A Rational Account of Anchor Effects in Hindsight Bias

Samarie Wilson, Somya Arora, Qiong Zhang, Thomas L. Griffiths

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

Abstract

Hindsight bias is exhibited when knowledge of an outcome (i.e., an anchor) affects subsequent recollections of previous predictions (i.e., an estimate). Hindsight bias usually leads to estimates being remembered as closer to the anchor than they actually were. The exact amount of hindsight bias exhibited depends on the anchor value and the anchor plausibility, with experimental results showing that hindsight bias is elicited only when the anchor is perceived to be plausible. In this paper we present a Bayesian model that captures the relationship between hindsight bias and anchor plausibility. This model provides a rational account of hindsight bias by considering memory recall as a statistical problem, where the goal is to reconstruct the original estimate using the anchor as new evidence. Simulations show that the modeled trends align closely with previously published human data.

Original languageEnglish (US)
Pages1327-1332
Number of pages6
StatePublished - 2021
Event43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 - Virtual, Online, Austria
Duration: Jul 26 2021Jul 29 2021

Conference

Conference43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021
Country/TerritoryAustria
CityVirtual, Online
Period7/26/217/29/21

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction

Keywords

  • Bayesian inference
  • anchor plausibility
  • hindsight bias
  • prior
  • rational analysis

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