helping behaviour experiments

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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Helping Behavior

The good samaritan experiment.

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

This article is a part of the guide:

• Social Psychology Experiments
• Milgram Experiment
• Bobo Doll Experiment
• Stanford Prison Experiment
• Asch Experiment

Browse Full Outline

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

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

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

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

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

The experiment was constructed as follows:

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

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

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

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

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

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

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

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

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

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

Conclusions

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

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

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

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

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

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The Social Psychology Perspectives On Helping Others

Why do people help?

Intrinsic motivators, extrinsic motivators.

Social responsibility is a feeling that a person has an obligation to act in such a way that benefits the whole society. With this, a person has a duty to fulfil to maintain the balance in his environment. A person may do this actively, for example donating money to government NGO’s, or passively, such as ensuring that he commits no harm to others with his deeds.

Why do people not help?

2 responses, leave a reply cancel reply.

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Social relations and presence of others predict bystander intervention: Evidence from violent incidents captured on CCTV

Lasse suonperä liebst.

1 Department of Sociology, University of Copenhagen, Copenhagen Denmark

Richard Philpot

2 Department of Psychology, Fylde College, Lancaster University, Lancaster United Kingdom

Wim Bernasco

3 Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam The Netherlands

4 Department of Spatial Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam The Netherlands

Kasper Lykke Dausel

Peter ejbye‐ernst, mathias holst nicolaisen, marie rosenkrantz lindegaard.

Are individuals willing to intervene in public violence? Half a century of research on the “bystander effect” suggests that the more bystanders present at an emergency, the less likely each of them is to provide help. However, recent meta‐analytical evidence questions whether this effect generalizes to violent emergencies. Besides the number of bystanders present, an alternative line of research suggests that pre‐existing social relations between bystanders and conflict participants are important for explaining whether bystanders provide help. The current paper offers a rare comparison of both factors—social relations and the number of bystanders present—as predictors of bystander intervention in real‐life violent emergencies. We systematically observed the behavior of 764 bystanders across 81 violent incidents recorded by surveillance cameras in Copenhagen, Denmark. Bystanders were sampled with a case–control design, their behavior was observed and coded, and the probability of intervention was estimated with multilevel regression analyses. The results confirm our predicted association between social relations and intervention. However, rather than the expected reversed bystander effect, we found a classical bystander effect, as bystanders were less likely to intervene with increasing bystander presence. The effect of social relations on intervention was larger in magnitude than the effect of the number of bystanders. We assess these findings in light of recent discussions about the influence of group size and social relations in human helping. Further, we discuss the utility of video data for the assessment of real‐life bystander behavior.

1. INTRODUCTION

In the presence of others, bystanders are less likely to intervene when they witness someone in need of help (Darley & Latané, 1968 ). This bystander effect is one of the most well‐established findings of psychology (Manning, Levine, & Collins, 2007 ), and is typically interpreted as the product of a diffusion of responsibility, by which the liability to help dilutes across the multiple bystanders present (Latané & Nida, 1981 ). Paradoxically, although the bystander research field was prompted by the violent 1964‐murder of Kitty Genovese, and the inaction of the witnesses present (but see Manning et al., 2007 ), experimental research has rarely examined bystander behavior in the context of violent attacks (Cherry, 1995 ; Liebst, Heinskou, & Ejbye‐Ernst, 2018 ). This omission is a result of the practical and ethical infeasibility of exposing participants to objectively or subjectively dangerous study conditions (Osswald, Greitemeyer, Fischer, & Frey, 2010 ).

By restricting the analysis of bystander behavior to laboratory settings—in which neither the victims nor the bystanders are exposed to danger—the field risks isolating itself away from the phenomenon it initially set out to explain (Mortensen & Cialdini, 2010 ). Confirming this concern, in the exceptionally few experimental studies that have simulated attacks, it is found that bystanders are equally (Fischer, Greitemeyer, Pollozek, & Frey, 2006 ), or more (Harari, Harari, & White, 1985 ) likely to intervene in the presence of others than when alone. Further, a meta‐analysis of the experimental literature concludes that the bystander effect attenuates, or even reverses, in high‐danger study contexts where the victims, the bystanders, or both are exposed to dangerous situations (Fischer et al., 2011 ).

Taken together, when uncoupling the experimental evidence into the trivial (e.g., a pencil spill, a door that needs to be answered) and the more dangerous emergencies, the classical bystander effect does not seem to generalize across both domains. Rather, in study contexts where intervention may be dangerous for participants, the presence of additional bystanders may provide welcome physical support that promotes intervention (Fischer & Greitemeyer, 2013 ). In line with this interpretation, observational evidence from real‐life emergencies captured by surveillance cameras shows a positive relationship between group size and the number of interventions (Levine, Taylor, & Best, 2011 ). Further, a cross‐national video analysis finds that at least one bystander intervenes in 9 out of 10 public space conflicts, with the likelihood of victim help increasing with greater bystander presence (Philpot, Liebst, Levine, Bernasco, & Lindegaard, 2019 ). The overall finding that individuals do intervene when it really matters aligns with cross‐cultural anthropological accounts suggesting that third‐party intervention in everyday conflicts is most likely a human universal (Boehm, 2000 ; Brown, 1991 ; Eibl‐Eibesfeldt, 1989 ; Fry, 2000 ).

Shifting away from a situational emphasis on how additional individuals promote nonintervention, or the potential reversal of such effect, an alternative line of research stresses the importance of social relations in bystander helping (Levine & Manning, 2013 ; Philpot, 2017 ; Swann & Jetten, 2017 ). Specifically, those bystanders who are affiliated with a person in an emergency situation are significantly more likely to intervene than those who are socially distant. This association is found not only across experimental and observational studies with humans (Levine, Cassidy, Brazier, & Reicher, 2002 ; Lindegaard et al., 2017 ; Slater et al., 2013 ) but also in nonhuman primates (de Waal, 2015 ). These findings are consistent with an evolutionary theory of cooperation that expects helping behavior to occur disproportionately between genetically related or reciprocating individuals (de Waal & Preston, 2017 ; Axelrod & Hamilton, 1981 ; Vázquez, Gómez, Ordoñana, Swann, & Whitehouse, 2017 ).

Besides de‐escalatory helping, which exists as the main focus of bystander research (Fischer et al., 2011 ), group membership is also associated with escalatory interventions by which third‐parties fight on behalf of their fellow group members (Black, 1993 ; Levine, Lowe, Best, & Heim, 2012 ; Phillips & Cooney, 2005 ; Swann, Gómez, Huici, Morales, & Hixon, 2010 ). Social relations between bystanders and conflict participants thus seem to foster not only de‐escalatory but also escalatory interventions.

Despite the coexistence of these partially competing accounts, few attempts have examined the relative contributions of the number of bystanders and social relations in explaining bystander intervention. This may result from the methodological circumstance that “laboratory studies of bystander intervention usually use strangers as research confederates who help to stage the helping dilemma” (Banyard, 2015 , p. 30). Fischer et al. ( 2011 ) included bystander‐victim familiarity as a moderator in their meta‐analysis and found that the magnitude of the bystander effect was not influenced by whether or not the bystander knew the victim. Similarly, a regression analysis of in‐depth interviews reports a significant bystander effect in a model in which social relations are the main predictor of bystander intervention (Phillips & Cooney, 2005 ). By contrast, an examination of real‐life bystander intervention in the aftermath of commercial robberies (Lindegaard et al., 2017 ) reports a weak reversed bystander effect in a model where social relations between victims and bystanders, again, dominates the intervention outcome. While these studies assess the net effects of these two factors, Levine and Crowther ( 2008 ) analyze the interaction between group size and social group identification and find that this inter‐relationship could both increase or decrease the likelihood of bystander intervention.

These few studies examining the two factors simultaneously indicate that social relations outperform the number of bystanders as a predictor of intervention, while the evidence regarding the positive, vis‐à‐vis the negative, direction of the bystander effect remains mixed. However, these studies tend to rely on ecologically limited experimental paradigms and retrospective accounts (Baumeister, Vohs, & Funder, 2007 ; Swann & Jetten, 2017 ). An exception is the work of Lindegaard et al. ( 2017 ), which used video‐based naturalistic observations of bystanders in the aftermath of nonfatal commercial robberies. However, by analyzing the period after the offenders had already left the setting, their study provides limited information on whether bystanders intervene in violent emergencies where intervention may be dangerous—that is, the condition proposed to attenuate or reverse the bystander effect. Overall, there is a dearth of direct comparisons of number of bystanders and social relations as predictors of bystander intervention in violent emergencies. The present study, which utilizes video recordings of public violent assaults, is the first systematic observational study to address this gap.

Given the dangerous nature of the violent situations under study, both for the antagonists and for potential interveners, we predicted a reversed bystander effect, with a positive association between the number of bystanders and the likelihood of bystander intervention (Hypothesis 1). We further predicted that bystanders who have a social relation with a conflict party are more likely to intervene than strangers (Hypothesis 2). As the evidence supporting the reversed bystander effect is less uniform than the evidence in favor of social relations, we expected that the effect of social relations on intervention will be larger in magnitude than the effect of the number of bystanders (Hypothesis 3). These hypotheses align with the majority of bystander research that considers intervention as unambiguously prosocial (i.e., helping behavior), and should therefore apply to de‐escalatory interventions. Whether these propositions also fit escalatory interventions, where bystanders become conflict participants, is an open question that we also explore in the empirical analysis.

We control for other factors that may be related to the intervention likelihood, including the bystander's gender (Cross, Copping, & Campbell, 2011 ; Eagly, 2009 ), whether the bystander is a member of the public or is serving an occupational role (e.g., bouncer, Hobbs, 2003 ), whether the event takes place in a nighttime drinking setting (Levine et al., 2012 ; Reynald, 2011 ), and two additional measures that may affect the bystander's intervention opportunity: the density of the situation (Macintyre & Homel, 1997 ), and the spatial proximity of the bystander to the conflict participants (Macintyre & Homel, 1997 ).

2. DATA AND METHODS

The data consists of 81 surveillance camera recordings of police‐reported public violent assaults in central Copenhagen between 2010 and 2012 (replication data and a Stata script are available as Supporting Information at osf.io/r25wu). 1 The clips were a subset of a wider sample ( N  = 164), 2 and were selected if they conformed to the following three criteria. Each clip captured an event of physical violence, with or without intervening bystanders. The clip had a quality (e.g., brightness and resolution) that rendered it possible to conduct a systematic behavioral coding. Each clip captured the duration of the situation with none, or only negligible, breaks in the coverage (see Nassauer & Legewie, 2012 ).

2.2. Coding procedure

The coding began by identifying the conflicting parties, in most cases, the two individuals between whom the situation initially manifested itself as a conflict. This encounter was identified from displays of direct physical violence or from nonverbal cues of anger and aggression (e.g., emphasizing gestures, forward body inclination, see Dael, Mortillaro, & Scherer, 2012 ). All individuals entering the ongoing conflict were defined as intervening bystanders.

With the use of a detailed observation codebook, four trained student assistants coded the bystander intervention behavior (Table A1 in the Appendix) and situational properties (Table A2 in the Appendix) of each clip. This codebook was compiled from existing variable definitions in the literature (e.g., “de‐escalatory” and “escalatory” intervention types, see Levine et al., 2011 ) and specified through in‐depth qualitative observations of a subsample of videos (see Eibl‐Eibesfeldt, 1989 ; Jones et al., 2016 ).

In addition to the visual information obtained from the video recordings, each clip was also coupled with a police case file that provided descriptive accounts of the event. Pre‐existing social relationships were by default inferred from nonverbal social behavioral cues observed in the footage (see Murphy, 2016 ). These cues included interactional displays of collective behavior‐in‐concert, such as moving in synchrony, shared focus and attention, and bodily proximity (Afifi & Johnson, 2005 ; Ge, Collins, & Ruback, 2012 ; Goffman, 1971 ). In ambiguous cases, coders validated these video‐based group assessments against the police case file descriptions.

2.3. Interrater reliability

To test the reliability of the variables included in the final analysis, we selected 20 (29%) of the video contexts and 35 (15%) of the intervening bystanders for double coding. All variables included in the analyses reached a Krippendorff's α value of ≥0.80, recommended by Krippendorff ( 2004 ) as the cutoff point for reliable interrater agreement (for the Krippendorff values of all coded variables see Tables A1 and A2 in the Appendix). Disagreements between the coders were resolved through discussion Before analysis.

2.4. Case–control sampling

Because the incidents involved many more nonintervening than intervening bystanders and because the behavioral coding is very time‐consuming, we applied a case–control approach (Keogh & Cox, 2014 ). Here, we randomly selected a sample of nonintervening “controls,” who were situated in the same time and place as the intervening “cases,” but without displaying the intervention outcome of interest (Grimes & Schulz, 2005 ). For sufficient statistical power, it is recommended to sample at least two, but no more than four, controls per case (Lewallen & Courtright, 1998 ). With 510 nonintervening bystanders and 215 intervening bystanders included in the study, our control‐to‐case ratio is 2.4:1 and thus within these recommended thresholds.

2.5. Estimation

To account for the hierarchical structure of our data, with bystanders nested into video contexts, data was estimated with two‐level regression models with a random intercept (Hox, Moerbeek, & van de Schoot, 2017 ). All estimations were calculated with Stata 14's “gllamm” module using the adaptive quadrature estimation technique (Rabe‐Hesketh, Skrondal, & Pickles, 2005 ). The data showed an average of nine individuals nested across the 81 contexts, offering a sufficient sample size to obtain unbiased fixed‐effect point estimates for most multilevel model specifications (McNeish & Stapleton, 2016 ).

2.6. Sampling weights

To make the randomly selected controls representative of the actual number of nonintervening bystanders in each context, data was modeled using sampling weights (Lohr, 2010 ). All interveners were assigned a weight of 1. Controls were assigned a weight equal to the total number of noninterveners per context divided by the number of selected controls. In the relatively few contexts where the number of selected controls exceeded the number of noninterveners, the controls were assigned a weight of 1. Before analysis, the weights were scaled to suit multilevel modeling (Carle, 2009 ).

2.7. Robustness tests

In addition to confirmatory tests of the three hypotheses and an exploratory comparison between escalatory and de‐escalatory intervention, we conducted sensitivity analyses to assess the robustness of our results against other reasonable data and model specifications (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016 ). These analyses included estimating combinations of independent variables using two alternative sampling weight scalings (Carle, 2009 ), and also the inclusion of the number of bystanders as a quadratic term, given that earlier research suggests that the negative association between number of bystanders and intervention diminishes curvilinearly with increasing numbers (Latané, 1981 ).

2.8. Measures

2.8.1. dependent variables.

