16c
Parisa R. Kaliush, Robert D. Vlisides-Henry, and Sheila E. Crowell
Markon (this volume) as well as Bornovalova, Choate, and Fatimah (this volume) provide thoughtful remarks on our chapter. Markon raises important points regarding the complexity of mechanistic research – namely, difficulties defining clear boundaries between hypothesized causes and effects. For example, he questions the mechanistic influences of neural disconnectivity on the development of emotion dysregulation, proposing that such neural phenomena may be physical manifestations of dysregulated emotional states rather than components of a causal process. He also introduces weak emergence as a way to conceptualize development of personality disorders (PDs). Weak emergence accounts for macroscopic phenomena (e.g., borderline personality disorder) that derive from lower-order parts (e.g., invalidating relationships) but cannot be reduced to simple explanations due to complexity and the lengthy timescales over which mechanistic pathways develop (Baumert et al., 2017).
Bornovalova and colleagues also address difficulties explaining the development of PDs. They argue that mechanistic research rarely extends beyond simple unidirectional relations (e.g., candidate genes to environments). In actuality, it is more likely that these phenomena exert reciprocal influences across time, and “genetic” or “environmental” factors are not as distinct as traditionally conceptualized. For instance, parenting has heritability estimates ranging from 12 to 37 percent (Kendler & Baker, 2007). Bornovalova and colleagues describe a theoretical perspective, “genetic control over exposure to the environment” (i.e., gene–environment correlation), which may prove more fruitful for testing reciprocal influences on PD development across the lifespan. We agree that grounding mechanistic research in theory is vital to disentangling elements of complex mechanistic processes (Hedström & Ylikoski, 2010).
In response to these rich commentaries, we delve briefly into the complexity and transactional nature of mechanisms in PD development. Specifically, we discuss nonlinear dynamical systems and psychopathology research. Dynamical systems models allow researchers to examine persons and/or variables across time (see e.g., Granic, O’Hara, Pepler, & Lewis, 2007; Pezard & Nandrino, 2001). Behaviors, traits, and other individual differences are assumed to have “attractors” and “repellers” (Granic et al., 2007; Kenrick et al., 2002). That is, across time one would expect some degree of stability in key characteristics as a person continuously repels from, or is attracted to, certain points. For example, a person may show some stability in their emotions, behaviors, or physiology around a mean (i.e., their set point). However, the theory also assumes that people are “open systems,” subject to frequent “perturbations” or disruptions, which pull them away from their typical set point. For instance, a person’s level of negative affect may be relatively stable and attracted (i.e., drawn back) to a moderate level. External factors (e.g., a fight with peers or a fun gathering) can perturb this system, temporarily shifting the set point toward higher or lower negative affect. When human characteristics are conceptualized as longitudinal open systems, researchers attend to a system’s stability and attractor/repeller points. Ultimately, one can argue from a dynamical systems perspective that current approaches to assessing personality disorder mechanisms present an incomplete picture of the dynamics of psychopathology over time.
In our chapter, we highlighted the complexity of PD assessment and multiple coexisting mechanisms. Yet only recently have dynamical systems approaches been applied to clinical research (see e.g., Gelfand & Engelhart, 2012). Unlike other areas of psychological science, the clinical implications behind dynamical systems research may lend to more nuanced interpretations of mechanisms. For example, our lab used nonlinear dynamics to characterize emotional and biological sensitivity among self-injuring, depressed, and control adolescent girls and their mothers during a 10-minute conflict discussion (Crowell et al., 2017). As hypothesized, we found that self-injuring adolescents were more sensitive – behaviorally and psychophysiologically – to their mothers’ conflict behaviors than depressed or control adolescents. In addition, we found that adolescents’ behaviors did not “drive” their mothers’ responses. Thus, we used a dynamical systems approach to complement a key developmental theory: that vulnerability for self-injury is greatest among adolescents who are more sensitive to family environments (Crowell, Beauchaine, & Linehan, 2009). Although we did not assess every possible mechanism, we sought to link our mechanistic research to theory and feasible treatment targets.
Moving forward, researchers should acknowledge implications associated with studying mechanisms in PDs. As noted in our original chapter, the notion of causality is implicit to mechanistic research. However, testing causal theories is often beyond the capacity of our methodological and statistical approaches. As highlighted in both commentaries, there are complexities that hinder current understanding of PD development and mechanistic pathways. We hypothesize that delving deeper into dynamical systems theory, weak emergence, and complex gene–environment associations will enrich our understanding and treatment of PDs.
References
Baumert, A., Schmitt, M., Perugini, M., Johnson, W., Blum, G., Borkenau, P., … Wrzus, C. (2017). Integrating personality structure, personality process, and personality development. European Journal of Personality, 31, 503–528.
Crowell, S. E., Beauchaine, T. P., & Linehan, M. M. (2009). A biosocial developmental model of borderline personality: Elaborating and extending Linehan’s theory. Psychological Bulletin, 135, 495–510.
Crowell, S. E., Butner, J. E., Wiltshire, T. J., Munion, A. K., Yaptangco, M., & Beauchaine, T. P. (2017). Evaluating emotional and biological sensitivity to maternal behavior among self-injuring and depressed adolescent girls using nonlinear dynamics. Clinical Psychological Science, 5, 272–285.
Gelfand, L., & Engelhart, S. (2012). Dynamical systems theory in psychology: Assistance for the lay reader is required. Frontiers in Psychology, 3, 1–3.
Granic, I., O’Hara, A., Pepler, D., & Lewis, M. D. (2007). A dynamic systems analysis of parent–child changes associated with successful “real-world” interventions for aggressive children. Journal of Abnormal Child Psychology, 35, 845–857.
Hedström, P., & Ylikoski, P. (2010). Causal mechanisms in the social sciences. Annual Review of Sociology, 36, 49–67.
Kendler, K. S., & Baker, J. H. (2007). Genetic influences on measures of the environment: A systematic review. Psychological Medicine, 37, 615–626.
Kenrick, D. T., Maner, J. K., Butner, J., Li, N. P., Becker, D. V., & Schaller, M. (2002). Dynamical evolutionary psychology: Mapping the domains of the new interactionist paradigm. Personality and Social Psychology Review, 6, 347–356.
Pezard, L., & Nandrino, J. L. (2001). Dynamic paradigm in psychopathology: “Chaos Theory,” from physics to psychiatry. L’Encephale, 27, 260–268.