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 Concept Videos

Machines01:19

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...

You might also read

Related Articles

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

Sort by
Same author

Managing Gaze Competition during Simultaneous Manual Action Control and Environment Monitoring.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

The Simons Collaboration on Ecological Neuroscience: Studying how the brain interacts with the world.

Neuron·2026
Same author

The organization of multiple motor memories.

Current opinion in neurobiology·2026
Same author

Rapid responses to reach errors are equally strong during fixation and visual pursuit.

Journal of neurophysiology·2026
Same author

Task-relevant haptic feedback improves asymptotic performance in de novo arm control acquisition.

Scientific reports·2026
Same author

Manifold Interactions between the Action-Mode Network and Sensorimotor Cortex during Human Motor Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026

Related Experiment Video

Updated: Jun 14, 2026

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

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

Published on: April 21, 2023

Multiple grasp-specific representations of tool dynamics mediate skillful manipulation.

James N Ingram1, Ian S Howard, J Randall Flanagan

  • 1Department of Engineering, University of Cambridge, Cambridge, UK. jni20@cam.ac.uk

Current Biology : CB
|March 30, 2010
PubMed
Summary
This summary is machine-generated.

The brain uses grasp-specific models for tool dynamics, not a single general model. This finding impacts understanding how the motor system learns and adapts to tool manipulation across different orientations.

More Related Videos

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

Related Experiment Videos

Last Updated: Jun 14, 2026

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

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

Published on: April 21, 2023

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace
09:11

Design and Use of an Apparatus for Presenting Graspable Objects in 3D Workspace

Published on: August 8, 2019

Area of Science:

  • Motor control and learning
  • Robotics and human-machine interaction
  • Cognitive neuroscience

Background:

  • Skillful tool use relies on understanding tool dynamics, specifically the relationship between applied force and motion.
  • This force-motion mapping is influenced by the tool's orientation within the hand.
  • Previous research has not fully clarified whether the motor system employs a unified dynamic representation or multiple context-specific ones.

Purpose of the Study:

  • To investigate how the motor system represents tool dynamics during manipulation with varying grasp orientations.
  • To determine if the brain uses a single, generalized dynamic model or multiple grasp-specific models for tool use.

Main Methods:

  • A novel robotic interface was used to allow subjects to manipulate a virtual tool.
  • The orientation of the tool relative to the hand was systematically varied during the task.
  • Subjects' ability to anticipate and parameterize forces applied to the tool was measured.

Main Results:

  • Subjects could quickly predict force direction based on visual cues of tool orientation.
  • With practice, subjects learned to adjust force magnitude but showed limited generalization across different tool orientations.
  • This limited generalization suggests that a single, general dynamic representation was not employed.

Conclusions:

  • The findings indicate that the motor system utilizes multiple, grasp-specific representations of object dynamics.
  • Each representation likely encodes the specific force-motion mapping for a particular grasp context.
  • The concept of grasp-specific representations offers a framework for understanding prior findings in dynamics learning.