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The Roots of Cognitive NeuroscienceBehavioral Neurology and Neuropsychology$
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Anjan Chatterjee and H. Branch Coslett

Print publication date: 2013

Print ISBN-13: 9780195395549

Published to Oxford Scholarship Online: January 2014

DOI: 10.1093/acprof:oso/9780195395549.001.0001

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Attractor Basins: A Neural Basis for the Conformation of Knowledge

Attractor Basins: A Neural Basis for the Conformation of Knowledge

A Unified Account of Patterns of Linguistic Impairment, Magnitude Estimation, and Hemispatial Neglect

Chapter:
(p.305) Chapter 15 Attractor Basins: A Neural Basis for the Conformation of Knowledge
Source:
The Roots of Cognitive Neuroscience
Author(s):

Stephen E. Nadeau

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195395549.003.0015

Parallel distributed processing (PDP), the science of population-encoded representations, enables us to understand how neural networks can support cognitive processes. Auto-associator networks are networks in which each unit is connected to many other units. The activity function of such networks has the capacity for settling into stable states, called attractor basins, which correspond to concept representations. The characteristics of attractor basins supported by substrates for semantic knowledge and the results of attenuation of this knowledge in semantic dementia serve both to illustrate the function of such systems and to reveal a paradox: the PDP properties of neural networks enable us to miraculously acquire large and apparently seamless domains of knowledge from a series of individual experiences, but at the cost of serious limitations, as manifested in semantic errors in semantic dementia and in the phenomenology defined by Steven’s power law of magnitude estimation and hemispatial neglect (including the cross-over effect).

Keywords:   parallel distributed processing, attractor basins, semantic knowledge, semantic dementia, steven’s power law, magnitude estimation, hemispatial neglect, cross-over effect

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