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 language | English (US) |
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Pages | 1327-1332 |
Number of pages | 6 |
State | Published - 2021 |
Event | 43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 - Virtual, Online, Austria Duration: Jul 26 2021 → Jul 29 2021 |
Conference
Conference | 43rd Annual Meeting of the Cognitive Science Society: Comparative Cognition: Animal Minds, CogSci 2021 |
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Country/Territory | Austria |
City | Virtual, Online |
Period | 7/26/21 → 7/29/21 |
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