MODELING VISUAL SEARCH: EVOLVING THE SELECTIVE ATTENTION FOR IDENTIFICATION MODEL (SAIM)

We present an extension of the Selective Attention for Identification model (SAIM) [1] in which feature extraction processes are incorporated. We show that the new version successfully models experimental results from visual search. We also predict the influence of a target cue on search. This extended version of SAIM may provide a powerful framework for understanding human visual attention.