to help is not as simple and straightforward as it might seem, and many factors need to be considered by those who might help. In this module, we will try to understand how the decision to help is made by answering the question: Who helps when and why?

Learning Objectives

  • Learn which situational and social factors affect when a bystander will help another in need.
  • Understand which personality and individual difference factors make some people more likely to help than others.
  • Discover whether we help others out of a sense of altruistic concern for the victim, for more self-centered and egoistic motives, or both.

Introduction

A younger man and woman help an elderly gentleman down the street.

Go to YouTube and search for episodes of “Primetime: What Would You Do?” You will find video segments in which apparently innocent individuals are victimized, while onlookers typically fail to intervene. The events are all staged, but they are very real to the bystanders on the scene. The entertainment offered is the nature of the bystanders’ responses, and viewers are outraged when bystanders fail to intervene. They are convinced that they would have helped. But would they? Viewers are overly optimistic in their beliefs that they would play the hero. Helping may occur frequently, but help is not always given to those in need. So  when  do people help, and when do they not? All people are not equally helpful— who  helps?  Why  would a person help another in the first place? Many factors go into a person’s decision to help—a fact that the viewers do not fully appreciate. This module will answer the question: Who helps when and why?

When Do People Help?

Social psychologists are interested in answering this question because it is apparent that people vary in their tendency to help others. In 2010 for instance, Hugo Alfredo Tale-Yax was stabbed when he apparently tried to intervene in an argument between a man and woman. As he lay dying in the street, only one man checked his status, but many others simply glanced at the scene and continued on their way. (One passerby did stop to take a cellphone photo, however.) Unfortunately, failures to come to the aid of someone in need are not unique, as the segments on “What Would You Do?” show. Help is not always forthcoming for those who may need it the most. Trying to understand why people do not always help became the focus of  bystander intervention  research (e.g., Latané & Darley, 1970).

To answer the question regarding when people help, researchers have focused on

  • how bystanders come to define emergencies,
  • when they decide to take responsibility for  helping , and
  • how the costs and benefits of intervening affect their decisions of whether to help.

Defining the situation: The role of pluralistic ignorance

The decision to help is not a simple yes/no proposition. In fact, a series of questions must be addressed before help is given—even in emergencies in which time may be of the essence. Sometimes help comes quickly; an onlooker recently jumped from a Philadelphia subway platform to help a stranger who had fallen on the track. Help was clearly needed and was quickly given. But some situations are ambiguous, and potential helpers may have to decide whether a situation is one in which help, in fact,  needs  to be given.

To define ambiguous situations (including many emergencies), potential helpers may look to the action of others to decide what should be done. But those others are looking around too, also trying to figure out what to do. Everyone is looking, but no one is acting! Relying on others to define the situation and to then erroneously conclude that no intervention is necessary when help is actually needed is called  pluralistic ignorance  (Latané & Darley, 1970). When people use the  inactions  of others to define their own course of action, the resulting pluralistic ignorance leads to less help being given.

Do I have to be the one to help?: Diffusion of responsibility

A huge crowd of people stand shoulder to shoulder during the 2010 World Cup.

Simply being with others may facilitate or inhibit whether we get involved in other ways as well. In situations in which help is needed, the presence or absence of others may affect whether a bystander will assume personal responsibility to give the assistance. If the bystander is alone, personal responsibility to help falls solely on the shoulders of that person. But what if others are present? Although it might seem that having more potential helpers around would increase the chances of the victim getting help, the opposite is often the case. Knowing that someone else  could  help seems to relieve bystanders of personal responsibility, so bystanders do not intervene. This phenomenon is known as  diffusion of responsibility  (Darley & Latané, 1968).

On the other hand, watch the video of the race officials following the 2013 Boston Marathon after two bombs exploded as runners crossed the finish line. Despite the presence of many spectators, the yellow-jacketed race officials immediately rushed to give aid and comfort to the victims of the blast. Each one no doubt felt a personal responsibility to help by virtue of their official capacity in the event; fulfilling the obligations of their roles overrode the influence of the diffusion of responsibility effect.

There is an extensive body of research showing the negative impact of pluralistic ignorance and diffusion of responsibility on helping (Fisher et al., 2011), in both emergencies and everyday need situations. These studies show the tremendous importance potential helpers place on the social situation in which unfortunate events occur, especially when it is not clear what should be done and who should do it. Other people provide important social information about how we should act and what our personal obligations might be. But does knowing a person needs help and accepting responsibility to provide that help mean the person will get assistance? Not necessarily.

The costs and rewards of helping

The nature of the help needed plays a crucial role in determining what happens next. Specifically, potential helpers engage in a  cost–benefit analysis  before getting involved (Dovidio et al., 2006). If the needed help is of relatively low cost in terms of time, money, resources, or risk, then help is more likely to be given. Lending a classmate a pencil is easy; confronting someone who is bullying your friend is an entirely different matter. As the unfortunate case of Hugo Alfredo Tale-Yax demonstrates, intervening may cost the life of the helper.

The potential rewards of helping someone will also enter into the equation, perhaps offsetting the cost of helping. Thanks from the recipient of help may be a sufficient reward. If helpful acts are recognized by others, helpers may receive social rewards of praise or monetary rewards. Even avoiding feelings of guilt if one does not help may be considered a benefit. Potential helpers consider how much helping will cost and compare those costs to the rewards that might be realized; it is the economics of helping. If costs outweigh the rewards, helping is less likely. If rewards are greater than cost, helping is more likely.

Do you know someone who always seems to be ready, willing, and able to help? Do you know someone who never helps out? It seems there are personality and individual differences in the helpfulness of others. To answer the question of who chooses to help, researchers have examined 1) the role that sex and gender play in helping, 2) what personality traits are associated with helping, and 3) the characteristics of the “prosocial personality.”

Who are more helpful—men or women?

A group of men and women stand together in a muddy field with shovels and wheelbarrows as they participate in an outdoor volunteer project.

In terms of individual differences that might matter, one obvious question is whether men or women are more likely to help. In one of the “What Would You Do?” segments, a man takes a woman’s purse from the back of her chair and then leaves the restaurant. Initially, no one responds, but as soon as the woman asks about her missing purse, a group of men immediately rush out the door to catch the thief. So, are men more helpful than women? The quick answer is “not necessarily.” It all depends on the type of help needed. To be very clear, the general level of helpfulness may be pretty much equivalent between the sexes, but men and women help in different ways (Becker & Eagly, 2004; Eagly & Crowley, 1986). What accounts for these differences?

Two factors help to explain sex and gender differences in helping. The first is related to the cost–benefit analysis process discussed previously. Physical differences between men and women may come into play (e.g., Wood & Eagly, 2002); the fact that men tend to have greater upper body strength than women makes the cost of intervening in some situations less for a man. Confronting a thief is a risky proposition, and some strength may be needed in case the perpetrator decides to fight. A bigger, stronger bystander is less likely to be injured and more likely to be successful.

The second explanation is simple socialization. Men and women have traditionally been raised to play different social roles that prepare them to respond differently to the needs of others, and people tend to help in ways that are most consistent with their gender roles. Female gender roles encourage women to be compassionate, caring, and nurturing; male gender roles encourage men to take physical risks, to be heroic and chivalrous, and to be protective of those less powerful. As a consequence of social training and the gender roles that people have assumed, men may be more likely to jump onto subway tracks to save a fallen passenger, but women are more likely to give comfort to a friend with personal problems (Diekman & Eagly, 2000; Eagly & Crowley, 1986). There may be some specialization in the types of help given by the two sexes, but it is nice to know that there is someone out there—man or woman—who is able to give you the help that you need, regardless of what kind of help it might be.

A trait for being helpful: Agreeableness

Graziano and his colleagues (e.g., Graziano & Tobin, 2009; Graziano, Habishi, Sheese, & Tobin, 2007) have explored how  agreeableness —one of the Big Five personality dimensions (e.g., Costa & McCrae, 1988)—plays an important role in  prosocial behavior . Agreeableness is a core trait that includes such dispositional characteristics as being sympathetic, generous, forgiving, and helpful, and behavioral tendencies toward harmonious social relations and likeability. At the conceptual level, a positive relationship between agreeableness and helping may be expected, and research by Graziano et al. (2007) has found that those higher on the agreeableness dimension are, in fact, more likely than those low on agreeableness to help siblings, friends, strangers, or members of some other group. Agreeable people seem to expect that others will be similarly cooperative and generous in interpersonal relations, and they, therefore, act in helpful ways that are likely to elicit positive social interactions.

Searching for the prosocial personality

Rather than focusing on a single trait, Penner and his colleagues (Penner, Fritzsche, Craiger, & Freifeld, 1995; Penner & Orom, 2010) have taken a somewhat broader perspective and identified what they call the  prosocial personality orientation . Their research indicates that two major characteristics are related to the prosocial personality and prosocial behavior. The first characteristic is called  other-oriented empathy : People high on this dimension have a strong sense of social responsibility, empathize with and feel emotionally tied to those in need, understand the problems the victim is experiencing, and have a heightened sense of moral obligation to be helpful. This factor has been shown to be highly correlated with the trait of agreeableness discussed previously. The second characteristic,  helpfulness , is more behaviorally oriented. Those high on the helpfulness factor have been helpful in the past, and because they believe they can be effective with the help they give, they are more likely to be helpful in the future.

Finally, the question of  why  a person would help needs to be asked. What motivation is there for that behavior? Psychologists have suggested that 1) evolutionary forces may serve to predispose humans to help others, 2) egoistic concerns may determine if and when help will be given, and 3) selfless, altruistic motives may also promote helping in some cases.

Evolutionary roots for prosocial behavior

Cave paintings from Western Australia appear to show an ancient family dressed in traditional clothes.

Our evolutionary past may provide keys about why we help (Buss, 2004). Our very survival was no doubt promoted by the prosocial relations with clan and family members, and, as a hereditary consequence, we may now be especially likely to help those closest to us—blood-related relatives with whom we share a genetic heritage. According to evolutionary psychology, we are helpful in ways that increase the chances that our DNA will be passed along to future generations (Burnstein, Crandall, & Kitayama, 1994)—the goal of the “selfish gene” (Dawkins, 1976). Our personal DNA may not always move on, but we can still be successful in getting some portion of our DNA transmitted if our daughters, sons, nephews, nieces, and cousins survive to produce offspring. The favoritism shown for helping our blood relatives is called  kin selection  (Hamilton, 1964).

But, we do not restrict our relationships just to our own family members. We live in groups that include individuals who are unrelated to us, and we often help them too. Why?  Reciprocal altruism  (Trivers, 1971) provides the answer. Because of reciprocal altruism, we are all better off in the long run if we help one another. If helping someone now increases the chances that you will be helped later, then your overall chances of survival are increased. There is the chance that someone will take advantage of your help and not return your favors. But people seem predisposed to identify those who fail to reciprocate, and punishments including social exclusion may result (Buss, 2004). Cheaters will not enjoy the benefit of help from others, reducing the likelihood of the survival of themselves and their kin.

Evolutionary forces may provide a general inclination for being helpful, but they may not be as good an explanation for why we help in the here and now. What factors serve as proximal influences for decisions to help?

Egoistic motivation for helping

Most people would like to think that they help others because they are concerned about the other person’s plight. In truth, the reasons why we help may be more about ourselves than others: Egoistic or selfish motivations may make us help. Implicitly, we may ask, “What’s in it  for me ?” There are two major theories that explain what types of reinforcement helpers may be seeking. The  negative state relief model  (e.g., Cialdini, Darby, & Vincent, 1973; Cialdini, Kenrick, & Baumann, 1982) suggests that people sometimes help in order to make themselves feel better. Whenever we are feeling sad, we can use helping someone else as a positive mood boost to feel happier. Through socialization, we have learned that helping can serve as a secondary reinforcement that will relieve negative moods (Cialdini & Kenrick, 1976).

The  arousal: cost–reward model  provides an additional way to understand why people help (e.g., Piliavin, Dovidio, Gaertner, & Clark, 1981). This model focuses on the aversive feelings aroused by seeing another in need. If you have ever heard an injured puppy yelping in pain, you know that feeling, and you know that the best way to relieve that feeling is to help and to comfort the puppy. Similarly, when we see someone who is suffering in some way (e.g., injured, homeless, hungry), we vicariously experience a sympathetic arousal that is unpleasant, and we are motivated to eliminate that aversive state. One way to do that is to help the person in need. By eliminating the victim’s pain, we eliminate our own aversive arousal. Helping is an effective way to alleviate our own discomfort.

As an egoistic model, the arousal: cost–reward model explicitly includes the cost/reward considerations that come into play. Potential helpers will find ways to cope with the aversive arousal that will minimize their costs—maybe by means other than direct involvement. For example, the costs of directly confronting a knife-wielding assailant might stop a bystander from getting involved, but the cost of some  indirect  help (e.g., calling the police) may be acceptable. In either case, the victim’s need is addressed. Unfortunately, if the costs of helping are too high, bystanders may reinterpret the situation to justify not helping at all. For some, fleeing the situation causing their distress may do the trick (Piliavin et al., 1981).

The egoistically based negative state relief model and the arousal: cost–reward model see the primary motivation for helping as being the helper’s own outcome. Recognize that the victim’s outcome is of relatively little concern to the helper—benefits to the victim are incidental byproducts of the exchange (Dovidio et al., 2006). The victim may be helped, but the helper’s real motivation according to these two explanations is egoistic: Helpers help to the extent that it makes them feel better.

Altruistic help

Although many researchers believe that  egoism  is the only motivation for helping, others suggest that  altruism —helping that has as its ultimate goal the improvement of another’s welfare—may also be a motivation for helping under the right circumstances. Batson (2011) has offered the  empathy–altruism model  to explain altruistically motivated helping for which the helper expects no benefits. According to this model, the key for altruism is empathizing with the victim, that is, putting oneself in the shoes of the victim and imagining how the victim must feel. When taking this perspective and having  empathic concern , potential helpers become primarily interested in increasing the well-being of the victim, even if the helper must incur some costs that might otherwise be easily avoided. The empathy–altruism model does not dismiss egoistic motivations; helpers not empathizing with a victim may experience  personal distress  and have an egoistic motivation, not unlike the feelings and motivations explained by the arousal: cost–reward model. Because egoistically motivated individuals are primarily concerned with their own cost–benefit outcomes, they are less likely to help if they think they can escape the situation with no costs to themselves. In contrast, altruistically motivated helpers are willing to accept the cost of helping to benefit a person with whom they have empathized—this “self-sacrificial” approach to helping is the hallmark of altruism (Batson, 2011).

A woman stops on the sidewalk to offer food to a man holding a sign reading "Homeless, please help Thank you."

Although there is still some controversy about whether people can ever act for purely altruistic motives, it is important to recognize that, while helpers may derive some personal rewards by helping another, the help that has been given is also benefitting someone who was in need. The residents who offered food, blankets, and shelter to stranded runners who were unable to get back to their hotel rooms because of the Boston Marathon bombing undoubtedly received positive rewards because of the help they gave, but those stranded runners who were helped got what they needed badly as well. “In fact, it is quite remarkable how the fates of people who have never met can be so intertwined and complementary. Your benefit is mine; and mine is yours” (Dovidio et al., 2006, p. 143).

A Red Cross volunteer assists an elderly lady from Mozambique, where a food distribution was taking place.

We started this module by asking the question, “Who helps when and why?” As we have shown, the question of when help will be given is not quite as simple as the viewers of “What Would You Do?” believe. The power of the situation that operates on potential helpers in real time is not fully considered. What might appear to be a split-second decision to help is actually the result of consideration of multiple situational factors (e.g., the helper’s interpretation of the situation, the presence and ability of others to provide the help, the results of a cost–benefit analysis) (Dovidio et al., 2006). We have found that men and women tend to help in different ways—men are more impulsive and physically active, while women are more nurturing and supportive. Personality characteristics such as agreeableness and the prosocial personality orientation also affect people’s likelihood of giving assistance to others. And, why would people help in the first place? In addition to evolutionary forces (e.g., kin selection, reciprocal altruism), there is extensive evidence to show that helping and prosocial acts may be motivated by selfish, egoistic desires; by selfless, altruistic goals; or by some combination of egoistic and altruistic motives. (For a fuller consideration of the field of prosocial behavior, we refer you to Dovidio et al. [2006].)

Test your Understanding

  • Batson, C. D. (2011).  Altruism in humans . New York, NY: Oxford University Press.
  • Becker, S. W., & Eagly, A. H. (2004). The heroism of women and men.  American Psychologist, 59 , 163–178.
  • Burnstein, E., Crandall, C., & Kitayama, S. (1994). Some neo-Darwinian decision rules for altruism: Weighing cues for inclusive fitness as a function of the biological importance of the decision.  Journal of Personality and Social Psychology, 67 , 773–789.
  • Buss, D. M. (2004).  Evolutionary psychology: The new science of the mind . Boston, MA: Allyn Bacon.
  • Cialdini, R. B., & Kenrick, D. T. (1976). Altruism as hedonism: A social developmental perspective on the relationship of negative mood state and helping.  Journal of Personality and Social Psychology, 34 , 907–914.
  • Cialdini, R. B., Darby, B. K. & Vincent, J. E. (1973). Transgression and altruism: A case for hedonism.  Journal of Experimental Social Psychology, 9 , 502–516.
  • Cialdini, R. B., Kenrick, D. T., & Baumann, D. J. (1982). Effects of mood on prosocial behavior in children and adults. In N. Eisenberg (Ed.),  The development of prosocial behavior  (pp. 339–359). New York, NY: Academic Press.
  • Costa, P. T., & McCrae, R. R. (1998). Trait theories in personality. In D. F. Barone, M. Hersen, & V. B. Van Hasselt (Eds.),  Advanced Personality  (pp. 103–121). New York, NY: Plenum.
  • Darley, J. M. & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility.  Journal of Personality and Social Psychology, 8 , 377–383.
  • Dawkins, R. (1976).  The selfish gene . Oxford, U.K.: Oxford University Press.
  • Diekman, A. B., & Eagly, A. H. (2000). Stereotypes as dynamic structures: Women and men of the past, present, and future.  Personality and Social Psychology Bulletin, 26 , 1171–1188.
  • Dovidio, J. F., Piliavin, J. A., Schroeder, D. A., & Penner, L. A. (2006).  The social psychology of prosocial behavior . Mahwah, NJ: Erlbaum.
  • Eagly, A. H., & Crowley, M. (1986). Gender and helping behavior: A meta-analytic review of the social psychological literature.  Psychological Review, 66 , 183–201.
  • Fisher, P., Krueger, J. I., Greitemeyer, T., Vogrincie, C., Kastenmiller, A., Frey, D., Henne, M., Wicher, M., & Kainbacher, M. (2011). The bystander-effect: A meta-analytic review of bystander intervention in dangerous and non-dangerous emergencies.  Psychological Bulletin, 137 , 517–537.
  • Graziano, W. G., & Tobin, R. (2009). Agreeableness. In M. R. Leary & R. H. Hoyle (Eds.),  Handbook of Individual Differences in Social Behavior . New York, NY: Guilford Press.
  • Graziano, W. G., Habashi, M. M., Sheese, B. E., & Tobin, R. M. (2007). Agreeableness, empathy, and helping: A person x situation perspective.  Journal of Personality and Social Psychology, 93 , 583–599.
  • Hamilton, W. D. (1964). The genetic evolution of social behavior.  Journal of Theoretical Biology, 7 , 1–52.
  • Latané, B., & Darley, J. M. (1970).  The unresponsive bystander: Why doesn’t he help?  New York, NY: Appleton-Century-Crofts.
  • Penner, L. A., & Orom, H. (2010). Enduring goodness: A Person X Situation perspective on prosocial behavior. In M. Mikuliner & P.R. Shaver, P.R. (Eds.),  Prosocial motives, emotions, and behavior: The better angels of our nature  (pp. 55–72). Washington, DC: American Psychological Association.
  • Penner, L. A., Fritzsche, B. A., Craiger, J. P., & Freifeld, T. R. (1995). Measuring the prosocial personality. In J. Butcher & C.D. Spielberger (Eds.),  Advances in personality assessment  (Vol. 10, pp. 147–163). Hillsdale, NJ: Erlbaum.
  • Piliavin, J. A., Dovidio, J. F., Gaertner, S. L., & Clark, R. D., III (1981).  Emergency intervention . New York, NY: Academic Press.
  • Trivers, R. (1971). The evolution of reciprocal altruism.  Quarterly Review of Biology, 46 , 35–57.
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The phenomenon whereby people intervene to help others in need even if the other is a complete stranger and the intervention puts the helper at risk.

Prosocial acts that typically involve situations in which one person is in need and another provides the necessary assistance to eliminate the other’s need.

Relying on the actions of others to define an ambiguous need situation and to then erroneously conclude that no help or intervention is necessary.

When deciding whether to help a person in need, knowing that there are others who could also provide assistance relieves bystanders of some measure of personal responsibility, reducing the likelihood that bystanders will intervene.

A decision-making process that compares the cost of an action or thing against the expected benefit to help determine the best course of action.

A core personality trait that includes such dispositional characteristics as being sympathetic, generous, forgiving, and helpful, and behavioral tendencies toward harmonious social relations and likeability.

Social behavior that benefits another person.

A measure of individual differences that identifies two sets of personality characteristics (other-oriented empathy, helpfulness) that are highly correlated with prosocial behavior.

A component of the prosocial personality orientation; describes individuals who have a strong sense of social responsibility, empathize with and feel emotionally tied to those in need, understand the problems the victim is experiencing, and have a heightened sense of moral obligations to be helpful.

A component of the prosocial personality orientation; describes individuals who have been helpful in the past and, because they believe they can be effective with the help they give, are more likely to be helpful in the future.

According to evolutionary psychology, the favoritism shown for helping our blood relatives, with the goals of increasing the likelihood that some portion of our DNA will be passed on to future generations.

According to evolutionary psychology, a genetic predisposition for people to help those who have previously helped them.

An egoistic theory proposed by Cialdini et al. (1982) that claims that people have learned through socialization that helping can serve as a secondary reinforcement that will relieve negative moods such as sadness.

An egoistic theory proposed by Piliavin et al. (1981) that claims that seeing a person in need leads to the arousal of unpleasant feelings, and observers are motivated to eliminate that aversive state, often by helping the victim. A cost–reward analysis may lead observers to react in ways other than offering direct assistance, including indirect help, reinterpretation of the situation, or fleeing the scene.

A motivation for helping that has the improvement of the helper’s own circumstances as its primary goal.

A motivation for helping that has the improvement of another’s welfare as its ultimate goal, with no expectation of any benefits for the helper.

An altruistic theory proposed by Batson (2011) that claims that people who put themselves in the shoes of a victim and imagining how the victim feel will experience empathic concern that evokes an altruistic motivation for helping.

According to Batson’s empathy–altruism hypothesis, observers who empathize with a person in need (that is, put themselves in the shoes of the victim and imagine how that person feels) will experience empathic concern and have an altruistic motivation for helping.

According to Batson’s empathy–altruism hypothesis, observers who take a detached view of a person in need will experience feelings of being “worried” and “upset” and will have an egoistic motivation for helping to relieve that distress.

Introduction to Social Psychology Copyright © 2023 by Dr. Jennifer Brown is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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When somebody is in trouble, many people ignore their plight. Experiment in helping behaviour - how many people will help, how many will be bystanders?

The Bystander Effect in Helping Behaviour: An Experiment

research helping behaviour

Peter Prevos | 3 January 2006 Last Updated | 1 November 2020 1960 words | 10 minutes

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In 1964, Kitty Genovese was murdered outside her home in New York, while 38 witnesses did nothing to save her. This incident sparked a public outcry and was the catalyst for a considerable amount of research into what motivates people to help others in obvious need or what prevents them from helping. 1 The common-sense explanation for this seeming lack of compassion are vague concepts such as ‘‘alienation’’ and ‘‘apathy’’. These explanations stem from the idea that our moral actions are determined by character traits. This explanation of morality has, however, been contradicted by results from contemporary research in social psychology. 2

Most research on helping behaviour has used experimental methodologies to study situations in which someone has a sudden need for help. Factors such as clarity, the urgency of the need and skin colour, gender, age or handicap of the ‘‘victim’’, how many potential helpers are present and the relationship between victim and subject have been manipulated. 3

Researchers comparing helping behaviour in rural and urban areas consistently find that helping strangers is more likely in less densely populated areas. North, Tarrant & Hargreaves found that participants are more likely to help when they are in a positive mood and stimulated by music. 4 Wegner & Crano found that that contrasting skin colour of the victim and helper can also be a determinant for helping behaviour. 5

Several studies have demonstrated that the presence of other observers reduces the likelihood that any one person will display a helping response. 6 Contrary to common sense, there does not seem to be safety in numbers as the victim appears to have a greater likelihood of receiving help when there is a single witness rather than a group. Two possible psychological explanations proposed to explain the bystander effect are diffusion of responsibility among bystanders and a social norms explanation.

Diffusion of Responsibility

Latané & Darley developed a model that bystanders follow to decide if they will provide help or not. According to this model, a bystander goes through a five-step decision tree before assistance is provided. Helping responses can, however, be inhibited at any stage of the process, and no support is provided:

  • The bystander needs to notice that an event is taking place, but may fail to do so and not provide help.
  • The bystander needs to identify the event as some form of emergency. The situation may be ambiguous, preventing from help being given.
  • The bystander needs to take responsibility for helping but might avoid taking responsibility by assuming that somebody else will ( diffusion of responsibility ).
  • The bystander needs to decide on the appropriate helping response, but may not believe themselves to be competent to do so.
  • The bystander needs to implement that response, but this may be against their interest to do so, especially in dangerous situations.

In the diffusion of responsibility in stage three, each bystander notices the event and recognises that help is required, but fails to act because they assume that somebody else will take responsibility. This can be viewed as a means of reducing the psychological cost of not helping. The cost (e.g. embarrassment and guilt) are shared among the group, reducing the likelihood of intervention.

Social Norms and Helping Behaviour

Bryan & Test have shown that the bystander effect does not seem to appear if a helping response is first modelled by another observer, which seems to contradict the diffusion of responsibility concept. 7 They suggest that this behaviour can be explained by the process of conformity to social norms. The social norms explanation holds that people use actions from others as cues to decide what an appropriate response to specific situations should be, as demonstrated by Asch’sAsch’s conformity experiments. 8 Cialdini, Reno & Kallgren conducted five experiments to determine how social norms influence littering in public places and concluded that norms have a considerable impact on behaviour. 9

The methodology employed by Bryan & Test is, however, not fully comparable with the traditional helping model as described by Latané & Nida. The study by Bryan & Test involved two separate events—the driver first sees a driver in need being helped by somebody and a while later sees another driver in need that is not being helped. Separating these two moments eliminates the possibility of diffusion of responsibility as there are no bystanders in the second situation and the subject is alone in his or her car.

