Proceedings of the Annual Meeting of the Cognitive Science Society

An individuals intelligence mindset describes their implicit beliefs about whether intelligence is fixed (fixed mindset)or malleable (growth mindset). Here, we introduce a computational framework to unify and build upon findings in themindsets literature. We postulate that individuals maintain a mental model of others skill, in which current skill is thesum of innate skill (1) and skill acquired from experience (growth potential (2) times fraction of potential realised (3)).An observed current skill level is consistent with multiple combinations of (1), (2), and (3). To disambiguate, the modelobserver performs probabilistic inference, which requires priors. In particular, we conceptualise a fixed mindset usinga high-variance prior over innate skill and a low-variance, low-mean prior over growth potential. Through proofs andsimulations, we demonstrate that our model accounts for empirical findings in terms of the latent psychological processes.Our results offer promise for a computational cognitive science of mindsets.