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Attention is more than prediction precision

Published online by Cambridge University Press:  10 May 2013

Howard Bowman ,
Marco Filetti ,
Brad Wyble  and
Christian Olivers
Howard Bowman
Affiliation:
Centre for Cognitive Neuroscience and Cognitive Systems, and the School of Computing, University of Kent at Canterbury, Kent CT2 7NF, United Kingdom. H.Bowman@kent.ac.ukM.Filetti@kent.ac.ukhttp://www.cs.kent.ac.uk/people/staff/hb5/http://www.cs.kent.ac.uk/people/rpg/mf266/
Marco Filetti
Affiliation:
Centre for Cognitive Neuroscience and Cognitive Systems, and the School of Computing, University of Kent at Canterbury, Kent CT2 7NF, United Kingdom. H.Bowman@kent.ac.ukM.Filetti@kent.ac.ukhttp://www.cs.kent.ac.uk/people/staff/hb5/http://www.cs.kent.ac.uk/people/rpg/mf266/
Brad Wyble
Affiliation:
Department of Psychology, Syracuse University, Syracuse, NY 13244. bwyble@gmail.comwww.bradwyble.com
Christian Olivers
Affiliation:
Department of Cognitive Psychology, Faculty of Psychology and Education, VU University Amsterdam, 1081 BT Amsterdam, The Netherlands. c.n.l.olivers@vu.nlhttp://olivers.cogpsy.nl
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Abstract

A cornerstone of the target article is that, in a predictive coding framework, attention can be modelled by weighting prediction error with a measure of precision. We argue that this is not a complete explanation, especially in the light of ERP (event-related potentials) data showing large evoked responses for frequently presented target stimuli, which thus are predicted.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 206 - 208
Copyright
Copyright © Cambridge University Press 2013 

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References

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