Decomposition of Internal Models in Motor Learning under Mixed Dynamic Environments

2006 ◽  
Author(s):  
Koji Ito ◽  
Tsutomu Imai ◽  
Naoki Tomi ◽  
Toshiyuki Kondo
Keyword(s):  
Motor Learning ◽  
Dynamic Environments ◽  
Internal Models

scholarly journals Composition and Decomposition of Internal Models in Motor Learning under Altered Kinematic and Dynamic Environments

1999 ◽  
Vol 19 (20) ◽  
pp. RC34-RC34 ◽  
Author(s):  
J. Randall Flanagan ◽  
Eri Nakano ◽  
Hiroshi Imamizu ◽  
Rieko Osu ◽  
Toshinori Yoshioka ◽  
...  
Keyword(s):  
Motor Learning ◽  
Dynamic Environments ◽  
Internal Models

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

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 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

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 Implicit Learning of Temporal Behavior in Complex Dynamic Environments

2020 ◽  
Author(s):  
Josh M. Salet ◽  
Wouter Kruijne ◽  
Hedderik van Rijn
Keyword(s):  
Motor Learning ◽  
Implicit Learning ◽  
Dynamic Environments ◽  
Temporal Behavior ◽  
Complex Environment ◽  
Complex Dynamic ◽  
Laboratory Findings ◽  
Cognitive Theories ◽  
Future Events ◽  
Motor Actions

AbstractHumans can automatically detect and learn to exploit repeated aspects (regularities) of the environment. Timing research suggests that such learning is not only used to anticipate what will happen, but also when. However, in timing experiments, the intervals to be timed are presented in isolation from other stimuli and explicitly cued, contrasting with naturalistic environments in which intervals are embedded in a constant stream of events and individuals are hardly aware of them. It is unclear whether laboratory findings from timing research translate to a more ecologically valid, implicit environment. Here we show in a game-like experiment, specifically designed to measure naturalistic behavior, that participants implicitly use regular intervals to anticipate future events, even when these intervals are constantly interrupted by irregular yet behaviorally relevant events. This finding extends previous research by showing that individuals not only detect such regularities but can also use this knowledge to decide when to act in a complex environment. Furthermore, this finding demonstrates that this type of learning can occur independently from the ordinal sequence of motor actions, which contrasts this work with earlier motor learning studies. Taken together, our results demonstrate that regularities in the time between events are implicitly monitored and used to predict and act on what happens when, thereby showing that laboratory findings from timing research can generalize to naturalistic environments. Additionally, with the development of our game-like experiment we demonstrate an approach to test cognitive theories in less controlled, ecologically more valid environments.

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.

scholarly journals Implicit learning of temporal behavior in complex dynamic environments

2021 ◽  
Author(s):  
Josh M. Salet ◽  
Wouter Kruijne ◽  
Hedderik van Rijn
Keyword(s):  
Motor Learning ◽  
Implicit Learning ◽  
Dynamic Environments ◽  
Temporal Behavior ◽  
Complex Environment ◽  
Complex Dynamic ◽  
Laboratory Findings ◽  
Cognitive Theories ◽  
Future Events ◽  
Motor Actions

AbstractHumans can automatically detect and learn to exploit repeated aspects (regularities) of the environment. Timing research suggests that such learning is not only used to anticipate what will happen, but also when it will happen. However, in timing experiments, the intervals to be timed are presented in isolation from other stimuli and explicitly cued, contrasting with naturalistic environments in which intervals are embedded in a constant stream of events and individuals are hardly aware of them. It is unclear whether laboratory findings from timing research translate to a more ecologically valid, implicit environment. Here we show in a game-like experiment, specifically designed to measure naturalistic behavior, that participants implicitly use regular intervals to anticipate future events, even when these intervals are constantly interrupted by irregular yet behaviorally relevant events. This finding extends previous research by showing that individuals not only detect such regularities but can also use this knowledge to decide when to act in a complex environment. Furthermore, this finding demonstrates that this type of learning can occur independently from the ordinal sequence of motor actions, which contrasts this work with earlier motor learning studies. Taken together, our results demonstrate that regularities in the time between events are implicitly monitored and used to predict and act on what happens when, thereby showing that laboratory findings from timing research can generalize to naturalistic environments. Additionally, with the development of our game-like experiment, we demonstrate an approach to test cognitive theories in less controlled, ecologically more valid environments.

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