A computational model of serial and parallel processing in visual search
Rachel F. Heaton
The following is a dissertation aimed at understanding what the various
phenomena in visual search teach us about the nature of human visual
representations and processes. I first review some of the major empirical
findings in the study of visual search. I next present a theory of visual
search in terms of what I believe these findings suggest about the
representations and processes underlying ventral visual processing. These
principles are instantiated in a computational model called CASPER (Concurrent
Attention: Serial and Parallel Evaluation with Relations), originally developed
by Hummel, that I have adapted to account for a range of phenomena in visual
search. I then describe an extension of the CASPER model to account for our
ability to search for visual items defined not simply by the features composing
those items but by the spatial relations among those features. Seven
experiments (four main experiments and three replications) are described that
test CASPER's predictions about relational search. Finally, I evaluate the fit
between CASPER's predictions and the empirical findings and show with three
additional simulations that CASPER can account for negative acceleration in
search functions for relational stimuli if one postulates that the visual
system is leveraging an emergent feature that bypasses relational processing.