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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. © 2006 Psychology Press Ltd.

Original publication

DOI

10.1080/13506280500195482

Type

Journal article

Journal

Visual Cognition

Publication Date

01/08/2006

Volume

14

Pages

985 - 1005