HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python
The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation m...
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| Published in: | Frontiers in neuroinformatics Vol. 7; p. 14 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Switzerland
Frontiers Research Foundation
01.01.2013
Frontiers Media S.A |
| Subjects: | |
| ISSN: | 1662-5196, 1662-5196 |
| Online Access: | Get full text |
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| Abstract | The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/ |
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| AbstractList | The diffusion model is a commonly used tool to infer latent psychological processes underlying decision making, and to link them to neural mechanisms based on reaction times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of reaction time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject / condition than non-hierarchical method, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g. fMRI) influence decision making parameters. This paper will first describe the theoretical background of drift-diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the chi-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/ The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ2-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/ The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation methods are useful for enhancing statistical power, allowing for simultaneous estimation of individual subject parameters and the group distribution that they are drawn from, while also providing measures of uncertainty in these parameters in the posterior distribution. Here, we present a novel Python-based toolbox called HDDM (hierarchical drift diffusion model), which allows fast and flexible estimation of the the drift-diffusion model and the related linear ballistic accumulator model. HDDM requires fewer data per subject/condition than non-hierarchical methods, allows for full Bayesian data analysis, and can handle outliers in the data. Finally, HDDM supports the estimation of how trial-by-trial measurements (e.g., fMRI) influence decision-making parameters. This paper will first describe the theoretical background of the drift diffusion model and Bayesian inference. We then illustrate usage of the toolbox on a real-world data set from our lab. Finally, parameter recovery studies show that HDDM beats alternative fitting methods like the χ(2)-quantile method as well as maximum likelihood estimation. The software and documentation can be downloaded at: http://ski.clps.brown.edu/hddm_docs/ |
| Author | Sofer, Imri Wiecki, Thomas V. Frank, Michael J. |
| AuthorAffiliation | Department of Cognitive, Linguistic and Psychological Sciences, Brown University Providence, RI, USA |
| AuthorAffiliation_xml | – name: Department of Cognitive, Linguistic and Psychological Sciences, Brown University Providence, RI, USA |
| Author_xml | – sequence: 1 givenname: Thomas V. surname: Wiecki fullname: Wiecki, Thomas V. – sequence: 2 givenname: Imri surname: Sofer fullname: Sofer, Imri – sequence: 3 givenname: Michael J. surname: Frank fullname: Frank, Michael J. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23935581$$D View this record in MEDLINE/PubMed |
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| Copyright | 2013. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2013 Wiecki, Sofer and Frank. 2013 |
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| Keywords | drift diffusion model software decision-making Bayesian modeling Python |
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| References | 22295983 - Neural Comput. 2012 May;24(5):1186-229 18183889 - Behav Res Methods. 2007 Nov;39(4):767-75 18085991 - Neural Comput. 2008 Apr;20(4):873-922 12412886 - Psychon Bull Rev. 2002 Sep;9(3):438-81 21585453 - Cogn Sci. 2008 Dec;32(8):1248-84 5416868 - Ergonomics. 1970 Jan;13(1):37-58 21603108 - J Stat Softw. 2010 Jul;35(4):1-81 19815782 - Psychon Bull Rev. 2009 Oct;16(5):798-817 21299302 - Psychol Methods. 2011 Mar;16(1):44-62 20064637 - Cogn Psychol. 2010 May;60(3):158-89 21471371 - J Neurosci. 2011 Apr 6;31(14):5365-77 15036882 - Trends Neurosci. 2004 Mar;27(3):161-8 18209015 - Biostatistics. 2008 Jul;9(3):523-39 18243170 - Cogn Psychol. 2008 Nov;57(3):153-78 22131410 - J Neurosci. 2011 Nov 30;31(48):17488-95 21946325 - Nat Neurosci. 2011 Sep 25;14(11):1462-7 18981414 - Proc Natl Acad Sci U S A. 2008 Nov 11;105(45):17538-42 19342495 - Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6539-44 18411528 - Behav Res Methods. 2008 Feb;40(1):61-72 19763151 - Nat Rev Genet. 2009 Oct;10(10):681-90 |
| References_xml | – reference: 18209015 - Biostatistics. 2008 Jul;9(3):523-39 – reference: 19763151 - Nat Rev Genet. 2009 Oct;10(10):681-90 – reference: 18085991 - Neural Comput. 2008 Apr;20(4):873-922 – reference: 5416868 - Ergonomics. 1970 Jan;13(1):37-58 – reference: 12412886 - Psychon Bull Rev. 2002 Sep;9(3):438-81 – reference: 18981414 - Proc Natl Acad Sci U S A. 2008 Nov 11;105(45):17538-42 – reference: 21299302 - Psychol Methods. 2011 Mar;16(1):44-62 – reference: 21585453 - Cogn Sci. 2008 Dec;32(8):1248-84 – reference: 21471371 - J Neurosci. 2011 Apr 6;31(14):5365-77 – reference: 18183889 - Behav Res Methods. 2007 Nov;39(4):767-75 – reference: 19815782 - Psychon Bull Rev. 2009 Oct;16(5):798-817 – reference: 21946325 - Nat Neurosci. 2011 Sep 25;14(11):1462-7 – reference: 22295983 - Neural Comput. 2012 May;24(5):1186-229 – reference: 19342495 - Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6539-44 – reference: 15036882 - Trends Neurosci. 2004 Mar;27(3):161-8 – reference: 18411528 - Behav Res Methods. 2008 Feb;40(1):61-72 – reference: 20064637 - Cogn Psychol. 2010 May;60(3):158-89 – reference: 18243170 - Cogn Psychol. 2008 Nov;57(3):153-78 – reference: 21603108 - J Stat Softw. 2010 Jul;35(4):1-81 – reference: 22131410 - J Neurosci. 2011 Nov 30;31(48):17488-95 |
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| SubjectTerms | Bayesian analysis Bayesian modeling Computer programs Decision Making Diffusion Drift Drift diffusion model Economic models Functional magnetic resonance imaging Hypothesis testing Methods Neuroscience Neurosciences Open source software Parameter estimation python Quantitative psychology Software |
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| Title | HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python |
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