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 uncertainty about the state, the feedback, or its relevance. Our experiments will focus on probing the resulting trial-by-trial learning. 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, uncertainty is a central factor for human behavior and quantitatively understanding its role is important beyond any specific modeling framework. The long term objectives of this research program are to answer basic and important questions in motor learning from a computational perspective and to provide tools for improving motor rehabilitation.

Public Health Relevance

The nervous system needs to learn in the presence of uncertainty within the functions of everyday life, and in the presence of disease. Based on statistical insights, this study will test key factors that affect the way the nervous system learns from visuo-motor errors. Specifically, we will understand how the times, magnitudes and the visual presentation of errors affect motor learning. Choices in robotic rehabilitation approaches result in how error feedback can be made effective and relevant through maximizing research testing. As we ask fundamental questions, the results are expected to generalize to a wide range of motor learning tasks.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS063399-02
Application #
7879971
Study Section
Sensorimotor Integration Study Section (SMI)
Program Officer
Chen, Daofen
Project Start
2009-09-01
Project End
2014-08-31
Budget Start
2010-09-01
Budget End
2011-08-31
Support Year
2
Fiscal Year
2010
Total Cost
$217,111
Indirect Cost
Name
Rehabilitation Institute of Chicago
Department
Type
DUNS #
068477546
City
Chicago
State
IL
Country
United States
Zip Code
60611
Benjamin, Ari S; Fernandes, Hugo L; Tomlinson, Tucker et al. (2018) Modern Machine Learning as a Benchmark for Fitting Neural Responses. Front Comput Neurosci 12:56
Chambers, Claire; Sokhey, Taegh; Gaebler-Spira, Deborah et al. (2018) The development of Bayesian integration in sensorimotor estimation. J Vis 18:8
Saeb, Sohrab; Lattie, Emily G; Kording, Konrad P et al. (2017) Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR Mhealth Uhealth 5:e112
Vilares, Iris; Kording, Konrad P (2017) Dopaminergic Medication Increases Reliance on Current Information in Parkinson's Disease. Nat Hum Behav 1:0129
Tabor, Abby; Thacker, Michael A; Moseley, G Lorimer et al. (2017) Pain: A Statistical Account. PLoS Comput Biol 13:e1005142
Chambers, Claire; Sokhey, Taegh; Gaebler-Spira, Deborah et al. (2017) The integration of probabilistic information during sensorimotor estimation is unimpaired in children with Cerebral Palsy. PLoS One 12:e0188741
Kong, Gaiqing; Zhou, Zhihao; Wang, Qining et al. (2017) Credit assignment between body and object probed by an object transportation task. Sci Rep 7:13415
Saeb, Sohrab; Cybulski, Thaddeus R; Schueller, Stephen M et al. (2017) Scalable Passive Sleep Monitoring Using Mobile Phones: Opportunities and Obstacles. J Med Internet Res 19:e118
Saeb, Sohrab; Lonini, Luca; Jayaraman, Arun et al. (2017) The need to approximate the use-case in clinical machine learning. Gigascience 6:1-9
Crevecoeur, Frédéric; Kording, Konrad P (2017) Saccadic suppression as a perceptual consequence of efficient sensorimotor estimation. Elife 6:

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