Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Imperfect Bayesian inference in visual perception
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Psychology.
2019 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 4, article id e1006465Article in journal (Refereed) Published
Abstract [en]

The main task of perceptual systems is to make truthful inferences about the environment. The sensory input to these systems is often astonishingly imprecise, which makes human perception prone to error. Nevertheless, numerous studies have reported that humans often perform as accurately as is possible given these sensory imprecisions. This suggests that the brain makes optimal use of the sensory input and computes without error. The validity of this claim has recently been questioned for two reasons. First, it has been argued that a lot of the evidence for optimality comes from studies that used overly flexible models. Second, optimality in human perception is implausible due to limitations inherent to neural systems. In this study, we reconsider optimality in a standard visual perception task by devising a research method that addresses both concerns. In contrast to previous studies, we find clear indications of suboptimalities. Our data are best explained by a model that is based on the optimal decision strategy, but with imperfections in its execution.

Place, publisher, year, edition, pages
2019. Vol. 15, no 4, article id e1006465
National Category
Psychology
Identifiers
URN: urn:nbn:se:uu:diva-382226DOI: 10.1371/journal.pcbi.1006465ISI: 000467530600005PubMedID: 30998675OAI: oai:DiVA.org:uu-382226DiVA, id: diva2:1306371
Funder
Swedish Research Council, 2015-00371Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-06-05Bibliographically approved

Open Access in DiVA

fulltext(2471 kB)37 downloads
File information
File name FULLTEXT01.pdfFile size 2471 kBChecksum SHA-512
2124eacb7db1db62939aa6b57dfba1d614f344e24ebd5cce0af730748dfa0b2431e5702f9c331fb800aabc2fe2727bd15b77dc2fa5f35e45e76765b65cae6f12
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Stengård, ElinaVan den Berg, Ronald
By organisation
Department of Psychology
In the same journal
PloS Computational Biology
Psychology

Search outside of DiVA

GoogleGoogle Scholar
Total: 37 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 51 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf