ArticleScientific reports2021
Dissociation between asymmetric value updating and perseverance in human reinforcement learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Deterministic dynamics of distributional multi-agent reinforcement learning.PLoS computational biology · 2026Article
- Disinformation elicits learning biases.eLife · 2026Article
- Attention modulates value normalization in human reinforcement learning by shaping reward encoding.Nature communications · 2026Article
- Context induces distortions in value representations across multiple elicitation methods and learning modalities.Nature communications · 2026Article
- Asymmetric Reinforcement Learning Explains Human Choice Patterns in Decision-making Under Risk.bioRxiv : the preprint server for biology · 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
- A habit and working memory model as an alternative account of human reward-based learning.Nature human behaviour · 2026Article
- Information uncertainty influences learning strategy from sequentially delayed rewards.PLoS computational biology · 2026Article
- Uncertainty and reward histories have distinct effects on decisions after wins and losses.Scientific reports · 2026Article
- How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently.Journal of experimental psychology. General · 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
- Article
- Dual process impairments in reinforcement learning and working memory systems underlie learning deficits in physiological anxiety.PLoS computational biology · 2025Article
- Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning.PLoS computational biology · 2025Article
- Beyond reward learning deficits: Exploration-exploitation instability reveals computational heterogeneity in value-based decision making in early psychosis.medRxiv : the preprint server for health sciences · 2025Article
- Eating disorder symptoms and emotional arousal modulate food biases during reward learning in females.Nature communications · 2025Article
- Externally Provided Rewards Increase Internal Preference, but Not as Much as Preferred Ones Without Extrinsic Rewards.Computational brain & behavior · 2025Article
- Individual-specific strategies inform category learning.Scientific reports · 2025Article
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2 authors.
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Abstract
The learning rate is a key parameter in reinforcement learning that determines the extent to which novel information (outcome) is incorporated in guiding subsequent actions. Numerous studies have reported that the magnitude of the learning rate in human reinforcement learning is biased depending on the sign of the reward prediction error. However, this asymmetry can be observed as a statistical bias if the fitted model ignores the choice autocorrelation (perseverance), which is independent of the outcomes. Therefore, to investigate the genuine process underlying human choice behavior using empirical data, one should dissociate asymmetry in learning and perseverance from choice behavior. The present study addresses this issue by using a Hybrid model incorporating asymmetric learning rates and perseverance. First, by conducting simulations, we demonstrate that the Hybrid model can identify the true underlying process. Second, using the Hybrid model, we show that empirical data collected from a web-based experiment are governed by perseverance rather than asymmetric learning. Finally, we apply the Hybrid model to two open datasets in which asymmetric learning was reported. As a result, the asymmetric learning rate was validated in one dataset but not another.
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