Symbol Grounding is an Empirical Problem: Neural Nets are Just a Candidate Component


Harnad, S (1993) Symbol Grounding is an Empirical Problem: Neural Nets are Just a Candidate Component. Proceedings of the Fifteenth Annual Meeting of the Cognitive Science Society Erlbaum.

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Description/Abstract

"Symbol Grounding" is beginning to mean too many things to too many people. My own construal has always been simple: Cognition cannot be just computation, because computation is just the systematically interpretable manipulation of meaningless symbols, whereas the meanings of my thoughts don't depend on their interpretability or interpretation by someone else. On pain of infinite regress, then, symbol meanings must be grounded in something other than just their interpretability if they are to be candidates for what is going on in our heads. Neural nets may be one way to ground the names of concrete objects and events in the capacity to categorize them (by learning the invariants in their sensorimotor projections). These grounded elementary symbols could then be combined into symbol strings expressing propositions about more abstract categories. Grounding does not equal meaning, however, and does not solve any philosophical problems.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Web & Internet Science
ePrint ID: 253367
Date Deposited: 25 May 2000
Last Modified: 27 Mar 2014 19:55
Further Information:Google Scholar
ISI Citation Count:5
URI: http://eprints.soton.ac.uk/id/eprint/253367

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