ArticleSocial cognitive and affective neuroscience2020
Using reinforcement learning models in social neuroscience: frameworks, pitfalls and suggestions of best practices.
Article in Social cognitive and affective neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.
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84 citing papers in PubMed.
- Dopamine and Temporal Discounting: Revisiting Pharmacology and Individual Differences.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Trial
- Pharmacological and pupillary evidence for the noradrenergic contribution to reinforcement learning in Parkinson's disease.Communications biology · 2025Trial
- Testosterone eliminates strategic prosocial behavior through impacting choice consistency in healthy males.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Trial
- Angiotensin blockade enhances motivational reward learning via enhancing striatal prediction error signaling and frontostriatal communication.Molecular psychiatry · 2023Trial
- Neurobehavioral differences in value-based decision making between people with cocaine use disorder and controls.Experimental and clinical psychopharmacology · 2026Article
- Impaired midfrontal‑motor theta phase synchronization characterizes maladaptive motivational behavior in people with obsessive‑compulsive disorder.PLoS biology · 2026Article
- Towards naturalistic social neuroscience: A multi-level framework integrating real-world phenotyping, neurobiology, and computational mechanisms.Cognitive, affective & behavioral neuroscience · 2026Review
- Reinforcement-learning signals support dynamic adaptive control during language switching.Communications biology · 2026Article
- Causal attributions shape the formation of novel ability self-beliefs.Communications psychology · 2026Article
- Recurrent Neural Network Exploration Strategies During Reinforcement Learning Depend on Network Capacity.Computational brain & behavior · 2026Article
- Does zero mean nothing? Investigating the attentional mechanism of the hidden-zero effect in risky decision-making.Psychonomic bulletin & review · 2026Article
- The neurocomputational mechanisms underlying the impact of social comparison on effort investment.Communications biology · 2026Article
- A Theoretical Framework for Studying the Phenomenon of Gaslighting.Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc · 2026Review
- Intention-Outcome Trade-Off in Moral Character Learning.Annals of the New York Academy of Sciences · 2026Article
- Estimating and learning personality traits of and from women with borderline personality disorder.Borderline personality disorder and emotion dysregulation · 2026Article
- Learn how to be effective: neural and computational insights into charity efficiency learning.Social cognitive and affective neuroscience · 2026Article
- Computational modeling reveals cognitive processes in simple rodent depression tests.Cell reports methods · 2025Article
- Learning from imagined experiences via an endogenous prediction error.Nature communications · 2025Article
- Do we advise as one likes? The alignment bias in social advice giving.PLoS computational biology · 2025Article
- Neurocomputational mechanisms of social presence effects on instrumental learning.Communications biology · 2025Article
24 more citing papers are in PubMed but not listed here.
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5 authors.
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Abstract
The recent years have witnessed a dramatic increase in the use of reinforcement learning (RL) models in social, cognitive and affective neuroscience. This approach, in combination with neuroimaging techniques such as functional magnetic resonance imaging, enables quantitative investigations into latent mechanistic processes. However, increased use of relatively complex computational approaches has led to potential misconceptions and imprecise interpretations. Here, we present a comprehensive framework for the examination of (social) decision-making with the simple Rescorla-Wagner RL model. We discuss common pitfalls in its application and provide practical suggestions. First, with simulation, we unpack the functional role of the learning rate and pinpoint what could easily go wrong when interpreting differences in the learning rate. Then, we discuss the inevitable collinearity between outcome and prediction error in RL models and provide suggestions of how to justify whether the observed neural activation is related to the prediction error rather than outcome valence. Finally, we suggest posterior predictive check is a crucial step after model comparison, and we articulate employing hierarchical modeling for parameter estimation. We aim to provide simple and scalable explanations and practical guidelines for employing RL models to assist both beginners and advanced users in better implementing and interpreting their model-based analyses.
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