Fischer Et Al 2006 By Stander Effect Essay

Citation: Slater M, Rovira A, Southern R, Swapp D, Zhang JJ, Campbell C, et al. (2013) Bystander Responses to a Violent Incident in an Immersive Virtual Environment. PLoS ONE 8(1): e52766. https://doi.org/10.1371/journal.pone.0052766

Editor: Frank Krueger, George Mason University/Krasnow Institute for Advanced Study, United States of America

Received: March 1, 2012; Accepted: November 22, 2012; Published: January 2, 2013

Copyright: © 2013 Slater et al. 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.

Funding: This work was funded by the UK EPSRC project “Visual and Behavioural Fidelity of Virtual Humans with Applications to Bystander Intervention in Violent Emergencies” (EP/F032420/1; EP/F030215/1; EP/F030355/1). http://www.epsrc.ac.uk. European Senior Research Grant TRAVERSE grant number 227985. http://erc.europa.eu/. The funders 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

A violent and unprovoked attack by one person on another unfolds in close view of an unrelated bystander: under what conditions will the bystander be likely to intervene to help the victim? In this paper we address the hypothesis that group affiliation between the bystander and the victim provides a powerful incentive for the bystander to try to intervene to stop the attack, or prevent harm to the victim, and in particular that this operates even though the perpetrator and victim are virtual human characters. Our experiment involved fans of an English football team, Arsenal. In one experimental condition (in-group) the fan conversed with a virtual character that was clearly an Arsenal supporter and in another condition the character was just a general football enthusiast but not an Arsenal fan (out-group). The virtual character was later threatened by a perpetrator that, in the in-group condition, specifically attacked his Arsenal affiliation. Our expectation was that based on group affiliation, those in the in-group would intervene more than those in the out-group. First we place this in the general context of studies of bystander intervention, and then describe the detailed design of the experiment and the results.

Research on the behaviour of bystanders in emergencies began with the response to the rape and murder of Kitty Genovese in New York in 1964. Social psychologists Bibb Latane and John Darley read a report on the murder in the New York Times suggesting that 38 witnesses had watched the murder unfold over 30 minutes from their apartment windows– and yet failed to intervene. In order to understand why this might have happened, they set out to create laboratory based experimental analogies of the event. They set up carefully choreographed situations in which bystanders were faced with a non-violent emergency situation while on their own or in the presence of others [1], [2]. The research led to the discovery of the ‘bystander effect’ – the idea that people are more likely to intervene on their own than in the presence of others [1]. This is one of the most reliable and robust findings in social psychology [3], [4].

However, as Cherry pointed out [5], through translating the events surrounding the Genovese murder into laboratory settings, Latane and Darley neglected some of the key features of the event. Despite the fact that the original murder involved violence by a man against a woman, subsequent experimental analogies tended to remove both the gendered nature of the attack and the violence. Although there are thousands of studies using non-violent emergency settings, it is possible to find only a few experiments that did retain violence as the emergency variable [6], [7], [8], [9], [10]. These found results that were at odds with the traditional bystander paradigm. In violent emergencies, what seemed to be most important about the likelihood of bystander intervention was not the presence of others, but rather the bystanders’ beliefs about the nature of the relationship between perpetrator and victim [9], [10]. In an experiment that did vary the number of bystanders to a violent emergency Harari et al. [6] showed that the presence of others actually enhanced the likelihood of bystander intervention in a simulated rape situation. This finding has been supported by contemporary work which presents violence to participants by means of a CCTV video link, where the presence of others is not found to inhibit helping [11] and can sometimes enhance it [12]. A recent meta-analysis by Fisher and colleagues [4] confirms that intervention behaviour in violent emergencies does not fit the traditional bystander effect explanation.

If violent emergencies are different in some way, it is important to understand the processes at work. Almost all violence research shares a similar limitation. In order to circumvent the practical and ethical problems of presenting violence in experimental settings, these experiments tend to avoid placing participants in direct contact with the violence itself. The only exception is the work described in [10] in which a role-play setting was used, and confederates actually staged a violent confrontation in front of naive participants who were also taking part in the role-play game. However, it is highly unlikely that contemporary ethics boards would allow this kind of design. The other studies either have the violence happening at a distance where it is possible to avoid the event [6] or present the violence as happening contemporaneously but where it can only be heard [7], [8], [9], or happening in another room where it can be seen on CCTV link [11]. This distancing of participants from the violence is required to satisfy the ethical and practical difficulties of experimental design, but may itself introduce psychological effects that interfere with the veridical nature of the situation. Imagining the violence, or having it happen in another room, is not the same as being physically where the violence erupts.

In [13] we argued that the use of immersive virtual environments (IVE) goes some way towards solving this problem, since there is mounting evidence that when people are faced with events and situations in an IVE they tend to behave and respond as if these were real [14]. IVEs portray a simulated computer generated reality at life size that is sensorially surrounding. Participants perceive this world through wide field-of-view stereo vision and sound. The form of perception involves more or less natural sensorimotor contingencies - meaning that the whole body is used for perception much as in physical reality, based at least on head-gaze direction and orientation achieved through head-tracking. This gives rise to the sensation of being in the virtual place that is depicted, a place-illusion. Additionally when there are dynamically unfolding events in the environment that personally refer to the participant, and where actions of the participant apparently cause responses in the virtual environment, this gives rise to a plausibility-illusion, meaning that events have the illusory quality of being real. When the participant has the double illusion - of being in the virtual place and where events that are happening are apparently really happening, this can give rise to behaviour and responses that are appropriate to the situation as if it were playing out in reality [15].

IVEs provide therefore a powerful tool for experimental studies in social psychology [16] and classic effects such as proxemics [17] where distances that people maintain between themselves are governed by social norms, have been reproduced several times in IVEs with respect to virtual humanoid characters [18], [19], [20]. Moreover, IVEs have been useful for experiments that would otherwise be difficult to carry out in any other way, such as the study of male risk taking in the presence of observers, specifically the differential effects of the observers being male or female [21].

Closer to the present study which focuses on responses to violence, the Stanley Milgram obedience paradigm [22] has been reproduced with IVE avoiding the ethical difficulties of deception [23], [24]. IVEs provide environments completely under control of a computer program but where people respond realistically. Every experimental condition can be exactly reproduced across trials as needed, and hence can be used for laboratory based experiments.

It has been argued before that IVEs provide an excellent tool for the study of prosocial behaviour [25]. The experiment described in the present study is specifically concerned with the likelihood of prosocial behaviour when participants are placed in direct proximity to violent behaviour. We explore the hypothesis that the psychological relationships between bystanders and the others involved are important in bystander behaviour, in this case specifically the relationship between the bystander and the victim [12], [26], [27], [28]. The experimental conditions provide a context where it is certain that the violence between perpetrator and victim is of the same magnitude and intensity for each experimental trial. Participants (n = 40) all supporters of the Arsenal Football Club, entered into a virtual reality that represents a bar. A male virtual human (V) approached and conversed with them about football for a few minutes. In one condition V wore an Arsenal football shirt and spoke enthusiastically about the club (in-group condition). In a second condition V wore an unaffiliated red sports shirt, and asked questions about Arsenal without special enthusiasm, using neutral responses and displaying ambivalence about Arsenal’s prospects (out-group condition). After a few minutes of this conversation another male virtual human (P, perpetrator) who had been sitting by the bar walked over to V (victim) and started an argument that he continually escalated until it became a physical attack (Figure 1).

Figure 1. The Victim and Perpetrator.

The Victim (V) is in the red shirt, with an Arsenal emblem in the in-group condition, and with a plain football shirt of the same colour in the out-group condition. The perpetrator (P) had been sitting by the bar. (a) P stood up to approach V and (b) started an argument. (c) As the argument progressed V made conciliatory statements and postures while (d) P became ever more aggressive finally pushing V violently against a wall.

https://doi.org/10.1371/journal.pone.0052766.g001

The main response variable was the extent to which the participant attempted to intervene during this confrontation. Interventions were verbal utterances or physical moves towards the two virtual characters and were coded from video recordings by two independent researchers (Methods). There were two binary factors group and LookAt. Group was whether V was in-group (Arsenal supporter) or out-group with respect to the participant. LookAt was whether or not occasionally during the confrontation V would look towards the participant or not (LookAt = ‘on’ or ‘off’). The experiment used a between-groups design, with n = 40, 10 participants allocated arbitrarily to one of the four cells of the 2×2 design. The degree of support for the Arsenal club was similar between the 4 experimental conditions (Text S1). At the end of their session they answered a questionnaire, and this was followed by an interview and debriefing. The data from two participants could not be used due to video recording failures.

