Compact representations of the environment allow humans to behave efficiently in a complex world. Reinforcement learning models capture many behavioral and neural effects but do not explain recent findings showing that structure in the environment influences learning. In parallel, Bayesian cognitive models predict how humans learn structured knowledge but do not have a clear neurobiological implementation. We propose an integration of these two model classes in which structured knowledge learned via approximate Bayesian inference acts as a source of selective attention. In turn, selective attention biases reinforcement learning towards relevant dimensions of the environment. An understanding of structure learning will help to resolve the fundamental challenge in decision science: explaining why people make the decisions they do.
Starting in 2022, the lab has decided to post here only the archival, open-access version of our publications. This is part of the movement to emphasize quality and content over the impact factor or prestige of the journal a paper is published in. Full citations (for referencing papers in your own work) can be found on pubmed and/or within the archival version, which will be updated once a paper is accepted for publication after peer review.