Perspectives and problems in motor learning

doi:10.1016/S1364-6613(00)01773-3

Trends in Cognitive Sciences

Volume 5, Issue 11, 1 November 2001, Pages 487-494

https://doi.org/10.1016/S1364-6613(00)01773-3 Get rights and content

Abstract

Movement provides the only means we have to interact with both the world and other people. Such interactions can be hard-wired or learned through experience with the environment. Learning allows us to adapt to a changing physical environment as well as to novel conventions developed by society. Here we review motor learning from a computational perspective, exploring the need for motor learning, what is learned and how it is represented, and the mechanisms of learning. We relate these computational issues to empirical studies on motor learning in humans.

Section snippets

Why do we need motor learning?

Learning involves changes in behaviour that arise from interaction with the environment and is distinct from maturation, which involves changes that occur independent of such interaction. The goal of learning is, in general, to improve performance. Whereas some simple species show no motor learning, the need for motor learning arises in species in which the organism's environment, body or task change. Specifically, when such changes are unpredictable, they cannot be pre-specified in a control

What is learned in motor learning?

From a computational viewpoint the brain is a processing system that converts inputs to outputs. The outputs are the motor commands acting on ensembles of muscles and the inputs are the aggregate of sensory feedback provided by our sense organs and derived internally from an efference copy of the descending motor command. Motor control can be thought of as the process of transforming sensory inputs into consequent motor outputs. The problem of motor learning is one of mastering and adapting

What is the computational basis of motor learning?

There are three principal ways in which the learning system can interact with the environment; these three ways correspond to three computational paradigms for learning: (1) supervised; (2) reinforcement; and (3) unsupervised learning. We consider the motor learning system taking in sensory inputs and producing motor outputs. In supervised learning, the environment provides, for each input, an explicit desired output or target. The goal of the learning system is to learn the mapping from inputs

What makes motor control difficult?

The algorithms discussed in the previous section can in theory be used to learn internal models required for skilled performance. However, there are several features of the human motor system that significantly complicate learning and control. First, there are considerable time delays in both the transduction and transport of sensory signals to the CNS. For example, visual input can take around 100 ms to be processed. When this sensory delay is combined with efferent delays associated with

How is motor learning represented?

In principle, internal models can be represented in motor memory in many different ways. How they are represented in the CNS has important functional implications. The representation determines the coordinate systems of the neurons’ code and what changes to the mapping are easy to learn. We can distinguish between two extremes of representation. Lookup tables simply store the output for each possible setting of the input. Lookup tables are infinitely flexible, but suffer from their inability to

What are the building blocks of motor learning?

Recently, focus has begun to shift away from examining learning of a single internal model to consider how we are able to learn a variety of tasks. Many situations that we encounter are derived from a combination of previously experienced situations, such as novel conjoints of manipulated objects and environments. Internal models can be regarded conceptually as motor primitives, which are the building blocks used to construct intricate motor behaviours with an enormous range. By modulating the

How does motor learning relate to perception and cognition?

As stated at the beginning of this article, direct information transmission between people, such as speech, arm gestures or facial expressions, is mediated through the motor system, which provides a common code for communication. An important idea in psychology is that perception of the action of others, including speech, involves the action system ⁵⁹. Others’ actions are decoded by activating one's own action system at a subthreshold level and there appear to be special neural mechanism for

Questions for future research

•
What are the common elements of motor learning and other forms of learning? Are the differences between motor and other forms of learning (e.g. perceptual) at a cellular level or a systems level?
•
How are internal models of external objects, such as tools, integrated with the internal models of our own body, such as the arm?
•
What are the characteristics of motor tasks that lead to competition in motor working memory and are there multiple motor working memory systems?
•
Are the internal models that

Acknowledgements

This work was supported by grants from the Wellcome Trust, the Medical Research Council, the BBSRC and the Human Frontier Science Project. Cartoons from CALVIN AND HOBBES © Bill Watterson. Reprinted with permission of Universal Press Syndicate. All rights reserved.

References (66)

E. Thelen
Spontaneous kicking in month-old infants: manifestation of a human central locomotor program
Behav. Neural. Biol.
(1981)
R.C. Miall et al.
Forward models for physiological motor control
Neural Netw.
(1996)
M. Desmurget et al.
Forward modeling allows feedback control for fast reaching movements
Trends Cognit. Sci.
(2000)
P.J. Cordo et al.
Sensory control of target acquisition
Trends Neurosci.
(1989)
M.I. Jordan et al.
Forward models: supervised learning with a distal teacher
Cognit. Sci.
(1992)
M. Kawato
Feedback-error-learning neural network for supervised learning
K. Doya
Complementary roles of basal ganglia and cerebellum in learning and motor control
Curr. Opin. Neurobiol.
(2000)
D.M. Wolpert et al.
Multiple paired forward and inverse models for motor control
Neural Netw.
(1998)
S. Schaal
Is imitation learning the route to humanoid robots?
Trends Cognit. Sci.
(1999)
D.M. Wolpert
Internal models in the cerebellum
Trends Cognit. Sci.
(1998)

F.A. Mussa-Ivaldi

Modular features of motor control and learning

Curr. Opin. Neurobiol.

