ArticlePLoS computational biology2017
Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 2 of them syntheses that pooled it.
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Who cites it
83 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Trust learning in the repeated trust game: A meta-analytic study.British journal of psychology (London, England : 1953) · 2026Pooled it
- Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling.Cognitive, affective & behavioral neuroscience · 2019Pooled it
- Dopamine D2-receptor blockade in humans disrupts the effect of effort on learning.PLoS biology · 2026Trial
- In Cocaine Dependence, Neural Prediction Errors During Loss Avoidance Are Increased With Cocaine Deprivation and Predict Drug Use.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2019Trial
- Prior preferences interfere with the associative learning of food values.Communications psychology · 2026Article
- Deterministic dynamics of distributional multi-agent reinforcement learning.PLoS computational biology · 2026Article
- Disinformation elicits learning biases.eLife · 2026Article
- Mechanistic corticostriatal circuit model predicts learning-dependent fMRI dynamics and individual reward bias in humans.bioRxiv : the preprint server for biology · 2026Article
- Eye of the beholder: Pupillary response reflects how subjective prior beliefs shape reinforcement learning with fake news.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Intention-Outcome Trade-Off in Moral Character Learning.Annals of the New York Academy of Sciences · 2026Article
- Excessive Flexibility? Recurrent Neural Networks Can Accommodate Individual Differences in Reinforcement Learning Through In-Context Adaptation.Computational brain & behavior · 2026Article
- Social information creates self-fulfilling prophecies in judgments of pain, vicarious pain, and cognitive effort.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Genuine learning biases persist after accounting for temporally decreasing learning rates: Insight from fitting six datasets.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Blaming luck, claiming skill: Self-attribution bias in error assignment.PLoS computational biology · 2025Article
- Asymmetric learning and adaptability to changes in relational structure during transitive inference.Communications psychology · 2025Article
- Learning reduces ingroup bias more with perceived losses than gains across cultures.NPJ science of learning · 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
- Ventromedial prefrontal cortex lesions disrupt learning to reward others.Brain : a journal of neurology · 2025Article
- Novelty as a drive of human exploration in complex stochastic environments.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning. We carried out two experiments: in the factual learning experiment, participants learned from partial feedback (i.e., the outcome of the chosen option only); in the counterfactual learning experiment, participants learned from complete feedback information (i.e., the outcomes of both the chosen and unchosen option were displayed). In the factual learning experiment, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account, relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice.
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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.