Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data

Jan P Gläscher; John P O'Doherty

doi:10.1002/wcs.57

Model-based approaches to neuroimaging

Standard

Model-based approaches to neuroimaging : combining reinforcement learning theory with fMRI data. / Gläscher, Jan P; O'Doherty, John P.

In: WIRES COGN SCI, Vol. 1, No. 4, 07.2010, p. 501-10.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Gläscher, JP & O'Doherty, JP 2010, 'Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data', WIRES COGN SCI, vol. 1, no. 4, pp. 501-10. https://doi.org/10.1002/wcs.57

APA

Gläscher, J. P., & O'Doherty, J. P. (2010). Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data. WIRES COGN SCI, 1(4), 501-10. https://doi.org/10.1002/wcs.57

Vancouver

Gläscher JP, O'Doherty JP. Model-based approaches to neuroimaging: combining reinforcement learning theory with fMRI data. WIRES COGN SCI. 2010 Jul;1(4):501-10. https://doi.org/10.1002/wcs.57

Bibtex

@article{02444c576206460e83239222bc5fd4ae,

title = "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 {\textcopyright} 2010 John Wiley & Sons, Ltd. For further resources related to this article, please visit the WIREs website.",

author = "Gl{\"a}scher, {Jan P} and O'Doherty, {John P}",

note = "Copyright {\textcopyright} 2010 John Wiley & Sons, Ltd.",

year = "2010",

month = jul,

doi = "10.1002/wcs.57",

language = "English",

volume = "1",

pages = "501--10",

journal = "WIRES COGN SCI",

issn = "1939-5078",

publisher = "John Wiley and Sons Inc.",

number = "4",

}

RIS

TY - JOUR

T1 - Model-based approaches to neuroimaging

T2 - combining reinforcement learning theory with fMRI data

AU - Gläscher, Jan P

AU - O'Doherty, John P

PY - 2010/7

Y1 - 2010/7

N2 - 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. For further resources related to this article, please visit the WIREs website.

AB - 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. For further resources related to this article, please visit the WIREs website.

U2 - 10.1002/wcs.57

DO - 10.1002/wcs.57

M3 - SCORING: Journal article

C2 - 26271497

VL - 1

SP - 501

EP - 510

JO - WIRES COGN SCI

JF - WIRES COGN SCI

SN - 1939-5078

IS - 4

ER -