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

Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any finite,...
Time and frequency -Domain Interpretation of Phase-lead Control01:24

Time and frequency -Domain Interpretation of Phase-lead Control

Phase-lead controllers are commonly used in various control systems to enhance response speed and stability. Adjusting the brightness on a television screen offers a practical example of phase-lead control. When contrast is enhanced, a phase-lead controller is employed. Mathematically, phase-lead control is identified when the first parameter is smaller than the second.
The design of phase-lead control involves the strategic placement of poles and zeros to balance steady-state error and system...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Distance Measurements by Taping01:18

Distance Measurements by Taping

Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.

You might also read

Related Articles

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

Sort by
Same author

Task-specific vs general measures of pain-related fear relative associations with spinal motor behaviour: a systematic review and meta-analysis.

Pain·2025
Same author

Restoring the complexity of walking in the elderly and its impact on clinical measures around the risk of falls.

Frontiers in network physiology·2025
Same author

Complex systems approaches to the adaptability of human functions and behavior in health, aging, and chronic diseases: protocol for a meta-narrative review.

Systematic reviews·2023
Same author

Adaptive Capacities and Complexity of Heart Rate Variability in Patients With Chronic Obstructive Pulmonary Disease Throughout Pulmonary Rehabilitation.

Frontiers in physiology·2021
Same author

Relationship between gait complexity and pain attention in chronic low back pain.

Pain·2021
Same author

Restoring Walking Complexity in Older Adults Through Arm-in-Arm Walking: Were Almurad et al.'s (2018) Results an Artifact?

Motor control·2021

Related Experiment Video

Updated: Jun 18, 2026

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

Long-range correlation in synchronization and syncopation tapping: a linear phase correction model.

Didier Delignières1, Kjerstin Torre, Loïc Lemoine

  • 1Motor Efficiency and Deficiency Laboratory, University Montpellier I, Montpellier, France. didier.delignieres@univ-montp1.fr

Plos One
|November 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new fractal timekeeper model to explain long-range correlations in syncopation tapping, extending previous timing models for a unified framework.

More Related Videos

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task
05:04

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Published on: September 21, 2017

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Related Experiment Videos

Last Updated: Jun 18, 2026

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task
05:04

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Published on: September 21, 2017

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Area of Science:

  • Cognitive Science
  • Neuroscience
  • Human Motor Control

Background:

  • Synchronization and syncopation tapping reveal distinct patterns of serial dependence in human timing.
  • Existing models primarily focus on synchronization, leaving syncopation timing less explained.
  • Understanding timing mechanisms is crucial for cognitive and motor control research.

Purpose of the Study:

  • To develop and validate a novel model explaining increased long-range correlations in syncopation tapping.
  • To extend the linear phase correction model to incorporate fractal dynamics.
  • To provide a unifying framework for event-based timing across different tapping paradigms.

Main Methods:

  • Extension of the linear phase correction model.
  • Introduction of a fractal timekeeper concept.
  • Incorporation of random-walk dynamics for half-period estimation.
  • Comparison of model simulations with experimental syncopation and synchronization tapping data.

Main Results:

  • The proposed model successfully accounts for the observed serial dependence patterns in syncopation tapping.
  • Simulated data closely matched experimental findings, validating the model's predictive power.
  • The fractal timekeeper and random-walk estimation effectively explain increased long-range correlations.

Conclusions:

  • The new model offers a unified explanation for event-based timing in both synchronization and syncopation.
  • Fractal dynamics and random-walk processes are key components in understanding complex human timing.
  • This work advances the modeling of cognitive and motor timing mechanisms.