In eyewitness research, multiple identification decisions in sequential lineups are typically prevented by telling participants that only their first identification decision counts. These first-yes-counts instructions are incompatible with standard police protocols prescribing that witnesses shall see the entire lineup. Horry et al. were the first to experimentally test how this discrepancy between eyewitness research and standard police protocols affects eyewitness identification decisions. Here, the two-high threshold eyewitness identification model was used to disentangle the effect of the first-yes-counts instructions on the detection and guessing processes underlying eyewitness identification decisions. We report both a reanalysis of Horry et al.’s data and a conceptual replication. Both the reanalysis and the results of the conceptual replication confirm that first-yes-counts instructions do not affect the detection of the culprit but decrease the probability of guessing-based selections. To improve the ecological validity, research on sequential lineups should avoid first-yes-counts instructions. In a typical sequential lineup, photographs of one suspect and a finite number of fillers are shown in a random sequence and an eyewitness makes an identification decision or a non-identification decision for each photograph separately. If an eyewitness makes only non-identification decisions, the lineup is rejected. At the other extreme, the sequential presentation implies, at least in principle, that an eyewitness can make as many identification decisions as there are photographs in the lineup. In eyewitness research, multiple identification decisions in sequential lineups are typically prevented by first-yes-counts instructions8. In these instructions, participants are informed that only their first identification decision counts. Sometimes, the lineup presentation continues after a first identification decision has been made and participants are shown the complete lineup . In other studies, lineup presentation is terminated immediately after the first identification decision . In sharp contrast, standard police protocols do not use first-yes-counts instructions31. Instead, lineup presentation continues after identification decisions and eyewitnesses can select further photographs if these provide a better match to the memory of the culprit. Given this discrepancy between research and police practice, it is important to understand the effects of first-yes-counts instructions on the processes involved in eyewitness identification decisions.Following science-based recommendations  and the lineup rejection rate was significantly higher (0.53 compared to 0.43) in the condition with first-yes-counts instructions compared to the condition without such instructions. This result was interpreted to indicate that first-yes-counts instructions increase participants’ response criterion. Such an interpretation seems plausible given that participants in the condition with first-yes-counts instructions knew that they had only one chance to make an identification decision. These participants may have been particularly hesitant to select a lineup member, not knowing whether another lineup member would follow which might be an even better match to their memory of the culprit and to which they would then no longer be able to make an identification decision. Whereas two response rates were used by Horry et al.32 as a basis for drawing conclusions about participants’ response criterion, receiver operating characteristics were used as a measurement model to assess whether the instructions affected participants’ ability to discriminate the culprit from the innocent suspect. In terms of the partial area under the curve, there was a numerical, but not statistically significant, difference between the sequential-lineup condition with first-yes-counts instructions  and the sequential-lineup condition without such instructions . Based on these two types of analyses, it is possible to draw the conclusions that first-yes-counts instructions induce a conservative response bias while leaving the overall ability to discriminate the culprit from the innocent suspect unaffected.Horry et al.7 but this finding may only be an artifact of first-yes-counts instructions. However, a necessary condition for the validity of these conclusions is that the findings on which they are based are reliable33. Therefore, an independent replication of Horry et al.’s32 sequential-lineup conditions is needed before drawing such far-reaching conclusions.If these conclusions are valid, then they have important implications in that they suggest that a sizeable number of previous eyewitness identification studies may lack ecological validity. Specifically, if first-yes-counts instructions affect the processes underlying eyewitness identification decisions, inferences based on data from laboratory studies using first-yes-counts instructions may not apply to lineups that follow standard police protocols. For instance, sequential lineups have been associated with particularly conservative responding32 conclusions. First, Horry et al.32 used two separate types of response rates—the suspect identification rate and the lineup rejection rate—which they implicitly combined in their conclusion about the effect of first-yes-counts instructions into a two-component response-bias measure, albeit a measure that is not readily quantifiable. At this point it is important to realize that there is no such thing as a measure without a measurement model. This means that by implicitly combining the suspect identification rate and the lineup rejection rate into a (not readily quantifiable) two-component response-bias measure, an ad-hoc measurement model was applied. In a verbal approximation, this measurement model might look something like this: A decreasing suspect identification rate and an increasing lineup rejection rate may be assumed to indicate a decreasing willingness to make an identification decision which may be seen as an index of an increasingly conservative response bias. This measurement model may seem intuitively plausible and thus may have some face validity. Nevertheless, it is preferable to rely on a measurement model that is not only intuitively plausible but one that has also been validated experimentally, that is, for which it has been demonstrated experimentally that it really measures the processes it is intended to measure. To anticipate, here we will use such a validated measurement model. Second, the effect of first-yes-counts instructions on the implicit, not readily quantifiable response-criterion measure was assessed by performing two separate statistical tests of which one showed no significant difference between the instruction conditions whereas the other did. Specifically, the suspect identification rate was only numerically but not significantly lower in the condition with first-yes-counts instructions compared to the condition without such instructions while the lineup rejection rate was significantly higher in the condition with first-yes-counts instructions compared to the condition without such instructions. The fact that only one of the two tests was statistically significant creates some ambiguity as to the difference in response bias between the instruction conditions: According to the first test with a nonsignificant result one has to conclude that there is no difference in response bias between the instruction conditions whereas according to the second test with a statistically significant result one has to conclude that such a difference in response bias between test conditions exists. To arrive at unambiguous conclusions, it is better to have a single measure that can be compared between conditions using a single statistical test. To anticipate, the measurement model we use here provides such a measure. Third, a completely different measurement model based on receiver operating characteristics was used for assessing the effect of the first-yes-counts instructions on the ability to discriminate the culprit from the innocent suspect. Instead of relying on two entirely different measurement models with different sets of underlying assumptions, it seems desirable to use one single measurement model based on one coherent set of assumptions for measuring various types of processes underlying eyewitness identification decisions. Ideally it should also be possible to demonstrate that this measurement model fits the data before measures are interpreted.Independent of the question of reliability, there are at least three threats to the validity of Horry et al.’s35. The 2-HT eyewitness identification model belongs to the class of multinomial processing tree models38. These models are used to provide estimates of measures representing the probabilities of latent processes from categorical data. Statistical hypotheses are tested directly at the level of these parameters. Based on the entire 2 × 3 data structure of eyewitness identification decisions , the 2-HT eyewitness identification model provides measures of the detection of the presence of the culprit (dP) and of the detection of the absence of the culprit in the lineup (dA), a measure of the biased selection of the suspect in unfair lineups (b) and a measure of selections based on guessing processes (g).The two-high threshold (2-HT) eyewitness identification model is such a measurement model. The model has been experimentally validated: Multiple experiments have shown that the model’s measures indeed reflect the processes they were designed to measuredP. If culprit detection fails, which occurs with probability 1 – dP, then the eyewitness may identify the culprit based on two types of non-detection-based processes. If the lineup is unfair in that the culprit stands out from the fillers so that it can be inferred who the culprit is without relying on memory, then biased selection of the culprit occurs with probability b. With probability 1 – b, no biased selection of the culprit occurs . In this case, it is still possible to select one of the lineup members as the culprit based on guessing with probability g. The culprit is then selected among the fillers with probability 1 ÷ lineup size . With probability 1 – (1 ÷ lineup size), one of the fillers is selected . The probability with which guessing leads to a culprit identification thus depends only on the lineup size and not on any cognitive process which is why this element of the model is a known constant and not a parameter that has to be estimated from data. In the present context it is important to realize that this constant is the same irrespective of whether first-yes-counts instructions are used, provided a proper randomization procedure is in place. Specifically, if an eyewitness selects one person out of N persons in the lineup based on guessing, then the sampling probability that the selected person, out of the N persons in the lineup, is the culprit is always 1 ÷ N, irrespective of the presence or absence of first-yes-counts instructions. If none of the lineup members is selected based on guessing, which occurs with probability 1 – g, then the lineup is falsely rejected.Figure dA, leading to the correct rejection of the lineup. With probability 1 – dA, the absence of the culprit is not detected, in which case the same non-detection-based processes occur with the same probabilities as in culprit-present lineups, the difference being that guessing and biased selection in culprit-absent lineups may lead to the selection of the innocent suspect rather than to the selection of the culprit (see34 and35 for more details).The lower tree of the model represents the processes that occur in culprit-absent lineups. The absence of the culprit and the fact that no one else in the lineup can possibly be the culprit is detected with probability dP which is conceptually similar, but not identical, to the discrimination between culprits and innocent suspects that is hypothesized to be reflected in the partial area under the curve in analyses based on receiver operating