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

• First Online: 01 January 2009

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• Jennifer L. Silva 2 ,
• Loren D. Marks Ph.D. &
• Katie E. Cherry 3  

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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.

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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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Jennifer L. Silva

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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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9.3 How the Social Context Influences Helping

Learning objective.

• Review Bibb Latané and John Darley’s model of helping behavior and indicate the social psychological variables that influence each stage.

Although emotional responses such as guilt, personal distress, and empathy are important determinants of altruism, it is the social situation itself—the people around us when we are deciding whether or not to help—that has perhaps the most important influence on whether and when we help.

Consider the unusual case of the killing of 28-year-old Katherine “Kitty” Genovese in New York City at about 3:00 a.m. on March 13, 1964. Her attacker, Winston Moseley, stabbed and sexually assaulted her within a few yards of her apartment building in the borough of Queens. During the struggle with her assailant, Kitty screamed, “Oh my God! He stabbed me! Please help me!” But no one responded. The struggle continued; Kitty broke free from Moseley, but he caught her again, stabbed her several more times, and eventually killed her.

The murder of Kitty Genovese shocked the nation, in large part because of the (often inaccurate) reporting of it. Stories about the killing, in the New York Times and other papers, indicated that as many as 38 people had overheard the struggle and killing, that none of them had bothered to intervene, and that only one person had even called the police, long after Genovese was dead.

Although these stories about the lack of concern by people in New York City proved to be false (Manning, Levine, & Collins, 2007), they nevertheless led many people to think about the variables that might lead people to help or, alternatively, to be insensitive to the needs of others. Was this an instance of the uncaring and selfish nature of human beings? Or was there something about this particular social situation that was critical? It turns out, contrary to your expectations I would imagine, that having many people around during an emergency can in fact be the opposite of helpful—it can reduce the likelihood that anyone at all will help.

Latané and Darley’s Model of Helping

Two social psychologists, Bibb Latané and John Darley, found themselves particularly interested in, and concerned about, the Kitty Genovese case. As they thought about the stories that they had read about it, they considered the nature of emergency situations, such as this one. They realized that emergencies are unusual and that people frequently do not really know what to do when they encounter one. Furthermore, emergencies are potentially dangerous to the helper, and it is therefore probably pretty amazing that anyone helps at all.

Figure 9.5 Latané and Darley’s Stages of Helping

To better understand the processes of helping in an emergency, Latané and Darley developed a model of helping that took into consideration the important role of the social situation. Their model, which is shown in Figure 9.5 “Latané and Darley’s Stages of Helping” , has been extensively tested in many studies, and there is substantial support for it.

Latané and Darley thought that the first thing that had to happen in order for people to help is that they had to notice the emergency. This seems pretty obvious, but it turns out that the social situation has a big impact on noticing an emergency. Consider, for instance, people who live in a large city such as New York City, Bangkok, or Beijing. These cities are big, noisy, and crowded—it seems like there are a million things going at once. How could people living in such a city even notice, let alone respond to, the needs of all the people around them? They are simply too overloaded by the stimuli in the city (Milgram, 1970).

Many studies have found that people who live in smaller and less dense rural towns are more likely to help than those who live in large, crowded, urban cities (Amato, 1983; Levine, Martinez, Brase, & Sorenson, 1994). Although there are a lot of reasons for such differences, just noticing the emergency is critical. When there are more people around, it is less likely that the people notice the needs of others.

You may have had an experience that demonstrates the influence of the social situation on noticing. Imagine that you have lived with a family or a roommate for a while, but one night you find yourself alone in your house or apartment because your housemates are staying somewhere else that night. If you are like me, I bet you found yourself hearing sounds that you never heard before—and they might have made you pretty nervous. Of course the sounds were always there, but when other people were around you, you were simply less alert to them. The presence of others can divert our attention from the environment—it’s as if we are unconsciously, and probably quite mistakenly, counting on the others to take care of things for us.

Latané and Darley (1968) wondered if they could examine this phenomenon experimentally. To do so, they simply asked their research participants to complete a questionnaire in a small room. Some of the participants completed the questionnaire alone, while others completed the questionnaire in small groups in which two other participants were also working on questionnaires.

A few minutes after the participants had begun the questionnaires, the experimenters started to release some white smoke into the room through a vent in the wall while they watched through a one-way mirror. The smoke got thicker as time went on, until it filled the room. The experimenters timed how long it took before the first person in the room looked up and noticed the smoke. The people who were working alone noticed the smoke in about 5 seconds, and within 4 minutes most of the participants who were working alone had taken some action. But what about the participants working in groups of three? Although we would certainly expect that having more people around would increase the likelihood that someone would notice the smoke, on average, the first person in the group conditions did not notice the smoke until over 20 seconds had elapsed. And although 75% of the participants who were working alone reported the smoke within 4 minutes, the smoke was reported in only 12% of the three-person groups by that time. In fact, in only three of the eight three-person groups did anyone report the smoke at all, even after it had entirely filled the room!

Interpreting

Even if we notice an emergency, we might not interpret it as one. The problem is that events are frequently ambiguous, and we must interpret them to understand what they really mean. Furthermore, we often don’t see the whole event unfolding, so it is difficult to get a good handle on it. Is a man holding an iPod and running away from a group of pursuers a criminal who needs to be apprehended, or is this just a harmless prank? Were the cries of Kitty Genovese really calls for help, or were they simply an argument with a boyfriend? It’s hard for us to tell when we haven’t seen the whole event (Piliavin, Piliavin, & Broll, 1976). Moreover, because emergencies are rare and because we generally tend to assume that events are benign, we may be likely to treat ambiguous cases as not being emergencies.

The problem is compounded when others are present because when we are unsure how to interpret events we normally look to others to help us understand them (this is informational social influence). However, the people we are looking toward for understanding are themselves unsure how to interpret the situation, and they are looking to us for information at the same time we are looking to them.

When we look to others for information we may assume that they know something that we do not know. This is often a mistake, because all the people in the situation are doing the same thing. None of us really know what to think, but at the same time we assume that the others do know. Pluralistic ignorance occurs when people think that others in their environment have information that they do not have and when they base their judgments on what they think the others are thinking .

Pluralistic ignorance seems to have been occurring in Latané and Darley’s studies, because even when the smoke became really heavy in the room, many people in the group conditions did not react to it. Rather, they looked at each other, and because nobody else in the room seemed very concerned, they each assumed that the others thought that everything was all right. You can see the problem—each bystander thinks that other people aren’t acting because they don’t see an emergency. Of course, everyone is confused, but believing that the others know something that they don’t, each observer concludes that help is not required.

Pluralistic ignorance is not restricted to emergency situations (Miller, Turnbull, & McFarland, 1988; Suls & Green, 2003). Maybe you have had the following experience: You are in one of your classes and the instructor has just finished a complicated explanation. He is unsure whether the students are up to speed and asks, “Are there any questions?” All the class members are of course completely confused, but when they look at each other, nobody raises a hand in response. So everybody in the class (including the instructor) assumes that everyone understands the topic perfectly. This is pluralistic ignorance at its worst—we are all assuming that others know something that we don’t, and so we don’t act. The moral to instructors in this situation is clear: Wait until at least one student asks a question. The moral for students is also clear: Ask your question! Don’t think that you will look stupid for doing so—the other students will probably thank you.

Taking Responsibility

Even if we have noticed the emergency and interpret it as being one, this does not necessarily mean that we will come to the rescue of the other person. We still need to decide that it is our responsibility to do something. The problem is that when we see others around, it is easy to assume that they are going to do something and that we don’t need to do anything. Diffusion of responsibility occurs when we assume that others will take action and therefore we do not take action ourselves . The irony of course is that people are more likely to help when they are the only ones in the situation than they are when there are others around.

Darley and Latané (1968) had study participants work on a communication task in which they were sharing ideas about how to best adjust to college life with other people in different rooms using an intercom. According to random assignment to conditions, each participant believed that he or she was communicating with either one, two, or five other people, who were in either one, two, or five other rooms. Each participant had an initial chance to give his opinions over the intercom, and on the first round one of the other people (actually a confederate of the experimenter) indicated that he had an “epileptic-like” condition that had made the adjustment process very difficult for him. After a few minutes, the subject heard the experimental confederate say,

I-er-um-I think I-I need-er-if-if could-er-er-somebody er-er-er-er-er-er-er give me a liltle-er-give me a little help here because-er-I-er-I’m-er-er having a-a-a real problcm-er-right now and I-er-if somebody could help me out it would-it would-er-er s-s-sure be-sure be good…because there-er-er-a cause I-er-I-uh-I’ve got a-a one of the-er-sei er-er-things coming on and-and-and I could really-er-use some help so if somebody would-er-give me a little h-help-uh-er-er-er-er-er c-could somebody-er-er-help-er-uh-uh-uh (choking sounds).…I’m gonna die-er-er-I’m…gonna die-er-help-er-er-seizure-er- (chokes, then quiet). (Darley & Latané, 1968, p. 379)

As you can see in Table 9.2 “Effects of Group Size on Likelihood and Speed of Helping” , the participants who thought that they were the only ones who knew about the emergency (because they were only working with one other person) left the room quickly to try to get help. In the larger groups, however, participants were less likely to intervene and slower to respond when they did. Only 31% of the participants in the largest groups responded by the end of the 6-minute session.

You can see that the social situation has a powerful influence on helping. We simply don’t help as much when other people are with us.

Table 9.2 Effects of Group Size on Likelihood and Speed of Helping

Perhaps you have noticed diffusion of responsibility if you have participated in an Internet users group where people asked questions of the other users. Did you find that it was easier to get help if you directed your request to a smaller set of users than when you directed it to a larger number of people? Consider the following: In 1998, Larry Froistad, a 29-year-old computer programmer, sent the following message to the members of an Internet self-help group that had about 200 members. “Amanda I murdered because her mother stood between us…when she was asleep, I got wickedly drunk, set the house on fire, went to bed, listened to her scream twice, climbed out the window and set about putting on a show of shock and surprise.” Despite this clear online confession to a murder, only three of the 200 newsgroup members reported the confession to the authorities (Markey, 2000).

To study the possibility that this lack of response was due to the presence of others, the researchers (Markey, 2000) conducted a field study in which they observed about 5,000 participants in about 400 different chat groups. The experimenters sent a message to the group, from either a male (JakeHarmen) or female (SuzyHarmen) screen name. Help was sought by either asking all the participants in the chat group, “Can anyone tell me how to look at someone’s profile?” or by randomly selecting one participant and asking “[name of selected participant], can you tell me how to look at someone’s profile?” The experimenters recorded the number of people present in the chat room, which ranged from 2 to 19, and then waited to see how long it took before a response was given.

It turned out that the gender of the person requesting help made no difference, but that addressing to a single person did. Assistance was received more quickly when help was asked for by specifying a participant’s name (in only about 37 seconds) than when no name was specified (51 seconds). Furthermore, a correlational analysis found that when help was requested without specifying a participant’s name, there was a significant negative correlation between the number of people currently logged on in the group and the time it took to respond to the request.

Garcia, Weaver, Moskowitz, and Darley (2002) found that the presence of others can promote diffusion of responsibility even if those other people are only imagined. In these studies the researchers had participants read one of three possible scenarios that manipulated whether participants thought about dining out with 10 friends at a restaurant ( group condition ) or whether they thought about dining at a restaurant with only one other friend ( one-person condition ). Participants in the group condition were asked to “Imagine you won a dinner for yourself and 10 of your friends at your favorite restaurant.” Participants in the one-person condition were asked to “Imagine you won a dinner for yourself and a friend at your favorite restaurant.”

After reading one of the scenarios, the participants were then asked to help with another experiment supposedly being conducted in another room. Specifically, they were asked: “How much time are you willing to spend on this other experiment?” At this point, participants checked off one of the following minute intervals: 0 minutes , 2 minutes , 5 minutes , 10 minutes , 15 minutes , 20 minutes , 25 minutes , and 30 minutes .

Figure 9.6 Helping as a Function of Imagined Social Context

Garcia et al. (2002) found that the presence of others reduced helping, even when those others were only imagined.

As you can see in Figure 9.6 “Helping as a Function of Imagined Social Context” , simply imagining that they were in a group or alone had a significant effect on helping, such that those who imagined being with only one other person volunteered to help for more minutes than did those who imagined being in a larger group.

Implementing Action

The fourth step in the helping model is knowing how to help. Of course, for many of us the ways to best help another person in an emergency are not that clear; we are not professionals and we have little training in how to help in emergencies. People who do have training in how to act in emergencies are more likely to help, whereas the rest of us just don’t know what to do and therefore may simply walk by. On the other hand, today most people have cell phones, and we can do a lot with a quick call. In fact, a phone call made in time might have saved Kitty Genovese’s life. The moral: You might not know exactly what to do, but you may well be able to contact someone else who does.

Latané and Darley’s decision model of bystander intervention has represented an important theoretical framework for helping us understand the role of situational variables on helping. Whether or not we help depends on the outcomes of a series of decisions that involve noticing the event, interpreting the situation as one requiring assistance, deciding to take personal responsibility, and deciding how to help.

Fischer et al. (2011) recently analyzed data from over 105 studies using over 7,500 participants who had been observed helping (or not helping) in situations in which they were alone or with others. They found significant support for the idea that people helped more when fewer others were present. And supporting the important role of interpretation, they also found that the differences were smaller when the need for helping was clear and dangerous and thus required little interpretation. They also found that there were at least some situations (such as when bystanders were able to help provide needed physical assistance) in which having other people around increased helping.

Although the Latané and Darley model was initially developed to understand how people respond in emergencies requiring immediate assistance, aspects of the model have been successfully applied to many other situations, ranging from preventing someone from driving drunk to making a decision about whether to donate a kidney to a relative (Schroeder, Penner, Dovidio, & Piliavin, 1995).

Key Takeaways

• The social situation has an important influence on whether or not we help.
• Latané and Darley’s decision model of bystander intervention has represented an important theoretical framework for helping us understand the role of situational variables on helping. According to the model, whether or not we help depends on the outcomes of a series of decisions that involve noticing the event, interpreting the situation as one requiring assistance, deciding to take personal responsibility, and implementing action.
• Latané and Darley’s model has received substantial empirical support and has been applied not only to helping in emergencies but to other helping situations as well.

Exercises and Critical Thinking

• Analyze the Kitty Genovese incident in terms of the Latané and Darley model of helping. Which factors do you think were most important in preventing helping?
• Recount a situation in which you did or did not help, and consider how that decision might have been influenced by the variables specified in Latané and Darley’s model.

Amato, P. R. (1983). The helpfulness of urbanites and small town dwellers: A test between two broad theoretical positions. Australian Journal of Psychology, 35 (2), 233–243.

Darley, J. M., & Latané, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility. Journal of Personality and Social Psychology, 8 (4, Pt.1), 377–383.

Fischer, P., Krueger, J. I., Greitemeyer, T., Vogrincic, C., Kastenmüller, A., Frey, D.,…Kainbacher, M. (2011). The bystander-effect: A meta-analytic review on bystander intervention in dangerous and non-dangerous emergencies. Psychological Bulletin, 137 (4), 517–537.

