title: Task-Dependent Learning of Attention.
abstract: In this article, we propose a neural network model for selective covert visual attention. This model can learn to focus its attention on important features depending on the task to be fulfilled by gating the flow of information from the lower to the higher levels of the visual system. The model is kept as simple as possible, but it is still capable of reproducing attentional behavior observed in psychological experiments. Computer simulations demonstrate that (1) it can learn categories to reduce reaction time without a decrease in performance, (2) the model reveals a performance similar to that of humans in feature and conjunction search, and (3) its learning dynamics are comparable with those of humans. Copyright 1997 Elsevier Science Ltd.
authors.affiliation: RWCP (Real World Computing Partnership) Novel Functions SNN (Foundation for Neural Networks) Laboratory, Department of Medical Physics and Biophysics, University of Nijmegen, Netherlands
authors.forename: Stan
authors.initials: S
authors.lastname: Gielen
authors.forename: Tom
authors.initials: T
authors.lastname: Heskes
authors.forename: Pierre
authors.initials: P
authors.lastname: van de Laar