Top-down guidance of visual search: A computational account

We present a revised version of the Selective Attention for Identification Model (SAIM), using an initial feature detection process to code edge orientations. We show that the revised SAIM can simulate both efficient and inefficient human search, that it shows search asymmetries, and that top-down expectancies for targets play a major role in the model's selection. Predictions of the model for top-down effects are tested with human participants, and important similarities and dissimilarities are discussed.