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Optimal Behavioral Hierarchy

Author(s): Solway, Alec; Diuk, Carlos; Córdova, Natalia; Yee, Debbie; Barto, Andrew G.; et al

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dc.contributor.authorSolway, Alec-
dc.contributor.authorDiuk, Carlos-
dc.contributor.authorCórdova, Natalia-
dc.contributor.authorYee, Debbie-
dc.contributor.authorBarto, Andrew G.-
dc.contributor.authorNiv, Yael-
dc.contributor.authorBotvinick, Matthew M.-
dc.date.accessioned2019-10-28T15:54:51Z-
dc.date.available2019-10-28T15:54:51Z-
dc.date.issued2014-08-14en_US
dc.identifier.citationSolway, Alec, Diuk, Carlos, Córdova, Natalia, Yee, Debbie, Barto, Andrew G, Niv, Yael, Botvinick, Matthew M. (2014). Optimal Behavioral Hierarchy. PLoS Computational Biology, 10 (8), e1003779 - e1003779. doi:10.1371/journal.pcbi.1003779en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr14t8h-
dc.description.abstractIn reinforcement learning, a decision maker searching for the most rewarding option is often faced with the question: what is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: how can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and describe an equivalence between the Bayesian model and temporal difference learning algorithms that have been proposed as models of reinforcement learning in humans and animals. According to our view, the search for the best option is guided by abstract knowledge about the relationships between different options in an environment, resulting in greater search efficiency compared to traditional reinforcement learning algorithms previously applied to human cognition. In two behavioral experiments, we test several predictions of our model, providing evidence that humans learn and exploit structured inductive knowledge to make predictions about novel options. In light of this model, we suggest a new interpretation of dopaminergic responses to novelty.en_US
dc.language.isoen_USen_US
dc.relation.ispartofPLoS Computational Biologyen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleOptimal Behavioral Hierarchyen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1371/journal.pcbi.1003779-
dc.date.eissued2014-08-14en_US
dc.identifier.eissn1553-7358-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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