ArticleNeural computation2022
A Normative Account of Confirmation Bias During Reinforcement Learning.
Article in Neural computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- Competing cognitive pressures on human exploration in the absence of trade-off with exploitation.Nature communications · 2026Article
- Uncertainty and reward histories have distinct effects on decisions after wins and losses.Scientific reports · 2026Article
- Neurocomputational mechanisms of social presence effects on instrumental learning.Communications biology · 2025Article
- Debiasing training reduces confirmation bias in national risk analysts.Scientific reports · 2025Article
- Asymmetric learning and adaptability to changes in relational structure during transitive inference.Communications psychology · 2025Article
- Apparent learning biases emerge from optimal inference: Insights from master equation analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- An Information-Theoretic Framework for Understanding Learning and Choice Under Uncertainty.Entropy (Basel, Switzerland) · 2025Article
- Evolving choice hysteresis in reinforcement learning: Comparing the adaptive value of positivity bias and gradual perseveration.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Understanding learning through uncertainty and bias.Communications psychology · 2025Review
- Human reinforcement learning processes and biases: computational characterization and possible applications to behavioral public policy.Mind & society · 2025Article
- Moderate confirmation bias enhances decision-making in groups of reinforcement-learning agents.PLoS computational biology · 2024Article
- Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts.PLoS computational biology · 2024Article
- The roots of polarization in the individual reward system.Proceedings. Biological sciences · 2024Article
- Article
- Confirmatory reinforcement learning changes with age during adolescence.Developmental science · 2023Article
- Efficient stabilization of imprecise statistical inference through conditional belief updating.Nature human behaviour · 2022Article
- As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.Psychological review · 2022Article
- Flexibility in valenced reinforcement learning computations across development.Child development · 2022Article
- Model Sharing in the Human Medial Temporal Lobe.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Reinforcement learning involves updating estimates of the value of states and actions on the basis of experience. Previous work has shown that in humans, reinforcement learning exhibits a confirmatory bias: when the value of a chosen option is being updated, estimates are revised more radically following positive than negative reward prediction errors, but the converse is observed when updating the unchosen option value estimate. Here, we simulate performance on a multi-arm bandit task to examine the consequences of a confirmatory bias for reward harvesting. We report a paradoxical finding: that confirmatory biases allow the agent to maximize reward relative to an unbiased updating rule. This principle holds over a wide range of experimental settings and is most influential when decisions are corrupted by noise. We show that this occurs because on average, confirmatory biases lead to overestimating the value of more valuable bandits and underestimating the value of less valuable bandits, rendering decisions overall more robust in the face of noise. Our results show how apparently suboptimal learning rules can in fact be reward maximizing if decisions are made with finite computational precision.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.