Graduation Year

2024

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Psychology

Major Professor

Marina Bornovalova, Ph.D.

Committee Member

Brent Small, Ph.D.

Committee Member

Fallon Goodman, Ph.D.

Committee Member

Jack Darkes, Ph.D.

Committee Member

Jay Michaels, Ph.D.

Keywords

attractors, damped oscillator, dynamical systems, Personality processes, symptom fluctuation

Abstract

With mounting evidence that personality is both stable and variable, personality scientists have called for comprehensive approaches that integrate stability and dynamics. Dynamical systems theory offers concepts and tools to systematically integrate the stable and dynamic components of personality while identifying the relationship between these components. This study extends the dynamical systems approach to personality pathology and investigates how pathological personality states vary in a patterned manner in relation to their equilibria or attractors, as opposed to random noise. Furthermore, I test if these patterned dynamics may describe important individual differences and predict behavioral outcomes. Using daily data pertaining to pathological personality states collected over 100 days in a sample of 101 individuals diagnosed with a personality disorder, I explored the dynamic patterns using idiographic dynamical systems models including phase-space methods (attractor landscapes, convex hull area) and stochastic differential equation models (damped oscillator). Across analyses, the findings support that pathological personality state domains are attractor governed for most individuals, and the properties of these attractors describe important individual differences. Specifically, the presence of a strong and flexible attractor was associated with lower severity of trait personality pathology. Findings suggest that personality pathology acts as a self-sustaining dynamical system.

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