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From: marty1@houdi.UUCP (M.BRILLIANT)
Newsgroups: comp.cog-eng,comp.ai
Subject: Re: The symbol grounding problem: Against Rosch & Wittgenstein
Message-ID: <1195@houdi.UUCP>
Date: Mon, 29-Jun-87 16:53:28 EDT
Article-I.D.: houdi.1195
Posted: Mon Jun 29 16:53:28 1987
Date-Received: Wed, 1-Jul-87 02:25:35 EDT
References: .... <6174@diamond.BBN.COM> <917@mind.UUCP> <1192@houdi.UUCP> <931@mind.UUCP>
Organization: AT&T Bell Laboratories, Holmdel
Lines: 42
Summary: OK, then a human-like categorizer must be able to vacillate
Xref: mnetor comp.cog-eng:158 comp.ai:589


In article <931@mind.UUCP>, harnad@mind.UUCP (Stevan Harnad) writes:
> marty1@houdi.UUCP (M.BRILLIANT) of AT&T Bell Laboratories, Holmdel asks:
> >	Why require 100% accuracy in all-or-none categorizing?... I learned
> >	recently that I can't categorize chairs with 100% accuracy. 
> 
> This is a misunderstanding. The "100% accuracy" refers to the
> all-or-none-ness of the kinds of categories in question. The rival
> theories in the Roschian tradition have claimed that many categories
> (including "bird" and "chair") do not have "defining" features. Instead,
> membership is either fuzzy or a matter of degree (i.e., percent)....

OK: once I classify a thing as a chair, there are no two ways about it:
it's a chair.  But there can be a stage when I can't decide.  I
vacillate: "I think it's a chair."  "Are you sure?"  "No, I'm not sure,
maybe it's a bed."  I would never say seriously that I'm 40 percent
sure it's a chair, 50 percent sure it's a bed, and 10% sure it's an
unfamiliar object I've never seen before.

I think this is in agreement with Harnad when he says:

> Categorization preformance (with all-or-none categories) is highly reliable
> (close to 100%) and MEMBERSHIP is 100%. Only speed/ease of categorization and
> typicality ratings are a matter of degree....
> This is not to deny that even all-or-none categorization may encounter
> regions of uncertainty. Since ALL category representations in my model are
> provisional and approximate .....  it is always possible that
> the categorizer will encounter an anomalous instance that he cannot classify
> according to his current representation.....
> ...... This still does not imply that membership is
> fuzzy or a matter of degree.....

So to pass the Total Turing Test, a machine should respond the way a
human does when faced with inadequate or paradoxical sensory data: it
should vacillate (or bluff, as some people do).  In the presence of
uncertainty it will not make self-consistent statements about
uncertainty, but uncertain and possibly inconsistent statements about
absolute membership.

M. B. Brilliant					Marty
AT&T-BL HO 3D-520	(201)-949-1858
Holmdel, NJ 07733	ihnp4!houdi!marty1