We defined bystander intervention as a binary variable, distinguishing bystanders who intervene into the conflict (with either escalatory or de‐escalatory acts) from bystanders that do not intervene. Decomposed bystander intervention was measured as a multinomial variable, distinguishing four possible bystanders based on their actions: nonintervention, only de‐escalatory acts, only escalatory acts, and a mix of de‐escalatory and escalatory acts. De‐escalatory acts included making open‐handed gestures, nonforceful touching, blocking contact between parties, holding a person back, hauling, and pushing the antagonists apart. Escalatory acts included pointing and threatening gestures, throwing a person, pushing, shoving, hitting, kicking, violence against a person on the ground, and weapon use (see Table A1 in the Appendix). Table ​ Table1 1 presents descriptive statistics of the dependent, independent, and control variables measured at the individual level. At the context‐level, at least one bystander intervened in 85.0% of the 81 videos. In total, there were 217 intervening bystanders, with an average of 2.7 interveners per situation.

Descriptive statistics of unweighted variables

2.8.2. Independent variables

The number of bystanders was a count of the individuals present in the emergency. This context‐level predictor was standardized by subtracting the mean and dividing by two standard deviations as to make it comparable to the effect sizes obtained from the binary predictors (see Gelman, 2008 ). The bystander's social relation was measured with a binary variable, distinguishing bystanders who have a social relationship to an individual involved in the conflict from bystanders who do not know any of the conflict parties.

2.8.3. Control variables

To control for omitted‐variable bias and based on findings of prior studies, we included five control variables. The bystander's gender was coded as male or female. This variable was included because of evidence showing that men tend to act more “heroically and chivalrously” in their helping behavior than women (Eagly, 2009 ; Taylor et al., 2000 ). Nighttime drinking settings were defined as situations occurring in proximity to a bar/nightclub or during the weekend nights. This control variable was included as evidence shows that bystander involvement is a pervasive aspect of these settings (Levine et al., 2012 ; Parks, Osgood, Felson, Wells, & Graham, 2013 ).

Further, given that most of our incidents occur in drinking settings, it is plausible that the intervention likelihood is shaped by whether the bystander is performing an occupational role, for example, as a bar staff or bouncer (Hobbs, 2003 ; Sampson, Eck, & Dunham, 2010 ). The occupational role of bystanders was captured with a binary variable, distinguishing bystanders who were at work from those who were not. Because physical proximity between individuals may facilitate helping behavior (Fujisawa, Kutsukake, & Hasegawa, 2006 ), we included a measurement of spatial proximity that distinguished whether the bystander was within a 2‐m radius from where the conflict initiated.

Finally, as levels of crowding may be associated with antisocial outcomes at public venues (Macintyre & Homel, 1997 ), we included people density as a control, distinguishing high density and low density situations. Density was assessed by whether it was possible to walk in a straight line across the setting without bumping into others present (low density) or not (high density).

Figure ​ Figure1 1 graphically shows the odds ratio estimates and associated confidence intervals of two multilevel binomial logistic regression models comparing bystander intervention with nonintervention. Full details of both models are presented in Table A3 in the Appendix. Both the key variables and control variables are listed on the vertical axis, while the effect sizes (odds ratios) are on the horizontal axis. The estimated odds ratios of the models are printed as dots and diamonds, respectively. The 95% percent confidence intervals are presented as horizontal lines around the estimates. The vertical line indicates an odds ratio of 1, reflecting the absence of a statistical association.

Multilevel binomial logistic regression estimates of bystander intervention. Complete results reported in Table ​ TableA3 A3 (Appendix)

The first model (estimates indicated in black with dots) includes only the two key variables, that is social relation and number of bystanders present. Contrary to the predicted reversed bystander effect, but in line with the classical bystander effect, we found that the number of bystanders is negatively associated with the likelihood of intervention. The effect size of this standardized variable (OR = 0.28) is medium‐large, as evaluated with Rosenthal's ( 1996 ) odds ratio effects size categories. Confirming our expectation, having a social relationship tie to a conflict party is positively associated with intervention. Compared to a stranger, the odds of intervening are more than 20 times larger for a bystander with a social relation to a conflict party. Even if assessed conservatively from the lower band of the confidence interval (95% CI = [9.98, 42.17]), the estimated odds ratio is very large.

In the second model (estimates shown in gray with diamonds) the five control variables are included to account for confounding relations with the key variables. Confounding is almost negligible, as the estimates of the two key variables are very similar to those in the first model (0.24 and 18.17, respectively). With respect to the control variables, only the bystander's gender is significantly related to intervention, with men's odds of intervention being 3.6 times larger than that of women.

Finally, a test of the effect size difference between the two key variables is statistically significant in both the first model ( χ 2 (a) = 85.52, p  < .001) and in the second model including control variables ( χ 2 (a) = 45.99, p  < .001). This confirms the third hypothesis, which states that the social relation predictor is more strongly associated with intervention than the number of bystanders predictor.

To further explore whether the associations of intervention with bystander numbers and social relations generalize across de‐escalatory and escalatory intervention types, we decomposed the intervening bystanders into three groups: those who displayed only de‐escalatory interventions, those who displayed only escalatory interventions, and those who displayed both de‐escalatory and escalatory interventions (the mixed group). We estimated two multilevel multinomial logistic regression models to distinguish effects of the key and control variables across these three groups and the nonintervention reference category. Details of both models are presented in Table A4 in the Appendix. To limit the amount of information displayed, Figure ​ Figure2 2 includes only the results of the model that includes both the key variables and the controls. Further, the variable that measured whether the bystander was acting in a professional role (“bystander at work”) is excluded because it completely separates the escalatory intervention from nonintervention (i.e., no bystanders at work intervened in an escalatory manner), a phenomenon that renders it impossible to estimate the effect of the predictor in a logistic model.

Multilevel multinomial logistic regression estimates of effects of key and control variables on decomposed bystander intervention. No intervention vs. de‐escalatory, escalatory, and mixed interventions. Complete results reported in Table ​ TableA4 A4 (Appendix)

From Figure ​ Figure2 2 it can be seen that increasing numbers of bystanders are statistically associated with lower odds of a de‐escalatory intervention, while escalatory, and mixed intervention outcomes are not statistically related to the outcome. Additional tests demonstrate that the effect size difference between de‐escalatory intervention (0.19) and escalatory intervention (0.68) is significant ( χ 2 (a) = 11.63, p  < .01) but not the differences involving mixed intervention (for full statistics, see the Supporting Information Material at osf.io/r25wu). Next, social relations have a positive and statistically significant effect on the odds of all three intervention types. The difference between the estimates of the de‐escalatory and the mixed intervention types is significant ( χ 2 (a) = 8.17, p  < .01) but not the differences involving escalatory intervention. Similar to the confirmatory analysis, gender is the only control variable significantly related to intervention. Men are more likely than women to display de‐escalatory, escalatory, and mixed interventions. These effects sizes do not significantly differ between the three intervention types.

We conducted a number of sensitivity tests to assess the robustness of our findings against alternative reasonable data and model specifications. These include an alternative scaling method for our sampling weights, and a curvilinear effect of number of bystanders. In Figures ​ Figures1 1 and ​ and2 2 and the corresponding Tables A3 and A4 in Appendix A , we used scaling method A as described by Carle ( 2009 ). Following Carle's recommendation, we also used scaling method B to verify that our findings did not depend on the scaling method selected. This proved to be the case, given that all estimates barely differed across the scaling methods. These results are available in the online Supporting Information Material at osf.io/r25wu.

Finally, given prior suggestions of a negative curvilinear association between number of bystanders and intervention (Latané, 1981 ), we estimated the four models shown in Tables A3 and A4 again, but with an added squared number of bystanders term. In support of this suggestion, the results demonstrate that for undifferentiated intervention and for de‐escalatory intervention, the negative effect of each additional bystander becomes significantly weaker (less negative) as the number of bystanders increases. For example, going from two to three bystanders reduces the likelihood of intervention more than going from 12 to 13 bystanders. These results are also available in the online Supporting Information Material.

4. DISCUSSION

Do people intervene into the danger of others at personal risk to themselves? The social sciences have a long tradition of stressing that third‐party individuals are indifferent to the plight of others (Cohen, 2001 ; Manning et al., 2007 ; Milgram, 1970 ). A particularly influential account is offered by the bystander research field, which stipulates that people rarely intervene to help, because of the collective apathy generated by being together with others. In the present study, relying on naturally occurring data, we contrasted the number of bystanders present against an alternative explanation of bystander involvement that puts social relations between bystanders and conflict participants front stage. Our confirmatory analysis provided no evidence for the reversed bystander effect (Hypothesis 1). Rather, we found that additional bystanders make individual intervention less likely, as expected under the classical bystander effect hypothesis. Further, we found compelling evidence that the bystanders' social relations with conflict participants are associated with bystander intervention (Hypothesis 2), and that the effect size is larger in magnitude than that of the number of bystanders predictor (Hypothesis 3).

Further, our subsequent exploratory analysis of decomposed bystander intervention suggests that the negative effect of bystander numbers mainly applies to de‐escalatory interventions, while social relations with conflict participants are highly predictive of all intervention types—whether de‐escalatory, escalatory, or mixed. Finally, the sensitivity analysis indicates that the negative effect of the number of bystanders on de‐escalatory intervention may diminish with increasing numbers of bystanders (i.e., a decreasing marginal effect), as suggested in earlier bystander research (Latané, 1981 ).

The bystander research field has for decades focused on people presence as the chief predictor of intervention behavior—initially as an explanation of nonintervention (Latané & Darley, 1970 ), and more recently, in dangerous contexts, as a facilitator of intervention (Fischer et al., 2011 ). Here, with the largest data set of video captured real‐life dangerous conflicts, we do not find evidence of a reversed bystander effect, but instead, a classical bystander effect. This is unexpected, given the recent paradigmatic shift toward an emergent consensus that additional bystanders offer physical support making intervention more likely, in particular in situations where nonintervention is dangerous for victims and intervention may be dangerous for interveners (Fischer & Greitemeyer, 2013 ; Fischer et al., 2011 ; Levine et al., 2011 ).

The reported negative association between bystander numbers and intervention may be received as evidence that bystanders become increasingly apathetic toward the needs of others when situated in more populated contexts (Latané & Darley, 1970 ). However, we also consider an alternative interpretation, not of collective apathy, but of helping saturation. Unlike the scarcely populated bystander experimental settings, public spaces often contain numerous individuals (with the current study finding an average of 18 bystanders per context), thus offering far more potential help‐givers than required to manage a typical conflict. This relatively fixed upper bound of required help‐givers has been shown to saturate at around three de‐escalatory bystanders (Levine et al., 2011 ). As such, additional bystanders beyond this point may be surplus to requirements and thus unlikely to intervene (see also Bloch, Liebst, Poder, Christensen, & Heinskou, 2018 ). The helping saturation hypothesis is empirically testable with CCTV footage because complete sequences of behavior of all participants are recorded second‐by‐second. By time stamping each recorded behavior, future investigations could statistically model the actions of bystanders as conditional on the previous behavior of other bystanders, and thus test the helping saturation hypothesis. 3

The very strong association between social relations and intervention adds to the accumulating body of evidence showing that group membership strongly predicts bystander helping (Levine, Cassidy, & Jentzsch, 2010 ; Lindegaard et al., 2017 ; Phillips & Cooney, 2005 ; Slater et al., 2013 ). Beyond peacekeeping, social relations may also induce escalatory aggressive interventions. Here, the intervener acts not as a mediator but as a partisan who fights on behalf of friends or group members (Black, 1993 ; Phillips & Cooney, 2005 ; Swann et al., 2010 ). Given the accumulating evidence supporting social relation as a key predictor of intervention behavior, it is unfortunate that helping research, and the social sciences more broadly, continue to emphasize the “power of situation,” at the expense of exploring further the role of social relations (Lefevor, Fowers, Ahn, Lang, & Cohen, 2017 ; Smith, 2015 ; Swann & Jetten, 2017 ). The current intervention study, which compares the effect of situational bystander presence to the effect of social relationships, finds the latter predictor many‐fold larger in magnitude. As such, people presence matters; in part, as a count in number, but more so as a consideration of the social ties existing between those present.

In addition to these two main predictors, we also included a number of control variables. Male bystanders were found to have a higher likelihood of intervention than females (across all intervention subtypes and model specifications). This is in line with evidence suggesting that although women are generally more helpful than men, males tend to be more strength‐intensive in their helping strategies (Becker & Eagly, 2004 ; Eagly, 2009 ), and as such, better positioned to engage in physical street violence interventions. Furthermore, occupational role (e.g., as a bouncer) was found to be a perfect predictor of the escalatory outcome category, with zero cases of bystander‐workers intervening in a purely escalatory manner (see Table S3 in the Supporting Information Material). This finding suggests that professional “place managers” are less prone to use excessive force than indicated in prior research (e.g., Roberts, 2009 ; Sampson et al., 2010 ).

In utilizing naturally occurring data, the current work contributes to the scholarly understanding of actual bystander behavior as situated in emergencies where intervention may be dangerous. This was rendered possible by the sampling of police‐reported events, all of which contained actual physical assaults. The current sample satisfies the call for research assessing bystander behavior in emergencies where intervention entails danger for the intervening person (Fischer et al., 2006 ), which is difficult to simulate ethically in the lab.

The reliance on police‐reported data also incurs several limitations. As police‐reported data are skewed toward more severely violent conflicts (Lindegaard & Bernasco, 2018 ; Tarling & Morris, 2010 ), our data does not capture the more mundane emergencies and nonviolent confrontations, commonplace in public settings (Copes, Hochstetler, & Forsyth, 2013 ; Philpot, Liebst, Møller, Lindegaard, & Levine, 2019 ). Furthermore, although bystander intervention was predominately de‐escalatory in our data, it is likely that the current sample under‐represents the proportion of de‐escalatory acts, while over‐representing the escalatory acts, in the intervention outcome. Specifically, while escalatory bystander interventions may exacerbate the conflict and make it of greater interest to the police, other conflicts successfully de‐escalated by bystanders, before they could become severe, are likely to be absent from our sample (Levine et al., 2012 ; Philpot, Liebst, Møller et al., 2019 ). As such, one should be wary of generalizing the current findings to bystander intervention occurring outside of high‐danger, police‐reported assaults (see Berk, 1983 ). Where possible, future research should prioritize random probability sampling of emergency incidents, violent, and mundane alike (Lindegaard & Bernasco, 2018 ; Philpot, Liebst, Møller et al., 2019 ).

A limitation of video clips captured by surveillance cameras is that there is no guarantee that violent conflicts can be observed in their entirety. In particular, because usually the cameras are fixed in space they cannot completely cover violent events that start in one place (e.g., inside a bar or club, or around the corner) and continue in front of the camera, or that they start in front of the camera and move out of sight. As a result, when interpreting findings it should be acknowledged that sometimes individuals who are bystanders in the recorded footage could have been antagonists in a phase of the conflict that took place outside the view of the camera. Thus, we should emphasize that the roles of “bystander” and “antagonist” remain situationally defined. To address this issue, and other issues of incomplete coverage, we suggest that future researchers try to triangulate observational CCTV data with information from other sources, including personal accounts of the individuals who were present during the incidents and, if available, police records (Philpot, Liebst, Møller et al., 2019 ).