The objective of this study is to test whether the diffusion of responsibility or the social norms explanation applies to helping behaviour in a non-emergency situation. If the diffusion of responsibility explanation is correct, then the number of people providing help will be less when non-helping bystanders are present than when no bystanders are present. The social norms explanation predicts that helping behaviour is increased when a bystander offers help as compared to when no bystanders are present.

Participants

The study consisted of a task where a naive subject had an opportunity to help the experimenter in a non-emergency situation. All subjects were selected randomly when the circumstances were suitable for undertaking the experiment. A confederate was used to act as a helping or non-helping bystander in the investigation. The experiment consisted of 135 trials in total. The data was obtained from 75 trials on four Monash University campuses, and 47 responses were obtained by distance education students working in the general community. The data was appended with thirteen observations by the author obtained in a municipal park in central Victoria.

Materials & Procedure

The experimenter looked for a person standing alone in a public place, with no other person present within ten metres. The subject was not participating in any specific activity to ensure they would notice the event. The experimenter then ‘‘accidentally’’ dropped a pile of loose pages from a manilla folder close to the subject. The subject was defined as helping if he or she picked up one or more pages within thirty seconds from the drop. In cases where a third person started helping, or the subject was not able to help, the trial was not included in the results.

In the control condition, only the subject and the experimenter were present. In the test conditions, a confederate was standing nearby, and the papers were dropped equidistant between the subject and the confederate. In one condition, the associate did not help, while in the other condition, the confederate started to pick up the papers, providing a model for the appropriate behaviour. The helping behaviour of the confederate bystander was the independent variable and the percentage of subjects helping to pick up the papers the dependent variable.

The raw data shows an increase in helping behaviour in those scenarios where a confederate is present, as summarised in figure 1. In the control situation, 41% (n=44) of the subjects provided help. With a non-helping bystander present, the helping behaviour of subjects increased to 46% (n=48), and for a helping bystander, the percentage of helping subjects was increased to 56% (n=43).

Figure 1. Results of helping behaviour experiment.

A $\chi^2$ test for goodness of fit at a 5% confidence level was undertaken to compare the results with the control situation. The presence of a non-helping confederate resulted in an increase of helping compared to the control situation (41% v.s. 46%), albeit not significant: χ 2 (1,n=48)=0.48, p>0.05. The presence of a helping confederate resulted in a significant increase over the control situation (41% v.s. 56%), $\chi^2 (1, n=43)=3.95, p<0.05)$.

The results show an increase in helping behaviour when a bystander is present, failing to support the diffusion explanation, which predicts a decrease in helping behaviour. The results do, however, not provide a firm ground to reject the diffusion explanation, as the increase is not statistically significant. The social norms explanation predicts that helping behaviour is increased when a bystander offers help as compared to when no bystanders are present. The results support the social norms explanation as there is a statistically significant increase in helping behaviour when first modelled by another bystander.

Although Latané & Nida have shown that the bystander effect has been replicated in many studies in many different circumstances, it has not occurred in 100% of the cases. It is unlikely that all these studies suffer from the same internal validity problems as this study. There could thus also be theoretical reasons for the abnormal results. Both the diffusion of responsibility explanation and the social norms explanation can be true simultaneously as the diffusion of responsibility is extinguished by a bystander who models the appropriate behaviour. Further research is required to untangle the relationship between the diffusion of responsibility mechanism and social norms as determinants for helping behaviour.

Methodology

The study suffers from some methodological problems, weakening its internal validity. Subject variables, such as gender and age, were not controlled, nor where they noted in the results. The data can thus not be tested for any significant effects of subject variables. There is also some doubt whether the methodology has been consistent because the experiment consists of groups of trials by different experimenters. There are also situational nuisance variables, such as weather conditions, location and time of day the investigation was held, which were not controlled because of the fragmented execution of the experiment. On a windy day, for example, the need to help to pick up the papers is much more apparent to any bystander. Situational variables can also influence mood, which in turn can influence helping behaviour. The increase in helping behaviour in the non-helping bystander condition has most likely been confounded by any of these uncontrolled variables.

Practical Application

Latané & Nida are pessimistic about the possibility of generating practical outcomes of the helping behaviour experiments. The significance of these experiments is of a more philosophical than practical nature. A critical aspect of the helping behaviour research is that it shows that our moral behaviour is not governed by moral virtues or character traits but by much more mundane social mechanisms. When things go wrong, it is usually the bystander who is being blamed for failing to act morally. We attribute these failures, like in the Genovese case, to expressions of bad character traits. Experiments in helping behaviour are valuable in that they can provide a greater understanding of why people fail to do what is morally expected and thus lead to greater tolerance and understanding of others.

Brehm, S. S., & Kassin, S. M. (1996). Social psychology (3rd ed.). Boston: Houghton Mifflin.

Harman, G. (1999). Moral philosophy meets social psychology: Virtue ethics and the fundamental attribution error. In Proceedings of the Aristotelian Society/ (Vol. CXIX, pp. 316–331).

Piliavin, J. A. (2001). Sociology of altruism and prosocial behavior. In N. J. Smelser & P. B. Baltes (Eds.), International encyclopedia of the social and behavioral sciences (pp. 411–415). Elsevier.

North, A. C., Tarrant, M., & Hargreaves, D. J. (2004). The effects of music on helping behavior: A field study. Environment and Behavior , 36(2), 266–275.

Wegner, D. M., & Crano, W. D.(1975). Racial factors in helping behavior: An unobtrusive field experiment. Journal of Personality and Social Psychology , 32(5), 901–905.

Latané, B., & Nida, S. (1981). Ten years of research on group size and helping. Psychological Bulletin , 89, 308–324.

Bryan, J. H., & Test, M. A. (1967). Models and helping: Naturalistic studies in aiding behavior. Journal of Personality and Social Psychology , 6, 400–407.

Asch, S.(1995). Opinions and social pressure. In E. Aronson (Ed.), Readings about the social animal (7 ed., pp. 17–26). New York: Freeman.

Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology , 58(6), 1015–1026.

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The Center for Compassion and Altruism Research and Education

Stanford School of Medicine

Helping Behavior

December 10, 2012 by CCARE Staff

This section is categorized by the type of measure.

Dan Batson paradigm – Empathic response to Kathy’s plight, perception of similarity and perception of need all measure how much compassion and sympathy we feel for others, particularly strangers.

  • Batson, D. C., Lishner, D. A., Cook, J., & Sawyer, S. (2010). Similarity and nurturance : Two possible sources of empathy for strangers. Basic and Applied Social Psychology, 27 (1), 37-41.

Recording time taken to offer assistance  after to exposure to violence.

  • Bushman, B. J. & Anderson C. (2009). Comfortably numb: desensitizing effects of violent media on helping others. Psychological Science, 20 (3), 273-7.

Opportunity to Give/Willingness or Likelihood of Helping – Participants read hypothetical scenarios where they have a chance to help strangers and indicate the likelihood of their helping based on how they would behave in each situation at the present moment.

  • On a scale from 1 (not at all likely) to 9 (very likely), responses to the scenarios were averaged to form the dependent measure of helping.
  • DeWall, C., Baumeister, R. F., Gailliot, M. T., & Maner, J. K. (2008). Depletion makes the heart grow less helpful: Helping as a function of self-regulatory energy and genetic relatedness. Personality And Social Psychology Bulletin, 34 (12), 1653-1662. doi:10.1177/0146167208323981 .

Bond and Lader (Visual Analogue) Mood Rating Scale (BLMRS) –  two items of the BLMRS specifically assess subjective prosocial effects:antagonistic/amicable and withdrawn/gregarious.

  • Dumont, G. H., Sweep, F. J., van der Steen, R. R., Hermsen, R. R., Donders, A. T., Touw, D. J., & … Verkes, R. J. (2009). Increased oxytocin concentrations and prosocial feelings in humans after ecstasy (3,4-methylenedioxymethamphetamine) administration. Social Neuroscience, 4 (4), 359-366. doi:10.1080/17470910802649470 .

The number of people approached for a contribution and the average amount of the contributions made can be measured in response to being asked for help.

  • Flynn, F. J., & Lake, V. (2008). “If you need help, just ask”: Underestimating compliance with direct requests for help. Journal of Personality and Social Psychology, 95 , 128 -143.

Dichotomous measure of whether voluntary help is provided measures prosocial behavior.

  • Grant, A. M., & Gino, F. (2010). A little thanks goes a long way: Explaining why gratitude expressions motivate prosocial behavior. Journal of Personality and Social Psychology, 98 (6), 946-955. doi:10.1037/a0017935 .

Charitable Giving Behavior by   self-reported satisfaction after either voluntary or mandatory giving.

  • Harbaugh, W. T., Mayr, U., & Burghart, D. R. (2007). Neural responses to taxation and voluntary giving reveal motives for charitable donations. Science, 316 (5831), 1622-1625. doi:10.1126/science.1140738 .

Magnitude of donations as a measure of altruism.

  • Reuter, M., Frenzel, C., Walter, N., Markett, S., & Montag, C. (2011). Investigating the genetic basis of altruism: The role of the COMT Val158Met polymorphism. Social Cognitive and Affective Neuroscience, 6 (5), 662-8 .

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  • Research article
  • Open access
  • Published: 03 September 2021

Predictors of help-seeking behaviour in people with mental health problems: a 3-year prospective community study

  • Carolin M. Doll   ORCID: orcid.org/0000-0002-4267-1668 1 , 2 ,
  • Chantal Michel 3 ,
  • Marlene Rosen 2 ,
  • Naweed Osman 1 ,
  • Benno G. Schimmelmann 3 , 4 &
  • Frauke Schultze-Lutter 1 , 3 , 5  

BMC Psychiatry volume  21 , Article number:  432 ( 2021 ) Cite this article

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The majority of people with mental illness do not seek help at all or only with significant delay. To reduce help-seeking barriers for people with mental illness, it is therefore important to understand factors predicting help-seeking. Thus, we prospectively examined potential predictors of help-seeking behaviour among people with mental health problems ( N  = 307) over 3 years.

Of the participants of a 3-year follow-up of a larger community study (response rate: 66.4%), data of 307 (56.6%) persons with any mental health problems (age-at-baseline: 16–40 years) entered a structural equation model of the influence of help-seeking, stigma, help-seeking attitudes, functional impairments, age and sex at baseline on subsequent help-seeking for mental health problems.

Functional impairment at baseline was the strongest predictor of follow-up help-seeking in the model. Help-seeking at baseline was the second-strongest predictor of subsequent help-seeking, which was less likely when help-seeking for mental health problems was assumed to be embarrassing. Personal and perceived stigma, and help-seeking intentions had no direct effect on help-seeking.

Conclusions

With only 22.5% of persons with mental health problems seeking any help for these, there was a clear treatment gap. Functional deficits were the strongest mediator of help-seeking, indicating that help is only sought when mental health problems have become more severe. Earlier help-seeking seemed to be mostly impeded by anticipated stigma towards help-seeking for mental health problems. Thus, factors or beliefs conveying such anticipated stigma should be studied longitudinally in more detail to be able to establish low-threshold services in future.

Peer Review reports

Worldwide, mental disorders are an immense economic burden for society [ 1 ]. On average, 29.2% of adults will develop a mental illness in their lifetime [ 2 ]. The majority of people with a mental disorder do not seek help from any health care professional [ 3 ], although help-seeking for mental health problems (HSmental) at an early stage is crucial to reduce the burden of mental illness, and social and personal financial costs, to prevent future relapses, and to improve social functioning, and quality of life [ 4 ].

Help-seeking for mental health problems and its predictors

HSmental is defined as an adaptive coping process that attempts to obtain external assistance to deal with mental health problems [ 5 ], including not only formal (e.g. psychiatrists) but also informal sources of help (e.g. friends) [ 5 ]. HSmental is predicted by different sociodemographic factors, such as older age [ 6 , 7 ], female sex [ 6 , 8 ], and lack of a current partner [ 9 ]. In addition, former positive help-seeking experiences [ 10 ], and more severe functional impairments [ 9 ], were positively correlated with HSmental. Furthermore, different types of stigma were identified as important barriers to HSmental [ 3 , 10 , 11 , 12 , 13 ]. Thereby, stigma is commonly divided into structural stigma, perceived stigma, self-stigma, personal stigma, and anticipated stigma. While structural stigma is defined on a macro-social level as institutional policies and practices, societal-level conditions, cultural norms, and institutional practices that constrain the opportunities, resources, and well-being for stigmatized populations [ 14 ]; perceived stigma that might be perceived as part of structural stigma [ 14 ] is expressed on the micro-social level by the community’s prejudices and negative stereotypes towards people with a mental illness [ 15 , 16 ]. Self-stigma is described as the affected persons’ internalisation of these stereotypes and prejudices [ 17 ], that were learned and hold in terms of personal stigma before developing a mental illness and identifying with the stigmatized group themselves [ 18 ]. Similar to this, personal stigma describes the unaffected individual’s own prejudice and negative stereotypes and it is often measured as the wish for social distance (WSD), which is basically the wish to avoid a specific group such as persons with a mental illness [ 19 ]. Furthermore, anticipated or perceived stigma [ 20 ] does not describe the experienced, but the anticipated stigmatization and discrimination by others in case one would become mentally ill oneself. This kind of stigma includes expectations that, for example, it would be embarrassing to get professional help when having a mental illness.

Personal stigma in terms of a wish for social distance (WSD) was negatively associated with help-seeking in a recent meta-analysis [ 12 ]. WSD seemed to lower the perceived need for professional help and, therefore, reduced the probability to be aware of one’s own illness; as a consequence, WSD minimized the likelihood to seek help [ 21 ]. Generally, WSD was more prominent in older than in younger persons with no differences between sexes [ 19 ]. Yet, stigma was a lower barrier to HSmental in studies with only female compared to only male participants [ 3 ]. In addition, perceived stigma has been negatively associated with help-seeking in adults [ 3 ] and with help-seeking intentions in adolescents [ 22 ]. In contrast to that, the intention to seek help was positively associated with actual HSmental [ 13 , 23 , 24 ]. Moreover, in a systematic review [ 11 ] but not in a longitudinal study [ 23 ], embarrassment about HSmental, i.e. anticipated stigma, was identified as a major barrier for HSmental in young people.

Most studies on HSmental were conducted only cross-sectionally [ 9 , 12 , 25 ] and frequently investigated only help-seeking intentions rather than actual help-seeking behaviour [ 6 ]. The longitudinal studies on HSmental, and stigma and attitudes have only a short follow-up of 6 months [ 8 , 26 , 27 ], small sample sizes [ 26 ], selected samples [ 28 , 29 ], or, at a large follow-up of 11 years, focussed only on the impact of attitudes toward mental health help-seeking and beliefs about the effectiveness of treatment but not of personal and perceived stigma and low functioning [ 23 ]. Thus, longitudinal studies of sufficient sample size of the impact of stigma and attitudes, and their interaction on actual HSmental are clearly needed.

Aims of the study

In order to address the lack of complex longitudinal studies on the impact of age, sex, various types of stigma, assumptions about own HSmental and functional deficit on actual HSmental behaviour within the following 3 years, we examined a complex structural equation model (SEM), which, based on the mainly cross-sectional findings described above, assumes the following effects on HSmental longitudinally:

Perceived stigma is negatively associated with HSmental [ 3 , 22 ].

Personal stigma (WSD) is negatively associated with HSmental [ 13 ].

Help-seeking intentions are positively associated with HSmental [ 13 , 23 , 24 ].

Anticipated stigma (embarrassment about HSmental) is negatively associated with HSmental [ 11 ].

Psychosocial functioning is negatively associated with HSmental [ 9 ].

Past treatment experiences are positively associated with HSmental [ 9 ].

Compared to male sex, female sex is more positively associated with HSmental [ 6 , 30 ].

Older age is positively associated with HSmental [ 6 , 7 , 9 ].

Considering not only direct but also indirect effects in our model, we aimed at detecting what predictors at baseline are linked to future help-seeking behaviour.

Study design

Our study is based on the longitudinal data of an add-on study to the ‘Bern Epidemiological At-Risk’ (BEAR) study [ 31 , 32 ]. At baseline, community participants of age 16 to 40 years were randomly drawn from the population register of the Canton Bern, Switzerland, first recruited for a telephone interview (response rate: 63.4%) and, at conclusion of the interview, for a questionnaire add-on study on stigma and mental health between June 2011 and June 2015 ( n  = 1519, response rate: 60.3%) [ 13 ]. At three-year follow-up, a preselected sub-sample of participants of the interview study were re-contacted between June 2015 and March 2018 ( n  = 1028, contact rate: 78.8%) [ 31 ] (Fig.  1 ). In the follow-up, 839 participants of the baseline interview study agreed to a second interview (response rate: 66.4%; see Fig. 1 ). Of these, 542 participants had participated in the add-on study at baseline and, thus, had available data on stigma and attitudes (Fig. 1 ). Since HSmental was our outcome variable of interest and the absence of HSmental in persons with and without mental health problems has to be evaluated differently – adequate behaviour in those without mental health problems but undesirable behaviour in those with mental health problems, we restricted our analyses to the 307 participants (56.6%) with mental disorders or relevant mental health problems past baseline, i.e., a positive response to any one screening question in the M.I.N.I. interview (Fig. 1 ).

figure 1

Recruitment process of the add-on study to the BEAR study according to the American Association for Public Opinion Research (AAPOR) Outcome Rate Calculator, version 3.1 [ 32 ]

For more information on recruitment see Additional file  1 . For all studies, verbal informed consent was obtained and recorded from all subjects prior to both starting the telephone interview and posting the questionnaires. This procedure was chosen to avoid delays between first personal phone contact and posting of written consent that might have led to loosing contact with potential participants, in doing so decreasing recruitment rate and, thereby, generalizability. This procedure of obtaining consent verbally as well as all other procedures contributing to this work and involving human subjects were approved by the independent ethical committee of the University of Bern (No. 172/09) prior to starting the study. Furthermore, all procedures comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Assessments

Based on previous studies [ 33 , 34 , 35 ], life-time and current help-seeking was assessed by using a modified version of the WHO Pathway-to-Care questionnaire [ 33 ].

DSM-IV non-substance-related axis-I disorders were assessed at follow-up with the MINI-International Neuropsychiatric Interview’ (M.I.N.I) [ 36 ], which was previously used in telephone surveys and considered to be comparable with face-to-face interviews [ 37 ]. Thereby, the presence of any subthreshold mental health problems that signal a need of professional assessment and, consequently, help-seeking was assumed when a screening question was affirmed [ 38 ].

As a global rating of current functioning at baseline, the ‘Social and Occupational Functioning Assessment Scale’ (Sofas) [ 39 ] with scores ranging from 0 to 100, was used. Thereby, a score ≤ 70 was considered as an indication of deficient functioning [ 40 , 41 ].

Personal stigma in terms of WSD was assessed at baseline with the adapted social distance scale developed by Link et al. [ 18 ]. In this, participants have to rate their willingness to socially interact in seven different situations with the person described in vignettes about patients with mental disorders (depression or schizophrenia) on a five-point Likert scale from 0 = ‘definitely willing’ to 4 = ‘definitely not willing’. Higher sum scores indicated stronger WSD.

Perceived stigma was assessed at baseline by presenting 10 items (Table  1 ) that describe public opinions about people with a mental illness (e.g. ‘Most people see it as an indicator of personal failure, when somebody is in a psychiatric clinic’). The participants had to rate these items on a five-point Likert scale from 1 = ‘nobody has this opinion’ to 5 = ‘everybody has this opinion’ according to the degree what they perceive as the public opinion.

In addition, anticipated stigma was measured by the question ‘How embarrassed would you be if your friends knew you were getting professional help for an emotional problem?’ with answer options ‘very embarrassed’ (=4), ‘somewhat embarrassed’ (=3), ‘not much embarrassed’ (=2), and ‘not at all embarrassed’ (=1).

Finally, help-seeking intentions were assessed by the question ‘If you had a serious emotional problem, would you seek professional help?’ with answer options ‘definitely’ (=4), ‘probably’ (=3), ‘probably not’ (=2), and ‘definitely not all embarrassed’ (=1). Finally, actual help-seeking behavior was binary assessed by the questions ‘Have you ever sought help for mental health problems?’ (at baseline) and ‘Have you sought help for mental health problems since the first interview?’ (at follow-up).

Statistical analyses

Categorical data were compared by χ 2 -tests, non-normally distributed ratio and ordinal data by Mann-Whitney U tests. Principal component analyses (PCA) with Varimax rotation were conducted on the 10 items on perceived stigma. Thereby, we used pairwise complete observations to deal with missing values. Next, the Kaiser-Meyer Olkin (KMO) measure was used to check the sampling adequacy for the analyses.

Secondly, we conducted a theoretically grounded SEM, which was based on the mainly cross-sectional findings described in the aims section. Within the SEM, we had 2% missing items. Based on the results of the PCA and previous studies, we formed one latent variable for ‘perceived stigma’. The variables ‘WSD’ [ 19 ], ‘help-seeking intention’ [ 13 , 23 , 24 ], ‘anticipated stigma [ 11 ], ‘age’ [ 7 ], ‘sex’ [ 30 ], ‘functional deficit’ [ 9 ], ‘help-seeking at baseline’ [ 9 , 10 , 13 ] were modelled as observed variables. The pathways from stigma via own help-seeking assumptions (i.e., help-seeking intentions) to help-seeking behaviour including all likely associations between latent and manifest variables were modelled in a SEM. To examine the bivariate relationships between the variables, we calculated Spearman’s correlation coefficients. To test also for indirect effects, we built mediation pathways within the model by labelling potential parameters in the regression as parameters. The statistical analyses were conducted in SPSS 25.0 and in the R language for statistical computing using the packages “lavaan” [ 42 ] and “psych” [ 43 ], respectively. Throughout, we considered a level of significance of α < .05.

Power analysis and sample characteristics

The calculation of the model was performed with N  = 307 participants with mental health problems or disorders. A power analysis of the final model, which contained 12 variables and df = 27 degrees of freedom, resulted in a power of 0.901 with an RMSEA = 0.06 and an α = 0.05. Of the 307 participants, 238 (77.5%) participants had not and 69 (22.5%) participants had sought help past baseline (Table 1 ). The average age was around 33 years with no difference between help-seekers and non-help-seekers. Revealing a small effect of sex, more female than male participants had sought help (Table 1 ). The majority of the participants were Swiss, frequently unmarried, and had a secondary school education (Table 1 ). There was a small effect towards help-seekers being more frequently unemployed or working in protected employment (Table 1 ). In addition, help-seekers had more frequently functional deficits at baseline or follow-up, and had more frequently reported HSmental already at baseline; all these differences revealed moderate effect size (Table 1 ). Help-seekers also had more often any current non-psychotic axis-I disorder at baseline or follow-up; yet, differences only revealed small-to-moderate effects (Table 1 ). Help-seekers, however, had not more frequently reported help-seeking intentions at baseline; this difference showed a moderate effect size (Table 1 ).

Factors of perceived stigma

In the PCA of the 10 perceived stigma items, the KMO measure indicated excellent or “meritorious” [ 44 ] sampling adequacy for the analyses (KMO = .85), and all KMO values for individual items were > .80 in the PCA, and thus above the acceptable limit of .5 [ 45 ]. Bartlett’s test of sphericity (χ 2 (45)  = 857.186, p  < 0.001) indicated that correlations between items were sufficiently large for PCA [ 45 ]. Two independent factors (‘perceived stigma, ‘no perceived stigma’) had an eigenvalue over Kaiser’s criterion of 1 and explained 50% of the variance (Table  2 ).

Association between stigma, assumptions about own help-seeking, and healthcare utilization

The fit indices of our longitudinal model (Fig.  2 ), CFI (guide value ≥0.95), RMSEA (guide value ≤0.06), SRMR (guide value ≤0.08) and 90% confidence intervals not containing 0.08) suggested good fit of our model to data [ 46 , 47 ]. As previously recommended [ 48 , 49 ], no post-hoc modifications were conducted but, for better overview, a model with only significant paths is displayed in the Additional file  2 .

figure 2

Final model of associations between stigma, assumptions about help-seeking and healthcare utilization with standardized path coefficients ( N  = 307)

In our model (Fig. 2 ), HSmental at follow-up was mostly related to functional deficit and HSmental at baseline but also, though to a lesser degree, to younger age and lower anticipated stigma at baseline. Furthermore, age exerted several other influences on other baseline variables: Older age was related to both stronger WSD and more help-seeking intentions, while younger age was related to more anticipated stigma. Sex did not influence HSmental at follow-up, but anticipated stigma was higher in males and help-seeking intentions were higher in females. Yet, unexpectantly, both baseline help-seeking intentions and baseline personal stigma in terms of WSD were not significantly related to HSmental at follow-up, yet, WSD decreased help-seeking intentions and increased anticipated stigma. In order to examine an indirect effect of personal stigma on HSmental at follow-up via anticipated stigma, we tested the pathway “personal stigma – anticipated stigma – HSmental at follow-up”; however, this also remained insignificant ( p  = 0.438). Perceived stigma at baseline was completely unrelated to any other variable in the model (Fig. 2 ).

Model fit indices: χ 2 (27)  = 32.174 with p  = 0.226, CFI = 0.985; SRMR = 0.047; RMSEA = 0.025 (90%CIs = 0.000, 0.054).

* p  ≤ 0.05; ** p  ≤ 0.01; *** p  ≤ 0.001; explained variance (R 2 ) for each endogenous variable in italics. In brackets, Odds Ratios for the endogenous variable “help-seeking within 3 years past baseline” are provided. Manifest variables are represented in rectangles, latent ones in ovals. Solid lines indicate significant paths, dashed lines indicate non-significant paths; in doing so, grey indicates positive, black negative correlations.

The bivariate correlations of the variables of the model are given in Table  3 . Contrary to the SEM, they indicated small to moderate correlations between HSmental at follow-up with help-seeking intentions and with female sex but not age. In line with the model, functional deficits and earlier HSmental as well as anticipated stigma, but not personal or perceived stigma were significantly related to subsequent HSmental. Personal and perceived stigma were also uncorrelated to HSmental at baseline (Table 3 ).

Our unique longitudinal study examined the complex associations of various types of stigma (personal, perceived and anticipated), help-seeking intentions, functional deficit and health care utilization in a community sample of persons with mental health problems using structural equation modelling. Contrary to the longitudinal studies with 3- and 6-month follow-ups [ 8 , 26 , 27 ], our follow-up period of 3 years, was sufficiently long to allow for the new emergence of mental health problems and related help-seeking. While in comparison to a study with 11-year follow up of persons with and without mental health problems [ 23 ], our study was still short enough to rule out significant recall bias and significant change in attitudes and did not confuse the interpretation of non-help-seeking by mixing persons with and without mental health problems. Furthermore, our sample was randomly selected from the general population of, at baseline, 16–40-year-olds, i.e., in an age range in that many mental disorders develop first [ 50 ] and sufficiently large to ensure good power. Thus, our results are likely more generalizable to Middle-European samples in an age range of highest risk to develop a first episode of mental illness than the ones reported from the much smaller, rather old convenience sample ( N  = 188; mean age: 50 years) of the 3- and 6-month follow-up studies that was also preselected for symptoms of depression only [ 8 , 26 ].

