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Incremental learning of perceptual and conceptual representations and the puzzle of neural repetition suppression

Overview of attention for article published in Psychonomic Bulletin & Review, June 2016
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Title
Incremental learning of perceptual and conceptual representations and the puzzle of neural repetition suppression
Published in
Psychonomic Bulletin & Review, June 2016
DOI 10.3758/s13423-015-0855-y
Pubmed ID
Authors

Stephen J. Gotts

Abstract

Incremental learning models of long-term perceptual and conceptual knowledge hold that neural representations are gradually acquired over many individual experiences via Hebbian-like activity-dependent synaptic plasticity across cortical connections of the brain. In such models, variation in task relevance of information, anatomic constraints, and the statistics of sensory inputs and motor outputs lead to qualitative alterations in the nature of representations that are acquired. Here, the proposal that behavioral repetition priming and neural repetition suppression effects are empirical markers of incremental learning in the cortex is discussed, and research results that both support and challenge this position are reviewed. Discussion is focused on a recent fMRI-adaptation study from our laboratory that shows decoupling of experience-dependent changes in neural tuning, priming, and repetition suppression, with representational changes that appear to work counter to the explicit task demands. Finally, critical experiments that may help to clarify and resolve current challenges are outlined.

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Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Italy 1 2%
Unknown 57 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 27%
Researcher 11 19%
Student > Master 7 12%
Student > Doctoral Student 4 7%
Other 3 5%
Other 9 15%
Unknown 9 15%
Readers by discipline Count As %
Psychology 17 29%
Neuroscience 11 19%
Agricultural and Biological Sciences 4 7%
Computer Science 3 5%
Linguistics 3 5%
Other 6 10%
Unknown 15 25%