characteristics and on the guessing-based-selection parameter g which provides a single, validated and unambiguous measure of the selection of lineup members based on guessing.Here we focus on the culprit-presence-detection parameter 32 to assess the effects of the first-yes-counts instructions on culprit-presence detection and guessing-based selection within a single coherent and experimentally validated measurement model. In addition to this reanalysis, an independent conceptual replication of the sequential-lineup conditions of Horry et al.32 was conducted, the data of which were also analyzed using the 2-HT eyewitness identification model. The central conclusion drawn from the analyses of Horry et al.32 was that the first-yes-counts instructions left detection-based processes unaffected but decreased the participants’ willingness to make identification decisions. If this conclusion is valid, then the first-yes-counts instructions should not affect the probability with which the presence of the culprit is detected whereas the probability of guessing-based selection should be lower in the condition with first-yes-counts instructions than in the condition without such instructions. In other words, the culprit-presence-detection parameter dP of the 2-HT eyewitness identification model should not differ between the condition with first-yes-counts instructions and the condition without such instructions whereas the guessing-based-selection parameter g of the 2-HT eyewitness identification model should be significantly lower in the condition with first-yes-counts instructions than in the condition without such instructions.In a first step, the 2-HT eyewitness identification model was used to reanalyze the sequential-lineup data reported by Horry et al.32 randomly assigned 896 participants to the conditions of a 3  × 2 (culprit presence: culprit present vs. culprit absent) × 2 (suspect position in the lineup: 2 vs. 5) between-subjects design. Here we focus on the effect of the first-yes-counts instructions. Therefore, we reanalyzed the identification decisions of the 559 participants in the two sequential-lineup conditions (with first-yes-counts instructions and without such instructions), aggregated across suspect position.Horry et al.32. Independent of the instructions, participants were able to make multiple identification decisions in both sequential-lineup conditions and if they did, they were presented with a second lap of the lineup to clarify their decision. In the condition with first-yes-counts instructions, each participant’s first identification decision in the initial lineup was used for the analyses. In the condition without such instructions, this was different. If participants made multiple identification decisions in the initial lineup, the unique identification decision given in the second lap was used for the analyses. For more details, see Horry et al.32.Participants watched one out of four possible mock-crime videos with the same content but a different culprit. Lineups were composed of one suspect and five fillers who matched the description of the suspect. In culprit-present lineups, the suspect was the culprit from the one video that had been seen. In culprit-absent lineups, the suspect was a culprit from one of the other videos that had not been seen. Participants were asked to identify a culprit in one lineup but they knew that the culprit did not have to be in the lineup. The instructions were identical for all participants except that the participants in the condition with first-yes-counts instructions received the following additional information: “If you respond ‘yes’ to a photo, you will not be able to change that decision, and you will not be able to respond ‘yes’ to any later photos”39 was used to obtain parameter estimates and to perform likelihood-ratio goodness-of-fit tests for which the α level was set to 0.05. The response frequencies underlying the analyses reported here are shown in Table 32 Table 3 in which the data are already aggregated across suspect position.For all analyses reported in this article, multiTreeb to be equal across the two instruction conditions. Second, for the same reason the culprit-absence-detection parameter dA was also set to be equal across the two conditions. The base model incorporating these two restrictions fit the data, G2(2) = 2.42, p = 0.298. The estimates of parameters b and dA were 0.00 (SE = 0.02) and 0.13 (SE = 0.12), respectively. The estimates of the culprit-presence-detection parameter dP and of the guessing-based-selection parameter g are displayed in Fig. We needed two instances of the model depicted in Fig. dP does not differ between the condition with first-yes-counts instructions and the condition without such instructions can be implemented by setting parameter dP to be equal between these conditions. If the model including this equality restriction provides a significantly worse fit to the data than the base model, then it is necessary to conclude that parameter dP differs between conditions. Parameter dP did not differ significantly between the condition with first-yes-counts instructions and the condition without such instructions, ΔG2(1) = 3.05, p = 0.081. In contrast, parameter g was significantly lower in the condition with first-yes-counts instructions than in the condition without first-yes-counts instructions, ΔG2(1) = 5.31, p = 0.021.In multinomial processing-tree models, hypotheses can be tested directly at the level of the postulated processes. For instance, the hypothesis that the culprit-presence-detection parameter 32. The presence or absence of first-yes-counts instructions did not affect the probability with which the culprit was detected whereas it affected the probability of guessing-based selection in the expected direction. Specifically, the culprit-presence-detection parameter dP of the 2-HT eyewitness identification model did not differ significantly between the condition with first-yes-counts instructions and the condition without such instructions whereas the guessing-based-selection parameter g of the 2-HT eyewitness identification model was significantly lower in the condition with first-yes-counts instructions than in the condition without such instructions.The results of this reanalysis are compatible with the conclusions reached by Horry et al.dP was lower in the condition with first-yes-counts instructions than in the condition without such instructions. This observation may fuel the suspicion that there might in fact be a difference in dP which could turn out to be statistically significant if the relevant statistical test were even more sensitive. This was one reason as to why we aimed at replicating the sequential-lineup conditions of Horry et al.32 and to test, based on even more datapoints and thus with even more sensitivity, whether first-yes-counts instructions have indeed no effect on culprit-presence detection while decreasing the probability of guessing-based selections.However, at a descriptive level the culprit-presence-detection parameter 33 because conclusions can only be valid if they are based on reliable data. What is more, an independent replication of critical findings is regarded as the gold standard in assessing the reliability of findings and as a necessary step in science to increase the trust in new findings40. We therefore tested the reliability of the findings of Horry et al.32 in an independent conceptual replication of their sequential-lineup conditions. We chose to perform a conceptual replication because, if successful, a test of the replicability of findings is even more powerful if one can show that the core finding does not depend on details of the experimental implementation. Our implementation differed in the following aspects from that used in the study of Horry et al.32. First, in the condition with first-yes-counts instructions, only one identification was technically possible. Second, participants saw each lineup only once. Third, if multiple identifications occurred in the condition without first-yes-counts instructions, we considered the last decision as a revision of any previous decisions; therefore, the last identification was used in the analysis. Fourth, we used a multiple-culprit mock crime; every participant was presented with four different lineups, two of which were culprit-present lineups and two were culprit-absent lineups. Finally, the positions of all lineup members, including the culprit and the innocent suspect, were randomly selected. Given these differences between the two studies, a replication of the results of Horry et al.32 would indicate that their results are reliable and robust in the sense that they do not depend on the details of the experimental implementation. In this case, the culprit-presence-detection parameter dP of the 2-HT eyewitness identification model should not differ between the condition with first-yes-counts instructions and the condition without such instructions whereas the guessing-based-selection parameter g should be significantly lower in the condition with first-yes-counts instructions than in the condition without such instructions.The reliability of findings is importanthttps://www.cint.com). Criteria for participation were legal age (≥ 18 years), good eyesight and German language skills. Given that the design of the present experiment comprised two groups, we aimed at recruiting about half as many participants as in the four-group experiments of Winter et al.35 in which about 750 participants were recruited. We thus aimed at collecting about 375 data sets and terminated the data collection at the end of the day at which this criterion was surpassed. Of the 421 participants who gave informed consent, 52 did not complete the study. Another 14 participants were excluded because they had failed to respond correctly to the attention check question, because they had indicated technical problems or because they had participated more than once which implies that they had watched the mock-crime video more than once. The final sample comprised 355 participants  with a mean age of 46 years (SD = 15). A sensitivity analysis using G*Power41 showed that given α = β = 0.05, a sample size of N = 355 and four identification decisions per participant, it was possible to detect an effect of the first-yes-counts instructions of size w = 0.10 on the model parameters.Participants were recruited using the Cint research panel  of their respective soccer clubs. The culprits insulted the victim, poked fun at him and tossed his belongings around. At the end of the video, the victim got knocked to the ground. The culprits continued to physically abuse the victim before they apparently noticed another person approaching (not visible in the video) and ran away shouting loudly. The two videos (henceforth Video 1 and Video 2) showed the same events , but the victims and the culprits were played by different actors. However, the victim in Video 1 matched the victim in Video 2 in terms of age, hair color, hair style and stature, as determined by the present authors. The same was true for the four culprits, that is, Hooligan A in Video 1 matched Hooligan A in Video 2, Hooligan B in Video 1 matched Hooligan B in Video 2 and so on. It was randomly determined which of the two parallel versions of the video was shown to a particular participant. The videos were about 130 s long and shown in a resolution of 885 × 500 pixels.Participants started the video by clicking on a “Start” button. Participants could not stop, fast forward or replay the video. Once the video had ended, participants were asked to answer an attention check question probing for the type of persons seen in the video. The correct response was to select “soccer fans” from a list of ten alternatives.Participants were informed that they had to identify the FC Bayern München hooligans from the video they had just seen. Participants received two-sided lineup instructions that emphasized both the need to identify the culprit if the culprit was present and the need to reject the lineup if the culprit