Our longitudinal analysis revealed expected associations of functional deficits and of earlier help-seeking for mental health problems with subsequent help-seeking that were similar to those cross-sectionally reported earlier from a larger baseline sample of the BEAR study [ 9 ]. Furthermore, it supported the negative effect of anticipated stigma on help-seeking for mental health problems [ 11 ]. With regard to age, the reported association of older age with help-seeking [ 6 , 7 , 9 ] was not supported by our study, in which the contrary effect, an association with younger age, was found. Other direct effects on help-seeking reported from cross-sectional studies could not be replicated longitudinally, such as the help-seeking reducing effect of perceived and personal stigma [ 3 , 13 , 22 ], which also did not show on the level of bivariate correlations, or the help-seeking increasing effect of earlier help-seeking intentions [ 13 , 23 , 24 ], and of female sex [ 6 , 8 ].

In line with previous results [ 9 ], our results indicated that functional deficits had the strongest effect on help-seeking for mental health problems at follow-up in both bivariate correlations and the model (Fig. 2 ). This indicates that persons with mental health problems are more likely to seek help, when they already experience functional impairments in some areas of life. These, however, only develop over the course of the disorder and/or when problems of multiple domains of mental disorder have already developed [ 9 ]. This important role of functional impairments in help-seeking for mental health problems is unfortunate in light of the fact that early help-seeking for mental health problems, in particular, is a prerequisite not only for preventing mental disorder but also for preventing impairments in functioning and quality of life [ 4 ].

The second most important predictor of help-seeking for mental health problems at follow-up was help-seeking for mental health problems at baseline, which is in line with other studies [ 9 , 19 ]. This might reflect an effect of familiarity and previous positive experience with mental health services that counteracts negative beliefs towards mental health services and professionals, which were found to be the most cited barriers to help-seeking in a recent review focussing on adolescents [ 10 ]. Thus, future longitudinal studies in even larger samples should also focus on the interplay between these variables and their predictors, including for example the type of mental health problems that help was sought for. This might be important in light of reports that help-seeking is predominately reported because of depressiveness, anxiety and interpersonal problems [ 51 ], possibly because these symptoms were reported to impact most strongly and persistent on self-perceived health status and quality of life [ 52 ], which might also affect help-seeking.

Contrary to some cross-sectional studies [ 3 , 22 ], but in line with a recent meta-analysis on the impact of various stigma types on help-seeking [ 12 ], perceived stigma had no significant effect on help-seeking or assumptions about own help-seeking. In the meta-analysis [ 12 ], only personal attitudes towards mental illness or help-seeking were significantly associated with active help-seeking, in particular own negative attitudes towards help-seeking (including anticipated stigma in terms of embarrassment about help-seeking) and personal stigma (including WSD). In our model, these effects were not independent of each other; rather, contrary to our hypotheses, personal stigma had no direct effect on help-seeking for mental health problems at follow-up, but increased anticipated stigma at baseline. Furthermore, anticipated stigma significantly decreased help-seeking for mental health problems at follow-up. This finding is contrary to that of another longitudinal study [ 23 ], in which anticipated stigma had no influence on future help-seeking for mental health problems. However, this study was an 11-year follow-up and, thus, this particular assumption about help-seeking might have changed over the 11 years, because anticipated stigma about help-seeking was more prominent in younger people in our model.

Despite the weak but significant correlation that is in line with earlier reports on a positive association between help-seeking intentions and help-seeking [ 13 , 23 , 24 ], help-seeking intentions were not related to help-seeking for mental health problems at follow-up in our model. This might be due to factors that might influence the association between help-seeking intentions and help-seeking differently over time, such as the perceived need for help-seeking [ 24 ], perceived accessibility, spatial and temporal distance from mental health services, treatment efficacy beliefs, and anticipated self-stigma [ 8 ] that should be studied in more detail in future studies. The impact of the perceived need for help-seeking, however, might be partially reflected in the independent role of functional deficit, the strongest predictor of help-seeking for mental health problems at follow-up, whereat the association between perceived need for help-seeking and functioning might be further moderated by aspects of autonomy and underestimation of symptoms [ 53 ]. Yet, the gap between intention and behaviour is well known [ 54 ] and clearly observable in our study, in which 84.7% of the persons with mental health problems stated an intention to seek professional help for a serious mental problem, but only 19.9% actually sought help. Thus, future research should carefully differentiate between help-seeking intention and behaviour, and focus more on the latter.

In line with previous findings [ 6 ], women had higher intention to seek help; yet, a sex effect on help-seeking for mental health problems could not be detected, albeit frequent report of more help-seeking for mental health problems in women [ 6 , 55 ] and the significant positive correlation between female sex and help-seeking for mental health problems. This indicates that the frequently reported sex effect on help-seeking for mental health problems may rather be mediated by other factors such as anticipated stigma about help-seeking, which was less severe in females.

Strengths and limitations

Besides the strengths of our well-powered study of being one of the very few longitudinal community studies and of observing multiple factors and their interrelations in one model of help-seeking in persons of the community with mental health problems, some limitations have to be discussed. First, our sample was restricted to German-speaking persons because of the language of the questionnaires and to persons between 16 to 40 years at baseline because of the study focus of the BEAR study on psychosis-risk symptoms; additionally, it was of mainly Middle-European background. Due to existing cultural differences regarding help-seeking [ 56 ] and stigma [ 3 ], our study may thus only generalize to the Western, Middle-European culture. Another limitation, which interview- or questionnaire-based studies usually have in common, is the probability of systematic response bias due to social desirability. Furthermore, as already discussed above, our model did not include all possible moderators of help-seeking for mental health problems itself as well as of some of the significant factors in the model.

With only 22.5% of persons with mental health problems seeking any help for these, our study confirmed a prominent treatment gap. Functional deficits, which may introduce a perceived need for help, had the strongest impact on help-seeking for mental health problems longitudinally. In doing so, younger men showed more anticipated stigma towards help-seeking at follow-up, which decreased help-seeking for mental health problems at follow-up. Surprisingly, personal and perceived stigma had no direct effect on help-seeking for mental health problems, nor had help-seeking intentions or sex. At a non-help-seeking rate of more than 75%, our study raises the questions, whether the health care system is offering enough low-threshold help-seeking opportunities for persons with emerging mental health problems, and how to design mental health care systems that are not associated with anticipated stigma, i.e., that identify features conveying anticipated stigma.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

American Association for Public Opinion Research

Bern Epidemiological At-Risk

Help-seeking for mental health problems

Wish for social distance

  • Structural equation model

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Acknowledgements

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This study was supported by a project-funding grant from the Swiss National Science Foundation (SNF), grant number 32003B_135381 (to Drs. Schultze-Lutter and Schimmelmann). The sponsor took no part in the analysis and the interpretation of the data. Open Access funding enabled and organized by Projekt DEAL.

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F.S.-L. and B.G.S. designed the study. F.S.-L. and M.C. supervised collection of the data. Under the supervision of F.S.-L., C.M.D. made the analyses and wrote the manuscript. M.R. contributed intellectual content to the manuscript. N.O. aided in revising the manuscript. All the authors were involved in discussing the findings. They all approved its final version.

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Verbal informed consent was obtained and recorded from all subjects prior to both starting the telephone interview or posting the questionnaires. All procedures contributing to this work and involving human subjects complied with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008, and were approved by the ethical committee of the University of Bern (No. 172/09).

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Drs Michel, Rosen, Schimmelmann, Schultze-Lutter, MSc Doll, and have declared that there are no conflicts of interest in relation to the subject of this study. Dr. Schimmelmann reports and received honoraria and is on the speakers’ board of Takeda (Shire) and Infectopharm.

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Doll, C.M., Michel, C., Rosen, M. et al. Predictors of help-seeking behaviour in people with mental health problems: a 3-year prospective community study. BMC Psychiatry 21 , 432 (2021). https://doi.org/10.1186/s12888-021-03435-4

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  • Published: 24 January 2024

Cortical regulation of helping behaviour towards others in pain

  • Mingmin Zhang 1 , 2   na1 ,
  • Ye Emily Wu   ORCID: orcid.org/0000-0001-8052-1073 1 , 2 , 3   na1 ,
  • Mengping Jiang 1 , 2 &
  • Weizhe Hong   ORCID: orcid.org/0000-0003-1523-8575 1 , 2 , 4  

Nature volume  626 ,  pages 136–144 ( 2024 ) Cite this article

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  • Neural circuits
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Humans and animals exhibit various forms of prosocial helping behaviour towards others in need 1 , 2 , 3 . Although previous research has investigated how individuals may perceive others’ states 4 , 5 , the neural mechanisms of how they respond to others’ needs and goals with helping behaviour remain largely unknown. Here we show that mice engage in a form of helping behaviour towards other individuals experiencing physical pain and injury—they exhibit allolicking (social licking) behaviour specifically towards the injury site, which aids the recipients in coping with pain. Using microendoscopic imaging, we found that single-neuron and ensemble activity in the anterior cingulate cortex (ACC) encodes others’ state of pain and that this representation is different from that of general stress in others. Furthermore, functional manipulations demonstrate a causal role of the ACC in bidirectionally controlling targeted allolicking. Notably, this behaviour is represented in a population code in the ACC that differs from that of general allogrooming, a distinct type of prosocial behaviour elicited by others’ emotional stress. These findings advance our understanding of the neural coding and regulation of helping behaviour.

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research helping behaviour

Data availability

All data and analyses necessary to understand the conclusions of the manuscript are presented in the main text and in Extended Data. Source data are provided with this paper.

Code availability

Code for behavioural analysis ( https://github.com/pdollar/toolbox and https://github.com/hongw-lab/Behavior_Annotator ), animal pose tracking ( https://github.com/murthylab/sleap/releases/tag/v1.2.9 ), analysis of mouse vocalizations ( https://github.com/rtachi-lab/usvseg ) 47 , microendoscopic imaging data analysis ( https://github.com/etterguillaume/MiniscopeAnalysis , https://github.com/zhoupc/CNMF_E and https://github.com/flatironinstitute/NoRMCorre ), ROC and SVM decoding analysis is available ( https://github.com/hongw-lab/Code_for_2024_ZhangM ) on GitHub.

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Acknowledgements

We thank M. Ma, S. Chaudhry, X. Zhang, L. Gu and S. Kim for technical assistance; C. Cahill for suggestions on pain-related experimental procedures; and members of the laboratory of W.H. for valuable comments. Schematics in Figs. 1a , 2a,m,p , 3a,d,i,n and 4a,f and Extended Data Figs. 4a , 9a and  10g,i were created with BioRender.com . This work was supported in part by National Institutes of Health grants (R01 MH130941, R01 NS113124, R01 MH132736, RF1 NS132912 and UF1 NS122124), a Packard Fellowship in Science and Engineering, a Keck Foundation Junior Faculty Award, a Vallee Scholar Award and a Mallinckrodt Scholar Award (to W.H.).

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These authors contributed equally: Mingmin Zhang, Ye Emily Wu

Authors and Affiliations

Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Mingmin Zhang, Ye Emily Wu, Mengping Jiang & Weizhe Hong

Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

Ye Emily Wu

Department of Bioengineering, Samueli School of Engineering, University of California, Los Angeles, Los Angeles, CA, USA

Weizhe Hong

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Contributions

M.Z., Y.E.W. and W.H. designed the study. M.Z. carried out all experiments. Y.E.W. and M.Z. carried out computational data analysis. M.J. assisted in some experiments. Y.E.W., M.Z. and W.H. wrote the manuscript. W.H. supervised the entire study.

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Correspondence to Weizhe Hong .

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Extended data figures and tables

Extended data fig. 1 behavioral responses of demonstrators and observers following melittin injection..

( a ) Example images showing saline- or melittin-injected paws. ( b ) Example raster plots showing self-licking behavior directed towards the melittin-injected paw or other paws in demonstrator animals. Each row indicates an individual demonstrator animal. ( c ) Time courses of the cumulative duration of self-licking behavior towards the melittin-injected paw and other paws. ( d ) Duration of self-licking behavior towards the melittin-injected paw and other paws during 5-minute intervals throughout the interaction period. ( e ) Total duration of self-licking behavior towards the melittin-injected paw and other paws. ( f ) Duration of allolicking towards the uninjected forepaws of demonstrators that were injected with either melittin or saline in the hind paw during 5-minute sliding windows throughout the interaction period. ( g ) Time courses of the cumulative duration of allolicking towards the uninjected forepaws. ( h , i ) Total duration (h) and number of bouts (h) of allolicking towards the uninjected forepaws of melittin- and saline-injected demonstrators. ( j - m ) Total duration of different behaviors displayed by dominant observers when interacting with subordinate demonstrators in pain and by subordinate observers when interacting with dominant demonstrators in pain, including investigation (j), general allogrooming (k), allolicking towards injured paws and uninjured paws (l), and general allogrooming and targeted allolicking combined (m). In (c, d), data are mean ± s.e.m. In (e, h, i, j-m), the center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the 10 th and 90 th percentiles. n  = 24 mice in (c-e), 12 mice per group in (f-i), and 13 mice per group in (j-m). (e, h, i) Wilcoxon signed-rank test. (j, k, m) Unpaired t-test. (l) Two-way repeated measures ANOVA with post hoc Bonferroni’s multiple comparisons test. All statistical tests are two-sided. **** P  < 0.0001, ** P  < 0.01, * P  < 0.05. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 .

Source Data

Extended Data Fig. 2 Behaviors of female observers towards female demonstrators in pain.

( a ) Example raster plots showing general allogrooming and targeted allolicking behaviors towards demonstrators injected with either melittin or saline (control). Each row indicates an individual observer animal, and the same observers were plotted for the control and melittin-injected groups. ( b ) Time courses of the cumulative duration of different behaviors towards demonstrators in pain and control animals, including investigation, general allogrooming, targeted allolicking towards injured paws, allolicking towards uninjured paws, and general allogrooming and targeted allolicking combined. Data are mean ± s.e.m. ( c ) Duration of various behaviors during 5-minute sliding windows throughout the interaction period. ( d - k ) Quantification of the duration (d-g) and bout number (h-k) of behaviors towards demonstrators in pain and control animals. In (d-k), the center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the minimum and maximum values. n  = 18 mice per group in (b-k). (d, e, g, h, i, k) Wilcoxon signed-rank test. (f, j) Two-way repeated measures ANOVA with post hoc Bonferroni’s multiple comparisons test. All statistical tests are two-sided. *** P  < 0.001, ** P  < 0.01, * P  < 0.05. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 .

Extended Data Fig. 3 Observers’ behaviors towards demonstrators experiencing pain induced by formalin injection.

( a ) Example raster plots showing general allogrooming and targeted allolicking behaviors towards formalin- and saline-injected demonstrators. Each row indicates an individual observer animal, and the same observers were plotted for the control and formalin-injected groups. ( b ) Time courses of the cumulative duration of different behaviors towards formalin- and saline-injected demonstrators, including investigation, general allogrooming, targeted allolicking towards injured paws, allolicking towards uninjured paws, and general allogrooming and targeted allolicking combined. Data are mean ± s.e.m. ( c ) Duration of various behaviors during 5-minute intervals throughout the interaction period. ( d - k ) Quantification of the duration (d-g) and bout number (h-k) of various behaviors towards formalin- and saline-injected demonstrators. In (d-k), the center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the minimum and maximum values. n  = 16 mice per group in (b-k). (d, e, g, h, i, k) Wilcoxon signed-rank test. (f, j) Two-way repeated measures ANOVA with post hoc Bonferroni’s multiple comparisons test. All statistical tests are two-sided. *** P  < 0.001, ** P  < 0.01, * P  < 0.05. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 .

Extended Data Fig. 4 Observers display general allogrooming but not targeted allolicking towards demonstrators in a stress state induced by acute restraint.

( a ) Schematic of the behavioral protocol for examining interaction between observer mice and demonstrators in stress. Created with BioRender.com . ( b ) Time courses of the cumulative duration of investigation, general allogrooming, and allolicking of paws towards stressed demonstrators and controls. Data are mean ± s.e.m. ( c ) Duration of various behaviors during 5-minute intervals throughout the interaction period. ( d ) Quantification of the duration of various behaviors towards stressed demonstrators and controls. The center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the minimum and maximum values. n  = 12 mice per group in (b-d). (d) Two-sided Wilcoxon signed-rank test. ** P  < 0.01. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 .

Extended Data Fig. 5 Allolicking assists others in coping with pain.

( a , b ) Duration of self-licking behavior exhibited by demonstrators and combined duration of self-licking by demonstrators and targeted allolicking by observers. The demonstrators were either isolated or housed with cage mates after receiving melittin injection. The demonstrators were divided into a “low prosocial” group (a) and a “high prosocial” group (b) according to the level of targeted allolicking and general allogrooming behaviors exhibited by the cage mates. The combined duration of allolicking and allogrooming by cage mates were <50 s in the “low prosocial” group, and ≥ 50 s in the “high prosocial” group. Grey bar: the amount of time that demonstrators spent self-licking when they were alone; blue bar: the amount of time that demonstrators spent self-licking when they were together with observers; red bar: the combined duration of self-licking and allolicking in a social setting. ( c , d ) Example spectrograms overlaid with behavior annotations showing the lack of ultrasonic vocalizations (USV) during interaction between observers and demonstrators in pain, as well as prior to the onset of allolicking or allogrooming behavior (c). This contrasts with frequent vocalizations emitted by pups (d). ( e - g ) Correlations between the duration of self-licking by demonstrators and allolicking (e) or allogrooming (f) by observers or the two behaviors combined (g). Solid lines represent linear regression lines and dashed lines indicate 95% confidence intervals. ( h ) Raster plots showing targeted allolicking by observers towards the melittin- and saline-injected paws of sedated demonstrators. ( i ) Onset latency of allolicking towards the injured paw of awake and sedated demonstrators. ( j , k ) The fraction of targeted allolicking towards the injured paw and allolicking toward the uninjured paw of awake (j) and sedated (k) demonstrators during different interaction periods. In (a, b, i-k), data are mean ± s.e.m. n  = 6 mice per group in (a), 18 mice per group in (b), 16 mice in (e-g), and 12 mice in the awake group and 18 mice in the sedated group in (i-k). (a, b) Friedman test with post hoc Dunn’s multiple comparisons test. (e-g) Linear regression. (i) Wilcoxon rank-sum test. (j, k) Two-way repeated measures ANOVA with post hoc Bonferroni’s multiple comparisons test. All statistical tests are two-sided. **** P  < 0.0001, ** P  < 0.01, * P  < 0.05. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 .

Extended Data Fig. 6 Response of ACC neurons to different states of others across demonstrators.

( a , b , f , g , k ) Pearson correlation of AUROC values (reflecting cells’ tuning properties) with respect to investigation (“inv”) towards others in neutral (a), pain (b, g, k) or stress (f) state between pairs of demonstrators. AUROC values were derived using data from each demonstrator and Pearson correlation coefficient was calculated for cells defined as significantly responsive using data pooled from all demonstrators. Each dot represents correlation between a pair of demonstrators. P values less than 10 −10 are plotted as 10 −10 for visualization purposes. ( c , h , l ) Correlation between AUROC values for the same state of others (neutral, pain, or stress) across pairs of demonstrators (“dem”): groups 1 and 3 in (c, h), group 1 in (l). Correlation derived from randomly shuffled data (grey bars): groups 2 and 4 in (c, h), group 2 in (l). Correlation between AUROC values for different states of others within the same demonstrators: groups 5 and 6 in (c, h), groups 3 and 4 in (l); for these groups, correlation was calculated separately for cells responsive to either state. ( d , i , m ) Overlap between activated cells in the same or different response types across pairs of demonstrators. ( e , j , n ) Fraction of cells from each response type that overlap with the other response type within the same demonstrators. Activated cells were defined using AUROC values derived from data from each individual demonstrator. In (c-e, h-j, l-n), the center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate data within 1.5× interquartile range. Data were from 11 mice in (a-e), 10 mice in (f-j), 6 mice in (k-n). (c, d) Kruskal-Wallis test with post-hoc Dunn’s multiple comparison test. (h, i, l) One-way ANOVA test with with post hoc Bonferroni’s multiple comparisons test. (m) Wilcoxon rank-sum test. All statistical tests are two-sided. **** P  < 0.0001, ** P  < 0.01. Details of statistical analyses and sample sizes are provided in Supplementary Table 1 .

Extended Data Fig. 7 Single-cell- and population-level representations of prosocial behaviors and different states of demonstrators.

( a ) Schematics illustrating dissociable and shared aspects in the neural representations of different states or behaviors at the single-cell and population levels. ( b - j ) Decoding performance using all cells and after removing significantly responsive cells in different groups of decoding analysis. Data in the “All cells” groups in (b-j) are the same as presented in Figs. 3 h, 3 p, 3s (left), 3t (left), 5e, 5 l, 5p, 5 m, and Extended Data Fig. 8n , respectively. ( k , l ) Fraction of variance explained by the first three PC (k) and PLS (l) components in the data used for decoding of others’ neutral versus pain state (Fig. 3h ). In (b-l), the center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the minimum and maximum values (b-j) or data within 1.5× interquartile range (k, l). n  = 11 mice in (b, h, j-l), 6 mice in (c-e, g), 8 mice in (f), 12 mice in (i). (b-j) Two-sided Wilcoxon signed-rank test. *** P  < 0.001, ** P  < 0.01, * P  < 0.05. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 .

Extended Data Fig. 8 Response of ACC neurons to others’ pain and stress states and during prosocial behaviors.

( a , b ) Schematic timelines showing the order of the presentation of different types of demonstrators and self-pain experiences for examining the neural representations of others’ stress versus pain state (a) and self-pain versus others’ pain (b). ( c , e ) Heatmaps showing average responses of all recorded ACC neurons during the 5 s before and after the onset of close investigation of demonstrators in neutral, stress, or pain state (c) as well as allolicking and allogrooming (e). Each row represents the activity of an individual cell aligned to the onset of close investigation, allolicking, or allogrooming towards demonstrators (time 0). Cells are clustered using K-means clustering using their activity dynamics. Clusters are separated by dashed horizontal lines. ( d , f ) Cells in clusters showing a trend of increased activity preferentially in response to one type of demonstrator or behavior in (c, e) are ordered by the time each cell takes to reach 50% of its maximum activity. ( g ) The expected and observed percentages of neurons activated by all three demonstrator types (naïve, stress, and pain) among the neurons activated by both stressed and pain-experiencing demonstrators. ( h , i ) Pair-wise distances between cells activated by demonstrators in stress or pain (h), or between cells activated during allolicking or allogrooming (i) within the field of view. Distances between cells within the same response type or from different response types are compared. Grey boxes show distances calculated after cell type identities were randomly shuffled. ( j ) Venn diagram showing the overlap between neurons activated during the observers’ self-licking after receiving melittin injection or when observing self-licking of melittin-injected demonstrators. ( k ) Fraction of variance accounted for by the first three PCs in the PCA analysis of population activity associated with allolicking and allogrooming as presented in Fig. 5g–i . ( l ) Venn diagram and example calcium traces of cells selectively activated during either allogrooming or investigation, but not both, towards demonstrators in pain or stress. ( m ) Heatmaps showing average responses of example cells (each row) activated selectively by either allogrooming or investigation (but not both) aligned to the onset of each type of behavior (time 0). ( n ) Performance of decoders trained on population activity in classifying allogrooming versus investigation. In (h, i, k, n), the center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the minimum and maximum values. n  = 4388 cells from 10 mice in (c, d, g), 5080 cells from 12 mice in (e, f), 10 mice per group in (h), 12 mice per group in (i), 2399 cells from 6 mice in (j), 6 mice per group in (k), 5406 cells from 13 mice in (l), 11 mice per group in (n). (h, i) Friedman test. (n) Wilcoxon signed-rank test. All statistical tests are two-sided. *** P  < 0.001. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 .

Extended Data Fig. 9 Behavioral effects of DREADD inhibition of ACC neurons.

( a ) Schematic of viral injection and experimental paradigm for DREADD inhibition experiments in mCherry-expressing control animals. Created with BioRender.com . ( b ) Time courses of the cumulative duration of general allogrooming, targeted allolicking, allogrooming and allolicking combined, and social investigation towards pain-experiencing demonstrators by observers that were injected with either CNO or saline. The observers expressed mCherry but not hM4Di. Data are mean ± s.e.m. ( c , d ) Quantification of the total duration (c) and bout number (d) of general allogrooming, targeted allolicking, allogrooming and allolicking combined, and social investigation towards pain-experiencing demonstrators by mCherry-expressing observers that were injected with either CNO or saline. ( e , f ) Correlation between the duration of investigation and allolicking (e) or allogrooming (f) directed towards demonstrators in pain during chemogenetic inhibition of ACC neurons. Solid lines: linear regression lines, dashed lines: 95% confidence intervals. ( g ) Schematic of the three-chamber social preference test. ( h ) Total time spent in the “social” and “non-social” zones in hM4Di-expressing animals injected with CNO or saline. ( i ) Sociability scores of hM4Di-expressing animals injected with CNO or saline. ( j , k ) Duration and bout number of allolicking (j) or investigation (k) displayed by hM4Di-expressing observers injected with CNO or saline towards melittin-injected demonstrators that were under sedation. In (c, d, h-k), the center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the 10 th and 90 th percentiles (h, i) or the minimum and maximum values (c, d, j, k). n  = 10 mice per group in (b-d), 16 mice in (e, f), 14 mice per group in (h, i), and 11 mice per group in (j, k). (c, d, i) Paired t-test. (e, f) Linear regression. (h) Two-way repeated measures ANOVA followed by post hoc Bonferroni’s multiple comparisons test. (j, k) Wilcoxon signed-rank test. All statistical tests are two-sided. ** P  < 0.01. * P  < 0.05. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 .

Extended Data Fig. 10 Optogenetic activation of ACC neurons and control experiments.

