Author:

Keywords:

Expectancy Valence Model, Bayesian Hierarchical Modeling, Cognitive Modeling, Reinforcement Learning, Science & Technology, Social Sciences, Physical Sciences, Mathematics, Interdisciplinary Applications, Social Sciences, Mathematical Methods, Psychology, Mathematical, Mathematics, Mathematical Methods In Social Sciences, Psychology, Expectancy Valence model, Bayesian hierarchical modeling, Cognitive modeling, Reinforcement learning, DECISION-MAKING, INDIVIDUAL-DIFFERENCES, STATISTICAL-INFERENCE, COGNITIVE MODELS, SIGNAL-DETECTION, PERFORMANCE, DEFICITS, ABUSERS, ADULTS, 0102 Applied Mathematics, 1701 Psychology, 1702 Cognitive Sciences, Experimental Psychology, 4901 Applied mathematics, 5204 Cognitive and computational psychology

Abstract:

The purpose of the popular Iowa gambling task is to study decision making deficits in clinical populations by mimicking real–life decision making in an experimental context. Busemeyer and Stout (2002) proposed an "Expectancy Valence" reinforcement learning model that estimates three latent components which are assumed to jointly determine choice behavior in the Iowa gambling task: weighing of wins versus losses, memory for past payoffs, and response consistency. In this article we explore the statistical properties of the Expectancy Valence model. We first demonstrate the difficulty of applying the model on the level of a single participant, we then propose and implement a Bayesian hierarchical estimation procedure to coherently combine information from different participants, and we finally apply the Bayesian estimation procedure to data from an experiment designed to provide a test of specific influence.