was absent. Participants were not informed about how many lineups were about to follow. Each lineup consisted of the facial photographs of six persons, one culprit or innocent suspect and five fillers. Participants saw two culprit-present lineups and two culprit-absent lineups.35 was used. In each of the two culprit-present lineups, a randomly selected face of one of the hooligans of the video the participants had seen was presented among the fillers. In each of the two culprit-absent lineups, the face of an innocent suspect was presented that the participants had not witnessed committing a crime. The innocent suspect was one of the hooligans from the video that the participants had not seen. For instance, if participants had seen Video 1, two randomly selected hooligans  from Video 1 served as the culprits in the two culprit-present lineups, while two of the hooligans from Video 2 (Hooligan A and Hooligan D in this example) served as the innocent suspects in the culprit-absent lineups. The same set of fillers was used in culprit-present and culprit-absent lineups. For each of the four lineups, five photographs of male adults aged between 18 and 29 years were selected from the Center for Vital Longevity Face Database of Minear and Park43 to serve as fillers. The fillers resembled the culprit and the matching innocent suspect in age, hair color, hair style and stature. Together with the fact that it was randomly determined whether participants saw Video 1 or Video 2, the crossed-lineup procedure ensures that the culprits and innocent suspects differ to the same degree, on average, from the fillers in the lineup. The crossed-lineup procedure is ecologically valid because the situation it creates corresponds closely to that of a real-world lineup in which the photograph of the suspect—whose status as culprit or innocent suspect is unknown to the police—stems from a different source  than the photographs of the fillers . Therefore, the photographs of the fillers may differ to some degree from the photograph of the suspect in terms of variables such as tint, resolution or softness, particularly in cases in which the photographs of the fillers are digitally manipulated46. All photographs showed the faces from a frontal view with a neutral facial expression against a black background with no clothes visible. The photographs were edited to harmonize face sizes and lighting conditions and were presented in a resolution of 142 × 214 pixels. The order of the lineups was randomized, as was the position of the culprit or innocent suspect and the fillers in each lineup.To manipulate the suspect’s guilt, the crossed-lineup procedureParticipants were randomly assigned either to the condition with first-yes-counts instructions or to the condition without such instructions. In the condition with first-yes-counts instructions, each lineup was preceded by a screen announcing the next lineup as the “1st lineup”, “2nd lineup” and so on and the following statement : “If you choose ‘Yes, was present’ for one face, this decision is irrevocable. You will not be able to select ‘Yes was present’ for any other face in the lineup”. A click on the “I am ready” button started the successive presentation of the photographs. Participants decided for each photograph whether it showed one of the culprits or not by clicking on the “Yes, was present” button or the “No, this person was not present” button, respectively. Either response initiated the presentation of the next photograph. Immediately after participants’ first and only identification decision in a particular lineup, the ‘Yes, was present’ button was disabled and its label changed to ‘A yes response is no longer possible’ for all remaining photographs of the lineup for which participants were only able to select the ‘No, this person was not present’ button.In the condition with no first-yes-counts instructions, each lineup was preceded by a screen announcing the next lineup as the “1st lineup”, “2nd lineup” and so on, without the first-yes-counts statement. A click on the “I am ready” button started the successive presentation of the photographs. Within each lineup, participants decided for each photograph whether it showed one of the culprits or not by clicking on the “Yes, was present” button or the “No, this person was not present” button, respectively. Either response initiated the presentation of the next photograph. In case of more than one identification decision in a lineup, the last decision was used in the analysis.32 and to that of a typical police lineup, participants also indicated how confident they were that their decision was correct. After every lineup, participants had to click on a “Continue” button to start the next lineup or, after the final lineup, to reach a screen on which questions were asked about possible technical difficulties during the online experiment. At the end of the experiment, all participants were informed that the crime shown in the video had been staged and they were informed about the purpose of the experiment.A lineup was counted as rejected if none of the members of the lineup was identified as one of the culprits. To make the procedure similar to the procedure used by Horry et al.32 in the reanalysis presented above. The biased-suspect-selection parameter b and the culprit-absence-detection parameter dA were each set to be equal across the two conditions. The base model incorporating these restrictions fit the data, G2(2) = 3.09, p = 0.214. The estimates of parameters b and dA were 0.01 (SE = 0.01) and 0.06 (SE = 0.04), respectively. The estimates of the culprit-presence-detection parameter dP and the guessing-based-selection parameter g are displayed in Fig. The response frequencies underlying the analysis reported here are shown in Table dP did not differ significantly between the condition with first-yes-counts instructions and the condition without such instructions, ΔG2(1) = 0.01, p = 0.936. In contrast, parameter g was significantly lower in the condition with first-yes-counts instructions than in the condition without such instructions, ΔG2(1) = 21.16, p < 0.001.Parameter 32 nicely fit with the results of the original study. The presence or absence of first-yes-counts instructions did not affect the probability with which the culprit was detected whereas guessing-based selection was significantly less likely to occur in the condition with first-yes-counts instructions than in the condition without such instructions. This confirms the conclusions drawn from the reanalysis of the data reported by Horry et al.32. The fact that there was again no difference between conditions in parameter dP may be seen as particularly remarkable given that the relevant statistical test here was based on more data points . As an aside, these procedural differences may be the reason why the probability of culprit-presence detection was lower and the probability of guessing-based selection was higher in the present conceptual replication than in the reanalysis of the Horry et al.32 data. However, the important point is that the relevant comparisons between the conditions with and without first-yes-counts instructions led to the same conclusions irrespective of these procedural changes. The conclusion about the effect of first-yes-counts instructions on the processes underlying eyewitness identification decisions thus do not depend on the details of the experimental implementation.Horry et al.35 have shown in their Experiment 3, using pre-lineup instructions that discourage all but the very certain identifications suffices to reach this goal.With respect to guessing-based selection, the conclusion drawn from both datasets analyzed here is that first-yes-counts instructions serve to reduce the probability with which this process occurs. Should one thus use first-yes-counts instructions if the goal is to reduce guessing-based selection? There is in fact a far simpler and more straightforward method for reducing the probability of guessing-based selection without affecting the probability of culprit-presence detection. As Winter et al.32 and the present conceptual replication were essentially fair , there was no basis for biased-suspect selection to differ between the condition with first-yes-counts instructions and the condition without such instructions. Therefore, the existing data do not allow us to draw firm conclusions about whether, and if so how, first-yes-counts instructions might affect biased-suspect selection in unfair lineups. The effect of first-yes-counts instructions on biased-suspect selection in unfair lineups is thus an issue for future research to explore.Given that the lineups used in both the experiment of Horry et al.47). Parallel to what Steblay et al.8 have stated, we know of no jurisdiction that has implemented a rule to take into account only the witnesses’ first identification decision in sequential lineups31. The reason as to why such a rule is avoided seems obvious. Legal systems typically come with a regulation as to how many fillers have to be in a lineup together with the suspect. For instance, in the United States a lineup usually consists of five fillers and the suspect48, in the United Kingdom a lineup usually consists of eight fillers and the suspect29 and in Germany a lineup usually consists of seven fillers and the suspect2. A first-yes-counts rule could be seen to undermine these regulations. Specifically, whenever an eyewitness would make an identification decision prior to seeing the last photo in the lineup and thus terminate the lineup presentation, it could be argued by defense lawyers that the actual lineup size was lower than the lineup size stipulated by the legal system. The most straightforward solution to the problem is to assume that if multiple identification decisions occur, later identification decisions imply a revision of earlier identification decisions which implies that the last identification decision in a lineup is the one to be used. Alternatively, in some legal systems eyewitnesses are obliged to perform multiple laps through a lineup before they are allowed to make one final identification . Either way, research on sequential lineups needs to implement one of the rules instead of the first-yes-counts rule if the aim is not to decrease the ecological validity of this research.Knowing that and how first-yes-counts instructions affect the processes underlying eyewitness identification decisions is important. This is so because one may now conclude that a sizeable number of previous eyewitness identification studies indeed lack ecological validity in that they underestimate the tendency of eyewitnesses to select the culprit based on guessing relative to real police lineups (e.g.,


