ArticleCurrent directions in psychological science2024
Hidden Reward: Affect and Its Prediction Errors as Windows Into Subjective Value.
Article in Current directions in psychological science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Emotional Context Modulates Neural Prediction-Error Encoding.Psychophysiology · 2026Article
- A dynamic affective surprise signal influences episodic memory.Cognition · 2026Article
- An Integrative Appraisal Model of Epistemic Curiosity.Affective science · 2025Review
- Self-utility distance as a computational approach to understanding self-concept clarity.Communications psychology · 2025Article
- Time matters: on the predictive power of current, short- and long-term expected valence in an experience based learning task.Frontiers in psychology · 2025Article
- Wanting to matter and learning to care: A neurodevelopmental window of opportunity for (Pro) social learning?Developmental cognitive neuroscience · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Scientists increasingly apply concepts from reinforcement learning to affect, but which concepts should apply? And what can their application reveal that we cannot know from directly observable states? An important reinforcement learning concept is the difference between reward expectations and outcomes. Such reward prediction errors have become foundational to research on adaptive behavior in humans, animals, and machines. Owing to historical focus on animal models and observable reward (e.g., food or money), however, relatively little attention has been paid to the fact that humans can additionally report correspondingly expected and experienced affect (e.g., feelings). Reflecting a broader "rise of affectivism," attention has started to shift, revealing explanatory power of expected and experienced feelings-including prediction errors-above and beyond observable reward. We propose that applying concepts from reinforcement learning to affect holds promise for elucidating subjective value. Simultaneously, we urge scientists to test-rather than inherit-concepts that may not apply directly.
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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.