Nonlinear dynamical patterns as personality theory for neurobiology and psychiatry

A J Mandell, K A Selz
Psychiatry 1995, 58 (4): 371-90
ADVANCES in the theory of nonlinear differential equations and their statistical representations have yielded a powerful, qualitatively descriptive yet quantitative language that captures characteristic patterns of behavior (what the psychoanalyst Roy Schafer calls "continuity, coherence, and consistency of action") that has begun to influence studies of complex systems in motion as diverse in specifics as signatory patterns of discharge of neurochemically defined single neurons and the dynamical structures characteristic of a particular composer's music. What might be called personality theories of neurobiological dynamics have arisen to replace neurobiological theories of personality. It is in this way that rigorously proven and powerful general mathematical insights have changed the face of determinism in research in brain and behavior. Two examples: (1) Very complicated looking behavior of neurobiological forced-dissipative (expanding and contracting) systems over time take place on low dimensional abstract surfaces on which only a few underlying abstract parameters control the action. (2) Independent of specific details (chemical, electrical, and/or behavioral), there exist a relatively few fundamental categories of behavior in time and transitions, among them a property called universality. Results from this new theoretical, in contrast with experimental, reductionism yield analogies with and new approaches to historically important dynamic ideas about personality and character patterns that are equally relevant to micro and macrocomplex systems such as neural membrane receptor proteins and individual personality styles. Research findings achieved over the past decade and a half in our laboratory and others in neurochemistry, neurophysiology, and animal and human behavior, as well as the results of a new demonstration experiment involving the prediction of dynamical category membership from abstract expressive motion in humans, are used to exemplify this use of a quantitative dynamic category theory across disciplinary levels in brain and behavior. Multiple measures of complexity adapted from current research in the statistical properties of chaos on unobtrusively observed and reconstructed orbits on the computer screen made by non-premorbid subjects executing content-free, computer-game-like tasks with a mouse, were used to reliably differentiate the "signatures" of two Axis II diagnoses as established using SCID-II criteria. Whereas the techniques of nonlinear systems have achieved some success in quantifying and stimulating the dynamical styles of relatively local phenomena such as the spontaneous behavior of neuronal membrane conductances, single neurons, neural networks, and field electrical events, we think that the real power of these techniques lies in their quantitative description and statistical prediction of global patterns of behavior of entire systems. For example, since the late 1970s our work has shown that these measures could be used to discriminate categories of drug action and dose when applied to patterns of rat exploratory behavior in space and time. The combination of abstract generality and quantitative precision of these methods suggests their usefulness as a cross-disciplinary language for fields like psychiatry that deal with complicated behavior of both neurobiological elements and "the whole person."

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