Results

Numbers of Interventions

Table 1 shows the means and standard errors of the numbers of interventions indicating that the mean number of interventions was higher for the in-group than the out-group, but that the LookAt factor had no effect. Two-way analysis of variance was carried out on the response variables, the number of physical (nPhys) and number of verbal (nVerbal) interventions. ANOVA for nPhys indicates that the mean is greater for the in-group than for the out-group condition (P = 0.02) but with no significant differences for the LookAt factor and no interaction effect. However, the residual errors of the fit were strongly non-normal (Shapiro-Wilk test P = 0.0008). To overcome this problem a square root transformation was applied to nPhys. This resulted in the same conclusions for group (P = 0.016, partial η2 = 0.15) and no significance for LookAt (P = 0.297, partial η2 = 0.03). The normality of the residuals is improved although not ideal (Shapiro-Wilk P = 0.034). For the response variable nVerbal the results were similar: ANOVA of nVerbal on group and LookAt shows no significant interaction term, group has significance level P = 0.095, and for LookAt P = 0.228. However, again the residual errors are far from normal (SW P = 0.0008). The square root transformation gives P = 0.060, partial η2 = 0.10 for group and P = 0.112, partial η2 = 0.07 for LookAt. The residual errors are compatible with normality (SW P = 0.24).

The factor LookAt represents whether the V avatar was programmed to occasionally look toward the participants. Additionally, the post experience questionnaire included the statement (VictimLooked) “After the argument started, the victim looked at me wanting help” which was scored on a scale from 1 (least agreement) to 7 (most agreement). VictimLooked therefore represents the belief of the participants as to whether the victim looked towards them for help. There is no significant difference between the mean VictimLooked score of those who were in the group LookAt = ‘on’ (mean 3.3, SD = 1.8, n = 20) and those in the group LookAt = ‘off’ (mean 4.0, SD = 1.5, n = 20) (P = 0.12, Mann-Whitney U). Hence the response to this question was not based on the number of actual looks of the victim towards the participant, and therefore was a belief. It turns out that VictimLooked plays a significant role in the number of interventions.

Figure 2 shows the scatter plots of nPhys and nVerbal on the questionnaire response VictimLooked for the out-group and in-group. These reveal a quite different relationship in the two cases. In the case of the in-group there is a positive association between the number of interventions (verbal or physical) and the perception that the victim was looking towards the participant for help. In the case of the out-group there appears to be no relationship in the nPhys case and a possible negative relationship in the nVerbal case. Using the same strategy as above in order to obtain residual errors compatible with normality, ANCOVA of nPhys0.5 on group with VictimLooked as a covariate shows that the slopes of the regression line are different between the in-group and out-group (P = 0.004, partial η2 = 0.22 for the slopes, SW P = 0.18). For the number of verbal interventions, using nVerbal0.5 the difference in slopes between in-group and out-group is significant at P = 0.004 (partial η2 = 0.22 for the slope, SW P = 0.12).

These results indicate that the response to the belief that the victim was looking towards the bystander for help was different between the in-group and out-group. For those in the in-group condition the greater their belief that the victim was looking to them for help the greater the number of verbal and physical interventions. For those in the out-group condition there is no such association. These results are further corroborated using multivariate analysis of variance on the response vector (nPhys0.5, nVerbal0.5) (Text S2).

Numbers of Interventions - Symbolic Regression

The previous section provided standard analyses for these types of data. Even though this revealed positive results consistent with our initial hypothesis, in this section we also employ a quite different method using symbolic regression, to throw further light on the experimental results. The purpose is to consider the relationship between the number of interventions, and the experimental factors, but now also including any possible influence of the subjective variables as elicited through the post-experience questionnaire (Table 2). Standard statistical analysis is based, amongst other things, on the assumption of linearity in the parameters. But in such a complex situation as the one under consideration, on what grounds is such an assumption valid when considering the multivariate influence of a number of factors potentially influencing bystander intervention? Symbolic regression does not rely on such linearity, being a method for discovering relationships between variables using the technique of genetic programming [29] (Text S3). It has recently been shown to be able to discover complex physical laws automatically [30], using a program called Eureqa, which was used in the analysis presented below. In the context that we apply this technique here, we consider it as a data reduction method. It allows us to succinctly represent the original data but with quite simple equations while preserving the variance in the original data. It is not a technique that can be compared with statistical significance testing, it is rather a data exploration method, that can lead to understanding of complex data, where models generated by this technique can be used for hypothesis formation in later experimental study.

The operators that were used for the symbolic regression were: Constant, +, −, ×, /, sqrt, exp, log. The program was run for both nPhys and nVerbal. The population size (number of formulae per generation) was chosen by Eureqa as 2560. For each analysis the program was run on a 40 core cluster (see Methods) and left running for many hours until the solution set of equations stabilized. The fitness metric used was mean absolute error.

We consider first nPhys. The Eureqa program was left to run for more than 2000 core hours. It reported 28 equations. Each has an associated size parameter that represents the complexity of the equation (ranging from 1, least, to 53, most complex), a fitness value, the square of the correlation coefficient between the response variable and the fitted values from the equation, and the Akaike Information Criterion (AIC). The AIC is an information theoretic measure of the relative goodness of fit of a model to the data. Smaller AIC values represent better goodness of fit, taking also into account the complexity of the model. The AIC is often used in model selection procedures, as discussed extensively in [31].

The model with the smallest AICs is shown in Eq (1). Here group is 0 for out-group and 1 for in-group. Similarly LookAt is 0 for ‘off’, and 1 for ‘on’. The other variables are from the questionnaire (Table 2).(1)

(R2 = 0.85, AIC = 108, Size = 26).

Figure 3a shows the relationship between the observed and fitted number of interventions based on Eq (1) (the diagram is very similar for all the top fitting equations generated). The high fitting equations all, of course, give similar results and Eq (1) is marginally preferred since it has high explanatory power (in terms of correlation) and the smallest AIC, and on the range of complexity of the models produced is about half way along the scale amongst all generated equations.

The equation shows a clear distinction between in-group and out-group. For the out-group (group = 0) the entire first term, on the left-hand side of the plus sign, vanishes (20 of the 28 equations generated have this exponential term). For the in-group (group = 1), it can be seen that LookAt has a very small but positive influence on the number of interventions but VictimLooked has a greater influence. As it ranges from 1 to 7 the number of interventions increases by 0.015*exp(VictimLooked), which is, for example, 2 for VictimLooked = 5, and 16 for VictimLooked = 7, other things being equal.

The second term only includes a few of the questionnaire variables. Examining this term, the number of interventions is proportional to concern about the safety of others, and the feeling that the fight should be stopped. It is inversely proportional to the feeling of wanting to get out, and the fear that other people might turn up to make things worse.

Now we turn to the number of verbal interventions nVerbal, and follow the same analysis. Here the genetic program ran for 1930 core hours. 28 equations were produced with size complexity ranging from 1 to 71. The equation with the lowest AIC is shown in Eq (2).(2)

(R2 = 0.93, AIC = 83, Size = 29).

As before all the high fitting equations give very similar results and we take Eq. (2) as representative. Figure 3b shows the plot of fitted by observed values over the data set for Eq. (2). Examining the equation we see this time there is no effect of group. The number of verbal interventions is proportional to the feeling of the need to stop the fight, and inversely proportional to the fear that other people might arrive and make things worse. Also there is a positive association with participant fears for their own safety. The most interesting variable again is VictimLooked, the belief that the V avatar was looking towards the participant for help. The variable MoveAway is strongly related with VictimLooked which must be taken into account otherwise the equations explode into huge values as VictimLooked increases. Figure 4 shows that there is a very strong positive correlation between these two variables (apart from 1 outlier) (r = 0.71, P = 3.3×10−7), with regression line MoveAway = −0.38+0.82VictimLooked. Moreover 22 out of the 28 equations include the exponential term involving these two variables. We maintain this relationship when examining the effect of VictimLooked on nVerbal rather than fixing MoveAway at a constant value, and taking this into account high values of VictimLooked are associated with a larger number of interventions.

