Projects per year
Abstract
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.
Original language | English |
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Pages (from-to) | 985-1005 |
Number of pages | 21 |
Journal | Visual Cognition |
Volume | 14 |
DOIs | |
Publication status | Published - 1 Aug 2006 |
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Dive into the research topics of 'Top down guidance of visual search: A computational account'. Together they form a unique fingerprint.Projects
- 2 Finished
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The Neural Architecture of Primate Visuospatial Attention
Humphreys, G.
Biotechnology & Biological Sciences Research Council
1/10/04 → 29/02/08
Project: Research Councils
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Memory and Selection in Vision: A Cognitive Neuroscience Approach
Humphreys, G. & Riddoch, J.
1/10/02 → 30/09/07
Project: Research Councils