The University of Southampton
University of Southampton Institutional Repository

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.

Text
harnad00.language.html - Other
Download (60kB)
Image
Image8.gif - Other
Download (5kB)
Image
Image10.gif - Other
Download (7kB)
Image
Image12.gif - Other
Download (10kB)
Image
Image54.gif - Other
Download (8kB)
Image
Image15.gif - Other
Download (7kB)
Image
Image18.gif - Other
Download (807B)
Image
Image16.gif - Other
Download (7kB)
Image
Image17.gif - Other
Download (807B)
Image
Image53.gif - Other
Download (9kB)
Image
Image14.gif - Other
Download (7kB)
Image
Image9.gif - Other
Download (5kB)
Image
Image13.gif - Other
Download (5kB)
Image
Image11.gif - Other
Download (6kB)

Show all 14 downloads.

More information

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

Identifiers

Local EPrints ID: 252616
URI: http://eprints.soton.ac.uk/id/eprint/252616
PURE UUID: 284e3aa3-c1f8-4000-8b51-3f14d5cdcc90
ORCID for Stevan Harnad: ORCID iD orcid.org/0000-0001-6153-1129

Catalogue record

Date deposited: 23 Feb 2000
Last modified: 07 Oct 2020 06:17

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×