The Interviews

After the experimental trial there was a short interview with the participants, followed by their debriefing where the purposes of the experiment were explained. The interviews concentrated on several main questions: their feelings and responses during their experience, the extent to which they judged their responses to be realistic, factors that might have increased their intervention, and factors that drew them out of the experience. Summaries of the interviews were coded into key codes and frequency tables constructed, using the HyperResearch software [32].

We consider first the responses and feelings of participants during their experience. Table 3 shows the codes and two example sentences of each code and Table 4 the code frequencies.

The impression from the interviews as shown in Table 4 is that those in the out-group tended to sympathize with or feel sorry for V. Also many of them wanted to just leave the situation, felt uninvolved, or a few found the situation silly. For those in the in-group it seems to be more anger and frustration that could be the driving force of their intervention, and their response was more likely to be a confrontational one. None of them felt uninvolved, found the situation funny or silly, felt sorry for V or wanted to leave. Some of the in-group expressed surprise at their own responses even though they were aware that it was virtual reality, whereas none of the out-group expressed such surprise. This fits with the fact that many of the out-group felt uninvolved and none of the in-group felt so.

Tables 5 and 6 give the results for the interview question regarding the authenticity of response in comparison with reality. We do not show the separate tables for in-group and out-group since there is no difference between them in this regard, although there is some suggestion of a difference between the LookAt groups. It seems that those in the LookAt ‘off’ group were more likely to remark on the lack of interaction, and to contrast their behaviour in virtual reality and reality. They were less likely to report their responses as being realistic. In the combined sample just over half found that their responses were realistic.

Participants were asked what might have increased or decreased their degree of intervention. The results are shown in Tables 7 and 8. Most frequently they said that if the setup had been more interactive (i.e., the characters responding to their actions after the argument had started) then they would have been more likely to intervene. There were two other aspects that are opposed. On the one side a number of participants said that they would have been more likely to intervene if the perpetrator had become more aggressive. On the other side some participants said that they might have intervened had the perpetrator been less aggressive. Others emphasized that had the victim explicitly called for help they would have been more likely to have intervened. Another important contributory factor could have been greater rapport - for example, the victim having been a friend - or someone in need such as a child.

Finally participants were asked to talk about technical factors that drew them out of the experience. It will be seen from the video (Video S1) that, for example, there is no lip sync when the characters talk. This is very obvious when looking at the video, but barely noticeable when immersed in the environment with the life-sized characters. The combination of gesture and natural turn taking in conversation, amongst other things, are probably factors in making this glaring defect not noticeable. Only 5 out of 40 people mentioned the lack of lip sync and it was the fifth most mentioned aspect in this question. Table 9 shows the list of topics raised by the participants and the number of times they were mentioned. By far the greatest number of issues were concerned with ‘plausibility’ of the situation itself, and the technical factors tend to come down lower in the list.

Discussion

The principal finding of this research with respect to the bystander issue is that participants in the in-group condition made more attempts at physical and verbal intervention than those in the out-group condition. Second, for those in the in-group the number of physical interventions was associated with the belief that the victim was looking towards them for help.

This second finding relies on the important distinction between the experimentally manipulated LookAt factor, and the questionnaire report after the experiment about how much the subjects thought that the victim was looking towards them for help (VictimLooked). To be clear, LookAt refers to whether or not in fact the program was making the victim sometimes look towards the participant. The second refers to the reported belief of the participant that the victim was looking towards him for help. The analysis of covariance (and Figure 2) showed that the belief that the victim was looking towards the participant for help had a differential effect depending on group. For those in the in-group condition, if they believed that the victim was looking towards them for help their number of interventions tended to be greater. For those in the out-group condition this relationship did not occur. This would not be surprising if it occurred in reality. If you consider you have group affiliation with someone and that person is looking to you for help surely this would be a more important event, more likely to move you to action, than if someone with whom you have no affiliation looks towards you for help. It is especially striking then that this also occurs also in virtual reality (where the only real people were the participants themselves): the more that the participants believed that the victim was looking towards them for help the more often did they intervene - but only those in the in-group condition.

Third, the use of symbolic regression as a data exploration method complemented and supported the results found from the classical analysis. Specifically, it provided a further demonstration that those in the in-group and out-group conditions responded quite differently to the influence of the LookAt factor and the VictimLooked response. Additionally, for those in both in-group and out-group the feeling that they should stop the argument was positively associated with an increased number of physical interventions, as was concern for the safety of the victim. However, the fear that other people might turn up to make the situation worse was inversely related to the number of physical interventions as was the feeling of wanting to get out.

The picture looks different for the number of verbal interventions. Here the group did not seem to play much role. Important factors contributing positively to the number of such interventions were the feelings by participants that they ‘should stop it’, concern for their own safety, and a strong perception that the victim was looking towards them for help. The factors that contributed negatively were the feeling of wanting to move away from the protagonists, and also the fear that other people might turn up to make the situation worse. However, in the vast majority of equations generated by the symbolic regression the belief that the victim was looking towards them for help is always together with the feeling of wanting to move away from the protagonists. These two variables have opposite effects, but in these data they are very strongly positively correlated. When VictimLooked is high, and MoveAway is held at its correlated value according to the regression relationship between them, then the number of verbal interventions becomes very high.

The out-group and in-group participants had about the same reported desire to stop the argument, the same level of feeling of being torn about intervening or not, and the same level of anxiety or fear. However, those in the in-group condition expressed greater anger and frustration, whereas those in the out-group condition were more likely to feel sorry for the victim, feel uninvolved or find the situation silly. Those in the in-group condition were more likely to react in a confrontational way compared with those in the out-group, who were looking more to defuse the situation. When we classify the verbal interventions as to whether they were more aimed at defusing the situation or more confrontational, amongst the out-group 17% were confrontational compared to 40% for the in-group, and 73% were defusing utterances compared to 60% for the in-group. These data suggest that the in-group were more likely to respond to the situation through anger and confrontation compared to the out-group, who were either less likely to become involved at all, or more likely to make verbal interventions to defuse the situation. This is not too surprising since by insulting the Arsenal affiliation of the victim in the in-group situation, the perpetrator was also of course indirectly insulting the participants who were all Arsenal supporters.

These data also suggest that physical interventions were more related to the safety of the victim, whereas verbal interventions were more related to safety of the self. The equations for the verbal interventions are more likely to include the ‘own safety’ than those for the physical interventions.

A final point regarding the ‘out-group’ is that in a sense it is not really an ‘out-group’ condition. Rather it is simply not ‘in-group’. Recalling the fact that all the participants were Arsenal supporters, for the ‘out-group’ the victim was portrayed as a football supporter of unknown affiliation (though highly unlikely to be Arsenal). The fact that there are clearly different results between the in-group and out-group condition is therefore a quite strong one: it is ‘in-group’ versus simply not ‘in-group’.

An important issue is the extent to which these findings are generalizable. We have shown an example where the group affiliation was a real one: strong supporters of a particular football team. This is unlike many laboratory based experiments where an abstract group affiliation is created for the purposes of the experiment. Our experimental manipulation involved activating the Arsenal affiliation through the virtual character V wearing an Arsenal football shirt, and talking enthusiastically about the club (in-group). The affiliation was not activated for those in the out-group condition, since V was not wearing an Arsenal shirt, and did not engage in enthusiastic conversation about the club. Our interest focused on the extent to which this activated (or not) psychological group affiliation impacted intervention behaviour. Our procedure was therefore designed to generate meaningful psychological group membership - the Arsenal fans were representative of a particular group. Our claim is that it is the perception that the victim belongs to the same group as the participant (in this context he was ‘one of us’) that leads people to be more likely to intervene. Hence our general hypothesis is that had the group identification been through some other means (social class, race, members of a tennis club, or even arbitrary groups conjured for an experiment) the results would have been similar.

