{"pk":49899,"title":"When Seating Matters: Modeling Graded Social Attitudes as Bayesian Inference","subtitle":null,"abstract":"Humans can quickly infer social relationships from minimal cues, such as where people choose to sit in a meeting room. We investigated how people make graded, context-sensitive judgments about social attitudes beyond simple proximity-based heuristics. Using controlled seating scenarios, we compared participants' judgments to the predictions of Bayesian models: the interaction-probability model, which captures how one person's seat choice affects the probability that another person will initiate the conversation, and the interaction-cost model, which accounts for the effort required based on how far apart they sit from each other. Results showed that participants' inferences aligned best with the interaction-cost model, indicating sensitivity to effort and moving trajectory, rather than relying solely on proximity. Our findings suggest that higher-order cognition refines perceptual cues, enabling nuanced, graded social reasoning essential for complex social interactions.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"Psychology; Social cognition; Theory of Mind; Bayesian modeling; Computational Modeling; Symbolic computational modeling"}],"section":"Papers with Poster Presentation","is_remote":true,"remote_url":"https://escholarship.org/uc/item/10d930rk","frozenauthors":[{"first_name":"Zihan","middle_name":"","last_name":"Wang","name_suffix":"","institution":"Yale University","department":""},{"first_name":"Julian","middle_name":"","last_name":"Jara-Ettinger","name_suffix":"","institution":"Yale University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2025-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/49899/galley/37861/download/"}]}