I received my degree in Clinical Psychology from the Pennsylvania State University, and completed my clinical internship at the VA Palo Alto Healthcare System. Prior to joining the faculty at Berkeley, I was a postdoctoral fellow at Stanford University.
Idiographic science and person-specific methodologies, Group-to-individual generalizability, Psychopathology, Configural Psychometrics
Primary Research Area: Clinical Science
*** NOT recruiting Psychology PhD students for the Fall 2027 admissions cycle ***
The Idiographic Dynamics Laboratory studies psychopathology at the level of the individual—and, increasingly, at the level of the discrete moments each individual experiences and the discrete elements and conditions that make up each moment. For nearly fifteen years the lab has pursued one question through successive reformulations: how do we build a clinical science that is at once actionable—capable of yielding decision-relevant information about a particular person—and generalizable, such that its inferences hold beyond the sample in hand? These two demands ordinarily trade against one another. Our work has been a sustained argument that this apparent trade-off is actually an artifact of the unit of analysis, and that selecting the right unit can dissolve the tension altogether.
The ID Lab started in 2013 as the first dedicated idiographic lab in Clinical Psychology. Motivated by the work of Peter Molenaar, we set out to demonstrate that group-level statistics do not, in general, describe the individuals who compose the group—a property known as nonergodicity, which our laboratory helped establish as an empirical cornerstone in clinical psychology (Fisher, Medaglia, & Jeronimus, 2018, PNAS). If the average person is a statistical fiction, then averaging over people is not a safe route to claims about any one of them. The lab's first phase was a direct response to this premise: it built person-specific (i.e. idiographic) modeling into a working paradigm, narrowing the unit of analysis from groups, to individuals, to moments within individuals.
Our current program narrows it once more—to the discrete elements of which those moments are composed—and in doing so changes what is meant to generalize. Person-specific parameters are themselves idiosyncratic; they are unlikely to travel across people. But structure can. Configural psychometrics treats clinical features as present-or-absent elements and asks, using set theory, combinatorics, probability, and information theory, which configurations of elements are minimally sufficient for a clinical outcome—the smallest load-bearing combinations of symptoms sufficient for a target outcome—and which individual elements are necessary across those configurations. At the between-person level, necessity and sufficiency applies to classifications such as diagnosis, distress, and impairment. At the intraindividual level, we examine the necessary and sufficient conditions for moments of acute risk or emotional vulnerability.
The configural psychometric approach is acategorical: it assumes no diagnostic categories in advance and lets the outcome define which configurations matter. And it is, by construction, actionable, because it speaks in the native currency of clinical decisions—a feature is present or it is not; a configuration is sufficient or it is not; one intervenes or one does not.
This inverts the logic of measurement itself. Where latent-variable models define a construct by its internal coherence and then ask, separately, how it relates to the world, the configural approach lets a construct earn its meaning from the work it does—its relation to observable outcomes. Applied to the internalizing disorders of the DSM, a handful of minimally sufficient configurations recovers essentially all of the diagnostic information contained in thousands of possible symptom combinations, reducing that combinatorial complexity by more than 99% while revealing which symptoms—worry, for one—are non-negotiable (Fisher, 2026).
For more information on Current Projects in the lab and instructions for Prospective Applicants, please visit dynamicpsychlab.com
Full list available on Google Scholar
Fisher, A. J. (in press). Beyond parametric ergodicity: A framework for structural generalizability. Philosophical Transactions of the Royal Society A.
Fisher, A. J. (2026a). Establishing minimally sufficient conditions reduces the complexity of symptom presentations in DSM internalizing disorders. Journal of Psychopathology and Clinical Science, 135, 554–566.
Fisher, A. J. (2026b). Constructs should do work: Reply to Forbes, Lorenzo-Luaces and Buss, Markon, and Paulus. Journal of Psychopathology and Clinical Science, 135, 578–580.
Leenaerts, N., & Fisher, A. J. (2026). setweaver: Building sets of variables in a probabilistic framework (R package, version 1.0.0). CRAN. (Software)
Mattoni, M., Fisher, A. J., Gates, K. M., Chein, J., & Olino, T. M. (2025). Group-to-individual generalizability and individual-level inferences in cognitive neuroscience. Neuroscience & Biobehavioral Reviews, 106024.
Cusack, C. E., Sandoval-Araujo, L. E., Hernández, J. C., Pennesi, J. L., Lazarus, G., Levinson, C. A., & Fisher, A. J. (2025). What's strength centrality got to do with it? Examining the stability of central symptoms across symptom ensembles and time in idiographic networks. Journal of Psychopathology and Clinical Science, 134, 571–584.
Kelley, S., Fisher, A. J., Lee, C. T., Gallagher, E., Hanlon, A., Robertson, I., & Gillan, C. (2023). Elevated emotion network connectivity is associated with fluctuations in depression. Proceedings of the National Academy of Sciences, 120(45), e2216499120.
Song, J., Howe, E., Oltmanns, J. R., & Fisher, A. J. (2022). Examining the concurrent and predictive validity of single items in ecological momentary assessments. Assessment, 10731911221113563.
Howe, E. S., & Fisher, A. J. (2022). Identifying and predicting posttraumatic stress symptom states in adults with posttraumatic stress disorder. Journal of Traumatic Stress, 35(5), 1508-1520.
Fisher, A. J., Howe, E., & Zong, Z. Y. (2022). Unsupervised clustering of autonomic temporal networks in clinically distressed and psychologically healthy individuals. Behaviour Research and Therapy, 154, 104105.
Soyster, P. D., Ashlock, L., & Fisher, A. J. (2022). Pooled and person-specific machine learning models for predicting future alcohol consumption, craving, and wanting to drink: A demonstration of parallel utility. Psychology of Addictive Behaviors, 36(3), 296.
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. (2015). Toward a dynamic model of psychological assessment: Implications for personalized care. Journal of Consulting and Clinical Psychology,83, 825-836
Person-specific data analysis (PSYCH 207)
Psychotherapy theory and practice (PSYCH 238)
Structural Equation Modeling (PSYCH 206)
Health Psychology (PSYCH 134)
