Psychological review最新文献

Covert identity signals permit the communication of group membership to ingroup members while avoiding potentially costly detection by members of other groups. If individuals are incentivized to detect others' group memberships, however, covert signals may not remain covert for very long. We propose a theoretical extension to the literature on covert signaling in which conventionalized identity signals can become destabilized when learned by outgroup individuals to be replaced by the emergence of new signaling conventions. We formalize this idea with both analytical and agent-based modeling of ingroup and outgroup individuals who learn about signals of group membership. Depending on the risk and associated cost of detection by the outgroup, the model yields three dynamic classes: saturation, where all identity signals become stable conventions and never go extinct; cycling, in which new signals emerge to replace old ones as they are learned by the outgroup; and suppression, in which informative identity signals never emerge. Our analysis has implications for understanding identity signaling, the emergence of conventions, coded speech, and the ebb and flow of fashion cycles. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Understanding the mechanisms enabling the learning and flexible use of knowledge in context-appropriate ways has been a major focus of research in the study of both semantic cognition and cognitive control. We present a unified model of semantics and control that addresses these questions from both perspectives. The model provides a coherent view of how semantic knowledge, and the ability to flexibly access and deploy that knowledge to meet current task demands, arises from end-to-end learning of the statistics of the environment. We show that the model addresses unresolved issues from both literatures, including how control operates over features that covary with one another and how control representations themselves are structured and emerge through learning, through a series of behavioral experiments and simulations. We conclude by discussing the implications of our approach to other fundamental questions in cognitive science, machine learning, and artificial intelligence. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Social norms shape a vast range of human behaviors, from everyday interactions to major life choices. Yet, existing theories of norm emergence typically focus either on why certain norms arise (substantive properties) or on how they spread and persist (dynamical properties), often making conflicting assumptions. Here, we propose a unified account in which norms prescribing how one ought to act emerge naturally from the fundamental algorithms that guide learning-whether in social or nonsocial settings. Our account builds on recent advances in decision making and emotion research that have highlighted "actor-critic" models as a core mechanism of learning from feedback. We extend this mechanism to social settings by assuming that it is not only we who critique our actions; others critique our actions as well. By simulating this interindividual form of learning, we show that it uniquely produces group behavior that exhibits both substantive and dynamical properties of real-world social norms, including prosociality, ingroup bias, stickiness, S-shaped curves, and local conformity/global diversity. Our framework thus offers a uniquely parsimonious way to bridge the gap between individual learning and group behavior. (PsycInfo Database Record (c) 2025 APA, all rights reserved).