Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
2005 Special Issue: Modelling divided visual attention with a winner-take-all network
Neural Networks - 2005 Special issue: IJCNN 2005
Journal of Cognitive Neuroscience
Towards cortex sized artificial neural systems
Neural Networks
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Centre-Surround Neural Field (CSNF) models were used to explain a possible mechanism by which information from different sources may be integrated into target likelihood maps that are then used to direct eye saccades. The CSNF model is a dynamic model in which each region in network excites near-by location and inhibits distant locations, thereby modeling competition for eye movements (saccades). The CSNF model was tested in a number of conditions analogous to a naturalistic search task in which the target was either (1) present in the expected location, (2) present in the unexpected location, or (3) absent. Simulations showed that the model predicted a pattern of accuracy results similar to those obtained by [Eckstein, M. P., Drescher, B. A., & Shimozaki, S. S. (2006). Attentional cues in real scenes, saccadic targeting, and Bayesian priors. Psychological Science, 17(11), 973-980] from human participants. However, the model predicts different saccadic latencies between conditions where Eckstein, Drescher, and Shimozaki (2006) found no significant differences. These discrepancies between model predictions and behavioural results are discussed. Additional simulations indicated that these models can also capture the qualitative flavor of eye movements in conditions with multiple targets as compared to [Findlay, J. M. (1997). Saccade target selection during visual search. Vision Research, 37(5), 617-631].