(1999)

A.M. Liberman et al.

On the relation of speech to language

Trends Cognit. Sci.

(2000)

G. Rizzolatti et al.

Language within our grasp

Trends Neurosci.

(1998)

J. Grezes

Does perception of biological motion rely on specific brain regions?

NeuroImage

(2001)

S.T. Grafton

Premotor cortex activation during observation and naming of familiar tools

NeuroImage

(1997)

I. Eibl-Eibesfeldt

The expressive behavior of the deaf-and-blind-born

R.N. Lemon

Cortical control of the primate hand. The 1992 GL brown prize lecture

Exp. Physiol.

(1993)

D.M. Wolpert

An internal model for sensorimotor integration

Science

(1995)

J.R. Flanagan et al.

The role of internal models in motion planning and control: evidence from grip force adjustments during movements of hand-held loads

J. Neurosci.

(1997)

R.S. Johansson

Sensory input and control of grip

Novartis Found. Symp.

(1998)

S.J. Blakemore

Perceptual modulation of self-produced stimuli: the role of spatio-temporal prediction

J. Cogn. Neurosci.

(1999)

J.A.S. Kelso

Dynamic Patterns: The Self-Organization of Brain and Behavior

(1995)

D.E. Rumelhart

Learning internal representations by back-propagating errors

Nature

(1986)

R. Sutton et al.

Reinforcement Learning

(1998)

N. Bernstein

The Coordination and Regulation of Movements

(1967)

A.E. Bryson et al.

Applied Optimal Control

(1975)

W.L. Nelson

Physical principles for economies of skilled movements

Biol. Cybern.

(1983)

T. Flash et al.

The co-ordination of arm movements: an experimentally confirmed mathematical model

J. Neurosci.

(1985)

Y. Uno

Formation and control of optimal trajectories in human multijoint arm movements: minimum torque-change model

Biol. Cybern.

(1989)

R.A. Schmidt

Motor output variability: a theory for the accuracy of rapid motor acts

Psychol. Rev.

(1979)

C.M. Harris et al.

Signal-dependent noise determines motor planning

Nature

(1998)

E. Oja

A simplified neuron model as a principal component analyzer

J. Math. Biol.

(1982)

R. Linsker

From basic network principles to neural architecture: emergence of spatial-opponent cells

Proc. Natl. Acad. Sci. U. S. A

(1986)

Cited by (523)

