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Abstract:
A novel neural network model of pre-attention processing in
visual-search tasks is presented. Using displays of line
orientations taken from Wolfe's experiments [1992], we study the
hypothesis that the distinction between parallel versus serial
processes arises from the availability of global information in the
internal representations of the visual scene. The model operates in
two phases. First, the visual displays are compressed via
principal-component-analysis. Second, the compressed data is
processed by a target detector module in order to identify the
existence of a target in the display. Our main finding is that
targets in displays which were found experimentally to be processed
in parallel can be detected by the system, while targets in
experimentally-serial displays cannot. This fundamental difference
is explained via variance analysis of the compressed
representations, providing a numerical criterion distinguishing
parallel from serial displays. Our model yields a mapping of
response-time slopes that is similar to Duncan and Humphreys's
"search surface" [1989], providing an explicit formulation of their
intuitive notion of feature similarity. It presents a neural
realization of the processing that may underlie the classical
metaphorical explanations of visual search.
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