Both early social psychologists and the modern, interdisciplinary scientific community have advocated for diverse team science. We echo this call and describe three common pitfalls of solo science illustrated by the target article. We discuss how a collaborative and inclusive approach to science can both help researchers avoid these pitfalls and pave the way for more rigorous and relevant research.

Drawing on interdisciplinary, feminist insights, we encourage social psychologists to embrace the active participation of marginalized groups in social disparities research. We explain (1) how the absence of marginalized groups' perspectives in research presents a serious challenge to understanding intergroup dynamics and concomitant disparities, and (2) how their inclusion could assuage some of social psychology's “fatal flaws.”

Until social psychology devotes as much attention to construct and external validity as it does to internal validity, the field will continue to produce theories that fail to replicate in the field and cannot be used to meliorate social problems.

We agree that external validity of social psychological experiments is a concern, we disagree these models are useless. Experiments, reconsidered from a situated cognition perspective and non-linearly combined with other methods (qualitative and simulations) allow grasping decision dynamics beyond bias outcomes. Dynamic (vs. discrete) insights regarding these processes are key to understand missing forces and bias in real-world social groups.

Ecological validity is key in science and laboratory experiments alone cannot fully explain complex real-world phenomena. Yet the three flaws Cesario proposes do not characterize the field and are not “methodological trickery,” (sect. 5, para. 5) designed to intentionally mislead practitioners. In school discipline alone, these alleged flaws are indeed addressed and laboratory experimentation has contributed to mitigation of a real-world problem.

Culture provides people with rich, detailed, implicit, and explicit knowledge about associations (what goes together) and contingencies (how situations are likely to unfold). These culture-based expectations allow people to get through their days without much systematic reasoning. Experimental designs that unpack these situated effects of culture on thinking, feeling, and doing can advance bias research and direct policy and intervention.

Cesario argues that experiments cannot illuminate real group disparities because they leave out factors that operate in ordinary life. But what Cesario calls flaws are, in fact, the point of the experimental method. Of all the topics in science, we have to wonder why racial discrimination would be uniquely unsuited for investigating with experiments. The argument to give up the most powerful scientific method to study one of the hardest problems we confront is laughable.

Although psychology has long professed that perception predicts action, the strength of the evidence supporting the statement depends on the ecological validity of the technologies and paradigms used, particularly those that track eye movements, supporting Cesario's argument. While right to call for ecological validity, Cesario's model fails to account for individual differences in visual experience perceivers have when presented with the same stimulus.

Cesario's critiques and suggestions for redesigning social psychology experiments echo Dahl's (2017) call for developmental researchers to use experimental and naturalistic methods in a complementary manner for understanding children's development. We provide examples of how naturalistic observations can rectify Cesario's missing flaws for developmental studies investigating children's social biases and help researchers derive theories they can then experimentally test.

There are two problems with the logic of Cesario's argument for abandoning existing research on social bias. First, laboratory findings of decisional bias have social significance even if Cesario is right that the research strips away real-world context. Second, the argument makes overly skeptical demands of a research program seeking complex causal linkages between micro- and macro-scale phenomena.

In psychology, causal inference – both the transport from lab estimates to the real world and estimation on the basis of observational data – is often pursued in a casual manner. Underlying assumptions remain unarticulated; potential pitfalls are compiled in post-hoc lists of flaws. The field should move on to coherent frameworks of causal inference and generalizability that have been developed elsewhere.

The target article raises important questions about the applicability of experimental social psychology research on topics with policy implications. This commentary focuses on the importance of attending to a variety of factors to improve ecological validity as well as considering the ultimate factors shaping behavior and the role of natural categories in the stability of stereotypes and their influence.

Cesario criticizes the experimental design of studies of bias by claiming that acting on stereotypes in the experimental situation might not be an “error” from a Bayesian perspective. However, social psychologists might have an ethical reason to label the observed decision-maker biases as “erroneous.” Decision-making can be considered as “biased” and “erroneous,” because it reflects illegal and morally condemnable discrimination.

Cesario argues that experimental studies of bias tell us little about why group disparities exist. We argue that Cesario's alternative approach implicitly frames understanding of group disparities as a false binary between “bias” and “group differences.” This, we suggest, will contribute little to our understanding of the complex dynamics that produce group disparities, and risks inappropriately rationalizing them.

Cesario misrepresents experimental social psychology. The discipline encompasses significantly more than implicit bias research, including controlled decision making and real-world behavioral observations. Paradoxically, while critiquing popular implicit bias tasks, Cesario also describes task refinements that have significantly advanced their external validity and our contextual understanding of bias. Thus rather than abandonment, a call for “continued improvement” is a far more sensible proposition.

Are the landscapes of real-world decisions adequately represented in our laboratory tasks? Are the goals and expertise of experimental participants the same as real-world decision-makers? Are we neglecting crucial forces that lead to group outcomes? Are the contingencies necessary for producing experimental demonstrations of bias present in the real world? In the target article, I argued that the answers to these questions are needed to understand whether and how laboratory research can inform real-world group disparities. Most of the commentaries defending experimental social psychology neglected to directly address these main arguments. The commentaries defending implicit bias only revealed the inadequacy of this concept for explaining group disparities. The major conclusions from the target article remain intact, suggesting that experimental social psychology must undergo major changes to contribute to our understanding of group disparities.

We don't yet have adequate theories of what the human mind is representing when it represents a social group. Worse still, many people think we do. This mistaken belief is a consequence of the state of play: Until now, researchers have relied on their own intuitions to link up the concept social group on the one hand and the results of particular studies or models on the other. While necessary, this reliance on intuition has been purchased at a considerable cost. When looked at soberly, existing theories of social groups are either (i) literal, but not remotely adequate (such as models built atop economic games), or (ii) simply metaphorical (typically a subsumption or containment metaphor). Intuition is filling in the gaps of an explicit theory. This paper presents a computational theory of what, literally, a group representation is in the context of conflict: It is the assignment of agents to specific roles within a small number of triadic interaction types. This “mental definition” of a group paves the way for a computational theory of social groups – in that it provides a theory of what exactly the information-processing problem of representing and reasoning about a group is. For psychologists, this paper offers a different way to conceptualize and study groups, and suggests that a non-tautological definition of a social group is possible. For cognitive scientists, this paper provides a computational benchmark against which natural and artificial intelligences can be held.

We apply the author's computational approach to groups to our empirical work studying and modelling riots. We suggest that assigning roles in particular gives insight, and measuring the frequency of bystander behaviour provides a method to understand the dynamic nature of intergroup conflict, allowing social identity to be incorporated into models of riots.

Folk-sociology is a set of intuitive assumptions that organize our spontaneous theories about society, including the notion that social groups are agent-like. Pietraszewski's model may explain this folk-sociological assumption in an elegant way. However, large-scale group dynamics include features that seem to escape agent-like descriptions. Therefore, one may want to find out whether the “event-grammar” proposed here can account for these features.

A crucial factor in how we perceive social groups involves the signals and cues emitted by them. Groups signal various properties of their constitution through coordinated behaviors across sensory modalities, influencing receivers' judgments of the group and subsequent interactions. We argue that group communication is a necessary component of a comprehensive computational theory of social groups.