Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

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.

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

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...

You might also read

Related Articles

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

Sort by

Same author

When is "now"? In the past to compensate for the sensation of time or in the future as a prediction of the temporal sensory horizon?

The Behavioral and brain sciences·2026

Same author

Natural scene segmentation dynamics reveal iterative Bayesian inference.

bioRxiv : the preprint server for biology·2026

Same author

Low confidence for perceptual completion of partially occluded objects.

Journal of vision·2026

Same author

Are we ready to tackle perceptual segmentation of natural scenes?

Vision research·2025

Same author

Long-term perceptual priors drive confidence bias that favors prior-congruent evidence.

PLoS computational biology·2025

Same author

Introducing Philosophy Corner.

Perception·2025

Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026

Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026

Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026

Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026

Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026

Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Measuring uncertainty in human visual segmentation.

Jonathan Vacher¹, Claire Launay², Pascal Mamassian¹

¹Laboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, Paris, France.

Plos Computational Biology

|September 25, 2023

Summary

This summary is machine-generated.

We developed a new method to map human visual segmentation by analyzing pixel judgments. This approach quantifies perceptual segmentation and provides benchmarks for computer vision algorithms.

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Related Experiment Videos

Last Updated: Jul 15, 2025

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

Area of Science:

Visual perception
Computational neuroscience
Computer vision

Background:

Visual segmentation is crucial for understanding scenes.
Existing methods lack quantitative measures for human perception.
Machine learning offers algorithms but lacks insight into human logic.

Purpose of the Study:

To develop a quantitative paradigm for measuring human perceptual segmentation maps.
To enable direct comparison between human perception and computational models.
To investigate the influence of image uncertainty on human segmentation.

Main Methods:

A novel approach combining pixel-based same-different judgments with model-based reconstruction.
Collection of perceptual data from human participants viewing natural images and textures.
Analysis of how image uncertainty affects individual variability and feature weighting.

Main Results:

The proposed method successfully reconstructs human segmentation maps.
The approach is robust to experimental variations and captures individual differences.
Image uncertainty was shown to influence human variability and feature weighting.

Conclusions:

The new paradigm provides a quantitative tool for studying visual segmentation.
It serves as a benchmark for evaluating segmentation algorithms.
It offers insights into the computational principles underlying human visual perception.