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Related Experiment Videos

Internal models for motor control and trajectory planning.

M Kawato1

  • 1ATR Human Information Processing Research Laboratories, ERATO, JST, Kawato Dynamic Brain Project, Seika-cho, Soraku-gun, 619-0288, Japan. kawato@hip.atr.co.jp

Current Opinion in Neurobiology
|December 23, 1999
PubMed
Summary
This summary is machine-generated.

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Internal models are crucial in neuroscience and cognitive science, supported by extensive data. Research highlights inverse dynamics model learning and the minimum variance model in motor control, suggesting combined kinematic and dynamic approaches.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience
  • Motor Control

Background:

  • Internal model concepts are increasingly prevalent in neuroscience and cognitive science.
  • These models are substantiated by behavioral, neurophysiological, and imaging data.
  • The structure and function of these models have been elucidated through empirical evidence.

Purpose of the Study:

  • To explore the role of internal models in motor control and learning.
  • To present evidence supporting inverse dynamics model learning.
  • To introduce and discuss the 'minimum variance model' as an advancement in trajectory planning.

Main Methods:

  • Analysis of behavioral, neurophysiological, and imaging data.

Related Experiment Videos

  • Unit recordings from cerebellar Purkinje cells to support inverse dynamics.
  • Theoretical integration of kinematic and dynamic approaches to trajectory planning.

    • Main Results:

    • Empirical data supports the widespread use and understanding of internal models.
    • Unit recordings provide direct evidence for inverse dynamics model learning.
    • The minimum variance model integrates disputed trajectory planning approaches, indicating dual kinematic and dynamic model utilization.

    Conclusions:

    • Internal models are fundamental to understanding motor control and cognitive processes.
    • Inverse dynamics model learning is a key mechanism supported by neural data.
    • The minimum variance model offers a unified framework for motor planning, integrating both kinematic and dynamic internal models.