The study of warfare from an evolutionary perspective has expanded rapidly over the last couple of decades. However, it has tended to focus on the ancestral origins, prevalence, and instruments of war rather than adaptationist analyses of its underlying psychology. I argue that our evolved coalitional psychology may contain a set of distinct evolved heuristics designed specifically for offensive and defensive coalitional aggression. Data from two survey experiments are presented, in which subjects were given scenarios depicting offensive or defensive aggression and were told to make decisions, for example, regarding their willingness to participate in the conflict, their opinions of others who did not choose to participate, and their expectations benefit. The results indicate that humans do indeed distinguish readily between these two domains and that their willingness to participate, as well as their emotional responses toward others, is highly contingent upon this informational cue in adaptively relevant ways. In addition, and consistent with parental investment theory, data reveal a range of sex differences in attitudes toward coalitional aggression in the two conflict domains. Beyond the study of warfare, this project has implications for our understanding of the relationship between individual behavior and group dynamics, as well as for our understanding of the mechanisms by which the psychological framing of political events can lead to important social outcomes. Debate on the evolution of war has typically been hamstrung by ambiguities regarding its ancestral origins and prevalence. Although the nature of ancestral environments is not unknowable, it is often incomplete, requiring integration of various lines of evidence across disciplines such as primatology, archaeology, and anthropology. This has produced two opposing arguments regarding the evolutionary origins of warfare. One group of scholars argues that we cannot infer the existence of warfare prior to direct archaeological evidence of its effects and therefore that warfare probably emerged no earlier than about 50,000 years ago . In contIn parallel to this disagreement, scholars also dispute the very nature of war—that is, how should war be defined? While some scholars prefer a more restrictive definition that requires a degree of social organization and weaponry, others embrace a broader view of warfare, such as “events in which coalitions of members of a group seek to inflict bodily harm on one or more members of another group” , p. 1294These adaptationist questions are necessary because, absent methodological innovations, there are only so many ways to interpret existing evidence of ancestral environments. Future research may reveal more ancient markers of human warfare; however, direct evidence of warfare does not fossilize well. Therefore, even if warfare is as evolutionarily old as some argue, it is unlikely to be reflected in the fossil record, and archaeology alone will necessarily be insufficient for concluding the matter on its own. Nevertheless, if we are to take adaptationism seriously, then scholarship has so far largely ignored one crucial source of evidence about ancestral environments: the information processing structure of our own evolved psychology. Just as the functional design of the human eye reveals information about the visual landscapes in which human perception evolved, so the design of our coalitional psychology reflects the statistical regularities of the coalitional landscape in which we evolved—including the prevalence and form of coalitional violence or warfare. Our adapted mind is a window into the ancestral past .Nonevolutionary theories of war are important, myriad, and useful. While it is beyond the scope of this study to review these here, it is sufficient to note that these theories tend to emphasize the formative pressures of resource competition, social stratification, social organization, weaponry, and threat environment in helping to explain both the origins and patterns of human warfare over time . New mod One exception is the framework established by There are many evolutionary theories of dyadic aggression , and theAdaptationist hypotheses on the psychology of human warfare do not necessarily dispute the relevance of variables emphasized by nonevolutionary theories of warfare, nor do they diminish the importance of cultural evolution in shaping the coalitional landscape over time. Rather, evidence of a specialized psychology of warfare helps us to understand how the mind tracks and processes these cues in the environment.The central claim of the following studies is that distinct forms of coalitional aggression constituted selection pressures that have resulted in psychological adaptations designed to reason adaptively within these domains. These two forms of coalitional aggression are offensive and defensive aggression—or more generally, taking versus holding territory or resources . Before Human coalitional aggression is as old as it is diverse in form. One common distinction is between raids and battles . Raids tAlthough the evolutionary endurance of multiple forms of coalitional violence suggests the need for distinct adaptationst explanations of each, it is also possible that there are computational challenges that are common to all or most forms of coalitional violence . For exaacross forms of warfare allows a clearer perspective on how unique forms of war may alter or calibrate these dynamics.Analysis thus proceeds by first examining the collective action dynamics that are likely common across multiple forms of warfare. This set of computational problems then forms the framework within which separate mechanisms for offensive and defensive coalitional behavior can be derived. In short, an understanding of the dynamics that are common own decision to participate, and the second relates to an individual’s ability and willingness to enforce the participation of others. The individual decision to participate in violence is both somatically and reproductively risky, though not unconditionally so. It is by now widely recognized that the regulation of one’s own participation in coalitional aggression should therefore be sensitive to adaptively relevant cues that reveal information relating to, for example, relative formability, the distribution of risk, the value of the collective goal, and the probability of success  to the entire group. However, offensive aggression that is successful in taking resources from another group is more likely to include a nonnegligible amount of privatized resources, including perishable food and mating opportunities, which asymmetrically accrue to the participants rather than the group as a whole. In short, the major distinction between offense and defense from which hypotheses are derived is that successful defensive coalitional aggression mirrors the structure of a public good, and successful offensive coalitional aggression confers benefits that are relatively more privatized among those who choose to participate in violence. Thus, these two types of aggression can be distinguished in terms of their respective ancestral cost–benefit structure.Ancestrally, one difference between offensive and defensive coalitional behavior would have been that the benefits of successful offensive aggression include nonpublic goods to a relatively greater degree than those derived from successful defensive aggression .4 For shadenfreude and reduced empathy for outgroupers in competitive environments fuel the motivational component of defensive action. Thus, there is a preexisting bias toward group support that motivates participation in defensive coalitional aggression. In contrast, the initiation of offensive aggression shifts the burden of coalitional action onto those who would initiate aggression. Unlike the case of defense, in which benefits are distributed relatively symmetrically and publicly, in offensive aggression benefits accrue asymmetrically and often privately to participants, and the relevant consideration in this domain will be that of personal gain, rather than group gain. Thus, the first two hypotheses propose that the calibration of behavior-regulatory mechanisms in the brain for participation in coalitional aggression will depend upon the domain of aggression. In offense, willingness to participate should be predicted by perceived personal gain, while in defense, willingness to participate should be predicted by group gain .In defense, an out-group initiates aggression, and the challenge is to respond quickly and effectively. The preexisting bonds characterized by kin-based hunter-gatherer bands facilitate a quick defensive response to external aggression. Additionally, intergroup dynamics such as Differences in the ancestral cost–benefit structure of offense and defense suggest that punitive and reward sentiments toward nonparticipants should operate differently in these two conflict domains. I have argued that the domain of offensive aggression shifts the motivational burden of coalitional action to the initiators and that their enforcement of the participation of others is a function of their ability and willingness to absorb the costs of this enforcement on others. Also as noted earlier, Sex differences may be adaptively contingent upon the domain of conflict. Sexual selection and parental investment theory have proven remarkably successful at predicting and explaining the zoological diversity of sexual dimorphism and have proven useful for explaining a range of sex differences in humans . This thcoalitional aggression has been reproductively advantageous for males more than for females  competition . On the  females . At the offensive coalitional aggression since the benefits of such action remain privatized among participants, even accepting the fact that the material benefits of such coalitional action may be shared among close family or allies.In terms of expected benefit, however, given that successful defense confers more in the way of public goods than successful offense, all group members experience the main benefits of successful defensive aggression regardless of sex. Despite the fact that investment asymmetries render female coalitional aggression a high-cost, low-benefit endeavor, this does not mean that aggression avoidance should be psychologically instantiated as an unconditional strategy in females. Despite the fact that the fitness benefits to females of engaging in coalitional violence are often low relative to males, the threat of invasion by a male coalition is a particularly costly prospect for females , such thHypothesis 1: In offense, willingness to participate will be triggered by expectations of personal benefit, even controlling for expectations of group benefit.Hypothesis 2: In defense, willingness to participate will be triggered by expectations of group benefit, even controlling for expectations of personal benefit.Hypothesis 3: Males will be more willing than females to participate in both offensive and defensive coalitional aggression.Hypothesis 4: There will be no sex difference in expected benefit from defensive coalitional aggression.Hypothesis 5: Males will expect greater benefit than females from offensive coalitional aggression.Hypothesis 6: In defense, but not in offense, punitive sentiment toward nonparticipants will be triggered by one’s willingness to participate.Hypothesis 7: In defense, reward sentiment toward participants will be triggered by one’s expected benefit from coalitional aggression, but in offense, reward sentiment toward participants will be triggered by one’s willingness to participate.Hypotheses were tested with an experiment-based survey in 2007. The subject population consisted of 195 undergraduate students  at the University of California, Santa Barbara, mostly political science majors between the ages of 18 and 39. The survey presented both an offensive and defensive scenario. Each subject read both scenarios, and the order in which the scenarios were presented was counterbalanced across subjects to minimize order effects. Thus, the conflict domain manipulation (offense/defense) followed a within-subjects implementation.Offense: Imagine that a few years from now, several oil-rich Middle Eastern countries get together and decide that to increase profits, they will dramatically raise the price of their oil. This price increase devastates US industry and causes high inflation in the USA. US gas prices triple, and several US oil companies go bankrupt. After talks with these Middle Eastern countries fail, the USA declares war on them. But war was unexpected, so the USA has allowed its army to get relatively small, and it must start drafting US citizens in order to have a chance of victory. How would you feel about this war?Defense: Imagine that a few years from now, the Russian people elect a new, warlike dictator who claims that Alaska should rightfully belong to Russia. Under this dictator, Russia invades and conquers Alaska. There is good evidence that Russia also intends to conquer more US territory, in addition to Alaska. In response to this invasion, the USA declares war on Russia. But because this war was unexpected, the USA has allowed its army to get relatively small, and it must start drafting US citizens in order to have a chance of winning this war. How would you feel about this war?Two major changes were made to these vignettes. First, both scenarios are rewritten in a nomadic context 1,000 years ago. This is done in order to “decouple,” as best as possible, modern views toward war in the context of nation-states and our beliefs regarding those states from the operation of the psychological adaptations themselves. For example, the above vignettes invoked the prospect of war with Russia and with Middle Eastern countries, all of which represent culturally significant out-groups with unique histories of interaction that are likely to affect the calibration of psychological adaptations in confounding ways .This experiment-based survey is adapted from and expands upon an earlier survey conducted by “Defense: Imagine that you and your friends and kinsmen are the members of a nomadic horse-riding people, the Pathans, in the year 1050. You and your group are tough and strong, but your life is meager. You cook over dung fires, drink fermented mare’s milk, sleep in yurts under skins, and freeze every winter. Your ancestral territory is on the dry steppe, but you also border on the prosperous Chinese province of Sinkiang. You have heard that the governor of the province is ambitious and intends to extend his control into your territory. If you lose your grasslands, you and your family will have nothing. The governor has assembled a war party, which has launched a series of nighttime raids on your people. Some of your women have been kidnapped, and some of your men were killed. You hear rumors that another raid is going to happen tonight. You and your friends and fellow tribesmen begin to discuss whether you should form a war party to repel the raiders.”