Close
This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Return to Session

A hierarchical Bayesian model for the progressive ratio test

Authors
Ms. Yiyang Chen
The Ohio State University ~ Psychology
Nicholas Breitborde
The Ohio State University, United States of America
Mario Peruggia
The Ohio State University, United States of America
Trisha Van Zandt
Ohio State University ~ Department of Psychology
Abstract

The progressive ratio test (Wolf et al., 2014) is commonly used to measure motivation, yet the number of studies investigating its underlying mechanisms is limited. In this paper, we present a hierarchical Bayesian model for the progressive ratio structure. This model may be used to investigate the underlying mechanisms of human behavior in progressive ratio tests, which can identify the factors contributing to participants' performance. A simulation study shows satisfactory parameter recovery results for this model. We apply the model to a progressive ratio data set involving people with schizophrenia, first-order relatives of the schizophrenia patients, and people without schizophrenia. Analysis reveals that the motivation of people with schizophrenia decreases faster as time elapses than that of people without schizophrenia, which may make them less compliant with long continuous treatment sessions.

Tags

Keywords

Bayesian Modeling
Cognitive Modeling

Topics

Cognitive Modeling
Bayesian Modeling
Discussion
New

There is nothing here, yet. Be the first to create a thread