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Related Experiment Video

Updated: Jul 9, 2026

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
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Published on: December 5, 2014

Credit assignment between body and object probed by an object transportation task.

Gaiqing Kong1,2,3,4, Zhihao Zhou1,2,3, Qining Wang1,2,3

  • 1School of Psychological and Cognitive Sciences, Peking University, Beijing, China.

Scientific Reports
|October 19, 2017
PubMed
Summary
This summary is machine-generated.

Movement error learning involves assigning credit to the object or body. This study quantifies this by showing the body accounts for 58% of learning, while the object accounts for 25%.

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Last Updated: Jul 9, 2026

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Area of Science:

  • Neuroscience
  • Motor Control
  • Human Movement Science

Background:

  • Movement error learning presents a credit assignment problem, requiring differentiation between misestimations of object properties and bodily states.
  • While simultaneous learning of object and body properties has been observed, their quantitative relationship remains underexplored.

Purpose of the Study:

  • To quantitatively investigate the partitioning of learning between object properties and body states during motor control.
  • To determine the relative contribution of object-based versus body-based learning following motor errors.

Main Methods:

  • A novel weight-transportation task was designed to elicit movement errors.
  • Participants' learning transfer was assessed across conditions involving switching hands, identical objects, or both.

Main Results:

  • Learning attributed to the object (same cup) was found to be 25%.
  • Learning attributed to the body (same hand) was found to be 58%.
  • These findings indicate a distinct partitioning of motor learning between environmental and internal factors.

Conclusions:

  • The nervous system appears to effectively partition motor learning between object-specific and body-specific adaptations.
  • The body's contribution to motor learning from errors is significantly larger than the object's contribution.