Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!utgpu!water!watmath!clyde!rutgers!princeton!mind!harnad From: harnad@mind.UUCP Newsgroups: comp.ai,comp.cog-eng Subject: Re: The symbol grounding problem Message-ID: <786@mind.UUCP> Date: Thu, 28-May-87 01:46:28 EDT Article-I.D.: mind.786 Posted: Thu May 28 01:46:28 1987 Date-Received: Fri, 29-May-87 04:44:04 EDT References: <764@mind.UUCP> <768@mind.UUCP> <770@mind.UUCP> <6174@diamond.BBN.COM> Organization: Cognitive Science, Princeton University Lines: 29 Keywords: icons, categories, symbols, grounding Xref: utgpu comp.ai:424 comp.cog-eng:98 Summary: Physical isomorphism is captured by physical invertibility Anders Weinstein of BBN wrote: > a point that I thought was clearly made in our earlier > discussion of the A/D distinction: loss of information, i.e. > non-invertibility, is neither a necessary nor sufficient condition for > analog to digital transformation. The only point that seems to have been clearly made in the sizable discussion of the A/D distinction on the Net last year (to my mind, at least) was that no A/D distinction could be agreed upon that would meet the needs and interests of all of the serious proponents and that perhaps there was an element of incoherence in all but the most technical and restricted of signal-analytic candidates. In the discussion to which you refer above (a 3-level bottom-up model for grounding symbolic representations in nonsymbolic -- iconic and categorical -- representions) the issue was not the A/D transformation but A/A transformations: isomorphic copies of the sensory surfaces. These are the iconic representations. So whereas physical invertibility may not have been more successful than any of the other candidates in mapping out a universally acceptable criterion for the A/D distinction, it is not clear that it can be faulted as a criterion for physical isomorphism. -- Stevan Harnad (609) - 921 7771 {bellcore, psuvax1, seismo, rutgers, packard} !princeton!mind!harnad harnad%mind@princeton.csnet harnad@mind.Princeton.EDU