The priming of concepts has been shown to influence peoples' subsequent actions, often unconsciously. We propose 3 mechanisms (psychological, cultural, and biological) as a unified explanation of such effects. (a) Primed concepts influence holistic representations of situations by parallel constraint satisfaction. (b) The constraints among representations stem from culturally shared affective meanings of concepts acquired in socialization. (c) Patterns of activity in neural populations act as semantic pointers linking symbolic concepts to underlying emotional and sensorimotor representations and thereby causing action. We present 2 computational models of behavioral priming that implement the proposed mechanisms. One is a localist neural network that connects primes with behaviors through central nodes simulating affective meanings. In a series of simulations, where the input is based on empirical data, we show that this model can explain a wide variety of experimental findings related to automatic social behavior. The second, neurocomputational model simulates spiking patterns in populations of biologically realistic neurons. We use this model to demonstrate how the proposed mechanisms can be implemented in the brain. Finally, we discuss how our models integrate previous theoretical accounts of priming phenomena. We also examine the interactions of psychological, cultural, and biological mechanisms in the control of automatic social behavior.

A Complex Systems Approach to the Study of Ideology: Cognitive-Affective Structures and the Dynamics of Belief Systems

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.