Full Program »
105. Drift Diffusion Model of Sustained Attention in First-Episode Psychosis: Analysis of Cognition and Symptoms
Recent trends in psychotic disorder research indicate cognitive deficits to be perhaps the most prevalent and defining feature of these disorders. At the same time, analyses of the association between cognitive functioning and psychotic symptoms have not demonstrated a strong relationship between the two variables. Dynamic decision making models such as the Drift Diffusion Model (DDM) may help to clarify the nature of the relationship between cognitive functioning and the severity of psychotic symptoms. DDM is a reaction time based decision-making model and allows for a more detailed analysis of cognitive performance. Specifically, by considering accuracy and reaction time data simultaneously, the DDM can incorporate more information than traditional descriptive/summary analyses, and thus offers an effective technique for modeling the underlying cognitive components of decision making processes. The DDM is an appealing approach as it has already been applied to the study of ADHD, depression, anxiety, aphasia and dyslexia. In schizophrenia, the DDM has been successfully applied in a cross-sectional analysis of reward vs. punishment learning. The current study will build upon this work by applying the DDM to longitudinal data from the Continuous Performance Task- Independent Pairs (CPT-IP) and the Positive and Negative Syndrome Scale (PANSS), with the goal of clarifying the nature of the relationship between cognition and symptomatology in first-episode psychosis.