As a final limitation, the very large effect size of group relations may, in part, be inflated because the coders (subconsciously, against their instructions) inferred the bystanders' relationship ties from whether or not the bystander intervened. In the current study, however, coders had detailed police case files accompanying each video, which were consulted to settle ambiguous video‐based assessments of group membership. It is important to note that there were few discrepancies during this qualitative validation. Adding to this, the reported association between group relations and intervention is what one may expect, given that all prior studies (to our best knowledge) testing this association report a positive effect, typically of substantial magnitude. However, future bystander research should, ideally, consider conducting formal interrater validity tests (in addition to standard interrater reliability tests) in which video‐based assessments are compared against ratings where group membership is definitively known (see Afifi & Johnson, 2005 ).

Cialdini ( 1980 ) describes a “full cycle” psychology, by which experimentation should be prompted by the naturalistic observation of social phenomena (e.g., the murder of Kitty Genovese), and, in turn, validated through systematic real‐world observation. The bystander research field, still largely contained in experimental work, is yet to fully confirm the ecological validity of its setup and findings. A case in point is that bystander studies typically compare rates of intervention when the bystander is alone versus when in the presence of a few others. The prevalence of numerous bystanders in public spaces suggests, however, that solitary conditions—similar to the simulation of nondangerous emergencies in the presence of strangers only—are over‐studied artifacts of the laboratory. With real‐life video data, we gain a greater understanding of how bystanders actually behave when together in numbers. This allows a reconsideration of whether nonintervention by individuals in populated settings reflects bystander apathy, or alternatively, bystander surplus. In taking such steps, the field may satisfy the final turn in Cialdini's ( 1980 ) cycle, and in doing so, recalibrate the “external invalidity” (Mook, 1983 ) of the experimental bystander paradigm toward a higher ecological validity.

Third‐party conflict intervention is a probable human universal. Our work shows that this needs to be understood together with another universal, noted by Brown ( 1991 ): in‐group favoritism. This bias toward one's own may promote de‐escalatory helping toward familiar individuals, as shown in the current study. However, the boundaries of “us” and “them” may also be an obstacle for the provision of assistance to strangers (Bloom, 2017 ), and may promote pro‐group partisan fighting on behalf of those known (Swann et al., 2010 ). We suggest that research gravitate away from chiefly using bystander counts to explain nonintervention. Rather, in our view, both the event and the non‐event of bystander involvement, as well as its helpful and harmful consequences, calls for an appreciation of the group processes existing between those present.

ACKNOWLEDGMENTS

This study was supported by the Danish Council for Independent Research (DFF‐6109‐00210) and the Velux Foundation. The funders had no role in the design of the study, data collection and analysis, decision to publish, or preparation of the manuscript. We additionally thank Camilla Bank Friis and Anne Laura Engmann Juul for their contributions to the coding of CCTV footage.

APPENDIX A. 

Summary of bystander intervention codes used to construct the outcome variables

Note: The above codes were used to construct the binary intervention outcome (i.e., any intervention or none), as well as the bystander intervention outcome decomposed into four outcomes (i.e., de‐escalatory, escalatory, mixed, and none). The Krippendorff's α 's of the de‐escalatory and escalatory intervention codes are .92 and .82, respectively. A mixed outcome is coded for bystanders displaying both escalatory and de‐escalatory interventions.

Summary of independent variable definitions and related Krippendorff's α 's

Multilevel binomial logistic regression estimates of bystander intervention

Abbreviations: CI, confidence interval; OR, odds ratio; *** p  < .001 ** p  < .01 * p  < .05.

Multilevel multinomial logistic regression estimates of decomposed bystander intervention

Abbreviations: CI, confidence interval; OR, odds ratio. *** p  < .001, ** p  < .01, * p  < .05.

Liebst LS, Philpot R, Bernasco W, et al. Social relations and presence of others predict bystander intervention: Evidence from violent incidents captured on CCTV . Aggressive Behavior . 2019; 45 :598–609. 10.1002/ab.21853 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

1 The study was approved by the Danish Data Protection Agency (reference 2015‐57‐0125‐0026).

2 Note that part of this video material is analyzed for another study purpose in Liebst et al. ( 2018 ).

3 We owe this observation to an anonymous reviewer of this journal.

• Afifi, W. A. , & Johnson, M. L. (2005). The nature and function of tie‐signs In Manusov V. L. (Ed.), The sourcebook of nonverbal measures: Going beyond words (pp. 189–198). Mahwah, NJ: Psychology Press. [ Google Scholar ]
• Axelrod, R. , & Hamilton, W. (1981). The evolution of cooperation . Science , 211 , 1390–1396. [ PubMed ] [ Google Scholar ]
• Banyard, V. L. (2015). Toward the next generation of bystander prevention of sexual and relationship violence: Action coils to engage communities . London: Springer. [ Google Scholar ]
• Baumeister, R. F. , Vohs, K. D. , & Funder, D. C. (2007). Psychology as the science of self‐reports and finger movements: Whatever happened to actual behavior? Perspectives on Psychological Science , 2 , 396–403. 10.1111/j.1745-6916.2007.00051.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Becker, S. W. , & Eagly, A. H. (2004). The heroism of women and men . American Psychologist , 59 , 163–178. [ PubMed ] [ Google Scholar ]
• Berk, R. A. (1983). An introduction to sample selection bias in sociological data . American Sociological Review , 48 , 386–398. 10.2307/2095230 [ CrossRef ] [ Google Scholar ]
• Black, D. (1993). The social structure of right and wrong . San Diego, CA: Academic Press. [ Google Scholar ]
• Bloch, C. , Liebst, L. S. , Poder, P. , Christiansen, J. M. , & Heinskou, M. B. (2018). Caring collectives and other forms of bystander helping behavior in violent situations . Current Sociology , 66 , 1049–1069. 10.1177/0011392118776365 [ CrossRef ] [ Google Scholar ]
• Bloom, P. (2017). Empathy and its discontents . Trends in Cognitive Sciences , 21 , 24–31. 10.1016/j.tics.2016.11.004 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boehm, C. (2000). Conflict and the evolution of social control . Journal of Consciousness Studies , 7 , 79–101. [ Google Scholar ]
• Brown, D. E. (1991). Human universals . New York, NY: McGraw‐Hill. [ Google Scholar ]
• Carle, A. C. (2009). Fitting multilevel models in complex survey data with design weights: Recommendations . BMC Medical Research Methodology , 9 , 49 10.1186/1471-2288-9-49 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cherry, F. (1995). The ‘stubborn particulars’ of social psychology: Essays on the research process . London: Routledge. [ Google Scholar ]
• Cialdini, R. B. (1980). Full‐cycle social psychology . Applied Social Psychology Annual , 1 , 21–47. [ Google Scholar ]
• Cohen, S. (2001). States of denial: Knowing about atrocities and suffering . Cambridge: Polity Press. [ Google Scholar ]
• Copes, H. , Hochstetler, A. , & Forsyth, C. J. (2013). Peaceful warriors: Codes for violence among adult male bar fighters . Criminology , 51 , 761–794. 10.1111/1745-9125.12019 [ CrossRef ] [ Google Scholar ]
• Cross, C. P. , Copping, L. T. , & Campbell, A. (2011). Sex differences in impulsivity: A meta‐analysis . Psychological Bulletin , 137 , 97–130. [ PubMed ] [ Google Scholar ]
• Dael, N. , Mortillaro, M. , & Scherer, K. R. (2012). Emotion expression in body action and posture . Emotion , 12 , 1085–1101. 10.1037/a0025737 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Darley, J. M. , & Latane, B. (1968). Bystander intervention in emergencies: Diffusion of responsibility . Journal of Personality and Social Psychology , 8 , 377–383. 10.1037/h0025589 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Eagly, A. H. (2009). The his and hers of prosocial behavior: An examination of the social psychology of gender . American Psychologist , 64 , 644–658. 10.1037/0003-066X.64.8.644 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Eibl‐Eibesfeldt, I. (1989). Human ethology . New York, NY: Aldine de Gruyter. [ Google Scholar ]
• 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 , 517–537. 10.1080/00224545.2012.697931 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fischer, P. , & Greitemeyer, T. (2013). The positive bystander effect: Passive bystanders increase helping in situations with high expected negative consequences for the helper . The Journal of Social Psychology , 153 , 1–5. 10.1080/00224545.2012.697931 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fischer, P. , Greitemeyer, T. , Pollozek, F. , & Frey, D. (2006). The unresponsive bystander: Are bystanders more responsive in dangerous emergencies? European Journal of Social Psychology , 36 , 267–278. 10.1002/ejsp.297 [ CrossRef ] [ Google Scholar ]
• Fry, D. P. (2000). Conflict management in cross‐cultural perspective In Aureli F., F. B. M., & de Waal (Eds.), Natural conflict resolution (pp. 334–351). Berkeley, CA: University of California Press. [ Google Scholar ]
• Fujisawa, K. K. , Kutsukake, N. , & Hasegawa, T. (2006). Peacemaking and consolation in Japanese preschoolers witnessing peer aggression . Journal of Comparative Psychology , 120 , 48–57. 10.1037/0735-7036.120.1.48 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gelman, A. (2008). Scaling regression inputs by dividing by two standard deviations . Statistics in Medicine , 27 , 2865–2873. 10.1002/sim.3107 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goffman, E. (1971). Relations in public, Microstudies of the public order . New York, NY: Basic Books. [ Google Scholar ]
• Grimes, D. A. , & Schulz, K. F. (2005). Compared to what? Finding controls for case‐control studies . The Lancet , 365 , 1429–1433. 10.1016/S0140-6736(05)66379-9 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Harari, H. , Harari, O. , & White, R. V. (1985). The reaction to rape by American male bystanders . The Journal of Social Psychology , 125 , 653–658. 10.1080/00224545.1985.9712039 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hobbs, D. (2003). Bouncers: Violence and governance in the night‐time economy . Oxford: Oxford University Press. [ Google Scholar ]
• Hox, J. J. , Moerbeek, M. , & van de Schoot, R. (2017). Multilevel analysis: Techniques and applications . London: Routledge. [ Google Scholar ]
• Jones, L. K. , Jennings, B. M. , Goelz, R. M. , Haythorn, K. W. , Zivot, J. B. , & de Waal, F. B. M. (2016). An ethogram to quantify operating room behavior . Annals of Behavioral Medicine , 50 , 487–496. 10.1007/s12160-016-9773-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Keogh, R. H. , & Cox, D. R. (2014). Case‐control studies . Cambridge: Cambridge University Press. [ Google Scholar ]
• Krippendorff, K. (2004). Reliability in content analysis . Human Communication Research , 30 , 411–433. 10.1111/j.1468-2958.2004.tb00738.x [ CrossRef ] [ Google Scholar ]
• Latané, B. (1981). The psychology of social impact . American Psychologist , 36 , 343–356. 10.1037/0003-066X.36.4.343 [ CrossRef ] [ Google Scholar ]
• Latané, B. , & Nida, S. (1981). Ten years of research on group size and helping . Psychological Bulletin , 89 , 308–324. 10.1037/0033-2909.89.2.308 [ CrossRef ] [ Google Scholar ]
• Latané, B. , & Darley, J. M. (1970). The unresponsive bystander: Why doesn't he help? New York, NY: Appleton‐Century‐Crofts. [ Google Scholar ]
• Lefevor, G. T. , Fowers, B. J. , Ahn, S. , Lang, S. F. , & Cohen, L. M. (2017). To what degree do situational influences explain spontaneous helping behaviour? A meta‐analysis . European Review of Social Psychology , 28 , 227–256. 10.1080/10463283.2017.1367529 [ CrossRef ] [ Google Scholar ]
• Levine, M. , Cassidy, C. , Brazier, G. , & Reicher, S. (2002). Self‐categorization and bystander non‐intervention: Two experimental studies . Journal of Applied Social Psychology , 32 , 1452–1463. 10.1111/j.1559-1816.2002.tb01446.x [ CrossRef ] [ Google Scholar ]
• Levine, M. , Lowe, R. , Best, R. , & Heim, D. (2012). We police it ourselves': Group processes in the escalation and regulation of violence in the night‐time economy: Night‐time economy violence . European Journal of Social Psychology , 42 , 924–932. 10.1002/ejsp.1905 [ CrossRef ] [ Google Scholar ]
• Levine, M. , Cassidy, C. , & Jentzsch, I. (2010). The implicit identity effect: Identity primes, group size, and helping . British Journal of Social Psychology , 49 , 785–802. 10.1348/014466609X480426 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levine, M. , & Crowther, S. (2008). The responsive bystander: How social group membership and group size can encourage as well as inhibit bystander intervention . Journal of Personality and Social Psychology , 95 , 1429–1439. 10.1037/a0012634 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levine, M. , & Manning, R. (2013). Social identity, group processes, and helping in emergencies . European Review of Social Psychology , 24 , 225–251. 10.1080/10463283.2014.892318 [ CrossRef ] [ Google Scholar ]
• Levine, M. , Taylor, P. J. , & Best, R. (2011). Third parties, violence, and conflict resolution: The role of group size and collective action in the microregulation of violence . Psychological Science , 22 , 406–412. 10.1177/0956797611398495 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lewallen, S. , & Courtright, P. (1998). Epidemiology in practice: Case‐control studies . Community Eye Health , 11 , 57–58. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Liebst, L. S. , Heinskou, M. B. , & Ejbye‐Ernst, P. (2018). On the actual risk of bystander intervention: A statistical study based on naturally occurring violent emergencies . Journal of Research in Crime and Delinquency , 55 , 27–50. 10.1177/0022427817710776 [ CrossRef ] [ Google Scholar ]
• Lindegaard, M. R. , & Bernasco, W. (2018). Lessons learned from crime caught on camera . Journal of Research in Crime and Delinquency , 55 , 155–186. 10.1177/0022427817727830 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lindegaard, M. R. , Liebst, L. S. , Bernasco, W. , Heinskou, M. B. , Philpot, R. , Levine, M. , & Verbeek, P. (2017). Consolation in the aftermath of robberies resembles post‐aggression consolation in chimpanzees . PLOS One , 12 ):e0177725 10.1371/journal.pone.0177725 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lohr, S. (2010). Sampling: Design and analysis . Boston, MA: Brooks/Cole. [ Google Scholar ]
• Macintyre, S. , & Homel, R. (1997). Danger on the dance floor: A study of interior design, crowding and aggression in nightclubs In Homel R. (Ed.), Policing for prevention: Reducing crime, public intoxication and injury (pp. 91–113). Monsey, NY: Criminal Justice Press. [ Google Scholar ]
• 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 , 555–562. 10.1037/0003-066X.62.6.555 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• McNeish, D. M. , & Stapleton, L. M. (2016). The effect of small sample size on two‐level model estimates: A review and illustration . Educational Psychology Review , 28 , 295–314. 10.1007/s10648-014-9287-x [ CrossRef ] [ Google Scholar ]
• Milgram, S. (1970). The experience of living in cities . Science , 167 , 1461–1468. [ PubMed ] [ Google Scholar ]
• Mook, D. G. (1983). In defense of external invalidity . American Psychologist , 38 , 379–387. 10.1037/0003-066X.38.4.379 [ CrossRef ] [ Google Scholar ]
• Mortensen, C. R. , & Cialdini, R. B. (2010). Full‐cycle social psychology for theory and application . Social and Personality Psychology Compass , 4 , 53–63. 10.1111/j.1751-9004.2009.00239.x [ CrossRef ] [ Google Scholar ]
• Murphy, N. A. (2016). What we know and the future of interpersonal accuracy research In Hall J. A., Mast M. S., & West T. V. (Eds.), The social psychology of perceiving others accurately (pp. 404–424). Cambridge: Cambridge University Press. [ Google Scholar ]
• Nassauer, A. , & Legewie, N. (2012). Erratum for PMID 21180585 . Sociological Methods & Research , 5 , 371 10.1177/0049124118769093 [ CrossRef ] [ Google Scholar ]
• Osswald, S. , Greitemeyer, T. , Fischer, P. , & Frey, D. (2010). What is moral courage? Definition, explication, and classification of a complex construct In Pury C., & Lopez S. (Eds.), The psychology of courage: Modern research on an ancient virtue (pp. 149–164). Washington, DC: APA. [ Google Scholar ]
• Parks, M. J. , Osgood, D. W. , Felson, R. B. , Wells, S. , & Graham, K. (2013). Third party involvement in barroom conflicts . Aggressive Behavior , 39 , 257–268. 10.1002/ab.21475 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Phillips, S. , & Cooney, M. (2005). Aiding peace, abetting violence: Third parties and the management of conflict . American Sociological Review , 70 , 334–354. 10.1177/000312240507000207 [ CrossRef ] [ Google Scholar ]
• Philpot, R. (2017). Beyond the dyad: The role of groups and third‐parties in the trajectory of violence (Doctoral dissertation), University of Exeter, Exeter.
• Philpot, R. , Liebst, L. S. , Levine, M. , Bernasco, W. , & Lindegaard, M. R. (2019). Would I be helped? Cross‐national CCTV footage shows that intervention is the norm in public conflicts . American Psychologist . [ PubMed ] [ Google Scholar ]
• Philpot, R. , Liebst, L. S. , Møller, K. K. , Lindegaard, M. R. , & Levine, M. (2019). Capturing violence in the night‐time economy: A review of established and emerging methodologies . Aggression and Violent Behavior , 46 , 56–65. 10.1016/j.avb.2019.02.004 [ CrossRef ] [ Google Scholar ]
• Rabe‐Hesketh, S. , Skrondal, A. , & Pickles, A. (2005). Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects . Journal of Econometrics , 128 , 301–323. 10.1016/j.jeconom.2004.08.017 [ CrossRef ] [ Google Scholar ]
• Reynald, D. M. (2011). Factors associated with the guardianship of places: Assessing the relative importance of the spatio‐physical and sociodemographic contexts in generating opportunities for capable guardianship . Journal of Research in Crime and Delinquency , 48 , 110–142. [ Google Scholar ]
• Roberts, J. C. (2009). Bouncers and barroom aggression: A review of the research . Aggression and Violent Behavior , 14 , 59–68. 10.1016/j.avb.2008.10.002 [ CrossRef ] [ Google Scholar ]
• Rosenthal, J. A. (1996). Qualitative descriptors of strength of association and effect size . Journal of Social Service Research , 21 , 37–59. 10.1300/J079v21n04_02 [ CrossRef ] [ Google Scholar ]
• Sampson, R. , Eck, J. E. , & Dunham, J. (2010). Super controllers and crime prevention: A routine activity explanation of crime prevention success and failure . Security Journal , 23 , 37–51. 10.1057/sj.2009.17 [ CrossRef ] [ Google Scholar ]
• Slater, M. , Rovira, A. , Southern, R. , Swapp, D. , Zhang, J. J. , Campbell, C. , & Levine, M. (2013). Bystander responses to a violent incident in an immersive virtual environment . PLOS One , 8 ):e52766 10.1371/journal.pone.0052766 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smith, C. (2015). To flourish or destruct: a personalist theory of human goods, motivations, failure, and evil . University of Chicago Press. [ Google Scholar ]
• Steegen, S. , Tuerlinckx, F. , Gelman, A. , & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis . Perspectives on Psychological Science , 11 , 702–712. 10.1177/1745691616658637 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Swann, W. B. , Gómez, Á. , Huici, C. , Morales, J. F. , & Hixon, J. G. (2010). Identity fusion and self‐sacrifice: Arousal as a catalyst of pro‐group fighting, dying, and helping behavior . Journal of Personality and Social Psychology , 99 , 824–841. 10.1037/a0020014 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Swann, W. B. , & Jetten, J. (2017). Restoring agency to the human actor . Perspectives on Psychological Science , 12 , 382–399. 10.1177/1745691616679464 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tarling, R. , & Morris, K. (2010). Reporting crime to the police . The British Journal of Criminology , 50 , 474–490. 10.1093/bjc/azq011 [ CrossRef ] [ Google Scholar ]
• Taylor, S. E. , Klein, L. C. , Lewis, B. P. , Gruenewald, T. L. , Gurung, R. A. R. , & Updegraff, J. A. (2000). Biobehavioral responses to stress in females: Tend‐and‐befriend, not fight‐or‐flight . Psychological Review , 107 , 411–429. 10.1037//0033-295X.107.3.411 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vázquez, A. , Gómez, Á. , Ordoñana, J. R. , Swann, W. B. , & Whitehouse, H. (2017). Sharing genes fosters identity fusion and altruism . Self and Identity , 16 , 684–702. 10.1080/15298868.2017.1296887 [ CrossRef ] [ Google Scholar ]
• de Waal, F. B. M. (2015). Prosocial primates In Schroeder D. A., & Graziano W. G. (Eds.), The Oxford handbook of prosocial behavior (pp. 61–85). Oxford: Oxford University Press. [ Google Scholar ]
• de Waal, F. B. M. , & Preston, S. D. (2017). Mammalian empathy: Behavioural manifestations and neural basis . Nature Reviews Neuroscience , 18 , 498–509. 10.1038/nrn.2017.72 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weina ge, Ge , Collins, R. T. , & Ruback, R. B. (2012). Vision‐based analysis of small groups in pedestrian crowds . IEEE Transactions on Pattern Analysis and Machine Intelligence , 34 , 1003–1016. 10.1109/TPAMI.2011.176 [ PubMed ] [ CrossRef ] [ Google Scholar ]