“Offense: Imagine that you and your friends and kinsmen are the members of a nomadic horse-riding people, the Pathans, in the year 1050. You and your group are tough and strong, but your life is meager. You cook over dung fires, drink fermented mare’s milk, sleep in yurts under skins, and freeze every winter. Your ancestral territory is on the dry steppe, but you also border on the prosperous Chinese province of Sinkiang. You sometimes enter these cities to trade, but the Chinese men and women laugh at your poor clothes, your dirt, and your lack of refinement. You do notice that the men are short, weak, and cowardly, despite the fact that they give themselves airs. The women are also beautiful and dressed in the best silks.You and your friends have heard that the Mongols to the North had attacked and taken over several cities and are now wealthy and powerful. The Chinese troops had run like rabbits. You and your friends and fellow tribesmen begin to discuss whether you should form a war party to seize one of the neighboring cities.”Second, the offense scenario specifically was reorganized in order clarify that the question is whether to initiate unprovoked aggression. In the offense scenario utilized by strongly disagree (1) to strongly agree (7). An identical battery of questions followed each scenario with minor modifications where contextually appropriate.Immediately following each scenario, subjects were asked a series of questions relating to their willingness to participate, the extent to which they personally expected to benefit, anticipated benefit to the group, expectations that others would join, desire to punish free riders, and desire to reward participants. Answers to each question were self-reported on a 7-point Likert-type scale from Willingness to participate is predicted by expected benefit to the group (“If my group succeeded…it would benefit us as a group”) in defense (Hypothesis 1) but by expected personal benefit  in offense (Hypothesis 2). Hypotheses 1 and 2 are expected if defense is a public good, while the benefits of offense tend to be subject to privatization.The first two hypotheses predict that the effect of expected group benefit and expected personal benefit on one’s willingness to participate is conditional upon the domain of offense versus defense . Therefore, these two hypotheses were tested using a linear mixed effects regression model that includes each of the main independent variables as well as interaction terms with condition on each of the independent variables. Due to high inter-item reliability across the four measures of participation, the dependent variable is a new variable that scales the four measures. Results  show thaMales are more willing than females to participate in both offensive and defensive aggression.A mixed effects regression model showed that condition did not moderate the effect of sex on willingness to participate . In otheMales expect greater benefit than females from offensive, but not from defensive, coalitional aggression.benefit me personally” and “If my group succeeded in repelling the invaders/seizing a city, it would benefit us as a group.” Again, mixed effects regression was used to examine the moderating effect of condition upon the relationship between sex and both types of expected benefit.I examined two measures of perceived benefit: “If my group succeeded in repelling the invaders/seizing a city, it would b = −0.271, p = .22); however, the moderating effect of condition on sex was in the predicted direction and statistically significant  in the second model. The interactions for each model are depicted in Results for two linear mixed effects regression models are presented in Willingness to participate will track punitive sentiment  in defense but not in offense.b = −0.447, p = .01). In other words, Price et al.’s study is replicated in defense, but the effect is weakened in offense. In contrast, and consistent with b = 0.031, p = .71) or when interacted with condition . Perceived benefit to self should predict sentiments for rewarding participants in defense, but willingness to participate should predict reward sentiment  in offense. Again, a replication of b = 0.600, p = .00). However, although the moderating effect of condition on perceived benefit was indeed in the predicted direction, it failed to reach statistical significance . Resulting interactions are depicted in Results of a mixed effects regression , Model 2both benefit to self and willingness to participate predict reward sentiment. One possible explanation for these findings derives from the earlier discussion of the distinction between defense and offense as public versus private goods. To the extent that the benefits from successful offense are mostly private goods but include a nonnegligible amount of public goods , it follows that benefit to self might still predict reward sentiment in offense as in defense. For example, if there are at least some public benefits that accrue as a consequence of offensive coalitional aggression, then it follows that there may some individuals who (1) expect the benefits of action will make them better off, either directly or through benefits that inhere to the group, and (2) for some reason may wish to participate but are not able to . In these instances, the  existence of a public good severs the narrow link between participation and perceived personal benefit, such that even nonparticipants can expect benefits—and when they do, they should act to reward participants to the extent that the value of their donation does not outweigh their on-average gain.It is noteworthy that in offense, Hypotheses 1–5 are generally supported by the data and help to reveal the effect of conflict domain (offense/defense) on the operation of mechanisms designed in part to regulate within-group participation in intergroup conflict. Results supported the broad pattern of evidence in favor of male aggressiveness, but again, conflict domain adds granularity to this relationship. For example, males are more willing to participate in both offense and defense, and males are less pessimistic than females about the group benefits of offensive aggression. Importantly, there was no meaningful sex difference in the perceived personal or group benefits of defensive aggression. This supports new research that downplays a purported unconditional preference for aggression in males and reveals one aspect of the domain specificity of such preferences.added premium of privatized benefits to the participants. Thus, future research should further investigate the cue structure of these two domains of aggression. Although there is already fruitful movement in this direction, the inferential possibilities are myriad.Hypotheses 6 was supported—offensive aggression weakens the relationship between participation and punitive sentiment due to the fact that nonparticipants in offense are not free riders as they are in defense. Nevertheless, one must remember that it is most likely that successful offensive coalitional aggression confers many types of benefits, some of which may indeed take the form of a public good. In other words, both defensive and offensive coalitional aggression sometimes confer some types of public goods when successful , but offensive coalitional aggression typically comes with the Hypothesis 7 received mixed support—willingness to participate strongly predicted reward sentiment in offense, as predicted, but perceived personal benefit predicted reward sentiment in both offense and defense. In other words, in defense, only perceived benefit significantly predicts reward sentiment toward nonparticipants, but in offense, both perceived benefit and willingness to participate predict reward sentiment.One of the implications of the previous study is that punitive sentiment may be designed, at least in part, to recruit labor. This is somewhat intuitive since punitive sentiment, by reversing fitness differentials between participants and free riders in the present, may serve to motivate participation in the future by deterring would-be defectors. Relatedly, the challenge of recruiting labor should be central to our coalitional psychology, especially given the ancestral imperative of relative numbers for intergroup aggression. Therefore, in Study 2, hypotheses are derived and tested regarding the effect of perceived labor levels on punitive sentiment toward nonparticipants and willingness to participate in offensive and defensive aggression.Coalitional aggression is predominantly a male activity ; howeverHypothesis 8: In defense, male participation should remain high and unaltered by perceived labor conditions; female participation should be greater when labor is perceived to be insufficient.Hypothesis 9: In offense, male participation should be highly incentivized by “nearly there” labor levels; female participation should remain low and unaltered by perceived labor levels.Hypotheses on willingness to participate:Furthermore, given the link between participation and punitive sentiment, the intuitive expectation is that recruitment—in the form of punitive sentiment toward nonparticipants—should follow the same pattern. In other words, we have the following parallel predictions regarding punitive sentiment:Hypothesis 10: In defense, male punitive sentiment should remain high and unaltered by perceived labor conditions; female punitive sentiment should be greater when labor is perceived to be insufficient.Hypothesis 11: In offense, male punitive sentiment should be highly incentivized by “nearly there” labor levels; female punitive sentiment should remain low and unaltered by perceived labor levels.Hypotheses on punitive sentiment:Before proceeding to tests and results, it is worthwhile to further elaborate the nature of the sex-specific conditional strategies in war. The divergence in strategies becomes apparent when we consider the marginal effects of hypothetical changes to labor levels in defense and offense. Each is examined in turn. Furthermore, given a greater baseline willingness of males to participate in coalitional aggression than females, there is no obvious theoretical reason why female participation should be as high or higher than male participation in defense when available labor is already sufficient. When labor is insufficient, however, it is possible that we may see a greater increase in female participation, particularly if this is viewed as necessary for victory and