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Typicality Rankings for Real-World Categories
Typicality Sharpens Category Representations inObject-Selective Cortex
Marius Cătălin Iordan¹, Michelle R. Greene¹, Diane M. Beck², Li Fei-Fei¹1Department of Computer Science, Stanford University 2Beckman Institute and Department of Psychology, University of Illinois at Urbana-Champaign
[email protected] [email protected] [email protected] [email protected]
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V1 V2 V3v hV4
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LOC
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CATE
GO
RY B
OUN
DARY
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Most Typical Least Typical
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LOC
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ION
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methods:16 images per exemplar (across 2 blocks)
passive viewing
analysis: use MVPA to characterize similarity and dissimilarity
of activity patterns within and between categories
Typical exemplars more similar to central category tendency in LOCTypical exemplars distinguish more strongly between categories in LOC
Suggests prototype representation for real-world objects
BirdsCatsDogsFishCarsBoatsPlanesTrains
more typical less typical
behavioral experimenttypicality rankings
for each 8 exemplars within their category
stimulus set8 categories
8 exemplars per category16 images per exemplar
ANIMALS(animate, natural)
VEHICLES(inanimate, man-made)
which is the best dog?faster recognition, faster categorization for typical items
Posner & Keele (1968), Rosch (1973), Rosch & Mervis (1975)
RT for recognition / categorization
Neural Typicality Hypothesis: Family Resemblance
relationship with central category tendencytypical items share more features in common with central tendency
differentiating between categoriessharper category boundaries between typical items compared to less typical items
typicality effect no typicality effect
LOC V1, V2,V3v, hV4
Whole-Brain Searchlight AnalysisfMRI Experiment Summary
Less typical exemplars more distinguishable in cIPLcIPL involved in category learning, memory of object context
Suggests contextual facilitation of categorization for atypical exemplars
Typicality may constitute a previously unexplored principle of organization for intra-category neural structure
New HypothesesIntermediate visual processing may prioritize the embedding of
behaviorally relevant dimensions of variance directly into neural representations
Selected References: Posner & Keele (1968) J. Exp. Psychol. ✦ Rosch (1973) Cogn. Dev. Acq. Lang. ✦ Rosch & Mervis (1975) Cogn. Psychol. ✦ Deng et al. (2011) CVPR ✦ Davis & Poldrack (in press) Cerebral Cortex ✦ Iordan, Greene, Beck, & Fei-Fei (in press) JOCN
