Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data
Abstract
The combination of functional magnetic resonance imaging (fMRI) with computational models for a given cognitive process provides a powerful framework for testing hypotheses about the neural computations underlying such processes in the brain. Here, we outline the steps involved in implementing this approach with reference to the application of reinforcement learning (RL) models that can account for human choice behavior during value‐based decision making. The model generates internal variables which can be used to construct fMRI predictor variables and regressed against individual subjects' fMRI data. The resulting regression coefficients reflect the strength of the correlation with blood oxygenation level dependent (BOLD) activity and the relevant internal variables from the model. In the second part of this review, we describe human neuroimaging studies that have employed this analysis strategy to identify brain regions involved in the computations mediating reward‐related decision making. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Neuroscience > Cognition
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Trial-by-trial estimates of computational quantities, such as prediction errors or precisions, can be used as parametric modulators in a general linear model of fMRI data, an approach commonly referred to as model-based fMRI.
“For example, trial-by-trial estimates of computational quantities, such as prediction errors or precisions, can be used as parametric modulators in a general linear model (GLM) of fMRI data, an approach commonly referred to as “model-based fMRI” (114).”
From Can Bayesian Theories of Autism Spectrum Disorder Help Improve Clinical Practice? (2016) · cites this paper as [114]
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- Authors
- Gläscher, J. P., & O'Doherty, J. P.
- Affiliations
- California Institute of Technology (US)
- ORCID
- Gläscher, Jan P.
- Venue
- WIREs Cognitive Science
- Publisher
- Wiley
- Published
- 2010-04-02
- Volume
- 1
- Issue
- 4
- Pages
- 501-510
- ISSN
- 1939-5078 (ISSN-L)
- 1939-5086
- OpenAlex
- W2016473481
- Cited by
- 104
- References
- 37
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 4 · 2023: 9 · 2022: 4 · 2021: 18 · 2020: 7 · 2019: 11 · 2018: 8 · 2017: 6 · 2016: 8 · 2015: 7 · 2014: 2 · 2013: 5 · 2012: 5
- FWCI
- 2.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Wiley Interdisciplinary Reviews Cognitive Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- reinforcement learning models, value-based decision making, computational psychiatry, BOLD signal, internal variables, neural computation, choice behavior
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