Levine, Daniel S.

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Daniel S. Levine
University of Texas
Arlington, Texas
Dr. Levine received his Ph.D. in applied mathematics at MIT in 1975, under
Professor Stephen Grossberg. He has been an associate professor of
mathematics at the University of Texas at Arlington since the f a l l of 1983,
and his research articles have been in the theory, modeling, and simulation
of biological (neural or ecological) processes using dynamical systems of
differential equations. In neural networks, Dr. Levine has written an 86-
page review article (Mathematical Biosciences, Vol. 66, 1983) and is
currently w r i t i n g a graduate textbook (Introduction to Neural and Cognitive
Modeling, Erlbaum, to appear in 1989). He is an editor of Neural Networks
and has been on the organizing committees of both 1987 and 1988
International Neural Network Society meetings. His recent work has centered
on interactions between cognitive and motivational variables in neural
networks. Dr. Levine has also been a commissioned officer at the National
Institute of Health; a postdoctoral trainee in physiology at UCLA; and an
assistant professor of mathematics at Rutgers University, the University of
Pittsburgh, and the University of Houston.
NEURAL MODELING OF SELECTIVE ATTENTION
Abstract
A neural network is presented in which there are modifiable, bidirectional
connections between nodes representing sensory events and other nodes repre-
senting reinforcement sources. There is also competition between sensory
nodes. Through these competitive and associative mechanisms, the presenter,
together with Stephen Grossberg, has simulated some data on attentionally
modulated Pavlovian conditioning. In particular, if two stimuli are pre-
sented simultaneously, and one of them has already been associated with a
primary reinforcer (such as electric shock or food), selective attention
occurs which inhibits the other stimulus from forming new associations.
Context changes can profoundly alter the dynamics of selective attention.
For example, if one stimulus has been paired with a reinforcer and that
stimulus combined wi t h another is paired with a greater or lesser amount of
that reinforcer, the second stimulus is no longer blocked. Also, selective
attention based on positive or negative reinforcement can compete with
selective attention based on other criteria. Nonmotivational criteria are
enhanced by frontal lobe damage, which weakens the sensory-reinforcement
linkage. For example, a frontally lesioned monkey can prefer a novel object
to one that has previously been rewarded. Also, a human frontal lobe
patient can persevere in a habit that was once, but is no longer, rewarding.
https://ntrs.nasa.gov/search.jsp?R=19910073804 2020-03-17T14:40:05+00:00Z
NOTES


Neural modeling of selective attention

Abstract

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