Literature DB >> 19447098

Learning shapes the representation of behavioral choice in the human brain.

Sheng Li1, Stephen D Mayhew, Zoe Kourtzi.   

Abstract

Making successful decisions under uncertainty due to noisy sensory signals is thought to benefit from previous experience. However, the human brain mechanisms that mediate flexible decisions through learning remain largely unknown. Comparing behavioral choices of human observers with those of a pattern classifier based on multivoxel single-trial fMRI signals, we show that category learning shapes processes related to decision variables in frontal and higher occipitotemporal regions rather than signal detection or response execution in primary visual or motor areas. In particular, fMRI signals in prefrontal regions reflect the observers' behavioral choice according to the learned decision criterion only in the context of the categorization task. In contrast, higher occipitotemporal areas show learning-dependent changes in the representation of perceived categories that are sustained after training independent of the task. These findings demonstrate that learning shapes selective representations of sensory readout signals in accordance with the decision criterion to support flexible decisions.

Entities:  

Mesh:

Year:  2009        PMID: 19447098     DOI: 10.1016/j.neuron.2009.03.016

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   17.173


  36 in total

1.  Category learning increases discriminability of relevant object dimensions in visual cortex.

Authors:  Jonathan R Folstein; Thomas J Palmeri; Isabel Gauthier
Journal:  Cereb Cortex       Date:  2012-04-05       Impact factor: 5.357

2.  Learning alters the tuning of functional magnetic resonance imaging patterns for visual forms.

Authors:  Jiaxiang Zhang; Alan Meeson; Andrew E Welchman; Zoe Kourtzi
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3.  Decoding vibrotactile choice independent of stimulus order and saccade selection during sequential comparisons.

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4.  Dissociable perceptual-learning mechanisms revealed by diffusion-model analysis.

Authors:  Alexander A Petrov; Nicholas M Van Horn; Roger Ratcliff
Journal:  Psychon Bull Rev       Date:  2011-06

Review 5.  Two-stage model in perceptual learning: toward a unified theory.

Authors:  Kazuhisa Shibata; Dov Sagi; Takeo Watanabe
Journal:  Ann N Y Acad Sci       Date:  2014-04-23       Impact factor: 5.691

6.  Cognitive changes in conjunctive rule-based category learning: An ERP approach.

Authors:  Rahel Rabi; Marc F Joanisse; Tianshu Zhu; John Paul Minda
Journal:  Cogn Affect Behav Neurosci       Date:  2018-10       Impact factor: 3.282

7.  Decoding the brain's algorithm for categorization from its neural implementation.

Authors:  Michael L Mack; Alison R Preston; Bradley C Love
Journal:  Curr Biol       Date:  2013-10-03       Impact factor: 10.834

8.  Electrophysiological correlates of learning-induced modulation of visual motion processing in humans.

Authors:  Viktor Gál; István Kóbor; Eva M Bankó; Lajos R Kozák; John T Serences; Zoltán Vidnyánszky
Journal:  Front Hum Neurosci       Date:  2010-01-06       Impact factor: 3.169

9.  Effects of category-specific costs on neural systems for perceptual decision-making.

Authors:  Stephen M Fleming; Louise Whiteley; Oliver J Hulme; Maneesh Sahani; Raymond J Dolan
Journal:  J Neurophysiol       Date:  2010-03-31       Impact factor: 2.714

10.  Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations.

Authors:  Signe Bray; Catie Chang; Fumiko Hoeft
Journal:  Front Hum Neurosci       Date:  2009-10-23       Impact factor: 3.169

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