As we move, accurate knowledge of the state of the body and the surrounding environment is critical, in particular when we adapt or learn to move in unfamiliar environments. Minimizing uncertainty can dramatically speed up movement learning - and many successful training strategies implicitly aim at delivering low uncertainty information to subjects, for example by providing "knowledge of performance". Recent behavioral studies highlight strategies to parametrically vary and quantify uncertainty. Within statistics we can identify three different kinds of uncertainty that relate to movement. (1) We can never know the exact state of body and environment, which leads to state uncertainty. (2) Noisy perception is not sufficient to tell us precisely which movement we actually performed resulting in feedback uncertainty. (3) Within the vast array of sensory stimuli, some may relate to our movement, while others may be irrelevant, causing relevance uncertainty. Our preliminary data indicates that these three kinds of uncertainty have a strong influence on motor learning. While recent studies under the umbrella term "intemal models" characterized the influence of uncertainty on motor control. little Is known about the influence of each kind of uncertainty on motor learning. In the proposed research we will characterize how the nervous system deals with uncertainty in motor learning. Subjects will move a cursor from a starting position to a target position in a virtual environment. Visual feedback will be manipulated to induce unce1tainty about the state, the feedback or its relevance. Our experiments will focus on probing the resulting trial-by-trialleaming. The proposed analysis of the influence of uncertainty on motor learning is driven by strong hypotheses derived from a statistical framework With the expected results we will either be able to refute Bayesian models that formalize how uncertainty affects learning or refute state space models that assume that uncertainty has no influence on learning. Importantly though, uncertainty is a central factor for human behavior and quantitatively understanding its role is impmtant beyond any specific modeling framework.
|Effective start/end date||4/1/15 → 3/31/20|
- Rehabilitation Institute of Chicago (81378 NU//2R01NS063399-06)
- National Institute of Neurological Disorders and Stroke (81378 NU//2R01NS063399-06)
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