It could be argued that the group of participants might have been too diverse in order to draw these types of conclusions. However, we argue that diversity of the sample is not relevant to this study. Arsenal fans are clearly made up of men, women, Londoners, working class, middle class, and people of different ethnic origins. However, the point is that under some circumstances they come to define themselves as members of the same group (in this case Arsenal fans) - and when this aspect of identity is important to them they are more likely to intervene to help a victim of violence when they think that person shares group membership with themselves in this context. Such group membership can be so powerful that it has been shown to at least temporarily cut across even racial bias in a context where group affiliation was created in a laboratory setting [33], [34]. It has further been argued with respect to the famous Milgram obedience and Zimbardo Stanford prison experiments [35] that group identification is an excellent predictor of conformity [36], [37]. For example, it was demonstrated, on the basis of the complete set of Milgram’s experiments, that the more that subjects identified with the experimenter and his causes (science, answering an important scientific problem) the more likely that they would administer the shocks. On the other hand they would be more likely to disobey the more that they identified with the Learner (representing the general community). Milgram’s original set of experiments provided a range of circumstances that led to varying degrees of identification with one of these groups (science or the community), and the degree of obedience varied accordingly.

Now we consider how our experiment could be improved. In [15] the concept of ‘plausibility’ of experiences in IVEs was introduced, referring to the illusion of participants that the virtual events are really happening (even though they know that this is not the case). It was argued that plausibility depends at least on three factors: (i) the extent to which there are events that refer personally to the participant, (ii) the extent to which the environment responds to actions of the participant, (iii) and the credibility of the scenario in terms of how much they fit expectations from a similar situation in reality. With respect to the technical setup there were no differences between in-group and out-group, and this is reflected in the fact that there are no differences in reported responses and feelings elicited through the interviews. However, the evidence does suggest (Table 6) a greater tendency for the group with LookAt ‘on’ to say that their responses were realistic, and for those with LookAt ‘off’ to mention the lack of interaction. This is consistent with (i) above.

However, an overwhelming conclusion from these data is that the plausibility of the experience would be greatly improved through more interactivity (i.e., (ii) above). Recall that there was an interactive episode at the start of the experiment, where in order to establish the in-group and out-group conditions, the eventual victim did have a conversation with the participant. However, once the argument started there was no further interaction in the sense that the virtual characters did not respond to anything that the participant said or did, except for the pre-programmed LookAt factor. Another aspect of plausibility that would need to be improved based on the results of this experiment is the credibility of the scenario itself (iii). As seen from Table 9 the types of factors that drew people out of the scenario were to do with the setting rather than the technical aspects of the display: no other people around in the pub, it did not look like a real English pub, and the dialogue with the victim itself not being realistic. More than 50% of the statements made in Table 9 refer to these types of general credibility, and the remainder are specific technical issues such as ‘Illumination not realistic’ or ‘Lack of facial animation’, none of which were commonly stated. By technical issues we refer to aspects of the scenario that require only programming to solve (such as the provision of lip sync). By more general credibility issues we refer to the simulation itself - aspects that require a better understanding of what needs to be there for this to be believable as a fight in a London pub.

Apart from the introduction of interactivity and other issues relating to credibility, there are several improvements for later versions of this experiment. For example, we have not said anything about the role of the social identity of the perpetrator with respect to the participant. Moreover there are clearly other issues involved - such as participant fear of being harmed by the perpetrator. This has not been considered at all, but could also be incorporated into an experiment through manipulation of the appearance of the perpetrator (for example, to look more or less menacing). Finally, future experiments will also manipulate the number of bystanders, and thus directly tackle the question of the role of the number of bystanders in intervention.

In this paper we have shown that immersive virtual reality can be usefully exploited to study the likelihood of bystander intervention in interpersonal violent incidents. The paradigm allows the investigation of what participants did do and think during an actual experience involving violence rather than their opinion of what they might do or what they think others might do - whether based on watching a video or on a verbal description of a situation [38]. Moreover we have exploited the powerful tool of genetic programming to explore these data in a deeper way than is possible with normal statistical methods, highlighted by the elegant distinction between the in-group and out-group conditions shown in Eq. (1).

Of course, there is still no proof that what participants would do in a physically real situation would match that which we find in virtual reality. However, as reported in the introduction to this paper there is evidence to suggest that people do respond realistically in IVEs. In fact since these experiments can never be carried out in reality, ultimately the question of the validity of people’s responses to the virtual situation can never be known through laboratory based experiments of any kind. However, our approach can be used in the process of constructing theories, that can then be further tested with the use of experiments in virtual reality, and moreover ultimately examine how well these theories fit what might be found in actual experiences in the field.

To conclude, we note that the findings for this type of research can also have implications for policy. For example, by creating an atmosphere where it is thought that not running away from a violent scene is the right thing to do, and by encouraging people to ask for help when they are victims of such a situation, it may be possible to engineer pro-social behaviour in specific circumstances where this is thought desirable by policy makers, and actually to manipulate the same variables to avoid it in other situations (e.g., “do not approach this man since he is considered armed and dangerous”). Here it would be a question of using the group to enforce social norms for the prevention of violent behaviour. The key to tackling the so called ‘walk–on-by’ society lies in using the power of group identification to promote social solidarity – and to persuade and empower bystanders to intervene, in situations where this is considered by the authorities to be appropriate.

Methods

Ethics Statement

The experiment was approved by the UCL Research Ethics Committee, and was carried out under written informed consent from each participant.

The Virtual Reality System

A four screen projection system driven by a 5 PC cluster was used. We refer to this by generic name ‘Cave’ being the type of system described in [39]. The Cave has three 3 m×2.2 m back-projected screens: front, left, and right, and a 3 m×3 m front projection surface on the floor. The computers in the cluster contain Intel Pentium 3.2 GHz processors with 1 gigabyte of RAM and Nvidia Quadro FX 5600 graphics cards. The display resolution is 1024×768 pixels for each screen.

The participants were fitted with Crystal Eyes shutter glasses that were synchronized with the projectors, delivering active stereo at 45 Hz each eye. Head-tracking was performed with an InterSense IS-900 tracking device.

The program was written using the XVR programming platform as described in [40]. The virtual characters were animated using the Hardware Accelerated Library for Character Animation, HALCA [41].

The Scenario

Two professional actors were hired to act the scene for the character animation motion capture. A Vicon motion capture system with 6 infrared cameras was used to capture their motions simultaneously. Sound was also recorded at the same time using Audacity software (audacity.sourceforge.net) with two wireless microphones attached to each actor. This raw data was then cleaned up, synchronized and split into pieces so that each one could be later assigned to a button on the interface to be played when needed during the study.

This article is about the psychological phenomenon. For the bystander effect in radiobiology, see Bystander effect (radiobiology).

The bystander effect, or bystander apathy, is a social psychological phenomenon in which individuals are less likely to offer help to a victim when other people are present. The greater the number of bystanders, the less likely it is that any one of them will help. Several factors contribute to the bystander effect, including ambiguity, cohesiveness and diffusion of responsibility.