likely to bring about that outcome.Defense, being a public good, is such that every additional unit of labor added to the effort only further decreases the cost–benefit ratio faced by each participating individual, since more labor only reduces costs in an environment in which benefits are public and fixed. for the execution of offensive coalitional aggression, each additional unit of labor actually increases the cost–benefit ratio, assuming that the benefits are private and fixed. For example, if there are 50 units of resources to be plundered from out-group X and 10 individuals are required in order to surprise and overwhelm out-group X and steal away with those resources, then each individual potentially receives 5 units as reward (plunder) on average. However, if 15 more individuals are added to the effort, then the cost–benefit ratio is effected in the following way. On the cost side, the fact that the raiders have more than doubled in size reduces the risk to which each is exposed , while also increasing the probability of success . Furthermore, as numbers expand, the offensive coalition at some point must inevitably lose the element of surprise by virtue of size alone, which was among the most critical factors determining success in ancestral landscapes. On the benefit side, with 25 total individuals participating in the raid, each individual now only receives two resource units on average. Thus, the expectation is that males will optimize participation and recruitment in offense according to the demands of the situation. Again, given the predominantly male character of coalitional aggression, the expectation would be that the above analysis explains male but not female motivation in offensive war. Instead, this perspective would predict female participation and recruitment to remain low in offense regardless of any perceived change in labor levels.In offense, every additional unit of labor to the effort does not invariably reduce the cost–benefit ratio experienced by participants. For example, when labor is insufficient for the successful execution of offensive coalitional aggression, participants (initiators) have a unique and powerful interest in recruiting labor since they have identified that the initiation of offensive aggression would benefit them. In contrast, however, when labor is sufficientextremely low labor levels, it may make little sense to invest the time and effort necessary to build a coalition; instead, what is more likely is that the effort will fizzle upon lack of interest and motivation. However, as labor levels begin to increase , it becomes increasingly worthwhile to engage in the endeavor until a critical threshold is reached, after which point the coalition members experience diminishing marginal returns per capita as more participants join, essentially “turning off” participation and recruitment. As …once the critical threshold required to overpower the enemy combatants is reached, the individual share of loot acquired through raiding out-groups is likely to decrease with the number of warriors participating in the raid, while the individual chances of survival for defenders are likely to increase with every additional man participating in the defense. (p. 975)In addition to the rapid diffusion of benefits that extra labor causes in offense, there is the additional problem of coalition management, which only becomes increasingly challenging with ever larger coalitions. In contrast, defense poses more of a basic coordination problem due to its immediate and public nature, and therefore, the extra labor is less of a burden and more of an asset.A further note is warranted regarding male participation and recruitment in offense. The above analysis suggests that there is likely a quadratic relationship in offense between participation and perceived labor levels. In other words, at and defense) described coalitional aggression that was unlikely to succeed if more people did not join, while the other half received a survey in which both vignettes  described coalitional aggression that would probably succeed even if more people did not join. The manipulations added to the end of the vignettes are:One hundred and eighty-eight undergraduate subjects participated in this study, of which 74 were male and 114 were female. The within-subjects design of offensive and defensive vignettes and questions was maintained. A new between-subjects manipulation was incorporated that altered the design in the following way. Although all subjects read an offense and a defense vignette as before, in this round, each subject reads vignettes in which either success was unlikely if more people do not join (labor insufficient) or success was likely even if more people do not join (labor sufficient). This was accomplished by adding a couple of sentences to the end of each vignette describing one or the other labor condition. For example, half of the subjects received a survey in which both vignettes . Is punitive sentiment a function of a three-way interaction between conflict type, labor condition, and sex? Results indicate that although the interaction term was in the predicted direction, the effect failed to reach statistical significance . Individual participation is predicted to be a function of the three-way interaction between conflict type, labor condition, and sex. Results of a mixed effects regression  support interaction between conflict type, labor condition, and sex, indicating that although more labor was tied to greater expectations of success for males in both offense and defense, the same was not also true for females . This surprising result will be addressed in the discussion below. No hypotheses were established regarding the effect of perceived labor levels on the expected probability of success. However, given the widely accepted importance of relative numbers for coalitional aggression that is often cited in the literature e.g., , it woul Importantly, as found in Study 1 and in previous studies, sex does predict willingness to participate reasonably well. Taken together, what this suggests is that the punitive sentiment system is sensitive to cues of participation rather than to the sex of the participant, since it is one’s participation, rather than sex, per se, that is more immediately indicative of the cost–benefit profile of the coalitional context. Nevertheless, due to the relatively small sample size and the fact that the three-way interaction between conflict type, labor condition, and sex was nevertheless in the predicted direction despite missing statistical significance, the idea is worth examining further. However, the current results offer limited support at best.As predicted by Results support the prediction that participation is a function of the three-way interaction of conflict type, labor condition, and sex. In offense, labor conditions affected strongly predicted male participation, while in defense, labor conditions affected female participation. In contrast to punitive sentiment, results provide greater support for the predictions regarding individual participation.negative illusion designed to compel the participation of others, if not specifically male participation. Of course, such negative illusions find their analogue in positive illusions, or overconfidence. In this regard, it is noteworthy that males are more prone to overconfidence than females in conflict scenarios , some care is necessary when outlining these issues.The current design faces limits to internal validity due to the fact that one of the main manipulations (offensive vs. defensive domains) was implemented using a within-subject survey experiment design, meaning that each subject received both manipulations, although the order of the manipulations was randomly varied in order to mitigate ordering effects. Furthermore, within-subjects designs yield a greater likelihood of committing type II errors, in which one fails to reject a false null hypothesis. In other words, within-subject manipulations may weaken a causal relationship that exists, leading the researcher to falsely conclude that the manipulation or treatment has no effect . In the context of these difficulties, however, it is noteworthy that hypotheses regarding the offense/defense manipulation were largely supported and in the predicted direction. Nevertheless, future research could expand and amend the design to overcome this limitation.Problems of external validity exist as well, largely due to the demographics of the subject population—almost exclusively an undergraduate population. Clearly, generalization is limited. Importantly, however, this student population is in fact the same age as most individuals in active combat and therefore may be ecologically valid for some reasons while problematic for others. Nevertheless, research must proceed in stages, in which initial tests establish proof of concept, and future studies aim to replicate findings and employ alternative methodologies that overcome these limitations, which are far from insurmountable. At no point is the argument made that any of the above results offer conclusive evidence of adaptations for warfare; rather, the findings are consistent with hypotheses that were derived from adaptationist analyses, drawing upon a range of theory and evidence, such as, but not limited to, known and inferred ancestral selection pressures. Both the assumptions upon which the model rests  as well as the hypotheses themselves are falsifiable. Improvements in internal and external validity require, at a minimum, further tests against alternative hypotheses, as well as basic replication and improvements in sample size and representativeness.initiation of violence, while acknowledging that as conflict unfolds, particularly in modern combat, the distinction between offense and defense can quickly become fluid and nebulous. Analysis is therefore restricted to the initiation of conflict, which is also often the practice of scholars of international relations  to misrepresent offensive aggression as defensive, particularly where their expected personal benefits are great. This dynamic may also lead to interesting international consequences, as the spread of democracy may indeed be accompanied to a certain degree by an ostensible norm against the initiation of offensive aggression and the competitive framing of aggression of any type as defensive. Thus, it should come as no surprise, given the significant downstream motivational consequences of war framing, that one of the most hotly debated considerations in any conflict, ranging from civil to international war, is the rather parochial question: “Who started it?” These studies indicate that the answer to this simple but powerful question may often be sufficient to trigger a host of downstream inferences and motivations regarding one’s own participation in conflict as well as one’s feelings toward others who may disagree.For example, one important institution that may interact with our coalitional psychology in important ways is democracy. Under democratic regimes with broad political enfranchisement, war-minded democratic executives face an acute labor recruitment problem when they seek to initiate offensive violence .15 ThiThis dynamic is evident in almost any debate on the relative merits of war but is particularly well illustrated by the 2003 American invasion of Iraq. The Bush administration made the case for preventive war against the Saddam Hussein regime by claiming that Hussein intended to acquire and use weapons of mass destruction (WMD). According to the administration and its supporters, the threat was imminent and indivisible, and therefore, preventive  aggression was necessary. Their opponents, however, insisted that Hussein could be contained and that WMD-related claims were exaggerated or false. The counterargument was, therefore, diametrically opposed: War was not inevitable, the threat was not shared, and American intervention would be offensive rather than preventive. Furthermore, and quite tellingly, opponents of war accused the administration