The clinical high risk for psychosis (CHR-P) population is important for understanding disease progression and treatment
however, standard approaches to identifying CHR-P individuals are expensive and labor-intensive. Focusing on neurocognitive mechanisms that underlie individual psychosis symptoms (positive, negative, and disorganization) may improve screening and identification. The present study examines whether a behavioral task battery that assays symptom mechanisms can identify CHR-P individuals and predict risk severity. Participants (