Garcia, S. M., Weaver, K., Moskowitz, G. B., & Darley, J. M. (2002). Crowded minds: The implicit bystander effect. Journal of Personality and Social Psychology, 83 (4), 843–853.

Latané, B., & Darley, J. M. (1968). Group inhibition of bystander intervention in emergencies. Journal of Personality and Social Psychology, 10 (3), 215–221.

Levine, R. V., Martinez, T. S., Brase, G., & Sorenson, K. (1994). Helping in 36 U.S. cities. Journal of Personality and Social Psychology, 67 (1), 69–82.

Manning, R., Levine, M., & Collins, A. (2007). The Kitty Genovese murder and the social psychology of helping: The parable of the 38 witnesses. American Psychologist, 62 (6), 555–562.

Markey, P. M. (2000). Bystander intervention in computer-mediated communication. Computers in Human Behavior, 16 (2), 183–188.

Milgram, S. (1970). The experience of living in cities. Science, 167 (3924), 1461–1468.

Miller, D. T., Turnbull, W., & McFarland, C. (1988). Particularistic and universalistic evaluation in the social comparison process. Journal of Personality and Social Psychology, 55 , 908–917.

Piliavin, J. A., Piliavin, I. M., & Broll, L. (1976). Time of arrival at an emergency and likelihood of helping. Personality and Social Psychology Bulletin, 2 (3), 273–276.

Schroeder, D. A., Penner, L. A., Dovidio, J. F., & Piliavin, J. A. (1995). The psychology of helping and altruism: Problems and puzzles . New York, NY: McGraw-Hill.

Suls, J., & Green, P. (2003). Pluralistic ignorance and college student perceptions of gender-specific alcohol norms. Health Psychology, 22 (5), 479–486.

Principles of Social Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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

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

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?

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

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).

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).

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.
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• 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.
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• Wood, W., & Eagly, A. H. (2002). A cross-cultural analysis of the behavior of women and men: Implications for the origins of sex differences.  Psychological Bulletin, 128 , 699–727.

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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• 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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Increased similarity of neural responses to experienced and empathic distress in costly altruism

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.).

Author information

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.

Corresponding author

Correspondence to Weizhe Hong .

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The authors declare no competing interests.

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Nature thanks Steve Chang, Christian Keysers, Ewelina Knapska and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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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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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

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

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?

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

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

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).

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.

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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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Gender and Helping Behavior. A Meta-Analytic Review of the Social Psychological Literature

Research output : Contribution to journal › Review article › peer-review

According to our social-role theory of gender and helping, the male gender role fosters helping that is heroic and chivalrous, whereas the female gender role fosters helping that is nurturant and caring. In social psychological studies, helping behavior has been examined in the context of short-term encounters with strangers. This focus has tended to exclude from the research literature those helping behaviors prescribed by the female gender role, because they are displayed primarily in long-term, close relationships. In contrast, the helping behaviors prescribed by the male gender role have been generously represented in research findings because they are displayed in relationships with strangers as well as in close relationships. Results from our meta-analytic review of sex differences in helping behavior indicate that in general men helped more than women and women received more help than men. Nevertheless, sex differences in helping were extremely inconsistent across studies and were successfully predicted by various attributes of the studies and the helping behaviors. These predictors were interpreted in terms of several aspects of our social-role theory of gender and helping.

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N2 - According to our social-role theory of gender and helping, the male gender role fosters helping that is heroic and chivalrous, whereas the female gender role fosters helping that is nurturant and caring. In social psychological studies, helping behavior has been examined in the context of short-term encounters with strangers. This focus has tended to exclude from the research literature those helping behaviors prescribed by the female gender role, because they are displayed primarily in long-term, close relationships. In contrast, the helping behaviors prescribed by the male gender role have been generously represented in research findings because they are displayed in relationships with strangers as well as in close relationships. Results from our meta-analytic review of sex differences in helping behavior indicate that in general men helped more than women and women received more help than men. Nevertheless, sex differences in helping were extremely inconsistent across studies and were successfully predicted by various attributes of the studies and the helping behaviors. These predictors were interpreted in terms of several aspects of our social-role theory of gender and helping.

AB - According to our social-role theory of gender and helping, the male gender role fosters helping that is heroic and chivalrous, whereas the female gender role fosters helping that is nurturant and caring. In social psychological studies, helping behavior has been examined in the context of short-term encounters with strangers. This focus has tended to exclude from the research literature those helping behaviors prescribed by the female gender role, because they are displayed primarily in long-term, close relationships. In contrast, the helping behaviors prescribed by the male gender role have been generously represented in research findings because they are displayed in relationships with strangers as well as in close relationships. Results from our meta-analytic review of sex differences in helping behavior indicate that in general men helped more than women and women received more help than men. Nevertheless, sex differences in helping were extremely inconsistent across studies and were successfully predicted by various attributes of the studies and the helping behaviors. These predictors were interpreted in terms of several aspects of our social-role theory of gender and helping.

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The Bystander Effect in Helping Behaviour: An Experiment

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).

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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Behavioral research + methods, examples, tools

TABLE OF CONTENTS

In this article, we have collated everything you need to know about behavioral research, its main methodologies, real use case scenarios as well as the best tried-and-tested tools to get stuck in!

What Is Behavioral Research?

Behavioral research is at the intersection of psychology, sociology, and anthropology, amongst others, and it is all about getting an understanding of human behavior. Within a product strategy context, behavioral research examines the various aspects of behavior. This occurs when the user interacts with a system or interface. Behavioral research deploys a range of different methodologies, such as but not limited to user testing , task analysis, behavioral observation, and mapping, with the main aim of understanding the cognitive mechanisms and processes that underpin action, memory, and decision-making.

Attitudinal vs Behavioral UX Research

Attitudinal and behavioral UX research are two approaches to conducting research that often complement each other. On the one hand, attitudinal UX research is laser-focused on observing and mapping the attitudes as well as perceptions of the users about a product or a service. Attitudinal research involves methodologies such as interviews and focus groups or surveys to gather data and insights about the users’ lived experiences when it comes to product usage. This approach unveils the motivations, pain points, and user needs.

On the other hand behavioral UX research is all about observing the actual behavior of a user while they interact with a product. Behavioral research deploys methodologies such as task analysis, user testing, eye-tracking, and heatmaps to gain insights into how users interact with an interface and pinpoint areas of improvement.

How Behavioral Research Helps Improve UX

Behavioral research is pivotal for better UX. Here are some key benefits that conducting behavioral research has to offer:

Predictions of User Behavior

Through behavioral research, researchers have an unmissable opportunity to observe and analyze user behavior. In this way, they can uncover patterns and trends that can predict future user behavior. Those informed data-backed predictions can be gold for both the enhancement of already existing features and the development of new futures since they allow UX designers to proactively meet user needs and tackle areas of improvement in time. Hence, behavioral research can lead to a more frictionless and meaningful user experience that meets the ever-evolving user needs.

Improved Personalization

Behavioral research can provide juicy insights and data into user preferences. These insights can be then leveraged to offer a more personalized approach to design. For instance, based on this data product teams can develop algorithms that can provide users with personalized recommendations on content. In this way, UX designers have an unmissable opportunity to craft interfaces that are more relatable and hence more pleasurable with higher user engagement.

Enhanced Accessibility

Last but not least, behavioral research plays a pivotal role in creating interfaces and systems that are widely accessible to people of all abilities. More specifically, behavior research can pinpoint barriers as well as help identify potential accessibility needs that need to be addressed. Having these insights about accessibility, UX designers can craft interfaces with accessibility features and alternative input methods. This ensures that the interface adheres to the main accessibility standards creating in this way more inclusive user experiences.

8 Essential Behavioral Research Methods

Here are the 8 essential behavior research methods you should master today:

User Testing

User testing is a behavioral research methodology that involves observing users while they interact with an interface. Worth noting is that the interface can be that of an actual product or a prototype . During the test, the researcher observes the user while they complete a specific task to gain insights into the challenges that they might encounter while completing that particular task. Hence, this method is great for pinpointing areas of improvement and crafting digital experiences that feel frictionless.

Watch our step-by-step guide to help you choose the best user testing tool. ⬇️

Task Analysis

Task analysis is another quintessential behavioral research methodology whereby complex tasks are broken down into small manageable steps. By breaking those complex tasks down and analyzing each step, product owners can gather invaluable insights about the cognitive mechanisms that underpin decision-making and user action. Designers can then put this knowledge into designing interfaces that feel more intuitive.

• Card Sorting

Card sorting is another behavioral research method where users are asked to categorize information intuitively. An excellent way to gather insights about the way your users consume information is card sorting . It also reveals how they perceive categories and their content. UX designers can then put this knowledge into practice and create meaningful labels for the information architecture of the digital product that is in line with the user’s mental models.

Try these UXtweak’s tools in practice ⬇️

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Try Tree Testing✅

Behavioral Mapping

Next up is behavioral mapping. This methodology revolves around creating a visual representation of the user interaction within a particular digital interface or a specific feature within this interface. By visualizing the movements of the user, researchers and designers can identify patterns. They can also identify areas where users show particular interest or areas where users make the most mistakes and rectify them if needed!

Behavioral Observation

On the other hand, behavioral observation is all about observing user interactions within a natural or testing setting. The observation can take the form of video recordings , field studies, or ethnographic studies to better understand how users behave in real-world scenarios and apply those findings to a digital product or service.

Contextual Inquiry

Contextual inquiry is a methodology whereby users are observed while they perform a specific task in their natural environment. This technique also involves the researcher simultaneously asking questions to get a deeper understanding of the motivations and goals that underpin the decision-making process.

Clickstream Analysis

Clickstream analysis is all about analyzing the clicks and the navigation paths that users follow within a digital interface. By doing so, UX researchers have an unmissable opportunity to identify areas of frustration with the so-called rage clicks , as well as think about ways of designing a path that guides the user to perform a specific task with the least clicks possible!

Log Analysis

Last but not least, log analysis involves the analysis of the data logs that are generated by the application stacks. The careful collection and analysis of these logs can give juicy insights into user behavior and usage patterns.

Top 10 Behavioral Research Tools

Here are the top 10 tried-and-tested behavioral research tools to get stuck in:

UXtweak is the only comprehensive user research platform you will ever need when it comes to behavioral research. The platform boasts a range of tools such as session recording, usability testing, card sorting, and more to help you capture and analyze user insights without a hitch.

Conduct UX Research with UXtweak!

The only UX research tool you need to visualize your customers’ frustration and better understand their issues

Heap is a platform that provides its users with the right tools to capture and analyze user data both across web and mobile applications. The top-notch data visualizations offered by Heap make it stand out from competitors in the market.

3. Mouseflow

Mouseflow is another behavior tool that can get you juicy insights into mouse movements and all sorts of user interactions including clicks and scrolls. This tool features heatmaps and funnel reports giving researchers an unmissable opportunity to improve conversion rates.

4. Clevertap

Clevertap is a customer engagement tool that offers researchers the opportunity to track behavior. Boasting tools like A/B testing, UX professionals can analyze user engagement and optimize every single customer touchpoint leading to frictionless user journeys.

5. Google Analytics 360

Google Analytics is a popular choice among businesses. This is a robust and versatile analytics tool with a range of features including but not limited to conversion tracking, user segmentation, real-time reporting, and goal tracking providing detailed data on user behavior.

6. Crazy Egg

This is another behavioral research tool featuring heatmaps, scroll maps, and confetti reports. This is a robust choice for businesses looking to optimize their digital products by leveraging behavioral research.

7. FullStory

FullStory is a comprehensive behavioral research tool that offers deep insights into user behavior. Its advanced search tools coupled with robust analytics features allow for quick identification of areas of improvement.

Hotjar is a stellar behavior analytics tool for UX professionals looking to delve into the world of behavioral analytics. Offering a wide range of features from session recordings to heatmaps, it can provide invaluable insights into user interaction.

9. Amplitude

Amplitude is yet another product analytics platform that can help businesses make sense of user behavior. Including a wide range of tools such as goal completion and tracking events as well as behavior cohort analysis this is a great tool to optimize user journeys and drive growth.

10. Smartlook

Finally, Smartlook is another comprehensive tool boasting session recordings and track conversion channels that can help product professionals identify sticky points and optimize the user experience.

Examples of Behavioral Research in Practice

Here are 3 real-life examples that have reaped the benefits of behavioral research and have seen exponential business growth:

Airbnb is one of the companies that consistently tap into behavioral research to enhance their product. Through rigorous research, the design at Airbnb has managed to leverage social proof to create a sense of trust in the platform. This strategy urges users to book more! This has been achieved through the use of verified user reviews. Additionally, badges like ‘Super Host’ or ‘Rare Find’ are awarded to listed homes based on user reviews.

Source: Airbnb

Duolingo is another great example of taking full advantage of behavioral research to enhance their language learning app. The popular language learning apps use design elements and copy to gamify the language learning process. This approach creates a sense of progress and accomplishment amongst users, boosting in this way engagement with the app. All these features are certainly not arbitrary. They are more the result of rigorous behavior research analyzing user behavior at every step of the way.

Source: Duolingo

Behavioral research can help companies craft more personalized experiences that resonate with users on a more personal level. Netflix is the epitome of this as the platform is all about personalized content. Netflix is feeding its algorithm with the insights gained from continuous behavior research studies. Based on these insights, the platform presents its users with movie recommendations based on the viewer’s preferences and past choices of films.

To sum it up

Behavioral research can play a pivotal role in creating relatable, frictionless, and inclusive digital experiences for products and services alike. By taking the time to analyze user interaction, businesses have an unmissable opportunity. This opportunity allows them to streamline processes and craft tailor-made experiences that exceed user expectations and drive loyalty and engagement. Companies the likes of Airbnb, Duolingo, and Netflix have managed to reap the advantages of behavioral research and have unlocked the potential of their products.

By giving a chance to UXtweak’s free trial you can shape experiences that change behaviors 🐝

FAQ: Behavioral Research

The behavioral approach in research is about observing and analyzing human behavior and gathering insights to craft memorable and relatable experiences that align with the user’s mental models.

The main tasks of a behavioral researcher are to design, conduct, and analyze research studies and experiments to study human behavior in a particular context.

There are an array of methodologies out there to conduct behavioral research. Task analysis, user testing, log analysis, contextual analysis, behavioral mapping, and observation are a few of those methods.

Elena is a T-shaped UX Researcher with a varied cross-industry marketing background. She holds a BA in English as SLA, a Master of Science in Management and a professional UX Design Diploma. She leverages her marketing background to bridge the gap between users and products, ensuring digital products meet user needs while hitting business goals. Her career highlights include helping Talanta, an educational start-up to scale into a global remote-first edtech SaaS business and becoming a guest lecturer at the University of Brighton International College.

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Looking to eat better? Exercise more? Get unstuck in life or career? Stanford scholars offer this research-backed advice for making moves in the new year. And a reminder from Graduate School of Business Professor Szu-chi Huang : Embrace process and reflection. “Health, well-being, success, love – these are journeys,” she says, “ not destinations.” 

