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

摘要

We present an extension of the Selective Attention for Identification model (SAIM) 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