The Idiographic Dynamics Lab at UC Berkeley engages in research at the individual level. Our group is currently interested in issues of Precision, Personalization, and Prediction in psychopathology and substance use.
Specifically, we are interested in extending the Precision Medicine paradigm to psychological and psychiatric domains—a Precision Behavioral Health model that complements biomedical approaches by leveraging behavioral data to customize and fine-tune behavioral interventions. We are interested in identifying actionable units of information at the behavioral level of analysis that will allow us to match patients, problems, and optimal interventions.
Additionally, we believe that the concept of personalization extends beyond treatment delivery and should encompass study design, data collection, and statistical analysis. Recent research in our lab has revealed marked heterogeneity in the temporal patterns, correlational structures, and predictive relationships in psychopathology and substance use.
Finally, given the heterogeneity in the timing and predictors of individual problems and behaviors, our group is currently working on methods for predicting individual behavior moment to moment, in order to identify when problems might occur. Building accurate prediction systems may allow researchers and clinicians to provide interventions when they are most needed (i.e. "just in time").
Prospective students interested in joining our lab should have a concentrated interest in idiographic science, personalization, prediction, and related issues.
Fisher, A.J., Bosley, H.G., Fernandez, K.C., Reeves, JW., Diamond, A.E., Soyster, P.D., & Barkin, J. (2019). Open trial of a personalized modular treatment for mood and anxiety. Behaviour Research and Therapy, 116, 69-79.
Fisher, A.J.,Medaglia, J.D., & Jeronimus, B.F. (2018). Lack of Group-to-Individual Generalizability is a Threat to Human Subjects Research. Proceedings of the National Academy of Sciences.
Fisher, A.J., Reeves, J.W., Lawyer, G., Medaglia, J.D., & Rubel, J.A. (2017). Exploring the Idiographic Dynamics of Mood and Anxiety with Network Analysis. Journal of Abnormal Psychology.
Fernandez, K.C., Fisher, A. J., & Chi, C. (2017). Development and Initial Implementation of the Dynamic Assessment Treatment Algorithm (DATA). PLoS ONE, 12(6): e0178806.
Fisher, A.J., Reeves, J.W., & Chi, C. (2016). Dynamic RSA: Examining parasympathetic regulatory dynamics via vector-autoregressive modeling of time-varying RSA and heart period. Psychophysiology, 53, 1093-1099.
Fisher, A.J. & Boswell, J.F. (2016). Enhancing the Personalization of Psychotherapy with Dynamic Assessment and Modeling. Assessment, 23, 496-506.
Fisher, A.J. (2015). Toward a dynamic model of psychological assessment: Implications for personalized care. Journal of Consulting and Clinical Psychology, 83, 825-836