Social-structure learning is the process by which social groups are identified on the basis of experience. Building on models of structure learning in other domains, we formalize this problem within a Bayesian framework. According to this framework, the probabilistic assignment of individuals to groups is computed by combining information about individuals with prior beliefs about group structure. Experiments with adults and children provide support for this framework, ruling out alternative accounts based on dyadic similarity. More broadly, we highlight the implications of social-structure learning for intergroup cognition, stereotype updating, and coalition formation.

中文翻译：

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社会结构学习

社会结构学习是根据经验识别社会群体的过程。在其他领域的结构学习模型的基础上，我们在贝叶斯框架内将这个问题形式化。根据此框架，通过将有关个人的信息与对群体结构的先前信念相结合，可以计算出个体对群体的概率分配。通过成人和儿童进行的实验为该框架提供了支持，并根据二元相似性排除了其他帐户。更广泛地讲，我们强调社会结构学习对群体间认知，刻板印象更新和联盟形成的影响。