Review Article | Published:

Principles of sensorimotor learning

Nature Reviews Neuroscience volume 12, pages 739751 (2011) | Download Citation

Abstract

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities — whether it is snowboarding or ballroom dancing — but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.

Key points

  • Learning movement skills involves a number of interacting components, such as information extraction, decision making, different classes of control, motor learning and its representations.

  • Skilled performance requires the effective and efficient gathering and processing of sensory information that is relevant to an action.

  • Decision-making processes involve determining what information to extract during the unfolding task and, based on this information, when to make the next movement and which movement to make.

  • Classes of control used to optimize motor performance include predictive, reactive and biomechanical control. Processes of motor learning can be distinguished by the types of information that the motor system uses as a learning signal. These include error-based learning, reinforcement learning, observational learning and use-dependent learning.

  • Representations in motor learning reflect the internal assumptions about the task structure and constrain the way in which learning occurs in response to errors. Such representations can be conceptualized in two ways, either as mechanistic or normative models.

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Acknowledgements

We thank the Wellcome Trust, the Canadian Institutes of Health Research and the Human Frontiers Science Programme for support. J.D. is supported by a Scholar award from the James S. McDonnell foundation.

Author information

Author notes

    • Daniel M. Wolpert
    • , Jörn Diedrichsen
    •  & J. Randall Flanagan

    All authors contributed equally to this work

Affiliations

  1. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.

    • Daniel M. Wolpert
  2. Institute of Cognitive Neuroscience, University College London, London WC1N 3AR, UK.

    • Jörn Diedrichsen
  3. Department of Psychology and Centre for Neuroscience Studies, Queen's University, Kingston, Ontario K7L 3N6, Canada.

    • J. Randall Flanagan

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Daniel M. Wolpert.

Glossary

Optimal

A system is said to be optimal if it minimizes some cost function under given constraints.

Saccade

A rapid movement of the eyes that changes fixation from one point to another.

Visuomotor mapping

Typically, the relationship between the hand's actual and visual locations that can be altered using devices (such as a prism) or virtual reality to examine visuomotor learning.

Noise

Random or unpredictable fluctuations and disturbances of neural, neuromuscular or environmental origin.

Bayesian inference

A method of statistical inference in which observations are used to calculate or update the probability distribution of hidden variables.

Visuo–haptic integration

The process that combines visual information (for example, the visual size of an object) and haptic information (for example, the felt size of a grasped object) into a single percept (for example, its size).

Efference copy

A copy of the outgoing (efferent) motor command that can be used in conjunction with a forward model to predict the sensory consequences of action.

Dynamics

The relationship between force and motion that can be altered using robotic interfaces to study the learning of novel dynamics.

Forward model

A neural simulator that predicts (in the causal — and hence, forward — direction) the sensory consequences of an action given the current state and efference copy of the motor command.

Optimal feedback control

Optimality that is applied to setting up time-varying feedback controllers to drive a movement so as to minimize a function that is typically a combination of accuracy and effort.

Impedance control

Impedance refers to the force produced by the limb to resist an externally induced motion (or deviation from desired motion). Impedance control changes this biomechanical behaviour of the limb by changing the configuration or stiffness through muscular co-contraction.

Force fields

A type of dynamic motor learning in which forces are applied to the hand by a robotic manipulandum and in which the force direction and magnitude depends on the state of the hand (for example, its position and velocity), allowing the perturbation to be plotted as a force field.

Solution manifold

The set of solutions that can each, on average (perhaps owing to noise), solve a task.

After-effect

The deviations of a system from pre-perturbation behaviour after learning when the perturbation is first removed.

Kinematics

This refers to the relationship between positional variables, such as joint angles and hand position.

Savings

This refers to the phenomenon that relearning of a perturbation or skill for a second time is faster than initial learning.

Declarative memory

Memories that can be consciously recalled, such as facts and events.

Procedural memory

Unconscious memories of skills and how to do things, such as being able to walk downstairs.

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DOI

https://doi.org/10.1038/nrn3112

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