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
Age range of 18 to 65.
Not currently having a psychiatric diagnosis determined after psychiatric evaluation by Drs. Tadayon-Nejad and Wei (both are board certified psychiatrists).
Ability to understand and perform experimental tasks, i.e. basic ability to communicate and comprehend tasks.
Ability to give informed consent.
Age range of 18 to 65.
Psychiatric diagnosis of any type of depressive disorders, any type of anxiety disorders or obsessive-compulsive disorder.
Primary or comorbid bipolar disorders are allowed but only if not in the acute manic phase.
Comorbidity with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are allowed.
Single group
Behavioral task performance
BehavioralMeasures of performance on behavioral tasks
Changes in DASS depression scale scores
Changes in computational parameter estimates related to gain/loss learning, reward/effort tradeoff and reward/predation risk tradeoffs will correlate with changes in DASS depression scale scores across time.
Changes in DASS anxiety scale scores
Changes in computational parameter estimates related to novelty driven exploration and reward/predation risk tradeoffs will be correlated with changes in DASS anxiety scale scores
Changes in OCI-R scores
Changes in computational parameter estimates related to the balance between model-based vs model-free reinforcement-learning will be correlated with changes in OCI-R symptoms across time.
OCI-R scores
Computational parameter estimates related to the balance between model-based vs model-free reinforcement-learning will be correlated with OCI-R scores.
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