title: Attention is more than prediction precision.
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.
authors.affiliation: Centre for Cognitive Neuroscience and Cognitive Systems, and School of Computing, University of Kent at Canterbury, Kent CT2 7NF, United Kingdom. H.Bowman@kent.ac.uk
authors.forename: Howard
authors.initials: H
authors.lastname: Bowman
authors.forename: Marco
authors.initials: M
authors.lastname: Filetti
authors.forename: Brad
authors.initials: B
authors.lastname: Wyble
authors.forename: Christian
authors.initials: C
authors.lastname: Olivers
mesh_terms: D001288:Attention / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology*; D019336:Cognitive Science / Q000639:trends*; D006801:Humans; D010465:Perception / Q000502:physiology*