1. Think like a designer 

Bill Burnett co-teaches a legendary course at Stanford that applies design thinking to the “wicked” problem of planning life and career. When approaching big decisions, he recommends using these power tools of his trade: Embrace curiosity – it’s the mindset that gives you the energy to overcome fear and procrastination; p rototype your way forward, through conversations and experiences; and reframe as a way to get unstuck from seemingly unsolvable problems.

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2. Build habits to last

BJ Fogg is the author of Tiny Habits: The Small Changes That Change Everything . In addition to focusing on bite-size objectives, he says it’s critical to be flexible in your quest for change and to lean into positive emotions. Specifically: H elp yourself do what you already want to do; h elp yourself feel successful; and i nvest time and energy (and money) to design your environment in a way that makes “good” behaviors easy and “bad” behaviors hard or impossible.

3. Embrace friendly competition 

Szu-chi Huang’s research has shown that while positive feedback motivates you to start a goal, negative feedback is more likely to motivate you to finish it. As such, she says it’s wise to l everage friendly support as you start to pursue a goal and then lean into friendly competition later on. Doing so can help you push through to the end. 

4. Maintain momentum with “good enough” 

Marily Oppezzo is a behavioral and learning scientist and a registered dietitian, and she frequently provides advice to faculty and staff through the university’s wellness programs. She emphasizes the importance of doing something , even if the action falls short of a loftier objective. Make a “good enough” version of your goals, she suggests, and give the ones you miss a head nod. (Not going to make that run happen? Just put on your tennis shoes.) “Some people, when they can’t make their goal at all, they put on blinders and try not to think about it,” Oppezzo says. “Instead, look your goal in the eye and say, ‘I see you and I’ll get you tomorrow.’ Maybe drive by the gym. Or take a bite of the celery before you have your ice cream. What this does is it gives your brain some practice, some mind share, and it adds to the rhythmicity of the habit that you’re starting to build.” 

5. Don’t get distracted by false controversy – and eat less meat

There’s more expert consensus than you’d think – especially when it comes to nutrition. If you’re confused by seemingly contradictory advice, Christopher Gardner suggests asking the questions “With what?” and “Instead of what?” (Eggs instead of Pop-Tarts = good; eggs instead of oats? Maybe not as much.) He also recommends the protein flip. Instead of putting steak or chicken at the center of your dinner plate, put grains, beans, and veggies there, with meat as a condiment or side dish.

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To Help or Not to Help? Prosocial Behavior, Its Association With Well-Being, and Predictors of Prosocial Behavior During the Coronavirus Disease Pandemic

Elisa haller.

1 Clinical Psychology and Intervention Science, Department of Psychology, University of Basel, Basel, Switzerland

Jelena Lubenko

2 Department of Health Psychology and Pedagogy, Riga Stradiņš University, Riga, Latvia

Giovambattista Presti

3 Kore University Behavioral Lab, Faculty of Human and Social Sciences, Kore University of Enna, Enna, Italy

Valeria Squatrito

Marios constantinou.

4 Department of Social Sciences, School of Humanities and Social Sciences, University of Nicosia, Nicosia, Cyprus

Christiana Nicolaou

5 Department of Nursing, Cyprus University of Technology, Limassol, Cyprus

Savvas Papacostas

6 Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

Gökçen Aydın

7 Department of Psychological Counseling and Guidance, Faculty of Education, Hasan Kalyoncu University, Gaziantep, Turkey

Yuen Yu Chong

8 The Nethersole School of Nursing, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China

Wai Tong Chien

Ho yu cheng, francisco j. ruiz.

9 Department of Psychology, Fundación Universitaria Konrad Lorenz, Bogotá, Colombia

María B. García-Martín

10 Faculty of Psychology, Universidad de La Sabana, Chía, Colombia

Diana P. Obando-Posada

Miguel a. segura-vargas, vasilis s. vasiliou.

11 Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom

Louise McHugh

12 School of Psychology, University College Dublin, Dublin, Ireland

Stefan Höfer

13 Department of Medical Psychology, Innsbruck Medical University, Innsbruck, Austria

Adriana Baban

14 Department of Psychology, Babes-Bolyai University (UBB), Cluj-Napoca, Romania

David Dias Neto

15 ISPA—Instituto Universitário, APPsyCI—Applied Psychology Research Center Capabilities and Inclusion, Lisbon, Portugal

Ana Nunes da Silva

16 Faculdade de Psicologia, Universidade de Lisboa, Alameda da Universidade, Lisbon, Portugal

Jean-Louis Monestès

17 LIP/PC2S Lab, Université Grenoble Alpes, Grenoble, France

Javier Alvarez-Galvez

18 Department of Biomedicine, Biotechnology and Public Health, University of Cadiz, Cadiz, Spain

Marisa Paez-Blarrina

19 Instituto ACT, Madrid, Spain

Francisco Montesinos

20 Department of Psychology, European University of Madrid, Madrid, Spain

Sonsoles Valdivia-Salas

21 Department of Psychology and Sociology, University of Zaragoza, Zaragoza, Spain

Dorottya Ori

22 Department of Mental Health, Heim Pal National Pediatric Institute, Budapest, Hungary

23 Institute of Behavioural Sciences, Semmelweis University, Budapest, Hungary

Bartosz Kleszcz

24 Bartosz Kleszcz Psychotherapy and Training, Sosnowiec, Poland

Raimo Lappalainen

25 Department of Psychology, University of Jyväskylä, Jyväskylä, Finland

Iva Ivanović

26 Department of Child Psychiatry, Institute for Children’s Diseases, Clinical Centre of Montenegro, Podgorica, Montenegro

David Gosar

27 Department of Child, Adolescent and Developmental Neurology, University Children’s Hospital, University Medical Center, Ljubljana, Slovenia

Frederick Dionne

28 Département de Psychologie, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada

Rhonda M. Merwin

29 Department of Psychiatry and Behavioral Science, Duke University, Durham, CA, United States

Maria Karekla

30 Department of Psychology, University of Cyprus, Nicosia, Cyprus

Angelos P. Kassianos

Andrew t. gloster, associated data.

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

The coronavirus disease (COVID-19) pandemic fundamentally disrupted humans’ social life and behavior. Public health measures may have inadvertently impacted how people care for each other. This study investigated prosocial behavior, its association well-being, and predictors of prosocial behavior during the first COVID-19 pandemic lockdown and sought to understand whether region-specific differences exist. Participants ( N = 9,496) from eight regions clustering multiple countries around the world responded to a cross-sectional online-survey investigating the psychological consequences of the first upsurge of lockdowns in spring 2020. Prosocial behavior was reported to occur frequently. Multiple regression analyses showed that prosocial behavior was associated with better well-being consistently across regions. With regard to predictors of prosocial behavior, high levels of perceived social support were most strongly associated with prosocial behavior, followed by high levels of perceived stress, positive affect and psychological flexibility. Sociodemographic and psychosocial predictors of prosocial behavior were similar across regions.

Introduction

The outbreak of the coronavirus disease (COVID-19) in late 2019 quickly led nations to declare states of emergency due to the overwhelming demands posed by the rapid spread of the virus globally. As a response to the pandemic, a range of behavioral measures were implemented, which drastically impacted people’s everyday life and functioning. In an attempt to curtail COVID-19 transmissions, many governments around the world declared national lockdowns, enforced curfews and travel bans, temporarily closed schools, businesses, cultural and recreational facilities, and issued “stay-at-home” recommendations or orders ( World Health Organization [WHO], 2020 ). These physical distancing and quarantine policies severely disrupted daily social interactions and resulted in increased social isolation ( Jarvis et al., 2020 ; Smith and Lim, 2020 ), a situation at odds with basic human needs to connect with others ( Baumeister and Leary, 1995 ). The absence of social interactions along with emerging feelings of fear, insecurity, and stress ( Luo et al., 2020 ; Wang et al., 2020 ) have limited the possibilities to assist others and may give rise to self-centered concern and disregard of others. Indeed, a variety of reactions characterized as selfish or antisocial were observed as a result of the pandemic and related measures. These included non-compliance with public health measures (e.g., mask-wearing), which increases risk of transmission, and issues such as overbuying and food stockpiling ( Dholakia, 2020 ; Wang et al., 2020 ), and even a sharp rise of reports and acts of racism and xenophobia against Asian people ( Ng, 2020 ), when the coronavirus spread in spring 2020. Moreover, recent research points to the importance of social norms when individuals inform their decisions about behaving more prosocially or in in a self-interested way ( Abel and Brown, 2020 ). Being exposed to positive role models might be curtailed due to a limited participation in social life.

However, this unique test of our social fabric simultaneously revealed prosocial responses, which encompass a variety of attitudes and voluntary actions that may be adopted by individuals, all aiming at helping, supporting, comforting, or caring for others ( Batson and Powell, 2003 ). During the first wave of the pandemic in 2020, solidarity and cooperation were reflected in various individual behaviors and collective efforts. For example, measures were adhered to by large parts of the population in order to protect the public health ( Margraf et al., 2021 ), as reflected by a rapid surge of millions of people across the globe practicing scientifically recommended hygiene measures and self-isolating. Cooperation was further manifested in musicians performing concerts from their balconies for the common good and people applauding from their windows to express gratitude to frontline and healthcare workers ( Taylor, 2020 ). Collective efforts to directly help those in need further entailed the establishment of formal volunteering endeavors and informal initiatives to support neighbors in communities ( Beardmore et al., 2020 ). Indeed, previous research has described the emergence of prosocial responses to shared experiences of adversity and suffering ( Staub, 2003 , 2005 ). According to this perspective, experiencing trauma generates a sense of shared fate and identity, which may lead to increased empathy with and a greater motivation to help those in need. Importantly, these outbursts of altruistic and prosocial behaviors have been observed in the various contexts of traumatic life events, such as interpersonal conflict, war, and natural disasters ( Kaniasty and Norris, 1995 ; Hartman and Morse, 2020 ). It has been suggested that in the context of such extreme and severe disasters, populous acts of cooperation and prosocial behavior are likely facilitated by a higher number of opportunities to help others ( Vollhardt, 2009 ). The COVID-19 crisis presented an emergency situation in which different helping behaviors were difficult to accomplish. The governmentally mandated lockdowns in the wake of the pandemic thus provided a unique context to study other-oriented behavior. Little is known about how periods of increased insecurity, stress, and social disruption – as experienced during the first COVID-19 lockdowns – are related to individuals’ readiness and opportunities to demonstrate prosocial behavior.

Engaging in prosocial behavior may be a potent protective factor during periods of global adversity, as it can be linked to benefits on the individual and to society as a whole: Acts that focus on benefitting others are particularly important in the face of widespread suffering caused by the COVID-19 pandemic, in which many individuals were confronted with financial hardship, social isolation, and the loss of loved ones. Furthermore, prosocial behavior is associated with personal benefits for the individual provider of help. This is supported by mounting evidence of different types of prosocial behavior being related to greater physical and mental health with studies showing that volunteering, family caregiving, and the provision of tangible help to close others are associated with greater longevity ( Harris and Thoresen, 2005 ; Poulin et al., 2013 ; Roth et al., 2013 ) and that spending money on others augments emotional well-being ( Aknin et al., 2013 ). Past research has shown that prosocial behavior buffers against the negative effects of daily stress on emotional well-being ( Raposa et al., 2016 ). Furthermore, it was found that time spent volunteering after a natural disaster is associated with increased feelings of belongingness ( Gordon et al., 2011 ). In the context of the current global crisis, a recent experimental study in a United States sample provides preliminary evidence that showing prosocial behavior during the COVID-19 pandemic increases positive affect, empathy, and social connectedness ( Varma et al., 2020 ), indicating that helping others has an immediate impact on the helper’s mood.

Prosocial behavior is in part driven by feelings of empathy and a concern for the welfare of others ( Eisenberg and Miller, 1987 ; Carlo et al., 1996 ). However, most previous research with regard to predictors of prosocial behavior has focused on individual emotional competencies, leaving out the situational and social context. Similarly, the majority of studies that examine predictors of prosocial behavior have been conducted during non-emergency situations. To the extent that individuals can show varied emotional and behavioral responses to the COVID-19 crisis, the factors influencing prosocial behavior may differ in terms of situational contexts, particularly those with an increased global burden and in times of (mandated) social distancing. Understanding prosocial responding during the COVID-19 pandemic therefore necessitates an examination of psychosocial variables that likely play an important role with regard to prosocial behavior.

Perceived Social Support

The availability of supportive relationships has been linked to increased levels of prosocial behavior. The social network provides an essential context in which prosocial behavior can be displayed. Moreover, conceptual links between emotional functioning and prosocial behavior have been suggested in that experiencing social support can foster social-emotional competencies ( Eisenberg and Fabes, 1990 ), such as providing help and support to others. In addition, the experience of social exclusion has been shown to be associated with decreases in prosocial acts ( Twenge et al., 2007 ). While social interactions have been compromised drastically during the first wave of COVID-19 lockdowns, the perception of social support or a lack thereof in a novel, encumbering situation might be sharpened. We therefore hypothesized that a high level of perceived social support would predict more prosocial behavior.

Psychological Flexibility

Psychological flexibility has been proposed as a cornerstone of health and well-being ( Kashdan and Rottenberg, 2010 ; Leonidou et al., 2019 ). Psychological flexibility comprises a range of intra- and interpersonal skills enabling an individual to shift their mindsets and adapt their behavioral repertoire to what a situation affords. It has been shown to protect from the negative effects of daily stress ( Gloster et al., 2017 ) and major life events ( Fonseca et al., 2020 ) on well-being and depressive symptoms. Research on psychological flexibility postulates that responding flexibly to situational demands allows individuals to prioritize areas in life that are meaningful and consistent with personal values ( Hayes et al., 2006 ; Villanueva et al., 2020 ). Additionally, researchers started to evaluate psychological flexibility as a potential public health target with preliminary evidence of its potency: In a recent study, it was found that a brief intervention strengthening psychological flexibility led to an increase in prosocial choices in couples ( Gloster et al., 2020c ). Another recent study from our research group has supported the dual roles of psychological flexibility and prosocial behaviors in mitigating the impact of illness perception toward COVID-19 on mental health in a sample of Hong Kong adults during the early phase of the pandemic ( Chong et al., 2021 ). Given that the COVID-19 lockdown measures may make it harder for individuals to shape their daily lives in accordance with their personally held values, we hypothesized that high levels of psychological flexibility would be positively associated with the occurrence of prosocial behavior.

Perceived Stress

A growing body of research supports the hypothesis that experiencing stress elicits prosocial behavior ( Buchanan and Preston, 2014 ). Proponents of this perspective postulate that affiliative behaviors present a way of coping with the adverse experiences of stress ( Midlarsky, 1991 ; Taylor et al., 2000 ). This view is supported by empirical data showing an increase in prosocial behavior under stress in some situations. For example, studies have shown that people exhibit prosocial behavior under time pressure ( Rand et al., 2012 ). In an experimental study, the experience of an acute stressor led to increases in trustworthiness and prosocial behavior in social interactions ( von Dawans et al., 2012 ), suggesting that feeling stressed contributes to the emergence of prosocial behaviors due to its stress-buffering properties. Alternatively, engaging in activities such as volunteering, helping, and supporting others require personal resources, which may increase stress ( Gloster et al., 2020a ). Within the pandemic, prosocial behaviors might be one way to overcome experiences of stress that result from being affected or by observing other peoples’ suffering during the pandemic. We therefore hypothesized that high levels of perceived stress would be positively related to prosocial behavior.

