scholarly journals Rethinking Motor Learning and Savings in Adaptation Paradigms: Model-Free Memory for Successful Actions Combines with Internal Models

Neuron ◽  
2011 ◽  
Vol 70 (4) ◽  
pp. 787-801 ◽  
Author(s):  
Vincent S. Huang ◽  
Adrian Haith ◽  
Pietro Mazzoni ◽  
John W. Krakauer
Keyword(s):  
Motor Learning ◽  
Internal Models ◽  
Model Free

scholarly journals The dynamics of motor learning through the formation of internal models

2019 ◽  
Vol 15 (12) ◽  
pp. e1007118 ◽  
Author(s):  
Camilla Pierella ◽  
Maura Casadio ◽  
Ferdinando A. Mussa-Ivaldi ◽  
Sara A. Solla
Keyword(s):  
Motor Learning ◽  
Internal Models

New roles for dopamine in motor skill acquisition: Lessons from primates, rodents, and songbirds

2021 ◽  
Author(s):  
Alynda N Wood
Keyword(s):  
Basal Ganglia ◽  
Motor Learning ◽  
Associative Learning ◽  
Skill Acquisition ◽  
Motor Skill ◽  
Human Life ◽  
Skill Learning ◽  
Motor Skill Acquisition ◽  
Model Free ◽  
Species Comparisons

Motor learning is a core aspect of human life, and appears to be ubiquitous throughout the animal kingdom. Dopamine, a neuromodulator with a multifaceted role in synaptic plasticity, may be a key signaling molecule for motor skill learning. Though typically studied in the context of reward-based associative learning, dopamine appears to be necessary for some types of motor learning. Mesencephalic dopamine structures are highly conserved among vertebrates, as are some of their primary targets within the basal ganglia, a subcortical circuit important for motor learning and motor control. With a focus on the benefits of cross-species comparisons, this review examines how "model-free" and "model-based" computational frameworks for understanding dopamine's role in associative learning may be applied to motor learning. The hypotheses that dopamine could drive motor learning either by functioning as a reward prediction error, through passive facilitating of normal basal ganglia activity, or through other mechanisms are examined in light of new studies using humans, rodents, and songbirds. Additionally, new paradigms that could enhance our understanding of dopamine's role in motor learning by bridging the gap between the theoretical literature on motor learning in humans and other species are discussed.

scholarly journals The dynamics of motor learning through the formation of internal models

2019 ◽  
Author(s):  
Camilla Pierella ◽  
Maura Casadio ◽  
Sara A. Solla ◽  
Ferinando A. Mussa-Ivaldi
Keyword(s):  
Motor Learning ◽  
Internal Models ◽  
Inverse Model ◽  
The Body ◽  
Alternative Possibilities ◽  
Dynamical Process ◽  
External Device ◽  
Learning Mechanisms ◽  
Forward Models ◽  
Inverse Models

AbstractA medical student learning to perform a laparoscopic procedure as well as a recently paralyzed user of a powered wheelchair must learn to operate machinery via interfaces that translate their actions into commands for the external device. Mathematically, we describe this type of learning as a deterministic dynamical process, whose state is the evolving forward and inverse internal models of the interface. The forward model predicts the outcomes of actions while the inverse model generates actions designed to attain desired outcomes. Both the mathematical analysis of learning dynamics and the performance observed in a group of subjects demonstrate first-order exponential convergence of the learning process toward a particular state that depends only on the initial inverse and forward models and on the supplied sequence of targets. Noise is not only present but necessary for the convergence of learning through the minimization of the difference between actual and predicted outcomes.Author summarySeveral studies have suggested that as we learn a new skill our brain forms representations, or “internal models”, of the skill and of the environment in which we operate. Theories of motor learning postulate that the brain builds forward models that predict the sensory consequences of motor commands, and inverse models that generate successful commands from planned movements. We test this hypothesis taking advantage of a special interface that generates a novel relation between the subject’s actions and the position of a cursor on a computer monitor, thus allowing subjects to control an external device by movements of their body. We recorded the motions of the body and of the cursor, and obtained estimates of both forward and inverse models. We followed how these estimates evolved in time as subjects practiced and acquired a new skill. We found that the description of learning as a simple deterministic process driven by the sequence of targets is sufficient to capture the observed convergence to a single solution of the inverse model among an infinite variety of alternative possibilities. This work is relevant to the study of fundamental learning mechanisms as well as to the design of intelligent interfaces for people with paralysis.

