Neural models of attention?

[Jonathan D. Cohen]

Several investigators have begun to address attentional phenomena using
connectionist models.  They include Schneider, Mozer and Phaff (see references
below).  My colleagues (Kevin Dunbar and Jay McClelland) and I have also
done some work in this area.  Our approach has been to view attention as
the modulatory influence that information in one part of the system has on
processing in other parts.  By modulation, we mean changes in the responsivity
of processing units.  We have implemented our ideas in a model of the
Stroop task, a standard psychological paradigm for studying selective
aspects of attention.  The reference and abstract for this paper are also
included below.

Jonathan Cohen
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Mozer, M. (1988).  A connectionist model of selective attention in visual
perception.  In the proceedings of the tenth annual conference of the Cognitive
Science Society.Hillsdale, NJ: Erlbaum,  pp. 195-201.

Phaff, R. H.  (1986).  A connectionist model for attention:  Restricting
parallel processing through modularity.  Unpublished doctoral dissertation,
University of Experimental Psychology, University of Leiden,  Netherlands.

Schneider, W. (1985).  Toward a model of attention and the development of
automatic processing.  In M.I. Posner & O.S.M. Marin (Eds.), Attention and
and Performance  XI (pp.475-492).  Hillsdale, NJ:  Lawrence Erlbaum.

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Cohen JD, Dunbar K & McClelland JL (in press).  On the control of automatic
processes:  A parallel distributed processing model of the Stroop effect.
Psychological Review, in press.  (I can handle a *limited* number of requests
for preprints)

                               Abstract
A growing body of evidence suggests that traditional views of
automaticity are in need of revision.  For example, automaticity has
often been treated as an all-or-none phenomenon, and traditional
theories have held that automatic processes are independent of
attention.  Yet recent empirical data suggest that automatic
processes are continuous, and furthermore are subject to
attentional control.  In this paper we present a model of attention
which addresses these issues.  Using a parallel distributed
processing framework we propose that the attributes of
automaticity depend upon the strength of a processing pathway
and that strength increases with training.  Using the Stroop effect
as an example, we show how automatic processes are continuous
and emerge gradually with practice.  Specifically, we present a
computational model of the Stroop task which simulates the time
course of processing as well as the effects of learning.  This was
accomplished by combining the cascade mechanism described by
McClelland (1979) with the back propagation learning algorithm
(Rumelhart, Hinton, & Williams, 1986).  The model is able to
simulate performance in the standard Stroop task, as well as
aspects of performance in variants of this task which manipulate
SOA, response set, and degree of practice.  In the discussion we
contrast our model with other models, and indicate how it relates
to many of the central issues in the literature on attention,
automaticity, and interference.