Positive Affect

A vast amount of evidence supports the association between positive mood and prosocial behavior. One prevailing theoretical assumption explaining this relationship is that prosocial behavior is a strategy to maintain positive mood. Experimental evidence shows that the induction of positive mood facilitates helping behavior ( Rosenhan et al., 1981 ) and that positive state affect leads to increased prosocial behavior in the work context ( George, 1991 ). Support of this association is provided by numerous studies manipulating positive affect in different ways, which show, for example, that prosocial behavior occurs following the reception of a surprise payment ( Isen and Levin, 1972 ) or after listening to happy thoughts ( O’Malley and Andrews, 1983 ). We therefore hypothesized that positive affect would contribute to the emergence of prosocial behavior during this time of elevated stress.

The Present Study

The positive impact of prosocial behavior on happiness has been documented in Western and non-Western societies and across socioeconomic status ( Dunn et al., 2008 ; Aknin et al., 2013 ). However, it remains unexplored whether this association exists during a long-lasting incisive event limiting humans’ social behavior faced by almost the entire world population concurrently. The current study aimed to extend our understanding of prosocial behavior in the face of an adverse global situation, which disrupts social life and everyday social interactions. First, the present study examined whether and to which extent people engage in prosocial behaviors during the first COVID-19 pandemic lockdowns. Second, this study investigated the relationship between prosocial behavior and well-being during the first wave of COVID-19 pandemic lockdowns. Third, we aimed at identifying predictors of prosocial behavior during the COVID-19 pandemic lockdowns by testing the relationships between sociodemographic and psychosocial variables and prosocial behavior. Lastly, we were interested in investigating region-specific differences in the extent of prosocial behavior, its association with well-being, and predictors of prosocial behavior.

Materials and Methods

The current study was part of the COVID-Impact project, an international cross-sectional online survey conducted in 78 countries worldwide ( Gloster et al., 2020b ). The aim of the COVID-Impact project was to explore the behavioral, emotional, and psychological response to the COVID-19 pandemic and related lockdown measures. Between April and June 2020, data were collected through social media, university mailing lists, and advertisements in professional associations using a secured google platform. Participants were first informed about the study aim, the procedure, and about risks and benefits of taking part. Those who chose to participate gave informed consent electronically. The survey was available in 22 different languages. Ethical approval was granted by the Cyprus National Bioethics Committee (ref.: EEBK EΠ 2020.01.60 on 3rd April 2020) and by the local ethics boards from the research teams involved in data collection.

The Mental Health Continuum-Short Form (MHCS-SF) was employed, which is a widely used and validated measure for positive mental health ( Lamers et al., 2011 ). The instrument comprises 14 items describing various feelings, which are rated on a 6-point Likert scale relating to the frequency of occurrence (0 = “never” to 6 = “every day”). The MHC-SF contains three subscales with three items measuring emotional, six items measuring psychological, and five items measuring social well-being. The scale revealed a Cronbach’s alpha of 0.91 in this sample. The total score is comprised of the sum score of all items (ranging from 0 to 70).

Prosocial Behavior

Prosocial behavior was measured using six out of 16 items of the Prosocialness Scale for Adults (PSA) ( Caprara et al., 2005 ). We were not able to include all items of the scale in order to avoid burdening respondents. The six items refer to statements about helping and sharing with friends and others, being available for volunteer activities, being empathetic with those in need, and spending time with lonely people. Participants were asked to rate the frequency of occurrence of the stated behavior on a 5-point Likert scale (1 = “never/almost never” to 5 = “always/almost always”). We used a sum score of the six items (range: 6–36), with higher values indicating more prosocial behavior. In this sample, Cronbach’s alpha was 0.83 for the scale.

Sociodemographic Status

Sociodemographic predictors included age, gender, education level, employment status, marital status, and living situation.

Characteristics Related to Quarantine/Self-Isolation

Participants responded to questions related to the amount of time in lockdown (in weeks) and the impact of the lockdown on their financial situation (“have got better,” “stayed the same,” “have gotten worse”) and on daily activities (“did not leave house,” “left house once only,” “left house a couple of times,” “left house more than three times per week”).

Social Support

The Oslo 3-item Social Support Scale (OSSS-3) was used to measure the availability of social support by asking about the number of close people, the extent of concern and interest, and the appraised ease of getting help from neighbors ( Dalgard et al., 2006 ). Analysis of the internal consistency revealed Cronbach’s alpha of 0.54. The sum score is classified in groups of different levels of social support: low (3–8), moderate (9–11), and high (12–14).

Psy-Flex is a 6-item instrument that measures psychological flexibility, a construct referring to a range of intra- and interindividual skills that allow an open and presently aware mindset as well as the clarification and pursuit of deeply held, personal values ( Gloster et al., 2021 ). Items were rated on a 5-point Likert scale related to the frequency of occurrence (1 = “very seldom” to 5 = “very often”). We used a sum score of all items. The internal consistency of the scale revealed a Cronbach’s alpha of 0.84 in this sample.

The 10-item Perceived Stress Scale (PSS) was used to measure the degree to which life situations of the past week are appraised as stressful ( Cohen et al., 1983 ). Respondents were asked to rate the frequency of feeling or thinking about life situations or events in a certain manner on a 5-point Likert scale (0 = “never” to 4 = “very often”). The sum score of all items was used in this sample with internal consistency (α = 0.89). The scores are classified into groups of low (0–13), moderate (14–26), and high stress (27–40).

The subscale of the Positive and Negative Affect Scale (PANAS; Watson et al., 1988 ) was used to measure positive affect. The subscale is comprised of 10 items that are scored on a 5-point Likert scale (1 = “very slightly/little” to 5 = “extremely”). Sum score of all items was calculated; internal consistency analysis revealed a Cronbach’s alpha of 0.90.

Statistical Analysis

Descriptive statistics included relative frequencies, means and standard deviations (SDs), or medians and interquartile ranges (IQR) of sociodemographic variables (age, gender, education level, employment status, marital status, children, and living situation), characteristics regarding the self-isolation/quarantine measures (weeks in quarantine, having been infected by COVID-19, impact of social isolation on financial situation and daily activities), and predictor and outcome variables for the overall sample and for all regions. Countries were grouped into eight geo-cultural regions (Southern Europe (=SE) includes Cyprus, Greece, Spain, Italy, Portugal, and Andorra; Eastern Europe (=EE) includes Latvia, Poland, Czech Republic, Hungary, Slovakia, Slovenia, Croatia, Ukraine, Romania, Serbia, Montenegro, and North Macedonia; Western Europe (=WE) includes Austria, Switzerland, Liechtenstein, Germany, Belgium, Netherlands, Luxembourg, France, United Kingdom, and Ireland; Northern Europe (=NE) includes Finland, Denmark, Sweden, Norway, and Iceland; Western Asia (=WA) includes Turkey, Azerbaijan, Lebanon, Israel, Jordan, Iran, Pakistan, Kuwait, Saudi-Arabia, and United Arab Emirates; and East Asia (=EA) includes Hong Kong, China, and Taiwan; Latin America (=LA) includes Mexico, El Salvador, Guadeloupe, Panama, Colombia, Ecuador, Brazil, Peru, Uruguay, Paraguay, Argentina, and Chile; North America (=NA) includes Canada, United States. In order to analyze regional variations of prosocial behavior, we compared the regions’ mean to the mean of the overall sample with a linear regression model for prosocial behavior centering around the grand mean and region as a predictor. Cohen’s d for the standardized difference was used to measure the magnitude of the effect with values. Bivariate correlation analysis ( r ) for the entire sample was used to assess associations between all study variables (predictors and outcome) with ≤0.10 referring to very small, ≤0.20 to small, ≤0.30 to moderate, ≤0.40 to large and >0.40 to very large effect sizes ( Funder and Ozer, 2019 ).

First, simple linear regressions were performed with prosocial behavior as predictor and well-being as outcome in the total sample and in each region’s subsample. Next, separate multiple regression analyses with the total sample and with the region-samples were performed with prosocial behavior as the dependent variable and each set of predictors (sociodemographic and psychosocial variables) as the independent variables. Standardized regression coefficients (Beta) with 95% Confidence Intervals (CI) were computed as indices of effect size in order to measure the strength of the association between each predictor and outcome variable. Variance Inflation Factors (VIF) were used to check for multicollinearity between the predictors. All analyses were first conducted with the overall sample and subsequently with the subsamples of each region. All analyses were computed using R software version 1.3.959 (R Core Team).

The sample comprised N = 9,496 participants from 60 countries grouped into eight regions: Southern Europe ( n = 2,820), Eastern Europe ( n = 2,269), Western Europe ( n = 2,107), Northern Europe ( n = 172), West Asia ( n = 720), and East Asia ( n = 520), Latin America ( n = 560), and North America ( n = 328). Regions with n < 100 were not considered in the sample. Table 1 presents relative frequencies and, where appropriate, measures of central tendency of the sociodemographic variables and characteristics related to quarantine/self-isolation for the entire sample and individual regions.

Sociodemographic and characteristics related to quarantine/self-isolation of the total sample and subsamples of each region.

Countries were clustered in regions as follows: Southern Europe (=SE) includes Cyprus, Greece, Spain, Italy, Portugal, and Andorra; Eastern Europe (=EE) includes Latvia, Poland, Czech Republic, Hungary, Slovakia, Slovenia, Croatia, Ukraine, Romania, Serbia, Montenegro, and North Macedonia; Western Europe (=WE) includes Austria, Switzerland, Liechtenstein, Germany, Belgium, Netherlands, Luxembourg, France, United Kingdom, and Ireland; Northern Europe (=NE) includes Finland, Denmark, Sweden, Norway, and Iceland; Western Asia (=WA) includes Turkey, Azerbaijan, Lebanon, Israel, Jordan, Iran, Pakistan, Kuwait, Saudi-Arabia, and United Arab Emirates; and East Asia (=EA) includes Hong Kong, China, Taiwan; Latin America (=LA) includes Mexico, El Salvador, Guadeloupe, Panama, Colombia, Ecuador, Brazil, Peru, Uruguay, Paraguay, Argentina, and Chile; North America (=NA) includes Canada, United States.

Frequency and Types of Prosocial Behavior

Overall, prosocial behavior was reported to occur often on average in the total sample ( M = 22.8, SD = 4.2). Different types of prosocial behaviors were reported with similar frequency on average. On a descriptive level, lowest levels were reported for being available for volunteering activities ( M = 3.2, SD = 1.2) and spending time with friends who feel lonely ( M = 3.4, SD = 1.0), higher levels for sharing with friends ( M = 3.9, SD = 0.92), willing to help ( M = 4.1, SD = 0.8), trying to help others ( M = 4.1, SD = 0.8), and being empathetic with those in need ( M = 4.1, SD = 0.8). With regard to regional variations, the reported levels of prosocial behavior were largely similar across regions. However, two regions reported slightly higher levels of prosocial behavior (Southern Europe, medium effect; Western Asia, large effect), and other two regions demonstrated lower levels of prosocial behavior (Eastern Europe, medium effect; Eastern Asia, very large effect) than the average sample. Average levels of prosocial behavior and differences between regions and their effect sizes can be found in Table 2 .

Average levels of prosocial behavior across regions.

M, mean; SD, standard deviation; CI, confidence interval, Cohen’s d for standardized difference between region mean and overall mean; moderate and large effects are printed in bold.

Regional variations were observed with regard to specific types of prosocial behavior. In most regions, about 45–55% of the respondents indicated being available for volunteer activities as “often” or “almost always”, whereas this was the case for only 20.8% in Eastern Europe, 27.3% in Northern Europe, and 26.1% in East Asia. A similar pattern emerged on spending time with friends who feel lonely: In most regions, between 47 and 62% indicated that this type of behavior occurred as “often” or “almost always”, while this was true for 34% in Eastern Europe, 37.8% in Northern Europe, and 36% in East Asia.

Relationship Between Prosocial Behavior and Well-Being

Scatterplot and Pearson’s correlation showed a moderate association between prosocial behavior and well-being ( r = 0.32). Simple linear regression revealed that prosocial behavior explained a significant amount of variance in well-being, F (1,9483) = 1,096, p < 0.001, R 2 adjusted = 0.104. The regression coefficient [ B = 1.07, CI (1.01, 1.13)] indicates that well-being increases by 1.07 for each unit of increase in prosocial behavior [β = 0.32, CI (0.30, 0.34), p < 0.001].

With regard to regional variations, prosocial behavior significantly positively predicted well-being in all regions with the largest effect in Latin America [β = 0.41, CI (0.29, 0.36), p < 0.001], West Asia [β = 0.38, CI (0.31, 0.45), p < 0.001] and East Asia [β = 0.35, CI (0.27, 0.44), p < 0.001] followed by Southern Europe [β = 0.32, CI (0.29, 0.36), p < 0.001], Northern Europe [β = 0.32, CI (0.23, 0.32), p < 0.001], Eastern Europe [β = 0.27, CI (0.24, 0.32), p < 0.001] and Western Europe [β = 0.27, CI (0.23, 0.31), p < 0.001], and North America [β = 0.24, CI (0.14, 0.35), p < 0.001]. Figure 1 presents the association between prosocial behavior and well-being across regions.

Association between prosocial behavior and well-being across regions.

Predictors of Prosocial Behavior

Descriptive statistics of all psychosocial variables can be found in Table 3 , displaying means and SDs for the outcome and predictor variables for the total sample and subsamples of each region.

Description of predictors of the total sample and subsamples of each region.

Table 4 presents means and SDs, as well as bivariate correlations among all psychological variables – predictors and outcome(s) – across the entire sample. Well-being showed a strong positive correlation with psychological flexibility, and positive affect, and a moderate to strong negative association with perceived stress. Prosocial behavior showed moderate positive correlations with well-being, social support, and positive affect; a weak positive correlation with psychological flexibility; and a weak negative correlation with perceived stress.

Correlation matrix for outcome and predictor variables.

Higher values indicate greater extent of the measured trait; strong correlations are printed in bold; all correlations are statistically significant, p < .001, two-tailed.

Table 5 displays the results of the multiple regression analysis for the overall sample [ F (24, 9407) = 88.92, p < 0.001, R 2 adjusted = 0.183]. Both sociodemographic and psychosocial predictors were significant, with several psychosocial predictors showing the largest effects on prosocial behavior in the overall sample. Arranged in order of predictor strength, a high level of perceived social support was the predictor that best explained prosocial behavior (relative to low levels of social support). Higher levels of perceived stress and positive affect also contributed significantly to variation in prosocial behavior. This was followed by female gender (relative to male), being retired (relative to working full time), and living with friends or roommates (relative to living alone) as positive predictors. Significant positive predictors of prosocial behavior were higher levels of psychological flexibility, being retired, being unemployed (relative to working full time), living with parents, and living with roommates (relative to living alone). Significant negative predictors of prosocial behavior were living with own family (relative to living alone), as well as being in a relationship and being widowed (relative to being married).

Sociodemographic and psychosocial predictors of prosocial behavior.

