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Statistical learning of temporal community structure in the hippocampus

Author(s): Schapiro, Anna C.; Turk-Browne, Nicholas B.; Norman, Kenneth A.; Botvinick, Matthew M.

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dc.contributor.authorSchapiro, Anna C.-
dc.contributor.authorTurk-Browne, Nicholas B.-
dc.contributor.authorNorman, Kenneth A.-
dc.contributor.authorBotvinick, Matthew M.-
dc.date.accessioned2019-10-28T15:54:10Z-
dc.date.available2019-10-28T15:54:10Z-
dc.date.issued2016-01en_US
dc.identifier.citationSchapiro, Anna C, Turk-Browne, Nicholas B, Norman, Kenneth A, Botvinick, Matthew M. (2016). Statistical learning of temporal community structure in the hippocampus. Hippocampus, 26 (1), 3 - 8. doi:10.1002/hipo.22523en_US
dc.identifier.issn1050-9631-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr18452-
dc.description.abstractThe hippocampus is involved in the learning and representation of temporal statistics, but little is understood about the kinds of statistics it can uncover. Prior studies have tested various forms of structure that can be learned by tracking the strength of transition probabilities between adjacent items in a sequence. We test whether the hippocampus can learn higher-order structure using sequences that have no variance in transition probability and instead exhibit temporal community structure. We find that the hippocampus is indeed sensitive to this form of structure, as revealed by its representations, activity dynamics, and connectivity with other regions. These findings suggest that the hippocampus is a sophisticated learner of environmental regularities, able to uncover higher-order structure that requires sensitivity to overlapping associations.en_US
dc.format.extent3 - 8en_US
dc.language.isoen_USen_US
dc.relation.ispartofHippocampusen_US
dc.rightsAuthor's manuscripten_US
dc.titleStatistical learning of temporal community structure in the hippocampusen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.1002/hipo.22523-
dc.date.eissued2015-10-13en_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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