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Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data

Cognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions can be well captured when the choice function i...

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Autores principales: Pedersen, Mads L., Frank, Michael J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811713/
https://www.ncbi.nlm.nih.gov/pubmed/35128308
http://dx.doi.org/10.1007/s42113-020-00084-w
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author Pedersen, Mads L.
Frank, Michael J.
author_facet Pedersen, Mads L.
Frank, Michael J.
author_sort Pedersen, Mads L.
collection PubMed
description Cognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions can be well captured when the choice function is replaced with a sequential sampling model such as the drift diffusion model. Hierarchical Bayesian parameter estimation further enhances the identifiability of distinct learning and choice parameters. One caveat is that these models can be time-consuming to build, sample from, and validate, especially when models include links between neural activations and model parameters. Here we describe a novel extension to the widely used hierarchical drift diffusion model (HDDM) toolbox, which facilitates flexible construction, estimation, and evaluation of the reinforcement learning drift diffusion model (RLDDM) using hierarchical Bayesian methods. We describe the types of experiments most applicable to the model and provide a tutorial to illustrate how to perform quantitative data analysis and model evaluation. Parameter recovery confirmed that the method can reliably estimate parameters with varying numbers of synthetic subjects and trials. We also show that the simultaneous estimation of learning and choice parameters can improve the sensitivity to detect brain–behavioral relationships, including the impact of learned values and fronto-basal ganglia activity patterns on dynamic decision parameters.
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spelling pubmed-88117132022-02-03 Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data Pedersen, Mads L. Frank, Michael J. Comput Brain Behav Article Cognitive models have been instrumental for generating insights into the brain processes underlying learning and decision making. In reinforcement learning it has recently been shown that not only choice proportions but also their latency distributions can be well captured when the choice function is replaced with a sequential sampling model such as the drift diffusion model. Hierarchical Bayesian parameter estimation further enhances the identifiability of distinct learning and choice parameters. One caveat is that these models can be time-consuming to build, sample from, and validate, especially when models include links between neural activations and model parameters. Here we describe a novel extension to the widely used hierarchical drift diffusion model (HDDM) toolbox, which facilitates flexible construction, estimation, and evaluation of the reinforcement learning drift diffusion model (RLDDM) using hierarchical Bayesian methods. We describe the types of experiments most applicable to the model and provide a tutorial to illustrate how to perform quantitative data analysis and model evaluation. Parameter recovery confirmed that the method can reliably estimate parameters with varying numbers of synthetic subjects and trials. We also show that the simultaneous estimation of learning and choice parameters can improve the sensitivity to detect brain–behavioral relationships, including the impact of learned values and fronto-basal ganglia activity patterns on dynamic decision parameters. 2020-12 2020-05-26 /pmc/articles/PMC8811713/ /pubmed/35128308 http://dx.doi.org/10.1007/s42113-020-00084-w Text en https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permiission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Pedersen, Mads L.
Frank, Michael J.
Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data
title Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data
title_full Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data
title_fullStr Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data
title_full_unstemmed Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data
title_short Simultaneous Hierarchical Bayesian Parameter Estimation for Reinforcement Learning and Drift Diffusion Models: a Tutorial and Links to Neural Data
title_sort simultaneous hierarchical bayesian parameter estimation for reinforcement learning and drift diffusion models: a tutorial and links to neural data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811713/
https://www.ncbi.nlm.nih.gov/pubmed/35128308
http://dx.doi.org/10.1007/s42113-020-00084-w
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