Two Preprints: Generalization & Representation, Sensorimotor Learning

[Mark Gluck]

TWO PRE-PRINTS AVAILABLE:

     1)  Stimulus Generalization and Representation in Adaptive Network
           Models of Category Learning

     2)  Sensorimotor Learning and the Cerebellum.

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 Gluck, M. A. (1991, in press). Stimulus generalization and
     representation in adaptive network models of category learning
     To appear in : Psychological Science.

                          Abstract

  An exponential-decay relationship relationship between the proba-
bility of generalization and psychological distance has received
considerable support from studies of stimulus generalization
(Shepard, 1958) and categorization (Nosofsky, 1984).  It is shown
here how an approximate exponential generalization gradient em-
erges in a "configural-cue" network model of human learning that
represents stimulus patterns in terms of elementary features and
pair-wise conjunctions of features (Gluck & Bower, 1988b; Gluck,
Bower, & Hee, 1989) from stimulus representation assumptions iso-
morphic to a special case of Shepard's (1987) theory of stimulus
generalization.  The network model can be viewed as a combination
of Shepard's theory and an associative learning rule derived from
Rescorla and Wagner's (1972) theory of classical conditioning.

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 Bartha, G. T., Thompson, R. F., & Gluck, M. A. (1991, in press)
        Sensorimotor learning and the cerebellum. In M. A. Arbib
        and J.-P. Ewert (Eds.), Visual Structures and Integrated
        Functions, Springer Research Notes in Neural Computing,
        Berlin: Springer-Verlag.

                              Abstract

  This paper describes our current work on integrating experimental
and theoretical studies of a simple form of sensorimotor learn-
ing: the classically conditioned rabbit eyelid closure response.
We first review experimental efforts to determine the neural
basis of the conditioned eyelid closure response and these sup-
port the role of the cerebellum as the site of the memory trace.
Then our current work to bring the modeling in closer contact
with the biology is described.  In particular, we extend our ear-
lier model of response topography to be more physiological in the
circuit connectivity, the learning algorithm, and the conditioned
stimulus representation. The results of these extensions include
a more realistic conditioned response topography and reinforce-
ment learning which accounts for an experimentally established
negative feedback loop.
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 with your hard-copy mailing address.

Or mail to: Mark A. Gluck, Department of Psychology, Jordan Hall, Bldg. 420,
 Stanford Univ., Stanford, CA  94305-2130