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Related Experiment Video

Updated: Jul 17, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

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Published on: April 21, 2023

Internal models underlying fingertip force control during object manipulation in humans.

A M Gordon1, I Salimi

  • 1Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
Summary

Predictive motor control for object manipulation is partially generalized to the non-practiced contralateral hand. While weight-based predictions transfer, precise center of mass (CoM) force adjustments do not, suggesting hierarchical control.

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Area of Science:

  • Motor control and learning
  • Human-robot interaction
  • Biomechanics

Background:

  • Skillful object manipulation relies on predictive fingertip force scaling.
  • Internal models, formed through experience, underpin this predictive control.
  • Generalizability of predictive control to the contralateral limb is not fully understood.

Purpose of the Study:

  • To investigate the transfer of predictive force control from the dominant (right) to the non-dominant (left) hand.
  • To determine if practice-based predictive control for object weight and center of mass (CoM) location generalizes across hands.
  • To explore the hierarchical nature of predictive motor control.

Main Methods:

  • Participants performed object lifting tasks with varying weights and CoM displacements.
  • Force transducers measured fingertip forces during lifts.
  • Predictive force scaling was assessed after practice with the right hand and subsequent single trial with the left hand.

Main Results:

  • Predictive force scaling for object weight generalized to the contralateral hand.
  • Precise force adjustments for CoM-induced tilting did not transfer to the contralateral hand.
  • Force rates were higher for heavier objects (800g) regardless of hand.

Conclusions:

  • Predictive control exhibits a dichotomy in generalizability: weight-based parameters transfer, while individuated digit control for CoM does not.
  • This suggests a hierarchical control system where higher-level parameters (weight) are globalized, while lower-level parameters (CoM adjustments) are not.
  • Findings have implications for understanding motor learning and designing assistive robotic devices.