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Computational modeling of behavioral tasks: An illustration on a classic reinforcement learning paradigm

Authors :
Suthaharan, Praveen
Corlett, Philip R.
Ang, Yuen-Siang
Source :
Tutorials in Quantitative Methods for Psychology, Vol 17, Iss 2, Pp 105-140 (2021)
Publication Year :
2021
Publisher :
Université d'Ottawa, 2021.

Abstract

There has been a growing interest among psychologists, psychiatrists and neuroscientists in applying computational modeling to behavioral data to understand animal and human behavior. Such approaches can be daunting for those without experience. This paper presents a step-by-step tutorial to conduct parameter estimation in R via three techniques: Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP) and Expectation-Maximization with Laplace approximation (EML). We first demonstrate how to simulate a classic reinforcement learning paradigm -- the two-armed bandit task -- for N = 100 subjects; and then explain how to develop the computational model and implement the MLE, MAP and EML methods to recover the parameters. By presenting a sufficiently detailed walkthrough on a familiar behavioral task, we hope this tutorial could benefit readers interested in applying parameter estimation methods in their own research.

Details

Language :
English, French
ISSN :
19134126
Volume :
17
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Tutorials in Quantitative Methods for Psychology
Publication Type :
Academic Journal
Accession number :
edsdoj.2294717180243ae8da2183d8b066456
Document Type :
article
Full Text :
https://doi.org/10.20982/tqmp.17.2.p105