Multisensory peripersonal space: Visual looming stimuli induce stronger response facilitation to tactile than auditory and visual stimulations
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Anticipating physical contact with objects in the environment is a key component of efficient motor performance. Peripersonal neurons are thought to play a determinant role in these predictions by enhancing responses to touch when combined with visual stimuli in peripersonal space (PPS). However, recent research challenges the idea that this visuo-tactile integration contributing to the prediction of tactile events occurs strictly in PPS. We hypothesised that enhanced sensory sensitivity in a multisensory context involves not only contact anticipation but also heightened attention towards near-body visual stimuli. To test this hypothesis, Experiment 1 required participants to respond promptly to tactile (probing contact anticipation) and auditory (probing enhanced attention) stimulations presented at different moments of the trajectory of a (social and non-social) looming visual stimulus. Reduction in reaction time as compared to a unisensory baseline was observed from an egocentric distance of around 2 m (inside and outside PPS) for all multisensory conditions and types of visual stimuli. Experiment 2 tested whether these facilitation effects still occur in the absence of a multisensory context, i.e., in a visuo-visual condition. Overall, facilitation effects induced by the looming visual stimulus were comparable in the three sensory modalities outside PPS but were more pronounced for the tactile modality inside PPS (84 cm from the body as estimated by a reachability judgement task). Considered together, the results suggest that facilitation effects induced by visual looming stimuli in multimodal sensory processing rely on the combination of attentional factors and contact anticipation depending on their distance from the body.
Kinesthetic vs. visual focus: No evidence for effects of practice modality in representation types after action imagery practice and action execution practice
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Action-imagery practice (AIP) is assumed to result in partly different action representations than action-execution practice (AEP). The present study investigated whether focusing on either kinesthetic or visual aspects of a task during practice amplifies or diminishes such differences between AIP and AEP. In ten sessions, four groups, using either AIP or AEP with either kinesthetic or visual focus, practiced a twelve-element sequence in a unimanual serial reaction time task. Tests involved the practice sequence, a mirror sequence, and a different sequence, each performed with the practice and transfer hand. In AIP and AEP, in both hands, reaction times (RTs) were shorter in the practice sequence than in the different sequence, indicating effector-independent visual-spatial sequence representations. Further, RTs were shorter in the practice hand than in the transfer hand in the practice sequence (but not in the different sequence), indicating effector-dependent representations in AEP and AIP. Although the representation types did not differ, learning effects were stronger in AEP than in AIP. Thus, although to a lower extent than in AEP, effector-dependent representations can be acquired using AIP. Contrary to the expectations, the focus manipulation did not have an impact on the acquired representation types. Hence, modality instructions in AIP may not have such a strong impact as commonly assumed, at least in implicit sequence learning.
Babies With Pierre Robin Sequence: Neuropsychomotor Development
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The Pierre Robin Sequence presents heterogeneous symptoms, and each newborn can manifest from mild breathing and feeding difficulties to severe complications, as well as a predisposition to present changes in growth and neuropsychomotor development in the first years of life.
The aims were to evaluate and associate the neuropsychomotor development of zero- to 12-month-old children with Pierre Robin sequence (PRS) in the personal-social, fine motor-adaptive, language, and gross motor aspects.
The subjects of the study were 17 infants of both sexes with PRS admitted to the special care unit (SCU) of a reference hospital in the interior of the state of São Paulo, Brazil, in the age range of 20 days to 263 days. Developmental assessments were performed using the Denver Development Screening Test II. The evaluations were carried out in the SCU, with duration of 30 minutes each. Statistical analysis was descriptive using the Mann-Whitney test, two-proportion equality test, and Spearman correlation. The level of significance was set at 0.05.
According to Denver Development Screening Test II, median 78.5 of the babies were at risk for developmental delay identified by the Denver II Test (n = 14, 82.4%). For the developmental areas analyzed by the test there was statistically significant difference in language area.
The babies aged up to 12 months with PRS in this study presented risks for delay in neuropsychomotor development in language, gross motor, fine motor-adaptive, and personal-social aspects, and this finding should be considered to set goals in family orientation and intervention.
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Gaze-specific motor memories for hand-reaching
2022, Current Biology
Numerous studies have proposed that our adaptive motor behaviors depend on learning a map between sensory information and limb movement,1, 2, 3 called an “internal model.” From this perspective, how the brain represents internal models is a critical issue in motor learning, especially regarding their association with spatial frames processed in motor planning.⁴^,⁵ Extensive experimental evidence suggests that during planning stages for visually guided hand reaching, the brain transforms visual target representations in gaze-centered coordinates to motor commands in limb coordinates, via hand-target vectors in workspace coordinates.6, 7, 8, 9 While numerous studies have intensively investigated whether the learning for reaching occurs in workspace or limb coordinates,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 the association of the learning with gaze coordinates still remains untested.²¹ Given the critical role of gaze-related spatial coding in reaching planning,22, 23, 24, 25, 26 the potential role of gaze states for learning is worth examining. Here, we show that motor memories for reaching are separately learned according to target location in gaze coordinates. Specifically, two opposing visuomotor rotations, which normally interfere with each other, can be simultaneously learned when each is associated with reaching to a foveal target and peripheral one. We also show that this gaze-dependent learning occurs in force-field adaptation. Furthermore, generalization of gaze-coupled reach adaptation is limited across central, right, and left visual fields. These results suggest that gaze states are available in the formation and recall of multiple internal models for reaching. Our findings provide novel evidence that a gaze-dependent spatial representation can provide a spatial coordinate framework for context-dependent motor learning.
Instrumental assessment of aero-resistive expiratory muscle strength rehabilitation devices
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Trends in Cognitive Sciences

ReviewPerspectives and problems in motor learning

Abstract

Section snippets

Why do we need motor learning?

What is learned in motor learning?

What is the computational basis of motor learning?

What makes motor control difficult?

How is motor learning represented?

What are the building blocks of motor learning?

How does motor learning relate to perception and cognition?

Questions for future research

Acknowledgements

Behav. Neural. Biol.

Neural Netw.

Trends Cognit. Sci.

Trends Neurosci.

Cognit. Sci.

Curr. Opin. Neurobiol.

Neural Netw.

Trends Cognit. Sci.

Trends Cognit. Sci.

Curr. Opin. Neurobiol.

Trends Cognit. Sci.

Trends Neurosci.

NeuroImage

NeuroImage

The expressive behavior of the deaf-and-blind-born

Cortical control of the primate hand. The 1992 GL brown prize lecture

Exp. Physiol.

An internal model for sensorimotor integration

Science

The role of internal models in motion planning and control: evidence from grip force adjustments during movements of hand-held loads

J. Neurosci.

Sensory input and control of grip

Novartis Found. Symp.

Perceptual modulation of self-produced stimuli: the role of spatio-temporal prediction

J. Cogn. Neurosci.

Dynamic Patterns: The Self-Organization of Brain and Behavior

Learning internal representations by back-propagating errors

Nature

Reinforcement Learning

The Coordination and Regulation of Movements

Applied Optimal Control

Physical principles for economies of skilled movements

Biol. Cybern.

The co-ordination of arm movements: an experimentally confirmed mathematical model

J. Neurosci.

Formation and control of optimal trajectories in human multijoint arm movements: minimum torque-change model

Biol. Cybern.

Motor output variability: a theory for the accuracy of rapid motor acts

Psychol. Rev.

Signal-dependent noise determines motor planning

Nature

A simplified neuron model as a principal component analyzer

J. Math. Biol.

From basic network principles to neural architecture: emergence of spatial-opponent cells

Proc. Natl. Acad. Sci. U. S. A

Review
Perspectives and problems in motor learning