Hebbian reweighting on stable representations in perceptual learning

Barbara Anne Dosher; Zhong-Lin Lu

doi:10.1556/lp.1.2009.1.4

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Learning & Perception

Volume/Issue:: Volume 1: Issue 1

Hebbian reweighting on stable representations in perceptual learning

Authors:

Barbara Anne Dosher

Barbara Anne Dosher Department of Cognitive Sciences, University of California, Irvine, CA, USA
Department of Cognitive Sciences, Center for Cognitive Neuroscience, Institute for Mathematical Behavioral Sciences, Center for Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA

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Zhong-Lin Lu

Zhong-Lin Lu Departments of Psychology and Biomedical Engineering, and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA

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Pages:: 37–58

Online Publication Date:: 01 Jun 2009

Publication Date:: 01 Jun 2009

Article Category:: Journal Article

DOI:: https://doi.org/10.1556/lp.1.2009.1.4

Keywords:: perceptual learning; plasticity; feedback; models

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Abstract

Perceptual learning is the improvement in perceptual task performance with practice or training. The observation of specificity in perceptual learning has been widely associated with plasticity in early visual cortex representations. Here, we review the evidence supporting the plastic reweighting of readout from stable sensory representations, originally proposed by Dosher and Lu (1998), as an alternative explanation of perceptual learning. A task-analysis that identifies circumstances in which specificity supports representation enhancement and those in which it implies reweighting provides a framework for evaluating the literature; reweighting is broadly consistent with the behavioral results and almost all of the physiological reports. We also consider the evidence that the primary mode of perceptual learning is through augmented Hebbian learning of the reweighted associations, which has implications for the role and importance of feedback. Feedback is not necessary for perceptual learning, but can improve it in some circumstances, and in some cases block feedback is also helpful — all effects that are generally compatible with an augmented Hebbian model (Petrov, Dosher and Lu, 2005). The two principles of perceptual learning through reweighting evidence from stable sensory representations and of augmented Hebbian learning provide a theoretical structure for the consideration of issues such as task difficulty, task roving, and cuing in perceptual learning.

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Learning & Perception

Print ISSN:: 1789-3186

Online ISSN:: 2060-9175

Learning & Perception
Language	English
Size
Year of Foundation	2009
Publication Programme	ceased
Volumes per Year
Issues per Year
Publisher	Akadémiai Kiadó
Publisher's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible Publisher	Chief Executive Officer, Akadémiai Kiadó
ISSN	1789-3186 (Print)
ISSN	2060-9175 (Online)