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Abstract
There is a long-held assumption that the degree of complexity (fractal dynamics) exhibited in neuro-motor output provides information about the system's health and adaptive capacity. To date, however, direct evidence of this relationship is limited, in part because there are opposing views on how system complexity should be observed and interpreted. One view argues that adaptability can be inferred according to the observed dynamics of a system functioning under minimal constraint (ie. self-selected unperturbed walking), in which pink noise fluctuations reflect the adaptive optimum. The opposing view contends that adaptive capacity is better represented by the observed changes in system dynamics corresponding to variations in task constraint. This contrast in perspective has often led to description of either minimally constrained, or strictly constrained behavioral dynamics, with little understanding of their combination and connection.
This dissertation incorporated the assessment of system dynamics under both minimal and task-relevant constraint according to an isometric force tracking paradigm. In Experiment 1, we observed subjects force in a no vision preferred-force, constant, sine, and pink noise tracking task. This study found that both minimally constrained force complexity, and task-relevant dynamical flexibility both predicted general force tracking ability. Moreover, minimally constrained dynamics did not correspond with pink noise. In Experiment 2, we collected minimally constrained force dynamics, and then had subjects practice either a pink noise or brown noise force target for 5 days. This was done to determine whether different task constraints elicited unique changes to minimally constrained force dynamics over practice. Results from Experiment 2 revealed similar alterations to individual's minimally constrained dynamics, in which both groups showed more complex force output. Still, this change did not correspond with pink noise behavior. Moreover, neither condition demonstrated superiority in adaptive performance of a transfer task.
Together, these findings support the necessity of unified framework in examining complexity according to both intrinsic and task-relevant constraint. To do so may improve insight into unique dynamical structures exhibited in specific movement paradigms. Moreover, additional research is necessary to understand the specific relations between practice and dynamical properties in order to facilitate specific improvement in adaptive control.
This dissertation incorporated the assessment of system dynamics under both minimal and task-relevant constraint according to an isometric force tracking paradigm. In Experiment 1, we observed subjects force in a no vision preferred-force, constant, sine, and pink noise tracking task. This study found that both minimally constrained force complexity, and task-relevant dynamical flexibility both predicted general force tracking ability. Moreover, minimally constrained dynamics did not correspond with pink noise. In Experiment 2, we collected minimally constrained force dynamics, and then had subjects practice either a pink noise or brown noise force target for 5 days. This was done to determine whether different task constraints elicited unique changes to minimally constrained force dynamics over practice. Results from Experiment 2 revealed similar alterations to individual's minimally constrained dynamics, in which both groups showed more complex force output. Still, this change did not correspond with pink noise behavior. Moreover, neither condition demonstrated superiority in adaptive performance of a transfer task.
Together, these findings support the necessity of unified framework in examining complexity according to both intrinsic and task-relevant constraint. To do so may improve insight into unique dynamical structures exhibited in specific movement paradigms. Moreover, additional research is necessary to understand the specific relations between practice and dynamical properties in order to facilitate specific improvement in adaptive control.