title: Learning the exception to the rule: model-based FMRI reveals specialized representations for surprising category members.
abstract: Category knowledge can be explicit, yet not conform to a perfect rule. For example, a child may acquire the rule "If it has wings, then it is a bird," but then must account for exceptions to this rule, such as bats. The current study explored the neurobiological basis of rule-plus-exception learning by using quantitative predictions from a category learning model, SUSTAIN, to analyze behavioral and functional magnetic resonance imaging (fMRI) data. SUSTAIN predicts that exceptions require formation of specialized representations to distinguish exceptions from rule-following items in memory. By incorporating quantitative trial-by-trial predictions from SUSTAIN directly into fMRI analyses, we observed medial temporal lobe (MTL) activation consistent with 2 predicted psychological processes that enable exception learning: item recognition and error correction. SUSTAIN explains how these processes vary in the MTL across learning trials as category knowledge is acquired. Importantly, MTL engagement during exception learning was not captured by an alternate exemplar-based model of category learning or by standard contrasts comparing exception and rule-following items. The current findings thus provide a well-specified theory for the role of the MTL in category learning, where the MTL plays an important role in forming specialized category representations appropriate for the learning context.
authors.affiliation: Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA. Thdavis@mail.utexas.edu
authors.forename: Tyler
authors.initials: T
authors.lastname: Davis
authors.forename: Bradley C
authors.initials: BC
authors.lastname: Love
authors.forename: Alison R
authors.initials: AR
authors.lastname: Preston
mesh_terms: D000328:Adult; D001921:Brain / Q000098:blood supply* / Q000502:physiology*; D001931:Brain Mapping; D003210:Concept Formation; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging; D008297:Male; D008960:Models, Psychological; D015233:Models, Statistical; D010100:Oxygen / Q000097:blood; D011237:Predictive Value of Tests; D011597:Psychomotor Performance; D021641:Recognition, Psychology / Q000502:physiology*; D055815:Young Adult
chemical_list: D010100:Oxygen