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From: aweinste@Diamond.BBN.COM (Anders Weinstein)
Newsgroups: comp.cog-eng,comp.ai
Subject: Re: The symbol grounding problem: Against Rosch & Wittgenstein
Message-ID: <6885@diamond.BBN.COM>
Date: Wed, 1-Jul-87 12:14:41 EDT
Article-I.D.: diamond.6885
Posted: Wed Jul  1 12:14:41 1987
Date-Received: Thu, 2-Jul-87 06:16:18 EDT
References: <.... <6174@diamond.BBN.COM> <917@mind.UUCP> <14184@teknowledge-vaxc.ARPA> <949@mind.UUCP>
Reply-To: aweinste@Diamond.BBN.COM (Anders Weinstein)
Organization: BBN Laboratories, Inc., Cambridge, MA
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Xref: mnetor comp.cog-eng:170 comp.ai:599

In article <949@mind.UUCP> harnad@mind.UUCP (Stevan Harnad) writes:
>
>>	There is no reliable, consensual all-or-none categorization performance
>>	without a set of underlying features?  That sounds like a restatement of
>>	the categorization theorist's credo rather than a thing that is so.
>
>If not, what is the objective basis for the performance? And how would
>you get a device to do it given the same inputs?

I think there's some confusion as to whether Harnad's claim is just an empty
tautology or a significant empirical claim. To wit: it's clear that we can
reliably recognize chairs from sensory input, and we don't do this by magic.
Hence, we can perhaps take it as trivially true that there are some
"features" of the input that are being detected. If we are taking this line
however, we have remember that it doesn't really say *anything* about the
operation of the mechanism -- it's just a fancy way of saying we can
recognize chairs. 

On the other hand, it might be taken as a significant claim about the nature
of the chair-recognition device, viz., that we can understand its workings as
a process of actually parsing the input into a set of features and actually
comparing these against what is essentially some logical formula in
featurese.  This *is* an empirical claim, and it is certainly dubitable:
there could be pattern recognition devices (holograms are one speculative
suggestion) which cannot be interestingly broken down into feature-detecting
parts.

Anders Weinstein
BBN Labs