University of Notre Dame

College of arts and letters, science of generosity, exploring an essential human value..

• Research Resources >
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Helping Behavior

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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• Published: 09 April 2019

Helping-Like Behaviour in Mice Towards Conspecifics Constrained Inside Tubes

• Hiroshi Ueno 1 , 2 ,
• Shunsuke Suemitsu 3 ,
• Shinji Murakami 3 ,
• Naoya Kitamura 3 ,
• Kenta Wani 3 ,
• Yosuke Matsumoto 4 ,
• Motoi Okamoto 2 &
• Takeshi Ishihara 3  

Scientific Reports volume  9 , Article number:  5817 ( 2019 ) Cite this article

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Prosocial behaviour, including helping behaviour, benefits others. Recently, helping-like behaviour has been observed in rats, but whether it is oriented towards rescue, social contact with others, or other goals remains unclear. Therefore, we investigated whether helping-like behaviour could be observed in mice similar to that in rats. Because mice are social animals widely used in neuroscience, the discovery of helping-like behaviour in mice would be valuable in clarifying the psychological and biological mechanisms underlying pro-sociability. We constrained mice inside tubes. Subject mice were allowed to move freely in cages with tubes containing constrained conspecifics. The subject mice released both cagemates and stranger mice but did not engage in opening empty tubes. Furthermore, the same behaviour was observed under aversive conditions and with anesthetised conspecifics. Interestingly, hungry mice opened the tubes containing food before engaging in tube-opening behaviour to free constrained conspecifics. Mice showed equal preferences for constrained and freely moving conspecifics. We demonstrated for the first time that mice show tube-opening behaviour. Furthermore, we partly clarified the purpose and motivation of this behaviour. An effective mouse model for helping-like behaviour would facilitate research on the mechanisms underlying prosocial behaviour.

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Introduction.

Prosocial behaviour comprises actions that benefit others 1 and is said to include informing, comforting, sharing, and helping 2 . Many animal species have been reported to exhibit prosocial-like behaviour 3 , 4 . The psychological mechanisms underlying prosocial behaviour are motivated by both selfish and unselfish factors. One of the difficulties here is to establish the psychological basis of prosocial behaviour in young humans before they have attained lingual expertise and in non-human animals 1 . Currently, the psychological basis of prosocial behaviour and its fundamental mechanisms remain unclear 5 , 6 , 7 , 8 . Interestingly, in recent years, it has been reported that rats also show prosocial behaviour 9 , 10 , 11 , 12 , 13 . Rats rescued others in various situations, they donated food, they groomed conspecifics, and they freed trapped conspecifics 9 , 12 .

Helping behaviour, a form of prosocial behaviour, involves acting for the benefit of others (e.g. rescuing others from difficult situations) in the absence of reward 14 . Ben-Ami Bartal et al . constrained rats inside tubes and showed that cagemate rats released the constrained individuals 9 , 10 , 11 . Similarly, Sato et al . placed rats in flooded conditions that are considered to be aversive, and cagemate rats released the individuals placed in this condition 12 . They speculated that helping behaviour is based on empathy 9 , 10 , 11 , 12 . In contrast, it has been suggested that rats interested in social contact would show helping behaviour towards conspecifics 15 , 16 , 17 . As described above, the motivation and purpose of this helping-like behaviour observed in rats remain undetermined. New experimental methods and models are needed.

In this study, we investigated whether mice, which are also rodents, would show helping-like behaviour, similar to that demonstrated by Ben-Ami et al . for rats. It is suggested that the neurobiological systems of prosocial behaviour are shared among mammalian species 18 . Mice are social animals widely used in neuroscience. Establishing prosocial, helping-like behaviour in mice would be of great value in the search for psychological and biological mechanisms that underlie pro-sociability.

In this study, the restraint apparatus of Ben-Ami et al . was modified to restrain mice. Confinement inside a tube causes psychological restraint stress without inducing pain to the mouse 19 . The tube restraining the mouse was capped with paper so that it could be easily accessed by the subject mice by eliminating the obstructing paper by chewing or crushing. A mouse cannot open the tube lid by chance. Thus, by investigating the tube-opening behaviour of the mouse in the following five states, we attempted to clarify a part of the psychological mechanism (interest, purpose, and motivation) of tube-opening behaviour in mice:

(1) Testing tube-opening behaviour to free cagemate mice. First, we investigated whether the mouse would show tube-opening behaviour to free cagemate mice constrained inside tubes; (2) Testing tube-opening behaviour to free cagemate and stranger mice. Changes in tube-opening behaviour of subject mice with respect to a cagemate or stranger restrained mouse were compared. Mouse empathy-like behaviour is considered to occur only for cagemate mice 9 , 20 , 21 , 22 . If this tube-opening behaviour is based on empathy, it was expected that the behaviour would differ depending on whether the restrained conspecific was a cagemate or a stranger; (3) Testing tube-opening behaviour to free anesthetised cagemate mice. We examined if subject mice would show tube-opening behaviour when the cagemate mice in the tube did not move or vocalise. We placed anesthetised cagemate mouse in the tube. If distress signals communicated to the free mouse are required for tube-opening behaviour to occur, the subject mouse was not expected to free an anesthetised mouse. (4) Testing tube-opening behaviour in hungry and non-hungry states. Next, we examined whether subject mice would preferentially open a tube containing food when hungry. We investigated whether tube-opening behaviour changes due to internal factors such as mood and the physical condition of the subject mouse. Moreover, in this experiment, we clarified whether the mouse could identify the contents of the tube. If the mice can identify the tube contents, and if the behaviour is based on self-interest, a difference in the behaviour was expected; (5) Testing tube-opening behaviour under aversive conditions. We examined whether the tube-opening behaviour of the subject mouse would change depending on the external environment. In order to induce discomfort to the mouse, we wet half of the home cage bedding with water and the changes in the tube-opening behaviour of the mouse was examined. In this experiment, we estimated the degree of motivation of the mouse to engage in tube-opening behaviour. We expected that tube-opening behaviour would not be performed under this condition if the motivation was not strong.

We used the mouse strain C57BL/6N, which is one of the most widely used animal models in biological research.

Training for opening the paper lid

Mice were subjected to training for opening the paper lid on being constrained inside 50-mL tubes; the front of the tube was closed with a paper lid and the rear was closed with a plastic lid (Fig.  1A ). The tube was placed inside a new home cage, and the mice managed to break through the paper lid and exit the tube. We practiced this exercise three times a day for 2 days. By the end of training, all mice had learned to open the paper lid and exit the tube.

Training for opening the paper lid and tube-opening behaviour test. ( A ) Sample picture and schematic diagram of training for opening the paper lid. One side of the 50-mL tube was closed with a paper lid and the other with a plastic lid. ( B ) Sample picture and schematic diagram of the mouse waiting for release from the 50 mL tube. A 1-cm-diameter hole in front of the mouse and a paper lid at the back. ( C ) A sample image of the tube, containing the mouse, covered at the back with a paper lid. ( D ) Sample picture and schematic diagram of the tube-opening behaviour test in the new home cage. A tube containing the cagemate mouse on one side and an empty tube on the other side. ( E ) Sample picture during the test of tube-opening behaviour. The test mouse freely moves in the cage. ( F ) Tube-opening behaviour test for constrained cagemate: latency to paper lid-opening in each trial. All data are presented as box plots. The p values were calculated uisng one-way repeated measures ANOVA ( F ). n = 14 animals.

Mice show tube-opening behaviour to free constrained cagemate mice

In this experiment, we investigated whether mice show tube-opening behaviour to free constrained cagemate mice. The back of the tube containing the mouse was closed with a paper lid (Fig.  1B,C ). The subject mouse could release the constrained mouse by gnawing on the paper lid. Constrained cagemate mice were located at the sides of a new home cage. We placed an empty tube on the other side (Fig.  1D ). The subject mouse was placed at the centre and allowed to explore the entire home cage for a 90-min session (Fig.  1E ). The latency to lid opening of the tube containing the cagemate mouse was measured. We performed the test once a day for 7 days. We observed that subject mice opened the paper lid of tubes containing cagemate mice (Sup. Video  1 ). Seventy percent of mice learned to open the lid. Mice that did not learn to open the lid were excluded from the analysis. The latency to lid opening was significantly reduced in the second trial (Fig.  1F ). In the subsequent trials, the latency remained almost unchanged. Subject mice opened the paper lid of the tube containing the cagemate mouse in all seven trials (Fig.  1F , F 6,91  = 2.3, p = 0.05). Some subject mice bit the lid of the empty tube. However, in all trials, subject mice did not open the paper lid of the empty tube placed on the other side of the cage (Fig.  1D,E ). We also observed that subject mice often exhibited tube-opening behaviour and then entered the tube.

