Neural Mechanisms Supporting Implicit and Explicit Sensorimotor Learning

TITLE of PROJECT Neural Mechanisms Supporting Implicit and Explicit Sensorimotor Learning PROJECT SUMMARY Successful goal-directed actions require a flexible motor control system, one that can quickly respond to changes in the body (e.g., muscle fatigue) and in the environment (e.g., a windy day). Such flexibility depends on the

operation of multiple learning processes. Implicit learning processes (i.e., implicit adaptation) keep the sensorimotor system exquisitely calibrated in an automatic manner, whereas explicit learning processes can facilitate rapid adjustments in a strategic, yet effortful manner. While the cerebellum and basal ganglia are

prominently featured in the motor learning literature, their contribution to sensorimotor adaptation remains unclear, in part because past studies have employed tasks that conflate implicit and explicit learning processes. To disentangle the specific contributions of the cerebellum and basal ganglia to sensorimotor adaptation, I will

use a set of behavioral tasks developed in my mentor’s lab that are designed to isolate the contribution of different learning processes. The results from this work have revised our current computational understanding of sensorimotor adaptation and have set the stage for taking a new look at the subcortical systems involved in this

form of learning. In the proposed studies, we will test patients with spinocerebellar ataxia (SCA) and Parkinson’s disease (PD) on these tasks. In terms of basic research, the results will be important in advancing our understanding of how distributed neural systems support motor learning. In terms of translational benefit, the

insights from this work will aid physical therapists to better tailor interventions that tap into intact learning mechanisms or enhance impaired ones. This NRSA F31 training plan encompasses two specific aims (three experiments) that will be conducted at UC Berkeley under the supervision of my sponsor, Prof. Richard Ivry. As a PI for 30-years, Prof. Ivry has trained 24

Ph.D. trainees and 21 post-doc fellows, many of whom hold faculty positions at research institutions. Under the supervision of Prof. Ivry, this proposal outlines a comprehensive training plan, centered on gaining fluency in computational modeling of behavior, methods in neuropsychology, writing and grantsmanship, presenting and

disseminating research, and clinical pedagogy and mentorship. I will benefit from frequent interactions with Prof. Hyosub Kim, a former post-doc with Prof. Ivry who is now an Assistant Professor at the Univ. of Delaware. Prof. Kim provides added expertise in computational modeling and mentorship as a trained physical therapist. I will

also benefit from mentorship provided by Prof. Robert Knight, a Professor and neurologist at UC Berkeley, who can provide additional training in patient evaluation and general training drawing from many years of stellar neuropsychological research with many patient groups and trainees. In summary, this training plan will build a

solid foundation for my future role as a PI, working at the intersection of cognitive neuroscience and rehabilitation.