Using reinforcement learning models in social neuroscience: frameworks, pitfalls, and suggestions of best practices Article Swipe
Lei Zhang
,
Lukas Lengersdorff
,
Nace Mikuš
,
Jan Gläscher
,
Claus Lamm
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/uthw2
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/uthw2
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.
Related Topics
Concepts
Reinforcement learning
Computer science
Artificial intelligence
Neuroimaging
Social neuroscience
Functional magnetic resonance imaging
Machine learning
Cognitive neuroscience
Scalability
Cognitive science
Functional neuroimaging
Outcome (game theory)
Cognition
Psychology
Neuroscience
Social cognition
Mathematical economics
Database
Mathematics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31234/osf.io/uthw2
- https://psyarxiv.com/uthw2/download
- OA Status
- gold
- Cited By
- 19
- References
- 126
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4233779237
All OpenAlex metadata
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https://openalex.org/W4233779237Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.31234/osf.io/uthw2Digital Object Identifier
- Title
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Using reinforcement learning models in social neuroscience: frameworks, pitfalls, and suggestions of best practicesWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-11-06Full publication date if available
- Authors
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Lei Zhang, Lukas Lengersdorff, Nace Mikuš, Jan Gläscher, Claus LammList of authors in order
- Landing page
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https://doi.org/10.31234/osf.io/uthw2Publisher landing page
- PDF URL
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https://psyarxiv.com/uthw2/downloadDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://psyarxiv.com/uthw2/downloadDirect OA link when available
- Concepts
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Reinforcement learning, Computer science, Artificial intelligence, Neuroimaging, Social neuroscience, Functional magnetic resonance imaging, Machine learning, Cognitive neuroscience, Scalability, Cognitive science, Functional neuroimaging, Outcome (game theory), Cognition, Psychology, Neuroscience, Social cognition, Mathematical economics, Database, MathematicsTop concepts (fields/topics) attached by OpenAlex
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19Total citation count in OpenAlex
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2024: 4, 2023: 4, 2022: 8, 2021: 1, 2020: 1Per-year citation counts (last 5 years)
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126Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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