The computational roots of positivity and confirmation biases in reinforcement learning

2022/05/31 by Stefano Palminteri, Maël Lebreton · 1 voice · 4 citations
Decision Sciences · Neuroscience · #Decision-Making and Behavioral Economics #Neural and Behavioral Psychology Studies #Neural dynamics and brain function

paper · doi:10.1016/j.tics.2022.04.005

openalex publication_date 2022/05/31 · crossref created 2022/05/31 · crossref issued 2022/07/01 · crossref published 2022/07/01 · crossref published-print 2022/07/01 · openalex created_date 2025/10/10 · crossref deposited 2025/10/23 · crossref indexed 2026/07/25 · openalex updated_date 2026/07/29

Citations

• Confirmation Bias: A Ubiquitous Phenomenon in Many Guises
• Acceptable losses: the debatable origins of loss aversion
• Habits without values.
• Prospect Theory: An Analysis of Decision under Risk
• Choice-confirmation bias and gradual perseveration in human reinforcement learning.

Cited by

• Even if suboptimal, novelty drives human exploration
• Disinformation elicits learning biases
• Noisy Memory Generates Value in Changing Environments
• Evolving choice hysteresis in reinforcement learning: Comparing the adaptive value of positivity bias and gradual perseveration

Discussions

• Exhibit #2: learning rate bias has been reported (by us and other groups) in volatile tasks or conditions where, normatively, learning rates should not decay, and, perhaps more importantly, empiricall [bsky, 3 points, 2 comments]