R 2 = 0.18 (p < 0.001); β, standardized regression coefficient; CI, confidence interval; *p < 0.05; **p < 0.001.

Table 6 demonstrates a simplified version of the multiple regression analyses for each region. With regard to regional variation, the multiple regression analyses revealed the following results: The strongest predictors of prosocial behavior were high levels of perceived social support in all regions with East Asia [β = 0.84, CI (0.44, 1.23), p < 0.001], West Asia [β = 0.75, CI (0.55,0.95), p < 0.001], Northern Europe [β = 0.72, CI (0.24, 1.20), p < 0.001], Eastern Europe [β = 0.67, CI (0.55,0.80), p < 0.001], Northern America [β = 0.64, CI (0.33, 0.95), p < 0.001], Latin America [β = 0.58, CI (0.33, 0.82), p < 0.001], Western Europe [β = 0.57, CI (0.43, 0.70), p < 0.001] and Southern Europe [β = 0.53, CI (0.43, 0.64), p < 0.001].

Simplified representation of significant predictors of prosocial behavior for each region.

Southern Europe (SE): n = 2,820; Western Europe (WE): n = 2,107; Northern Europe (NE): n = 172; Eastern Europe (EE): n = 2,269; North America (NA): n = 328; Latin America (LA): n = 560; Western Asia (WA): n = 720; Eastern Asia (EA): n = 520; X = significant positive predictor, −X = significant negative predictor, both: p < 0.001.

The second strongest predictor was a high level of perceived stress in East Asia [β = 0.55, CI (0.22, 0.89), p < 0.001], North America [β = 0.44, CI (0.08, 0.79), p < 0.001], Southern Europe [β = 0.35, CI (0.21, 0.50), p < 0.001], Western Europe [β = 0.32, CI (0.15, 0.49), p < 0.001], Latin America [β = 0.24, CI (−0.09, 0.56), p < 0.001], and Eastern Europe [β = 0.23, CI (0.14, 0.43), p < 0.001]; and the third one was positive affect in Latin America [β = 0.29, CI (0.18, 0.40), p < 0.001], Southern Europe [β = 0.27, CI (0.23, 0.31), p < 0.001], North America [β = 0.24, CI (0.11, 0.38), p < 0.001], West Asia [β = 0.24, CI (0.16, 0.31), p < 0.001], East Asia [β = 0.24, CI (0.15, 0.33), p < 0.001], Eastern Europe [β = 0.23, CI (0.19, 0.28), p < 0.001] and Western Europe [β = 0.15, CI (0.10, 0.20), p < 0.001]. Lastly, psychological flexibility positively predicted prosocial behavior in East Asia [β = 0.18, CI (0.09, 0.27), p < 0.001], North America [β = 0.17, CI (0.04, 0.31), p < 0.001], Western Europe [β = 0.15, CI (0.12, 0.22), p < 0.001], Southern Europe [β = 0.11, CI (0.07, 0.16), p < 0.001], Latin America [β = 0.10, CI (0.00, 0.20), p < 0.001], and Eastern Europe [β = 0.07, CI (0.02, 0.12), p < 0.001] and Western Asia [β = 0.07, CI (0.00, 0.14), p < 0.001].

The COVID-19 pandemic fundamentally changed humans’ social lifes and day-to-day behaviors. The lockdown measures – imposed in many countries as a means to control the outbreak of the virus – was a concept-unheard of by most people at the time. Curfews, bans on gatherings, and the standstill of the public life dramatically impacted the frequency and quality of social interactions and resulted in the social isolation of many individuals. On the one hand, these restrictions conflict with the humans’ need to connect with others and to engage in positive social interactions, such as prosocial behavior. On the other hand, the severity of this crisis highlights the importance of prosocial behavior for a functioning society and the public mental health. This is why this study’s objective was to investigate the extent of prosocial behavior, its relation to well-being and factors predicting prosocial acts.

Prosocial Behavior During the Pandemic

The present study revealed that, overall, prosocial behavior was reported to occur frequently during the first COVID-19 related lockdown in spring 2020. This finding largely supports the notion that in response to the social dilemma individuals do not shy away from supporting each other. Contrary to commonly held beliefs of panic and egoistic acts following disasters, humans tend to engage in various types of benevolent behaviors, as observed in diverse catastrophes ( Zaki, 2020 ). Our results further indicated that helping behavior might occur universally given that the extent of reported prosocial behavior was comparable across eight regions covering 60 different countries around the world. Lower levels of prosocial behavior were observed in Eastern Europe and East Asia, which is likely explained by a combination of cultural, historic and political factors as well as country-specific regulations due to the pandemic. Indeed, when compared with Western families, traditional Asian families are more likely to emphasize family obligations and respect for hierarchical relations, implying that the prosocial tendencies are more likely to be displayed toward family members and peers (e.g., seniors), rather than other community members and strangers ( Padilla-Walker et al., 2018 ). For Asian families, these motives may have been underrepresented in the Prosocialness Scale for Adults. On the other hand, lower levels of prosocial behavior in Eastern Europe compared to the overall sample might be explained by lower levels of social trust that has previously been observed in post-communist countries (e.g., Bjørnskov, 2007 , 2021 ). If a society is characterized by doubts that most other people are behaving according to social norms, this might reduce the demonstration of prosocial behaviors. However, until replicated and specifically tested, these interpretations must remain speculative. Looking into specific types of prosocial behavior, our results show that spending time with friends who feel lonely and being available for volunteering were reported to occur the least frequently on average, a pattern observed across all regions. Given that state regulations involved restrictions drastically impacting social life, it is plausible that respondents refrained from spending time with friends. Research in a representative sample in the United Kingdom has shown that the number of daily contacts reduced substantially as a consequence of the physical distance measures in March 2020 ( Jarvis et al., 2020 ). With regard to volunteering, it might be that this type of prosocial activity was less favorable due to a lack of volunteering options, or due to perceived danger in volunteering activities that involved social contact with others, as has been suggested in studies on informal and inexperienced volunteering ( Whittaker et al., 2015 ). Another reason could be the lack of time and resources to commit to volunteering, particularly in working parents who were prone to experiencing high levels of distress due to the competing demands of childcare and employment as well as financial insecurity ( Cheng et al., 2021 ).

Importantly, we found that prosocial behavior was consistently associated with well-being across all regions, a finding consistent with a large body of evidence of a positive link between various types of prosocial behavior and well-being ( Hui et al., 2020 ). One possible explanation of this result is that doing good to others feels good and that emotional rewards of helping are inherent to human nature. Previous studies established causal effects of prosocial acts on well-being (e.g., Martela and Ryan, 2016 ), with one study suggesting this phenomena to be a human universal, due to their finding that spending money on others leads to increases in well-being across cultures ( Aknin et al., 2013 ). Our finding extends the current knowledge by indicating that the link between prosocial behavior and well-being is robust to the emotional and social intricacies of a global crisis. Due to the cross-sectional nature of this study, we cannot exclude the alternative explanation that individuals with high levels of well-being are more inclined to engage in prosocial behavior. However, there is preliminary causal evidence that generous actions during the pandemic result in positive affect, empathy, and social connectedness ( Varma et al., 2020 ).

The present study examined sociodemographic and psychosocial predictors of prosocial behavior. Female gender, being retired, being unemployed, and living with parents were positively associated with prosocial behavior in the total sample. The role of retirement with regard to prosocial behavior has been studied extensively, as transitioning to retirement has been linked to increases in prosocial behavior ( Fasbender et al., 2016 ). While previous research has primarily focused on volunteering in older age ( Bjälkebring et al., 2021 ), our study suggests that retirement is positively related to prosocial behavior more broadly. Non-working populations might actively seek ways to be involved in social life, granting them opportunities to display prosocial behavior on the one hand, and satisfying psychological needs (i.e., meaning in life) on the other hand. With regard to unemployment, it is reasonable to assume that a shared understanding of being in need might facilitate prosocial behavior in this segment of the population. Previous research has produced cross-cultural evidence that individuals from lower socioeconomic backgrounds are more inclined to engage in prosocial behaviors compared to well-situated populations ( Piff et al., 2010 ; Wang and Murnighan, 2014 ). Furthermore, in the context of the Spanish economic crisis, research showed that financial threat (i.e., due to unemployment) was related to increased helping behavior, suggesting that empathetic concern might give rise to prosocial actions ( Alonso-Ferres et al., 2020 ). Similarly, those affected by unemployment during the COVID-19 pandemic might have been more prone to empathizing with and helping those in need. Interestingly, having an own family was negatively associated with engaging in prosocial acts. Given that a large proportion of our sample reported having children, this finding is likely explained by a lack of time parents are facing: The pandemic-induced governmental measures required working parents to navigate childcare (incl. home-schooling) and their work, oftentimes transferred to home-office, which put a tremendous burden on families and parents’ work-life-balance ( Del Boca et al., 2020 ).

Prosocial behavior was most strongly associated with the perception of having social support in the overall sample, in particular high levels as compared to low levels of social support. Importantly, this finding was consistent across all regions, highlighting the central role of perceived social support with regard to benevolent actions independent of culture or society. Everyday helping behavior has been shown to occur more frequently with family and friends ( Amato, 1990 ; Padilla-Walker and Carlo, 2014 ), with close others being the building blocks of a mutually supportive network. The availability of a caring and reliable network implicates positive interactions with strong social ties, which facilitates prosocial behavior ( Barry and Wentzel, 2006 ). Perceived social stress, positive affect, and psychological flexibility were also connected to elevated prosocial behavior. The former might trigger prosocial behaviors due to a “tend-and-befriend”-response ( Taylor et al., 2000 ), while a person who is more psychologically flexible may be able to temporarily disengage of his/her own emotions and focus on those in need of help ( Chong et al., 2021 ).

With regard to regional variations, this study found similar patterns of meaningful predictors of prosocial behavior across the eight regions with one exception regarding psychosocial predictors worth mentioning: High levels of perceived stress were related to higher levels of reported prosocial behavior in all regions, except for Latin America, Western Asia, and Northern Europe. Based on the theoretical assumptions discussed in the introduction, one explanation could be that for individuals from these regions, prosocial actions are not employed as a way to regulate stress. Another explanation would be that engaging in prosocial behavior does not represent a source of stress in individuals from these regions, because prosocial acts might be culturally ingrained. It needs to be considered that these sub-samples are largely comprised of respondents from Colombia and Turkey, respectively, with societies that are characterized by collectivistic values and norms.

While psychosocial predictors showed comparable patterns across regions, sociodemographic variables gave a patchy picture when comparing regions. Some inconsistencies between regions merit further discussion: For example, unemployment was negatively related to prosocial behavior in Western Asia (consisting predominantly of Turkish respondents), whereas being unemployed predicted prosocial behavior in Northern Europe and Eastern Asia. While this finding might be explained by cultural differences, welfare-state measures existing in some countries might contribute to this finding. For example, the Nordic welfare states have a strong social support system and they have ensured easily accessible unemployment benefits as a response to the economic impact of the COVID-19 crisis ( Greve et al., 2021 ). Experiencing financial security despite not having a job might put unemployed individuals emotionally, financially and time-wise in a situation that allows to start or continue an investment in prosocial behavior. Respondents from Western Asia, Eastern Europe and Northern America were also more inclined to prosocial behaviors when being affected by a worsening financial situation due to the pandemic (relative to facing a better financial situation), corresponding to the previous line or argumentation that economic downturn might give rise to prosocial responding in some regions. It should be noted that different sample sizes per region may also account for these differences in results.

The importance of these findings lay in the fact that prosocial behavior impacts individual well-being and society as a whole. Prosocial behaviors can drive meaningful change on a societal level by contributing to positive collective outcomes, such as resilience, solidarity and social connectedness in communities ( Drury et al., 2009 ). Moreover, engaging in prosocial behavior might counteract adverse effects produced by the pandemic and related measures on the mental health and well-being of vulnerable groups. In the context of the COVID-19 pandemic, situations ranging from facing financial loss to being separated from loved ones pose an imminent threat to the mental health around the globe ( Brooks et al., 2020 ; Luo et al., 2020 ). Importantly, social isolation and perceived loneliness were found to be strongly associated with depression, anxiety, self-harm and reduced well-being during the first lockdowns in spring 2020 ( Gloster et al., 2020b ; Luo et al., 2020 ), conditions that affected large parts of the population ( Salari et al., 2020 ). Given the deleterious effects of the pandemic and associated measures on mental health, prosocial behavior has been suggested as a therapeutic target during the COVID-19 pandemic ( Holmes et al., 2020 ), due to the positive effect it may exhibit on the mental health of both the providers and the receivers of support and help, with first intervention proposals addressing the impact of acts of kindness on mental health being on the way ( Miles et al., 2021 ). Lastly, prosocial behavior has been discussed as a target for policy and intervention with regard to disease containment. Preliminary evidence demonstrates that prosocial emotions can be used as one path to instigate behavior change with studies showing that prosocial behavior is positively related to adhering to policy-relevant health behaviors including physical distancing, staying at home when sick, and adhering to hygiene recommendations ( Pfattheicher et al., 2020 ; Campos-Mercade et al., 2021 ). Consistent with the idea of collective cooperation, a study on preventive actions demonstrates that people exhibit greater intend to engage in preventive efforts, such as distancing, when public health messages are framed as a way to help or protect others rather than appeals focused on the individual benefit of such behavior ( Jordan et al., 2020 ).

Several limitations need to be acknowledged when interpreting the results of the current study: First, the cross-sectional study design did not allow for causal inferences nor for any accounts on the fluctuation of prosocial behavior across time. Second, we used a self-report measure of prosocial behavior, which might be prone to socially desirable reporting. While this is a common challenge in behavior research, anonymous survey administration could reduce the tendency of responding in a way that was viewed favorable by most societies. Additionally, using self-report measures was the only feasible way of assessing prosocial behavior in a large-scale, online survey. Third, despite the generally large sample size afforded by the wide reach of countries around the globe, in-country sample sizes varied substantially and were very small in some countries. While this problem was circumvented with the clustering of different countries into larger, geographically and culturally comparable regions, it needs to be considered that neither the regions were representative of included countries, nor the samples of each country.

The pandemic-induced lockdowns served the goal of reducing the transmission of the COVID-19 virus. It simultaneously created social isolation, with undesirable impact on the public mental health globally. There has been a pressing need for social cohesion and helping behavior(s), likely influencing individual well-being. The findings of the present study are reassuring that even when experiencing complications of a global crisis, prosocial behavior consistently occurred across the world. Such behavior was associated with better well-being across all regions. Future policy efforts should create ways of incorporating the social network of a person and address malleable psychological competencies, in order to facilitate prosocial behavior in the process of fighting the spread of the virus.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by Cyprus National Bioethics Committee. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

EH has conducted analysis. All authors contributed to the article and approved the submitted version.

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.

This work was supported by grants from the Swiss National Science Foundation awarded to ATG (PP00P1_ 163716/1 and PP00P1_190082). The funder provided support in the form of salaries for authors (EH and ATG) but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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• Introduction
• Conclusions
• Article Information

BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared).

The data points represent means. Error bars represent the SEs.

