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Categorization training results in shape- and category-selective human neural plasticity.


ABSTRACT: Object category learning is a fundamental ability, requiring the combination of "bottom-up" stimulus-driven with "top-down" task-specific information. It therefore may be a fruitful domain for study of the general neural mechanisms underlying cortical plasticity. A simple model predicts that category learning involves the formation of a task-independent shape-selective representation that provides input to circuits learning the categorization task, with the computationally appealing prediction of facilitated learning of additional, novel tasks over the same stimuli. Using fMRI rapid-adaptation techniques, we find that categorization training (on morphed "cars") induced a significant release from adaptation for small shape changes in lateral occipital cortex irrespective of category membership, compatible with the sharpening of a representation coding for physical appearance. In contrast, an area in lateral prefrontal cortex, selectively activated during categorization, showed sensitivity posttraining to explicit changes in category membership. Further supporting the model, categorization training also improved discrimination performance on the trained stimuli.

SUBMITTER: Jiang X 

PROVIDER: S-EPMC1989663 | biostudies-literature | 2007 Mar

REPOSITORIES: biostudies-literature

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Categorization training results in shape- and category-selective human neural plasticity.

Jiang Xiong X   Bradley Evan E   Rini Regina A RA   Zeffiro Thomas T   Vanmeter John J   Riesenhuber Maximilian M  

Neuron 20070301 6


Object category learning is a fundamental ability, requiring the combination of "bottom-up" stimulus-driven with "top-down" task-specific information. It therefore may be a fruitful domain for study of the general neural mechanisms underlying cortical plasticity. A simple model predicts that category learning involves the formation of a task-independent shape-selective representation that provides input to circuits learning the categorization task, with the computationally appealing prediction o  ...[more]

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