scholarly journals Learning to shape virtual patient locomotor patterns: internal representations adapt to exploit interactive dynamics

2019 ◽  
Vol 121 (1) ◽  
pp. 321-335 ◽  
Author(s):  
Christopher J. Hasson ◽  
Sarah E. Goodman
Keyword(s):  
Internal Model ◽  
Rhythmic Activity ◽  
Virtual Patient ◽  
Internal Models ◽  
Target Pattern ◽  
Locomotor Pattern ◽  
Model Free ◽  
Interactive Dynamics ◽  
Locomotor Patterns ◽  
Locomotor Rehabilitation

This work aimed to understand the sensorimotor processes used by humans when learning how to manipulate a virtual model of locomotor dynamics. Prior research shows that when interacting with novel dynamics humans develop internal models that map neural commands to limb motion and vice versa. Whether this can be extrapolated to locomotor rehabilitation, a continuous and rhythmic activity that involves dynamically complex interactions, is unknown. In this case, humans could default to model-free strategies. These competing hypotheses were tested with a novel interactive locomotor simulator that reproduced the dynamics of hemiparetic gait. A group of 16 healthy subjects practiced using a small robotic manipulandum to alter the gait of a virtual patient (VP) that had an asymmetric locomotor pattern modeled after stroke survivors. The point of interaction was the ankle of the VP’s affected leg, and the goal was to make the VP’s gait symmetric. Internal model formation was probed with unexpected force channels and null force fields. Generalization was assessed by changing the target locomotor pattern and comparing outcomes with a second group of 10 naive subjects who did not practice the initial symmetric target pattern. Results supported the internal model hypothesis with aftereffects and generalization of manipulation skill. Internal models demonstrated refinements that capitalized on the natural pendular dynamics of human locomotion. This work shows that despite the complex interactive dynamics involved in shaping locomotor patterns, humans nevertheless develop and use internal models that are refined with experience.NEW & NOTEWORTHY This study aimed to understand how humans manipulate the physics of locomotion, a common task for physical therapists during locomotor rehabilitation. To achieve this aim, a novel locomotor simulator was developed that allowed participants to feel like they were manipulating the leg of a miniature virtual stroke survivor walking on a treadmill. As participants practiced improving the simulated patient’s gait, they developed generalizable internal models that capitalized on the natural pendular dynamics of locomotion.

Limb motion dictates how motor learning arises from arbitrary environmental dynamics

2013 ◽  
Vol 109 (10) ◽  
pp. 2466-2482 ◽  
Author(s):  
Gary C. Sing ◽  
Simon P. Orozco ◽  
Maurice A. Smith
Keyword(s):  
Motor Learning ◽  
Adaptive Response ◽  
Prolonged Exposure ◽  
High Amplitude ◽  
Internal Models ◽  
Body Motion ◽  
Dependent Manner ◽  
Adaptive Responses ◽  
Motion State ◽  
Systems Identification

A key idea in motor learning is that internal models of environmental dynamics are internally represented as functions of spatial variables including position, velocity, and acceleration of body motion. We refer to such a representation as motion dependent. The evidence for a motion-dependent representation is, however, primarily based on examination of the adaptation to motion-dependent dynamic environments. To more rigorously test this idea, we examined the adaptive response to perturbations that cannot be well approximated by motion-state: force-impulses—brief, high-amplitude pulses of force. The induced adaptation characterizes the impulse response of the system—a widely used technique for probing system dynamics in engineering systems identification. Here we examined the adaptive responses to two different force-impulse perturbations during human voluntary reaching movements. We found that although neither could be well approximated by motion-state ( R2< 0.18 in both cases), both perturbations induced single-trial adaptive responses that were ( R2> 0.87). Moreover, these responses were similar in shape to those induced by low-fidelity motion-based approximations of the force-impulses ( r > 0.88). Remarkably, we found that the motion dependence of the adaptive responses to force-impulses persisted, even after prolonged exposure ( R2> 0.95). During a 300-trial training period, trial-to-trial fluctuations in the position, velocity, and acceleration of motion accurately predicted trial-to-trial fluctuations in the adaptive response, and the adaptation gradually became more specific to the perturbation, but only via reorganization of the structure of the motion-dependent representation. These results indicate that internal models of environmental dynamics represent these dynamics in a motion-dependent manner, regardless of the nature of the dynamics encountered.