and its supporters of seeking to initiate war for the sake of private interests and gains, such as oil, rather than the public good.This is exactly how an evolved coalitional psychology should operate in the prelude to war: Cues regarding public or private gains, for example, should loom large, and individuals should actively scan and evaluate the defensive claims of supporters for evidence of deception. Ancestrally, the costs of acting upon false beliefs regarding the nature of coalitional aggression could have generated serious fitness consequences. For example, joining offensive aggression that you falsely believe to be defensive could lead to great risk for little gain at best or in making the ultimate sacrifice for the private gain of another at worst; conversely, falsely categorizing group defense as offensive would risk not only your group’s defeat but also your own survival and reproductive future. Given that the costs of being wrong about defense are likely greater than the costs of being wrong about offense, error management may result in a psychology that is relatively susceptible to being convinced that offensive aggression is actually defensive.Given that the labor recruitment problem is generally more easily overcome for defensive than for offensive aggression, one important corollary is established: where both labor recruitment challenges and the potential personal benefits to initiators are great, those who would seek to initiate offensive aggression should, all things equal, differentially act to misrepresent offensive aggression as defensive for the sake of “cheating” the problem of labor recruitment. This misrepresentation on behalf of initiators may even occur through self-deception, in which the greater the benefit perceived by those who would seek to initiate aggression, the more likely they are to truly believe that the initiation of aggression is necessary, either for the sake of preventing future aggression or defending the status of the group, for example.Thus, as mentioned briefly above, it can be nearly impossible in practice to determine whether any given instance of warfare is “actually” defensive or offensive. And it may be that pure cases of either offense or defense are relatively uncommon, as any given episode of coalitional aggression may indeed contain mixed motives. However, this complexity is not an argument against the existence of separate psychologies. Rather, the complexity itself, as suggested above, is the product of the delicate interplay of these two psychologies. In other words, the reliable fuzziness of political claims in the run-up to war is likely the direct consequence of the recursive interplay of individuals attempting to resolve and enforce issues of participation and recruitment across society. In this sense, a distinction between offense and defense is indeed subjective, but the content of this subjectivity is provided by the recursive interplay of (1) a species-typical evolved coalitional psychology designed to operate according to privileged hypotheses specific to the domains of warfare, (2) unique situation-specific variables relating to intracoalition variables  and intercoalition variables , and (3) unique individual-level variables such as sex, personal formidability, number of siblings, personal prior history in aggression, status position relative to others, and so on. It is no surprise then that wars are fought for myriad reasons and develop in endlessly unique ways; yet, this is because of, rather than despite, an evolved psychology that is both highly conditional and deeply complex.products of shared identities, rather than when they are designed in order to produce them.Importantly, separate psychologies of offense and defense may also play a role in peacetime alliance politics. For example, modern notions of “collective security” in international relations institutionalize the concept of the indivisibility of threat by asserting that a threat to one is a threat to all. The evolutionary calculus, of course, is that shared threat motivates individual willingness to support the alliance, and thus, modern security institutions essentially attempt to exploit this defense psychology in order to artificially convert a dyadic nonshared threat into a broader shared threat. Collective security is therefore effective when a defense psychology is triggered among the members, such that collective action is facilitated and deterrence against external threats is enhanced. The psychological logic behind the institutional effort is sound; however, institutions alone cannot “dictate” the experience of a shared threat to its members, which of course suggests that institutions that are built on preexisting shared group boundaries and identities are more successful at coordinating collective action than institutions that are built for the purpose of creating shared group boundaries. This is evident, for example, in the very different historical trajectories experienced by the North Atlantic Treaty Organization on the one hand and the Southeast Asian Treaty Organization on the other. In other words, institutions are often more successful when they are the The existence of psychological adaptations for warfare does not allow the inference that humans are naturally war-prone and that warfare is inevitable . Instead


Dear Editor,et alRecently, we have read the correspondence entitled “How video games can help medical students become better surgeons in the future – correspondence” by Muhammad Learning video games has gained popularity as a method for medical education, health promotion, and illness prevention due to their enjoyable and engaging features including competition and feedback elements. The surgical healthcare workers were tested and trained using digital video game–based learning. In addition to their clinical hours, trainee surgeons can develop their surgical abilities outside operating rooms by playing video games. Since then, playing video games has been a successful way to trade surgeonset al.For aspiring surgeons to minimize errors and expedite surgery, nontechnical skills including excellent communication skills, teamwork, leadership, attention, and decision-making are also essential. Playing video games facilitates the blending of challenges and learning. It guarantees achieving the main aim in stealth mode learning, and players are encouraged to play until they succeedet al.For surgical oncology, laparoscopic suturing, and the use of ocular lasers, it is also essential to have appropriate vision and focusing abilities to prevent over sectioning of the operative margins. Green and BavelierFacial disfigurement following surgery in oral cancer patients may often prove to be socially stigmatizing and predisposing patients to mental health disorders. Reconstructive surgery is a key to the management, its quality determines patient survival and morbidity. Microsurgical reconstruction of the oral and maxillofacial area is a challenging procedure which requires unique motor skills and coordinated hand-eye movement among the surgeons. Despite modernized and advanced surgical tools, surgical outcome is greatly influenced by the surgical skills. A well-defined preoperative plan by the oncosurgeon is essential and governs the postoperative success of the oncological procedure. Better abilities in assessing and sectioning the primary tumor invaded borders together with normal surrounding tissue, followed by suturing, are especially needed in oncosurgery. Since gaming can enhance dexterity and surgical procedural skills and has a great impact on curriculum-based learning, it must be included in the curriculum for training oral cancer surgeons.Not applicable.None.S.S., P.R., and S.K.R.S. devised the concept, performed the literature search, and drafted the letter.The authors declare that they have no financial conflict of interest with regard to the content of this report.None.Not applicable.


In video streaming applications, especially during live streaming events, video traffic can account for a significant portion of the network traffic and can lead to severe network congestion. For such applications, multicast provides an efficient means to deliver the same content to a large number of users simultaneously. However, in multicast, if the base station transmits content at rates higher than what can be decoded by users with the worst channels, these users will experience outages. This makes the multicast system’s performance dependent on the weakest users in the system. Interestingly, video streams can tolerate some packet loss without a significant degradation in the quality experienced by the users. This property can be leveraged to improve the multicast system’s performance by reducing the dependence of the multicast transmissions on the weakest users. In this work, we design a loss-tolerant video multicasting system that allows for some controlled packet loss while satisfying the quality requirements of the users. In particular, we solve the resource allocation problem in a multimedia broadcast multicast services (MBMS) system by transforming it into the problem of stabilizing a virtual queuing system. We propose two loss-optimal policies and demonstrate their effectiveness using numerical examples with realistic traffic patterns from real video streams. It is shown that the proposed policies are able to keep the loss encountered by every user below its tolerable loss. The proposed policies are also able to achieve a significantly lower peak SNR degradation than the existing schemes. The popularity of video streaming platforms such as Netflix and YouTube has led to a fundamental shift in the way that video content is consumed online. Users increasingly prefer to stream content on the go over cellular wireless networks. As a result, during live video streaming events , the same video content is transmitted to thousands of users over orthogonal spectral resources. This massive influx of video traffic consumes a substantial fraction of the limited amount of spectrum available for use by cellular systems, leading to severe network congestion and degraded quality of service. For such services, multicast transmission provides an excellent solution ,2 that cA major bottleneck in multicast transmissions is that, in order to serve all user equipments (UEs) in a multicast group, data cannot be transmitted at a rate greater than what can be decoded by the UE with the weakest channel in the group. As a result, UEs with good channel conditions are constantly forced to settle for lower rates despite their high channel quality indicator (CQI) values, leading to user dissatisfaction and low system throughput. This work proposes a novel method to overcome these issues by exploiting the loss-tolerant nature of video streams. It has been shown that video streams can tolerate as much as 40% packet loss  without A compressed video stream is made up of a group of pictures (GoP). A GoP comprises a series of intra-coded (I), predicted (P) and bidirectional predicted (B) frames. I frames are self-contained and do not require other frames to be decoded. P frames are dependent on their preceding I frames to be correctly decoded, and B frames are dependent on both preceding and following I and/or P frames to be correctly decoded. Although the actual number of I, P and B frames in a GoP depends upon the size of the GoP used, the number of B frames is at least twice the number of I and P frames combined .It is difficult to estimate the impact of the loss of I and P frames on the video quality . HoweverIn this section, we present the relevant state of the art for the problem addressed in this work. The relevant literature can be broadly considered under the following three categories. (a) The study of problems related to optimal resource allocation in wireless multicast transmission. These include multicast transmission for video streaming, as well as other forms of data. (b) The study of the problem of joint grouping and resource allocation in wireless multicast transmission. The grouping problem