Mice also show tube-opening behaviour to free constrained stranger mice

Next, we investigated whether the subject mice would show a preference for opening the tube containing a cagemate over that containing a stranger mouse (Fig.  2 ). In this test, cagemate and unfamiliar C57BL/6N male (stranger) mice that had no previous contact with the subject mouse were placed into one of the transparent tubes located on both sides of a new home cage (Fig.  2A ). Subject mice showed no significant differences in the time spent in the two cage areas (Fig.  2B , t 16  = −1.369, p = 0.213). No significant difference was detected between the cagemate and stranger mouse conditions with respect to the latency to lid-opening (Fig.  2C,D , T = 23, n = 9, p = 0.825). Five of the nine mice opened the paper lid of the tube containing the cage mate first. These results suggested that there was no difference in tube-opening behaviour to free cagemate and stranger mice.

Tube-opening behaviour test for both cagemate and stranger mice, and tube-opening behaviour test for anesthetised cagemate mice. ( A ) Schematic diagram of the test. Tube-opening behaviour test for cagemate and stranger mice: time spent in the area ( B ) and latency to lid-opening ( C ). ( D ) Individual latency to lid-opening in the tube-opening behaviour test for both cagemate and stranger mice. ( E ) Schematic diagram of this test. ( F ) Tube-opening behaviour test for anesthetised cagemate mice: time spent in the anesthetised cagemate area or empty area. ( G ) Comparison of the latency to lid-opening for an anesthetised and a non-anesthetised mouse. All data are presented as box plots. The p values were calculated using paired t-test ( B,F ), Wilcoxon signed-rank test ( C ), and one-way repeated measures ANOVA ( G ). n = 9 animals per test.

Tube-opening behaviour to free constrained anesthetised cagemate mice

We prevented the constrained mouse from expressing or feeling distress by anesthetizing it. We performed this experiment by placing an anesthetised cagemate in a tube. Subject mice spent almost the same amount of time in both areas (Fig.  2F , t 16  = 0.006, p = 0.996). We observed that subject mice opened the paper lid of the tube containing the anesthetised cagemate (Sup. Video  2 ). We compared the latency to lid-opening when the cagemate was in an anesthetised and in an un-anesthetised state. Subject mice demonstrated equivalent latencies to lid-opening in both states (Fig.  2G , F 1,16  = 0.0, p = 0.987). These results suggested that the mice exhibited the similar behaviour towards their cagemates irrespective of their state (anesthetised or un-anesthetised).

Tube-opening behaviour in hungry and non-hungry states

For this experiment, we used two transparent tubes, one containing food and the other containing a cagemate mouse (Fig.  3A,B ). Next, we examined whether subject mice would preferentially open the tube containing the food when in a hungry state. To induce hunger, we restricted access to food from the day before this test. Subject mice spent a significantly longer time in the area with the tube containing the food than in the area with tube containing the cagemate (Fig.  3C , t 20  = −3.975, p = 0.003). We measured the time spent in the area before opening the tube containing the cagemate. Therefore, this result also includes the time that mice spent in eating, near the food tube. The latency to lid-opening was significantly shorter for the tube containing the food than for the one containing the cagemate (Fig.  3D , U = 28, n 1  = n 2  = 11, p = 0.034).

Tube-opening behaviour test in hungry and non-hungry states. ( A ) Upper row: Sample picture of the 50-mL tube containing food. It is closed with a paper lid. Lower row: sample picture during the test of tube-opening behaviour in the hungry state. The tube containing the cagemate is located on the left side and the tube containing the food on the right side. The test mouse moves freely around the cage. ( B ) Schematic diagram of this test. The cagemate area refers to the half of the home cage with the tube containing the cagemate mouse, and the feed area refers to the half of the home cage with the tube containing the food. Tube-opening behaviour test for cagemate versus feed in the food-deprived state: time spent in the area ( C ) and latency to lid-opening ( D ). Tube-opening behaviour test for cagemate versus feed in the non-food deprived state: time spent in the area ( E ) and latency to lid-opening ( F ). All data are presented as box plots. The p values were calculated using paired t-test ( C,E ) and the Mann-Whitney’s U -test ( D,F ). n = 11 animals per test.

Next, we performed the same experiment using subject mice that were not food deprived (Fig.  3E,F ). There were no significant differences between the time spent in the area with the tube containing the food and the tube containing the cagemate (Fig.  3E , t 20  = 0.090, p = 0.930). Subject mice that were not in a hungry state did not actively engage in opening the lid of the tube containing the food (Fig.  3F , U = 4, n 1  = n 2  = 11, p = 0.001). These results indicate that hungry subject mice prioritise food acquisition over helping constrained conspecifics.

Tube-opening behaviour under aversive conditions

We examined whether the subject mouse would engage in tube-opening behaviour to free constrained cagemate mice even when the tube-opening procedure could not be easily performed. We wet half of the home cage bed, creating an environment where the subject mouse would be reluctant to approach the tube containing the cagemate (Fig.  4A ). Subject mice spent less time in the area with the cagemate mouse on the wet bedding than in the empty dry bedding area (Fig.  4B , t 16  = 2.424, p = 0.030). Consistently, subject mice showed reduction in the average speed in the wet bedding area than in the dry bedding area (Fig.  4C , t 17  = −6.219, p < 0.001). We observed that subject mice opened the paper lid of the tube on the wet bedding containing the cagemate. We compared the latency to lid-opening for the cagemate- containing tubes placed on wet and dry bedding (Fig.  4D , F 1,16  = 0.65, p = 0.803). Subject mice showed equivalent latency to lid-opening in both states. These results suggest that subject mice show tube-opening behaviour to free constrained cagemate mice even under aversive conditions.

Tube-opening behaviour test under aversive conditions. ( A ) Schematic diagram of this test. Half of the home cage contains wet bedding and the other half contains dry, normal bedding. Tube containing the cagemate mouse on the wet bedding. ( B ) Tube-opening behaviour test for cagemate under the aversive condition: time spent in the area. ( C ) Average speed of the test mouse in each bedding. ( D ) Comparison of the latency to lid-opening when the tube contains the cagemate is placed on wet or normal bedding. ( E ) Latency to lid-opening in various conditions of the tube-opening behaviour test. All data are presented as box plots. ( E ) Statistical significance is represented by top bars: *p < 0.05. The p values were calculated using paired t-test ( B,C ) and one-way repeated measures ANOVA ( D,E ). n = 9 animals per test.

Comparison among the different conditions

We compared the latency to lid-opening for the tube containing the cagemate under different conditions (Fig.  4E , F 5,33  = 4.245, p = 0.003). The latency to lid-opening was significantly increased in the hungry state compared to that in the other conditions. There were no significant differences among the other conditions.

Degree of motivation towards constrained and non-constrained cagemates

We investigated whether the motivation for the helping-like behaviour towards a constrained cagemate would be equivalent to the interest towards a non-constrained cagemate. There were no significant differences in the time spent in each area when both areas were empty (Fig.  5D , T = 61, n = 16, p = 0.784). Subject mice showed a preference for spending time around the cage with the non-constrained cagemate mouse (Fig.  5D , T = 9, n = 16, p = 0.001). Next, constrained cagemate mice were placed in transparent cages. Subject mice showed a preference for spending time around the cage with the constrained cagemate mouse (Fig.  5D , T = 23, n = 16, p = 0.010). Next, both non-constrained and constrained cagemate mice were placed in transparent cages, which were placed at the corners of the chamber. No significant differences were found between the time spent around the cage with the non-constrained cagemate mouse and that around the opposite-positioned cage, with the constrained cagemate mouse (Fig.  5D , T = 43, n = 16, p = 0.213). These results indicate that subject mice showed equal preference for the constrained and non-constrained cagemate mice (Fig.  5E , F 3,62  = 7.784, p < 0.001; test 1 vs. test 2: p = 0.008; test 1 vs. test 3: p = 0.217; test 1 vs. test 4: p = 1.000; test 2 vs. test 3: p = 1.0; test 2 vs. test 4: p < 0.001; test 3 vs. test 4: p = 0.017).

Preference tests for constrained cagemate and non-constrained cagemate mice in the social interaction test apparatus. ( B ) Schematic diagram of the apparatus of this experiment. Two transparent cages [(a), and (b)] are placed at both ends of a rectangular apparatus, and half of the area of the apparatus is taken as the respective area [area (a) and area (b)]. A radius of 20 cm around the transparent cage was set around the cage [around cage (a) and around cage (b)]. ( B ) Sample picture of the transparent cages containing constrained and non-constrained cagemates. ( C ) Test schedule. For each mouse, four tests were conducted according to the contents of the table. Cagemates in each state were placed in transparent cages (a) and (b). Preference tests for constrained cagemate and intact mice: time spent around the cage ( D ), and preference index defined as (time spent around cage (a))/(time spent around cage (a) + time spent around cage (b)). All data are presented as box plots. The p values were calculated using Wilcoxon signed-rank test ( D ) and one-way ANOVA ( E ). n = 16 animals per trial.

In this study, we investigated for the first time whether mice show tube-opening behaviour to free conspecifics.

Mice engaged in tube-opening behaviour to free conspecifics constrained inside tubes. Since the mice did not open the paper lid of empty tubes, their behaviour can be considered to be as motivated by the constrained conspecifics or restraining apparatus, or specific aspects of these. It has already been reported that rats exhibit similar behaviours, by rescuing other rats trapped in tubes or in flooded environments 9 , 10 , 11 , 12 . Considering that the mouse is also a rodent, the results of this study are consistent with those of the previous reports.

In the previous studies, the helping behaviour exhibited by rats involved a very simple task of moving a lid to release the trapped conspecific 9 , 10 , 11 , 12 . In this experiment, we constructed a more complicated task, in which tube-opening behaviour could only performed with damage to the paper lid, which required time and effort. The latency of opening the paper lid was shortened through repeated trials. Moreover, the act was selectively carried out for the tube containing a conspecific individual. Thus, in our study, the behaviour may not be attributed to chance. In this paradigm, mice had to exert focused effort in chewing or crushing the paper to release their cagemate. This study shows that the mouse performed the tube-opening behaviour with a purpose.

Previous studies have suggested that rats are prosocial and that their helping behaviour was motivated by empathy 9 , 10 , 11 , 12 . It has been previously reported that rodents exhibit empathetic behaviour only towards cagemates 9 , 20 , 21 , 22 . However, in this study, the mice showed tube-opening behaviour both to free cagemates and stranger conspecifics. In addition, all mice were accustomed to the tube environment in our present study, and it can be considered that they were not fearful of the tube and thus could be interested in exploring its contents. Subject mice often exhibited tube-opening behaviour to free conspecifics and then entered the tube. Therefore, the act of opening the tube performed here may be based on other motives such as seeking social contact with the restrained mouse rather than expressing empathy 15 , 16 , 17 or by interest in the restraining device. Further research is needed to clarify the underlying motivation for engaging in this type of behaviour 23 .

In the present study, even in the absence of expression of distress or vocalisation by the anesthetised mouse in the tube, the subject mouse exhibited tube-opening behaviour. It was previously considered that a voiced warning would be necessary to induce helping-like behaviour 10 , 24 . Our mouse data contradicts a previous rat study 10 . However, distress may not be signalled solely by alarm calls; most likely, for mice as well as for humans, it is multi-modal, and includes olfactory as well as visual and auditory components. In fact, the anesthetised mice were not in the normal state seen in the home cage. Previous studies have shown that animals have the ability to observe the movement of others and identify their state. Mice are attentive to cagemates that show abnormal behaviours 25 , 26 . Hyperalgesia in mice observing a mouse in pain depends on the visual input of the conspecific in pain 27 . The present data at least suggest that auditory information was not indispensable for the subject mice to engage in tube-opening behaviour to free conspecifics.

Hungry mice tended to open the paper lid of the tube containing the food before engaging in tube-opening behaviour to free conspecifics. However, the hungry mice opened the paper lid of the tube containing constrained conspecifics within the test time. First, this result clearly shows that the mice recognise objects in the tube using sight or olfaction. Second, it indicates that tube-opening behaviour in mice changes based on internal factors. In previous studies, rats prioritised fellow rescue rather than bait reward 9 , 10 , 11 , 12 . This was attributed to the ability to understand and actively respond to the emotional state of conspecifics 9 , 10 , 11 , 12 . Our mouse data contradict previous rat studies. However, in these studies, the rats had not been starved. Therefore, the deviation from our results may be attributable to the state of hunger and species differences. It has long been demonstrated that mouse behaviour changes depending on hunger and satiety status. Generally, when mice are subjected to maze tasks, a bait is used as a reward and the animals are tested while hungry 28 , 29 . Our results suggest that the partner’s emotional state is not a casual factor for the tube-opening behaviour.

Mice engaged in tube-opening behaviour to free conspecifics even at the cost of personal discomfort. A wet floor is considered a stressor and is commonly used in stress paradigms, especially chronic unpredictable stress 30 . We showed that the mice walked slower on the wet bedding, which indicated that the experience was aversive. Nevertheless, the mice did walk on the wet bedding to help their constrained conspecifics. This result shows that there is a strong motivation, purpose, and/or interest in the tube-opening behaviour to free conspecifics.

Social interest in mice can be directly measured through their approach or avoidance behaviour 31 , 32 . In this study, mice showed a similar degree of motivation towards constrained and freely moving conspecifics, which showed that they neither avoided nor had increased interest towards the constrained mice. Rescue behaviour as a prosocial behaviour is psychologically oriented towards the goal of reducing another’s suffering. However, our results indicate that mice do not fear or show heightened interest towards the constrained state. Therefore, it is possible that the tube-opening behaviour observed here is not rescue or prosocial behaviour.

Our results indicate the possibility that the tube-opening behaviour of the mice may not be oriented towards the rescue of restrained conspecifics from suffering. The mental processes of mice, rats, or apes are unknown and it is difficult to decipher the motivational states of such animals. Whether the underlying motivation for the tube-opening behaviour involves empathy, sociality, or other factors is unknown, and further research is needed. However, our results show that mice can show rescue-like behaviour as do rats. An effective mouse model for rescue-like behaviour research will facilitate the study of aspects of this behaviour, which would otherwise not be possible.

This study clearly suggests that mice show helping-like behaviour towards conspecifics. The mouse identifies the contents of the restraining tube and engages in tube-opening behaviour to free other mice. This tube-opening behaviour depends on internal factors. It is not clear whether this rescue-like behaviour is prosocial, aimed towards rescuing conspecifics; however, our results clearly showed that engaging in this tube-opening behaviour is strongly motivated.

All animal experiments were performed in accordance with the U.S. National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals (NIH Publication No. 80–23, revised in 1996) and approved by the Committee for Animal Experiments at Kawasaki Medical School Advanced Research Center. All efforts were made to minimise the number of animals used and their suffering. Animals were purchased from Charles River Laboratories (Kanagawa, Japan) and housed in cages (five animals per cage) with food and water provided ad libitum under a 12-h light/dark cycle at 23 °C–26 °C. We used C57BL/6N male mice aged 10 weeks. All behavioural tests were conducted in behavioural testing rooms between 08.00 and 18.00 h during the light phase of the circadian cycle. After the tests, all equipment was cleaned with 70% ethanol and super hypochlorous water to prevent bias based on olfactory cues. Behavioural tests were performed according to the test order described below.

