View in EDS PDF Full Text

Attention is more than prediction precision.

Saved in:
Bibliographic Details
Title: Attention is more than prediction precision.
Authors: Bowman, Howard, Filetti, Marco, Wyble, Brad, Olivers, Christian
Source: Behavioral & Brain Sciences. Jun2013, Vol. 36 Issue 3, p206-208. 3p.
Subjects: Attention, Prediction models, Coding theory, Evoked potentials (Electrophysiology), Stimulus & response (Biology), Sensory neurons, Brain mapping
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. [ABSTRACT FROM AUTHOR]
Copyright of Behavioral & Brain Sciences is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
FullText Links:
  – Type: pdflink
Text:
  Availability: 0
Header DbId: pbh
DbLabel: Psychology and Behavioral Sciences Collection
An: 87581821
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Attention is more than prediction precision.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Bowman%2C+Howard%22">Bowman, Howard</searchLink><br /><searchLink fieldCode="AR" term="%22Filetti%2C+Marco%22">Filetti, Marco</searchLink><br /><searchLink fieldCode="AR" term="%22Wyble%2C+Brad%22">Wyble, Brad</searchLink><br /><searchLink fieldCode="AR" term="%22Olivers%2C+Christian%22">Olivers, Christian</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Behavioral+%26+Brain+Sciences%22">Behavioral & Brain Sciences</searchLink>. Jun2013, Vol. 36 Issue 3, p206-208. 3p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Coding+theory%22">Coding theory</searchLink><br /><searchLink fieldCode="DE" term="%22Evoked+potentials+%28Electrophysiology%29%22">Evoked potentials (Electrophysiology)</searchLink><br /><searchLink fieldCode="DE" term="%22Stimulus+%26+response+%28Biology%29%22">Stimulus & response (Biology)</searchLink><br /><searchLink fieldCode="DE" term="%22Sensory+neurons%22">Sensory neurons</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+mapping%22">Brain mapping</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Behavioral & Brain Sciences is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=87581821
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1017/S0140525X12002324
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 3
        StartPage: 206
    Subjects:
      – SubjectFull: Attention
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Coding theory
        Type: general
      – SubjectFull: Evoked potentials (Electrophysiology)
        Type: general
      – SubjectFull: Stimulus & response (Biology)
        Type: general
      – SubjectFull: Sensory neurons
        Type: general
      – SubjectFull: Brain mapping
        Type: general
    Titles:
      – TitleFull: Attention is more than prediction precision.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Bowman, Howard
      – PersonEntity:
          Name:
            NameFull: Filetti, Marco
      – PersonEntity:
          Name:
            NameFull: Wyble, Brad
      – PersonEntity:
          Name:
            NameFull: Olivers, Christian
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2013
              Type: published
              Y: 2013
          Identifiers:
            – Type: issn-print
              Value: 0140525X
          Numbering:
            – Type: volume
              Value: 36
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Behavioral & Brain Sciences
              Type: main
ResultId 1