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. 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.

Public Health Relevance

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. 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 #
5R01NS063399-05
Application #
8535220
Study Section
Sensorimotor Integration Study Section (SMI)
Program Officer
Chen, Daofen
Project Start
2009-09-01
Project End
2014-08-31
Budget Start
2013-09-01
Budget End
2014-08-31
Support Year
5
Fiscal Year
2013
Total Cost
$208,546
Indirect Cost
$32,486
Name
Rehabilitation Institute of Chicago
Department
Type
DUNS #
068477546
City
Chicago
State
IL
Country
United States
Zip Code
60611
Berniker, Max; Jarc, Anthony; Kording, Konrad et al. (2016) A Probabilistic Analysis of Muscle Force Uncertainty for Control. IEEE Trans Biomed Eng 63:2359-2367
Vaidya, Mukta; Kording, Konrad; Saleh, Maryam et al. (2015) Neural coordination during reach-to-grasp. J Neurophysiol 114:1827-36
Kilteni, Konstantina; Maselli, Antonella; Kording, Konrad P et al. (2015) Over my fake body: body ownership illusions for studying the multisensory basis of own-body perception. Front Hum Neurosci 9:141
Sternad, Dagmar; Körding, Konrad Paul (2015) Carrot or stick in motor learning. Nat Neurosci 18:480-1
Jonas, Eric; Kording, Konrad (2015) Automatic discovery of cell types and microcircuitry from neural connectomics. Elife 4:e04250
Acuna, Daniel E; Berniker, Max; Fernandes, Hugo L et al. (2015) Using psychophysics to ask if the brain samples or maximizes. J Vis 15:
Kording, Konrad Paul (2014) Bayesian statistics: relevant for the brain? Curr Opin Neurobiol 25:130-3
Wei, Kunlin; Glaser, Joshua I; Deng, Linna et al. (2014) Serotonin affects movement gain control in the spinal cord. J Neurosci 34:12690-700
Antos, Stephen A; Albert, Mark V; Kording, Konrad P (2014) Hand, belt, pocket or bag: Practical activity tracking with mobile phones. J Neurosci Methods 231:22-30
Fernandes, Hugo L; Stevenson, Ian H; Vilares, Iris et al. (2014) The generalization of prior uncertainty during reaching. J Neurosci 34:11470-84

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