Trial-by-Trial Human Generalization of Sense into Action

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DESCRIPTION (provided by applicant): Despite the centrality of motor learning to basic and clinical neuroscience, we know very little about the quantitative role neural systems play in the transformation of senses into adapted control. The experiments presented here will challenge normal human subjects with perturbations of varying strengths, durations, frequency of application, biases, and spatial complexities both within and across movements. The variability of these perturbations will generate a template with which we will identify subtleties in trial-by-trial adaptation. These interrogations, coupled with novel state space analyses, will enable a thorough understanding of the transformation of specific sensory experiences into immediate, incremental adaptations in predictive control, greatly enhancing our quantitative understanding of human motor adaptation. Experience enables us to build internal dynamic models of our movement environment. Investigators of this internal dynamic adaptation have hypothesized two components of learning: the abstraction of an error signal from previous movements and the application of this error to either specify or generalize learning across movement space. Human trial-by-trial adaptation has, to date, suggested that adaptation constantly scales with sensed error and generalizes broadly across movement space. However, preliminary results from the PI have discovered surprising flexibility in both components of learning: sensory feedback can induce adaptation strikingly disproportional to movement error, and environments can induce narrowing of generalization across movement space. Here we propose to identify the necessary sensory experiences to induce these newly established changes in the fundamental computations people execute to transform single movement sense into incremental adaptation. These results will illuminate how the nervous system performs real-timed signal processing to improve motor performance. The resultant models will help the neuroscience and biological modeling community to better connect behavior to its underlying physiological basis. These insights will also be of use to investigate the full repertoire of normal motor control and how control fails in disease states. We aim to formulate the scientific basis of how rehabilitation can optimally help patients generalize beyond clinical training to improve motor function in their daily lives.