Assessing the Potential of Computational Modeling in Clinical Science

• Type of Material: Article
• Authors: Peter Frank Hitchcock, Emory University; Angela Radulescu, Preinceton Unversity; Yael Niv, Princeton University; Chris R. Sims, Drexel University
• Language: English
• Date: 2017-06
• Publisher: ResearchGate
• Publication Version: Preprint (Prior to Peer Review)
• Copyright Statement: Authors
• Final Published Version (URL): https://www.researchgate.net/publication/315896048_Assessing_the_Potential_of_Computational_Modeling_in_Clinical_Science
• Title of Journal or Parent Work: ResearchGate
• Grant/Funding Information: This work was supported by ARO grant W911NF-14-1-0101 (YN), NIMH grant R01MH098861 (YN & AR), and NSF research grant DRL-1560829 (CRS).
• Keywords: test-retest reliability; trial-by-trial modeling; psychometrics; computational psychiatry; individual differences
• Research Categories: Psychology, Developmental; Biology, Neuroscience

Abstract

There has been much recent interest in using reinforcement learning (RL) model parameters as outcome measures in clinical science. A prerequisite to developing an outcome measure that might co-vary with a clinical variable of interest (such as an experimental manipulation, intervention, or diagnostic status) is first showing that the measure is stable within the same subject, absent any change in the clinical variable. Yet researchers often neglect to establish test-retest reliability. This is especially a problem with behavioral measures derived from laboratory tasks, as these often have abysmal test-retest reliability. Computational models of behavior may offer a solution. Specifically, model-based analyses should yield measures with lower measurement error than simple summaries of raw behavior. Hence model-based measures should have higher test-retest reliability than behavioral measures. Here, we show, in two datasets, that a pair of RL model parameters derived from modeling a trial-and-error learning task indeed show much higher test-retest reliability than a pair of raw behavioral summaries from the same task. We also find that the reliabilities of the model parameters tend to improve with time on task, suggesting that parameter estimation improves with time. Our results attest to the potential of computational modeling in clinical science.