A coincidence detector neural network model of selective attention

Abstract

A computational model of selective attention is implemented to account for findings from an experiment on selective attention that was conducted. The model successfully reproduces the latency data of human participants by relying on the interaction between a bottom-up saliency map and the top-down influences from spatial and semantic goals. The model offers a biologically-plausible way of operationalizing perceptual load and provides insights about the possible brain mechanisms that underlie related empirical findings.


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