Training for paper-lid opening

Mice were subjected to training for opening the paper lid on being constrained inside 3-cm diameter transparent plastic cylinders (50-mL tubes); for this, the front of the tube was closed with a paper lid and the rear was closed with a plastic lid (Fig.  1A ). The tube was placed inside a new home cage, and after the mice managed to break through the paper lid and exit the tube, they were allowed to act freely for 5 min. The exercise was performed three times a day for 2 days. All the mice were able to open the paper lid and exit the tubes.

Tube-opening behaviour to free cagemate mice

We used 50-mL tubes with a cut tip. We closed the back of the empty tube and of the tubes containing constrained mice with a paper lid (Fig.  1B,C ). In this test, a familiar mouse (cagemate) was placed in one of the transparent tubes located at the sides of a new home cage. We placed an empty tube on the other side (Fig.  1D ). The constrained cagemate mice had also been trained to open the paper lid. The subject mouse was placed at the centre and allowed to explore the entire home cage for a 90-min session (Fig.  1E ). The latency to lid-opening of the tube containing the cagemate mouse was measured. After lid-opening, the mice were allowed to act freely for 5 min. The mice had access to also open the paper lid of the empty tube. We performed the test once a day for 7 days. Seventy percent of the mice learned to open the lid. Mice that did not learn to open the lid were excluded from the analysis. The same subject mice were tested in all conditions. The data were recorded on video.

Tube-opening behaviour to free cagemate and stranger mice

In this test, a familiar mouse (cagemate) or unfamiliar C57BL/6N male mouse (stranger) that had no previous contact with the subject mouse was placed in one of the transparent tubes located at the sides of a new home cage (Fig.  2A ). The subject mouse was placed at the centre and allowed to explore the entire home cage for a 30-min session. The test was terminated when the mouse opened the paper lid. The cagemate area referred to the half of the home cage with the tube containing the cagemate, and the stranger area referred to the half of the home cage with the tube containing the stranger mouse (Fig.  2A ). The latency to lid opening and the amount of time spent in each area during the 30-min sessions were measured. The time spent in the area was measured only until one of the tubes was opened. Mice that did not open either of the two tubes were excluded from the analysis. The data were recorded on video and analysed using the ANY-MAZE software.

Tube-opening behaviour to free anesthetised cagemate mice

The cagemate mice were deeply anesthetised with a high dose of sodium pentobarbital (50 mg/kg, i.p.). The anesthetised mice were placed in transparent tubes that were closed on one side with a paper lid. The anesthetised cagemate area referred to the half of the home cage with the tube containing the anesthetised cagemate mouse, and the empty area referred to the half of the home cage with the empty tube (Fig.  2E ). The latency to lid opening and the amount of time spent in each area during the 30-min sessions were measured. The time spent in the area was measured only until the tube was opened. We compared the latency to lid-opening for tubes containing the anesthetised mice and tubes containing unanesthetised mice. The test was terminated when the mouse opened the paper lid. Mice that did not open either of the two tubes were excluded from the analysis. The data were recorded on video and analysed using the ANY-MAZE software.

To induce hunger, we restricted access to food from the day before the test. For the test, we used a transparent tube containing food and closed on one side with a paper lid (Fig.  3A ). In this test, a tube containing a cagemate and a tube containing food were placed on opposite sides of a new home cage (Fig.  3A,B ). The cagemate area referred to the half of the home cage with the tube containing the cagemate, and the feed area referred to the half of the home cage with the tube containing the food (Fig.  3B ). The latency to lid opening and the amount of time spent in each area during 30-min sessions were measured. The time spent in the area was measured only until the tube containing the cagemate was opened. The test was terminated when the mouse opened the paper lid. Mice that did not open either of the two tubes were excluded from the analysis. The data were recorded on video and analysed using the ANY-MAZE software. We performed the same experiment using subject mice that were not food deprived (Fig.  3E,F ).

We wet half of the home cage bedding with water. Thus, half of the home cage contained wet bedding and the other half contained dry, normal bedding. A transparent tube containing a cagemate mouse was placed on the wet bedding (Fig.  4A ). The latency to lid-opening and the amount of time spent in each area during 30-min sessions were measured. The time spent in the area was measured only until the tube was opened. We also analysed the average speed at which the subject mouse moved in each area. We compared the latency to lid-opening under normal and aversive conditions. The test was terminated when the mouse opened the paper lid. Mice that did not open either of the two tubes were excluded from the analysis. The data were recorded on video and analysed using the ANY-MAZE software.

Preference tests for constrained and non-constrained cagemate mice using social interaction test apparatus

The apparatus had a rectangular shape (30 × 60 × 40 cm). Two transparent cages (7.5 × 7.5 × 10 cm, with several holes with a diameter of 1 cm) [(a), and (b)] were placed at both ends of the rectangular apparatus (Fig.  5A,B ). Each mouse was placed in the box for 10 min and allowed free exploration for habituation. In this test, we tested each mouse according to the table schedule (Fig.  5C ). In test 1, both cage (a) and cage (b) were empty. In test 2, cage (a) was empty and a non-constrained cagemate was placed inside cage (b). In test 3, a constrained cagemate was placed inside cage (a) and cage (b) was empty. In test 4, a constrained cagemate was placed inside cage (a) and a non-constrained cagemate was placed inside cage (b). The non-constrained cagemate mouse was placed inside the transparent cage, which allowed nose contact between the bars but prevented the mice from fighting with each other. The subject mouse was placed at the centre and allowed to explore the entire box for a 10-min session. One side of the rectangular area was identified as the cage (a) area and the other as the cage (b) area. The amount of time spent in each area and around each cage during 10-min sessions was measured. We also analysed the average speed at which each subject mouse moved in each trial. The apparatus was cleaned after each phase of this test. In this test we used naive mice, not used in other tests. The data were recorded on video and analysed using the ANY-MAZE software.

Statistical analyses

Statistical analysis was conducted using the SPSS software (IBM Corp, Tokyo, Japan). If the variables were found to be non-normally distributed, non-parametric analysis was used. We used the non-parametric Mann-Whitney U -test, Wilcoxon signed-rank test, Student’s t -test, paired t -test, one-way ANOVA, or one-way repeated measures ANOVA. A p value < 0.05 was regarded as statistically significant. Data are shown as box plots.

Jensen, K. Prosociality. Curr. Biol. 26 , R748–R752 (2016).

Article   CAS   Google Scholar  

Olmstead, M. C. & Kuhlmeier, V. A. Prosociality in Comparative Cognition . 369–365 (Cambridge University Press, 2015).

Dugatkin, L. A. Animal cooperation among unrelated individuals. Naturwissenschaften. 89 , 533–541 (2002).

ADS   CAS   PubMed   Google Scholar  

Krause, J. & Ruxton, G. D. Living in groups . (Oxford University Press, 2002).

Brosnan, S. F. et al . Chimpanzees (Pan troglodytes) do not develop contingent reciprocity in an experimental task. Anim. Cogn. 12 , 587–597 (2009).

Article   Google Scholar  

Brosnan, S. F. Understanding social decision-making from another species’ perspective. Learn. Behav. 46 , 101–102 (2017).

Silk, J. B. et al . Chimpanzees are indifferent to the welfare of unrelated group members. Nature 437 , 1357–1359 (2005).

Article   ADS   CAS   Google Scholar  

Schmelz, M., Grueneisen, S., Kabalak, A., Jost, J. & Tomasello, M. Chimpanzees return favors at a personal cost. Proc. Natl. Acad. Sci. USA 114 , 7462–7467 (2017).

Ben-Ami Bartal, I., Decety, J. & Mason, P. Empathy and pro-social behavior in rats. Science 334 , 1427–1430 (2011).

Article   ADS   Google Scholar  

Ben-Ami Bartal, I., Rodgers, D. A., Sol Bernardez Sarria, M., Decety, J. & Mason, P. Pro-social behavior in rats is modulated by social experience. Elife 3 , e01385 (2014).

Ben-Ami Bartal, I. et al . Anxiolytic treatment impairs helping behavior in rats. Front. Psychol. 7 , 850 (2016).

Sato, N., Tan, L., Tate, K. & Okada, M. Rats demonstrate helping behavior toward a soaked conspecific. Anim. Cogn. 18 , 1039–1047 (2015).

Wrighten, S. A. & Hall, C. R. Support for altruistic behavior in rats. Open J Soc. Sci. 04 , 93–102 (2016).

Google Scholar  

Cronin, K. A. Prosocial behaviour in animals: the influence of social relationships, communication and rewards. Anim. Behav. 84 , 1085–1093 (2012).

Silberberg, A. et al . Desire for social contact, not empathy, may explain “rescue” behavior in rats. Anim. Cogn. 17 , 609–618 (2014).

Schwartz, L. P. et al . Does a rat release a soaked conspecific due to empathy? Anim. Cogn. 20 , 299–308 (2017).

Hachiga, Y. et al . Does a rat free a trapped rat due to empathy or for sociality? J. Exp. Anal. Behav. 110 , 267–274 (2018).

Decety, J., Ben-Ami Bartal, I., Uzefovsky, F. & Knafo-Noam, A. Empathy as a driver of prosocial behaviour: highly conserved neurobehavioural mechanisms across species. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371 , 20150077 (2015).

Glavin, G. B., Paré, W. P., Sandbak, T., Bakke, H. K. & Murison, R. Restraint stress in biomedical research: an update. Neurosci. Biobehav. Rev. 18 , 223–249 (1994).

de Vignemont, F. & Singer, T. The empathic brain: how, when and why? Trends Cogn. Sci. 10 , 435–441 (2006).

Leiberg, S. & Anders, S. The multiple facets of empathy: a survey of theory and evidence. Prog. Brain. Res. 156 , 419–440 (2006).

Panksepp, J. & Panksepp, J. B. Toward a cross-species understanding of empathy. Trends Neurosci. 36 , 489–496 (2013).

Vasconcelos, M. et al . Pro-sociality without empathy. Biol. Lett. 8 , 910–912 (2012).

Lavery, J. J. & Foley, P. J. Altruism or Arousal in the Rat? Science 140 , 172–173 (1963).

Yang, H. et al . Altered behavior and neural activity in conspecific cagemates co-housed with mouse models of brain disorders. Physiol. Behav. 163 , 167–176 (2016).

Ueno, H. et al . Empathic behavior according to the state of others in mice. Brain Behav. 8 , e00986 (2018).

Langford, D. J. et al . Social modulation of pain as evidence for empathy in mice. Science 312 , 1967–1970 (2006).

Pioli, E. Y. et al . An automated maze task for assessing hippocampus-sensitive memory in mice. Behav. Brain. Res. 261 , 249–257 (2014).

Peng, M. et al . Battery of behavioral tests in mice to study postoperative delirium. Sci. Rep. 6 , 29874 (2016).

Karagiannides, I. et al . Chronic unpredictable stress regulates visceral adipocyte-mediated glucose metabolism and inflammatory circuits in male rats. Physiol. Rep. 2 , e00284 (2014).

Crawley, J. N. Microbial Status and Genetic Evaluation of Mice and Rats: Proceedings of the 1999 US/Japan Conference. (National Academies Press (US), 2000).

Crawley, J. N. Designing mouse behavioral tasks relevant to autistic-like behaviors. Ment. Retard. Dev. Disabil. Res. Rev. 10 , 248–258 (2004).

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Acknowledgements

We thank the Kawasaki Medical School Central Research Institute for providing the instruments to carry out this work. The authors would like to thank Editage ( www.editage.jp ) for the English language review. This work was supported by a Grant Aid for Kawasaki University of Medical Welfare Scientific Research Fund.

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Hiroshi Ueno

Department of Medical Technology, Graduate School of Health Sciences, Okayama University, Okayama, 700-8558, Japan

Hiroshi Ueno & Motoi Okamoto

Department of Psychiatry, Kawasaki Medical School, Kurashiki, 701-0192, Japan

Shunsuke Suemitsu, Shinji Murakami, Naoya Kitamura, Kenta Wani & Takeshi Ishihara

Department of Neuropsychiatry, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, 700-8558, Japan

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All authors had complete access to all the study data and assume complete responsibility for the integrity of the data and accuracy of the data analysis. Study concept and design: H.U., M.O. and T.I. Acquisition of data: H.U. and S.S. Analysis and interpretation of data: H.U. and S.S. Drafting of the manuscript: H.U. and M.O. Critical revision of the manuscript for important intellectual content: S.M., N.K., K.W., Y.M. and T.I. Statistical analysis: H.U. and S.S. Study supervision: M.O. and T.I.

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Ueno, H., Suemitsu, S., Murakami, S. et al. Helping-Like Behaviour in Mice Towards Conspecifics Constrained Inside Tubes. Sci Rep 9 , 5817 (2019). https://doi.org/10.1038/s41598-019-42290-y

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Received : 19 June 2018

Accepted : 27 March 2019

Published : 09 April 2019

DOI : https://doi.org/10.1038/s41598-019-42290-y

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Open Access

Peer-reviewed

Research Article

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

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

* E-mail: [email protected]

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

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

• Georg Primes, 
• Martin Fieder

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

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

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

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

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

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

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

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

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Material and methods

Study description.

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

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

Genotypic data

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

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

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

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

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

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

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

Genetic association analysis

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

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

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

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

Genomic inflation

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

Genetic variance estimation

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

Genetic correlation

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

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

Gene set analysis

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

Supporting information

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

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

S1 File. Gene-set analysis.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Acknowledgments

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

• 1. Eisenberg N, Fabes RA, Spinrad TL. Prosocial development [Internet]. Wiley Online Library; 1998 [cited 2017 Apr 24]. Available from: http://onlinelibrary.wiley.com/doi/10.1002/9780470147658.chpsy0311/full
• View Article
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• 39. Westberg L, Hasse Walum H. Oxytocin and vasopressin gene variation and the neural basis of social behaviors. The Oxford Handbook of Molecular Psychology. 2015:145.
• 46. Health and Retirement Study. Produced and distributed by the University of Michigan with funding from the National Institute of Aging (grant numbers U01AG009740, RC2AG036495, and RC4AG039029), Ann Arbor, MI. 2016.
• 54. Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2013. 2014.

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The Good Samaritan Experiment: Why do people help each other?

You’re on your way to enjoy a free period when you see someone has dropped all of their class supplies. There are papers and notebooks scattered all over the floor, and they’re struggling to pick them all up. You think to yourself, should I help them? You make up your mind and decide to help the other student, but why? 

You may think you helped the other student because you’re a kind person. However, it’s actually because you had the time to spare. People are more likely to help a stranger in need if they aren’t in a hurry. If you had been late to class or on your way to an important meeting, it’s likely you would have ignored the other student. Personality doesn’t really affect helping behavior. While you may still be kind, the real cause of helping behavior is most likely because you had plenty of time. 

The Good Samaritan Experiment

The Good Samaritan Experiment was conducted in 1973 by John Darley and Daniel Batson at Princeton University’s Theological Seminary with participants who were studying to become religious leaders. The researchers hoped to discover whether helping is more motivated by personal characteristics or by the environment. 