Social psychology research[edit]

The bystander effect was first demonstrated in the laboratory by John M. Darley and Bibb Latané in 1968 after they became interested in the topic following the murder of Kitty Genovese in 1964.[1] These researchers launched a series of experiments that resulted in one of the strongest and most replicable effects in social psychology.[2] In a typical experiment, the participant is either alone or among a group of other participants or confederates. An emergency situation is staged and researchers measure how long it takes the participants to intervene, if they intervene. These experiments have found that the presence of others inhibits helping, often by a large margin.[3] For example, Bibb Latané and Judith Rodin (1969) staged an experiment around a woman in distress. 70 percent of the people alone called out or went to help the woman after they believed she had fallen and was hurt, but when there were other people in the room only 40 percent offered help.[4]

Variables affecting bystanders[edit]

Emergency versus non-emergency situations[edit]

Latané and Darley performed three experiments to test bystander behavior in non-emergency situations[5] Their results indicated that the way in which the subjects were asked for help mattered. In one condition, subjects asked a bystander for his or her name. More people provided an answer when the students gave their name first. In another condition, the students asked bystanders for a dime. When the student gave an explanation, such as saying that their wallet had been stolen, the percentage of people giving assistance was higher (72%) than when the student just asked for a dime (34%). Additional research by Faul, Mark, et al., using data collected by EMS officials when responding to an emergency, indicated that the response of bystanders was correlated with the health severity of the situation.[6]

According to Latané and Darley, there are five characteristics of emergencies that affect bystanders:[5]

  1. Emergencies involve threat of harm or actual harm
  2. Emergencies are unusual and rare
  3. The type of action required in an emergency differs from situation to situation
  4. Emergencies cannot be predicted or expected
  5. Emergencies require immediate action

Due to these five characteristics, bystanders go through cognitive and behavioural processes:

  1. Notice that something is going on
  2. Interpret the situation as being an emergency
  3. Degree of responsibility felt
  4. Form of assistance
  5. Implement the action choice

Notice: To test the concept of "noticing," Latane and Darley (1968) staged an emergency using Columbia University students. The students were placed in a room—either alone, with two strangers or with three strangers to complete a questionnaire while they waited for the experimenter to return. While they were completing the questionnaire smoke was pumped into the room through a wall vent to simulate an emergency. When students were working alone they noticed the smoke almost immediately (within 5 seconds). However, students that were working in groups took longer (up to 20 seconds) to notice the smoke. Latané and Darley claimed this phenomenon could be explained by the social norm of what is considered polite etiquette in public. In most western cultures, politeness dictates that it is inappropriate to idly look around. This may indicate that a person is nosy or rude. As a result, passers-by are more likely to be keeping their attention to themselves when around large groups than when alone. People who are alone are more likely to be conscious of their surroundings and therefore more likely to notice a person in need of assistance.

Interpret: Once a situation has been noticed, a bystander may be encouraged to intervene if they interpret the incident as an emergency. According to the principle of social influence, bystanders monitor the reactions of other people in an emergency situation to see if others think that it is necessary to intervene. If it is determined that others are not reacting to the situation, bystanders will interpret the situation as not an emergency and will not intervene. This is an example of pluralistic ignorance or social proof. Referring to the smoke experiment, even though students in the groups had clearly noticed the smoke which had become so thick that it was obscuring their vision, irritating their eyes or causing them to cough, they were still unlikely to report it. Only one participant in the group condition reported the smoke within the first four minutes, and by the end of the experiment, no-one from five of eight groups had reported the smoke at all. In the groups that did not report the smoke, the interpretations of its cause, and the likelihood that it was genuinely threatening was also less serious, with no-one suggesting fire as a possible cause, but some preferring less serious explanations, such as the air-conditioner was leaking.[7] Similarly, interpretations of the context played an important role in people's reactions to a man and woman fighting in the street. When the woman yelled, "Get away from me; I don't know you," bystanders intervened 65 percent of the time, but only 19 percent of the time when the woman yelled, "Get away from me; I don't know why I ever married you."[4]

General bystander effect research was mainly conducted in the context of non-dangerous, non-violent emergencies. A study (2006) tested bystander effect in emergency situations to see if they would get the same results from other studies testing non-emergencies. In situations with low potential danger, significantly more help was given when the person was alone than when they were around another person. However, in situations with high potential danger, participants confronted with an emergency alone or in the presence of another person were similarly likely to help the victim.[8] This suggests that in situations of greater seriousness it is more likely that people will interpret the situation as one in which help is needed and will be more likely to intervene.

Degree of Responsibility: Darley and Latané determined that the degree of responsibility a bystander feels is dependent on three things:

  1. Whether or not they feel the person is deserving of help
  2. The competence of the bystander
  3. The relationship between the bystander and the victim

Forms of Assistance: There are two categories of assistance as defined by Latané and Darley:

  1. Direct intervention: directly assisting the victim
  2. Detour intervention. Detour intervention refers to reporting an emergency to the authorities (i.e. the police, fire department)

Implementation: After going through steps 1-4, the bystander must implement the action of choice.

In one study done by Abraham S. Ross, the effects of increased responsibility on bystander intervention were studied by increasing the presence of children. This study was based on the reaction of 36 male undergraduates presented with emergency situations. The prediction was that the intervention would be at its peak due to presence of children around those 36 male undergraduate participants. This was experimented and showed that the prediction was not supported and was concluded as "the type of study did not result in significant differences in intervention."[9]

A meta-analysis (2011) of the bystander effect[10] reported that "The bystander effect was attenuated when situations were perceived as dangerous (compared with non-dangerous), perpetrators were present (compared with non-present), and the costs of intervention were physical (compared with non-physical). This pattern of findings is consistent with the arousal-cost-reward model, which proposes that dangerous emergencies are recognized faster and more clearly as real emergencies, thereby inducing higher levels of arousal and hence more helping." They also "identified situations where bystanders provide welcome physical support for the potentially intervening individual and thus reduce the bystander effect, such as when the bystanders were exclusively male, when they were naive rather than passive confederates or only virtually present persons, and when the bystanders were not strangers."

An alternative explanation has been proposed by Stanley Milgram, who hypothesized that the bystanders′ callous behavior was caused by the strategies they had adopted in daily life to cope with information overload. This idea has been supported to varying degrees by empirical research.[11]

Timothy Hart and Ternace Miethe used data from the National Crime Victimization Survey (NCVS) and found that a bystander was present in 65 percent of the violent victimizations in the data. Their presence was most common in cases of physical assaults (68%), which accounted for the majority of these violent victimizations and less likely in robberies (49%) and sexual assaults (28%). The actions of bystanders were most frequently judged by victims as "neither helping nor hurting" (48%), followed by "helping" (37%), "hurting" (10%), and "both helping and hurting" (3%). Half of the attacks in which a bystander was present occurred in the evening, where the victim and bystander were strangers.[12]

Ambiguity and consequences[edit]

Ambiguity is one factor that affects whether or not a person assists another in need. In some cases of high ambiguity, it can take a person or group up to 5 times as long before taking action than in cases of low ambiguity. In these cases, bystanders determine their own safety before proceeding. Bystanders are more likely to intervene in low ambiguity, insignificant consequence situations than in high ambiguity, significant consequence situations.

Understanding of environment[edit]

Whether or not a bystander intervenes may have to do with their familiarity of the environment where the emergency occurs. If the bystander is familiar with the environment, they are more likely to know where to get help, where the exits are, etc.[5] Bystanders who are in an environment in which they are not familiar with the surroundings are less likely to give help in an emergency situation.

Priming the bystander effect[edit]

Research done by Garcia et al. (2002) indicate that priming a social context may inhibit helping behavior.[13] Imagining being around one other person or being around a group of people can affect a person's willingness to help.

Cohesiveness and group membership[edit]

Main article: Group cohesiveness

Group cohesiveness is another variable that can affect the helping behaviour of a bystander. As defined by Rutkowski et al., cohesiveness refers to an established relationship (friends, acquaintances) between two or more people.[14] Experiments have been done to test the performance of bystanders when they are in groups with people they have been acquainted with. According to Rutkowski et al., the social responsibility norm affects helping behavior. The norm of social responsibility states that "people should help others who are in need of help and who are dependent on them for it." As suggested by the research, the more cohesive a group, the more likely the group will act in accordance to the social responsibility norm. To test this hypothesis, researchers used undergraduate students and divided them into four groups: a low cohesive group with two people, a low cohesive group with four people, a high cohesive group with two people, and a high cohesive group with four people. Students in the high cohesive group were then acquainted with each other by introducing themselves and discussing what they liked/disliked about school and other similar topics. The point of the experiment was to determine whether or not high cohesive groups were more willing to help a hurt "victim" than the low cohesive groups. The four member high cohesive groups were the quickest and most likely groups to respond to the victim who they believed to be hurt. The four member low cohesive groups were the slowest and least likely to respond to the victim.

