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From: harnad@mind.UUCP (Stevan Harnad)
Newsgroups: comp.ai,sci.lang
Subject: Re: Pinker & Prince Reply (On Modeling and Its Constraints)
Keywords: connectionism, symbolic rules, learnability, past tense formation
Message-ID: <2834@mind.UUCP>
Date: 3 Sep 88 20:06:45 GMT
References: <2816@mind.UUCP> <2817@mind.UUCP> <2818@mind.UUCP> <2831@mind.UUCP>
Organization: Cognitive Science, Princeton University
Lines: 58



Pinker & Prince attribute the following 4 points (not quotes) to me,
indicating that they sharply disgree with (1) and (2) and have no
interest whatsoever in discussing (3) and (4).:

   (1) Looking at the actual behavior and empirical fidelity of connectionist
   models is not the right way to test connectionist hypotheses.

This was not the issue, as any attentive follower of the discussion
can confirm. The question was whether Pinker & Prince's article was to
be taken as a critique of the connectionist approach in principle, or
just of the Rumelhart & McClelland 1986 model in particular.

   (2) Developmental, neural, reaction time, and brain-damage data should be
   put aside in evaluating psychological theories. 

This was a conditional methodological point; it is not correctly stated
in (2): IF one has a model for a small fragment of human cognitive
performance capacity (a "toy" model), a fragment that one has no reason
to suppose to be functionally self-contained and independent of the
rest of cognition, THEN it is premature to try to bolster confidence in
the model by fitting it to developmental (neural, reaction time, etc.)
data. It is a better strategy to try to reduce the model's vast degrees of
freedom by scaling up to a larger and larger fragment of cognitive
performance capacity. This certainly applies to past-tense learning
(although my example was chess-playing and doing factorials). It also
seems to apply to all cognitive models proposed to date. "Psychological
theories" will begin when these toy models begin to approach lifesize;
then fine-tuning and implementational details may help decide between
asymptotic rivals.

[Here's something for connectionists to disagree with me about: I don't
think there is a solid enough fact known about the nervous system
to warrant "constraining" cognitive models with it. Constraints are
handicaps; what's needed in the toy world that contemporary modeling
lives in is more power and generality in generating our performance
capacities. If "constraints" help us to get that, then they're useful
(just as any source of insight, including analogy and pure fantasy can
be useful). Otherwise they are just arbitrary burdens. The only
face-valid "constraint" is our cognitive capacity itself, and we all
know enough about that already to provide us with competence data
till doomsday. Fine-tuning details are premature; we haven't even come
near the station yet.]

   (3) The meaning of the word "learning" should be stipulated to apply
   only to extracting statistical regularities from input data.

   (4) Induction has philosophical priority over innatism.

These are substantive issues, very relevant to the issues under discussion
(and not decidable by stipulation). However, obviously, they can only be
discussed seriously with interested parties.
-- 
Stevan Harnad   ARPANET:  harnad@mind.princeton.edu         harnad@princeton.edu
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