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Learning and selective attention

Nature Neuroscience volume 3, pages 1218–1223 (2000)Cite this article

Abstract

Selective attention involves the differential processing of different stimuli, and has widespread psychological and neural consequences. Although computational modeling should offer a powerful way of linking observable phenomena at different levels, most work has focused on the relatively narrow issue of constraints on processing resources. By contrast, we consider statistical and informational aspects of selective attention, divorced from resource constraints, which are evident in animal conditioning experiments involving uncertain predictions and unreliable stimuli. Neuromodulatory systems and limbic structures are known to underlie attentional effects in such tasks.

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Figure 1: Uncertainty and unreliability.
Figure 2: Prediction with the Kalman filter up to time tF.
Figure 3: Architecture for prediction.
Figure 4: Competitive combination of predictions.

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Acknowledgements

We are grateful to Nathaniel Daw, Anthony Dickinson, Alex Kacelnik, Theresa Long, Terry Sejnowski, and Rich Sutton for discussions covering many of these issues, and particularly to Nathaniel Daw for comments on an earlier version of this paper. The authors' work is funded by the Gatsby Charitable Foundation (P.D. and S.K.), the National Science Foundation (S.K.) and NIDA (P.R.M.).

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Authors and Affiliations

  1. Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London, WC1N 3AR, UK

    Peter Dayan & Sham Kakade

  2. Division of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, 77030, Texas, USA

    P. Read Montague

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  1. Peter Dayan
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Correspondence to Peter Dayan.

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Dayan, P., Kakade, S. & Montague, P. Learning and selective attention. Nat Neurosci 3 (Suppl 11), 1218–1223 (2000). https://doi.org/10.1038/81504

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