( a ) Example raster plots showing an overall increase in allolicking/allogrooming during the 3-minute laser-on periods in ACC optogenetic activation experiments, compared to the 1.5-minute laser-off periods immediately before and after stimulation. ( b ) Duration of investigation behavior towards demonstrators in pain during periods of optogenetic activation of ACC neurons (laser-on phases) compared to laser-off phases. ( c , d ) Correlation between the duration of investigation and allolicking (c) or allogrooming (d) during optogenetic activation. Solid lines: linear regression lines, dashed lines: 95% confidence intervals. ( e , f ) The probability of allolicking (e) and investigation (f) during the 30 s before and after the onset of laser stimulation in experiments where stimulations were initiated after the first three minutes of the interaction. ( g ) Schematic of viral injection and experimental paradigm for light-stimulation experiments the ACC in EYFP-expressing control animals. ( h ) Quantification of the total duration of general allogrooming, targeted allolicking, and allogrooming and allolicking combined towards pain-experiencing demonstrators by EYFP-expressing observers during laser-on and laser-off periods. ( i ) Schematic of viral injection and experimental paradigm for optogenetic activation of excitatory neurons in the prelimbic cortex (PrL). ( j ) Example image showing ChR2-EYFP expression. Scale bar, 500 μm. IL, infralimbic cortex. ( k ) Quantification of the total duration of general allogrooming, targeted allolicking of the injured paw, allolicking of uninjured paws, and allogrooming and targeted allolicking combined towards pain-experiencing demonstrators by observers during laser-on and laser-off periods. ( l , m ) Comparison of the duration (l) and bout number (m) of self-licking behavior displayed by melittin-injected subject animals during optogenetic activation of ACC neurons versus periods without laser stimulation. In (e, f), data are mean ± s.e.m. In (h, k), the center line in the boxplots indicates the median, the box limits indicate the upper and lower quartiles, and the whiskers indicate the minimum and maximum values. n  = 18 mice per group in (b), 18 mice in (c, d), 80 trials from 16 mice per group in (e, f), 22 mice per group in (h), 11 mice per group in (l, m), and 8 mice per group in (k). (b, e, f, h, k, l, m) Wilcoxon signed-rank test. (c, d) Linear regression. All statistical tests are two-sided. ** P  < 0.01. ns, not significant. Details of statistical analyses are provided in Supplementary Table 1 . g , i , Created with BioRender.com .

Supplementary information

Supplementary information.

Supplementary Table 1 and Notes 1–6.

Reporting Summary

Supplementary video 1.

Mice exhibit affiliative allogrooming towards partners in pain. An observer mouse exhibits affiliative allogrooming towards a demonstrator experiencing pain.

Supplementary Video 2

Mice exhibit targeted allolicking towards partners in pain. An observer mouse exhibits targeted allolicking specifically directed towards the injured paw of a demonstrator.

Source data

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Zhang, M., Wu, Y.E., Jiang, M. et al. Cortical regulation of helping behaviour towards others in pain. Nature 626 , 136–144 (2024). https://doi.org/10.1038/s41586-023-06973-x

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Help-Seeking Behaviors Among Older Adults: A Scoping Review

1 Department of Gerontology, Simon Fraser University, Vancouver, BC, Canada

Ryan Churchill

Indira riadi, lucy kervin, andrew v. wister.

2 Gerontology Research Centre, Simon Fraser University, Vancouver, BC, Canada

Theodore D. Cosco

3 Oxford Institute of Population Ageing, University of Oxford, Oxford, UK

Associated Data

Supplemental Material, sj-pdf-1-jag-10.1177_07334648211067710 for Help-Seeking Behaviors Among Older Adults: A Scoping Review by Kelly Teo, Ryan Churchill, Indira Riadi, Lucy Kervin, Andrew V. Wister, and Theodore D. Cosco in Journal of Applied Gerontology

Supplemental Material, sj-pdf-2-jag-10.1177_07334648211067710 for Help-Seeking Behaviors Among Older Adults: A Scoping Review by Kelly Teo, Ryan Churchill, Indira Riadi, Lucy Kervin, Andrew V. Wister, and Theodore D. Cosco in Journal of Applied Gerontology

Supplemental Material, sj-pdf-3-jag-10.1177_07334648211067710 for Help-Seeking Behaviors Among Older Adults: A Scoping Review by Kelly Teo, Ryan Churchill, Indira Riadi, Lucy Kervin, Andrew V. Wister, and Theodore D. Cosco in Journal of Applied Gerontology

Although older adults may experience health challenges requiring increased care, they often do not ask for help. This scoping review explores the factors associated with the help-seeking behaviors of older adults, and briefly discusses how minority ethnic populations can face additional challenges in help-seeking, due to factors such as language barriers and differing health beliefs. Guided by Arksey and O’Malley’s scoping review framework and the Preferred Reporting Items for Systematic Reviews and Meta-AnalysesScoping Review guidelines, a systematic search of five databases was conducted. Using a qualitative meta-synthesis framework, emergent themes were identified. Data from 52 studies meeting inclusion criteria were organized into five themes: formal and informal supports, independence, symptom appraisal, accessibility and awareness, and language, alternative medicine and residency. Identifying how factors, including independence and symptom appraisal, relate to older adults’ help-seeking behaviors may provide insights into how this population can be supported to seek help more effectively.

Introduction

Efforts to understand why many older adults do not seek help, even while experiencing grave symptoms, have highlighted the importance of understanding older adults’ help-seeking behaviors for their physical and mental health challenges ( Woods et al., 2005 ). Older adults are less likely to access mental health services than their younger counterparts and even among those that do seek help, older adults are less likely to be offered treatment for their mental health challenges ( Mackenzie et al., 2008 ; Woods et al., 2005 ). Such disparities in healthcare access have led to further research and efforts to support efficient and early help-seeking behavior among older adults, including the development of personal emergency response systems and the introduction of health checks in general practice ( Porter & Markham, 2012 ; Woods et al., 2005 ). Even so, disparities in healthcare access and a lack of help-seeking continues to persist ( Woods et al., 2005 ).

Failure to exhibit and appropriately identify help-seeking behavior delays opportunities to diagnose or treat older patients in a timely manner, which may further exacerbate symptoms and increase future care costs ( Arthur-Holmes et al., 2020 ; Blakemore et al., 2018 ). This is especially a concern among minority ethnic older adults, whose continued underrepresentation in research leads to a poor understanding of what strategies may improve their help-seeking behavior and subsequent health outcomes ( George et al., 2014 ; Korte et al., 2011 ). Compared to their counterparts, minority ethnic groups underutilize healthcare services, despite demonstrating a greater need for services ( Greenwood & Smith, 2015 ; Walton & Anthony, 2017 ).

This scoping review aims to garner an in-depth understanding of the help-seeking behaviors of older adults, and to further explore those behaviors as they present in minority ethnic older adults. For this review, help-seeking behavior is defined as: any action taken by an older adult who perceives themselves as having a physical or mental health challenge, with the intent to find an appropriate remedy. The type of support that individuals pursue can include seeking formal support services (e.g., from clinicians, psychologists, and religious leaders) or informal support services (e.g., from family and friends). This review is meant to identify the ways in which older adults exhibit (or do not exhibit) help-seeking behaviors, and how they may experience barriers (and facilitators) from informal and formal supports.

This scoping review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines ( Tricco et al., 2018 ; Supplementary File 1 ) and is registered with the Open Science Framework ( Teo, 2020 ). Further methodological details, additional definitions, and the published protocol for this scoping review are available online ( Teo et al., 2021 ). The methods were also informed by Arksey and O’Malley (2005) , which are described below.

Identifying the Research Question

This review was undertaken with one main research question: Which factors are associated with help-seeking behavior among older adults? An additional sub-question was also included: How do cultural backgrounds, values, and differences influence help-seeking behavior among various older adult populations?

Identifying Relevant Studies

The databases MEDLINE/PubMed, Web of Science, PsycINFO, CINAHL, and Scopus were searched from January 2005 to the date of search commencement in January 2021. Separate search strategies were used to address the two research questions, using three key concepts: “help-seeking behavior,” “older adults,” and “ethnic minorities.” Further details of the search strategy are detailed in Supplementary File 2 . No language restrictions were imposed.

Study Selection

After removing duplicates in EndNote, a pilot screen of 50 articles was conducted to ensure consistency and reliability. Two reviewers then conducted title/abstract and full-text screens of the same articles independently, based on the eligibility criteria ( Table 1 ). Discrepancies between reviewers were reconciled through discussion and consultation with a third reviewer when necessary. Reference lists were hand-searched for potential missing articles.

Inclusion and Exclusion Criteria.

Charting the Data

An Excel data extraction spreadsheet was created to extract the following data: authorship, year/journal of publication, population characteristics, location, methodology, limitations, help-seeking barriers or facilitators and any other pertinent information.

Collating, Summarizing, and Reporting the Results

Following a qualitative meta-synthesis framework ( Erwin et al., 2011 ), each study was carefully read and help-seeking factors were matched and compared with subsequent articles. They were then organized in the following manner: first-order constructs were recorded as direct factors, quotes, and findings from the studies themselves, second-order constructs were interpretive themes that formed the basis of each category, and third-order constructs were developed from the aggregation of multiple categories that led to the development of new themes.

Included Studies

From the searches, 2824 unique articles were identified. A total of 52 articles met inclusion criteria ( Figure 1 ).

An external file that holds a picture, illustration, etc.
Object name is 10.1177_07334648211067710-fig1.jpg

Preferred reporting items for systematic reviews and meta-analyses flowchart.

Study Characteristics

The number of studies was highest in Europe ( n = 18), followed by North America ( n = 16), Asia ( n = 12), Australia ( n = 5), and New Zealand ( n = 1). Most studies used qualitative methods ( n = 26; Table 2 ), followed by quantitative ( n = 24; Table 3 ) and mixed methods ( n = 2; Table 4 ). More papers focused on physical health challenges (e.g., chronic conditions, pain; n = 28), over mental health challenges (e.g., depression; n = 11). Thirteen studies discussed both physical and mental health challenges or discussed general health challenges. Tables 2 ​ 2 – 4 summarize the characteristics of included studies while key findings from each article regarding health challenges, help-seeking behaviors, barriers, and facilitators are summarized in Supplemental Table 1 .

Study Demographics: Qualitative Study Demographics.

Study Demographics: Quantitative Study Demographics.

Study Demographics: Mixed Methods Study Demographics.

Overview of Findings

Formal and informal supports.

From the themes that emerged from this review, distinctions between seeking help from informal and formal supports are important to discuss. Within formal support, several articles described how mistrust, perceptions of a physician’s role, and past interactions with formal healthcare providers can negatively influence older adults’ help-seeking behaviors. Here, the notion that physicians could either not help at all or any more than they already were helping was prevalent ( Dollard et al., 2014 ; Elias & Lowton, 2014 ; Garg et al., 2017 ). This perspective was reinforced by past experiences of help-seeking, when misdiagnosis or non-diagnosis occurred ( Clarke et al., 2014 ; Polacsek et al., 2019 ). Furthermore, dismissal of health concerns and a lack of respect exhibited by a healthcare provider reinforced older adults’ reluctance to share any new challenges at subsequent consultations ( Gore-Gorszewska, 2020 ; Lawrence et al., 2006 ; Makris et al., 2015 ). Conversely, a positive relationship with healthcare providers was a facilitator to help-seeking. For example, positive views of both the healthcare system and psychological treatment were found to increase the likelihood of using such services ( Begum et al., 2012 ; Gur-Yaish et al., 2016 ). Feeling heard by the providers, gaining a sense of security, and being given practical information and assistance were deemed to be important components of this relationship ( Clarke et al., 2014 ; Waterworth et al., 2018 ), as was older adults being included in the decision-making process ( Polacsek et al., 2019 ).

When it came to informal supports, the availability of such often reduced the need or desire for formal support, as the older adult’s concerns were alleviated by those around them ( Begum et al., 2012 ; Hohls et al., 2021 ; Lau et al., 2014 ; Mechakra-Tahiri et al., 2011 ). Here, social support allowed opportunities to exchange health information, disclose, and receive endorsements/recommendations for treatments ( Aubut et al., 2021 ; Begum et al., 2012 ; Chen, 2020 ; Chung et al., 2018 ; Frost et al., 2020 ; Hurst et al., 2013 ; Lawrence et al., 2006 ; McCabe et al., 2017 ; Mechakra-Tahiri et al., 2011 ; Polacsek et al., 2019 ; Schneider et al., 2014 ). Unsolicited social support also facilitated help-seeking, when decisions for the older adult were made on their behalf by others ( Begum et al., 2012 ; Canvin et al., 2018 ; Lawrence et al., 2006 ), simply offered or given ( Miller et al., 2016 ) or when family and friends would influence, persuade, or coerce older adults to seek help ( Dollard et al., 2014 ; Elias & Lowton, 2014 ; Hurst et al., 2013 ; Johnston et al., 2010 ).

Independence

Older adults often indicated the preference to not want others involved in their health challenges when help-seeking was viewed as a threat to their independence ( Aubut et al., 2021 ; Canvin et al., 2018 ; Elias & Lowton, 2014 ; Frost et al., 2020 ; Johnston et al., 2010 ; Lee et al., 2020 ; Miller et al., 2016 ). Related to this were desires to be seen in a positive light and notions of not wanting to bother others or be a burden ( Begum et al., 2012 ; Canvin et al., 2018 ; Chen, 2020 ; Clarke et al., 2014 ; Dollard et al., 2014 ; Elias & Lowton, 2014 ; Gore-Gorszewska, 2020 ; Horton & Dickinson, 2011 ; Hurst et al., 2013 ; Johnston et al., 2010 ; Lawrence et al., 2006 ; Lee et al., 2020 ; Miller et al., 2016 ). As such, for help-seeking to occur, older adults had to view this behavior as a functional way of increasing their own independence. For example, Johnston et al. (2010) reasoned that effective alarm users were more likely to be positive about using such aids after a fall, as it improved an older adult’s confidence in living alone and provided reassurance to their families. Talking therapies and loved ones setting a routine with the older adult were also effective options that allowed participants to reach their own solutions and receive informal help without feeling like they were imposing on another’s schedule ( Frost et al., 2020 ; Miller et al., 2016 ).

Symptom Appraisal

When confronted with a symptom, older adults would commonly self-assess their health first to determine whether seeking help was necessary ( Hurst et al., 2013 ; Hwang & Jeong, 2012 ; Tsai & Tsai, 2007 ; Waterworth et al., 2018 ). As such, there was often a delay in help-seeking as many underestimated the seriousness of their conditions ( Garg et al., 2017 ; Murata et al., 2010 ) and would subsequently wait to see if the problems went away or attempt to self-manage their conditions, with informal support being a common first choice of help ( Altizer et al., 2014 ; Begum et al., 2012 ; Canvin et al., 2018 ; Djukanović et al., 2015 ; Frost et al., 2020 ; Garrido et al., 2011 ; Hurst et al., 2013 ; Hwang & Jeong, 2012 ; Johnston et al., 2010 ; Lee et al., 2020 ; McCabe et al., 2017 ; Tsai & Tsai, 2007 ). For example, Kelly et al. (2011) found that despite acknowledgment of hearing impairment, those in the non-consulting group did not experience communication breakdowns or experienced them in predictable ways, resulting in lower levels of anxiety and a lack of help-seeking. Furthermore, experiencing comorbidities made issues such as activity-restricting back pain or sexual problems seem less of a health priority ( Makris et al., 2015 ; Schaller et al., 2020 ; Schneider et al., 2014 ). During this symptom appraisal process, older adults would also often attribute their poor health as normal or inevitable due to age ( Canvin et al., 2018 ; Clarke et al., 2014 ; Elias & Lowton, 2014 ; Frost et al., 2020 ; Gore-Gorszewska, 2020 ; Hurst et al., 2013 ; Hwang & Jeong, 2012 ; Kharicha et al., 2013 ; Lee et al., 2005 ; Makris et al., 2015 ; Mukherjee, 2019 ; Polacsek et al., 2019 ; Waterworth et al., 2018 ). This “demedicalization of health problems” ( Elias & Lowton, 2014 , p. 977–978) was further exacerbated and reinforced when older adults did present these issues to formal health professionals, only to be met with dismissal, limited treatment options, or ageist and patronizing comments ( Frost et al., 2020 ; Gore-Gorszewska, 2020 ; Makris et al., 2015 ; Polacsek et al., 2019 ).

On the contrary, the decision to seek help from health professionals was based on several determinants, such as when symptoms were more noticeable/unfamiliar or when symptoms affected their daily lives ( Altizer et al., 2014 ; Begum et al., 2012 ; Canvin et al., 2018 ; Clarke et al., 2014 ; Dollard et al., 2014 ; Elias & Lowton, 2014 ; Frost et al., 2020 ; Horng et al., 2014 ; Hurst et al., 2013 ; Hwang & Jeong, 2012 ; Kharicha et al., 2013 ; Lee et al., 2020 ; Makam et al., 2016 ; Mukherjee, 2019 ; Schneider et al., 2014 ; Stenzelius et al., 2006 , 2007 ; Stoller et al., 2011 ; Waterworth et al., 2018 ). For example, one study found that those with painful physical symptoms were more likely to seek formal help (e.g., from mental health services) than those without ( Bonnewyn et al., 2009 ), while another found that the duration and intensity of pain were associated with seeking help ( Hartvigsen et al., 2006 ).

Accessibility and Awareness

In regards to accessibility and awareness of formal services, older adults expressed related issues such as costs, long wait times, short consultation times or having busy schedules as help-seeking barriers ( Djukanović et al., 2015 ; Dollard et al., 2014 ; Frost et al., 2020 ; Garg et al., 2017 ; Garrido et al., 2011 ; Hannaford et al., 2019 ; Kagan et al., 2018 ; Lawrence et al., 2006 ; Lee et al., 2012 , 2020 ; Murata et al., 2010 ; Polacsek et al., 2019 ; Schneider et al., 2014 ; Waterworth et al., 2018 ). Location and lack of available transportation also made it more difficult for older adults to seek help, especially among those living in rural areas or those with physical constraints ( Chen, 2020 ; Chung et al., 2018 ; Frost et al., 2020 ; Garg et al., 2017 ; Garrido et al., 2011 ; Gur-Yaish et al., 2016 ; Mukherjee, 2019 ; Murata et al., 2010 ; Polacsek et al., 2019 ; Waterworth et al., 2018 ). Furthermore, a lack of available information and awareness of services was evident ( Aubut et al., 2021 ; Chung et al., 2018 ; Djukanović et al., 2015 ; Garrido et al., 2011 ; Gore-Gorszewska, 2020 ; Horton & Dickinson, 2011 ; Lee et al., 2020 ; Mukherjee, 2019 ; Polacsek et al., 2019 ; Tsai & Tsai, 2007 ), where limited knowledge also limited older adults’ understanding of the severity of their issue. This was particularly true for symptoms related to heart issues ( Hwang & Jeong, 2012 ; McCabe et al., 2017 ).

To facilitate resource use and seeking activity, a greater understanding of resources/services, convenience of service use, and higher health literacy were notable factors ( Eriksson-Backa et al., 2018 ; Polacsek et al., 2019 ). Issues of location and transportation led to the importance of being able to speak to someone on the phone for health-related supports, especially for those living in rural areas ( Waterworth et al., 2018 ). However, the adoption of telephone consultations varied, with some experiencing challenges due to hearing problems or invoking fears of not knowing who they were speaking to ( Frost et al., 2020 ).

Language, Alternative Medicine, and Residency

Among minority ethnic populations, several factors, including language differences, were a notable impediment to help-seeking ( Chung et al., 2018 ; Horton & Dickinson, 2011 ; Krishnan & Lim, 2012 ; Mukherjee, 2019 ; Tsai & Tsai, 2007 ). As Chung et al. (2018) describes, a lack of English proficiency and lack of information provided in Korean limited older Korean immigrants’ social activities, enhanced their preference for a Korean-speaking doctor, and increased their reliance on others for support. Similarly, Krishnan and Lim’s (2012) study on older Indian men living in Singapore found that those who experienced language discordance were more likely to be dissatisfied with care. Others expressed the notion that it would be inappropriate to share personal problems with strangers ( Lau et al., 2014 ; Lawrence et al., 2006 ). Additional cultural factors and beliefs included the use of alternative medicine due to mistrust of American providers and Western medicines ( Chung et al., 2018 ) and the idea that help-seeking was futile for reasons such as luck, sin, karma, or destiny ( Horton & Dickinson, 2011 ; McGowan & Midlarsky, 2012 ; Mukherjee, 2019 ; Tsai & Tsai, 2007 ). However, this was not only unique to articles studying minority ethnic groups, as several others highlighted similar beliefs related to superstition/fatalism and the seeking of help from religious leaders over other formal sources ( Hannaford et al., 2019 ; Hurst et al., 2013 ; Johnston et al., 2010 ; Lawrence et al., 2006 ; McGowan & Midlarsky, 2012 ; Pickard & Guo, 2008 ; Pickard & Tang, 2009 ).

Facilitators to help-seeking among minority ethnic older adults included a longer length of residency in the country where one immigrated, which led to an improved ability to communicate with healthcare providers in English and a greater familiarity with how to navigate the healthcare system ( Chung et al., 2018 ). In addition, the availability of community organizations that offered translation services or home care helpers enhanced opportunities to access healthcare and programs, find information, and socialize with others ( Chung et al., 2018 ).

This scoping review demonstrates several thematic areas that explicate why older adults avoid help-seeking when health challenges arise. Many factors overlap, highlighting the way in which a decision to seek help among older adults can be intricate and complex. For instance, once a decision to seek help has been made, informal supports are common first choices for help, which is consistent with Cantor’s (1979) hierarchical-compensatory model of social supports. In circumstances where informal supports are available, help-seeking can end when needs are adequately met by these individuals. Informal support may also encourage seeking of formalized support such as that of healthcare professionals, or a blending of informal and formal support seeking may occur. However, even if older adults are at a stage where they are willing to seek help from formal avenues, there are structural barriers (e.g., costs or location) that can prevent them from doing so ( Chen, 2020 ; Chung et al., 2018 ; Johnston et al., 2010 ; Murata et al., 2010 ).

Further barriers identified by this review include negative perceptions of and relationships with healthcare providers and perceived threats to independence. This is consistent with reviews studying the help-seeking behaviors of other sub-populations, such as Indigenous communities ( Fiolet et al., 2021 ), women with urinary incontinence ( Koch, 2006 ), and men ( Yousaf et al., 2015 ), where avoiding help is linked to barriers such as inappropriate responses from service providers, the need for independence and control, and fear related to shame and repercussions. Symptom appraisal was also a prevalent factor among the older adults in this review, with serious or novel symptoms prompting help-seeking. This finding is similar to the aforementioned reviews, with other groups also highlighting seriousness and the feeling like a crisis had been reached as a turning point in the decision to seek help ( Fiolet et al., 2021 ; Koch, 2006 ).

A sub-focus on minority ethnic older adults highlights additional barriers that further inhibit this group’s ability to adequately seek help for their needs. These issues include cultural beliefs, immigration, and language, which are intermixed with issues of mistrust, structural barriers, and symptom appraisal. This pattern of help-seeking among older adults is similar to findings among younger minority ethnic populations, where traditional/cultural beliefs, and the employing of alternative strategies is often invoked ( Richards, 2019 ; Rüdell et al., 2008 ). To our knowledge, this is the first scoping review conducted on this topic, and thereby provides a comprehensive overview of existing literature and the most prevalent help-seeking factors for older adults. In doing so, these findings highlight the importance of including minority ethnic populations in research and imply the need for strategies that reduce barriers to help-seeking at all levels: among older adults themselves, their social networks, and formal services.

Limitations

Although the review findings will inform future research, our conclusions are limited by the methodological quality of the included studies and aspects of the review strategy itself. It is possible that there are studies that should have been captured in this search but were not, due to the search strategy, the lack of standardization of help-seeking terms or the indirect ways that help-seeking may have been addressed. Furthermore, 11 studies were unavailable to be retrieved through the institutional library accessible to the reviewers and requests sent to the authors for full-texts (via ResearchGate) were not answered, preventing possible inclusion of these studies and their results. The findings could have also been limited by the inclusion criteria, whereby articles excluding perspectives of stakeholders (e.g., caregivers) could have prevented a discussion of additional help-seeking factors. In addition, despite a secondary focus to explore the help-seeking behaviors of minority ethnic older adults, only nine articles were uncovered. The age cut-off of including only older adults aged 65 years and older may have introduced a selection bias that reduces the relevancy of these findings in countries with lower life expectancies, and thus should be a future research consideration. Furthermore, due to the ways in which language is constantly evolving, the search terms may have prevented the capturing of articles that identified specific populations or used terms such as people of color, Black and Minority Ethnic, or racially minoritized.

Future Directions

In consideration of the knowledge gaps in this field, a greater focus on specific minority ethnic populations, help-seeking interventions, and evaluations of such interventions are needed. Greater education efforts among older adults and their networks are also necessary, in recognition of how older adults may inadequately appraise their health or be unaware of available services. Patient engagement strategies that allow older adults to remain as independent as possible can also prevent fears of disempowerment or a lack of control in decisions that occur because of help-seeking. Furthermore, greater training for providers serving the older adult population is needed, where respect for autonomy and diversity are salient considerations.

Evidently, the help-seeking behaviors of older adults are complex interactions of various factors, considerations, and experiences. This scoping review has indicated a need to address the barriers that older adults experience when a need for help arises. It also suggests the need for comprehensive changes that involve the older adult themselves in decision-making when possible, while considering their relationships, cultural values, and beliefs in a holistic way. Through addressing and recognizing these factors, older adults can be better empowered and prepared to seek help, without delay.

Supplemental Material

Authors’ contributions: All authors contributed to the development of this manuscript. KT conceived the idea for this review, conducted screening and data extraction/analysis of studies, and prepared the first draft of the results and manuscript. RC conducted a second, independent screen of the studies at both the abstract/title and full-text stages. TDC helped refine the research question, provided review expertise and was available as a third reviewer to resolve discrepancies when necessary. IR, LK, and AVW supported the conceptualizing and refining of the study. All authors supported the editing and revising of this paper and have approved the final manuscript for submission.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

Supplemental Material: Supplemental material for this article is available online.

Kelly Teo https://orcid.org/0000-0003-2405-6466

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Helping Behavior

The good samaritan experiment.

Most people, in the Western and Middle Eastern worlds, understand the story of the Good Samaritan, and how it relates to helping behavior.

This article is a part of the guide:

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  • 1 Social Psychology Experiments
  • 2.1 Asch Figure
  • 3 Bobo Doll Experiment
  • 4 Good Samaritan Experiment
  • 5 Stanford Prison Experiment
  • 6.1 Milgram Experiment Ethics
  • 7 Bystander Apathy
  • 8 Sherif’s Robbers Cave
  • 9 Social Judgment Experiment
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  • 12 Ross’ False Consensus Effect
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  • 15 Hawthorne Effect
  • 16 Self-Deception
  • 17 Confirmation Bias
  • 18 Overjustification Effect
  • 19 Choice Blindness
  • 20.1 Cognitive Dissonance
  • 21.1 Social Group Prejudice
  • 21.2 Intergroup Discrimination
  • 21.3 Selective Group Perception

In this famous parable, a Rabbi and a Levite ignore an injured man and pass by, with a Samaritan being the only one to stop and help.

In the modern world, this parable is becoming increasingly relevant. There are many examples of victims of crime being ignored and not helped; you just need to open a newspaper or watch the news on television.

With this in mind, in 1978, an experiment was constructed, by Darley and Batson, to test the possible facts behind this story and study altruistic behavior.

The variables to be tested were the relative haste of the participant, and how occupied their minds were with other matters; it has been argued that, because the thoughts of the Rabbi and the Levite were on religious and spiritual matters, they might have been too distracted to stop and help.

