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

Bar Graph01:07

Bar Graph

23.4K
A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
23.4K
Review and Preview01:13

Review and Preview

11.7K
Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
11.7K
Review and Preview01:10

Review and Preview

8.5K
In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
8.5K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

45.8K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
45.8K
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

16.7K
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...
16.7K
Relative Frequency Histogram01:14

Relative Frequency Histogram

6.5K
The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
6.5K

You might also read

Related Articles

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

Sort by
Same author

Face pareidolia is sensitive to spectral power and orientation energy.

i-Perception·2025
Same author

The impact of simulated cataract on face learning.

Scientific reports·2025
Same author

Contrast negation increases face pareidolia rates in natural scenes.

Journal of vision·2025
Same author

Effects of maze appearance on maze solving.

Attention, perception & psychophysics·2025
Same author

Is visual information use during facial emotion recognition related to eating disorder symptoms in college-aged men and women? An experimental study.

Journal of eating disorders·2024
Same author

The role of texture summary statistics in material recognition from drawings and photographs.

Journal of vision·2023

Related Experiment Video

Updated: Feb 19, 2026

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
06:07

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

Published on: May 15, 2019

9.1K

Children's use of visual summary statistics for material categorization.

Benjamin Balas1

  • 1Department of Psychology and Center for Visual and Cognitive Neuroscience, North Dakota State University, Fargo, ND, USA.

Journal of Vision
|November 2, 2017
PubMed
Summary

Children

Area of Science:

  • Visual perception
  • Developmental psychology
  • Material recognition

Background:

  • Adult material perception from images is understood, but developmental aspects are not.
  • Key questions remain about when children develop adult-like material categorization and the visual features they use.

Purpose of the Study:

  • To investigate the developmental trajectory of material perception in school-age children.
  • To determine the visual features children use for material categorization across different ages.
  • To examine the role of visual summary statistics in developing material perception.

Main Methods:

  • Two experiments were conducted with school-age children (5-10 years) and adults.
  • Participants categorized natural materials (metal, stone, water, wood) using real and synthetic images.

More Related Videos

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

7.3K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K

Related Experiment Videos

Last Updated: Feb 19, 2026

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm
06:07

Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

Published on: May 15, 2019

9.1K
Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
07:31

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

7.3K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.3K
  • Synthetic images were generated using the Portilla-Simoncelli algorithm to isolate visual summary statistics.

    • Main Results:

    • Young children struggled disproportionately with categorizing synthetic images, indicating material-specific developmental changes in information use.
    • These categorization difficulties were reduced when participants matched real and synthetic images without explicit labeling.
    • Children demonstrated adult-like abilities in encoding and comparing images based on summary statistics.

    Conclusions:

    • The ability to map visual summary statistics to material category labels develops gradually throughout childhood.
    • While children can process visual summary statistics similarly to adults, the interpretation and labeling of these statistics mature over time.