ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2015
The Good, the Bad, and the Irrelevant: Neural Mechanisms of Learning Real and Hypothetical Rewards and Effort.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- When piloting health services interventions, what predicts real world behaviours? A systematic concept mapping review.BMC medical research methodology · 2020Pooled it
- Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis.Human brain mapping · 2018Pooled it
- Neural signatures of risk-taking adaptions across health, bipolar disorder, and lithium treatment.Molecular psychiatry · 2025Trial
- Using functional connectivity changes associated with cognitive fatigue to delineate a fatigue network.Scientific reports · 2020Trial
- Beyond negative valence: 2-week administration of a serotonergic antidepressant enhances both reward and effort learning signals.PLoS biology · 2017Trial
- Environmental uncertainty shapes human effort learning.PLoS biology · 2026Article
- Temporal dynamics of effort discounting: The roles of cognitive load and depression.Cognitive, affective & behavioral neuroscience · 2026Article
- Active disambiguation guides inferring controllability and cause in social interactions.Nature communications · 2025Article
- Ventromedial prefrontal cortex lesions disrupt learning to reward others.Brain : a journal of neurology · 2025Article
- Illuminating the impact of stress: In vivo approaches to track stress-related neural adaptations.Neurobiology of stress · 2025Article
- Social Effort Discounting Reveals Domain-General and Social-Specific Motivation Components.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Distinct roles of the orbitofrontal cortex, ventral striatum, and dopamine neurons in counterfactual thinking of decision outcomes.Science advances · 2023Article
- Multiple routes of communication within the amygdala-mPFC network: A comparative approach in humans and macaques.Current research in neurobiology · 2023Review
- Review
- Altered motivation of effortful decision-making for self and others in subthreshold depression.Depression and anxiety · 2022Article
- The effect of apathy and compulsivity on planning and stopping in sequential decision-making.PLoS biology · 2022Article
- Prefrontal cortex interactions with the amygdala in primates.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Review
- Constructing Others' Beliefs from One's Own Using Medial Frontal Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Computational modelling of social cognition and behaviour-a reinforcement learning primer.Social cognitive and affective neuroscience · 2021Article
- Are visual working memory and episodic memory distinct processes? Insight from stroke patients by lesion-symptom mapping.Brain structure & function · 2021Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
Natural environments are complex, and a single choice can lead to multiple outcomes. Agents should learn which outcomes are due to their choices and therefore relevant for future decisions and which are stochastic in ways common to all choices and therefore irrelevant for future decisions between options. We designed an experiment in which human participants learned the varying reward and effort magnitudes of two options and repeatedly chose between them. The reward associated with a choice was randomly real or hypothetical (i.e., participants only sometimes received the reward magnitude associated with the chosen option). The real/hypothetical nature of the reward on any one trial was, however, irrelevant for learning the longer-term values of the choices, and participants ought to have only focused on the informational content of the outcome and disregarded whether it was a real or hypothetical reward. However, we found that participants showed an irrational choice bias, preferring choices that had previously led, by chance, to a real reward in the last trial. Amygdala and ventromedial prefrontal activity was related to the way in which participants' choices were biased by real reward receipt. By contrast, activity in dorsal anterior cingulate cortex, frontal operculum/anterior insula, and especially lateral anterior prefrontal cortex was related to the degree to which participants resisted this bias and chose effectively in a manner guided by aspects of outcomes that had real and more sustained relationships with particular choices, suppressing irrelevant reward information for more optimal learning and decision making. SIGNIFICANCE STATEMENT: In complex natural environments, a single choice can lead to multiple outcomes. Human agents should only learn from outcomes that are due to their choices, not from outcomes without such a relationship. We designed an experiment to measure learning about reward and effort magnitudes in an environment in which other features of the outcome were random and had no relationship with choice. We found that, although people could learn about reward magnitudes, they nevertheless were irrationally biased toward repeating certain choices as a function of the presence or absence of random reward features. Activity in different brain regions in the prefrontal cortex either reflected the bias or reflected resistance to the bias.
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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.