The experiment was constructed as follows:

The experiment researchers had three hypotheses that they wanted to test ;

  • People thinking about religion and higher principles would be no more inclined to show helping behavior than laymen.
  • People in a rush would be much less likely to show helping behavior.
  • People who are religious for personal gain would be less likely to help than people who are religious because they want to gain some spiritual and personal insights into the meaning of life.

Religious studies students on a study course were recruited for this experiment, and had to fill in a questionnaire about religious affiliations and beliefs, to help evaluate and judge the findings of hypothesis 3.

The students were given some religious teaching and instruction and then were told to travel from one building to the next. Between the two buildings was a man lying injured and appearing to be in desperate need of assistance.

The first variable in this experiment was the amount of urgency impressed upon the subjects, with some being told not to rush and others being informed that speed was of the essence.

The relative mindset of the subject was also tested, with one group being told that they would be giving lectures on procedures in the seminary, the others that they would be giving a talk about the 'Good Samaritan'.

The experimenters constructed a six point plan of assessing helping behavior, ranging from apparently failing to even notice the victim, to refusing to leave until help was found, and the victim was in safe hands.

The results of the experiment were interesting, with the relative haste of the subject being the overriding factor; when the subject was in no hurry, nearly two thirds of people stopped to lend assistance. When the subject was in a rush, this dropped to one in ten.

People who were on the way to deliver a speech about helping others were nearly twice as likely to help as those delivering other sermons, showing that the thoughts of the individual were a factor in dictating helping behavior.

Religious beliefs did not appear to make much difference on the results; being religious for personal gain, or as part of a spiritual quest, did not appear to make much of a noticeable impact on the amount of helping behavior shown.

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Conclusions

It seems that the only major explanation for people failing to stop and help a victim is how obsessed with haste they are.

Even students going to speak about the Good Samaritan were less likely to stop and offer assistance, if they were rushing from one place to another.

It seems that people who were in a hurry did not even notice the victim, although, to be fair, once they arrived at their destination and had time to think about the consequences, they felt some guilt and anxiousness.

This, at least, indicates that ignoring the victim was not necessarily a result of uncaring attitude, but of being so wrapped up in their own world that they genuinely did not notice the victim.

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Martyn Shuttleworth (Aug 8, 2008). Helping Behavior. Retrieved Feb 23, 2024 from Explorable.com: https://explorable.com/helping-behavior

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Stanford Medicine study identifies distinct brain organization patterns in women and men

Stanford Medicine researchers have developed a powerful new artificial intelligence model that can distinguish between male and female brains.

February 20, 2024

sex differences in brain

'A key motivation for this study is that sex plays a crucial role in human brain development, in aging, and in the manifestation of psychiatric and neurological disorders,' said Vinod Menon. clelia-clelia

A new study by Stanford Medicine investigators unveils a new artificial intelligence model that was more than 90% successful at determining whether scans of brain activity came from a woman or a man.

The findings, published Feb. 20 in the Proceedings of the National Academy of Sciences, help resolve a long-term controversy about whether reliable sex differences exist in the human brain and suggest that understanding these differences may be critical to addressing neuropsychiatric conditions that affect women and men differently.

“A key motivation for this study is that sex plays a crucial role in human brain development, in aging, and in the manifestation of psychiatric and neurological disorders,” said Vinod Menon , PhD, professor of psychiatry and behavioral sciences and director of the Stanford Cognitive and Systems Neuroscience Laboratory . “Identifying consistent and replicable sex differences in the healthy adult brain is a critical step toward a deeper understanding of sex-specific vulnerabilities in psychiatric and neurological disorders.”

Menon is the study’s senior author. The lead authors are senior research scientist Srikanth Ryali , PhD, and academic staff researcher Yuan Zhang , PhD.

“Hotspots” that most helped the model distinguish male brains from female ones include the default mode network, a brain system that helps us process self-referential information, and the striatum and limbic network, which are involved in learning and how we respond to rewards.

The investigators noted that this work does not weigh in on whether sex-related differences arise early in life or may be driven by hormonal differences or the different societal circumstances that men and women may be more likely to encounter.

Uncovering brain differences

The extent to which a person’s sex affects how their brain is organized and operates has long been a point of dispute among scientists. While we know the sex chromosomes we are born with help determine the cocktail of hormones our brains are exposed to — particularly during early development, puberty and aging — researchers have long struggled to connect sex to concrete differences in the human brain. Brain structures tend to look much the same in men and women, and previous research examining how brain regions work together has also largely failed to turn up consistent brain indicators of sex.

test

Vinod Menon

In their current study, Menon and his team took advantage of recent advances in artificial intelligence, as well as access to multiple large datasets, to pursue a more powerful analysis than has previously been employed. First, they created a deep neural network model, which learns to classify brain imaging data: As the researchers showed brain scans to the model and told it that it was looking at a male or female brain, the model started to “notice” what subtle patterns could help it tell the difference.

This model demonstrated superior performance compared with those in previous studies, in part because it used a deep neural network that analyzes dynamic MRI scans. This approach captures the intricate interplay among different brain regions. When the researchers tested the model on around 1,500 brain scans, it could almost always tell if the scan came from a woman or a man.

The model’s success suggests that detectable sex differences do exist in the brain but just haven’t been picked up reliably before. The fact that it worked so well in different datasets, including brain scans from multiple sites in the U.S. and Europe, make the findings especially convincing as it controls for many confounds that can plague studies of this kind.

“This is a very strong piece of evidence that sex is a robust determinant of human brain organization,” Menon said.

Making predictions

Until recently, a model like the one Menon’s team employed would help researchers sort brains into different groups but wouldn’t provide information about how the sorting happened. Today, however, researchers have access to a tool called “explainable AI,” which can sift through vast amounts of data to explain how a model’s decisions are made.

Using explainable AI, Menon and his team identified the brain networks that were most important to the model’s judgment of whether a brain scan came from a man or a woman. They found the model was most often looking to the default mode network, striatum, and the limbic network to make the call.

The team then wondered if they could create another model that could predict how well participants would do on certain cognitive tasks based on functional brain features that differ between women and men. They developed sex-specific models of cognitive abilities: One model effectively predicted cognitive performance in men but not women, and another in women but not men. The findings indicate that functional brain characteristics varying between sexes have significant behavioral implications.

“These models worked really well because we successfully separated brain patterns between sexes,” Menon said. “That tells me that overlooking sex differences in brain organization could lead us to miss key factors underlying neuropsychiatric disorders.”

While the team applied their deep neural network model to questions about sex differences, Menon says the model can be applied to answer questions regarding how just about any aspect of brain connectivity might relate to any kind of cognitive ability or behavior. He and his team plan to make their model publicly available for any researcher to use.

“Our AI models have very broad applicability,” Menon said. “A researcher could use our models to look for brain differences linked to learning impairments or social functioning differences, for instance — aspects we are keen to understand better to aid individuals in adapting to and surmounting these challenges.”

The research was sponsored by the National Institutes of Health (grants MH084164, EB022907, MH121069, K25HD074652 and AG072114), the Transdisciplinary Initiative, the Uytengsu-Hamilton 22q11 Programs, the Stanford Maternal and Child Health Research Institute, and the NARSAD Young Investigator Award.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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Key influences on university students’ physical activity: a systematic review using the Theoretical Domains Framework and the COM-B model of human behaviour

  • Catherine E. B. Brown 1 ,
  • Karyn Richardson 1 ,
  • Bengianni Halil-Pizzirani 1 ,
  • Lou Atkins 2 ,
  • Murat Yücel 3   na1 &
  • Rebecca A. Segrave 1   na1  

BMC Public Health volume  24 , Article number:  418 ( 2024 ) Cite this article

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Physical activity is important for all aspects of health, yet most university students are not active enough to reap these benefits. Understanding the factors that influence physical activity in the context of behaviour change theory is valuable to inform the development of effective evidence-based interventions to increase university students’ physical activity. The current systematic review a) identified barriers and facilitators to university students’ physical activity, b) mapped these factors to the Theoretical Domains Framework (TDF) and COM-B model, and c) ranked the relative importance of TDF domains.

Data synthesis included qualitative, quantitative, and mixed-methods research published between 01.01.2010—15.03.2023. Four databases (MEDLINE, PsycINFO, SPORTDiscus, and Scopus) were searched to identify publications on the barriers/facilitators to university students' physical activity. Data regarding study design and key findings (i.e., participant quotes, qualitative theme descriptions, and survey results) were extracted. Framework analysis was used to code barriers/facilitators to the TDF and COM-B model. Within each TDF domain, thematic analysis was used to group similar barriers/facilitators into descriptive theme labels. TDF domains were ranked by relative importance based on frequency, elaboration, and evidence of mixed barriers/facilitators.

Thirty-nine studies involving 17,771 participants met the inclusion criteria. Fifty-six barriers and facilitators mapping to twelve TDF domains and the COM-B model were identified as relevant to students’ physical activity. Three TDF domains, environmental context and resources (e.g., time constraints), social influences (e.g., exercising with others), and goals (e.g., prioritisation of physical activity) were judged to be of greatest relative importance (identified in > 50% of studies). TDF domains of lower relative importance were intentions, reinforcement, emotion, beliefs about consequences, knowledge, physical skills, beliefs about capabilities, cognitive and interpersonal skills, social/professional role and identity, and behavioural regulation. No barriers/facilitators relating to the TDF domains of memory, attention and decision process, or optimism were identified.

Conclusions

The current findings provide a foundation to enhance the development of theory and evidence informed interventions to support university students’ engagement in physical activity. Interventions that include a focus on the TDF domains 'environmental context and resources,' 'social influences,' and 'goals,' hold particular promise for promoting active student lifestyles.

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Prospero ID—CRD42021242170.

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Physical activity (PA) has a powerful positive impact on all aspects of health. Regular PA can prevent and treat noncommunicable diseases [ 1 , 2 ], build resilience against the development of mental illness [ 3 ], and attenuate cognitive decline [ 4 ]. Given these pervasive health benefits, increasing participation in PA is recognised as a global priority by international public health organisations. Indeed, a core aspect of the World Health Organisation’s action plan for a “healthier world” is to achieve a 15% reduction in the global prevalence of physical inactivity by 2030 [ 5 ].

Despite international efforts to reduce physical inactivity, university students frequently do not meet the recommended level of PA required to attain its health benefits. Approximately 40–50% of university students are physically inactive [ 6 ], many of whom attribute their inactivity to unique challenges associated with university life. For many students, the transition to university coincides with new academic, social, financial, and personal responsibilities [ 7 ], disrupting established routines and imposing additional barriers to the initiation or maintenance of healthy lifestyle habits such as regular PA [ 8 ]. Students’ PA tends to decline further during periods of high stress and academic pressure, such as exams and assignment deadlines [ 9 ]. This pattern has been observed across diverse university populations and cultural contexts [ 10 , 11 , 12 ], highlighting the importance of understanding the factors that contribute to physical inactivity among this cohort globally.

Understanding the barriers and facilitators to PA in the context of the university setting is an important step in developing effective, targeted interventions to promote active lifestyles among university students. A recently published systematic review found that lack of time, motivation, access to places to practice PA, and financial resources were primary barriers to PA for undergraduate university students [ 13 ]. A corresponding and complementary synthesis of the facilitators of PA, however, has not yet been conducted. Such a synthesis would be valuable in enabling a comprehensive understanding of the factors that influence students' PA and identifying facilitators that could be leveraged in intervention design. Furthermore, applying theoretical frameworks to understand barriers and facilitators to PA can guide the development of theory-informed, evidence-based interventions for university students that purposely and effectively target factors that influence their participation in PA.

The Theoretical Domains Framework (TDF) [ 14 , 15 , 16 ] and the COM-B model of behaviour [ 17 ] are two robust, gold-standard frameworks frequently used to examine the determinants of human behaviour. The TDF is an integrated framework of 14 theoretical domains (see Additional file 1 for domains, definitions, and constructs) which provide a comprehensive understanding of the key factors driving behaviour. The TDF was developed through expert consensus, synthesising 33 psychological theories (such as social cognitive theory [ 18 , 19 ] and the theory of planned behaviour [ 20 , 21 ] and 128 theoretical constructs (such as ‘competence’, ‘goal priority’, etc.) across disciplines identified as most relevant to the implementation of behaviour change interventions. Identifying the relative importance of theoretical domains allows intervention designers to triage which behaviour change strategies should be prioritised in intervention development [ 22 , 23 ]. The TDF has been widely applied by researchers and practitioners to systematically identify which theoretical domains are most relevant for understanding health behaviour change and policy implementation across a range of contexts, including education [ 24 ], healthcare [ 25 ], and workplace environments [ 26 ].

The 14 TDF domains map onto the COM-B model (Fig.  1 ), which is a broader framework for understanding behaviour and provides a direct link to intervention development frameworks. The COM-B model posits that no behaviour will occur without sufficient capability, opportunity, and motivation. Where any of these are lacking, they can be strategically targeted to support increased engagement in a desired behaviour, including participation in PA. Within the COM-B model, capability can be psychological (e.g., knowledge to engage in the necessary processes) or physical (e.g., physical skills); opportunity can be social (e.g., interpersonal influences) or physical (e.g., environmental resources); and motivation can be automatic (e.g., emotional reactions, habits) or reflective (e.g., intentions, beliefs). The COM-B model was developed through a process of theoretical analysis, empirical evidence, and expert consensus as a central part of a broader framework for developing behaviour change interventions known as the Behaviour Change Wheel (BCW) [ 17 ].

figure 1

The TDF domains linked to the COM-B model subcomponents

Note. Reproduced from Atkins, L., Francis, J., Islam, R., et al. (2017) A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems. Implementation Science 12, 77.  https://doi.org/10.1186/s13012-017-0605-9

Using the TDF and COM-B model to understand the barriers and facilitators to university students’ participation in PA is valuable to inform the development of effective evidence-based interventions that are tailored to address the most influential determinants of behaviour change. As such, this systematic review aimed to: a) identify barriers and facilitators to university students’ participation in PA; b) map these factors using the TDF and COM-B model; and c) determine the relative importance of each TDF domain.

Study design

The systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 27 ]. The review protocol was registered on PROSPERO (CRD42021242170).

Search strategy

Search terms and parameters were developed in collaboration with a Monash University librarian with expertise in systematic review methodology. The following databases were searched on 15.03.2023 to identify relevant literature: MEDLINE, PsycINFO, and SPORTDiscus. Key articles were also selected for citation searching via Scopus. In consultation with a librarian, these databases were selected due to their unique scope, relevance, broad coverage, and utility. This process ensured the identified literature aligned with the aim and research topic of our systematic review. A 01.01.2010—15.03.2023 publication period was purposefully specified to account for the significant advancements in digital fitness support and tracking tools within the past decade [ 28 ], All available records were searched using the following combination of concepts in the title or abstract of the article: 1) barriers, facilitators, or intervention, Footnote 1 2) physical activity, 3) university, and 4) students. Each search concept was created by first developing a list of search terms relevant to each concept (e.g., for the ‘physical activity’ concept search terms included ‘physical exercise’, ‘physical fitness’, ‘sports’, ‘inactive’, ‘sedentary’, etc.). To create each concept, search terms were then searched collectively using the operator ‘OR’. Each search concept was then combined into the final search by using the operator ‘AND’. Search terms related to concepts 1, 2 and 3 included indexed terms unique and relevant to each database (i.e., Medical Subject Heading Terms for MEDLINE, Index Terms for PsycINFO, and Thesaurus terms for SPORTDiscus). The search was performed according to Boolean operators (e.g., AND, OR) (see Additional file 2 for the complete search syntax for MEDLINE). Unpublished studies were not sought.

Selection criteria

Articles were included if they: (a) reported university students’ self-reported barriers and/or facilitators to physical activity or exercise Footnote 2 ; (b) were written in English; and (c) were peer-reviewed journal articles. Articles encompassed studies directly investigating barriers and/or facilitators to students’ participation in PA and physical exercise intervention studies, where the latter reported participants’ self-reported barriers and/or facilitators to intervention adherence (see Table  1 below for full criteria).

Study selection

Identified articles were uploaded to EndNote X9 software [ 30 ]. A duplication detection tool was used to detect duplicates, which were then screened for accuracy by CB prior to removal. The remaining articles were uploaded to Covidence to enable blind screening and conflict resolution. Articles were screened at the title and abstract level against the inclusion and exclusion criteria by author CB, and 25% were independently screened by BP. The full text of studies meeting the inclusion criteria was then screened against the same criteria by CB, and 25% were again independently screened by BP. Differences were resolved by an independent author (KR). Inter-rater agreement in screening between CB and BP was high (0.96 for title and abstract screening, 0.83 for full-text screening). The decision to dual-screen 25% of studies was strategically chosen to balance thoroughness with efficiency, ensuring both the validity of the screening criteria and the reliability of the primary screener’s decisions. This approach aligns with the protocols used in similar systematic reviews in the field (e.g., [ 31 , 32 ]).

Data extraction

Key article characteristics were extracted, including the author/s, year of publication, country of origin, participant characteristics (e.g., enrolment status, exercise engagement [if reported]), sample size, research design, methods, and analytical approach. Barriers and facilitators were also extracted for each article and subsequently coded according to the 14 domains of the TDF and six subcomponents of the COM-B model. Quantitative data were only extracted if ≥ 50% of students endorsed a factor as a barrier or facilitator. This cut-off criterion was applied to maintain focus on the most common variables of influence and aligns with other reviews synthesising common barriers and facilitators to behaviour change (e.g., [ 26 , 33 ]).

A coding manual was developed to guide the process of mapping barriers and facilitators to the TDF and COM-B. All articles were independently coded by at least two authors (CB and BS, BP or KR). The first version of the manual was developed a priori, based on established guides for applying the TDF and COM-B model to investigate barriers and facilitators to behaviour [ 14 , 34 ], and updated as needed via regular consultation with a co-author and TDF/COM-B designer LA to ensure the accuracy of the data extraction. Barriers and facilitators were only coded to multiple TDF domains if deemed essential to accurately contextualise the core elements of the barrier/facilitator, and when the data in individual papers was described in sufficient detail to indicate that more than one domain was relevant. For example, if ‘lack of time due to competing priorities’ was reported as a barrier to PA, this encompassed both the ‘environmental context and resources’ (i.e., time) and ‘goals’ (i.e., competing priorities) domains of the TDF. Coding conflicts were resolved via discussion with LA.

Data analysis

The following three-step method was utilised to synthesise quantitative and qualitative data:

Framework analysis [ 35 ] was conducted to deductively code barriers and facilitators onto TDF domains and COM-B subcomponents. This involved identifying barriers and facilitators in each article, extracting and labelling them, and determining their relevance against the definitions of the TDF domains and COM-B subcomponents. This process involved creating tables to assist in the systematic categorisation of barriers and facilitators into relevant TDF domains and COM-B subcomponents.

Within each TDF domain, thematic analysis [ 36 ] was conducted to group similar barriers and facilitators together and inductively generate summary theme labels.

The relative importance of each TDF domain was calculated according to frequency (number of studies), elaboration (number of themes) and the identification of mixed barriers/facilitators regarding whether a theme was a barrier or facilitator within each domain (e.g., if some participants reported that receiving encouragement from their family to exercise was a facilitator, and others reported that lack of encouragement from their family to exercise was a barrier). The rank order was determined first by frequency, then elaboration, and finally by mixed barriers/facilitators.

This methodology follows previous studies using the TDF and COM-B to characterise barriers and facilitators to behaviour change and rank their relative importance [ 22 , 23 ].

Study characteristics

Following the removal of duplicates, 6,152 articles met the search criteria and were screened based on title and abstract. A total of 5,995 articles were excluded because they did not meet the inclusion criteria (see Fig.  2 below for the PRISMA flowchart). After the title and abstract screening, 157 full-text articles were retrieved and assessed for eligibility. One additional article was identified and included following citation searching of selected key articles. Thirty-nine articles met the inclusion criteria (see Additional file 3 for a summary of these studies). Eight studies were conducted in the USA, seven in Canada, three in Germany, two each in Qatar, Spain, the United Arab Emirates, and the United Kingdom, and one each in Australia, Belgium, Columbia, Egypt, Ireland, Japan, Kuwait, Malaysia, New Zealand, Saudi Arabia, South Africa, Sri Lanka, and Uganda.

figure 2

PRISMA flowchart illustrating the article selection process

Relative importance of TDF domains and COM-B components

Twelve of the 14 TDF domains and all six subcomponents of the COM-B model were identified as relevant to university students' PA. The rank order of relative importance of TDF domains and associated COM-B subcomponents are presented in Table  2 . The three most important domains were identified in at least 54% of studies.

Barriers and facilitators to student’s physical activity

Within the TDF domains, 56 total themes were identified, including 26 mixed barriers/facilitators, 18 facilitators and 12 barriers (Table  3 ). The barriers and facilitators identified within each TDF domain are summarised below (with associated COM-B subcomponent presented in parentheses), in order of relative importance:

1. Environmental context and resources (Physical Opportunity) ( n  = 90% studies)

The most frequent barrier to PA across all TDF domains was ‘lack of time’, most often in the context of study demands. Time constraints were exacerbated by long commutes to university, family responsibilities, involvement in co-curricular activities, and employment commitments. Students’ need for ‘easily accessible exercise options, facilities and equipment’ was a recurring theme. PA was deemed inaccessible if exercise facilities and other infrastructure to support PA, such as bike paths and running trails, were situated too far from the university campus or students’ residences, or if fitness classes were scheduled at inconvenient times. ‘Financial costs’ emerged as a theme. The costs associated with accessing exercise facilities, equipment and programs consistently deterred students from engaging in PA. The desire for ‘safe and enjoyable’, ‘weather appropriate’ environments for PA were frequently reported. Participating in outdoor PA in green spaces or near water increased enjoyment, provided the environment felt safe and weather conditions were suitable for PA. Factors related to students’ home, work, and university environment impacted their participation in ‘incidental PA’. Incidental PA was influenced by whether students engaged in domestic house chores, and manual work, and actively commuted to university and between classes on-campus. Students’ ‘access to a variety of physical activities’ and ‘information provision regarding on-campus exercise options’ impacted their PA. Students most often had access to a wide variety of physical activities, however, it could be difficult to access information about what types of activities were available on-campus and how to sign up to participate. The ‘lack of personalised physical activities to cater to individual fitness needs’ was a barrier, particularly for students with low levels of PA who required beginner-oriented programs. Another barrier was the ‘lack of university policy and promotion to encourage PA’, which led students to perceive that there was no obligation to participate in PA and that the university did not value it. ‘Health-concerning behaviours associated with university’, including poor diet, increased alcohol intake and sedentary behaviour, negatively impacted students’ PA. ‘Listening to music while exercising’ was a facilitator.

2. Social influences (Social Opportunity) ( n  = 72% studies)

Within social influences, ‘exercising with others’ emerged as the most frequent theme. Doing so increased students’ accountability, enjoyment and motivation, and helped them to overcome feelings of intimidation when exercising alone. Having a lack of friends to exercise with was a particular concern for students who were new to exercise or infrequently participated in PA. Receiving ‘encouragement from others to be physically active’, such as family members, friends, peers, and fitness instructors, shaped students’ values toward PA and enhanced their motivation and self-efficacy. Students’ family members, friends and teachers discouraged PA if it was not valued, or in favour of other priorities, such as academic commitments. Another recurrent theme was ‘competition or relative comparison to others’. While most students were motivated by competition, a minority felt demotivated if they compared themselves to others with higher PA standards, especially if they failed to achieve similar PA goals. Sociocultural norms influenced barriers/facilitators to PA across different cultures, and between various groups, such as international versus domestic students, and women versus men. Students from Japan and Hawaii viewed PA as an important part of their culture, in contrast to students from the Philippines who described the opposite. Participation in PA enabled international students to integrate with domestic students and learn about the local culture, however cultural segregation was a barrier to participation in university team sports. For female students from some middle-eastern countries, including Saudi Arabia, the UAE and Qatar, cultural norms made it impermissible for women to engage in PA, particularly compared to men. Religion also differentially impacted barriers/facilitators between women and men. Muslim women reported that Islamic practices, such as needing to engage in PA separately from men, be accompanied by a male family member while going outdoors, or dress modestly, posed additional barriers to PA. However, one study reported that Islamic teachings generally encouraged PA for both women and men by emphasising the importance of maintaining good health. Other gender-specific barriers were identified. Women often felt unwelcome or intimidated by men in exercise facilities, partly due to the perception that these facilities were tailored toward “masculine” sports and/or dominated by men. ‘Being stared at while engaging in PA’ was another barrier, impacting both women and students with a disability. A less common facilitator was the influence of both positive and negative ‘exercise role models’. For example, students practiced PA because they aspired to be like someone who was physically active, or because they did not want to be like someone who was not physically active.

3. Goals (Reflective Motivation) ( n  = 54%)

‘Prioritisation of PA compared to other activities’ was the most common theme within goals. Students frequently prioritised other activities, such as study, social activities, or work, over PA. However, those who played team sports or regularly practiced PA were more inclined to prioritise it for its recognised health benefits (i.e., stress management), and its role in enhancing confidence. Additional facilitators included ‘engaging in PA to achieve an external goal’, such as improving one’s appearance, and ‘setting specific PA-related goals’ as a means to enhance accountability.

4. Intentions (Reflective Motivation) ( n  = 44%)

Within intentions, ‘motivation to engage in PA’ was the most common theme. Students most often noted a lack of self-motivation for PA. Less frequent barriers included perceiving PA as an obligatory or necessary "chore", and ‘failing to follow through on intentions to engage in PA’. Conversely, ‘self-discipline to engage in PA’ emerged as a facilitator that assisted students in maintaining a regular PA routine.

5. Reinforcement (Automatic Motivation) ( n  = 38%)

The most frequent facilitator within reinforcement was ‘experiencing the positive effects of PA’ on their health and wellbeing. These included physical health benefits (i.e., maintaining fitness), psychological benefits (i.e., stress reduction), and cognitive health benefits (i.e., enhanced academic performance). Conversely, barriers arose from ‘experiencing discomfort during or after PA’ due to pain, muscle soreness or fatigue. ‘Past and current habits and routines’ was a theme. Students were more likely to participate in PA if they had established regular exercise routines, and that forming these habits at an early age made it easier to maintain them later in life. However, maintaining a regular PA routine was difficult in the context of inflexible university schedules. Students’ ‘sense of accomplishment in relation to PA’ was a theme. Students were less likely to feel a sense of accomplishment after participating in PA if it was not physically challenging. Consistent facilitators were ‘receiving positive feedback from others’ after engaging in PA, such as compliments, and ‘receiving incentives’, such as reducing the cost of gym memberships if students participated in more PA. ‘Experiencing a sense of achievement’ after reaching a PA-related goal or winning a sports match also served as a facilitator.

6. Emotion (Automatic Motivation) ( n  = 38%)

‘Enjoyment’ was the most frequently cited emotional theme. Most students reported that PA was fun and/or associated with positive feelings, however, a minority described PA as unenjoyable, boring, and repetitive. Students’ ‘poor mental health and negative affectivity’ (such as feeling sad, stressed or self-conscious, as well as fear of injury and pain), adversely impacted their motivation to be physically active.

