Core A: Administrative The administrative core is organized to support the overall program project via three main components: Project Direction, Program Integration and the organization of a Satellite Conference on Motor Learning PROGRAM DIRECTION The program director, with the assistance of a project coordinator, will be responsible for the following duties: 1. Insure that the evolving plans of the four projects and modeling core are well integrated. 2. Arrange PPG meetings and facilitate weekly communication among the motor control groups. 3. Communicate with an External Advisory Board. 4. Disseminate information about PPG activities to the public sector. 5. Coordinate recruitment and career development of young investigators in the PPG. 6. Oversee shared efforts by post-doctoral fellows between laboratories. 7. Organize three satellite conferences on motor learning. PROGRAM INTEGRATION The single most important source of integration in this project is a common behavioral task used across projects. Maintenance of integration is achieved by: 1. Reorganization of team members. 2. Physical Proximity. 3. Group Videoconferences. 4. Group Meetings. 5. Shared Fellows. 6. Communications with an External Advisory Board. 7. Dissemination of information to the public sector. 8. Recruitment and career development. SATELLITE CONFERENCE ON MOTOR LEARNING We will integrate our work from this program project grant with the larger motor science community by organizing three Conferences in Motor Learning. The goal is to bring together a broad range of expertise in motor skill learning, from computational modeling to empiric research.

Public Health Relevance

The proposed work is central to the problem of understanding the mechanisms where practice leads to reorganization of the human motor system in the face of aging, neurodegeneration, stroke or brain injury. Understanding these mechanisms has an impact on the design of therapies directed at preserving function, developing compensator movements and ultimately, developing novel motor capacity.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Program Projects (P01)
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National Institute of Neurological Disorders and Stroke Initial Review Group (NSD)
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University of California Santa Barbara
Santa Barbara
United States
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Soto, Fabian A; Vucovich, Lauren; Musgrave, Robert et al. (2015) General recognition theory with individual differences: a new method for examining perceptual and decisional interactions with an application to face perception. Psychon Bull Rev 22:88-111
Lawlor, Patrick Nathan; Kalisky, Tomer; Rosner, Robert et al. (2014) Conceptualizing cancer drugs as classifiers. PLoS One 9:e106444
Ashby, F Gregory (2014) Is state-trace analysis an appropriate tool for assessing the number of cognitive systems? Psychon Bull Rev 21:935-46
Devarajan, Karthik; Cheung, Vincent C K (2014) On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data. Neural Comput 26:1128-68
Smith, J David; Johnston, Jennifer J R; Musgrave, Robert D et al. (2014) Cross-modal information integration in category learning. Atten Percept Psychophys 76:1473-84
Acuna, Daniel E; Wymbs, Nicholas F; Reynolds, Chelsea A et al. (2014) Multifaceted aspects of chunking enable robust algorithms. J Neurophysiol 112:1849-56
Fernandes, Hugo L; Stevenson, Ian H; Vilares, Iris et al. (2014) The generalization of prior uncertainty during reaching. J Neurosci 34:11470-84
Klimm, Florian; Bassett, Danielle S; Carlson, Jean M et al. (2014) Resolving structural variability in network models and the brain. PLoS Comput Biol 10:e1003491
Smith, J David; Boomer, Joseph; Zakrzewski, Alexandria C et al. (2014) Deferred feedback sharply dissociates implicit and explicit category learning. Psychol Sci 25:447-57
Barany, Deborah A; Della-Maggiore, Valeria; Viswanathan, Shivakumar et al. (2014) Feature interactions enable decoding of sensorimotor transformations for goal-directed movement. J Neurosci 34:6860-73

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