People Reconstruct Others’ Cognitive Processes by Representing Their Minds as Computational Systems

Everyday social interactions require us to infer the cognitive processes happening in otherminds—their reasoning, distractions, and recall. Despite the importance of theseinferences, computational models of social reasoning focus exclusively on attributions ofmental states like knowledge and preferences, leaving the mechanisms underlying cognitiveprocess inference largely uncharacterized. Here we introduce Bayesian Inverse Reasoning(BIR), a new computational framework for social cognition that represents other minds ascomputational systems and performs Bayesian inference over abstract representations ofcomputation to reconstruct the cognitive processes unfolding in other minds. Across sixexperiments, we show that this framework quantitatively captures how people make gradedinferences about what others are thinking about (Experiment 1), their reasoning speed(Experiment 2), whether they engaged in recall (Experiment 3), and whether they weredistracted (Experiment 4). These inferences were fine-grained and easily evoked. Wefurther used the BIR framework to probe the algorithmic nature of this capacity, revealingthat people use first-person thinking to estimate the computational demands of differentproblems (Experiment 5), yet integrate these self-derived estimates into a causal model ofother minds in a way that produces unbiased inferences about others (Experiment 6).Together, these findings reveal a previously unknown cognitive architecture underlyinghuman social cognition: one in which people represent minds not merely as containers ofbeliefs and desires, but as computational engines engaged in dynamic, flexible informationprocessing.