7. Beliefs about consequences (Reflective Motivation) ( n  = 31%)

‘Beliefs about the physical health consequences of PA’ was the most recurrent barrier/facilitator. Most students understood that PA was essential for maintaining good health and preventing illness. However, some students who rarely or never engaged in PA believed they could delay pursuing an active lifestyle until they were older without compromising their health. Participating in PA to ‘maintain or improve one’s physical appearance’ acted as a facilitator. This motivation was most often cited in contexts such as increasing or decreasing weight, changing body shape or enhancing muscle tone. Beliefs about the positive environmental, occupational and psychological impacts of PA also served as facilitators. Students were motivated to participate in PA due to the environmental benefits of using active transport. They also acknowledged the importance of being physically fit for work and believed that being active was beneficial for mental health. ‘Receiving advice to participate in PA from a credible source’, such as a health professional, further facilitated students’ motivation to be active.

8. Knowledge (Psychological Capability) ( n  = 28%)

'Knowledge about the benefits of PA’, encompassing an understanding of the various types of benefits (i.e., physical, mental, or cognitive) and the biological mechanisms by which PA brings about these changes was identified as the most common knowledge theme. Being aware of these benefits positively influenced students’ motivation to be physically active. Conversely, students’ lack of knowledge about the gym environment and the programs available were barriers to PA. Regarding the gym environment, students’ ‘lack of knowledge about how to navigate through the gym, what exercises to do, and how to use exercise equipment’ amplified feelings of intimidation. Likewise, ‘lack of knowledge about the types of exercise programs and activities that were available on-campus, and how to sign up to participate’ were all barriers. A unique theme emerged concerning ‘knowledge about how to adapt physical activities for students with a disability’. Students with a disability described how fitness instructors often had a limited understanding of how to modify activities to enable them to participate. However, students with a disability were able to overcome this barrier if they possessed their own knowledge about how to tailor physical activities to meet their specific needs.

9. Physical skills (Physical Capability) ( n  = 21%)

The most prevalent theme within physical skills was ‘having the physical skills and fitness to participate in PA’. A lack of physical skills was most frequently a hindrance to PA. Additional obstacles to PA included being physically inhibited due to a ‘lack of energy’ or ‘physical injury’.

10. Beliefs about capabilities (Reflective Motivation) ( n  = 18%)

Within beliefs about capabilities, ‘self-efficacy to participate in PA’ was the most recurrent theme. Students who doubted their success in becoming physically active or who lacked confidence in their ability to initiate PA or participate in sport were less motivated to take part. A less frequent facilitator was students’ ‘self-affirmation to participate in PA’, often referring to positive cognitions about one’s own physical abilities.

11. Cognitive and interpersonal skills (Psychological Capability) ( n  = 15%)

‘Time-management’ was the only theme identified within cognitive and interpersonal skills. Students who struggled to manage their time effectively found it difficult to incorporate regular PA into their daily routine.

12. Social/professional role and identity (Reflective Motivation) ( n  = 8%)

The most frequent theme within social/professional role and identity was ‘perceiving PA as a part of one’s self-identity’. Students who engaged regularly in PA often considered it integral to their identity. Conversely, students who perceived they did not align with the aesthetic and superficial stereotypes commonly associated with the fitness industry felt less motivated to be active. A specific facilitator emerged among physiotherapy students, who were motivated to be active due to the emphasis on PA within their profession.

13. Behavioural regulation (Psychological Capability) ( n  = 3%)

Within the domain of behavioural regulation, two facilitators were equally prevalent: ‘self-monitoring of PA’ and ‘feedback on progress towards a PA-related goal’. By keeping track of their step count and receiving feedback on walking goals, students were motivated to exceed the average number of daily steps or achieve their personal PA targets.

14. Memory, attention, and decision process (Psychological Capability); Optimism (Reflective Motivation) ( n  = 0%)

No barriers or facilitators relating to the TDF domains of memory, attention and decision process, or optimism were identified.

This systematic review used the TDF and COM-B model to identify barriers and facilitators to PA among university students and rank the relative importance of each TDF domain. It is the first review to apply these frameworks in the context of increasing university students’ participation in PA. Twelve TDF domains across all six sub-components of the COM-B model were identified. The three most important TDF domains were ‘environmental context and resources’, ‘social influences’, and ‘goals’. The most common barriers and facilitators were ‘lack of time’, ‘easily accessible exercise options, facilities and equipment’, ‘exercising with others’, and ‘prioritisation of PA compared to other activities’.

The most common barrier to PA was perceived lack of time. This is consistent with previous findings among university students [ 13 , 74 ] and across other populations [ 24 ], For students, lack of time was frequently attributed to a combination of competing priorities and underdeveloped time management skills. Students predominantly prioritised study over PA, as performing well at university is a valued goal and there is a common perception that spending time exercising (at the expense of study) will impede their academic success [ 53 , 58 ]. Evidence from cognitive neuroscience research, however, suggests that this is a mistaken belief. In addition to its broad physical and mental health benefits, a growing body of evidence demonstrates regular PA can change the structure and function of the brain.

These changes can, in turn, enhance numerous aspects of cognition, including memory, attention, and processing speed [ 4 , 75 , 76 , 77 ], and buffer the negative impact of stress on cognition [ 78 ], all of which are important for academic success. However, students are typically unaware of the brain and cognitive health benefits of PA and its potential to improve academic performance, particularly compared to the physical health benefits [ 37 , 40 , 64 ]. Interventions that position participating in PA as a conduit for helping, rather than hindering, academic goals could increase the relative importance of PA to students and therefore increase their motivation to regularly engage in it. The impact that interventions of this nature have on students’ PA is yet to be empirically assessed.

Ineffective time management also contributed to students’ perceived lack of time for PA. Students reported tendencies to procrastinate in the face of overwhelming academic workloads, which left limited time for PA [ 53 ]. Additionally, students lacked an understanding of how to organise time for PA around academic timetables, social and family responsibilities, co-curricular activities, and employment commitments [ 9 , 44 , 53 , 59 ]. To address these challenges, efforts to develop students’ time management skills will be useful for enabling students to regularly participate in PA. Goal-setting and action planning are two specific examples of such skills that can be integrated into interventions to help students initiate and maintain a PA routine [ 79 ]. For example, goal-setting could involve setting a daily PA goal, and action planning could involve planning to engage in a particular PA at a particular time on certain days.

While the most common determinants of university students’ PA levels were not influenced by specific demographic characteristics, several barriers disproportionately impacted women and students with a disability. These findings are in keeping with evidence that PA is lower among these equity-deserving groups compared with the general population [ 68 , 80 ]. For women, particularly those from Middle Eastern cultures, restrictions were often tied to religious practices and sociocultural norms that limited their opportunities to engage in PA [ 45 , 48 , 66 ]. Additionally, a substantial number of women felt intimidated or self-conscious when exercising in front of others, especially men [ 48 , 49 ]. They also felt that exercise facilities were more often tailored towards the needs of men, leading to a perception that they were unwelcome in exercise communities [ 45 , 48 ]. Consequently, women expressed a desire for women-only spaces to exercise to help them overcome these gender-specific barriers to PA [ 47 , 48 , 66 ]. Furthermore, students with a disability faced physical accessibility barriers and perceived stigmatisation that deterred them from PA [ 50 , 52 ]. The lack of accessible exercise facilities and suitable equipment, programs, and education regarding how to adapt physical activities to accommodate their needs limited their opportunity and ability to participate [ 52 ]. Moreover, students with a disability felt stigmatised by others for not fitting into public perceptions of ‘normality’ or the aesthetic values and beauty standards often portrayed by the fitness industry [ 50 ]. These barriers for both equity-deserving groups of students are deeply rooted in historical stereotypes that have traditionally excluded women and people with a disability from engaging in various types of PA [ 81 , 82 ]. Despite growing awareness of these issues, PA inequalities persist due to narrow sociocultural norms, and a lack of diverse representation and inclusion in the fitness industry and associated marketing campaigns [ 83 , 84 ]. A concerted effort to address PA inequalities across the university sector and fitness industry more broadly is needed. One approach for achieving this is to develop interventions that are tailored to the unique needs of equity-deserving groups, emphasise inclusivity, diversity, and empowerment, and feature women and people with a disability being active.

The “This Girl Can” [ 85 ] and “Everyone Can” [ 86 ] multimedia campaigns are two examples of health behaviour interventions that were co-developed with key stakeholders (i.e., women and people with a disability, respectively) to tackle PA inequalities. The “This Girl Can” campaign has reached over 3 million women and girls, projecting inclusive and positive messages that aim to empower them to be physically active. Following the widespread reach of the “This Girl Can” campaign, the “Everybody Can” campaign was launched to support the inclusion of people with a disability in the PA sector. Although not tailored for university students, these campaigns provide a useful example for developing interventions that are specifically designed to address key barriers preventing women and people with a disability from participating in PA.

Across the tertiary education sector globally, efforts to elevate opportunities and motivation to include PA as a core part of the student experience will be beneficial for promoting students’ PA at scale. Two intervention approaches that can be implemented to facilitate such an endeavour are environmental restructuring and enablement [ 17 ]. These intervention approaches should involve the provision of accessible low-cost exercise options, facilities, and programs, integrating PA into the university curriculum, and mobilising student and staff leadership to encourage students’ participation in PA [ 9 ]. Although there is evidence that these approaches can be effective in promoting sustained PA throughout students’ university years and beyond [ 87 ], implementation measures such as these are complex. Implementation requires aligning student activity levels with broader university goals and is further complicated by having to compete with other funding priorities and resource allocations. Notably, due to the negative impact of the COVID-19 pandemic on university students’ physical and mental health [ 88 , 89 ], the post-pandemic era has seen many universities prioritise enhancing student health and wellbeing alongside more traditional strategic goals like academic excellence and workforce readiness. Despite the potential for PA to be used as a vehicle for supporting these strategic goals there is an absence of data on the extent to which this is occurring in the university sector. The limited evidence in this area suggests that some universities have made efforts to support students’ mental health by referring students who access on-campus counselling services to PA programs [ 90 ]. However, the uptake and efficacy of such initiatives is rarely assessed, and even less is known about whether PA is being used to support other strategic goals, such as academic success. Therefore, while the potential is there for the university sector to use PA to support students’ mental health and academic performance, to be successful this needs to become a strategic university priority. Given that these strategic priorities are set at the senior leadership level, engaging senior university staff in intervention design and promotion efforts is important to enhance the value of PA in the tertiary education sector.

Implications for intervention development

The current findings provide a high-level synthesis of the most common barriers and facilitators to university students’ physical activity. These findings can be leveraged with behavioural intervention development tools and frameworks (e.g., the BCW [ 17 ], Obesity-Related Behavioural Intervention Trials model [ 91 ], Intervention Mapping [ 92 ], and the Medical Research Council guidelines for developing complex interventions [ 93 , 94 ]) to develop evidence-based interventions and policies to promote PA. Given that the TDF and COM-B model are directly linked to the BCW framework, applying this process may be particularly useful to translate the current findings into an intervention.

Additionally, current findings can be triangulated with data directly collected from key stakeholders to assist in the development of context-specific interventions. Best practice principles for developing behavioural interventions recommend this approach to ensure a deep understanding of the barriers and facilitators that need to be targeted to increase the likelihood of behaviour change [ 17 ]. Consulting stakeholders directly (i.e., university students and staff) to understand their perspectives on the barriers and facilitators to students’ PA also enables an intervention to be appropriately tailored to the target population’s needs and implementation setting. Studies continue to demonstrate the effectiveness of this approach, especially when framed within the context of frameworks directly linked to intervention development frameworks, such as the TDF [ 95 ].

Strengths and limitations

The findings of this review should be considered with respect to its methodological strengths and limitations. The credibility and reliability of the research findings are supported by a systematic approach to screening and analysing the empirical data, along with the use of gold-standard behavioural science frameworks to classify barriers and facilitators to PA. The inclusion of qualitative, quantitative, and mixed-methods studies of both barriers and facilitators to students’ PA allowed for a comprehensive understanding of the factors that influence students’ PA that have not previously been captured.

While the present review elucidates students’ own perspectives of the factors that influence their activity levels, other stakeholders such as university staff, will also influence the adoption, operationalisation, and scale of PA interventions in a university setting. It will be important for future research to explore factors that influence university decision-makers in these roles to inform large-scale strategies for promoting students' PA.

Additionally, only one study included in the review used the TDF to explore barriers and facilitators to PA [ 47 ]. Therefore, it is possible that certain TDF domains may not have been identified because students were not asked relevant questions to assess the influence of those domains on their PA. For instance, domains such as ‘memory, attention, and decision process’, and ‘optimism’ are likely to play a role in understanding the barriers and facilitators to PA despite not being identified in this review.

Moreover, quantitative data were only extracted if ≥ 50% of students endorsed the factor as a barrier or facilitator to PA. This threshold was purposefully applied to maintain a focus on the TDF domains most universally relevant to the broad student population in the context of understanding their barriers and facilitators to PA. It is possible that less frequently reported barriers and facilitators, which may not be as prominently featured in the results, could be relevant to specific groups of students, such as those identified as equity-deserving.

Lastly, a quality appraisal of the included studies was not undertaken. This decision was informed by the aim of the review, which was to describe and synthesise the literature to subsequently map data to the TDF and COM-B rather than assess the effectiveness of interventions or determine the strength of evidence. However, this decision, combined with dual screening 25% of the studies and excluding unpublished studies and grey literature, may introduce sources of error and bias, which should be considered when interpreting the results presented.

PA is an effective, scalable, and empowering means of enhancing physical, mental, and cognitive health. This approach could help students reach their academic potential and cope with the many stressors that accompany student life, in addition to setting a strong foundation for healthy exercise habits for a lifetime. As such, understanding the barriers and facilitators to an active student lifestyle is beneficial. This systematic review applied the TDF and COM-B model to identify and map students’ barriers and facilitators to PA and, in doing so, provides a pragmatic, theory-informed, and evidence-based foundation for designing future context-specific PA interventions. The findings from this review highlight the importance of developing PA interventions that focus on the TDF domains ‘environmental context and resources’, ‘social influences’, and ‘goals’, for which intervention approaches could involve environmental restructuring, education, and enablement. If successful, such strategies could make a significant contribution to improving the overall health and academic performance of university students.

Availability of data and materials

The review protocol is available on PROSPERO. The datasets used and/or analysed during the current study and materials used are available from the corresponding author on reasonable request.

The term ‘intervention’ was included to identify student barriers and facilitators to engaging in implemented physical activity interventions.

Physical exercise is defined as “a subset of physical activity that is planned, structured, and repetitive”, and purposefully focused on the improvement or maintenance of physical fitness, whereas physical activity is defined as “any bodily movement produced by skeletal muscles that results in energy expenditure” [ 96 ].

Abbreviations

Behaviour Change Wheel

Capability, Opportunity, Model-Behaviour

  • Physical activity

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

International Prospective Register of Systematic Reviews

Theoretical Domains Framework

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The authors extend their gratitude to the funder, the nib foundation, for its financial support, which was instrumental in facilitating this research. We are also indebted to the Wilson Foundation and the David Winston Turner Endowment Fund for their generous philanthropic contributions, which have supported the BrainPark research team and facility where this research was conducted. Special thanks are owed to the library staff at Monash University for their expertise in conducting systematic reviews, which helped inform the selection of databases and the development of the search strategy.

This research was supported by nib foundation. The nib foundation had no role in the design of the study and collection, analysis, and interpretation of data, and in writing the manuscript. The views expressed are those of the authors and not necessarily those of the nib foundation.

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Catherine E. B. Brown, Karyn Richardson, Bengianni Halil-Pizzirani & Rebecca A. Segrave

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CB, KR, BP, LA and RS developed the review protocol. CB and BP conducted the search and screened articles, and KR resolved conflicts. CB, KR, BP, LA and RS extracted the barriers and facilitators, mapped barriers and facilitators to the TDF and COM-B model, and interpreted the results. CB drafted the paper. All authors read, revised, and approved the submitted version.

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Brown, C.E.B., Richardson, K., Halil-Pizzirani, B. et al. Key influences on university students’ physical activity: a systematic review using the Theoretical Domains Framework and the COM-B model of human behaviour. BMC Public Health 24 , 418 (2024). https://doi.org/10.1186/s12889-023-17621-4

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research helping behaviour

ORIGINAL RESEARCH article

The moderating role of spirituality and gender in canadian and iranian emerging adolescents’ theory of mind and prosocial behavior.

Nadia Khalili
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  • 1 Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada
  • 2 Department of Educational Studies, Brock University, St. Catharines, ON, Canada

Introduction: While research has found a link between ToM and prosociality in terms of caring and helping others which may also vary across cultures, the moderating role of spirituality and culture of this association in emerging adolescence has received little attention.

Methods: The current study empirically “examined” the role of spirituality and gender in relation to ToM and prosocial behavior in Canadian and Iranian emerging adolescents. A total of 300 (153 girls) emerging adolescents ( M = 11.502, SD = 2.228) were recruited from Montreal, Canada and Karaj, Iran. A series of double moderation analysis and ANOVA was conducted.

Results and discussion: Results indicated the difference between direct and indirect influences of ToM and its interactions with culture, gender, and spirituality on prosocial behavior. This implies an emerging complex framework which suggests the dynamic nonlinear interactions between these factors. Implications for youth’s social-emotional understanding will be discussed.

Introduction

This study examined the potential moderating role of gender, spirituality and culture in the link between Theory of Mind (ToM) and emerging adolescents’ prosociality. ToM refers to mental abilities to understand and explain others’ minds and predict their behaviors ( Peterson and Wellman, 2019 ; Lecce and Devine, 2021 ), and prosocial behavior refers to voluntary actions that benefit others ( Eisenberg, 2003 ; Imuta et al., 2016 ). According to theoretical studies, ToM and prosocial development reciprocally interact with each other ( Weller and Lagattuta, 2014 ; Lane and Bowman, 2021 ), while empirical studies have yielded inconsistent results ( Imuta et al., 2016 ). Furthermore, although earlier research suggests that social and contextual factors such as spirituality and culture may play a moderating role between prosocial and theory of mind abilities ( Wang et al., 2022 ), this has rarely been examined. Thus, the current study aimed to explore the moderating role of spirituality and gender in the relations between ToM and prosocial behavior in Canadian and Iranian youth.

ToM development and prosocial behaviors in children and adolescents

Theory of mind ability refers to the inference and reasoning of others’ mental states such as intention, belief, desire, and emotion ( Peterson and Wellman, 2019 ), and plays a key role in a person’s social life. Studies with youth show that ToM has a reciprocal interaction with numerous social–emotional variables ( Razza and Blair, 2009 ; Shakoor et al., 2011 ; Slaughter et al., 2015 ; Białecka-Pikul et al., 2017 ) such as children’s friendship ( Hughes, 2011 ; Bosacki, 2021 ; Gazelle et al., 2022 ), popularity ( Slaughter et al., 2015 ), social competence ( Wainryb and Brehl, 2006 ; Devine and Apperly, 2022 ), and moral development ( Hughes and Leekam, 2004 ; Leslie et al., 2006 ). For example, studies show that those youth who are challenged in their ability to mentalize or read another person’s mind may also be at risk for social well-being and social relationships ( Bagwell et al., 1998 ; Hughes and Leekam, 2004 ). However, empirical findings suggest that ToM has a complex relationship with social communications, such as prosocial behavior ( Hughes and Leekam, 2004 ; Derksen et al., 2018 ). For instance, children with advanced ToM abilities have the potential for both antisocial and prosocial behavior ( Hughes and Leekam, 2004 ; Imuta et al., 2016 ; Derksen et al., 2018 ). Prosocial behavior is one of the crucial aspects of children’s social competency, which develops from infancy to adulthood from simple concrete assistance to complex abstract supports ( Warneken and Tomasello, 2006 , 2007 ; Hastings et al., 2015 ).

According to earlier studies understanding others’ mental states such as needs, desires, emotions, thoughts, and intentions eases and promotes children’s prosocial development, which further advances their ToM abilities ( Eisenberg and Fabes, 1998 ; Astington et al., 2002 ; Sy et al., 2003 ; Hay and Cook, 2007 ; Weller and Lagattuta, 2014 ). There have been theoretical links made about the reciprocal interactions between ToM development and prosocial behavior. According to social-construct theory, children develop and strengthen their ToM skills through their social interactions ( Carpendale and Lewis, 2015 ). For example, studies show high levels of prosocial behaviors (sharing, comforting, cooperating) relate to sophisticated ToM skills ( Eisenberg and Fabes, 1998 ; Astington, 2003 ; Weller and Lagattuta, 2014 ). Alternatively, some have argued that understanding others’ desires, feelings, and intentions facilitates children’s ability to engage in prosocial behavior toward others ( Hoffman, 2000 ; Hay and Cook, 2007 ; Dunfield, 2014 ).

According to Imuta et al. (2016) , despite the theoretical justification of reciprocal interaction between ToM development and prosocial behavior, empirical studies have illustrated inconsistencies in this relationship. That is, empirical studies show inconsistencies in the directions of inter-relations between ToM and prosocial behaviors ( Imuta et al., 2016 ; Lane and Bowman, 2021 ). For example, Hughes and Leekam (2004) and Derksen et al. (2018) suggested that ToM development could positively, negatively, or neutrally influence and be influenced by social relationships. Underwood and Moore (1982) found positive relations between prosocial behavior and 3–13 years old’s affective and cognitive perspective taking. While other studies show that affective ToM (e.g., emotion recognition) has a stronger correlation with prosocial behavior (e.g., sharing and cooperating) rather than cognitive ToM ( Carlo et al., 2010 ; Imuta et al., 2016 ).

One valid ecological methodology to investigate the complex link between ToM and prosocial behavior is to examine the moderators of this association. A few studies suggested age and gender moderate the relations between ToM and antisocial behavior (e.g., Gomez-Garibello and Talwar, 2015 ; Mizokawa and Hamana, 2020 ). Yet to our knowledge, only one study to date has examined the moderating role of gender on the associations between ToM and prosocial behavior in Italian children ( Longobardi et al., 2019 ). Cross-cultural research in this domain (social cognition and prosocial behaviors) continues to remain sparse, especially with young adolescents ( Chen et al., 2021 ; Wang et al., 2022 ).

Spirituality

Exploring factors that moderate this relation may resolve the inconsistencies found between ToM and prosocial behavior. One neglected social–emotional factor that potentially moderates the relation between ToM and prosocial behavior is spirituality. Both theoretical and empirical studies suggest that spirituality is centred on meaningful and conscious social relationships, which result in prosocial behavior ( Hardy and Carlo, 2005a , b ; Pandya, 2017 ). These studies found that spiritual people value acts of collective compassion such as helping, serving, and caring for others, which are prosocial behaviors ( Hardy and Carlo, 2005a , b ; Pandya, 2017 ). To date, only a few studies examined the relation between ToM and spirituality (e.g., Van Elk and Aleman, 2017 ; Testoni et al., 2019 ). These studies found a link between the ToM network in brain and different aspect of spirituality such as serving, thinking about God, and praying. Thus, such studies suggest that the ability to enact spirituality such as caring for and helping others requires ToM or the ability understand others’ thoughts and emotions ( Barrett, 2004 ).

However, diverse types of spirituality emphasize various kinds of relationships. For example, existential spirituality emphasizes internal or emotional strength and development ( Post and Wade, 2009 ), while religious spirituality focuses on external development such as institutional and religious community engagement ( Spilka et al., 2003 ; Vittengl, 2018 ). Religious spirituality can measure either individual relationships with God or social relationships. In contrast, existential spirituality is not necessarily related to social relationships, it can be considered a mental state shaped by social influences ( Saunders and Fernyhough, 2016 ).

Despite numerous studies on spirituality and prosocial behaviors, as well as ToM and prosocial behaviors, to date, no study examines the relations among these three factors. Furthermore, because previous studies evaluated the above relationships with social-based spirituality, exploring the link between individual emotional spirituality, ToM, and prosocial behavior is necessary to understand the relationship between spirituality and ToM more comprehensively. In this research, both religious and existential spirituality were measured as states of being and feeling spiritual, which were not examined in previous studies.

Culture is another potential moderator on the relation between ToM and prosocial behavior that has received little attention. Past studies showed mixed findings regarding the influence of culture on ToM, and prosocial behavior separately, while both are key factors in children’s well-being, health, and social relationships ( Martí-Vilar et al., 2019 ; Souza et al., 2021 ). Thus, discovering the links between cultures and the above social emotional factors is vital to advancing our understanding of how culture affects youths’ mentalization skills and their social relationships.

Culture, however, is not a monolithic phenomenon. Theoretical explanations of culture consider the differences between the various cultures of the world. In the social sciences, most cross-cultural studies have focused on the contrast between North America and China as a prime example of Western individualism versus Eastern collectivism ( Chen et al., 2021 , 2022 ). However, studies of the Middle East can also contribute to the understanding of collectivist cultures. In contrast to East Asian countries such as China, Middle Eastern countries’ political and cultural constructions have been influenced by Islam ( Rabiei, 2013 ; Rezapour et al., 2019 ). Linguistic diversity and political conflicts in the Middle East also have shaped socio-cultural differences within this region ( Rabiei, 2013 ).

Therefore, collectivistic roots and values in the Middle East are notably different from those in other parts of Asia ( Shahaeian, 2015 ; Shohoudi Mojdehi et al., 2020 ). As one of the world’s oldest civilizations in this area ( Barrington, 2012 ), Iran has experienced several changes in religion, language, and political structure throughout its history ( Abrahamian, 2021 ). Thus, research from this area has a specific context and could contribute to cross-cultural studies by illuminating the cultural differences in social–emotional development in emerging adolescents. Thus, a comparison of data in Canadian and Iranian culture could yield wider insights about the cultural influences on social emotional development, clarifying the relative benefits and challenges of monocultural versus multicultural contexts.

ToM abilities and prosocial behavior occur within a social context that partly depends on the communication styles valued in diverse cultures. Collectivist cultures such as East Asian and Middle Eastern countries follow a high-context communication style, while individualist cultures such as North America follow a low-context communication style ( Chitakornkijsil, 2010 ; Mojdehi et al., 2020 ). High-context communications emphasize information and usually entail ambiguity and indirect messages, which can be understood through nonverbal actions such as gestures that require little or no speech or text ( Nishimura et al., 2008 ; Mojdehi et al., 2020 ). Word choice and context are thus important to convey deep meanings through short sentences or words. In contrast, low-context communications emphasize direct speech and literal meaning with the use of precise words that do not depend on the context to be understood ( Gudykunst et al., 1988 ; Mojdehi et al., 2020 ).

