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dc.contributor.authorTartaglia, E. M.en_US
dc.contributor.authorClarke, Aaronen_US
dc.contributor.authorHerzog, M. H.en_US
dc.date.accessioned2018-04-12T10:59:46Z
dc.date.available2018-04-12T10:59:46Z
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/11693/37004
dc.description.abstractMany of the decisions we make in our everyday lives are sequential and entail sparse rewards. While sequential decision-making has been extensively investigated in theory (e.g., by reinforcement learning models) there is no systematic experimental paradigm to test it. Here, we developed such a paradigm and investigated key components of reinforcement learning models: the eligibility trace (i.e., the memory trace of previous decision steps), the external reward, and the ability to exploit the statistics of the environment's structure (model-free vs. model-based mechanisms). We show that the eligibility trace decays not with sheer time, but rather with the number of discrete decision steps made by the participants. We further show that, unexpectedly, neither monetary rewards nor the environment's spatial regularity significantly modulate behavioral performance. Finally, we found that model-free learning algorithms describe human performance better than model-based algorithms.en_US
dc.language.isoEnglishen_US
dc.source.titleFrontiers in Psychologyen_US
dc.relation.isversionofhttp://dx.doi.org/10.3389/fpsyg.2017.00312en_US
dc.subjectExplorationen_US
dc.subjectQ-learningen_US
dc.subjectReinforcement learningen_US
dc.subjectSARSA (λ)en_US
dc.subjectSequential decision makingen_US
dc.titleWhat to choose next? a paradigm for testing human sequential decision makingen_US
dc.typeArticleen_US
dc.departmentInterdisciplinary Program in Neuroscience (NEUROSCIENCE)en_US
dc.departmentAysel Sabuncu Brain Research Center (BAM)en_US
dc.citation.spage1en_US
dc.citation.epage11en_US
dc.citation.volumeNumber8en_US
dc.identifier.doi10.3389/fpsyg.2017.00312en_US
dc.publisherFrontiers Research Foundationen_US
dc.contributor.bilkentauthorClarke, Aaron
dc.identifier.eissn1664-1078


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