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Open Access Publications from the University of California

When Seating Matters: Modeling Graded Social Attitudes as Bayesian Inference

Creative Commons 'BY' version 4.0 license
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.