Only few studies examined the gender as a moderator between ToM and prosocial behavior ( Smith and McSweeney, 2007 ; Bosacki et al., 2020 ), however to date, no study has yet to examine the interactions among adolescents’ gender, spirituality, ToM and prosociality between cultures. According to gender socialization theory, boys and girls behave differently in social situations because of their different nurturing practices and experiences ( Leaper and Farkas, 2015 ). Some empirical studies provided evidence for this theory by associating girls with caring behaviors and boys with competitive and assertive behaviors ( Kuhnert et al., 2017 ; Quenneville et al., 2022 ).

However, inconsistencies in past studies necessitate further investigations in this area. For example, Hughes et al. (2011) found no significant differences in boys’ and girls’ ToM understanding, whereas Białecka-Pikul et al. (2017) and Bosacki (2000) suggested girls are more advanced in these abilities. According to three studies ( Longobardi et al., 2016 , 2019 ; Van der Graaff et al., 2018 ), girls scored higher in prosocial behaviors, while four other studies ( Roberts and Strayer, 1996 ; McMahon et al., 2006 ; Braza et al., 2009 ; Longobardi et al., 2016 ) suggested stronger prosocial behaviors in boys. The present study contributes to the literature by investigating how gender and its interaction with spirituality may moderate the relation between ToM and prosocial behavior across two cultures (Canada and Iran).

Current study

Few previous ToM studies have focused on emerging adolescence, which is a sensitive transitional period from childhood to adolescence, including significant social, emotional, and cognitive changes that shape young people’s identities ( Crocetti, 2017 ; Bosacki et al., 2018 ). With the increase of gender-role stereotypes and peer interactions during this transition, emerging adolescents have reciprocal and complex interactions with culture and other social context such as spirituality ( Bosacki and Moore, 2004 ; Andrews et al., 2021 ).

Considering this context, the current study examines the links between culture, gender, ToM, prosocial behavior, and spirituality in emerging adolescents. Previous research has mainly investigated the association between cognitive ToM (i.e., the ability to make inferences about others’ thoughts and beliefs) and prosocial behavior, recent studies, however, have investigated this link with affective ToM (i.e., the ability to understand others’ emotional states) which may be more strongly correlated with prosocial behavior ( Imuta et al., 2016 ). In this study, to measure ToM, we use the Reading the Mind in the Eyes (RME) test, which evaluates various mind states (e.g., skeptical, accusing, anticipating, reflective, worried, upset, serious, and nervous) which, includes both cognitive and affective ToM ( Baron-Cohen et al., 2001 ; Prevost et al., 2014 ).

To explores whether culture, gender, and spirituality moderate the relations between ToM and prosocial behaviors in emerging adolescents, this study answers three questions: (1) Do ToM, prosocial behavior and spirituality differ across gender and culture? If so, (2) do gender/culture/spirituality serve as moderators in the relation among ToM, prosocial behavior, (3) What direct and indirect influences do ToM ability have on prosocial behavior in emerging adolescents?

Based on past studies that show gender and cultural differences in prosocial behavior, and spirituality among emerging adolescents ( Renouf et al., 2010 ; Bosacki et al., 2018 ; Aival-Naveh et al., 2019 ), the present study also predicts that culture and gender differences influence these variables. We also hypothesize that girls will perform higher on ToM, prosocial behavior, and spirituality ( Bosacki, 2000 ; Leaper and Farkas, 2015 ; Białecka-Pikul et al., 2017 ; Wang et al., 2022 ). Furthermore, given the mixed findings of past studies on the direct and indirect influence of ToM on prosocial behavior ( Lane and Bowman, 2021 ), we expected the same conclusions. Therefore, we hypothesized an emerging complex framework which suggest the dynamic interaction between components that influence ToM and prosociality. According to this framework, relationships between ToM and prosocial behavior vary in contextual conditions. ( Gershkoff-Stowe and Thelen, 2004 ; Blijd-Hoogewys and van Geert, 2017 ).

Participants

A total of 300 emerging adolescents between 10 and 12 years of age were recruited from Canada and Iran. Canadian adolescent participants ( n = 150; 78 females; M years = 11.502, SD = 2.228) were through schools in Canada. Iranian participants ( n = 150, 75 females, M years = 11.502, SD = 2.228) were recruited for participation in this study through schools in Karaj, Iran.

Spiritual well-being

The spiritual well-being scale measures both religious and existential well-being, including sense of purpose and meaning in life. The 20-item measure uses a 6-point Likert-type scale ranging from 1 (strongly agree) to 6 (strongly disagree). For the overall scale, scores range from 20 to 120 points; higher scores indicating greater levels of spiritual well-being. Internal consistency coefficients range from 0.82 to 0.94 for the religious well-being subscale, 0.78 to 0.86 for the existential well-being subscale, and from 0.89 to 0.94 for the whole scale. Reliability between 1st and 10th week of testing ranged from 0.82 to 0.99 ( Bufford et al., 1991 ).

Children spiritual lives

This questionnaire is a self-report, 31-item scale, developed by Moore et al. (2016) based on a previous qualitative study by Moore et al. (2012) . The questionnaire is designed for students from different religious and cultural backgrounds, specifically in North America (see Moore et al., 2016 ). This measure examines three main factors in relation to spirituality: comfort that “focus on God as a source of support and comfort”, omnipresence that “concerns the ubiquity of God” and duality, a believe that we have a spirit apart from body” ( Khalili et al., 2022 , p. 30). The participants were asked to respond to items using a Likertscale, between 1 (strongly disagree) and 5 (strongly agree). Inter reliability of this questionnaire was reported between 0.80 and 1.00 on all interviews ( Moore et al., 2012 ).

Reading the mind in the eye test third edition

To measure affective ToM, we used the RMET. Past studies show adequate internal consistency for this frequently used measurement with children and youth (α = 0.86; Baron-Cohen et al., 2001 ; Caputi et al., 2018 ). The questionnaire includes 36 items, each item contains a picture of an expression with the eyes with four different words indicating four different emotions. The participant should choose the word that best describe the expression. Each item has one correct answer with one point. A higher score indicated a higher ability of reading others emotion.

Prosocial behavior

To measure prosocial behavior, we used teacher ratings. In particular, we used a subscale of Children’s Social Behavior Scale (CSBS) – Teacher-Rated, including 4 items, used for measuring prosocial behavior. CSBS is a five-point Likert at scale1 = this is never true of this child, to 5 = this is almost always true of this child. This 15-item survey has three subscales: relational aggression, physical aggression, and prosocial behaviors which measure children’s behaviors with their peers through teachers ( Denham, 1986 ).

Upon obtainment of ethical clearance from the participating universities and school boards, informed letters of consent were sent to principals, teachers, parents, and students. Once written and informed parental consent, and child assent were obtained, self-report pencil-and-paper tasks were administered by the research team within classrooms, or within an alternate room in the school.

Preliminary analyses

To investigate the moderating role of spirituality, gender, and culture in the link between ToM and prosociality, we conducted a series of double moderation using R studio. Results indicated that spirituality, gender, culture, and their interaction moderate the association between ToM and prosociality. Data clearing involved the exclusion of 6 participants from Canada, and 20 from Iran, who did not indicate their gender, and 54 Iranian participants who completed less than 50% of the questions. We also removed four outliers from the Canadian dataset. The final sample consisted of 143 Canadian children, and 78 Iranian children ( F = 120, M = 101). Normality, additivity, and homogeneity assumptions were checked for the remaining 216 participants; no significant violence was found in assumption except normalcy of prosocial behavior which does not influence our analysis.

Descriptive analysis and ANOVA

Descriptive analysis in Table 1 indicates means and standard deviations for ToM (RME), prosocial behavior, spiritual well-being subscales, and children’s spiritual lives subscales. To investigate the cultural and gender effect on our participants’ perception of spirituality, ToM, and prosocial behavior, we conducted a series of univariate analyses of variance (two-way ANOVA) that included gender (female/male) and culture (Canada/Iran) as independent variables. ToM, prosocial behavior, existential well-being, religious well-being, omnipresence, comfort, and duality were the dependent variables.

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Table 1 . Descriptive statistics.

Independent observations, normality, and homogeneity assumptions were met. Participants were randomly recruited without any interaction to affect each other’s answers, and each record represents a distinct person; thus, observations were independent. A normality check is needed for a small sample size, N < 25 per subgroup; Since the sample contained 221 participants with four groups (girls/boys/Canadian/Iranian subgroups) (4*25 = 100), we assumed there was no violation of the normality assumption. Lastly, because our sample sizes were not equal in gender, homogeneity was tested by running Levene’s Test of Equality of Error Variances. Levene’s Test was not significant for the effect of culture and gender on any of variables except for omnipresence ( p = 0.004). Therefore, we ran the Welch unequal variances test, to determine whether the different group sized impacted homogeneity assumption for omnipresence as indicated by Levene’s test, F (1, 217.581) = 4.926, p < 0.05. Therefore, our ANOVAs were robust, and statistical assumptions were met.

Cultural effects

A main effect for culture was found for all variables (see Table 2 for descriptives): ToM abilities, F (1,217) = 37.558, p < 0.001, η 2 =0.148; Prosocial behavior, F (1,217) = 7.966, p < 0.01, η 2 =0.035; Comfort, F (1,217) = 64.645, p < 0.001, η 2 =0.230; Omnipresence, F (1,217) = 54.815, p < 0.001, η 2 =0.202; Duality F (1,217) = 84.755, p < 0.001, η 2 =0 .279 ; Religious well-being, F (1,217) = 12.519, p < 0.001, η 2 =0.055; And existential well-being F (1,217) = 49.384, p < 0.001, η 2 =0.185 (see Table 2 for descriptives) .

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Table 2 . Descriptive statistics of subgroups divided by culture & gender.

Gender effects

A significant main effect of gender was found for ToM abilities ( F (1,217) = 16.815, p < 0.01, η 2 =0 .072); Omnipresence ( F (1,217) = 7.238, p < 0.01, η 2 =0.032); Duality ( F (1,217) = 8.675, p < 0.01, η 2 =0.038); Existential well-being ( F (1,217) = 5.631, p < 0.05, η 2 =0.025). However, there was no significant main effect of gender on religious well-being ( F (1,217) = 0.002, p = 0.960, η 2 =0.000); Prosocial behavior ( F (1,217) = 2.770, p = 0.302, η 2 =0.001); And comfort ( F (1,217) = 2.488, p = 0.116, η 2 =0.001) (see Table 2 for descriptives) .

Culture*gender effects

The interaction of culture and gender has a significant effect on ToM ( F (1,277) = 5.771, p < 0.05, η 2 =0.026); Religious well-being ( F (1,217) = 3.965, p < 0.018); And existential well-being ( F (1,217) = 7.209, p < 0.01, η 2 =0.032) . However there was no significant influence of the interaction of culture and gender on prosocial behavior ( F (1,217) = 2.572, p = 0.110, η 2 =0.012); Comfort ( F (1,217) = 0.078, p = 0.780, η 2 =0.000); Omnipresence ( F (1,217) = 008, p = 927); And duality ( F (1,217) = 0.499, p = 481, η 2 =0.002) (see Table 3 for descriptives).

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Table 3 . Descriptive statistics of gender*culture.

Moderating role of culture, gender, and spirituality

To determine whether the relation between ToM and prosocial behavior was moderated by culture, gender, and spirituality, we conducted a series of double moderations. As moderating variables could change the magnitude or the direction of the relations between IVs and DVs, we tested whether these moderators would either strengthen or weaken the effect of the ToM on the prosocial behavior; and if so, whether in the positive or inverse direction. For the purpose of the moderating analysis, gender was dummy coded, and all continuous variables were mean centralized. Therefore, the coefficient of IVs and moderators will be interpreted as the effect of these variables on DV at the mean level of the other independent variables.

Spirituality and gender

To examine the moderating role of spirituality and gender on the relations between ToM and prosocial behavior, we conducted several double moderations using R studio and packages “devtools” and “doomlab/MeMoBootR.” In all models, ToM was entered as an independent variable (“ X ”), and prosocial behavior entered as a dependent variable (“ Y ”). The interaction of gender with subscales of spiritual well-being and children’s spiritual lives was examined as pair moderators. Only existential well-being*gender, F (5,215) = 6.526, p < 0.001, η 2 =0.131, and duality*gender, F (5,215) = 7.489, p < 0.001, η 2 =0.148, showed a significant moderation effect on the relations between ToM and prosocial behaviors which will be discussed below.

Existential well-being and gender

When Existential well-being and gender are entered as moderators, Existential well-being ( b = 6.204, t (1.708) = 3.632, p < 0.00), while Gender ( b = 0.265, t (0.097) = 2.734, p < 0.01), negatively predicts prosocial behavior and ToM ( b = 1.263, t (0.454) = 2.782, p < 0.01) positively predict prosocial behavior. Moderation effects of the interaction between IVs and Ms. such as ToM*existential well-being ( b = −0.265, t (0.097) = −2.734, p < 0.01) and ToM*gender (b = −0.486, t (0.157) = −3.096, p < 0.01) negatively predict prosocial behavior (see Table 4 ). In other words, existential well-being negatively influences and weakens the impact of ToM on prosocial behavior. Furthermore, the relation between ToM and prosocial behavior was different in males and females. Gender served as a moderating variable as results showed that ToM had a negative influence on prosocial behaviors in males, but not in females. That is, males with high levels of ToM were more likely to demonstrate low levels of prosocial behaviors.

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Table 4 . Moderating role of existential well-being and gender on the relationship between ToM and prosocial behaviour.

The overall model shows that all individual predictors, ToM, existential, gender, ToM*existential well-being, and ToM* gender, together predict 13.1% of the variance in prosocial behavior. The interaction of ToM and existential well-being contributes to a 3% variance of prosocial behavior in this model. The interaction of ToM and gender contributes to a 3.8% variance of prosocial behavior in this mode. Both interactions together contribute to a 6.3% of variances in prosocial behavior.

The conditional effects of the focal variables show that a high level of existential well-being had a strong negative moderating influence on the relations between female ToM and prosocial behavior ( b = −0.601, t (0.140) = −4.289, p < 0.001), while no significant moderation effect was found for males. That is, high levels of existential well-being related to high ToM, and low levels of prosocial behavior. In contrast, low levels of ToM predicted high levels of prosocial behavior, but only in girls. A moderate level of existential well-being has a moderate negative effect on the relations between female ToM and prosocial behaviors ( b = −0.416, t (0.118) = −3.503, p < 0.001), whereas no significant moderation effect was found for males. Accordingly, moderate level of existential well-being related to higher levels of ToM and in turn, predicted low prosocial behavior. In contrast, low levels of ToM predicted moderate levels of prosocial behavior, but only in girls. Low levels of existential well-being showed a moderate positive influence on the relation between ToM and prosocial behavior in males ( b = 0.309, t (0.140) = 2.205, p < 0.05), whereas no significant moderation effect was found for the female group in this model. In other words, boys who reported low levels of existential well-being were also more likely to demonstrate high levels of ToM which in turn predicted moderate levels of prosocial behavior. In contrast, low levels of ToM predicted low levels of prosocial behavior but only in boys (see Figure 1 ).

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Figure 1 . Moderating role of existential well-being and gender in the relationship between ToM and prosociality.

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Table 5 . Moderating role of duality and gender on the relationship between ToM and prosocial behaviour.

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Table 6 . Moderating role of culture and gender on the relationship between ToM and prosocial behaviour.

Duality and gender

When duality and gender were entered as moderators, ToM ( b = 0.750, t (0.192) = 3.893, p < 0.001), duality, ( b = 4.531, t (0.928) = 4.879, p < 0.001), and gender ( b = 9.793, t (2.781) = 3.512, p < 0.001), positively predicted prosocial behavior. The interaction between ToM and duality ( b = −0.189, t (0.044) = −4.218, p < 0.001), and the interaction between ToM and gender, ( b = −0.607, t (0.159) = −3.808, p < 0.001), negatively predicted prosocial behavior (see Table 5 ). In other words, duality weakened the influence of ToM on prosocial behavior, and negatively influenced the relationship between ToM and prosocial behavior. Furthermore, the relations between ToM and prosocial behavior were different amongst males and females.

The overall model showed that all individual predictors, ToM, duality, gender, ToM*duality, and ToM* gender, together predicted 14.8% of the variance in prosocial behavior. Specifically, the interaction of ToM and duality contributed to a 7% variance of prosocial behavior. The interaction of ToM and gender contributed to a 5.7% variance of prosocial behavior in this model. Taken together, both interactions contributed to 10.4% of variances in prosocial behavior. In other words, the moderators in this model—gender and duality—together contributed to 10.4% variances in outcome.

The conditional effects of the focal variables show that a high level of duality has a strong negative moderating influence on the relations between female ToM and prosocial behavior ( b = −0.755, t (0.146) = −5.150, p < 0.001) whereas no significant moderation effect was found for the male group in this model. Accordingly, high levels of duality and high levels of ToM predict low levels of prosocial behavior, and high level of duality and low ToM predict high levels of prosocial behavior but only in girls . A moderate level of duality has a moderate but statistically significant impact on the negative relation between female ToM and prosocial behaviors ( b = −0.519, t (0.121) = −4.264, p < 0.001) whereas no significant moderation effect was found for males. Thus, with moderate levels of duality, high levels of ToM predicted low levels of prosocial behavior, and low levels of ToM predicted moderate prosocial behavior, but again, only in girls. Low levels of duality have a moderate positive influence on the relations between male ToM and prosocial behavior ( b = 0.324, t (0.120) = 2.2683, p < 0.01), whereas low levels of duality have a moderate negative influence on the relations between female ToM and prosocial behavior ( b = −0.300, t (0.120) = −2.353, p < 0.05). So, with low duality, high ToM predicted moderate levels of prosocial behavior in boys, and low levels of prosocial behavior in girls. However, low ToM predicted low levels of prosocial behavior in boys, and moderate levels of prosocial behavior in girls (see Figure 2 ).

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Figure 2 . Moderating role of duality and gender in the relationship between ToM and prosociality.

Gender and culture

When gender and culture were entered as moderators, the overall model was significant: F (5,215) = 6.169, p < 0.001, η 2 =0.125. Gender, ( b  = 9.244, t (2.816) = 3.282, p < 0.001), positively predicts prosocial behavior. Culture, ( b  = −7.284, t (2.882) = −2.527, p < 0.05), and the interaction between ToM and gender, ( b  = −0.606, t (0.161) = −3.752, p < 0.001), negatively predicted prosocial behavior. While ToM, ( b  = −0.544, t (0.409) = −1.329, p  = 0.185), and the interaction between ToM and culture, ( b  = 250, t (0.170) = 1.470, p  = 0.142), was not significant (see Table 6 ). In other words, gender moderated the relations between ToM and prosocial behavior in this model, whereas culture does not moderate these relations.

The focal predictors’ conditional effects showed a strong negative relation between Canadian female ToM and prosocial behavior ( b = −0.650, t (0.50) = −4.315, p < 0.001), whereas we found no significant effect for males. That is, high ToM predicted moderate levels of prosocial behavior, and low ToM predicted high levels of prosocial behavior only in Canadian females. There was also a moderate negative relation between Iranian female ToM and prosocial behavior ( b = −0.400, t (0.154) = −2.596, p < 0.05), whereas no significant effect was found for the males. In other words, high ToM predicted low levels of prosocial behavior, and low ToM will predict moderate levels prosocial behavior only in Iranian females (see Figure 3 ).

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Figure 3 . Moderating role of gender and culture in the relationship between ToM and prosociality.

Culture and duality

When duality and culture are entered as moderators, the overall model was significant: F (5,215) = 4.563, p < 0.001, η 2 = 0.095. Duality, ( b  = 3.368, t (1.197) = 2.812, p < 0.01), positively predicted prosocial behavior. The interaction between ToM and duality, ( b  = −1.390, t (0.055) = −2.499, p < 0.05), negatively predicted prosocial behavior. While the effect of ToM, ( b  = 0.452, t (0.596) = 0.758, p  = 0.449), culture, ( b  = −0.066, t (3.487) = −0.019, p  = 0.984), and the interaction between ToM and culture: ( b  = −0.062, t (0.196) = −3.189, p  = 0.750), on prosocial behavior were not significant (see Table 7 ). In other words, compared to culture, duality was the only variable to moderate the relations between ToM and prosocial behavior.

The conditional effects of the focal predictors showed a moderate negative relation between ToM and prosocial behavior ( b = −0.650, t (0.146) = −5.150, p < 0.001) in Canadians with a high level of duality, and high negative relation between ToM and prosocial behaviour ( b  = −0.650, t (0.146) = −5.150, p  < 0.001) in Iranians with a high level of duality, while no significant effect was found for other levels of duality in each country (see Figure 4 ).

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Figure 4 . Moderating role of duality and culture in the relationship between ToM and prosociality.

Culture and existential well-being

When duality and culture were entered as moderators, the overall model was significant: F (5,215) = 4.845, p  < 0.001, η 2 = 0.148. Existential well-being, ( b  = 5.267, t (2.059) = 2.557, p  < 0.05), and the interaction between ToM and existential well-being, ( b  = −0.243, t (0.119) = 2.557, p  < 0.05), positively predicted prosocial behavior. However, ToM, ( b  = 0.970, t (0.920) = 1.054, p  = 0.292), culture, ( b  = −1.551, t (3.424) = -0.453, p  = 0.651), and the interactions between ToM and culture: ( b  = −0.016, t (0.206) = −0.082, p  = 0.934), did not have impact on prosocial behavior (see Table 8 ). In other words, existential well-being moderated the relation between ToM and prosocial behavior in this model, whereas culture did not serve as a moderator.

The conditional effects of the focal predictors showed a moderate negative relation between ToM and prosocial behavior ( b  = −0.331, t (0.113) = −2.921, p  < 0.01) in Canadian with a high level of existential well-being, whereas no significant effect was found for other levels of existential well-being on this model. Thus, with high existential well-being, high levels of ToM predicted low levels of prosocial behavior, and low levels of ToM predicted high levels of prosocial behavior (see Figure 5 ).

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Figure 5 . Moderating role of existential well-being and culture in the relationship between ToM and prosociality.

The main purpose of this study was to investigate the complex relations between ToM, spirituality, and prosocial development in emerging adolescents in Canada and Iran. More specifically, we investigated the direct and indirect role of emerging adolescents’ ToM ability from two different countries on their prosocial behavior. We also explored if the social-contextual factors of spirituality, culture, and gender served as moderators in the relations between ToM and prosocial behavior. First, we explored the role gender and culture played in emerging adolescents’ prosocial behaviors and their perceptions of spirituality, as well as the ability to recognize emotions in others (affective ToM). Further, to explore the moderating role of gender, culture, and spirituality in emerging adolescents, we investigated the direct and indirect influence of ToM on prosociality.

A key finding from our study was that our results demonstrated significant cultural differences between Canadian and Iranian participants’ ToM ability. These findings suggest the significant differences between understanding others’ thoughts and emotions among Iranian and Canadian youth could be due to differences in sociographic and linguistic factors, parenting styles, and value preferences ( Mason and Morris, 2010 ; Renouf et al., 2010 ; Aival-Naveh et al., 2019 ). Compared to Canadian youth, the lower score of ToM in Iranian youth supports past studies that show that in collectivist cultures, teachers and parents are more likely to discourage mental state’ talk in everyday conversations ( Adams et al., 2012 ). In contrast, studies show in Western countries, parent–child conversations are more likely to use mental or internal state language, which in turn may help to strengthen children’s ToM abilities ( Adams et al., 2012 ; Mayer and Tra ̈uble, 2013 ). Furthermore, high context communication style in countries such as Iran emphasize information using ambiguous and indirect messages that could be understood through context and word choice ( Shohoudi Mojdehi et al., 2019 ). Therefore, the present study’s results suggest that the “reading the mind in the eyes” test could be interpreted differently by Iranian and Canadian youth, due to their different social experience.

The significant differences found in between Iranian and Canadian adolescents’ existential well-being, duality, comfort, and omnipresence, suggests differences in the perception of spirituality among youth in these two countries. Interestingly, Iranian students living in a religious (Islamic) country which declared itself officially Shie and all its institutions follow and teach Islamic laws ( Evason, 2016 ), showed lower scores in existential well-being, duality, and omnipresence, which were found to be highly related to religiosity, compared to Canadian youth who lived within a secular environment. These findings are similar to Hardy et al. (2022) that suggested religious deidentification among British youth and Büssing et al. (2012) that reported a low level of religiosity among Christian adolescents who attended a highly religious school within a strong faith-based community. They interpreted the adolescents’ lower scores as a result of living within a secular society with the unpopularity of religious beliefs.

Within the present study, the present Iranian participants lived within a country lead by a religious government that controlled schools and institutions. We interpret our findings as a sign of dramatic changes in young people’s religious beliefs and secular shifts in Iran due to a critical view of the link between political power and religious authority in Iran ( Arab and Maleki, 2020 ). In line with Arab and Maleki (2020) the current findings provide novel evident to suggest that within the context of governmental and religious pressure, younger Iranian generations are changing their beliefs and attitudes from high religiosity to more anti-religious sentiments. Cognitive resistance and rejection of some religious demands could also be another reason for the low religious and spiritual scores found in the present study’s sample of Iranian youth ( Büssing et al., 2012 ).

The significant differences found between Canadian and Iranian adolescents’ prosocial behaviors suggest cultural differences in their social abilities, such as helping and serving others reported by their parents and teachers. Our study aligns with findings from Luria et al.’s study ( 2015 ), that found a slight but positive relation between individualist culture and prosocial behavior. However, our findings contradict those of Parboteeah et al. (2004) , and Lampridis and Papastylianou (2017) , which showed a positive relation between collectivism and prosocial behavior. On the other hand, Ringov and Zollo (2007) and Onder (2011) found no relations between prosocial behavior and national culture, shared values, behaviors, customs, and norms shared by the population of a certain country ( Beugelsdijk and Welzel, 2018 ; Martí-Vilar et al., 2019 ). These findings suggest that social–emotional development is complex in nature and could be understood by examining many factors, including national culture.

The interaction between gender and culture showed a significant influence on participants’ ToM ability and existential and religious well-being. In contrast, this interaction between gender and culture failed to have an influence on prosocial behavior, comfort, omnipresence, and duality. Past studies show that compared to boys, girls generally score higher in ToM ability ( Spilka et al., 2003 ; Saunders and Fernyhough, 2016 ; Vittengl, 2018 ), and spiritual beliefs regarding omnipresence and duality ( Bosacki et al., 2018 ). However our findings on omnipresence support past studies that show no significant differences in omnipresence between girls and boys ( Bosacki et al., 2018 ). The findings support gender-role socialization theory that suggests gender-role stereotypes encourage girls to demonstrate more nurturing and caring behaviors with others, and thus may be the reason for their higher scores in social–emotional factors such as understanding others’ thoughts and emotions ( Eisenberg and Fabes, 1998 ; Bosacki, 2000 ; Charman et al., 2002 ; Leaper and Farkas, 2015 ; Białecka-Pikul et al., 2017 ; Wang et al., 2022 ). Similarly, the present results support past studies that show compared to males, females were more likely to reflect higher levels of religious spirituality – particularly the dimensions of omnipresence and duality which focus on external development and their social relationships and relationships with God ( Spilka et al., 2003 ; Saunders and Fernyhough, 2016 ; Vittengl, 2018 ).