Altruism research suggests that helping behaviour is more likely when there are similarities between the helper and the person being helped. Recent research has considered the role of similarity, and more specifically, shared group membership, in encouraging bystander intervention. In one experiment (2005), researchers found that bystanders were more likely to help an injured person if that person was wearing a football jersey of a team the bystander liked as opposed to a team the bystander did not like. However, when their shared identity as football fans was made salient, supporters of both teams were likely to be helped, significantly more so than a person wearing a plain shirt.[15]

The findings of Mark Levine and Simon Crowther (2008) illustrated that increasing group size inhibited intervention in a street violence scenario when bystanders were strangers, but encouraged intervention when bystanders were friends. They also found that when gender identity is salient, group size encouraged intervention when bystanders and victims shared social category membership. In addition, group size interacted with context-specific norms that both inhibit and encourage helping. The bystander effect is not a generic consequence of increasing group size. When bystanders share group-level psychological relationships, group size can encourage as well as inhibit helping.[16]

These findings can be explained in terms of self-categorization and empathy. From the perspective of self-categorization theory, a person’s own social identity, well-being is tied to their group membership so that when a group based identity is salient, the suffering of one group member can be considered to directly affect the group. Because of this shared identity, referred to as self-other merging, bystanders are able to empathize, which has been found to predict helping behaviour. For example, in a study relating to helping after eviction both social identification and empathy were found to predict helping. However, when social identification was controlled for, empathy no longer predicted helping behaviour.[17]

Cultural differences[edit]

In discussing the case of Wang Yue and a later incident in China, in which CCTV footage from a Shanghaisubway showed passengers fleeing from a foreigner who fainted, UCLA anthropologist Yunxiang Yan asserted that the reactions can be explained by deeply seated historical cultural differences in Chinese agrarian society, in which there was a stark contrast between how individuals associated with ingroup and outgroup members, saying, "How to treat strangers nicely is one of the biggest challenges in contemporary Chinese society...The prevailing ethical system in traditional China is based on close-knit community ties, kinship ties." He continued, "A person might treat other people in the person's social group very, very nicely...But turn around, when facing to a stranger, and (a person might) tend to be very suspicious. And whenever possible, might take advantage of that stranger."[18]

Diffusion of responsibility[edit]

Main article: Diffusion of responsibility

Darley and Latané (1968) conducted research on diffusion of responsibility.[19] The findings suggest that in the case of an emergency, when people believe that there are other people around, they are less likely or slower to help a victim because they believe someone else will take responsibility. People may also fail to take responsibility for a situation depending on the context. They may assume that other bystanders are more qualified to help, such as doctors or police officers, and that their intervention would be unneeded. They may also be afraid of being superseded by a superior helper, offering unwanted assistance, or facing the legal consequences of offering inferior and possibly dangerous assistance. For this reason, some legislations, such as "Good Samaritan Laws" limit liability for those attempting to provide medical services and non-medical services in an emergency.

Organizational Ombuds practitioners' research[edit]

A 2009 study published by International Ombudsman Association in the Journal of the International Ombudsman Association suggests that—in reality—there are dozens of reasons why people do not act on the spot or come forward in the workplace when they see behavior they consider unacceptable.[20]

The most important reasons cited for not acting were: the fear of loss of important relationships in and out of the workplace, and a fear of "bad consequences." There also were many reasons given by people who did act on the spot or come forward to authorities.

This practitioners' study suggests that the "bystander effect" can be studied and analyzed in a much broader fashion. The broader view includes not just a) what bystanders do in singular emergencies, b) helping strangers in need, when c) there are (or are not) other people around. The reactions of bystanders can also be analyzed a) when the bystanders perceive any of a wide variety of unacceptable behavior over time, b) they are within an organizational context, and c) with people whom they know. The practitioners' study reported many reasons why some bystanders within organizations do not act or report unacceptable behavior. The study also suggests that bystander behavior is, in fact, often helpful, in terms of acting on the spot to help,and reporting unacceptable behavior (and emergencies and people in need.) The ombuds practitioners' study suggests that what bystanders will do in real situations is actually very complex, reflecting views of the context and their managers (and relevant organizational structures if any) and also many personal reasons.

In support of the idea that some bystanders do indeed act responsibly, Gerald Koocher and Patricia Keith Spiegel wrote a 2010 article related to an NIH-funded study which showed that informal intervention by peers and bystanders can interrupt or remedy unacceptable scientific behavior.[21]

What Would You Do?[edit]

John Quiñones' primetime show, What Would You Do? on ABC, tests the bystander effect. Actors are used to act out (typically non-emergency) situations while the cameras capture the reactions and actions of innocent bystanders. Topics include cheating on a millionaire test, an elderly person shoplifting, racism and homophobia.

Non-computer versus computers: computer mediated intervention[edit]

Research suggests that the bystander effect may be present in computer-mediated communication situations.[22] Evidence demonstrates that people can be bystanders even when they cannot see the person in distress. In the experiment, 400 online chat groups were observed. One of two confederates were used as victims in each chat room: either a male victim whose screen name was Jake Harmen or a female victim whose screen name was Suzy Harmen. The purpose of the experiment was to determine whether or not the gender of the victim mattered, if the size of each chat group had any effect and if asking for a person's help by directly using their screen name would have any effect. Results indicated that the gender of the victim had no effect on whether or not a bystander assisted the victim. Consistent with findings of Latané and Darley, the number of people present in the chat room did have an effect. The response time for smaller chat groups was quicker than in the larger chat groups. However, this effect was nonexistent when the victim (Suzy or Jake) asked for help from a specific person in the chat group. The mean response time for groups in which a specific person was called out was 36.38 seconds. The mean response time for groups in which no screen name was pointed out was 51.53 seconds. A significant finding of the research is that intervention depends on whether or not a victim asked for help by specifying a screen name. The group size effect was inhibited when the victim specifically asked a specific person for help. The group size effect was not inhibited if the victim did not ask a specific person for help.

Children as bystanders[edit]

Although most research has been conducted on adults, children can be bystanders too. A study conducted by Robert Thornberg in 2007 came up with seven reasons why children do not help when another classmate is in distress. These include: trivialisation, dissociation, embarrassment association, busy working priority, compliance with a competitive norm, audience modelling, and responsibility transfer.[jargon][23] In a further study, Thornberg concluded that there are seven stages of moral deliberation as a bystander in bystander situations among the Swedish schoolchildren he observed and interviewed: (a) noticing that something is wrong, i.e., children pay selective attention to their environment, and sometimes they don't tune in on a distressed peer if they're in a hurry or their view is obstructed, (b) interpreting a need for help—sometimes children think others are just playing rather than actually in distress or they display pluralistic ignorance, (c) feeling empathy, i.e., having tuned in on a situation and concluded that help is needed, children might feel sorry for an injured peer, or angry about unwarranted aggression (empathic anger), (d) processing the school's moral frames—Thornberg identified five contextual ingredients influencing children's behavior in bystander situations (the definition of a good student, tribe caring, gender stereotypes, and social-hierarchy-dependent morality), (e) scanning for social status and relations, i.e., students were less likely to intervene if they didn't define themselves as friends of the victim or belonging to the same significant social category as the victim, or if there were high-status students present or involved as aggressors—conversely, lower-status children were more likely to intervene if only a few other low-status children were around, (f) condensing motives for action, such as considering a number of factors such as possible benefits and costs, and (g) acting, i.e., all of the above coalesced into a decision to intervene or not. It is striking how this was less an individual decision than the product of a set of interpersonal and institutional processes.[24]

Implications of research[edit]

South African murder trials[edit]

In an effort to make South African courts more just in their convictions, the concept of extenuating circumstances came into being.[25] However, no concrete definition of extenuating circumstances was ever made. The South African courts began using the testimony of expert social psychologists to define what extenuating circumstances would mean in the justice system. Examples include: deindividuation, bystander apathy, and conformity. In the case of S. vs. Sibisi and Others (1989) eight members of the South African Railways and Harbours Union were involved in the murder of four workers who chose not to join in the SARHWU strike. Psychologists Scott Fraser and Andrew Colman presented evidence for the defense using research from social psychology. Social anthropologist Boet Kotzé provided evidence for the defense as well. He testified that African cultures are characterized by a collective consciousness. Kotzé testified that the collective conscious contributed to the defendants' willingness to act with the group rather than act as individuals. Fraser and Colman stated that bystander apathy,deindividuation, conformity and group polarization were extenuating factors in the killing of the four strike breakers. They explained that deindividuation may affect group members' ability to realize that they are still accountable for their individual actions even when with a group. They also used research on bystander apathy by Latané and Darley to illustrate why four of the eight defendants watched as the other four defendants killed four men. The testimonies of Fraser and Colman helped four of the defendants escape the death penalty.