The data points indicate estimated marginal means for weekly loss of control eating, overeating, and binge eating, and the shaded areas represent the 95% CIs. Scores range from 0 (not at all) to 10 (very much), with higher scores indicating greater intensity of each behavior per week.

Trial Protocol

eTable 1. Descriptive Statistics and Reliabilities of Primary and Secondary Outcomes

eTable 2. Baseline Differences in Demographic Characteristics

eTable 3. Weekly Trends in Eating Disorder–Related Behaviors

eTable 4. Sensitivity Analyses for Primary and Secondary Outcomes

eTable 5. Moderator Analyses of Participant Characteristics on the Primary Outcome

eTable 6. Temporal Trajectories in Ecological Momentary Assessment Data

eTable 7. Negative Effects Attributed to the Web-Based Intervention

eTable 8. Dropout Over Time in the Control and Intervention Group

Data Sharing Statement

• Digital Interventions to Close the Treatment Gap for Binge Eating JAMA Network Open Invited Commentary May 16, 2024 Andrea K. Graham, PhD

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Pruessner L , Timm C , Barnow S , Rubel JA , Lalk C , Hartmann S. Effectiveness of a Web-Based Cognitive Behavioral Self-Help Intervention for Binge Eating Disorder : A Randomized Clinical Trial . JAMA Netw Open. 2024;7(5):e2411127. doi:10.1001/jamanetworkopen.2024.11127

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Effectiveness of a Web-Based Cognitive Behavioral Self-Help Intervention for Binge Eating Disorder : A Randomized Clinical Trial

• 1 Department of Psychology, Heidelberg University, Heidelberg, Germany
• 2 Department of Psychology, University of Osnabrück, Osnabrück, Germany
• Invited Commentary Digital Interventions to Close the Treatment Gap for Binge Eating Andrea K. Graham, PhD JAMA Network Open

Question   Does a web-based cognitive behavioral self-help intervention improve outcomes in patients with binge eating disorder (BED)?

Findings   In this randomized clinical trial involving 154 patients with BED, access to a web-based cognitive behavioral self-help intervention was superior to a waiting-list condition. The intervention significantly reduced the number of objective binge eating episodes compared with the control group.

Meaning   These findings highlight the value of web-based cognitive behavioral self-help interventions as a promising solution to support individuals with BED and address the substantial treatment gap in this population.

Importance   Binge eating disorder (BED) is one of the most frequent eating pathologies and imposes substantial emotional and physical distress, yet insufficient health care resources limit access to specialized treatment. Web-based self-help interventions emerge as a promising solution, offering more accessible care.

Objective   To examine the effectiveness of a web-based cognitive behavioral self-help intervention for individuals with BED.

Design, Setting, and Participants   This 2-arm, parallel-group randomized clinical trial conducted from January 15, 2021, to August 3, 2022, in Germany and other German-speaking countries enrolled patients aged 18 to 65 years who met the diagnostic criteria for BED (according to the Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]). Data analysis occurred between January 27 and September 4, 2023, following our statistical analysis plan.

Interventions   Participants were randomized to a web-based self-help intervention or a waiting-list control condition.

Main Outcomes and Measures   The primary outcome was a change in objective binge eating episodes from baseline to after treatment. Secondary outcomes included global eating pathology, clinical impairment, work capacity, well-being, comorbid psychopathology, self-esteem, and emotion regulation.

Results   A total of 1602 patients were screened, of whom 154 (mean [SD] age, 35.93 [10.59] years; 148 female [96.10%]) fulfilled the criteria for BED and were randomized (77 each to the intervention and control groups). The web-based intervention led to significant improvements in binge eating episodes (Cohen d , −0.79 [95% CI, −1.17 to −0.42]; P  < .001), global eating psychopathology (Cohen d , −0.71 [95% CI, −1.07 to −0.35]; P  < .001), weekly binge eating (Cohen d , −0.49 [95% CI, −0.74 to −0.24]; P  < .001), clinical impairment (Cohen d , −0.75 [95% CI, −1.13 to −0.37]; P  < .001), well-being (Cohen d , 0.38 [95% CI, 0.01 to 0.75]; P  = .047), depression (Cohen d , −0.49 [95% CI, −0.86 to −0.12]; P  = .01), anxiety (Cohen d , −0.37 [95% CI, −0.67 to −0.07]; P  = .02), self-esteem (Cohen d , 0.36 [95% CI, 0.13 to 0.59]; P  = .003), and emotion regulation (difficulties: Cohen d , −0.36 [95% CI, −0.65 to −0.07]; P  = .01 and repertoire: Cohen d , 0.52 [95% CI, 0.19 to 0.84]; P  = .003).

Conclusion and Relevance   In this randomized clinical trial of a web-based self-help intervention for patients with BED, the findings confirmed its effectiveness in reducing binge eating episodes and improving various mental health outcomes, highlighting a scalable solution to bridge the treatment gap for this condition.

Trial Registration   ClinicalTrials.gov Identifier: NCT04876183

Binge eating disorder (BED) is one of the most prevalent eating disorders, impacting 1.0% to 2.8% of the population over their lifetimes. 1 - 3 Defined by recurrent episodes of uncontrolled overeating, BED contributes to obesity, hypertension, and type 2 diabetes 4 - 7 and undermines quality of life, occupational performance, and social relationships. 4 , 8 , 9 Without intervention, BED progresses to a chronic condition 10 and may lead to premature death. 8 , 9 , 11

While cognitive behavioral therapy (CBT) has demonstrated its effectiveness as an evidence-based BED intervention, 12 , 13 treatment rates for this disorder are reduced compared with other psychiatric conditions, including anorexia nervosa and bulimia nervosa. 14 - 16 Various barriers prevent individuals from seeking face-to-face psychotherapy, including limited access, clinician unawareness, sociocultural stigma, and treatment costs. 17 As a result, there is a substantial treatment gap for BED.

The rapid evolution of technology provides new avenues for delivering interventions that can address this gap and make evidence-based BED treatments more accessible. 18 - 21 Web-based cognitive behavioral interventions have gained prominence due to their advantages in terms of availability, cost-effectiveness, ease of implementation, and reduced social stigma, thereby circumventing the barriers of conventional BED treatments. 18 , 22 - 24

Although preliminary studies indicate that digital interventions hold promise for addressing BED, there remains a critical need for rigorous scientific investigations under naturalistic conditions. 19 , 25 - 28 This randomized clinical trial aims to fill this gap by examining the effectiveness of a web-based cognitive behavioral self-help intervention for BED compared with a waiting-list control group in an ecologically valid setting. To amplify translational potential and broaden generalizability, we analyzed alterations in key eating disorder symptoms, clinical impairment, well-being, comorbid psychopathology, self-esteem, and emotion regulation.

Given the limited understanding of the outcomes of web-based BED treatments compared with interventions targeting subclinical eating psychopathology 24 , 29 or other mental disorders, 30 - 32 this study evaluated patients with full-threshold BED to enhance clinical applicability. Furthermore, few studies have investigated how these interventions impact participants’ everyday lives. 33 Therefore, our study used ecological momentary assessment (EMA) and weekly symptom monitoring to capture real-time changes in binge eating and its underlying mechanisms.

This randomized clinical trial was conducted from January 15, 2021, to August 3, 2022, to examine the effectiveness of a web-based cognitive behavioral self-help intervention for BED. 34 Participants were randomly assigned to an intervention group with direct access to the web-based treatment or a waiting-list control group. Assessments were completed at baseline (study entrance) and at 6 weeks (midtreatment) and 12 weeks (posttreatment) following baseline. This study followed the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline. All participants provided written informed consent, and the study was approved by the institutional review board at Heidelberg University. Upon completing all assessments, participants received a reimbursement of €30 (US $32.40). Data were stored anonymously following European Union regulations.

Nationwide recruitment of participants took place in Germany with additional provisions for the participation of German-speaking individuals from other European countries. Recruitment channels encompassed eating disorder treatment centers, a waiting list of individuals expressing interest in the web-based program, social media platforms, mailing lists, and self-help forums.

Participants completed a digital registration process to undergo eligibility screening, provide informed consent, and schedule a clinical interview, including the Eating Disorder Examination Interview 35 and the Diagnostic Interview for Psychological Disorders. 36 All diagnostic interviews were conducted by trained researchers; supervision was overseen by licensed clinical psychologists (L.P. and C.T.). Interrater reliability was determined during the initial interviews conducted by each interviewer to ensure the accuracy of the diagnostic decisions and consistent implementation of the interviews from the outset of the data collection. This assessment demonstrated significant agreement (with Cohen κ = 0.90 for 19 interviews).

Inclusion criteria encompassed (1) being aged 18 to 65 years, (2) owning a smartphone with internet access, (3) C1-level (advanced) proficiency in German, and (4) a diagnosis of BED based on the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition). 37 Exclusion criteria entailed (1) a body mass index below 18.5 (calculated as weight in kilograms divided by height in meters squared), (2) current psychotherapy or pharmacotherapy for eating disorders, (3) anorexia nervosa or bulimia nervosa, (4) bipolar disorder or psychotic disorders, (5) acute substance dependence, (6) current severe depressive episodes, and (7) acute suicidality. These exclusion criteria were chosen due to their potential to act as contraindications for web-based self-help interventions. 38 Other comorbidities were not excluded to mirror standard clinical practice.

After the baseline assessment, a 1:1 randomization ratio was executed by an independent researcher unaffiliated with the project using an automated and anonymized process. 39 To uphold allocation concealment, the participants’ identities and group assignments were unknown by the study personnel, including the individual who initiated the randomization. 40 An automated notification informed participants of their randomization results. Moreover, measures used to maintain blinding of the data analysis and clinical interviews, ensuring an unbiased approach throughout the study, are documented in the study protocol ( Supplement 1 ). 34

Participants in the intervention group received access to the 12-week web-based intervention (Selfapy). 34 , 41 - 43 This program is rooted in CBT and uses evidence-based exercises to elucidate risk factors and mechanisms of BED based on a diathesis-stress model. 34 , 41 Central to the program is a core curriculum of 6 mandatory modules focused on self-monitoring of binge eating, psychoeducation, and emotion regulation, complemented by 6 elective specialization areas tailored to participants’ preferences. Each module incorporated a blend of texts, videos, audio, and interactive exercises. 34 , 41 To ensure a personalized and engaging learning experience while maintaining consistent participant progression, the intervention used a sequential module-access strategy, with modules becoming accessible after completing the preceding ones. Email reminders were sent to participants who delayed starting the program to foster initial and sustained engagement, and an integrated messaging feature was provided to facilitate inquiries about the intervention content.

Participants assigned to the control group did not receive access to the web-based intervention during the study. They were notified that they would gain access after a 12-week waiting period. To mimic naturalistic conditions in which individuals seeking help for BED might explore various treatment options, all participants, regardless of group assignment, could seek other types of pharmaceutical and psychological treatments. This design choice also facilitated the ethical consideration of not withholding effective treatment. Importantly, we closely monitored the initiation of other health care services in both groups throughout the study 44 to assess their impact on the intervention’s effectiveness.

The primary outcome was a reduction in objective binge eating episodes from baseline to after treatment (using the Eating Disorder Examination Questionnaire). 45 Secondary outcome measures were global eating psychopathology (using the Eating Disorder Examination Questionnaire); weekly loss of control eating, overeating, and binge eating (using the Weekly Binges Questionnaire) 46 ; clinical impairment (using the Clinical Impairment Assessment Questionnaire scale) 47 ; well-being (using the World Health Organization Well-Being Index-5 questionnaire) 48 ; and work capacity (using the Institute for Medical Technology Assessment Productivity Cost Questionnaire). 49 Exploratory outcomes were depressive symptoms (using the Patient Health Questionnaire-9), 50 anxiety symptoms (using the Generalized Anxiety Disorder Scale-7), 51 self-esteem (using the Rosenberg Self-Esteem Scale), 52 and emotion regulation (using the Difficulties in Emotion Regulation Scale 53 and the Heidelberg Form for Emotion Regulation Strategies 54 ). Detailed descriptions of all scales and psychometric properties are provided in the study protocol ( Supplement 1 ) and eTable 1 in Supplement 2 .

Additional measures included an EMA protocol 55 on participants’ mobile devices for 5 days at baseline and after treatment. Furthermore, the study assessed measures of online intervention attitudes (using the Attitudes Toward Psychological Online Interventions scale), 56 treatment expectations (using the Patients’ Questionnaire on Therapy Expectation and Evaluation scale), 57 adverse effects (using the Negative Effects Questionnaire), 58 and health care service utilization (using the Client Sociodemographic and Service Receipt Inventory). 44

Data analysis occurred between January 27 and September 4, 2023, following our statistical analysis plan. Multilevel models with random intercepts, the fixed effects of time and treatment, and the interaction effect of time and treatment were conducted to examine whether there was a more substantial change in the intervention group compared with the control group in primary and secondary outcomes. These models were selected due to their capacity to address the hierarchical data structure, with observations nested within participants, and their flexibility in accommodating missing data. 59 We assessed the size of the main and interaction effects by estimating Cohen d based on Feingold’s recommendations. 60 To evaluate the clinical significance of our findings, a minimal clinically important difference (MCID) score was computed (MCID = SD b [√(1  − r )]), in which b indicates baseline and r represents the retest reliability of the primary outcome. This method aligns with other trials in the eating disorders field, 61 , 62 ensuring that the MCID reflects a change larger than the measurement error alone. Power analyses 63 based on an intraclass correlation of 0.40, a power of 0.80, and an α level of .05 indicated a required sample of at least 152 participants or more. Statistical analyses were performed using R, version 4.3.1 (R Project for Statistical Computing). A 2-sided P  < .05 was considered statistically significant.

As sensitivity analyses, we performed the last observation carried forward approach and the multiple imputations by chained equations technique to deal with missing data. 64 False discovery rate-adjusted P values were calculated using the Bonferroni-Holm correction. 65 Additionally, we explored the impact of potential moderators, such as treatment expectations, 57 online intervention attitudes, adherence, and health care service utilization. 44 Last, we assessed the percentage of individuals in the intervention group who encountered adverse effects. 58

Participant enrollment and study flow are depicted in the CONSORT flow diagram ( Figure 1 ). Of 1602 patients initially screened, 154 participants (mean [SD] age, 35.93 [10.59] years; 148 female [96.10%], 5 male [3.25%], and 1 nonbinary [0.65%]) fulfilled the criteria for BED and were ultimately randomized. Among the total participants, 77 were randomized to the web-based self-help intervention group and 77 to the waiting-list control condition. Table 1 depicts the baseline characteristics, which were comparable across groups (eTable 2 in Supplement 2 ).

Participants in the intervention group revealed a greater reduction in binge eating episodes compared with the control group. The estimated interaction effect between treatment and time, assessing differential changes from baseline to after treatment across the two groups, was −7.91 (95% CI, −11.68 to −4.15), with a Cohen d of −0.79 (95% CI, −1.17 to −0.42; P  < .001). Figure 2 illustrates the trajectory of the primary outcome in both groups. In the intervention group, the mean (SD) of binge eating episodes decreased from 14.79 (9.60) at baseline to 6.07 (6.71), marking a significant reduction (coefficient, −9.02 [95% CI, −11.31 to −6.72] and Cohen d , −1.00 [95% CI, −1.30 to −0.70]; P  < .001) that surpassed the clinically meaningful threshold (MCID ≥ 3.97 episodes; reliability = 0.84). 62 As expected, the mean (SD) number of binge eating episodes in the control group decreased from 15.01 (10.30) to 14.33 (17.58), indicating no significant change (coefficient, −1.21 [95% CI, −4.34 to 1.93] and Cohen d , −0.09 [95% CI, −0.33 to 0.15]; P  = .44).