A Proposed Postural Control Theory Synthesizing Optimal Feedback Control Theory, Postural Motor Learning, and Cerebellar Supervision Learning

2020 ◽  
Vol 127 (6) ◽  
pp. 1118-1133
Author(s):  
Nathan Morelli ◽  
Matthew Hoch
Keyword(s):  
Feedback Control ◽  
Motor Learning ◽  
Control Theory ◽  
Postural Control ◽  
Motor Development ◽  
Internal Models ◽  
Optimal Feedback Control ◽  
Optimal Feedback ◽  
Task Goal ◽  
Feedback Control Theory

Multiple theories regarding motor learning and postural control development aim to explain how the central nervous system (CNS) acquires, adjusts, and learns postural behaviors. However, few theories of postural motor development and learning propose possible neurophysiologic correlates to support their assumptions. Evidence from behavioral and computational models support the cerebellum’s role in supervising motor learning through the production of forward internal models, corrected by sensory prediction errors. Optimal Feedback Control Theory (OFCT) states that the CNS learns new behaviors by minimizing the cost of multi-joint movements that attain a task goal. By synthesizing principles of the OFCT, postural sway characteristics, and cerebellar anatomy and its internal models, we propose an integrated learning model in which cerebellar supervision of postural control is governed by movement cost functions.

scholarly journals External error attribution dampens efferent-based predictions but not proprioceptive changes in hand localization

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Raphael Q. Gastrock ◽  
Shanaathanan Modchalingam ◽  
Bernard Marius ’t Hart ◽  
Denise Y. P. Henriques
Keyword(s):  
Motor Learning ◽  
Implicit Learning ◽  
Internal Models ◽  
Hand Position ◽  
Source Attribution ◽  
External Sources

AbstractIn learning and adapting movements in changing conditions, people attribute the errors they experience to a combined weighting of internal or external sources. As such, error attribution that places more weight on external sources should lead to decreased updates in our internal models for movement of the limb or estimating the position of the effector, i.e. there should be reduced implicit learning. However, measures of implicit learning are the same whether or not we induce explicit adaptation with instructions about the nature of the perturbation. Here we evoke clearly external errors by either demonstrating the rotation on every trial, or showing the hand itself throughout training. Implicit reach aftereffects persist, but are reduced in both groups. Only for the group viewing the hand, changes in hand position estimates suggest that predicted sensory consequences are not updated, but only rely on recalibrated proprioception. Our results show that estimating the position of the hand incorporates source attribution during motor learning, but recalibrated proprioception is an implicit process unaffected by external error attribution.

scholarly journals Tandem internal models execute motor learning in the cerebellum

2018 ◽  
Vol 115 (28) ◽  
pp. 7428-7433 ◽  
Author(s):  
Takeru Honda ◽  
Soichi Nagao ◽  
Yuji Hashimoto ◽  
Kinya Ishikawa ◽  
Takanori Yokota ◽  
...  
Keyword(s):  
Motor Control ◽  
Motor Learning ◽  
Internal Models ◽  
Cerebellar Degeneration ◽  
Tandem Configuration ◽  
Behavioral Adaptations ◽  
Behavioral Impairments ◽  
Complex Forms ◽  
Learning And Cognition ◽  
The Brain

In performing skillful movement, humans use predictions from internal models formed by repetition learning. However, the computational organization of internal models in the brain remains unknown. Here, we demonstrate that a computational architecture employing a tandem configuration of forward and inverse internal models enables efficient motor learning in the cerebellum. The model predicted learning adaptations observed in hand-reaching experiments in humans wearing a prism lens and explained the kinetic components of these behavioral adaptations. The tandem system also predicted a form of subliminal motor learning that was experimentally validated after training intentional misses of hand targets. Patients with cerebellar degeneration disease showed behavioral impairments consistent with tandemly arranged internal models. These findings validate computational tandemization of internal models in motor control and its potential uses in more complex forms of learning and cognition.

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