Drawing on Bayesian probability theory, we propose a generalization of affect control theory (BayesACT) that better accounts for the dynamic fluctuation of identity meanings for self and other during interactions, elucidates how people infer and adjust meanings through social experience, and shows how stable patterns of interaction can emerge from individuals’ uncertain perceptions of identities. Using simulations, we illustrate how this generalization offers a resolution to several issues of theoretical significance within sociology and social psychology by balancing cultural consensus with individual deviations from shared meanings, balancing meaning verification with the learning processes reflective of change, and accounting for noise in communicating identity. We also show how the model speaks to debates about core features of the self, which can be understood as stable and yet malleable, coherent and yet composed of multiple identities that may carry competing meanings. We discuss applications of the model in different areas of sociology, implications for understanding identity and social interaction, as well as the theoretical grounding of computational models of social behavior.

In recent years, scholars have come to understand emotions as dynamic and socially constructed—the product of interdependent cultural, relational, situational, and biological influences. While researchers have called for a multilevel theory of emotion construction, any progress toward such a theory must overcome the fragmentation of relevant research across various disciplines and theoretical frameworks. We present affect control theory as a launching point for cross-disciplinary collaboration because of its empirically grounded conceptualization of social mechanisms operating at the interaction, relationship, and cultural levels, and its specification of processes linking social and individual aspects of emotion. After introducing the theory, we illustrate its correspondence with major theories of emotion construction framed at each of four analytical levels: cultural, interactional, individual, and neural.

Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.

Affect control theory and the stereotype content model share explanatory goals and employ compatible measurement strategies but have developed in largely separate literatures. The present article examines the models’ commensurability and discusses new insights that can be gained by comparing theories. We first demonstrate that the unique measurement dimensions used by each theory (evaluation/potency/activity vs. warmth/competence) describe much of the same semantic content. We then show how simulation techniques developed by affect control theorists can be applied to the study of interactions with stereotyped groups. These simulations indicate broad consistencies between the theories’ predictions but highlight three distinctive emphases of affect control theory. Specifically, affect control models predict that actors are motivated to behave in ways that (1) are consistent with self-meanings, (2) maintain cultural norms about the suitability of behaviors and emotions to role relations, and (3) account for behavior and emotion in prior interactions.

This paper compares affective meanings of various stereotyped social groups in U.S., German, and Japanese cultures along the three basic dimensions of emotional experience (evaluation, potency, and activity). Analyses exploring similarities in affective meanings between respondents revealed considerable consensus within cultures, but less across cultures. These analyses indicated greater consensus for the U.S. and German sample than for the Japanese sample, supporting past research which indicates that Japanese social perception is more contextualized than in Western cultures. Analyses of cross-cultural differences also identified meaningful patterns of culture-specific deviation, interpretable in terms of the placement of each national sample on cultural dimensions such as power distance, masculinity, and individualism/collectivism. We argue that affective meanings reflect the social order of specific cultures, making variations in consensus significant as affective meanings guide intergroup behavior and emotion.