Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Learning arm kinematics and dynamics.

C G Atkeson1

  • 1Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge 02139.

Annual Review of Neuroscience
|January 1, 1989
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Interpersonal interactions for haptic guidance during balance exercises.

Gait & posture·2018
Same author

Implementing projection pursuit learning.

IEEE transactions on neural networks·1996
Same author

Inferring limb coordination strategies from trajectory kinematics.

Journal of neuroscience methods·1987
Same author

Deducing planning variables from experimental arm trajectories: pitfalls and possibilities.

Biological cybernetics·1987
Same author

Kinematic features of unrestrained vertical arm movements.

The Journal of neuroscience : the official journal of the Society for Neuroscience·1985
Same journal

Body-Brain Integration: The Lower Brainstem in Sleep-Wake Regulation.

Annual review of neuroscience·2026
Same journal

Planning in the Brain: It's Not What You Think It Is.

Annual review of neuroscience·2026
Same journal

The Emerging Neurobiology of Psychedelics: Critical Periods, Metaplasticity, and Extracellular Matrix Remodeling.

Annual review of neuroscience·2026
Same journal

Rethinking Predictive Processing.

Annual review of neuroscience·2026
Same journal

Path Integration in Alzheimer's Disease: Orientation, Movement, and Theta Rhythmicity.

Annual review of neuroscience·2026
Same journal

The Cellular and Circuit Basis of Temperature Sensation in <i>Drosophila</i>.

Annual review of neuroscience·2026
See all related articles

Internal models, like tabular and structured representations, influence motor learning. Structured models excel with accurate system knowledge, while tabular models offer flexibility when system structure is unknown, aiding motor control research.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • Internal models of the motor apparatus are crucial for interpreting performance errors, especially in complex multijoint movements.
  • Understanding these models bridges computational theories and physiological explorations of motor learning.
  • Motor learning research is vital for deciphering the brain's sophisticated solutions to motor control challenges.

Purpose of the Study:

  • To review how different internal model representations (tabular vs. structured) impact motor learning capabilities.
  • To explore the benefits and drawbacks of each model type in motor control.
  • To highlight the role of experimental psychophysics in advancing motor learning theories.

Main Methods:

  • Review of existing literature on internal models in motor control.

Related Experiment Videos

  • Analysis of the properties of tabular and structured internal models.
  • Discussion of experimental studies in motor learning and psychophysics.

    • Main Results:

    • Structured models facilitate efficient learning and generalization when system structure is accurately known.
    • Tabular models provide flexibility and are useful when system structure is unknown or approximated.
    • Combining different representations is an active area of research.

    Conclusions:

    • The choice of representation in internal models significantly affects a system's learning capacity and generalization.
    • Investigating motor learning patterns can elucidate the underlying control architectures and representations used by the nervous system.
    • Future research directions include combining model types and further experimental psychophysics studies.