In the experiment, the participants were first asked to fill out surveys assessing whether their motivations for being religious were intrinsic or extrinsic. They were then split into two groups. Half the participants were told to prepare a speech on job opportunities while the other half were told to prepare a speech about the Good Samaritan, a Biblical story about a victim ignored by several holy people and eventually saved by someone considered an enemy, the Samaritan. The participants were told to travel to a different building to give their speech. 

Unbeknownst to the participants, the researchers had assigned them to one of three groups. Some participants were told that if they left immediately, they would be early, others were told they would be on-time, and the remainder were told they were already late. Along the path to the building, each participant ran into a stranger who had fallen in an alleyway. The stranger coughed and moaned, signaling that they needed help. 

How It Works

So, which of the participants decided to help the stranger? Overall, 40% of the participants offered some help to the stranger. When the participants believed they were early for their speech, 63% of them helped the stranger. In moderate hurry situations, when participants believed they would be on time, 45% of them helped the stranger. In high hurry situations, only 10% of people helped the stranger. Even when people were on their way to give a speech about helping, they were less likely to help if they were in a rush. 

Initially, the results of this study can be disheartening. How could so many people be so self-centered that they would neglect a person who clearly needs help? Darley and Batson believe that the participants only ignored the stranger because arriving to give their speech on time would help the experimenters. Conflicting obligations, rather than cruelty, could explain low helping numbers in high hurrying situations. 

It is important to keep the Good Samaritan Experiment in mind when you see someone who needs help. It is natural to worry about upholding obligations and promises. But, if you slow down for a minute, the situation becomes clearer. More often than not, people will understand lateness or absence caused by offering help to someone suffering. Don’t let being in a hurry stop you from doing something good.

Think Further

• When you are deciding whether to help someone, what factors do you consider?
• How has “hurriedness” impacted your decisions to help others? 
• Based on this experiment, what can we do as a society to encourage helping behaviors?

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• Cunningham, M. R. (1979). Weather, mood, and helping behavior: Quasi experiments with the sunshine samaritan. Journal of Personality and Social Psychology, 37 (11), 1947–1956. https://doi.org/10.1037/0022-3514.37.11.1947
• Guéguen, N., & De Gail, M.-A. (2003). The Effect of Smiling on Helping Behavior: Smiling and Good Samaritan Behavior. Communication Reports, 16 (2), 133–140. https://doi.org/10.1080/08934210309384496
• McManus, Ryan M et al. “What We Owe to Family: The Impact of Special Obligations on Moral Judgment.” Psychological science vol. 31,3 (2020): 227-242. doi:10.1177/0956797619900321
• Shenker, Israel. “Test of Samaritan Parable: Who Helps the Helpless?” The New York Times , 10 Apr. 1971, https://www.nytimes.com/1971/04/10/archives/test-of-samaritan-parable-who-helps-the-helpless.html.

This gave students an opportunity to watch a video to identify key factors in our judicial system, then even followed up with a brief research to demonstrate how this case, which is seemingly non-impactful on the contemporary student, connect to them in a meaningful way

This is a great product. I have used it over and over again. It is well laid out and suits the needs of my students. I really appreciate all the time put into making this product and thank you for sharing.

Appreciate this resource; adding it to my collection for use in AP US Government.

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Accountability

Prosocial Behavior: 12 Examples, Activities & Findings

She swerved to avoid it, sending her car into a spin across the freeway until it finally came to a stop in the fast lane.

In a daze, she realized that someone was knocking on her passenger-side door, asking her if she needed help. Yes, she did. And with her permission, he hopped into her car, gunned it across the freeway, and parked behind his own vehicle. Then he hopped back into his car and drove off, leaving Dr. Marsh , a Georgetown University professor of psychology, wondering this:

Why would somebody risk his life to help a stranger when there was clearly no possibility of a payoff at all?

Before you continue, we thought you might like to download our three Positive Relationships Exercises for free . These detailed, science-based exercises will help you or your clients build healthy, life-enriching relationships.

This Article Contains:

What is prosocial behavior 2 theories in psychology, 3 real-life examples of prosocial behavior, 4 thought-provoking findings and experiments, prosocial behavior in child development, 2 ways to increase prosocial behavior, 3 helpful activities, assessing prosociality: questionnaires and scales, prosocial behavior, antisocial behavior, and altruism, relevant positivepsychology.com resources, a take-home message.

Prosocial behavior is any behavior that is intended to benefit another person or persons (Dunfield, 2014). Examples include volunteer work, donating money, or helping a neighbor move a heavy item of furniture. The most striking type of prosocial behavior is altruism, where a person takes on a cost to help another person with no expectation or possibility of receiving a benefit in return.

This is what Dr. Marsh experienced from the anonymous driver who put in time and effort to help her to safety and asked for no compensation in return.

When you engage in prosocial behavior, the goal of your behavior is to address another person’s needs. Generally speaking, people’s needs fall into three categories:

• Instrumental needs, where an individual experiences difficulty achieving a goal on their own
• Unmet desires, where an individual does not have access to a required resource
• Emotional distress, such as grief or loneliness

When you help a person reach a goal, share your resources, or provide comfort, you are engaging in prosocial behavior.

Scientists and philosophers have proposed numerous theories to explain the paradox of prosocial behavior. Why do people willingly impose costs on themselves to benefit others rather than focusing solely on benefiting themselves?

Theoretical explanations of prosocial behavior fall into two broadly defined categories. The first category contains evolution-based theories that explain prosocial behavior as adaptations to the pressures inherent in social living.

Kin selection theory explains why you are more likely to help genetic relatives than friends or strangers. If you help people who share genes with you, you increase their chances of survival and ensure that your genes remain (or increase) in the gene pool (Hamilton, 1963, 1964).

Reciprocal altruism theory points out that helping non-kin can also be adaptive if the recipients of your generosity can be relied upon to reciprocate help when you need it (Trivers, 1971).

Scientists Robert Axelrod and William Hamilton (1981) summarized prosocial behavior in the natural world this way:

The theory of evolution is based on the struggle for life and the survival of the fittest. Yet cooperation is common between members of the same species and even between members of different species.

The second broad category of theories includes those that attribute prosocial tendencies to individual differences in social learning experiences, mood, and ability to empathize (Bierhoff, 2005).

For example, a large meta-analysis found that the strongest predictor of prosocial behavior is the ability to empathize with feelings and viewpoints of other people (Bierhoff, Klein, and Kramp, 1991).

Other studies have found that children and adults are more willing to help or share with others when they are in a happy mood than when they are in a neutral or negative mood (Rosenhan, Underwood, & Moore, 1974).

For example, rats will work a latch to free a trapped rat or rescue a drowning one, even when turning their backs would allow them to obtain a tasty reward (Sato, Tan, Tate, & Okada, 2015).

Vervet monkeys give alarm calls to warn fellow monkeys of the presence of predators, even though doing so puts them at risk of attack (Cheney & Seyfarth, 1990).

Over 115 episodes of humpback whales intervening in killer whale attacks on unrelated species have been documented by marine biologists (Pitman et al., 2017).

People engage in prosocial behavior when they donate time or money to charitable causes, help a friend move heavy furniture, run errands for someone who is ill, and encourage someone who feels like giving up.

In each case, we offer time and effort to ease someone else’s burden or improve their wellbeing.

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According to standard economic theories that are taught in business schools and political science, the most rational choice in any situation is the one that maximizes benefits to you, regardless of the impact on others (Anand, Pattanaik, & Puppe, 2009).

To put it another way, you behave rationally only when you behave selfishly. Yet decades of research in experimental economics, experimental psychology, and anthropology have proven otherwise. When making decisions, people take the impact their choices have on others seriously.

The most dramatic demonstrations come from studies based on Dictator and Ultimatum economic games, such as the following.

In the Dictator game, a sum of money is given to one person, and that person has complete authority to decide whether to keep or share the money with another person.

According to standard economic theories, the rational thing to do is to keep all the money for yourself. But that is not what people do. Instead, dictators freely give away about 15–35% of the money to their partners – strangers they just met and will probably never see again (Camerer, 2003).

This result has been replicated worldwide, from small-scale hunter–gatherer societies to large industrialized societies (Henrich et al., 2005).

In the Ultimatum game, one party is given the right to propose how the sum should be divided, and another party (the responder) can either accept or reject the offer. If the offer is rejected, nobody gets any money.

According to standard economic theories, proposers should offer the minimum amount possible, and responders should accept whatever is offered (because something is better than nothing). But that is not what people do. Proposers typically offer 40–50%, and responders routinely reject offers of less than 20% (Camerer, 2003).

Even more surprising is the observation that people are often willing to pay a penalty to be given the opportunity to punish a player who behaves selfishly in Dictator and Ultimatum games, even if they are not playing the game but merely watching it (Fehr & Gächter, 2002).

Worldwide, people’s choices appear to be motivated by concerns about fairness, often creating norms (social rules) that are intended to promote prosocial behavior.

Prosocial individuals are typically sought after as partners, friends, and mates. Those who behave selfishly are avoided because they signal their willingness to exploit rather than help their partners (von Rueden, 2014).

Because infants can’t talk, these methods rely on other kinds of measurable behaviors such as how long they look at displays that differ in theoretically relevant ways or which choices they make when given a chance to reach for different types of toys. Surprisingly, infants show strong prosocial as well as in-group biases from a very early age.

Infants as young as six months prefer individuals who help others in distress over those who harm others or stand by while another is being harmed.

In one series of experiments, six-month-old infants were shown video clips of a red disk straining to roll up a hill (Hamlin, Bloom, & Wynn, 2007). A yellow square raced into view and pushed the circle up the hill. A blue triangle then appeared and pushed the circle back down to the bottom of the hill.

The infants watched this display repeatedly until they became bored and looked away. Then they were presented with a tray containing a yellow square and blue triangle and were allowed to choose one. Infants overwhelming chose the yellow square.

This result has been replicated in a variety of experiments using different types of actors behaving in either prosocial or antisocial ways.

Other studies have found that infants in this age group prefer individuals who punish people who harm others (Hamlin, Wynn, Bloom, & Mahajan, 2011).

By nine months of age, infants prefer individuals who help those who are like them, and they prefer individuals who harm those who are not like them. For example, in one set of studies, nine-month-olds preferred individuals who harmed puppets that didn’t share their food preferences (Hamlin, Mahajan, Liberman, & Wynn, 2013).

Between 12 and 36 months of age, young children readily engage in prosocial behaviors such as helping, comforting, sharing, and cooperating with others (Brownell, 2013).

By the third year of life, children also show a marked precocity for learning social rules and monitoring compliance with them. For example, they actively enforce rules during games even when they are spectators rather than players (Cummins, 1996; Schmidt & Tomasello, 2012).

By the age of four, children become adept at taking multiple factors into consideration when deciding how to partition resources, such as effort, need, group membership, cost, and past experiences with different individuals (Fehr, Bernhard, & Rockenbach, 2008).

During middle childhood, children begin to use prosocial lying to protect another’s feelings or, in some cultures, to appear modest. Their cognitive skills have also matured sufficiently to allow them to appreciate that harm is sometimes necessary to achieve a greater good, such as pulling someone off an unsafe play structure to prevent them from injury (Evans & Lee, 2014).

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How would one go about improvinf prosocial behavior? We offer two options below.

Nudge people toward prosocial choices

Nobel Laureate Richard Thaler and coauthor Cass Sunstein introduced a powerful means for steering people’s choices in specific directions, called nudging, which involves arranging choices in a way that shifts preferences predictably without forbidding any options.

For example, rather than giving employees the choice of whether or not to enroll in a retirement program, the “ Save More Tomorrow” program automatically enrolls employees but gives them the right to opt out at any time.

Programs like these increased retirement savings by as much as $30 billion over the past decade (Malito, 2018).

Improve empathy skills

Empathy essentially means putting yourself in another’s shoes.

Emotional empathy means feeling the same emotion that another person is feeling. If the person is sad, you feel sad as well. If they feel happy, you feel happy.

Cognitive empathy means seeing things from another person’s perspective, understanding why and how they are interpreting and responding to events taking place. Countless studies have repeatedly shown that individuals who excel at cognitive and emotional empathy find it easier to cooperate with, help, and defuse conflicts between others (Stocks, Lishner, & Decker, 2009).

One of the best ways to improve empathy skills is to read fiction and biographies. When you read a novel or biography, the story unfolds in a character’s own words, putting you right there inside their minds and feelings.

Neuroscience studies have reported that when reading fiction, there is more activity in parts of the brain that are involved in simulating what other people are thinking (Tamir, Bricker, Dodell-Feder, & Mitchell, 2016). Other studies have found that reading fictional narratives increased self-reported empathy and empathetic skills over time (Bal & Veltkamp, 2013).

Psychologists Rodolfo Barragan and Carol Dweck (2014) found that even one-year-olds quickly begin to respond to new playmates as people to help and share with after playing games like these.

Hone your skills at reading emotional facial expressions. It is easier to behave in prosocial ways if you are adept at interpreting facial expressions and anticipating what people want or what they’ll do. Courses for adults to improve emotion reading skills have been developed by Dr. Paul Ekman, a psychologist and expert in the fields of emotions, nonverbal communication , and deception detection.

Play party games that encourage perspective taking. Game designer, artist, and professor Mary Flanagan developed a subtle, less preachy approach to improving social coordination skills called the  Awkward Moment Card Game , which requires players to choose solutions to awkward social problems. Adults and children have been found to improve their perspective-taking skills after playing the game regularly.

The most widely used and respected assessment instrument is the Prosocial Tendencies Measure (Carlo & Randall, 2002). The measure was initially developed to use with college-aged students and young adults and was later modified to use with middle and high-school-aged adolescents.

It is an extensive scale of 23 items, which distinguish the following six types of prosocial behaviors:

• Altruistic (example item: I feel that if I help someone, they should help me in the future .)
• Anonymous (example item: I tend to help needy others most when they do not know who helped them .)
• Dire (example item: I tend to help people who are in a real crisis or need .)
• Emotional (example item: I tend to help others, particularly when they are emotionally distressed .)
• Compliant (example item: When people ask me to help them, I don’t hesitate. )
• Public (example item: I can help others best when people are watching me .)

Another widely used instrument is the Prosocialness Scale for Adults (Caprara, Steca, Zelli, & Capanna, 2005). The scale is composed of 17 items and classifies behaviors and feelings into four types: sharing, helping, taking care of, and empathy with others.

Notably, the scores people receive on these questionnaires are predictive of their behavior in Dictator and Ultimatum games. For example, individuals who score high on altruism tend to make generous offers in these economic games (Rodrigues, Nagowski, Mussel, & Hewig, 2018; Zhao, Ferguson, & Smillie, 2016).

The National Mentoring Resource Center offers a useful online questionnaire for assessing the prosocial behavior of children between the ages of 6–11 years.

Altruism is an extreme version of prosocial behavior because it involves imposing costs on yourself solely to benefit others.

