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theses

Movement variability is inherently present in natural behaviors. In recent years, this source of information has been of great interest to motor control physiologists as it constitutes a physical, quantifiable form of sensory feedback to aid in the planning and execution of complex actions. In marked contrast, the psychological and psychiatric arenas mainly rely on verbal descriptions and interpretations of behavior through observation. Whether in the laboratory setting conducting basic science research, or in the clinic diagnosing and evaluating patients, behavior has not been physically quantified using proper statistical methods. Consequently, a large gap exists between the body’s manifestations of mental states (whether normative or pathological) and their descriptions (through observational inventories). This disconnect is partly responsible for a disembodied approach in the psychological and neural sciences, whereby contributions of the peripheral nervous system to central control, executive functions, and decision making processes are poorly understood. Furthermore, the present gap between mind and body interactions severely impedes progress in translating basic scientific outcomes into clinical applications. How can we transition from a psychological theorizing approach to better understand complex behaviors in a more objective manner? This dissertation introduces a novel, objective, statistical framework and motor control paradigm that characterizes the stochastic signatures of movement variability present in the continuous stream of natural behaviors. We characterize a new class of movements that occurs largely beneath conscious awareness but are interminably present in all complex behaviors. The moment-by-moment assessment of the continuous flow of sensory-motor fluctuations is possible for goal-directed segments of complex actions, (which researchers have focused on for decades), and this newly introduced class of supplementary, goal-less movements. To illustrate these points, we present the application of our statistical framework to perceptual tasks involving visual illusions, as well as to the automatic detection of sensory-motor disruptions in individuals with schizophrenia. Lastly, we discuss the potential impact our discoveries have on translating basic science research into practical, societal applications. We conclude that analyses of motor output variability can transform the way in which we currently conduct research, determine clinical diagnoses, and administer therapeutic interventions in the social, health, and neural sciences.

ETD_6196

Ph.D.

Includes bibliographical references

by Jillian Nguyen

doi:10.7282/T3S184H0

ETD doctoral

The author owns the copyright to this work.