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Fitting Human Decision Making Models using Python

Alejandro Weinstein
Universidad de Valparaiso, Chile
Advanced Center for Electrical and Electronic Engineering

Wael El-Deredy
Universidad de Valparaiso, Chile
Advanced Center for Electrical and Electronic Engineering

Stéren Chabert
Universidad de Valparaiso, Chile

Myriam Fuentes
Universidad de Valparaiso, Chile

Abstract

A topic of interest in experimental psychology and cognitive neuroscience is to understand how humans make decisions. A common approach involves using computational models to represent the decision making process, and use the model parameters to analyze brain imaging data. These computational models are based on the Reinforcement Learning (RL) paradigm, where an agent learns to make decisions based on the difference between what it expects and what it gets each time it interacts with the environment. In the typical experimental setup, subjects are presented with a set of options, each one associated to different numerical rewards. The task for each subject is to learn, by taking a series of sequential actions, which option maximizes their total reward. The sequence of actions made by the subject and the obtained rewards are used to fit a parametric RL model. The model is fit by maximizing the likelihood of the parameters given the experiment data. In this work we present a Python implementation of this model fitting procedure. We extend the implementation to fit a model of the experimental setup known as the \textquotedbl{}contextual bandit\textquotedbl{}, where the probabilities of the outcome change from trial to trial depending on a predictive cue. We also developed an artificial agent that can simulate the behavior of a human making decisions under the RL paradigm. We use this artificial agent to validate the model fitting by comparing the parameters estimated from the data with the known agent parameters. We also present the results of a model fitted with experimental data. We use the standard scientific Python stack (NumPy/SciPy) to compute the likelihood function and to find its maximum. The code organization allows to easily change the RL model. We also use the Seaborn library to create a visualization with the behavior of all the subjects. The simulation results validate the correctness of the implementation. The experimental results shows the usefulness and simplicity of the program when working with experimental data. The source code of the program is available at https://github.com/aweinstein/FHDMM.

Keywords

decision making modeling, reinforcement learning

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