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[Correction Notice: An Erratum for this article was reported in Vol 123(4) of Journal of Personality and Social Psychology (see record 2023-02979-001). In the error, the Study 2 heading Computational Mode of Learning should instead appear as Computational Model of Learning. All versions of this article have been corrected.] How do humans learn, through social interaction, whom to depend on in different situations? We compared the extent to which inferred trait attributes—as opposed to learned reward associations previously examined as part of feedback-based learning—could adaptively inform cross-context social decision-making. In four experiments, participants completed a novel task in which they chose to “hire” other players to solve math and verbal questions for money. These players varied in their trait-level competence across these contexts and, independently, in the monetary rewards they offered to participants across contexts. Results revealed that participants chose partners primarily based on context-specific traits, as opposed to either global trait impressions or material rewards. When making choices in novel contexts—including determining who to choose for social and emotional support—participants generalized trait knowledge from past contexts that required similar traits. Reward-based learning, by contrast, demonstrated significantly weaker context-sensitivity and generalization. These findings suggest that people form context-dependent trait impressions from interactive feedback and use this knowledge to make flexible social decisions. These results support a novel theoretical account of how interaction-based social learning can support context-specific impression formation and adaptive decision-making. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
