English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Humans actively sample evidence to support prior beliefs

Kaanders, P., Sepulveda, P., Folke, T., Ortoleva, P., & De Martino, B. (2022). Humans actively sample evidence to support prior beliefs. eLife, 11: e71768. doi:10.7554/eLife.71768.

Item is

Files

show Files

Locators

show
hide
Locator:
https://elifesciences.org/articles/71768 (Publisher version)
Description:
-
OA-Status:
Not specified

Creators

show
hide
 Creators:
Kaanders, P1, Author                 
Sepulveda, P, Author
Folke, T, Author
Ortoleva, P, Author
De Martino, B, Author
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: No one likes to be wrong. Previous research has shown that participants may underweight information incompatible with previous choices, a phenomenon called confirmation bias. In this paper, we argue that a similar bias exists in the way information is actively sought. We investigate how choice influences information gathering using a perceptual choice task and find that participants sample more information from a previously chosen alternative. Furthermore, the higher the confidence in the initial choice, the more biased information sampling becomes. As a consequence, when faced with the possibility of revising an earlier decision, participants are more likely to stick with their original choice, even when incorrect. Critically, we show that agency controls this phenomenon. The effect disappears in a fixed sampling condition where presentation of evidence is controlled by the experimenter, suggesting that the way in which confirmatory evidence is acquired critically impacts the decision process. These results suggest active information acquisition plays a critical role in the propagation of strongly held beliefs over time.

Details

show
hide
Language(s):
 Dates: 2022-04
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.71768
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: eLife
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge : eLife Sciences Publications
Pages: 29 Volume / Issue: 11 Sequence Number: e71768 Start / End Page: - Identifier: ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X