Table 2 details the treatment effects across all outcomes. The intervention group exhibited significantly reduced global eating psychopathology compared with the control group (Cohen d , −0.71 [95% CI, −1.07 to −0.35]; P  < .001). Figure 3 further illustrates that the intervention group experienced significantly greater decreases in weekly loss of control eating (Cohen d , −0.56 [95% CI, −0.81 to −0.31]; P  < .001), weekly overeating (Cohen d , −0.44 [95% CI, −0.69 to −0.18]; P  < .001), and weekly binge eating (Cohen d , −0.49 [95% CI, −0.74 to −0.24]; P  < .001) than the control group (eTable 3 in Supplement 2 ).

Clinical impairment also revealed a notable decrease in the intervention group (Cohen d , −0.75 [95% CI, −1.13 to −0.37]; P  < .001) . Additionally, the intervention group experienced a significantly greater improvement in well-being compared with the control group (Cohen d , 0.38 [95% CI, 0.01-0.75]; P  = .047). However, there were no meaningful between-group differences regarding changes in work capacity (Cohen d , 0.05 [95% CI, −0.33 to 0.43]; P  = .80).

The intervention group revealed significantly more substantial decreases in depression (Cohen d , −0.49 [95% CI, −0.86 to −0.12]; P  = .01) and anxiety (Cohen d , −0.37 [95% CI, −0.67 to −0.07]; P  = .02) compared with the control group. Furthermore, improvements in self-esteem (Cohen d , 0.36 [95% CI, 0.13 to 0.59]; P  = .003) and emotion regulation abilities (difficulties: Cohen d , −0.36 [95% CI, −0.65 to −0.07]; P  = .01 and repertoire: Cohen d , 0.52 [95% CI, 0.19 to 0.84]; P  = .003) were found in the intervention group compared with the control group.

Sensitivity analyses for all primary and secondary confirmatory outcomes are summarized in eTable 4 in Supplement 2 , reaffirming the results obtained in our primary statistical models. These analyses consistently revealed significant reductions in binge eating episodes, improvements in eating psychopathology, and diminished clinical impairment. However, the impact on well-being did not consistently appear across different sensitivity analyses, indicating less robust effects on this metric. Modeling changes using a continuous time variable replicated the main findings for all outcomes.

Moderators, including baseline symptom severity, treatment expectations, online intervention attitudes, health care service utilization, and demographics, did not impact treatment effects. At baseline, 23 participants (14.94%) received in-person psychotherapy, aligning with typical treatment rates for BED. 66 Participants across both groups showed low current psychotropic medication use (10 participants [6.49%]). Throughout the study, an additional 13 participants (8.44%) in both groups sought in-person psychotherapy, and 2 (1.30%) initiated new psychotropic medication. Treatment-seeking intentions indicated that 26 participants (16.88%) were currently on a waiting list for in-person psychotherapy and aimed to use the web-based intervention to bridge this waiting time. None of these health care service–related variables differed between groups or acted as moderators (eTable 5 in Supplement 2 ).

Additional analyses of EMA data are reported in eTable 6 in Supplement 2 . These indicated that the intervention effects could also be demonstrated momentarily in participants’ everyday lives, revealed by reductions in binge eating, overeating, shape concerns, and weight concerns in the intervention group compared with the control group.

Findings among participants in the intervention group who provided complete data on the web-based program's adverse effects are detailed in eTable 7 in Supplement 2 . While these were overall minimal, the resurgence of distressing memories was recorded as a more frequent adverse effect in 20 of 64 patients (31.25%) at midtreatment and in 8 of 59 patients (13.56%) at posttreatment. These analyses provide a comparative perspective, indicating that negative effects were lower than those reported for face-to-face psychotherapy. 67

The dropout rate was 17.53%, with no significant difference between the intervention and control groups ( χ 2  = 1.62; P  = .20) (eTable 8 in Supplement 2 ), indicating comparable retention across study arms. The number of modules completed in the intervention was used as an indicator of treatment dose. Most participants in the intervention group (71 [92.22%]) began the intervention. The participants completed a mean (SD) of 7.65 (4.07) modules, including the 6 core modules and 2 optional secialization areas. Notably, a greater treatment dose, evidenced by completing more modules, was associated with more substantial reductions in eating psychopathology (β, −0.38 [95% CI, −0.63 to −0.12]; P  < .001). Additionally, a significant proportion of the control group (61 [79.22%]) enrolled in the program after the study had concluded, highlighting the continued need for the intervention.

Binge eating disorder is a frequent and debilitating disorder with considerable societal and personal burdens. 1 , 7 , 9 However, traditional treatments have faced constraints, such as limited access, stigma, and high cost, 17 , 18 underscoring the need to explore alternative intervention delivery methods. The findings of this randomized clinical trial present new possibilities for addressing this substantial public health challenge by building on the widespread adoption of digital technologies. 18 , 24 , 29

Notably, we found supportive evidence for the effectiveness of a 12-week, web-based cognitive behavioral self-help program for BED. The intervention group outperformed the control group in reducing binge eating episodes and several key outcomes, including global eating psychopathology and clinical impairment. The observed effect sizes mirrored or exceeded those reported in trials investigating other digital interventions for eating disorders 19 , 25 - 28 and in-person guided and unguided self-help interventions for BED. 68 - 70 Furthermore, the effect sizes were consistent with or even surpassed established findings from studies exploring the impact of conventional, in-person CBT interventions, 12 , 13 underlining the clinical relevance of the results. Moreover, by not limiting our inclusion criteria to individuals with comorbid obesity, 69 - 71 our research offers evidence for the broad applicability of web-based interventions in enhancing eating disorder–related outcomes across the BED spectrum.

In addition to the significant reductions in eating psychopathology, the intervention extended its benefits to diverse aspects of participants’ quality of life. Receiving access to the program was linked to improvements in well-being, depressive symptoms, anxiety, self-esteem, and emotion regulation. This expansion beyond eating psychopathology was further substantiated by weekly symptom monitoring and real-time data that were collected in everyday contexts, which revealed that participants in the intervention group experienced tangible improvements in day-to-day symptoms and quality of life.

Together, these findings showcase the potential of digital interventions to enhance participants’ everyday experiences by addressing core eating-related symptomatology and the emotional and psychological mechanisms frequently linked with BED. 72 , 73 To reflect naturalistic conditions, we permitted participants from both study groups to seek additional professional assistance, which may account for the minor improvements in global eating psychopathology and depressive symptoms observed within the control group. Notably, our analysis confirmed that the engagement in psychotherapy or pharmacotherapy following randomization was equally low across both groups and did not moderate the web-based intervention’s outcomes. The positive effects of the intervention further persisted when considering participants’ attitudes toward online interventions, treatment expectations, and demographic characteristics as moderators. Additionally, the effectiveness of self-guided web-based interventions depends on participant motivation, highlighting the importance of monitoring dropout and retention rates. 22 , 71 Our results indicate low dropout levels, underlining our findings’ robustness

Regarding the study’s limitations, it is crucial to acknowledge that our sample’s socioeconomic and demographic composition may reflect disparities in help-seeking behavior and digital literacy among socioeconomic groups. Our study highlights the necessity for targeted strategies to engage male and older populations, who are often underrepresented, in studies of web-based interventions for BED. 19 , 28 Moreover, while self-report measures are indispensable for assessing subjective experiences and complex behaviors, these methods are vulnerable to social desirability. 55 To increase the accuracy of self-reported information, our study complemented traditional questionnaires with real-time EMA and weekly reports. Future research may benefit from additional methodologies to minimize these biases, including double-blind designs and integrating objective measures where feasible. Additionally, the temporal scope of our study, focusing on postintervention changes, calls for future research to extend the assessment period, ideally incorporating a 12-month follow-up to capture the program’s long-term effects. Finally, while our findings posit the effectiveness of the web-based intervention compared with a waiting-list control condition, it is imperative that subsequent investigations include comparisons with alternative control conditions, including face-to-face CBT, 74 , 75 and evaluate the practical implementation of these interventions in health care settings.

This randomized clinical trial demonstrated that offering access to a web-based intervention significantly enhanced the daily lives of participants with BED. The provision of accessible and effective treatment options holds promise for improving the everyday experiences of patients with BED, as well as for diminishing its adverse health effects. Providing these programs to those in need of treatment can contribute to alleviating the burden that BED places on patients, their families, and society.

Accepted for Publication: March 7, 2024.

Published: May 16, 2024. doi:10.1001/jamanetworkopen.2024.11127

Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License . © 2024 Pruessner L et al. JAMA Network Open .

Corresponding Author: Luise Pruessner, MS, Department of Psychology, Heidelberg University, Hauptstr 47-51, 69117 Heidelberg, Germany ( [email protected] ).

Author Contributions: Ms Pruessner and Mr Hartmann had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Pruessner, Timm, Hartmann.

Drafting of the manuscript: Pruessner.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Pruessner, Hartmann.

Obtained funding: Pruessner, Timm, Barnow.

Administrative, technical, or material support: Pruessner, Timm.

Supervision: Pruessner, Barnow, Rubel.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by the European Regional Development Fund awarded to Selfapy. An independent evaluation of the web-based intervention for eating disorders was commissioned by Selfapy to the Department of Psychology at Heidelberg University (to the team of Dr Timm, Prof Barnow, Ms Pruessner, and Mr Hartmann) to ensure adherence to the highest scientific standards. The publication costs were funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) and Heidelberg University.

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 3 .

Additional Contributions: We express our appreciation to the following individuals for their significant contributions to participant recruitment and data collection, integral to their thesis work: Maria Brinkhof, BS; Katrin Fischer, MS; Leonie Hans, BS; Tanja Hauser, MS; Nina Helwig, MS; Luisa Jung, BS; Theresa Kloss, BS; Lena Komorowski, MS; Laura Kristalis, MS; Jana Reich, BS; Elena Rettweiler, BS; Marlene Sayer, MS; and Leoni Weintz, MS (all affiliated with Heidelberg University at the time of the study). No additional compensation was provided for their contributions beyond their customary remuneration.

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Adolescence

Effortful helping in teenagers at risk for psychopathy, new research shows adolescents with conduct problems are less willing to help..

Updated May 10, 2024 | Reviewed by Davia Sills

• What Changes During Adolescence?
• Find counselling to support kids and teens
• Adolescents with conduct problems often display antisocial behavior.
• People with conduct problems and high callous-unemotional traits are at risk of developing psychopathy.
• Research finds those at risk of developing psychopathy are especially unwilling to make an effort to help.
• The data highlight differences between individuals and new targets for research on behavioral interventions.

This post was written by Anne Gaule, Ph.D., and Essi Viding, Ph.D., with edits from Patricia Lockwood, Ph.D., and Jo Cutler, Ph.D.

Helping other people—be it your friend moving house or a colleague with their work—often requires effort. However, research has shown that helping others or engaging in what psychologists call "prosocial" behaviors has a range of positive impacts on our social relationships, our physical and mental well-being, and even our longevity.

This is as true when we are young as it is in adulthood. There is good evidence that when young people engage in prosocial behaviors, this helps them to form the building blocks they need to establish good relationships with others, which, in turn, can be protective against common mental health and behavior problems .

However, there is a group of young people that engage in antisocial behavior, have difficulty in their social relationships, and also appear to show worryingly low levels of prosocial behaviors. This pattern of behaviors in adolescence is termed "conduct problems." A new study explores how teenagers with conduct problems engage in effortful prosocial behaviors and highlights important factors we may need to consider when designing interventions to support this vulnerable group.

Individual differences matter

As outlined above, research indicates that young people with conduct problems show low levels of prosocial behaviors. However, just because someone behaves antisocially does not mean that they will never behave prosocially. Individual differences among adolescents with conduct problems (or psychological characteristics that define who we are and how we process information about the world) have not received a lot of attention when it comes to prosocial behavior. These differences matter—especially when we are trying to design effective interventions.

One source of individual differences that is thought to be very important when considering how conduct problems develop and are expressed is a person’s level of what researchers call callous-unemotional traits. These traits include having difficulty empathizing with other people, lacking remorse for poor behavior, and not placing importance on social relationships. Research has shown that adolescents with conduct problems who also show high levels of these traits have poorer treatment outcomes and are at a greater risk for continued antisocial behavior and psychopathy in adulthood.

Given the social and psychological benefits that prosocial behaviors can have during development, understanding how these traits influence prosocial behavior in young people with conduct problems could be hugely important.

The importance of effort

When we consider prosocial behaviors in young people with conduct problems and how these impact their social relationships, it is important to think about what people generally value when it comes to prosocial behavior. Of course, this probably varies from person to person, but as a general rule, it seems as though people care a lot about effort. For example, if a friend is moving house and you wish to support them, simply saying that you will help probably is not enough to contribute to the good relationship you have with that person—you have to actually show up and help with the packing!

Supporting this, a research study carried out in 2018 found that people often care more about the personal sacrifice involved in a prosocial action than about the social benefit produced by the action itself, as they take this sacrifice to be an indication of a person’s moral character.

Therefore, if we want to get a full picture of prosocial behaviors, we need to understand how willing people are to put in effort to help others. This is especially important in the context of social relationships and understanding how willingness to help others may vary between people, such as those with conduct problems. Surprisingly, however, this is rarely looked at in research studies—which often look at whether people donate money to help others.

Building our understanding of prosocial behavior in adolescents with conduct problems

In our recent study, published in the Journal of Child Psychology and Psychiatry , we tested a sample of 94 adolescent boys between the ages of 11 and 16 from mainstream and specialist provision schools in the UK. The teenagers played a game where they could squeeze a hand-held gripper to earn points towards gift vouchers—with rounds where they played for themselves and prosocial rounds where they played to earn vouchers for another boy at a different school.

On each round of the game, the boys had to first choose if they wanted to have a go at squeezing the gripper to get more points towards vouchers, or if they would rather rest. If they chose to exert effort for a higher number of points, they were then asked to squeeze the gripper with the required force in order to earn their points.

We found that boys who met our criteria for having conduct problems chose less prosocially than the boys without conduct problems. However, once choices had been made, there was a distinction within the group of boys with conduct problems related to levels of callous-unemotional traits. Boys with conduct problems who were also rated by their teachers as being high in callous-unemotional traits put in considerably less effort to help others (relative to for themselves) compared to both boys without conduct problems and boys with conduct problems with lower levels of these traits.

So, while boys with conduct problems overall were less likely to choose to help someone else, once the choice to be helpful was made, only those boys who had both conduct problems and callous unemotional traits made less effort to follow through.

Implications

Our findings underscore the importance of nuance when trying to understand social behavior. By looking at prosocial effort as well as prosocial choices, and by also accounting for differences among adolescents with conduct problems, we have demonstrated a number of things. First, it is not enough to just study prosocial choices; knowing about people’s willingness to make effort for others is also important if we want to fully understand adolescents’ social lives.

Furthermore, even among adolescents with conduct problems, there is substantial variation in prosociality. If we want to promote more prosocial behavior in adolescents who have behavioral problems, we need to understand the precise difficulties that they have, which may need to be targeted specifically in interventions. Our study clearly shows that we should not treat all the young people in this vulnerable group the same way and that some of them may need more help than others.

Gaule, A., Martin, P., Lockwood, P. L., Cutler, J., Apps, M., Roberts, R., Phillips, H., Brown, K., McCrory, E. J., & Viding, E. (2024). Reduced prosocial motivation and effort in adolescents with conduct problems and callous-unemotional traits. Journal of Child Psychology and Psychiatry . https://doi.org/10.1111/jcpp.13945

Johnson, S. G. B. (2020). Dimensions of Altruism: Do Evaluations of Prosocial Behavior Track Social Good or Personal Sacrifice? OSF. https://doi.org/10.31234/osf.io/r85jv

Memmott-Elison, M. K., & Toseeb, U. (2023). Prosocial behavior and psychopathology: An 11-year longitudinal study of inter- and intraindividual reciprocal relations across childhood and adolescence. Development and Psychopathology , 35 (4), 1982–1996. https://doi.org/10.1017/S0954579422000657

Post, S. G. (2005). Altruism, Happiness, and Health: It’s Good to Be Good. International Journal of Behavioral Medicine , 12 (2), 66–77.

Patricia Lockwood, Ph.D., is a Wellcome Trust/Royal Society Sir Henry Dale Fellow (Associate Professor) and Jacobs Foundation Research Fellow at the University of Birmingham, UK, where she leads the Social Decision Neuroscience Lab. Jo Cutler, Ph.D., is a Postdoctoral Research Fellow in the Social Decision Neuroscience Lab at the University of Birmingham, UK.

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Under stress, an observer is more likely to help the victim than to punish the perpetrator

While performing a bystander intervention task in a brain scanner, stressed participants had different patterns of neural activation than non-stressed participants, and were more likely to help the victim.

Being stressed while witnessing injustice may push your brain towards altruism, according to a study published on May 14 in the open-access journal PLOS Biology by Huagen Wang from Beijing Normal University, China, and colleagues.

It takes more cognitive effort to punish others than it does to help them. Studies show that when witnessing an act of injustice while stressed, people tend to behave selflessly, preferring to help the victim than to punish the offender. This aligns with theories proposing that distinct brain networks drive intuitive, fast decisions and deliberate, slow decisions, but precisely how a bystander's brain makes the trade-off between helping and punishing others in stressful situations is unclear.

To better understand the neural processes driving third-party intervention in the face of injustice, Wang and colleagues recruited 52 participants to complete a simulated third-party intervention task in an fMRI (functional magnetic resonance imaging) scanner, where they watched someone decide how to distribute an endowment of cash between themself and another character, who had to passively accept the proposal. The participant then decided whether to take money away from the first character, or give money to the second. Roughly half of these participants submerged their hands in ice water for three minutes right before starting the task to induce stress.

Acute stress affected decision-making in extremely unfair situations, where the participant witnessed someone keep the vast majority of the cash they were supposed to split with someone else. The researchers observed higher dorsolateral prefrontal cortex (DLPFC) activation -- a brain region typically linked to mentalizing and decision-making -- when stressed participants chose to punish an offender. Computational modeling revealed that acute stress reduces bias towards punishment, raising the likelihood that someone will help a victim instead.

The authors state that their findings suggest that punishing others requires more deliberation, cognitive control, and reliance on calculations than helping a victim. These results align with a growing body of evidence suggesting that stressed individuals tend to act more cooperatively and generously, perhaps because people devote more of their cognitive resources towards deciding how to help the victim, rather than punishing the offender.

The authors add, "Acute stress shifts third-party intervention from punishing the perpetrator to helping the victim."

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Story Source:

Materials provided by PLOS . Note: Content may be edited for style and length.

Journal Reference :

• Huagen Wang, Xiaoyan Wu, Jiahua Xu, Ruida Zhu, Sihui Zhang, Zhenhua Xu, Xiaoqin Mai, Shaozheng Qin, Chao Liu. Acute stress during witnessing injustice shifts third-party interventions from punishing the perpetrator to helping the victim . PLOS Biology , 2024; 22 (5): e3002195 DOI: 10.1371/journal.pbio.3002195

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Behavior Therapy First for Young Children with ADHD

At a glance.

Experts recommend behavior therapy as the first treatment for children under 6 years of age living with attention-deficit/hyperactivity disorder (ADHD). The most effective type is parent training in behavior management, where parents work with therapists to manage ADHD. Talk to your doctor about behavior management therapy.

Many children with severe symptoms of ADHD are diagnosed before 6 years of age. Young children with ADHD need the right treatment for ADHD. Experts recommend using behavior therapy first to help young children with ADHD.

ADHD is one of the most common enduring conditions of childhood and affects many children, including very young children. Those with more severe symptoms are often diagnosed earlier. Read about trends in diagnosis and medication treatment for ADHD.

Being easily distracted, impulsive, and highly active is normal for young children, but when the symptoms are severe and persistent, it can cause problems. Young children with ADHD are more likely than those without ADHD to have difficulties in early education programs or school, including problems with peer relationships, learning, and a higher risk of injuries.

My young child has been diagnosed with ADHD, now what?

For older children, the best treatment is often a combination of behavior therapy and medication. But for children under 6 years of age, experts recommend that ADHD be treated with behavior therapy first, before trying medication. Behavior therapy is the recommended treatment for ADHD in children under 6 years of age. The type of behavior therapy that is most effective for this age is parent training in behavior management , meaning that therapists work with parents and teach them the skills needed to help their child better manage their ADHD.

Need help? Get information and support from the National Resource Center on ADHD.

How can parent training in behavior management help my child?

Children who have ADHD act in ways that are often challenging for parents. Children may often forget things they are told, be overly active, and act before thinking. They might not be able to get positive attention the way that other children can; they tend to misbehave and might be punished more frequently than other children. Even if children with ADHD really try to follow rules, they might not be able to. This can have a negative impact on their self-image and cause them to give up trying or to act up more often.

A therapist skilled in behavior management can help parents understand how ADHD affects their child. Parent training in behavior management is used to help change problem behaviors by building parenting skills, improving the relationship between parents and their child with ADHD, and helping children manage their own behaviors.

Others (childcare providers, preschool teachers, and other caregivers) can also help to manage the behavior of preschoolers who have ADHD by becoming educated about the disorder and by being trained in behavioral techniques.

The recommended first choice

Behavior therapy first‎.

• Children under 6 are more likely than older children to experience side effects from ADHD medications, such as increased heart rate and blood pressure, trouble sleeping, loss of appetite, and a loss of energy.
• The long-term effects of ADHD medications on children under 6 are not known since ADHD medications have not been well-studied in young children.
• Behavior therapy works as well as medication in young children with ADHD in helping to manage symptoms. Studies have shown that families who receive training in behavior therapy notice improvements for several years after treatment.

Parent training in behavior management has evidence as an effective treatment. There are several programs that meet the criteria of the Agency for Healthcare Research and Quality for effective treatments.

What CDC is doing

CDC works to help families get the right care, at the right time.

• CDC is working with states and partner agencies to increase awareness as well as to identify best practices in support of behavior therapy for ADHD.
• CDC is using national surveys to understand how many children have ADHD and how they are treated.
• CDC is learning more about how children with ADHD are diagnosed.
• CDC funds the National Resource Center on ADHD to provide evidence-based information about ADHD to families and professionals.

What you can do

• Talk to your doctor about behavioral therapy first.
• Share information about behavioral therapy for ADHD with other families.

More information

• About ADHD | CDC
• Improving Access to Children's Mental Health Care | CDC
• CHADD - Improving the lives of people affected by ADHD
• American Academy of Pediatrics (AAP), Subcommittee on Children and Adolescents with Attention-Deficit/Hyperactivity Disorder. Clinical Practice Guideline for the Diagnosis, Evaluation, and Treatment of Attention-Deficit/Hyperactivity Disorder in Children and Adolescents. Available at: https://publications.aap.org/pediatrics/article/144/4/e20192528/81590/Clinical-Practice-Guideline-for-the-Diagnosis?autologincheck=redirected . Accessed on December 13, 2023
• U.S. Food & Drug Administration (FDA). Treating and dealing with ADHD. Available at: https://www.fda.gov/consumers/consumer-updates/dealing-adhd-what-you-need-know . Accessed on December 13, 2023

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Overthought This

Self-Compassion Helps People Achieve Weight Loss Goals, Research Shows

Posted: February 5, 2024 | Last updated: February 5, 2024

Findings from a study at Drexel University suggest that self-compassion can help people engage in healthier weight loss behavior by helping them become less demoralized by setbacks.

Losing weight is extremely difficult because high-calorie, delicious food is very accessible. Despite best intentions, it’s common to wind up overeating. These setbacks can be frustrating and demoralizing and often lead people to abandon their goals. 

Self-Compassion Helps People Bounce Back From Setbacks

A new study from the Center for Weight, Eating and Lifestyle Sciences ( WELL Center ) in Drexel University’s College of Arts and Sciences explored whether practicing self-compassion – or treating oneself with the same care and kindness that people typically offer to their loved ones – helps people become more resilient to these overeating setbacks.

Self-Compassion Led to Better Self Control and Better Mood

Recently published in Appetite , researchers found that when study participants had more self-compassionate responses to their lapse, they reported better mood and self-control over their eating and exercise behavior in the hours following the lapse. 

The findings suggest that self-compassion can help people engage in healthier weight loss behavior by helping them become less demoralized by setbacks.  

People Worry Self-Compassion Will Lead To Inadequacy

“Many people worry that self-compassion will cause complacency and lead them to settle for inadequacy, but this study is a great example of how self-compassion can help people be more successful in meeting their goals,” said Charlotte Hagerman, PhD, an assistant research professor in the College and lead author. 

“The road to achieving difficult goals—especially weight loss—is paved with setbacks. Practicing self-compassion helps people cope with self-defeating thoughts and feelings in response to setbacks, so that they are less debilitated by them. In turn, they can more quickly resume pursuing their goals.”

How the Study Was Conducted

Hagerman and colleagues collected data from a group of 140 participants who were trying to lose weight through a group-based lifestyle modification program. Participants responded to surveys on their smartphones multiple times a day to report whether they had experienced a dietary lapse – eating more than they intended, a food they didn’t intend, or at a time they didn’t intend – and the extent to which they were responding to that lapse with self-compassion. 

The researchers also asked about participants’ moods and how well they had been able to practice self-control over their eating and exercise behavior since the last survey they responded to.

It’s Not About a Lack of Willpower

Hagerman noted that weight loss and maintenance are extremely difficult, and people typically blame themselves for a lack of willpower.

“In reality, we live in a food environment that has set everyone up to fail. Practicing self-compassion rather than self-criticism is a key strategy for fostering resilience during the difficult process of weight loss,” said Hagerman.

How to Use Self-Compassion

Hagerman suggested, “The next time you feel the urge to criticize yourself for your eating behavior, instead try speaking to yourself with the kindness that you would speak to a friend or loved one.”

Examples of Using Compassion

For example, instead of a person saying to his or herself, “You have no willpower,” reframe it to a kinder – and truer – statement: “You’re trying your best in a world that makes it very difficult to lose weight.”  Hagerman added that this isn’t letting yourself “off the hook” but giving yourself grace to move forward in a highly challenging process.

Self-Compassion and Accountability Working Together

People don’t have to make themselves feel worse in order to make improvements.

“It can be easy for the message of self-compassion to get muddied, such that people practice total self-forgiveness and dismiss the goals they set for themselves,” said Hagerman. “But we’ve shown that self-compassion and accountability can work together.”

Future of This Research

The research team hopes this will lead to more effective interventions that teach people how to practice self-compassion in the moments that they experience setbacks, such as overeating or weight gain. 

They also hope to study the best strategies to teach people how to practice true self-compassion, reducing self-blame and criticism while also holding themselves accountable to their personal standards and goals.

Journaling for Mindfulness and Self Compassion

One way to grow your self-compassion is through journaling.

• Here’s how to start mindfulness journaling with prompts.

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Chapter 6: Helping

6 – Helping and Prosocial Behavior

Introduction, learning objectives:.

• Identify the factors that influence helping
• Describe diffusion of responsibility
• Describe pluralistic ignorance
• Identify personality characteristics that may influence altruistic behavior
• 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.

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?

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

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?

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

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 ).

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).

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 ].)

Reflection/Discussion

In 1964, a young female named Kitty Genovese was stabbed outside of her home in Queens, New York. While 38 of her neighbors heard her cries for help, they did not intervene or call the police. The death of Kitty Genovese, and the apparent level of apathy of her neighbors, was appalling to the public at the time. It was shocking to think that so many individuals could ignore the cries of someone in need. The following video reviews the original Kitty Genovese case, which initially sparked interest in the response (or lack thereof) of bystanders. Watch this video about Kitty Genovese

What would cause so many individuals to hear and witness such a horrific event and not respond? Some argued it was a lack of concern and a level of dehumanization within the environment. Latane and Darley reviewed the Kitty Genovese case and felt that there were other factors at play in the case. Specifically, they hypothesized that that the very fact that there were so many witnesses may have impacted the response of bystanders. Latane and Darley (1968) conducted various experiments in order to test this hypothesis. One of their studies, the Smoke Filled Room, examined the influence that having others present can have on our willingness to act or respond, even in an emergency. The video below provides a simulated example of the study.

Currently, many question the accuracy of the original Kitty Genovese case. Is it true that no one really intervened or attempted to call for help? The following video provides updated information regarding the case:

Kitty Genovese case video

What influences someone’s desire to help? Watch the following simulation on helping and respond to the questions included in the video:

Please answer the 3 multiple choice questions below.

What would you do? The Kitty Genovese case sparked debate about bystander apathy and the diffusion of responsibility. When someone hears about the case, or other similar examples, it is common to think that we would respond differently. But would you? A popular television show attempted to test the idea in real life to see how people truly respond in emergency situations. The following video clip shows some of these scenarios.

What would you do? Video

So, diffusion of responsibility indicates that many individuals may not respond or act in an emergency, especially if several others are present, because they anticipate that someone else will likely respond. But what about situations that are not emergencies? Does diffusion of responsibility apply to our willingness to respond to daily tasks, such as responding to an email? Somers (2013) applies the concept of apathy to mass emails in the following article. Can you think of other examples where this apathy may present in relation to a diffusion of responsibility?

Read this article on diffusion of responsibility.

Helping and Prosocial Behavior Resources

Poepsel, D. L. & Schroeder, D. A. (2020). Helping and prosocial behavior. In R. Biswas-Diener & E. Diener (Eds), Noba textbook series: Psychology. Champaign, IL: DEF publishers. Retrieved from Helping and Prosocial Behavior

Outside Resources

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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.

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.

Social Psychology Copyright © by Jennifer Croyle is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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