A review of medial temporal lobe and caudate contributions to visual category learning

E. M. Nomura, Paul J Reber*

*Corresponding author for this work

Research output: Contribution to journalReview article

50 Scopus citations

Abstract

Here we review recent functional neuroimaging, neuropsychological and behavioral studies examining the role of the medial temporal lobe (MTL) and the caudate in learning visual categories either by verbalizeable rules or without awareness. The MTL and caudate are found to play dissociable roles in different types of category learning with successful rule-based (RB) categorization depending selectively on the MTL and non-verbalizeable information-integration (II) category learning depending on the posterior caudate. These studies utilize a combination of experimental cognitive psychology, mathematical modeling (Decision Bound Theory (DBT)) and cognitive computational modeling (the COVIS model of Ashby et al. [1998. A neuropsychological theory of multiple systems in category learning. Psychological Review 105, 442-481]) to enhance the understanding of data obtained via functional magnetic resonance imaging (fMRI). The combination of approaches is used to both test hypotheses of the cognitive model and also to incorporate hypotheses about the strategies used by participants to direct analysis of fMRI data. Examination of the roles of the MTL and caudate in visual category learning holds the promise of bridging between abstract cognitive models of behavior, systems neuroscience, neuropsychology, and the underlying neurophysiology of these brain regions.

Original languageEnglish (US)
Pages (from-to)279-291
Number of pages13
JournalNeuroscience and Biobehavioral Reviews
Volume32
Issue number2
DOIs
StatePublished - Jan 29 2008

Keywords

  • Categorization
  • Decision bound modeling
  • Information-integration
  • Rule-based

ASJC Scopus subject areas

  • Behavioral Neuroscience

Fingerprint Dive into the research topics of 'A review of medial temporal lobe and caudate contributions to visual category learning'. Together they form a unique fingerprint.

  • Cite this