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The Adaptive Advantage of Symbolic Theft Over Sensorimotor Toil: Grounding Language in Perceptual Categories

The Adaptive Advantage of Symbolic Theft Over Sensorimotor Toil: Grounding Language in Perceptual Categories
The Adaptive Advantage of Symbolic Theft Over Sensorimotor Toil: Grounding Language in Perceptual Categories
Using neural nets to simulate learning and the genetic algorithm to simulate evolution in a toy world of mushrooms and mushroom-foragers, we place two ways of acquiring categories into direct competition with one another: In (1) "sensorimotor toil," new categories are acquired through real-time, feedback-corrected, trial and error experience in sorting them. In (2) "symbolic theft," new categories are acquired by hearsay from propositions - boolean combinations of symbols describing them. In competition, symbolic theft always beats sensorimotor toil. We hypothesize that this is the basis of the adaptive advantage of language. Entry-level categories must still be learned by toil, however, to avoid an infinite regress (the "symbol grounding problem"). Changes in the internal representations of categories must take place during the course of learning by toil. These changes can be analyzed in terms of the compression of within-category similarities and the expansion of between-category differences. These allow regions of similarity space to be separated, bounded and named, and then the names can be combined and recombined to describe new categories, grounded recursively in the old ones. Such compression/expansion effects, called "categorical perception" (CP), have previously been reported with categories acquired by sensorimotor toil; we show that they can also arise from symbolic theft alone. The picture of natural language and its origins that emerges from this analysis is that of a powerful hybrid symbolic/sensorimotor capacity, infinitely superior to its purely sensorimotor precursors, but still grounded in and dependent on them. It can spare us from untold time and effort learning things the hard way, through direct experience, but it remain anchored in and translatable into the language of experience.
evolution, language, neural nets, artificial life, learning, categorisation, categorical perception
117-142
Cangelosi, Angelo
c6e85857-1799-4bdf-96bf-615a8c6111ea
Harnad, Stevan
442ee520-71a1-4283-8e01-106693487d8b
Cangelosi, Angelo
c6e85857-1799-4bdf-96bf-615a8c6111ea
Harnad, Stevan
442ee520-71a1-4283-8e01-106693487d8b

Cangelosi, Angelo and Harnad, Stevan (2001) The Adaptive Advantage of Symbolic Theft Over Sensorimotor Toil: Grounding Language in Perceptual Categories. Evolution of Communication, 4 (1), 117-142.

Record type: Article

Abstract

Using neural nets to simulate learning and the genetic algorithm to simulate evolution in a toy world of mushrooms and mushroom-foragers, we place two ways of acquiring categories into direct competition with one another: In (1) "sensorimotor toil," new categories are acquired through real-time, feedback-corrected, trial and error experience in sorting them. In (2) "symbolic theft," new categories are acquired by hearsay from propositions - boolean combinations of symbols describing them. In competition, symbolic theft always beats sensorimotor toil. We hypothesize that this is the basis of the adaptive advantage of language. Entry-level categories must still be learned by toil, however, to avoid an infinite regress (the "symbol grounding problem"). Changes in the internal representations of categories must take place during the course of learning by toil. These changes can be analyzed in terms of the compression of within-category similarities and the expansion of between-category differences. These allow regions of similarity space to be separated, bounded and named, and then the names can be combined and recombined to describe new categories, grounded recursively in the old ones. Such compression/expansion effects, called "categorical perception" (CP), have previously been reported with categories acquired by sensorimotor toil; we show that they can also arise from symbolic theft alone. The picture of natural language and its origins that emerges from this analysis is that of a powerful hybrid symbolic/sensorimotor capacity, infinitely superior to its purely sensorimotor precursors, but still grounded in and dependent on them. It can spare us from untold time and effort learning things the hard way, through direct experience, but it remain anchored in and translatable into the language of experience.

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More information

Published date: 2001
Additional Information: Special Issue on Grounding
Keywords: evolution, language, neural nets, artificial life, learning, categorisation, categorical perception
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 257709
URI: https://eprints.soton.ac.uk/id/eprint/257709
PURE UUID: 0a0dbc06-fb03-462b-9616-6551b2e380c3
ORCID for Stevan Harnad: ORCID iD orcid.org/0000-0001-6153-1129

Catalogue record

Date deposited: 19 Jun 2003
Last modified: 06 Jun 2018 13:05

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