In contrast, the present results showed that compared to girls, boys scored higher in existential well-being which could suggest that boys showed a greater tendency to emphasize a sense of self-agency and internal strength development ( Post and Wade, 2009 ). However, some studies show no significant gender differences in ToM abilities and spirituality ( Hughes et al., 2011 ; Khalili et al., 2022 ), which is also consistent with our findings in that there was no main effect of gender on religious well-being and comfort among participants from both countries. These findings thus support the claims that gender may add to the complexity of the relations between social emotional factors such as ToM and spiritual development in adolescents, particularly across different cultures ( Bosacki et al., 2018 ; Quenneville et al., 2022 ).

The present results did not show any significant gender differences on prosocial behavior, which is in contrast to previous studies that suggested higher scores of prosociality in girls ( Eisenberg et al., 2010 ; Iglesias Gallego et al., 2020 ). One justification of these findings could be a connection between gender roles and social–emotional development factors, which aligns with social learning theory – that people learn by observing, imitating, and modelling behaviors in society ( Bandura and Walters, 1977 ; Nabavi, 2012 ; Jans-Beken et al., 2018 ; Khalili et al., 2022 ). Recent studies show the changing nature of societal gender-role expectations ( Andrews et al., 2021 ; Quenneville et al., 2022 ). For example, Coyne et al. (2021) explored how “princess culture” (in Disney movies and other entertainment) influences gender stereotypes in behavior and body esteem as children learn gender roles through their interaction with their environment, including popular media. These findings demonstrate a generational shift in gender-role stereotypic beliefs and attitudes that may influence children’s socialization, suggesting a need to study how children and adolescents’ social–emotional factors have also been influenced by gender ( Gazelle et al., 2022 ).

In general, the present study found that Canadian girls scored higher in most of the social–emotional factors than boys and Iranian girls, while Iranian boys scored lower for most social–emotional factors (e.g., ToM, prosocial behavior, and spirituality). These findings align with Çelik and Deniz (2008) that girls from Western cultures tend demonstrate stronger abilities in social and emotional competencies compared to girls and boys from other cultures. Furthermore, stronger curriculum concentration on social–emotional well-being and development in Canadian educational systems compared to Iranian educational systems could be another reason for lower scores in Iranian participants ( Schonert-Reichl and Hymel, 2007 ; Ahrari et al., 2022 ). These cultural and gender variations in our findings suggested the need for studies to explore the moderating role of gender and culture on the indirect influence of adolescents’ ToM on prosocial behavior.

Our moderating analysis suggest that ToM has a direct positive influence on prosocial behavior in both countries, which supports theoretical studies that suggest understanding others’ feelings and thoughts facilitates one’s ability to help and serve others (prosocial behavior) ( Hoffman, 2000 ; Hay and Cook, 2007 ; Dunfield, 2014 ). Although many studies ( Megías-Robles et al., 2020 ) refer to “reading the mind in the eyes” as a measurement of affective ToM (understanding others’ feelings), the test evaluates different mind states (e.g., skeptical, accusing, anticipating, reflective, worried, upset, serious, and nervous) which includes both cognitive and affective ToM ( Baron-Cohen et al., 2001 ; Prevost et al., 2014 ). Therefore, as this test does not categorize these mind states, we conclude that regardless of a children’s culture and level of ToM ability, it is more likely that youth with a higher capacity to understand other’s mental states, both cognitive and affective, show prosocial behaviors.

Our findings support the importance of information expressed in the eye region and its vital link with social interactions ( Grossmann, 2017 ; Schmidtmann et al., 2020 ). It is also worth mentioning that although the “reading the mind in the eyes” test includes cognitive understanding, it is different from other cognitive tasks such as “False belief understanding,” which are built on reasoning and strongly related to memory, language, and executive function ( Guajardo et al., 2009 ; Diaz and Farrar, 2018 ). Thus, it is important for future research to examine the influence of children’s ToM ability through different tasks to measure prosocial behavior in everyday life.

Our results showed that indirect influence of ToM on prosocial behaviors was negatively moderated by gender, which was significant in girls but not boys. These findings contradict some past studies that suggest a strong link between the girls’ mindreading abilities and prosocial behaviors ( Leaper and Farkas, 2015 ). We also found that girls with a higher level of ToM ability were less likely to show prosocial behavior while relations were found between boys’ mindreading and prosocial behaviors. Our results specifically contradicts Longobardi et al. (2019) findings that suggested gender positively moderated the relation between ToM and prosocial behavior in Italian children, and emerging adolescents boys and not in girls. In other words, their findings suggested no associations between ToM and prosocial behaviors in girls, while our results showed a negative association between these two factors in girls only. However, our results did not show a significant associaton between ToM and prosocial behaviors in boys. These results support neither global patterns nor cultural differences. Still, they suggest individual differences and the complex relationship between social–emotional abilities and the role of contextual conditions such as gender and culture in mentalization skills and spirituality.

Surprisingly, the impact of ToM on prosocial behavior was negatively moderated with spirituality, existential well-being and duality. However, we did not find any moderating role of religious well-being, omnipresence, and comfort in this relationship. The interactions between gender and spirituality showed that girls’ and boys’ different levels of perception of spirituality were associated with various levels of prosociality. Girls with high existential well-being/duality and high ToM showed low prosocial behavior, but no relations were found for boys. Girls with moderate existential well-being/duality and high ToM showed low prosocial behavior, but again not for boys.

Compared to girls, boys with low existential well-being/duality and high ToM showed moderate levels of prosocial behavior. Boys with low existential well-being and low ToM showed low levels of prosocial behavior, but no relations were found for girls. Girls with low duality and high ToM showed low levels of prosocial behavior. Girls with low duality and low ToM showed moderate levels of prosociality. Boys with low existential well-being/duality and high ToM showed average levels of prosocial behavior. These findings contrast with past theoretical and empirical studies that suggest that spirituality is associated with prosocial behavior and ToM ability and suggest a more complex pattern ( Barrett, 2004 ; Hardy and Carlo, 2005a , b ; Pandya, 2017 ; Van Elk and Aleman, 2017 ; Testoni et al., 2019 ).

Our findings suggest considering social–emotional development as a complex system which could be understood through a nonlinear analysis. The relations between the components of this system, such as the interconnections among ToM, prosocial behavior, gender, culture, and spirituality, will be better understood through a nonlinear analysis. Nonlinear systems are defined as systems that do not have additivity, and homogeneity are known as the two main characteristics of linear systems ( Heeger, 2000 ; Boissevain and Mitchell, 2018 ; Tabassum et al., 2018 ). Additivity means that if we add two components to the system, the result will not be different from merely adding separate elements. Homogeneity states that the strength of the output will change proportionately with the input if we increase the power of the input ( Antsaklis and Michel, 2006 ).

These characteristics imply that how added components to the system interact with each other is more crucial than the number of them. For example, the interaction of two added components could positively, negatively, or neutrally influence the system, which is the opposite of additivity. Increasing the strength of the input also could either decrease or increase the power of input in a system, which is against homogeneity. From a nonlinear perspective, the influence of a factor on other variables cannot be measured separately due to reciprocal interactions among variables ( Van Geert, 2003 ; Lowie, 2012 ). Our study and findings support this perspective by considering the direct and indirect interactions between different levels of spirituality and prosociality with gender and culture in emerging adolescents’ ToM development.

To elaborate, the results suggest a large difference between Canadians’ and Iranians’ ToM abilities and moderate differences between women’s and men’s ToM abilities. Therefore, from a linear perspective, we should expect a large or medium difference between Canadian females’ ToM ability compared to other participants. Still, our results show a slight difference between Canadian adolescent females’ ToM ability compared to other participants. Considering the positive influence of ToM ability on prosocial behavior, we should expect a moderate or large influence of the interaction of culture and gender on prosocial behavior. However, our results suggest that the interaction between culture and gender did not influence prosocial behavior, which supports the complexity and nonlinearity of ToM development and its relations to other social–emotional factors such as prosociality, culture, and gender.

Our moderating analysis results contrast with Fodor’s (1983) and Parboteeah et al.'s (2004) , who discussed the computational nature of social–emotional developments. We suggest a more dynamic and fluid nature of complex nonlinear interactions may help to better untangle the complex links between social-cognitive and social- emotional-cultural factors. By shedding light on the influence of factors such as gender and culture on input, internal structure, and output of a relationship between ToM and prosocial behavior, we propose a shift in perspective ( Lowie, 2012 ).

For example, the influence of gender on ToM, prosocial behavior, and the relations between ToM and prosocial behavior (internal structure) is a moderating model. This model illustrates a moderate gender influence on ToM ability, no gender influence on prosocial behavior, and the negative moderating role of gender on the relationship between ToM and prosocial behavior, while ToM positively predicts prosocial behavior. This suggests a more dynamic and fluid nature of gender influence on the moderating system of ToM and prosocial behavior. Not only does the input (ToM) and output (prosocial behavior) play a role, but the internal structure (the relations between them) is also influenced by gender. Interestingly, these influences are neither linear, nor consistently positive or negative. To elaborate, females showed a high level of ToM ability; and this ToM ability positively predicted prosocial behaviors. Therefore, from a linear perspective, we expected a high level of prosociality in females, while our results showed the opposite- a low level of prosociality in females with a high level of ToM ability.

Overall, this study is novel and contributes to the literature on cross-cultural social cognition, prosociality and spirituality, as this was the first study to examine gender and cultural differences in the relations among mentalization skills, prosocial behaviors and perceptions of spirituality in Canadian and Iranian young adolescents.

Another novel aspect of this study was that it uses a measure of Iranian children perceptions of spirituality, entitled Children Spiritual Lives Questionnaire developed by Moore et al. (2016) . Given the lack of research on adolescent’s spiritual understanding within a social cognitive context, this contributes to the cross-cultural literature in the field of young people’s spiritual and psychosocial development. Furthermore, this study was the first to examine the moderating role of spirituality on the relations between young adolescents’ ToM and prosocial behavior across two different countries.

Limitation, implications, and future direction

One of the main limitations in this study was the number of missing variables in the Iranian sample. Consequently, an unequal number of participants in culture and gender comparisons became another limitation of this study. Furthermore, we only examined the moderating role of two types of spirituality, existential and religious spirituality, on the relations between ToM and prosocial behaviors. Future studies are needed to investigate the moderating role of other types of spirituality on the relations among ToM and prosocial behavior in an equal sample size from both countries. In addition, the present study focused on the preferred gender identity of participants (male/female) – but given the changing definitions of gender and gender identities are conceived as more fluid – future studies should address this complexity ( Andrews et al., 2021 ; Quenneville et al., 2022 ). Lastly, in the present study we used only one of the ToM measurements. Given the multifaceted mentalizing ability of ToM ( Devine and Apperly, 2022 ), future studies should aim to more comprehensive measures of ToM to compare the direct and indirect effect of different ToM tasks on prosociality and spirituality among youth from different cultures.

In sum, our study applied a complex ecological valid method to analyze the data to understand the complexity of ToM development and its relations with other social–emotional factors such as prosociality, spirituality, culture, and gender. Furthermore, our study suggests a complex nonlinear perspective as a theoretical framework for understanding social–emotional development. Our results indicate that Canadian adolescent females scored the highest in most of the social–emotional factors examined in this study, whereas Iranian adolescent males scored the lowest. Furthermore, Canadian participants generally showed higher scores in all factors than Iranian participants, except in religious well-being, which was higher in Iranian participants. These findings suggest that adolescent females are becoming the leaders in social–emotional development, whereas adolescent males need to continue to develop these abilities. Also, these abilities were more developed among Canadian youth compared to Iranian youth which may reflect cultural, political, and educational differences between the two countries.

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Table 7 . Moderating role of culture and duality on the relationship between ToM and prosocial behaviour.

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Table 8 . Moderating role of culture and existential well-being on the relationship between ToM and prosocial behaviour.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by McGill University Research Ethics Board-II (REB#58-0615). Parents of the children provided their written informed consent and children provided verbal assent.

Author contributions

NK: substantial contributions to the conception of the work; the acquisition, analysis, and interpretation of data for the work and drafting and revising it critically for important intellectual content. SB and VT: substantial contributions to the conception of the work and the acquisition, drafting the work and revising it critically for important intellectual content. All authors contributed to the article, approved the submitted version and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

This research was supported in part by a grant from the Social Sciences and Humanities Research Council of Canada with grant no. 435–2015-0010 awarded to SB and VT. VT is Professor and Canada Research Chair in the Department of Educational and Counseling Psychology at McGill University. Her research is in the area of developmental psychology with an emphasis on social–emotional development. SB is a Professor in the Department of Graduate and Undergraduate Studies in Education at Brock University. Her research and teaching interests focus on social cognitive development in children and youth.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: theory of mind, prosociality, spirituality, culture, gender

Citation: Khalili N, Bosacki S and Talwar V (2023) The moderating role of spirituality and gender in Canadian and Iranian emerging adolescents’ theory of mind and prosocial behavior. Front. Psychol . 14:1134826. doi: 10.3389/fpsyg.2023.1134826

Received: 30 December 2022; Accepted: 01 February 2023; Published: 27 March 2023.

Reviewed by:

Copyright © 2023 Khalili, Bosacki and Talwar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Nadia Khalili, [email protected]

†ORCID: Nadia Khalili, http://orcid.org/0000-0002-4571-2656 Sandra Bosacki, http://orcid.org/0000%E2%80%930001%E2%80%939583-4582 Victoria Talwar, http://orcid.org/0000-0001-9806-0279

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Foraged food.

ADHD may have been an evolutionary advantage, research suggests

Traits associated with the neurodevelopmental disorder could have helped early humans when foraging for food

Traits common to attention deficit hyperactivity disorder (ADHD), such as distractibility or impulsivity, might have been an evolutionary advantage for our ancestors by improving their tactics when foraging for food, researchers have said.

ADHD is a neurodevelopmental disorder with symptoms including impulsiveness, disorganisation and difficulty focusing. While estimates of prevalence have varied, diagnoses have been rising in many countries, including the UK .

Now, researchers say while some of these traits tend to be viewed negatively, they might have helped people seek out new patches for foraging.

Dr David Barack of the University of Pennsylvania, who was the first author of the research, said the study offered a potential explanation for why ADHD was more prevalent than expected from random genetic mutations alone and – more broadly – why traits such as distractibility or impulsivity were common.

“If [these traits] were truly negative, then you would think that over evolutionary time, they would be selected against,” he said. “Our findings are an initial data point, suggestive of advantages in certain choice contexts.”

Writing in the journal Proceedings of the Royal Society B: Biological Sciences , Barack and colleagues reported how they analysed data from 457 adults who completed an online foraging game in which they had to collect as many berries as possible within eight minutes.

The number of berries obtained from each bush decreased with the number of times it was foraged.

During the task, participants could either continue to collect berries from the bushes in their original location or move to a new patch – although the latter cost them time.

The team also screened participants for ADHD-like symptoms – although they stress this did not constitute a diagnosis – finding 206 participants had positive results.

The researchers found that participants with higher scores on the ADHD scale spent shorter periods of time in each patch of bushes than those with lower scores. In other words, they were more likely to abandon their current patch and hunt for a new one. Crucially, the team found such participants also gained more points in the game than those with lower scores on the ADHD scale.

The researchers said their results chimed with other work that suggested populations with nomadic lifestyles that benefited from exploring tended to have genes associated with ADHD.

However, they added the study had limitations, including that ADHD-like symptoms were based on self-report.

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Barack said it was necessary to carry out experiments involving people diagnosed with ADHD and real-world foraging tasks, not least as the latter would involve far more effort to move between patches than in an online game.

Michael J Reiss, a professor of science education at University College London, who was not involved in the work, said while ADHD appeared to be linked to serious negative consequences, he and his colleagues had argued it may help in situations where physical activity and rapid decision-making were highly valued.

“It is great to see experimental evidence from David Barack and colleagues that participants who score highly for ADHD are more likely to switch their foraging activities in ways that can indeed be characterised as impulsive. In our evolutionary past such behaviour may sometimes have been highly advantageous,” he said.

“ADHD can be a serious problem but it’s a problem in large measure because of today’s environments.”

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Young kids failing to get adequate nutrition in early childcare centres, research suggests

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Health Young kids failing to get adequate nutrition in early childcare centres, research suggests

Two toddlers eating apples segments

The first 2,000 days of life — from conception to age five — is when we go through the most rapid and extensive brain development that sets the foundation for ongoing life.

During this critical time, some children spend up to 10,000 hours in long day care, Karen Thorpe, who heads up the Child Development, Education and Care Group at the Queensland Brain Institute, said.

"A large part of their nutrition relies on what they receive in those centres, and brains don't function without food."

That’s the drive behind an extensive research program led by Professor Thorpe and her colleague Bonnie Searle investigating the quality of food and nutrition in Australia's early childhood education and care sector.

Their research suggests there are serious deficiencies in the amount and type of food provided within the sector, especially in disadvantaged areas.

Impact of food insecurity and disadvantage

There are two sources of young children's food in childcare centres. Some centres provide the food and some rely on parents sending it in with the child.

In a study of more than 1,600 centres in Queensland , Professor Thorpe and her colleagues found childcare centres in disadvantaged or remote communities were less likely to provide food.

"An alternate way of putting this," Professor Thorpe said, "is parents are required to bring food from home, and these are the families least able to provide food and many are living in circumstances of food insecurity."

Lack of food was a major issue, according to a recently published study by Dr Searle, who compared food quality, mealtime environment, and interactions in metropolitan childcare centres which provided food versus those which didn't.

"What concerned us most was that there wasn't enough food, although across the board the quality of food was poor and did not align with Australian dietary guidelines," Dr Searle said.

"And the situation was worse when parents had to send in food.

"In the centres where the parents were experiencing the highest levels of disadvantage, the children were arriving hungry and the educators were asking the children not to eat their food all at once so it'd last the whole day.

"And we witnessed educators giving their own food to children."

Childcare workers are low paid and often come from the same communities as the children so, according to Professor Thorpe, may themselves be experiencing food insecurity.

'The good, the bad, and the ugly'

The research also found poor food supply affected the behaviour of toddlers and preschoolers through the day.

"The quality of emotional interactions was lower and conflict increased across the day," Dr Searle said.

Professor Thorpe said the emotional environment in early education and care was very important. 

"It is that which predicts children's outcomes, not only as they enter school, but right through to their secondary school years."

Bonnie Searle and Karen Thorpe sitting on a green bench

The findings match the experience of Tamika Hicks, an educator and former centre owner.

Ms Hicks, who has 23 years' experience in the sector, said she's seen the good, the bad, and the ugly.

"The bad is where children are just income earners, fed poor food that is low cost, high carbs, not a lot of protein, high saturated fat.

"Then their behaviour spikes, then they get labelled for different things.

"Then educators are getting burnout because they're dealing with different behaviours at the end of the day and it's a vicious cycle. That's the ugly."

The United Workers' Union (UWU), which represents workers in the early childhood education and care sector, got similar findings when they surveyed their members.

"We found the system for providing meals for little children in long day care centres isn't really set up to make sure children get all the nutrition they need," said Helen Gibbons, the UWU's executive director of early childhood.

"It's really set up around profits, what's affordable for those services and what's easy to make."

A core problem, according to Professor Thorpe, is that the quality standards by which early childhood education and care services are judged don't directly address what and how much the kids are eating but are more focused on hygiene in food preparation, allergy prevention, and nutrition education.

In addition, she said, quality inspectors assessing a centre can't necessarily rely on what they're being told.

"We go into centres and observe and sometimes we will see menus that look very healthy, but that is not what the children eat."

The Australian Competition and Consumer Commission has recently published its report into the market for the supply of childcare services. 

The Productivity Commission is also investigating childcare, and has published a  draft report .

But Professor Thorpe said neither reports directly addressed issues around food and nutrition.

So what are the solutions?

Professor Thorpe says there are two solutions which go together.

The first is to provide targeted food subsidies to centres in areas of disadvantage.

"Australia has a very good database which can indicate which services there are. If we can't do it for all we can at least do it for our most disadvantaged," she said.

The second solution, she suggested, was to ensure early childhood education and care's national quality framework and quality standards against which these services are rated "look at the right things".

A spokesperson for the Minister for Early Childhood Education Anne Aly said there were requirements under the national quality framework to ensure that food provided by a service was nutritious and adequate in quantity.

"Services that choose to provide food are required to have policies and procedures relating to nutrition and dietary requirements. This is monitored by state and territory regulatory authorities," they said in a statement.

"The government will consider the final recommendations of the Productivity Commission inquiry and the future of the early learning system as we chart a course to universal early childhood education and care."

Hear more detail about the research project on the Health Report and subscribe to the podcast .

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    Helping behavior is any action performed by an individual that benefits other individual/s. Helping does not imply a pro-social motivation or unselfish intent to alleviate others' needs or increase their welfare as an end in itself.

  10. Real-life helping behaviours in North America: A genome-wide ...

    Introduction. Prosocial behaviour-voluntary behaviour intended to benefit others []-is essential for social functioning in humans, who, next to eusocial insects, form the largest cooperative living groups on Earth.Extensive research has been conducted focusing on individual differences in this multifaceted trait that covers concepts such as helping, cooperation, altruism, and empathy [2-4]

  11. The Psychology Behind Helping and Prosocial Behaviors: An ...

    The Psychology Behind Helping and Prosocial Behaviors: An Examination from Intention to Action Jennifer L. Silva, Loren D. Marks Ph.D. & Katie E. Cherry Chapter First Online: 01 January 2009 1706 Accesses 16 Citations 6 Altmetric Abstract

  12. Helping Behavior

    In an attempt to clarify several issues, we identify four distinct research fields concerning the evolution of helping: (1) basic social evolution theory that studies helping within the framework of Hamilton's inclusive fitness concept, i.e. direct and indirect benefits, (2) an ecological approach that identifies settings that promote life histo...

  13. Social Psychology: Helping Behavior

    Researchers suggest that people are most likely to help others in certain circumstances: They have just seen others offering help. They are not in a hurry. They share some similarities with the person needing help. They are in a small town or a rural setting. They feel guilty. They are not preoccupied or focused on themselves. They are happy.

  14. Helping and Prosocial Behavior

    Helping and Prosocial Behavior ... At the conceptual level, a positive relationship between agreeableness and helping may be expected, and research by Graziano et al. (2007) has found that those higher on the agreeableness dimension are, in fact, more likely than those low on agreeableness to help siblings, friends, strangers, or members of ...

  15. The Bystander Effect in Helping Behaviour: An Experiment

    With a non-helping bystander present, the helping behaviour of subjects increased to 46% (n=48), and for a helping bystander, the percentage of helping subjects was increased to 56% (n=43). Figure 1. Results of helping behaviour experiment. A χ 2 test for goodness of fit at a 5% confidence level was undertaken to compare the results with the ...

  16. Helping in Organizations: A Review and Directions for Future Research

    Helping has long been a central component of organizational citizenship behavior (OCB), and yet our knowledge of the full spectrum of helping processes in organizations is limited. Most helping research in the OCB literature has focused on individuals' tendencies to help across situations, including antecedents and outcomes of those general ...

  17. (PDF) Ten Years of Research on Group Size and Helping

    Helping behavior was studied as a function of urban density. Four requests for help (for the time, for directions, for change of a quarter, and for the person's name) were solicited in three areas ...

  18. Helping Behavior

    Helping Behavior. If a helping behavior has arisen completely through enforcement, the primary evolutionary adaptation is in the enforcer, rather than the helping individual. From: Encyclopedia of Ecology (Second Edition), 2008. Related terms: ... based on two decades of research, concluded that young people - often from middle-class or ...

  19. Helping Behavior

    Helping Behavior. This section is categorized by the type of measure. Dan Batson paradigm - Empathic response to Kathy's plight, perception of similarity and perception of need all measure how much compassion and sympathy we feel for others, particularly strangers. Batson, D. C., Lishner, D. A., Cook, J., & Sawyer, S. (2010).

  20. Predictors of help-seeking behaviour in people with mental health

    The majority of people with mental illness do not seek help at all or only with significant delay. To reduce help-seeking barriers for people with mental illness, it is therefore important to understand factors predicting help-seeking. Thus, we prospectively examined potential predictors of help-seeking behaviour among people with mental health problems (N = 307) over 3 years.

  21. Cortical regulation of helping behaviour towards others in pain

    Humans and animals exhibit various forms of prosocial helping behaviour towards others in need 1,2,3. Although previous research has investigated how individuals may perceive others' states 4,5 ...

  22. Help-Seeking Behaviors Among Older Adults: A Scoping Review

    Introduction Efforts to understand why many older adults do not seek help, even while experiencing grave symptoms, have highlighted the importance of understanding older adults' help-seeking behaviors for their physical and mental health challenges ( Woods et al., 2005 ).

  23. Helping Behavior and the Good Samaritan

    Most people, in the Western and Middle Eastern worlds, understand the story of the Good Samaritan, and how it relates to helping behavior. In this famous parable, a Rabbi and a Levite ignore an injured man and pass by, with a Samaritan being the only one to stop and help. In the modern world, this parable is becoming increasingly relevant.

  24. Stanford Medicine study identifies distinct brain organization patterns

    A new study by Stanford Medicine investigators unveils a new artificial intelligence model that was more than 90% successful at determining whether scans of brain activity came from a woman or a man.. The findings, published Feb. 20 in the Proceedings of the National Academy of Sciences, help resolve a long-term controversy about whether reliable sex differences exist in the human brain and ...

  25. Key influences on university students' physical activity: a systematic

    Physical activity is important for all aspects of health, yet most university students are not active enough to reap these benefits. Understanding the factors that influence physical activity in the context of behaviour change theory is valuable to inform the development of effective evidence-based interventions to increase university students' physical activity.

  26. Full article: Research on the Relations Among Personality Traits

    Citation 13 In the long-term exploration and research of sports psychologists, the factors that explain and predict people's exercise behavior have been highly summarized, and now the protective motivation theory, Citation 14 self-determination theory, Citation 15 and the theory of planned behavior have been formed.

  27. Frontiers

    1 Department of Educational and Counselling Psychology, McGill University, Montreal, QC, Canada; 2 Department of Educational Studies, Brock University, St. Catharines, ON, Canada; Introduction: While research has found a link between ToM and prosociality in terms of caring and helping others which may also vary across cultures, the moderating role of spirituality and culture of this ...

  28. ADHD may have been an evolutionary advantage, research suggests

    Last modified on Tue 20 Feb 2024 19.03 EST. Traits common to attention deficit hyperactivity disorder (ADHD), such as distractibility or impulsivity, might have been an evolutionary advantage for ...

  29. Young kids failing to get adequate nutrition in early childcare centres

    The research also found poor food supply affected the behaviour of toddlers and preschoolers through the day. "The quality of emotional interactions was lower and conflict increased across the day ...