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

Color Vision01:24

Color Vision

1.7K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.7K
Stereotype Content Model02:16

Stereotype Content Model

15.6K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
15.6K
Perceptual Constancy01:12

Perceptual Constancy

1.6K
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
1.6K
Factorial Design02:01

Factorial Design

14.7K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
14.7K
Factors Affecting Perception01:25

Factors Affecting Perception

2.9K
Perception is influenced by perceptual set, context, motivation, and emotion. Perceptual set, or perceptual expectancy, refers to the tendency to perceive things in a particular way, influenced by previous experiences and expectations. This phenomenon affects the interpretation of stimuli, creating a set of mental tendencies and assumptions that impact sensory perceptions of sound, taste, touch, and sight.
An illustrative example of a perceptual set is the scenario where an airline pilot told...
2.9K

You might also read

Related Articles

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

Sort by
Same author

Using functional MRI neurofeedback to modulate self-blame in major depressive disorder: A pilot study.

NeuroImage. Clinical·2026
Same author

Large Language Models Estimate Fine-Grained Human Color-Concept Associations.

Cognitive science·2026
Same author

The colors of images preferred by individual voxels can be used to delineate functionally distinct visually responsive brain areas.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Understanding the opaque-is-more bias and saturated-is-more bias for colormap data visualizations.

Attention, perception & psychophysics·2026
Same author

Affective Color Scales for Colormap Data Visualizations.

IEEE transactions on visualization and computer graphics·2025
Same author

Global organisation of structural covariance networks derived from parcellated cortical surface area in atypical populations.

bioRxiv : the preprint server for biology·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: Feb 26, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.7K

Modeling color preference using color space metrics.

Karen B Schloss1, Laurent Lessard2, Chris Racey1

  • 1Department of Psychology, University of Wisconsin-Madison, 1202 West Johnson Street, Madison, WI 53706, USA; Wisconsin Institute for Discovery, University of Wisconsin-Madison, and Wisconsin Institutes for Discovery, 330 N. Orchard St., Madison, WI 53715, USA.

Vision Research
|July 19, 2017
PubMed
Summary
This summary is machine-generated.

Predicting color preferences is complex. The best model, "LabC Cyl2," uses CIELAB space with hue, lightness, and chroma harmonics to capture individual differences in color perception and judgment.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.9K

Related Experiment Videos

Last Updated: Feb 26, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

9.7K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.9K

Area of Science:

  • Color Science
  • Psychophysics
  • Computational Modeling

Background:

  • Color preferences are complex, with significant individual variation.
  • Modeling color preferences using metric color spaces can explain some variance.
  • Previous models using linear regression on color space dimensions showed limitations.

Purpose of the Study:

  • To improve models predicting color preferences by exploring different color metric specifications.
  • To compare the predictive power of various color spaces, coordinate systems, and factor degrees.
  • To identify the most parsimonious and accurate model for color preference prediction.

Main Methods:

  • Conducted a large-scale analysis of color space models.
  • Compared models differing in color space (cone-contrast vs. CIELAB), coordinate system (Cartesian vs. cylindrical), and factor degrees (1st vs. 1st and 2nd).
  • Utilized k-fold cross-validation to prevent overfitting and ensure fair model comparison.

Main Results:

  • The 2nd-harmonic Lch model in CIELAB space ("LabC Cyl2") provided the best fit.
  • This model incorporates 1st and 2nd harmonics of hue, lightness, and chroma.
  • It effectively captures hue opponency and simultaneous liking/disliking of opponent hues.

Conclusions:

  • The "LabC Cyl2" model offers a superior method for describing and predicting color preferences.
  • This approach can characterize group and individual color preference patterns.
  • The modeling framework may be applicable to other domains linking sensory appearance to cognitive or affective judgments.