Bayesian Models of Attention

Angela J. Yu

in The Oxford Handbook of Attention

Published in print January 2014 | ISBN: 9780199675111
Published online April 2014 | e-ISBN: 9780191753015 | DOI:
Bayesian Models of Attention

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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.

Keywords: attentional selection; Bayesian inference; behavioural goals; information processing; action selection; attention for learning; covert attention; overt attention

Article.  17744 words. 

Subjects: Cognitive Neuroscience

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