# Computations in human motor learning > **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $491,429 ## Abstract Project Summary The long-term goal of our laboratory is to understand the computations underlying human motor learning and thereby provide a framework to examine the neural underpinnings of learning, the deficits seen in neurological disorders and how learning mechanisms can be leveraged in rehabilitation. Motor learning is the fundamental process that involves changes in motor behavior arising from interaction with the environment. Humans spend a lifetime learning, storing and refining a multitude of motor memories appropriate for different contexts. Current studies of motor learning have focused almost exclusively on adaptation of individual memories in isolation. Con- sequently, the principles underlying how the brain coordinates its repertoire of memories are largely unknown. Our key hypothesis is that the process of contextual inference, estimating the probability with which each exist- ing motor memory is appropriate for the current situation, controls the creation of new memories and the degree to which different memories are expressed and updated. Our objective is to understand what leads to the cre- ation of new memories compared to the modification of existing motor memories, and how existing memories are recalled and updated. We have developed the COIN (COntextual INference) model to formalize the role of contextual inference in motor learning. The COIN model performs contextual inference in a more principled and comprehensive way than any previous model and can explain key findings traditionally attributed to adaptation as arising instead from contextual inference, such as spontaneous recovery, savings, anterograde interference and changes in learning rates. In contrast to current models, a critical feature of the COIN model is that it can determine, in a principled manner, whether a new memory should be created or existing memories adapted. To both test and develop the model, we will use behavioral studies in humans using novel robotic interfaces and virtual reality which allow us to control a participant’s sensorimotor experience during motor learning tasks. In Aim 1 we will determine the conditions under which new motor memories are created. In Aim 2 we will determine the rules by which existing motor memories are updated. While Aims 1 and 2 focus on reaching movements in the plane which make a large body of previous research comparable, Aim 3 moves towards more naturalistic tasks of manipulating objects in three-dimensions. In Aim 3 we will determine how motor memories are organized into fam- ilies to allow efficient learning and generalization for contexts that share similar properties. Voluntary movement is fundamental to human existence, yet many diseases such as stroke, degenerative disease, and developmental disorders, impair human movement over the life span. By establishing a new framework of motor learning, this project will contribute to our ultimate goal of developing assays to understand deficits in neurological disorde... ## Key facts - **NIH application ID:** 10347375 - **Project number:** 5R01NS117699-02 - **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES - **Principal Investigator:** Daniel Wolpert - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $491,429 - **Award type:** 5 - **Project period:** 2021-02-15 → 2026-01-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10347375 ## Citation > US National Institutes of Health, RePORTER application 10347375, Computations in human motor learning (5R01NS117699-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10347375. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*