Understanding Motivation with the Progressive Ratio Task: a Hierarchical Bayesian Model

Yiyang Chen, Nicholas J.K. Breitborde, Mario Peruggia, Trisha Van Zandt

Research output: Contribution to journalArticlepeer-review

Abstract

The progressive ratio task (e.g., Wolf et al., Schizophrenia Bulletin, 40(6):1328–1337, 2014) is often used to assess motivational deficits of individuals with mental health conditions, yet the number of studies investigating its underlying mechanisms is limited. In this paper, we present a hierarchical Bayesian model for the cognitive structure of the progressive ratio task. This model may be used to investigate the underlying mechanisms of human behavior in progressive ratio tasks, 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-degree relatives of people with schizophrenia, and people without schizophrenia. Our analysis reveals that people with schizophrenia are more affected by elapsed time than people without schizophrenia, tending to lose motivation to exert effort as they spend more time and effort in the task, regardless of the effort-reward ratio. The first-degree relatives show intermediate effects of time and effort-reward optimization between people with and without schizophrenia, which indicates that first-degree relatives might share some deficits with people with schizophrenia, only not as severe.

Original languageEnglish (US)
JournalComputational Brain and Behavior
DOIs
StateAccepted/In press - 2022

Keywords

  • Bayesian hierarchical modeling
  • Computational psychiatry
  • Individual differences
  • Motivation
  • Progressive ratio task
  • Schizophrenia

ASJC Scopus subject areas

  • Developmental and Educational Psychology
  • Neuropsychology and Physiological Psychology

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