Literature DB >> 16426719

Event-related fMRI of category learning: differences in classification and feedback networks.

Deborah M Little1, Silvia S Shin, Shannon M Sisco, Keith R Thulborn.   

Abstract

Eighteen healthy young adults underwent event-related (ER) functional magnetic resonance imaging (fMRI) of the brain while performing a visual category learning task. The specific category learning task required subjects to extract the rules that guide classification of quasi-random patterns of dots into categories. Following each classification choice, visual feedback was presented. The average hemodynamic response was calculated across the eighteen subjects to identify the separate networks associated with both classification and feedback. Random-effects analyses identified the different networks implicated during the classification and feedback phases of each trial. The regions included prefrontal cortex, frontal eye fields, supplementary motor and eye fields, thalamus, caudate, superior and inferior parietal lobules, and areas within visual cortex. The differences between classification and feedback were identified as (i) overall higher volumes and signal intensities during classification as compared to feedback, (ii) involvement of the thalamus and superior parietal regions during the classification phase of each trial, and (iii) differential involvement of the caudate head during feedback. The effects of learning were then evaluated for both classification and feedback. Early in learning, subjects showed increased activation in the hippocampal regions during classification and activation in the heads of the caudate nuclei during the corresponding feedback phases. The findings suggest that early stages of prototype-distortion learning are characterized by networks previously associated with strategies of explicit memory and hypothesis testing. However as learning progresses the networks change. This finding suggests that the cognitive strategies also change during prototype-distortion learning.

Mesh:

Year:  2006        PMID: 16426719     DOI: 10.1016/j.bandc.2005.09.016

Source DB:  PubMed          Journal:  Brain Cogn        ISSN: 0278-2626            Impact factor:   2.310


  9 in total

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

Authors:  Xiong Jiang; Evan Bradley; Regina A Rini; Thomas Zeffiro; John Vanmeter; Maximilian Riesenhuber
Journal:  Neuron       Date:  2007-03-15       Impact factor: 17.173

2.  Episodic and prototype models of category learning.

Authors:  Richard J Tunney; Gordon Fernie
Journal:  Cogn Process       Date:  2011-04-10

Review 3.  Categorization = decision making + generalization.

Authors:  Carol A Seger; Erik J Peterson
Journal:  Neurosci Biobehav Rev       Date:  2013-03-30       Impact factor: 8.989

4.  Dorsal striatum mediates deliberate decision making, not late-stage, stimulus-response learning.

Authors:  Nole M Hiebert; Adrian M Owen; Ken N Seergobin; Penny A MacDonald
Journal:  Hum Brain Mapp       Date:  2017-09-25       Impact factor: 5.038

5.  Dissociating hippocampal and basal ganglia contributions to category learning using stimulus novelty and subjective judgments.

Authors:  Carol A Seger; Christina S Dennison; Dan Lopez-Paniagua; Erik J Peterson; Aubrey A Roark
Journal:  Neuroimage       Date:  2011-01-19       Impact factor: 6.556

6.  Frontoparietal networks involved in categorization and item working memory.

Authors:  Kurt Braunlich; Javier Gomez-Lavin; Carol A Seger
Journal:  Neuroimage       Date:  2014-12-04       Impact factor: 6.556

7.  An ERP analysis of recognition and categorization decisions in a prototype-distortion task.

Authors:  Richard J Tunney; Gordon Fernie; Duncan E Astle
Journal:  PLoS One       Date:  2010-04-12       Impact factor: 3.240

8.  Functional imaging analyses reveal prototype and exemplar representations in a perceptual single-category task.

Authors:  Helen Blank; Janine Bayer
Journal:  Commun Biol       Date:  2022-09-01

9.  Network changes in the transition from initial learning to well-practiced visual categorization.

Authors:  Joe DeGutis; Mark D'Esposito
Journal:  Front Hum Neurosci       Date:  2009-11-12       Impact factor: 3.169

  9 in total

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