Laws[edit]

Some parts of the world have included laws that hold bystanders responsible when they witness an emergency.

  1. The Charter of human rights and freedoms of Quebec states that "[e]very person must come to the aid of anyone whose life is in peril, either personally or calling for aid, unless it involves danger to himself or a third person, or he has another valid reason".[26] It is therefore a legal obligation to assist people in danger in Quebec.
  2. Likewise, the Brazilian Penal Code states that it is a crime not to rescue (or call emergency services when appropriate) injured or disabled people including those found under grave and imminent danger as long as it safe to do so. This also includes abandoned children.[27]
  3. The German penal code makes it a crime for a person to fail to render aid in cases of accidents or other common dangers, unless such person would thereby endanger themselves or it would be contrary to some other important obligation.[28]

In the US, Good Samaritan laws have been implemented to protect bystanders who acted in good faith. Many organizations are including bystander training. For example, the United States Department of the Army is doing bystander training with respect to sexual assault. Some organizations routinely do bystander training with respect to safety issues. Others have been doing bystander training with respect to diversity issues.[29][30] Organizations such as American universities are also using bystander research to improve bystander attitudes in cases of rape. Examples include the InterAct Sexual Assault Prevention program[31] and the Green Dot program.[32] Others have been critical of these laws for being punitive and criminalizing the problem they are meant to address.[33]

Many institutions have worked to provide options for bystanders who see behavior they find unacceptable. These options are usually provided through complaint systems—so bystanders have choices about where to go. One option that is particularly helpful is that of an organizational ombudsman, who keeps no records for the employer and is near-absolutely confidential.

Notable examples[edit]

Kitty Genovese[edit]

Main article: Murder of Kitty Genovese

The case of Kitty Genovese is often cited and occasionally criticized as an example of the "bystander effect". It is also the case that originally stimulated social psychological research in this area. On March 13, 1964 Genovese, 28 years old, was on her way back to her apartment in Queens, New York, from work at 3am when she was stabbed, sexually assaulted, and murdered in front of multiple witnesses.[34] According to newspaper accounts, the attack lasted for at least a half an hour during which time Genovese screamed and pleaded for help. The murderer attacked Genovese and stabbed her, then fled the scene after attracting the attention of a neighbor. The killer then returned ten minutes later and finished the assault. Newspaper reports after Genovese's death claimed that 38 witnesses watched the stabbings and failed to intervene or even contact the police until after the attacker fled and Genovese had died. This led to widespread public attention, and many editorials. Psychology researchers Latané and Darley attributed the lack of help by witnesses to diffusion of responsibility. Because each witness saw others witnessing the same event, they assumed that the others would be taking responsibility and calling the police, and therefore did nothing to stop the situation themselves.[35]

According to an article published in American Psychologist in 2007, the original story of Genovese's murder was exaggerated by the media. Specifically, there were not 38 eyewitnesses, the police were contacted at least once during the attack, and many of the bystanders who overheard the attack could not actually see the event. The authors of the article suggest that the story continues to be misrepresented in social psychology textbooks because it functions as a parable and serves as a dramatic example for students.[36]

Raymond Zack[edit]

Main article: Death of Raymond Zack

On Memorial Day, 2011, 53-year-old Raymond Zack, of Alameda, California, walked into the waters off Robert Crown Memorial Beach and stood neck deep in water roughly 150 yards offshore for almost an hour. His foster mother, Dolores Berry, called 9-1-1 and said that he was trying to drown himself. (There are conflicting reports about Zack's intentions.[37]) Firefighters and police responded but did not enter the water. The firefighters called for a United States Coast Guard boat to respond to the scene. According to police reports, Alameda police expected the firefighters to enter the water.[38] Firefighters later said that they did not have current training and certifications to perform land-based water rescue. Dozens of civilians on the beach, and watching from their homes across from the beach, did not enter the water, apparently expecting public safety officers to conduct a rescue. Eventually, Zack collapsed in the water, apparently from hypothermia. Even then, nobody entered the water for several minutes. Finally, a good samaritan entered the water and pulled Zack to shore. Zack died afterwards at a local hospital.[39][40][41]

Wang Yue[edit]

Main article: Death of Wang Yue

In October 2011, a 2-year-old girl, Wang Yue, was hit by a small, white van in the city of Foshan, China, then run over by a large truck when she was not moved by bystanders. A total of 18 people ignored her, some going so far as to walk around the blood. The girl was left for seven minutes before a recycler, Chen Xianmei, picked up the toddler and called for help. The child died eight days later.[42][43]

Reversal of bystander effect[edit]

Role of public self-awareness[edit]

In many cases, we believe that individuals like to view themselves as noble by offering help. This concept is related to the notion of self-awareness. If it is true that people focus on the impression they make on others, then public-awareness through the use of accountability cues can stimulate people to give help to each other. However, being surrounded by a lot of bystanders does not automatically create public self-awareness. In fact, in some cases being surrounded by bystanders allows an individual to hide within the crowd and feel anonymous. According to Dr. van Bommel, by introducing an accountability cue, the aim is to eliminate feelings of anonymity and increase the likelihood of them offering help.[44]

Experiments[edit]

To investigate if people would help others in the presence of bystanders alone or when introducing an accountability cue, van Bommel[44] set out to perform two experiments. For the first experiment, participants were invited to join an online forum. A sample of 86 students was used and in the forum participants saw their own name and the names of the other people that were online in the left upper half of the screen. To operationalize the accountability cue, participants' names were displayed in either red or black. Red (salient condition), utilized to attract attention and stand out, was used to raise public self-awareness while black (non-salient) was used for participants to have the same color as other bystanders. In random order, five different messages were presented to the participants. Each of these messages was a personal story of a person in distress. The number of responses a participant typed measured the helping behavior from participants and as expected, the results showed that names in black (non-salient condition) were linked to the bystander effect with the amount of help being 2. However, when names were in the red (salient condition), the bystander effect was reversed with the amount of help being 3. This exemplifies the role of self-awareness in the reversal of bystander effect.

In order to further investigate the reversal of the bystander effect, van Bommel performed a second experiment, also reported in the article, with a sample size of 111 students. Unlike the first study, they wanted all participants in the forum to be indistinguishable from other bystanders on the forum. Therefore, the manipulation of public self-awareness was induced independently from the Internet forum. The procedure was identical to the first study, with a few variations. To induce public awareness in this study, webcams were mounted on participant’s computers. Participants could not see the video feed, but to make this more salient, they asked participants before they started to check whether the camera was on by looking at a LED-indicator underneath the webcam. To make the conditions resemble each other as close as possible, they asked participants in the camera absent condition to check if the LED-indicator for Num-Lock was on. Similar to the first experiment, five different messages were presented to the participants and the number of responses a participant typed measured the helping behavior from participants. Once again, the results were as expected. When the camera was not present, the bystander effect existed with the average amount of help being 1.5. However, when a camera was present, the bystander effect was reversed with the amount of help doubling to 3. That said, the findings revealed the importance of public self-awareness in the bystander effect.

