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

Variability: Analysis01:11

Variability: Analysis

263
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
263
Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

98.0K
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. 
98.0K
Random and Systematic Errors01:20

Random and Systematic Errors

14.0K
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...
14.0K
Random Error01:04

Random Error

5.3K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
5.3K
Standard Error of the Mean01:13

Standard Error of the Mean

9.7K
The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
9.7K
Sampling Distribution01:12

Sampling Distribution

15.8K
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
15.8K

You might also read

Related Articles

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

Sort by
Same author

The sense of agency is reduced by stimulus variability but has a limited effect on perceived variability.

Psychological research·2026
Same author

Reappraising intuitions about consciousness.

Frontiers in psychology·2026
Same author

The visual nature of social interaction and its impact on overall mood judgments.

Cognition & emotion·2026
Same author

When seeing follows believing: The role of social likelihood in demographic estimation.

Acta psychologica·2025
Same author

Amphiphilic Poly(SBMA-<i>co</i>-EGDEA) Coatings for Marine Antifouling: Insights into Design Criteria for Hydrophobic Monomers.

Biomacromolecules·2025
Same author

Effect of multi-refresh-rate method on user experience: sustained attention and inattentional blindness.

Cognitive research: principles and implications·2025

Related Experiment Video

Updated: Nov 12, 2025

Author Spotlight: Assessing the Reliability of Doppler Ultrasound in Measuring Leg Blood Flow
09:18

Author Spotlight: Assessing the Reliability of Doppler Ultrasound in Measuring Leg Blood Flow

Published on: December 15, 2023

3.2K

Perceived variability reflects the reliability of individual items.

Jinhyeok Jeong1, Sang Chul Chong2

  • 1The Graduate Program in Cognitive Science, Yonsei University, Seoul, South Korea.

Vision Research
|March 21, 2021
PubMed
Summary

Humans accurately perceive visual variability by weighting items based on their reliability, not just their range or standard deviation. This weighted standard deviation model explains how the visual system efficiently processes complex visual information.

Keywords:
Ensemble perceptionOrientation perceptionStatistical representationVariability perceptionWeighted standard deviation

More Related Videos

Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

27.5K
Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

9.2K

Related Experiment Videos

Last Updated: Nov 12, 2025

Author Spotlight: Assessing the Reliability of Doppler Ultrasound in Measuring Leg Blood Flow
09:18

Author Spotlight: Assessing the Reliability of Doppler Ultrasound in Measuring Leg Blood Flow

Published on: December 15, 2023

3.2K
Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

27.5K
Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations
09:07

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations

Published on: September 16, 2015

9.2K

Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computer Vision

Background:

  • Humans can rapidly and accurately compute variability in visual scenes, aiding information processing and decision-making.
  • The precise mechanisms by which the visual system computes perceived variability remain largely unknown.

Purpose of the Study:

  • To investigate how the human visual system computes perceived variability.
  • To differentiate between various computational models of variability (e.g., range, standard deviation, weighted standard deviation) and identify which best explains human perception.

Main Methods:

  • Conducted three psychophysical experiments involving participants judging the variability of item orientations in visual arrays.
  • Designed stimuli to distinguish between different potential measures of variability.
  • Analyzed participant responses to determine the best-fitting model of perceived variability.

Main Results:

  • Perceived visual variability was best explained by a weighted standard deviation model.
  • This model incorporates the reliability of individual items, down-weighting potential outliers and emphasizing precisely encoded salient items.
  • Human perception of variability is not solely based on simple statistical measures like range or standard deviation.

Conclusions:

  • The visual system employs an efficient and flexible reliability-based weighted standard deviation to represent visual variability.
  • This finding provides a novel insight into the computational strategies used for processing complex visual environments.
  • Understanding variability computation is crucial for fields ranging from human-computer interaction to artificial intelligence.