refers to the problem of creating multicast groups of UEs, which could be based on the content requested by the UEs, the channel quality of UEs or, in the case of multi-layer video streaming, the number of enhancement layers that a UE can receive. (c) The study of optimal multicast streaming strategies specifically for multi-layer video transmission.The most important literature from each of these categories is summarized in the following subsections.A resource allocation algorithm for live video streaming that allocates resources based on the channel quality and priority of UEs is proposed in . The proThe problem of grouping and resource allocation for lossless multicast streaming has been studied in ,2. The oResource allocation for MBMS operation on-demand has been studied in . The autIn , the autExisting approaches do not leverage the unique loss-tolerant nature of video streams to optimize resource allocation in multicast video streaming. This work exploits this property to design efficient resource allocation policies for video multicasting. A loss-tolerant mechanism for video streaming is proposed that allows for controlled packet losses without significantly impacting the quality of service. Each UE has a certain tolerance for loss, which could be a function of several factors, such as the type of video being streamed, the type of subscription , the device being used to stream the video or the channel quality experienced by the UE.Moreover, most of the existing wireless multicast literature assumes the rate achievable by a UE to be the same across all PRBs. This assumption significantly simplifies the resource allocation problem. Without the channel variability over PRBs, all PRBs are equivalent for a multicast group/UE and the problem of resource allocation is simplified to only determining the number of PRBs to be allocated to a multicast group. This work takes into account the fact that, due to fast fading, the CQI of a UE may also vary for different PRBs within a sub-frame. Therefore, while determining the allocation of PRBs to groups, the identity of the PRBs to be allocated also needs to be specified.The main contributions of this paper are summarized below. Since multicast services in fifth-generation (5G) communications are termed MBMS, the terms MBMS and multicast services will be used interchangeably through the rest of this paper.A loss-tolerant mechanism for multicast video streaming is proposed that exploits the loss-tolerant nature of videos to improve the system performance and utilize the available bandwidth more efficiently. The proposed mechanism allows for controlled packet losses while satisfying the quality of service requirements of the users.The problem of resource allocation in loss-tolerant MBMS systems is converted to the problem of stabilizing a fictitious virtual queuing system. It is proven that stabilizing the constructed virtual token queues is equivalent to satisfying the loss requirements of the users .Two loss-optimal policies are proposed for the allocation of resources in loss-tolerant MBMS systems, namely loss optimal resource allocation (LORA) and priority LORA (p-LORA). An algorithm for the efficient polynomial time implementation of these policies is also provided . These pThe performance of the proposed policies is evaluated using extensive simulations. Since these policies are designed for video streaming, traces from actual videos ,5 are usUnlike the multicast streaming mechanisms in the existing literature , the proAlthough we do not consider multi-layered video transmission in this work, the proposed policies can also be extended to these applications by considering each enhancement layer as a separate stream. In this case, the loss tolerances would also be a function of the enhancement layer being transmitted.n is denoted as Vectors are written in boldface letters, e.g., L different video streams. There are M UEs in the system, each subscribed to one of the L video streams. UEs subscribed to video stream k belongs to is denoted by k belongs to the group Consider a 5G multicast system with L video streams, a data packet arrives at the beginning of each sub-frame and is transmitted in the same sub-frame. The size of the arriving packet for group For each of the t is denoted in form of the allocation vector L given byj is allotted to t. However, if t is completely defined by the value of In each sub-frame k can successfully decode in PRB j of sub-frame t  in .(Average packet loss)The vector of the average packet losses of all UEs is given byDefinition 4.Γ, denoted by Γ, i.e.,The feasible region of a resource allocation policy where fined in  and ℓΓ¯  (ℓΓ¯ in .(Feasible region of a policy)Definition 5.Γ.The feasible region of the system is the set of loss vectors (Feasible region of the system)Using the above definitions, the optimal resource allocation policy can now be defined as follows.Definition 6.The optimal resource allocation policy Problem Statement.Based on the above definitions, the main objective of this work is to determine the optimal resource allocation policy Remark 1.While the focus of this work is on video streaming, it should be emphasized that the considered problem and our proposed resource allocation framework are not uniquely applicable to video streaming, but more generally to any loss-tolerant multicast transmission.token is used to refer to the virtual entities that make up the queues, and the tokens are used to keep track of the losses of the individual UEs. The basic idea is that tokens in each queue arrive at a rate proportional to the loss tolerance of the corresponding user. For users with stricter reliability constraints, i.e., lower loss tolerances, more tokens arrive on average. Whenever a UE is served, a token is removed from the corresponding queue. Thus, the length of the queue of a user is an indicator of how much loss a user has encountered. The state of this queuing system is completely described by the lengths of these virtual queues. An overview of the system is depicted in In this section, we show that the considered problem can be solved by converting it into the problem of stabilizing a queuing system. In particular, we construct a virtual queuing system k is denoted by k in sub-frame t and has the expected value k with this expected arrival rate k is served in at least For all k has been served in sub-frame t under policy k at the beginning of sub-frame t under policy Denote by As mentioned above, we will show in the following that stabilizing the virtual queuing system Definition 7.Γ if it holds that The constructed queuing system Since the eNodeB (eNB) knows both the loss requirements and the channel states of the UEs, it has all the information needed to maintain the virtual queuing system. In the following, we establish the relationship between the stability region of the constructed queuing system and the feasible region of the optimal resource allocation policy In this section, we prove that stabilizing the constructed virtual queuing system N is the number of PRBs in a sub-frame and L is the number of multicast groups. In 5G communication systems, channel states are identified using a finite number of integral values termed the CQI values. The Third Generation Partnership Project (3GPP) standards  in  denotes U to a unique vertex from V results in allocation equivalent to In the following lemma, we show that an MWBM of Lemma 1.Maximum weight bipartite matching for graph Proof. Detailed proof is given in The same MWBM can be used to implement We study the performance of the proposed allocation algorithms in an MBMS system. We consider a cell with UEs distributed uniformly at random through the cell. There are igned in  for the igned in . Signal-igned in . Other rAs described in http://trace.eas.asu.edu/ (accessed on 23 July 2019)) [Since the proposed policies are intended for use with video streaming services, we use traces from actual videos to generate realistic video traffic patterns in the simulations. These video traces have been obtained from the Arizona State University Video Trace Library (y 2019)) ,5. The vSince I and P frames are needed to decode other frames in a GoP, we ensure that all I and P frames are transmitted without any loss by allocating sufficient resources and transmitting at the rate corresponding to the weakest UE. We use the proposed lossy allocation policies only to send the B frames. This is a recommended practice in network simulations with video traces , since iFirst, we compare the losses encountered by the UEs to their loss tolerances. For this, we run the simulations for the entire duration of all five videos  degradation due to the packet losses in The video traces obtained from the ASU repository contain the PSNR values of each frame in a video trace. Using these, the PSNR of a GoP is obtained by adding the PSNR values of the frames in this GoP. We then find the average PSNR of a video stream as the average over all the GoPs in this stream. This is used as the representative PSNR value for the transmitted video stream.The PSNR of the video stream received by a UE is similarly calculated by taking the sum of the PSNR values of the frames that were successfully received by this UE within a GoP, followed by averaging over all the GoPs. We then calculate the average received PSNR of a stream by taking the average of the PSNR values over all the UEs in the group. Finally, the PSNR degradation is calculated as the difference in the average PSNR of the transmitted and received video streams. From the figure, it can be seen that EXP-Q leads to the largest degradation in the PSNR. Both LORA and p-LORA result in significantly less loss in the PSNR of the received video streams.t and the average loss As discussed in These simulation results clearly demonstrate the effectiveness of the proposed policies. The use of traces of actual videos further strengthens the case for the use of loss-tolerant allocation policies to stream video content.L and users per video stream K in the following. For the traffic data, we again use the realistic video content from the above simulations.After having shown that the proposed algorithms work effectively on real video stream data, we now focus on the influences of different parameters. In particular, we consider a varying number of video streams L and the number of users per group K. However, the computational times in the simulation do not purely depend on the total number of UEs in the system First, we compare the average computational time of the LORA and p-LORA algorithms in L and K. In Next, we analyze the packet losses for different combinations of Video streams can tolerate a certain amount of packet loss without a significant degradation in the quality perceived by the end user. In this paper, we have leveraged this property to improve the performance of multicast video streaming in MBMS. In particular, we have considered an MBMS system where users can tolerate a certain amount of packet loss depending on the type of video that they are streaming and the quality of their channel. For such a system, we have addressed the resource allocation problem by constructing a virtual queuing system to represent the actual loss-tolerant MBMS system. It has been shown that an optimal resource allocation policy corresponds to a policy that stabilizes the constructed virtual queuing system. Furthermore, we have proposed two algorithms, namely LORA and p-LORA, for resource allocation in loss-tolerant multicast video streaming services. Both LORA and p-LORA are optimal in the sense that their resulting resource allocation fulfills the loss requirements of all the users. Interestingly, this also implies that no policy can perform better, even if it uses information about future channel states. Additionally, we have proposed an MWBM algorithm that provides an efficient polynomial time implementation of the proposed policies. To compare our policies, we have modified the EXP-Q rule  for use We have performed extensive simulations using video traces from actual video streams  to study