Statistical learning of temporal community structure in the hippocampus

Anna C. Schapiro, Nicholas B. Turk-Browne, Kenneth A. Norman, Matthew M. Botvinick

Research output: Contribution to journalArticlepeer-review

155 Scopus citations


The 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.

Original languageEnglish (US)
Pages (from-to)3-8
Number of pages6
Issue number1
StatePublished - Jan 1 2016

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience


  • Background connectivity
  • Event representation
  • FMRI
  • Pattern analysis
  • Transition probability


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