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

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

112.8K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
112.8K
Random and Systematic Errors01:20

Random and Systematic Errors

15.7K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
15.7K
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

55.3K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
55.3K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

11.2K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
11.2K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.7K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

16.8K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
16.8K

You might also read

Related Articles

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

Sort by
Same author

Signal combination in flutter vibration perception.

PloS one·2026
Same author

Pathological suppression of binocular vision in stroke.

Optometry and vision science : official publication of the American Academy of Optometry·2026
Same author

Neural responses to binocular in-phase and anti-phase stimuli.

Vision research·2026
Same author

Neural correlates of the deployment of spatial attention, and their modulation by repetitive movements.

PloS one·2025
Same author

Evaluating the effective segmentation of human lateral geniculate nucleus.

Brain structure & function·2025
Same author

Illusory finger stretching and somatosensory responses.

Neuropsychologia·2025
Same journal

Impact of crowding on visual appearance and performance in amblyopia.

Vision research·2026
Same journal

Editorial for VSI Amblyopia: Advances in Amblyopia Research.

Vision research·2026
Same journal

Computational and mathematical models in vision: Quantitative approaches to understanding visual perception.

Vision research·2026
Same journal

Complex interactions between lightness, chroma, and hue in color ensemble perception.

Vision research·2026
Same journal

Driving with autism spectrum disorder: Exploring the impact of tactile hazard warnings on gaze behavior and hazard responses.

Vision research·2026
Same journal

Early visual processing in adults with ADHD: evidence from contrast sensitivity, spatial integration, and external noise.

Vision research·2026
See all related articles

Related Experiment Video

Updated: Mar 10, 2026

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

4.2K

Individual differences in internal noise are consistent across two measurement techniques.

Greta Vilidaite1, Daniel H Baker1

  • 1Department of Psychology, University of York, United Kingdom.

Vision Research
|December 7, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a nonlinear gain control model to estimate internal noise in visual processing, offering a more accurate method than traditional linear models and revealing noise

Keywords:
Contrast perceptionDouble-passEquivalent noiseGain control modelInternal noise

More Related Videos

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.9K
The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
09:10

The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements

Published on: December 5, 2025

1.0K

Related Experiment Videos

Last Updated: Mar 10, 2026

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

4.2K
Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.9K
The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
09:10

The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements

Published on: December 5, 2025

1.0K

Area of Science:

  • Visual Neuroscience
  • Psychophysics

Background:

  • Internal noise fundamentally limits visual processing efficiency.
  • Previous internal noise estimation relied on the equivalent noise paradigm with linear models, which have limitations.
  • White noise masks can introduce noise and suppress neural signals, complicating linear model accuracy.

Purpose of the Study:

  • To propose and validate a nonlinear gain control model for estimating internal noise.
  • To compare the proposed model's noise estimates with the equivalent noise paradigm and a direct measure (double-pass consistency).
  • To investigate the relationship between model parameters and psychophysical measures of noise.

Main Methods:

  • Developed a nonlinear gain control model fitted to contrast discrimination data.
  • Compared noise estimates from the gain control model and the equivalent noise paradigm against double-pass consistency.
  • Utilized datasets from seven and later forty observers under refined conditions.

Main Results:

  • The gain control model provided more accurate predictions of double-pass consistency than a linear model.
  • The noise parameter in the gain control model strongly correlated with consistency scores.
  • The gain control parameter did not correlate with consistency scores, a distinction missed by the equivalent noise paradigm.

Conclusions:

  • The nonlinear gain control model offers a more precise method for estimating internal noise in visual processing.
  • Internal noise estimation is distinct from neural gain control, a differentiation enabled by the proposed model.
  • Both contrast discrimination and double-pass paradigms are sensitive tools for studying internal noise and individual differences in vision.