Modeling visual search experiments: Selective Attention for Identification Model (SAIM)
Dietmar Heinke, Glyn W. Humphreys
Abstract Draft version
We have presented a computational model called Selective Attention for Identification Model (SAIM), that can account for a broad range of psychological and neuropsychological phenomena on attention (Heinke & Humphreys, in press). In this paper we report on work that extends SAIM to model data from visual search tasks. The results show that SAIM can capture important aspects of findings in visual search experiments, including variations of search slopes with the similarity between targets and distractors. SAIM is also capable of simulating experimantal findings for redundant targets, including the violation of the Miller inequality. SAIM explains these experimental findings as the consequence of competitive processes involved in object identification.
- Heinke, D. and Humphreys, G. W. (2003).
- Attention, spatial representation and visual neglect: Simulating emergent attention and spatial memory in the Selective Attention for Identification Model (SAIM).
Psychological Review, 110(1):29-87. Abstract Complete draft (1.3MB)
Neurocomputing, 44:811-816, 2002.