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Distinct Mechanisms in Visual Category Learning

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Publisher Springer
Date 2007 Nov 13
PMID 17993211
Citations 20
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Abstract

The ways in which visual categories are learned, and in which well-established categories are represented and retrieved, are fundamental issues of cognitive neuroscience. Researchers have typically studied these issues separately, and the transition from the initial phase of category learning to expertise is poorly characterized. The acquisition of novel categories has been shown to depend on the striatum, hippocampus, and prefrontal cortex, whereas visual category expertise has been shown to involve changes in inferior temporal cortex. The goal of the present experiment is to understand the respective roles of these brain regions in the transition from initial learning to expertise when category judgments are being made. Subjects were explicitly trained, over 2 days, to classify realistic faces. Subjects then performed the categorization task during fMRI scanning, as well as a perceptual matching task, in order to characterize how brain regions respond to these faces when not explicitly categorizing them. We found that, during face categorization, face-selective inferotemporal cortex, lateral prefrontal cortex, and dorsal striatum are more responsive to faces near the category boundary, which are most difficult to categorize. In contrast, the hippocampus and left superior frontal sulcus responded most to faces farthest from the category boundary. These dissociable effects suggest that there are several distinct neural mechanisms involved in categorization, and provide a framework for understanding the contribution of each of these brain regions in categorization.

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References
1.
OCraven K, Downing P, Kanwisher N . fMRI evidence for objects as the units of attentional selection. Nature. 1999; 401(6753):584-7. DOI: 10.1038/44134. View

2.
Schacter D, Wagner A . Medial temporal lobe activations in fMRI and PET studies of episodic encoding and retrieval. Hippocampus. 1999; 9(1):7-24. DOI: 10.1002/(SICI)1098-1063(1999)9:1<7::AID-HIPO2>3.0.CO;2-K. View

3.
Bunge S . How we use rules to select actions: a review of evidence from cognitive neuroscience. Cogn Affect Behav Neurosci. 2005; 4(4):564-79. DOI: 10.3758/cabn.4.4.564. View

4.
Heekeren H, Marrett S, Bandettini P, Ungerleider L . A general mechanism for perceptual decision-making in the human brain. Nature. 2004; 431(7010):859-62. DOI: 10.1038/nature02966. View

5.
Reber P, Gitelman D, Parrish T, Marsel Mesulam M . Dissociating explicit and implicit category knowledge with fMRI. J Cogn Neurosci. 2003; 15(4):574-83. DOI: 10.1162/089892903321662958. View