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Social Collaboration and Bayesian Mentalizing under Uncertainty
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DOI：10.1007/s12559-026-10601-x.png)
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Abstract
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Humans possess a remarkable ability to infer the underlying mental states of others based solely on observed behavior, even amidst various uncertainties. While computational cognitive modeling has shed light on human mentalizing, the impact of uncertainty and its meta-cognitive accounting during inference remains less explored. In this paper, we model the role of uncertainty in mentalizing and its effect on social collaboration, positing two hypotheses: firstly, that human mentalizing operates under the premise that observed behaviors arise from rational adaptations to latent mental states and the task environment, and secondly, that the inference of these states is probabilistic. Our model suggests that decision-makers exhibit greater caution during collaboration when faced with uncertain inferences due to having a meta-cognitive access to the posterior probabilities of their inferences. We validate the model using data from two human participant experiments, illustrating that a computational perspective on uncertainty in mentalizing improves our understanding of social cognition.
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