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

You might also read

Related Articles

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

Sort by
Same author

Association between internet addiction and mindfulness.

Frontiers in psychology·2026
Same author

Epidemiology, genetics, neuroimaging, clinical features, and treatment between overall and performance-only social anxiety disorder: a narrative review.

Dialogues in clinical neuroscience·2026
Same author

Entropy-dependent human motor modulation consistent with morphological computation in a single subject.

Frontiers in robotics and AI·2026
Same author

Adaptive laboratory evolution optimizes an engineered phosphite utilization pathway in Synechococcus elongatus PCC 7942.

Journal of bioscience and bioengineering·2025
Same author

Epigenetic aging in anxiety disorders: diagnostic subtype differences and associations with social functioning.

BMC medicine·2025
Same author

Factors Contributing to the Success of Counterattacks Examined Through Reaction Time.

Perceptual and motor skills·2025

Related Experiment Video

Updated: Sep 6, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.8K

Estimating Information Processing of Human Fast Continuous Tapping from Trajectories.

Hiroki Murakami1, Norimasa Yamada2

  • 1Graduate School of Health and Sport Sciences, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota, Aichi 470-0393, Japan.

Entropy (Basel, Switzerland)
|June 24, 2022
PubMed
Summary

This study explores human movement information processing using Shannon entropy. Difficult tasks show information processing early in movement, with a novel method enhancing accuracy.

Keywords:
Fittsinformation entropymutual information

More Related Videos

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

5.8K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K

Related Experiment Videos

Last Updated: Sep 6, 2025

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

13.8K
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

5.8K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K

Area of Science:

  • Human motor control
  • Information theory
  • Cognitive psychology

Background:

  • Fitts' law describes the speed-accuracy trade-off in human motor tasks.
  • Traditional Fitts' law calculations differ from Shannon's information entropy.
  • Understanding information processing in motor control is crucial.

Purpose of the Study:

  • To estimate information entropy and mutual information of human movement trajectories.
  • To analyze moment-by-moment information processing in a continuous aiming task.
  • To compare two methods for calculating information processing in motor tasks.

Main Methods:

  • Applied Shannon's information theory to analyze human movement trajectories.
  • Calculated information entropy and mutual information for continuous aiming tasks.
  • Utilized two encoding methods: 3D coordinates and directional coordinates.

Main Results:

  • Information entropy magnitude and structure varied with the index of difficulty.
  • Information processing occurred in the first half of the trajectory for difficult tasks.
  • The novel encoded method yielded higher information processing values than the conventional method.

Conclusions:

  • The study provides a new perspective on information processing in motor control.
  • The novel encoding method shows potential for more accurate estimation of information processing.
  • Findings contribute to understanding the cognitive mechanisms underlying human movement.