Proceedings of the Annual Meeting of the Cognitive Science Society

Humans regularly perform tasks that require combining infor-mation across several sources of information to learn, reason,and make decisions. Bayesian models provide a computa-tional framework, and a normative account, for how humanscarry out these tasks. However, exact inference is intractablein most real-world situations, and extensive empirical workshows that human behavior often deviates significantly fromthe Bayesian optimum. A promising possibility is that peopleinstead approximate rational solutions using bounded avail-able resources. In this workshop, we bring together lead-ing researchers from cognitive science, neuroscience and ma-chine learning to build a better understanding of bounded op-timality in how humans learn, reason and make decisions.