# The computational nature of memory modification

## Author(s): Gershman, Samuel J.; Monfils, Marie-H; Norman, Kenneth A.; Niv, Yael

To refer to this page use: http://arks.princeton.edu/ark:/88435/pr16n00
DC FieldValueLanguage
dc.contributor.authorGershman, Samuel J.-
dc.contributor.authorMonfils, Marie-H-
dc.contributor.authorNorman, Kenneth A.-
dc.contributor.authorNiv, Yael-
dc.date.accessioned2019-10-28T15:54:58Z-
dc.date.available2019-10-28T15:54:58Z-
dc.date.issued2017-03-15en_US
dc.identifier.citationGershman, Samuel J, Monfils, Marie-H, Norman, Kenneth A, Niv, Yael. (2017). The computational nature of memory modification. eLife, 6 (10.7554/eLife.23763en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr16n00-
dc.description.abstractRetrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.en_US
dc.language.isoen_USen_US
dc.relation.ispartofeLifeen_US
dc.rightsFinal published version. This is an open access article.en_US
dc.titleThe computational nature of memory modificationen_US
dc.typeJournal Articleen_US
dc.identifier.doidoi:10.7554/eLife.23763-
dc.date.eissued2017-03-15en_US
dc.identifier.eissn2050-084X-
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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