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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Predictability and Robustness in the Manipulation of Dynamically Complex Objects.

Dagmar Sternad1, Christopher J Hasson2,3

  • 1Department of Biology, Electrical and Computer Engineering, and Physics, Northeastern University, 134 Mugar Life Science Building, 360 Huntington Avenue, Boston, Massachusetts, 02115, USA. D.Sternad@neu.edu.

Advances in Experimental Medicine and Biology
|December 31, 2016
PubMed
Summary
This summary is machine-generated.

Humans develop predictable and robust strategies for manipulating objects, like a coffee cup, by learning to manage complex forces. This research quantifies these learned motor control strategies for better understanding human movement and neurological conditions.

Keywords:
Cart-and-pendulumChaosCup-and-ballDystoniaEnergy marginExperimental paradigmHand trajectoryHapticmasterNoiseNonlinear fluid dynamicsTheory-based

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Area of Science:

  • Neuroscience
  • Human Motor Control
  • Robotics

Background:

  • Complex object manipulation is crucial for daily living but poorly understood.
  • Human neuromotor control must compensate for complex interaction forces, such as when transporting a liquid.

Purpose of the Study:

  • To test the hypothesis that humans learn predictable and robust interaction strategies.
  • To quantitatively assess human control strategies for object manipulation.

Main Methods:

  • A cart-and-pendulum system modeled a coffee cup task in a virtual environment.
  • Subjects used a robotic manipulandum to interact with a virtual cup containing a rolling ball.
  • Predictability was quantified using mutual information; robustness was measured by energy margin.

Main Results:

  • Practice led to the development of predictable and robust movement strategies.
  • Observed strategies were not explained by common criteria like smoothness maximization or force minimization.
  • The findings suggest humans create internal models for predictable and robust interactions.

Conclusions:

  • Humans learn to make object interactions predictable and robust, essential for tasks like handling a coffee cup.
  • This research provides a framework for studying motor control deficits in neurological disorders and aging.
  • The developed paradigm offers insights into conditions affecting manual dexterity, including dystonia and multiple sclerosis.