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 literal, but not remotely adequate, or simply metaphorical. 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. (shrink)

Research suggests that the mind contains a set of adaptations for detecting alliances: an alliance detection system, which monitors for, encodes, and stores alliance information and then modifies the activation of stored alliance categories according to how likely they will predict behavior within a particular social interaction. Previous studies have established the activation of this system when exposed to explicit competition or cooperation between individuals. In the current studies we examine if shared political opinions produce these same effects. In particular, (...) if participants will spontaneously categorize individuals according to the parties they support, even when explicit cooperation and antagonism are absent, and if party support is sufficiently powerful to decrease participants’ categorization by an orthogonal but typically-diagnostic alliance cue . Evidence was found for both: Participants spontaneously and implicitly kept track of who supported which party, and when party cross-cut race—such that the race of targets was not predictive of party support—categorization by race was dramatically reduced. To verify that these results reflected the operation of a cognitive system for modifying the activation of alliance categories, and not just socially-relevant categories in general, an identical set of studies was also conducted with in which party was either crossed with sex or age . As predicted, categorization by party occurred to the same degree, and there was no reduction in either categorization by sex or by age. All effects were replicated across two sets of between-subjects conditions. These studies provide the first direct empirical evidence that party politics engages the mind’s systems for detecting alliances and establish two important social categorization phenomena: that categorization by age is, like sex, not affected by alliance information and that political contexts can reduce the degree to which individuals are represented in terms of their race. (shrink)

Cognitive scientists have focused too narrowly on the acquisition of data and on the methods to extract patterns from those data. We argue that a successful science of the mind requires widening our focus to include the problems being solved by cognitive processes. Frameworks that characterize cognitive processes in terms of instrumental problem‐solving, such as those within the evolutionary social sciences, become necessary if we wish to discover more accurate descriptions of those processes.

Describing a cognitive system at a mechanistic level requires an engineering task analysis. This involves identifying the task and developing models of possible solutions. Evolutionary psychology and Bayesian modeling make complimentary contributions: Evolutionary psychology suggests the types of tasks that human brains were designed to solve, while Bayesian modeling provides a rigorous description of possible computational solutions to such problems.

The psychology underlying revenge in an intergroup context is built around a small handful of recurrent interaction types. Analyzing the cost/benefit calculations of each agent's role within these interaction types provides a more precise way to characterize intergroup conflict and revenge. This in turn allows for more precise models of the psychology of intergroup conflict to be proposed and tested.

The target article is an appeal to allow explicit computational theorizing into the study of social groups. Some commentators took this proposal and ran with it, some had questions about it, and some were confused or even put off by it. But even the latter did not seem to outright disagree – they thought the proposal was mutually exclusive with some other enterprise, when in fact it is not. Unfortunately, scientists studying social groups have not yet avoided the thread-bare trope (...) of the blind men studying the different parts of the elephant: We see mutual exclusivity when we should see complementarity. I hope we can all take the next steps of examining how the different enterprises and approaches within our area of research might all fit together into a unified whole. (shrink)