Favoring Generalists over Specialists: How Attentional Biasing Improves Perceptual Category LearningReportar como inadecuado


Favoring Generalists over Specialists: How Attentional Biasing Improves Perceptual Category Learning


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Abstract

A model of cortical learning is proposed, which incorporates supervised feedback using two forms of attention: i feature-specific attention which allows the network to learn associations between specific feature conjunctions or categories and outputs, and ii nonspecific attentional -vigilance- which biases this learning when the associations appear to be incorrect. Attentional vigilance improves learning if it favors, via modulatory weights, generalist categories over specialist categories. A biologically plausible neural network is proposed which implements these computational principles and which outperforms several classifiers on classification benchmarks.Rights

Copyright 1999 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and - or special permission.

CAS-CNS Technical Reports -



Autor: Williamson, James - -

Fuente: https://open.bu.edu/







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