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

Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

526
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
526
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.1K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.1K
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

471
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
471
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

151
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
151
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

626
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
626
Causality in Epidemiology01:21

Causality in Epidemiology

1.1K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.1K

You might also read

Related Articles

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

Sort by
Same author

PCK2 Promotes Ribosome Biogenesis in Cervical Cancer Cells via Interaction with hnRNPK.

Reproductive sciences (Thousand Oaks, Calif.)·2026
Same author

Subinhibitory Concentrations of Rifampicin Synergize with Linezolid to Delay Resistance Evolution in Clinical Methicillin-Resistant Staphylococcus Aureus.

Microorganisms·2026
Same author

Impact of IFN-γ-Pretreated Umbilical Cord Mesenchymal Stem Cells Implanted in Mesh on Pelvic Organ Prolapse.

Tissue engineering. Part A·2026
Same author

The Novel Compound SIC-19 Triggers CUL4B-Mediated Ubiquitination and Degradation of SIK2.

Molecular and cellular biology·2026
Same author

FLIM Reveals Red Light-Induced Changes in Murine Hair Follicles.

Biosensors·2026
Same author

FABP5 confers resistance to drug-induced ROS toxicity in cervical cancer cell lines by suppressing the PPARγ/CPT1A signaling pathway.

Discover oncology·2026

Related Experiment Video

Updated: Oct 30, 2025

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

10.1K

A unifying Bayesian framework accounting for spatiotemporal interferences with a deceleration tendency.

Youguo Chen1, Chunhua Peng2, Andrew Avitt3

  • 1Key Laboratory of Cognition and Personality (Ministry of Education), Center of Studies for Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing 400715, China.

Vision Research
|July 4, 2021
PubMed
Summary

The Kappa effect, a spatiotemporal illusion, shows a deceleration tendency. This study suggests this effect arises from the Weber-Fechner law, not stimulus uncertainty or constant speeds.

Keywords:
Bayesian estimationDeceleration tendencyKappa effectSpatiotemporal interferenceWeber–Fechner law

More Related Videos

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.3K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.2K

Related Experiment Videos

Last Updated: Oct 30, 2025

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

10.1K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.3K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.2K

Area of Science:

  • Cognitive psychology
  • Perception science
  • Computational neuroscience

Background:

  • Spatiotemporal interference, like the Kappa effect, demonstrates interplay between spatial and temporal information processing.
  • The Kappa effect is characterized by an increased time estimation with increased stimulus distance, exhibiting a deceleration tendency.
  • Existing models, including classical (constant speed) and slowness (stimulus uncertainty) models, offer explanations for this phenomenon.

Purpose of the Study:

  • To investigate the underlying mechanism of the deceleration tendency in the Kappa effect.
  • To test the hypothesis that the Weber-Fechner law explains the Kappa effect, integrating a Bayesian framework with classical models.
  • To differentiate between predictions from linear and logarithmic scale models of time perception.

Main Methods:

  • Two time discrimination tasks were designed, manipulating stimulus location uncertainty and distance.
  • A unifying Bayesian framework was integrated with the classical model to derive predictions.
  • Behavioral data were analyzed to compare the accuracy of Bayesian, linear, and slowness models.

Main Results:

  • Stimulus location uncertainty did not significantly modulate time estimations in Experiment 1.
  • Experiment 2 demonstrated that a Bayesian model, operating on logarithmic scales, provided more accurate behavioral predictions than a linear model.
  • The observed deceleration tendency aligns with predictions derived from the Weber-Fechner law.

Conclusions:

  • The deceleration tendency observed in the Kappa effect is attributed to the Weber-Fechner law.
  • This finding challenges explanations based solely on constant speeds or stimulus location uncertainty.
  • The study highlights the utility of a Bayesian framework for understanding perceptual phenomena in time perception.