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A Phenomenological Reappraisal of Dynamical Systems in Psychopathology.

Evan J. Kyzar, George H Denfield, Jasper Feyaerts, Louis Sass, Barnaby Nelson

Psychopathology August 18, 2025 DOI: 10.1159/000548025 (opens in new tab)

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AI-extracted from the abstract
Characteristics Theoretical or philosophical paper Peer reviewed
Topics Philosophy of mind
Keywords Delusions Dynamical systems Psychosis Subjective experiences
Citations 1
Key points Argues that integrating phenomenological psychopathology with dynamical systems theory can improve the specification of core symptoms and theoretical understanding of symptom evolution in psychopathology research.

Abstract

Dynamical systems theory (DST) has recently gained traction as a framework to describe and predict the progression of psychopathology. However, a number of challenges to the application of DST to psychopathology have arisen, including the heterogeneity of symptom measures and the lack of theoretical underpinnings to describe the temporal unfolding of psychiatric illnesses. In this article, we aim to show how the integration of methods from phenomenology may strengthen the application of DST in psychopathology research. We explore how phenomenological psychopathology can improve DST-based investigations in two key ways: (1) by specifying the core symptoms of interest in psychopathological states in a more precise manner by focusing on subjective experiences, and (2) by deepening our theoretical understanding of how these symptoms evolve in severity over time. We show how incorporating phenomenologically informed measures of experience can complement DST using clinical high risk (CHR) for psychosis as a test case, and we demonstrate the utility of combining phenomenologically informed theory and DST by examining the ipseity-disturbance model (IDM) of psychosis development. We close by offering a vision for the broader integration of DST and phenomenological research methods within psychopathological research. Phenomenological investigations can synergize with and advance the use of DST to better understand and predict psychiatric disorders and transitions in states of mental health.

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