See also[edit]

References[edit]

  1. ^Darley, J. M. & Latané, B. (1968). "Bystander intervention in emergencies: Diffusion of responsibility". Journal of Personality and Social Psychology. 8: 377–383. doi:10.1037/h0025589. 
  2. ^Fischer, Peter; Krueger, Joachim I.; Greitemeyer, Tobias; Vogrincic, Claudia; Kastenmüller, Andreas; Frey, Dieter; Heene, Moritz; Wicher, Magdalena; Kainbacher, Martina. "The bystander-effect: A meta-analytic review on bystander intervention in dangerous and non-dangerous emergencies". Psychological Bulletin. 137 (4): 517–537. doi:10.1037/a0023304. PMID 21534650. 
  3. ^Hudson, James M. & Bruckman, Amy S. (2004). "The Bystander Effect: A Lens for Understanding Patterns of Participation". Journal of the Learning Sciences. 13 (2): 165–195. doi:10.1207/s15327809jls1302_2. 
  4. ^ abMeyers, D. G. (2010). Social Psychology (10th Ed). New York: McGraw- Hill. ISBN 978-0-07-337066-8. 
  5. ^ abcDarley, J. M., & Latane, B. (1970). The unresponsive bystander: why doesn't he help? New York, NY: Appleton Century Crofts.
  6. ^Faul, M., Aikman, S. N., & Sasser, S. M. (2016). Bystander Intervention Prior to The Arrival of Emergency Medical Services: Comparing Assistance across Types of Medical Emergencies. Prehospital Emergency Care, 1-7. doi:10.3109/10903127.2015.1088605
  7. ^Latané, B; Darley, J.M. (1968). "Group inhibition of bystander intervention in emergencies". Journal of Personality and Social Psychology. 10: 308–324. doi:10.1037/h0026570. 
  8. ^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 (2): 267–278. doi:10.1002/ejsp.297. 
  9. ^Ross, Abraham (1971). "Effect of increase responsibility on bystander intervention: presence of children". Journal of Personality and Social Psychology. 19 (3): 306–310. doi:10.1037/h0031459. 
  10. ^Fischer, P; Krueger, JI; Greitemeyer, T; Vogrincic, C; Kastenmüller, A; Frey, D; Heene, M; Wicher, M; Kainbacher, M. "The bystander-effect: a meta-analytic review on bystander intervention in dangerous and non-dangerous emergencies". Psychol Bull. 137: 517–37. doi:10.1037/a0023304. PMID 21534650. 
  11. ^Christensen, K. & Levinson, D. (2003). Encyclopedia of community: From the village to the virtual world, Band 1, p. 662.
  12. ^Hart, T.; Miethe, T. (2008). "Exploring Bystander Presence and Intervention in Nonfatal Violent Victimization: When Does Helping Really Help?". Violence and Victims. 23 (5): 637–651. doi:10.1891/0886-6708.23.5.637. 
  13. ^Garcia, S.M.; Weaver, K.; Darley, J.M.; Moskowitz, G.B. (2002). "Crowded minds: the implicit bystander effect". Journal of Personality and Social Psychology. 83 (4): 843–853. doi:10.1037/0022-3514.83.4.843. 
  14. ^Rutkowski, G. K.; Gruder, C. L.; Romer, D. (1983). "Group cohesiveness, social norms, and bystander intervention". Journal of Personality and Social Psychology. 44 (3): 545–552. doi:10.1037/0022-3514.44.3.545. 
  15. ^Levine, Mark; Prosser, A; Evans, D & Reicher, S (1968). "Identity and Emergency Intervention: How social group membership and inclusiveness of group boundaries shape helping behaviours". Personality and Social Psychology Bulletin. 31: 443–453. doi:10.1177/0146167204271651. 
  16. ^Levine, Mark; Crowther, Simon (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 (6): 1429–1439. doi:10.1037/a0012634. PMID 19025293. 
  17. ^Batson, C Daniel; Karen Sager; Eric Garst; Misook Kang; Kostia Rubchinsky; Karen Dawson (September 1997). "Is empathy-induced helping due to self-other merging?". Journal of Personality and Social Psychology. 73 (3): 495–509. doi:10.1037/0022-3514.73.3.495. 
  18. ^Langfitt, Frank (1 September 2014). "Why Did Crowd Flee Shanghai Subway After Foreigner Fainted?". NPR. Retrieved 2 September 2014. 
  19. ^Darley, J.M.; Latané, B. (1968). "Bystander intervention in emergencies: diffusion of responsibility". Journal of Personality and Social Psychology. 8 (4): 377–383. doi:10.1037/h0025589. 
  20. ^Rowe, Mary; Wilcox, Linda; Gadlin, Howard (2009). "Dealing with—or Reporting—'Unacceptable' Behavior—with additional thoughts about the 'Bystander Effect'"(PDF). Journal of the International Ombudsman Association. 2 (1): 52–64. 
  21. ^Koocher, G. and Spiegel, K. S. "Peers Nip Misconduct in the Bud". (July 22, 2010) Nature 466, 438-440.
  22. ^Markey, P.M. (2000). "Bystander intervention in computer-mediated communication". Computers in Human Behavior. 16 (2): 183–188. doi:10.1016/S0747-5632(99)00056-4. 
  23. ^Thornberg, R (2007). "A classmate in distress: schoolchildren as bystanders and their reasons for how they act". Social Psychology of Education. 10: 5–28. doi:10.1007/s11218-006-9009-4. 
  24. ^Thornberg, Robert (2010). "A student in distress: Moral frames and bystander behavior in school". Elementary School Journal. 110 (4): 585–608. doi:10.1086/651197. 
  25. ^Colman, A.M. (1991). "Crowd psychology in South African murder trials". American Psychologist. 46 (10): 1071–1079. doi:10.1037/0003-066x.46.10.1071. 
  26. ^Charter of human rights and freedoms Section 2
  27. ^"DEL2848". Planalto.gov.br. Retrieved 2011-10-27. 
  28. ^§323c StGB (the German criminal code)
  29. ^Scully, M.; Rowe, M. (2009). "Bystander training within organizations". Journal of the International Ombudsman Association. 2 (1): 1–9. 
  30. ^See also http://www.clemson.edu/olweus/ for an overview of a use of the Olweus Bullying Prevention Program.
  31. ^Ahrens, C. E.; Rich, M. D.; Ullman, J. B. (2011). "Rehearsing for real life: The impact of the InterACT sexual assault prevention program on self-reported likelihood of engaging in bystander interventions". Violence Against Women. 17 (6): 760–776. doi:10.1177/1077801211410212. 
  32. ^Coker, A. L.; Cook-Craig, P.; Williams, C. M.; Fisher, B. S.; Clear, E. R.; Garcia, L. S.; Hegge, L. M. (2011). "Evaluation of green dot: An active bystander intervention to reduce sexual violence on college campuses". Violence Against Women. 17 (6): 777–796. doi:10.1177/1077801211410264. 
  33. ^Meyer, Doug. "The Gentle Neoliberalism of Modern Anti-bullying Texts: Surveillance, Intervention, and Bystanders in Contemporary Bullying Discourse". Sexuality Research & Social Policy. 
  34. ^Manning, Rachel; Levine, Mark; Collins, Alan (September 2007). "The Kitty Genovese murder and the social psychology of helping: The parable of the 38 witnesses". American Psychologist. 62 (6): 555–562. doi:10.1037/0003-066x.62.6.555. PMID 17874896. 
  35. ^DARLEY, JOHN M.; LATANE, BIBB. "BYSTANDER INTERVENTION IN EMERGENCIES: DIFFUSION OF RESPONSIBILITY". Journal of Personality and Social Psychology. 8 (4, Pt.1): 377–383. doi:10.1037/h0025589. 
  36. ^Manning, R.; Levine, M.; Collins, A. (2007). "The Kitty Genovese murder and the social psychology of helping: The parable of the 38 witnesses"(PDF). American Psychologist. 62 (6): 555–562. doi:10.1037/0003-066X.62.6.555. PMID 17874896. 
  37. ^Many issues about this incident are disputed. Roughly one year after the incident, Raymond Zack's family filed a "Failure of Duty" lawsuit against the City of Alameda and the County of Alameda.
  38. ^Alameda police released redacted police reports to the media after the event that confirm this.
  39. ^"Alameda Police Release Memorial Day Drowning 911 Calls". San Francisco. 2011-06-08. 
  40. ^"The Death of Raymond Zack: No Heroes, Only Bystanders". 2011-06-01. 
  41. ^"Such callous disregard for life". Orange County. 2011-06-10. 
  42. ^"Chinese toddler run over twice after being left on street". London: Telegraph. 2011-10-17. Retrieved 2011-10-27. 
  43. ^Oct 22, 2011 5:03 PM ET (2011-10-22). "The bystander effect—Canada—CBC News". Cbc.ca. Retrieved 2011-10-27.

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