ArticleNature communications2018
Reference-point centering and range-adaptation enhance human reinforcement learning at the cost of irrational preferences.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- Article
- Article
- Attention modulates value normalization in human reinforcement learning by shaping reward encoding.Nature communications · 2026Article
- Reference-point dependent reinforcement learning in humans and rats.Nature communications · 2026Article
- Context induces distortions in value representations across multiple elicitation methods and learning modalities.Nature communications · 2026Article
- Hybrid neural-cognitive models reveal how memory shapes human reward learning.Nature human behaviour · 2026Article
- Brain network reconfiguration during reward prediction error processing.Network neuroscience (Cambridge, Mass.) · 2026Article
- Action repetition biases choice in context-dependent decision-making.Communications psychology · 2025Article
- Social inequity disrupts reward-based learning.Communications psychology · 2025Article
- Distributional dual-process model predicts strategic shifts in decision-making under uncertainty.Communications psychology · 2025Article
- Investigating Learning, Decision-Making, and Mental Health in Pregnancy: Insights From a UK Cohort Study.Computational psychiatry (Cambridge, Mass.) · 2025Article
- Relative Value Encoding in Large Language Models: A Multi-Task, Multi-Model Investigation.Open mind : discoveries in cognitive science · 2025Article
- Human reinforcement learning processes and biases: computational characterization and possible applications to behavioral public policy.Mind & society · 2025Article
- Comparing experience- and description-based economic preferences across 11 countries.Nature human behaviour · 2024Article
- Connected in Bad Times and in Good Times: Empathy Induces Stable Social Closeness.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Recent Opioid Use Impedes Range Adaptation in Reinforcement Learning in Human Addiction.Biological psychiatry · 2024Article
- Foraging in a non-foraging task: Fitness maximization explains human risk preference dynamics under changing environment.PLoS computational biology · 2024Article
- Neural and computational underpinnings of biased confidence in human reinforcement learning.Nature communications · 2023Article
- Article
- 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
5 authors.
Funding
Abstract
In economics and perceptual decision-making contextual effects are well documented, where decision weights are adjusted as a function of the distribution of stimuli. Yet, in reinforcement learning literature whether and how contextual information pertaining to decision states is integrated in learning algorithms has received comparably little attention. Here, we investigate reinforcement learning behavior and its computational substrates in a task where we orthogonally manipulate outcome valence and magnitude, resulting in systematic variations in state-values. Model comparison indicates that subjects' behavior is best accounted for by an algorithm which includes both reference point-dependence and range-adaptation-two crucial features of state-dependent valuation. In addition, we find that state-dependent outcome valuation progressively emerges, is favored by increasing outcome information and correlated with explicit understanding of the task structure. Finally, our data clearly show that, while being locally adaptive (for instance in negative valence and small magnitude contexts), state-dependent valuation comes at the cost of seemingly irrational choices, when options are extrapolated out from their original contexts.
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.