Leveraging Computationally Derived Measures of Individual Differences in Learning and Decision-making to Predict Psychiatric Diagnosis, Symptoms and Changes in Symptom Severity Across Time
California Institute of Technology
Summary
This study investigates the computational mechanisms associated with psychiatric disease dimensions. The study will characterize the relationship between computational parameter estimates of task performance and psychiatric symptoms and diagnoses with a longitudinal approach over a 12 month interval. Participants will be healthy participants recruited through Prolific an on-line crowdsourcing service, and psychiatric patients and healthy participants recruited via UCLA Psychiatry Clinics and UCLA's STAND Program
Description
The goal of computational psychiatry is to gain knowledge about underlying neurocomputational processes that underpin psychiatric disorders and to leverage this knowledge for improving diagnosis and treatment. A key step toward achieving this goal is to develop measures of individual differences in computations obtained from a single individual that are reliable, robust and meaningfully relevant to psychiatric dysfunction. In order to attain these objectives, it is essential we substantiate relationships between candidate computational mechanisms and diagnostic categories, symptom dimensions a…