Abstract
Humans can attend to and categorise objects individually, but also as groups. We present a computational model of how visual attention is allocated to single objects and groups of objects, and how single objects and groups are classified. We illustrate the model with a novel account of the role of stimulus similarity in visual search tasks, as identified by Duncan and Humphreys [1].
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Walles, H., Robins, A., Knott, A. (2013). A Neural Network Model of Visual Attention and Group Classification, and Its Performance in a Visual Search Task. In: Cranefield, S., Nayak, A. (eds) AI 2013: Advances in Artificial Intelligence. AI 2013. Lecture Notes in Computer Science(), vol 8272. Springer, Cham. https://doi.org/10.1007/978-3-319-03680-9_11
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DOI: https://doi.org/10.1007/978-3-319-03680-9_11
Publisher Name: Springer, Cham
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