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Abstract
The evolutionary importance of the orienting reflex (OR) has been known
for decades. To paraphrase Pavlov, if an animal does not orient to novelty
in its environment, it will not live long. Although much is known about
the OR, the computation of novelty at the neuronal level is not fully
understood. A variety of stimuli trigger an OR, and at some level they
must elicit the same pattern of low-level neural activation. Sokolov and
colleagues posit that “expectancy neurons” compare incoming stimuli with
what is expected in short-term memory. This study outlines the development
of a connectionist simple recurrent network (SRN) that displays orienting
behavior without having been taught to do so. Participants in laboratory
experiments reliably orient to scene changes (cuts) in television programs.
In order to capture the dynamic nature of visual processing, a series
of television clips was input to the SRN frame by frame. The task of the
network was to pre! dict the pixel values for the subsequent frame. The
model learns to “expect” consistency in the visual world, and predictions
falter noticeably at scene boundaries. When model expectancy is plotted
over time as perfect performance minus error, the resulting curve closely
resembles a cardiac response curve characteristic of orienting to scene
changes. Model data are compared to human EKG data recorded while participants
watched television clips. Thus, this simulation suggests that neurons
that learn to expect consistency from incoming stimuli are sufficient
to trigger an OR without higher-order processing.
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