Psychopathy is an extreme version of antisocial behavior because harm is imposed on others solely to the benefit of oneself, without regard to the suffering inflicted on others.

Extraordinary altruists – such as those who donate kidneys to others – show exceptional sympathetic neural responses to others’ emotions (particularly fear), which drive them to sympathetic action (Brethel-Haurwitz et al., 2018).

In contrast, psychopaths show a deficiency in this kind of neural response and a corresponding reduction in empathy for others’ distress (Blair, 2013).

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At PositivePsychology.com, we offer many resources with which to develop your prosocial behavior skills.

This article will teach you how to regulate emotions and not act impulsively. Another good read is this article that will show you how to improve communication skills .

Whether your goal is to get your kids to clean up their rooms or to get your boss to extend a work deadline, it is important to frame the request in a way that is unlikely to be perceived as a threat, demand, or negative evaluation by the other person.

Finding your own purpose, perhaps through reading any of these meaning of life books , may lead you to find that serving others is what brings you happiness.

Lastly, we strongly recommend reading this article about altruism , which explains the concept in great depth.

If you’re looking for more science-based ways to help others build healthy relationships, this collection contains 17 validated positive relationships tools for practitioners. Use them to help others form healthier, more nurturing, and life-enriching relationships.

Decades of research in cognitive science, developmental science, neuroscience, evolutionary biology, and anthropology have quite clearly shown that we are born with prosocial biases and that the strength of these biases varies across individuals and societies.

Our early learning experiences and cultural pressures shape these biases, either strengthening or weakening this inborn tendency to help or hinder others.

Adults and children tend to prefer to interact with people who display prosocial behavior and to avoid those who behave selfishly.

Historically, societies that favor cooperative effort and prosocial behavior thrive, while those that prefer self-interest eventually self-destruct.

We hope you enjoyed reading this article. Don’t forget to download our three Positive Relationships Exercises for free .

• Anand, P., Pattanaik, P., & Puppe, C. (Eds.) (2009). The handbook of rational and social choice . Oxford: Oxford University Press.
• Axelrod, R., & Hamilton, W. D. (1981). The evolution of cooperation. Science, 211 , 1390–1396.
• Bal, P. M., & Veltkamp, M. (2013). How does fiction reading influence empathy? An experimental investigation of the role of emotional transportation. PLoS ONE , 8 , e55341.
• Barragan, R., & Dweck, C. S. (2014). Rethinking natural altruism: Simple reciprocal interactions trigger children’s benevolence. Proceedings of the National Academy of Sciences of the United States of America , 111 (48), 17071–17074.
• Bierhoff, H. W. (2005). The psychology of compassion and prosocial behaviour. In P. Gilbert (Ed.), Compassion: Conceptualisations, research and use in psychotherapy (pp. 148–167). London: Routledge.
• Bierhoff, H. W., Klein, R., & Kramp, P. (1991). Evidence for the altruistic personality from data on accident research. Journal of Personality , 59 (2), 263–280.
• Blair, R. J. R. (2013). Psychopathy: cognitive and neural dysfunction. Dialogues in Clinical Neuroscience , 15 , 181–190.
• Brethel-Haurwitz, K. M., Cardinale, E. M., Vekaria, K. M., Robertson, E. L., Walitt, B., VanMeter, J. W., & Marsh, A. A. (2018). Extraordinary altruists exhibit enhanced self–other overlap in neural responses to distress. Psychological Science , 29 , 1631–1641.
• Brownell, C. A. (2013). Early development of prosocial behavior: Current perspectives. Infancy , 18 , 1–9.
• Camerer, C. F. (2003). Behavioural studies of strategic thinking in games. Trends in Cognitive Sciences , 7 (5), 225–231.
• Carlo, G., & Randall, B. A. (2002). The development of a measure of prosocial behaviors for late adolescents. Journal of Youth and Adolescence , 31 , 31–44.
• Caprara, G. V., Steca, P., Zelli, A., & Capanna, C. (2005). A new scale for measuring adults’ prosocialness. European Journal of Psychological Assessment , 21 , 77–89.
• Capraro, V., Jagfeld, G., Klein, R., Mul, M., & Van De Pol, I. (2019). Increasing altruistic and cooperative behaviour with simple moral nudges. Scientific Reports, 9, 11880.
• Cheney, D., & Seyfarth, R. (1990). How monkeys see the world . Chicago, IL: University of Chicago Press.
• Cummins, D. D. (1996) Evidence of deontic reasoning in 3- and 4-year-old children. Memory & Cognition, 24, 823–829.
• Dunfield K. A. (2014). A construct divided: Prosocial behavior as helping, sharing, and comforting subtypes. Frontiers in Psychology, 5 , 958.
• Evans, A. D., & Lee, K. (2014). Lying, morality, and development. In M. Killen & J. G. Smetana (Eds.) Handbook of moral development, 2nd ed., (pp. 361–384). New York, NY: Psychology Press.
• Fehr, E., Bernhard, H., & Rockenbach, B. (2008). Egalitarianism in young children. Nature , 454 , 1079–1083.
• Fehr, E., & Gächter, S. (2002). Altruistic punishment in humans. Nature , 415 (6868), 137–140.
• Hamilton, W. D. (1963). The evolution of altruistic behavior. The American Naturalist , 97 (896), 354–356.
• Hamilton, W. D. (1964). The genetical evolution of social behaviour I, II. Journal of Theoretical Biology , 7 , 1–52.
• Hamlin, J. K., Wynn, K., & Bloom, P. (2007). Social evaluation by preverbal infants. Nature , 450 , 557–559.
• Hamlin, J. K., Wynn, K., Bloom, P., & Mahajan, N. (2011). How infants and toddlers react to antisocial others. Proceedings of the National Academy of Sciences of the United States of America , 108 , 19931–19936.
• Hamlin, J. K., Mahajan, N., Liberman, Z, & Wynn, K. (2013). Not like me = bad: Infants prefer those who harm dissimilar others. Psychological Science , 24 , 589–594.
• Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., … & Henrich, N. S. (2005). “Economic man” in cross-cultural perspective: Behavioral experiments in 15 small-scale societies. The Behavioral and Brain Sciences , 28 , 795–855.
• Malito, A. (2018, January 6). Nobel Prize winner Richard Thaler may have added $29.6 billion to retirement accounts. MarketWatch online.  Retrieved from https://www.marketwatch.com/story/nobel-prize-winner-richard-thaler-may-have-added-296-billion-to-retirement-accounts-2017-10-09
• Pitman, R. L., Deecke, V. B., Gabriele, C. M., Srinivasan, M., Denkinger, J., … & Schulman‐Janiger, A. (2017). Humpback whales interfering when mammal-eating killer whales attack other species: Mobbing behavior and interspecific altruism? Marine Mammal Science , 33 , 7–58.
• Rodrigues, J., Nagowski, N., Mussel, P., & Hewig, J. (2018). Altruistic punishment is connected to trait anger, not trait altruism, if compensation is available. Heliyon , 4, e00962.
• Rosenhan, D. L., Underwood, B., & Moore, B. (1974). Affect moderates self-gratification and altruism. Journal of Personality and Social Psychology , 30 (4), 546–552.
• Sato, N., Tan, L., Tate, K., & Okada, M. (2015). Rats demonstrate helping behavior toward a soaked conspecific. Animal Cognition , 18 , 1039–1047.
• Schmidt, M. F. H., & Tomasello, M. (2012). Young children enforce social norms. Current Directions in Psychological Science , 21 , 232–236.
• Stocks, E. L., Lishner, D. A., & Decker, S. K. (2009). Altruism or psychological escape: Why does empathy promote prosocial behavior? European Journal of Social Psychology , 39 , 649–665.
• Tamir, D. I., Bricker, A., Dodell-Feder, D., & Mitchell, J. P. (2016). Reading fiction and reading minds : The role of simulation in the default network. Social, Cognitive, and Affective Neuroscience, 11 , 215–224.
• Trivers, R. L. (1971). The evolution of reciprocal altruism. The Quarterly Review of Biology, 46 , 35–57.
• von Rueden, C. (2014). The roots and fruits of social status in small-scale human societies. In J. T. Cheng, J. L. Tracy, & C. Anderson (Eds.), The psychology of social status (pp. 179–200). New York, NY: Springer.
• Zhao, K., Ferguson, E., & Smillie, L. D. (2016). Prosocial personality traits differentially predict egalitarianism, generosity, and reciprocity in economic games. Frontiers in Psychology , 7 , 1137.

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How to Perform Behavioral Experiments

Test how real your assumptions are and you might change your life.

  Thomas Barwick  / Stone / Getty Images Plus

Psychotherapists sometimes encourage clients to perform behavioral experiments that test the reality of their beliefs. It’s a powerful cognitive behavioral therapy technique that can help people recognize that their assumptions aren’t necessarily accurate.

What you think and believe isn't always true. But holding onto some of those beliefs might cause you to suffer.

For example, someone who believes they are destined to be an “insomniac” might try several different behavioral experiments in an attempt to uncover whether specific strategies might help them sleep better, like exercising in the morning and turning off their screens an hour before bedtime.

How It Works

Cognitive behavioral therapists help individuals become aware of their problems and the thoughts, emotions, and beliefs about their problems. The therapist helps identify inaccurate thoughts and thought patterns that contribute to the problem.

Then, they help people challenge their irrational or unproductive thoughts by asking questions and encouraging them to consider alternative ways to view an issue.

Therapists often ask questions that help clients look for exceptions to their rules and assumptions. For example, a therapist who is working with an individual who insists, “No one ever likes me,” might ask, “When was a time when someone did like you?”

This could help the client see that their assumptions aren’t 100% accurate.

But changing thought patterns aren’t always effective in changing deeply held core beliefs. This is in part because we’re constantly looking for evidence that supports our beliefs.

Someone who believes no one ever likes her might automatically think not getting a response from a text message is further proof that people dislike her. Meanwhile, she may view an invitation to a party as a “sympathy invite” from someone who feels sorry for her, not as proof that people actually like her.

When changing thought patterns aren’t effective in changing a person’s beliefs, changing their behavior first may be the best option.

An individual who accomplishes something they assumed they couldn’t do may begin to see themselves differently. Or an individual who sees that people don’t respond the way they assumed they would may let go of their unhealthy beliefs about other people.

Using behavioral experiments to gather evidence can chip away at self-limiting beliefs and help individuals begin to see themselves, other people, or the world in a different manner.

Studies show that cognitive behavioral therapy is effective in treating a variety of issues, including anxiety, depression , sleep disorders, substance abuse issues , and PTSD .

Press Play For Advice On Reframing

Hosted by Amy Morin, LCSW, this episode of The Verywell Mind Podcast shares tips for reframing your self-limiting beliefs, featuring Paralympic gold medalist Mallory Weggemann.

Follow Now : Apple Podcasts / Spotify / Google Podcasts

The Process

Behavioral experiments can take many forms. For some individuals a behavioral experiment might involve taking a survey to gather evidence about whether other people hold certain beliefs. For others it might involve facing one of their fears head on.

No matter what type of behavioral experiment a client is conducting, the therapist and the client usually work together on the following process:

• Identifying the exact belief/thought/process the experiment will target
• Brainstorming ideas for the experiment
• Predicting the outcome and devising a method to record the outcome
• Anticipating challenges and brainstorming solutions
• Conducting the experiment
• Reviewing the experiment and drawing conclusions
• Identifying follow-up experiments if needed

The therapist and the client work together to design the experiment. Then, the client conducts the experiment and monitors the results. The therapist and the client usually debrief together and discuss how the results affect the client’s belief system.

The therapist may prescribe further experiments or ongoing experiments to continue to assess unhealthy beliefs.

Psychotherapists may assist individuals in designing a behavioral experiment that can counteract almost any distorted way of thinking. Here are a few examples of behavioral experiments:

• A woman believes people will only like her if she is perfect. Her perfectionist tendencies create a lot of stress and anxiety. She agrees to conduct a behavioral experiment that involves making a few mistakes on purpose and then monitoring how people respond. She sends an email with a few typos and sends a birthday card with a grammatical error to see how people respond.
• A man believes he’s socially awkward. Consequently, he rarely attends social events—and when he does, he sits in the corner by himself. His behavioral experiment involves going to one social event per week and talking to five people. He then gauges how people to respond to him when he acts outgoing and friendly.
• A woman worries her boyfriend is cheating on her. She checks his social media accounts throughout the day to see what he is doing. Her behavioral experiment is to stop using social media for two weeks and see if her anxiety gets better or worse.
• A man struggles to stay asleep at night. When he wakes up, he turns on the TV and watches it until he falls asleep again. His behavioral experiment is to read a book when he wakes up to see if it helps him fall back to sleep faster.
• A woman with depression doesn’t go to work on days when she feels bad. On these days she stays in bed all day watching TV. Her behavioral experiment involves pushing herself to go to work on days she’s tempted to stay in bed to see if getting out of the house improves her mood.
• A man with social anxiety avoids socializing at all costs. He thinks he won’t have anything worthwhile to contribute to conversations. His behavioral experiment is to start attending small social events to see if his interactions with others go as poorly as he anticipates.

A Word From Verywell

If you’re interested in testing some of the potentially self-limiting beliefs you’ve been holding onto, try designing your own behavioral experiment. If you’re not certain how to get started, want some help designing the experiment, or would like to learn more about how to recognize irrational beliefs, then contact a cognitive behavioral therapist.

If you aren’t sure where to find one, speak to your physician. Your doctor may be able to refer a cognitive behavioral therapist who can assist you.

David D, Cristea I, Hofmann SG. Why Cognitive Behavioral Therapy Is the Current Gold Standard of Psychotherapy .  Frontiers in Psychiatry . 2018;9. doi:10.3389/fpsyt.2018.00004.

Hofmann SG, Asnaani A, Vonk IJJ, Sawyer AT, Fang A. The Efficacy of Cognitive Behavioral Therapy: A Review of Meta-analyses .  Cognitive Therapy and Research . 2013;36(5):427-440. doi:10.1007/s10608-012-9476-1.

By Amy Morin, LCSW Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

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Behavioral Experiment

Our thoughts and beliefs determine how we feel, and how we act, at any given moment. Even thoughts that are irrational, or lack evidence, impact our mood and behavior. A behavioral experiment is a CBT tool for testing our thoughts and beliefs, and replacing those that are irrational and harmful with healthy alternatives. What makes behavioral experiments so powerful is that we get to challenge our thoughts in the real world, as opposed to just hypothetically.

In the Behavioral Experiment worksheet, clients will identify one of their irrational thoughts, plan an experiment to test it, and execute the experiment. After the experiment is complete, they will describe their experience, how they felt, and how their original thought has changed.

When planning an experiment, be sure it is realistic so clients are more likely to follow through. During the next session, follow up and discuss the results of the experiment. Focus on the expectation versus reality of the experiment, and how the original thought might change as a result.

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Notice: Opening a fillable worksheet directly within an internet browser (e.g. Internet Explorer, Chrome, or Safari) may prevent work from being saved. Instead, the file should be saved to your device and opened with a PDF reader.

To learn more or share these instructions, visit the Fillable Worksheet Instructions .

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