2 TRs: Categorical Perception and Neural Nets

[Stevan Harnad]

The following two tech reports are available by anonymous ftp from
directory /pub/harnad on princeton.edu. Full ftp instructions follow
the abstracts.
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(1)  Categorical Perception and the Evolution
     of Supervised Learning in Neural Nets

     S Harnad*, SJ Hanson*,** & J Lubin*
     *Princeton University
     **Siemens Research Center

[Presented at 1991 AAAI Symposium on Symbol Grounding: Problem and
Practice]

ABSTRACT: Some of the features of animal and human categorical
perception (CP) for color, pitch and speech are exhibited by neural
net simulations of CP with one-dimensional inputs: When a backprop net
is trained to discriminate and then categorize a set of stimuli, the
second task is accomplished by "warping" the similarity space
(compressing within-category distances and expanding between-category
distances). This natural side-effect also occurs in humans and
animals. Such CP categories, consisting of named, bounded regions of
similarity space, may be the ground level out of which higher-order
categories are constructed; nets are one possible candidate for the
mechanism that learns the sensorimotor invariants that connect
arbitrary names (elementary symbols?) to the nonarbitrary shapes of
objects. This paper examines how and why such compression/expansion
effects occur in neural nets.

[Retrieve by anonymous ftp in binary mode as (compressed) file
harnad91.cpnets.Z from directory /pub/harnad on princeton.edu,
instructions below]
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(2)    Connecting Object to Symbol in Modeling Cognition

                   Stevan Harnad
                   Department of Psychology
                   Princeton University
                   Princeton NJ 08544

[To appear in Clark, A. & Lutz, R. (Eds) (1992) "CONNECTIONISM IN
CONTEXT," Springer-Verlag]

Connectionism and computationalism are currently vying for hegemony in
cognitive modeling. At first glance the opposition seems incoherent,
because connectionism is itself computational, but the form of
computationalism that has been the prime candidate for encoding the
"language of thought" has been symbolic computationalism, whereas
connectionism is nonsymbolic. This paper examines what is and is not a
symbol system. A hybrid nonsymbolic/symbolic system will be sketched in
which the meanings of the symbols are grounded bottom-up in the
system's capacity to discriminate and identify the objects they refer
to. Neural nets are one possible mechanism for learning the invariants
in the analog sensory projection on which successful categorization is
based. "Categorical perception," in which similarity space is "warped"
in the service of categorization, turns out to be exhibited by both
people and nets, and may mediate the constraints exerted by the analog
world of objects on the formal world of symbols.

[Retrieve by anonymous ftp in binary mode as (compressed) file
harnad92.symbol.object.Z from directory /pub/harnad on princeton.edu]

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