Preprint: Stimulus Sampling & Distributed Representations

[Mark Gluck]

PRE-PRINT AVAILABLE:

            Stimulus Sampling and Distributed Representations
                 in Adaptive Network Theories of Learning

                            Mark A. Gluck

                       Department of Psychology
                         Stanford University

      [To appear in: A. Healy, S. Kosslyn, & R. Shiffrin (Eds.),
        Festschrift for W. K. Estes. NJ: Erlbaum, 1991/in press]

                             ABSTRACT:

  Current adaptive network, or "connectionist", theories of human
learning are reminiscent of statistical learning theories of the
1950's and early 1960's, the most influential of which was
Stimulus Sampling Theory, developed by W. K. Estes and colleagues
(Estes, 1959; Atkinson & Estes, 1963). This chapter reviews
Stimulus Sampling Theory, noting some of its strengths and
weaknesses, and compares it to a recent network model of human
learning (Gluck & Bower, 1986, 1988a,b).  The network model's LMS
learning rule for updating associative weights represents a
significant advance over Stimulus Sampling Theory's more
rudimentary learning procedure.  In contrast, Stimulus Sampling
Theory's stochastic scheme for representing stimuli as
distributed patterns of activity can overcome some limitations of
network theories which identify stimulus cues with single active
input nodes.  This leads us to consider a distributed network
model which embodies the processing assumptions of our earlier
network model but employs stimulus-representation assumptions
adopted from Stimulus Sampling Theory.  In this distributed
network, stimulus cues are represented by the stochastic
activation of overlapping populations of stimulus elements (input
nodes).  Rather than replacing the two previous learning
theories, this distributed network combines the best established
concepts of the earlier theories and reduces to each of them as
special cases in those training situations where the previous
models have been most successful.

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