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

1.3K
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.
1.3K
Histogram01:05

Histogram

16.9K
The histogram is a graphical representation in the x-y form of data distribution in a data set. The horizontal x-axis is labeled with what the data represents (for instance, distance from your home to school). The vertical y-axis is labeled either frequency or relative frequency (or percent frequency or probability).
A histogram graph consists of contiguous (adjoining) boxes. The heights of the bars correspond to frequency values. The graph will have the same shape with respective labels. The...
16.9K
Labeling Emotion01:20

Labeling Emotion

589
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
589
Relative Frequency Histogram01:14

Relative Frequency Histogram

6.3K
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...
6.3K
The Influence of Affect on Cognition01:29

The Influence of Affect on Cognition

248
Positive affect significantly influences cognitive processes, including evaluation, memory, creativity, and social judgments. Compared to negative affect, positive emotional states promote more favorable interpretations of stimuli, cognitive flexibility, and heuristic processing. These effects highlight emotions' powerful role in shaping how individuals perceive, remember, and interact with the world.Influence on Evaluation and AttributionWhen individuals experience positive affect, they are...
248
Modified Boxplots00:57

Modified Boxplots

10.8K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
10.8K

You might also read

Related Articles

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

Sort by
Same author

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

Cognitive science·2026
Same author

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

Attention, perception & psychophysics·2026
Same author

Drawings of THINGS: A large-scale drawing dataset of 1854 object concepts.

Behavior research methods·2026
Same author

AI-Enhanced Semantic Feature Norms for 786 Concepts.

Topics in cognitive science·2025
Same author

EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts.

IEEE transactions on visualization and computer graphics·2025
Same author

Perceptual and Cognitive Foundations of Information Visualization.

Annual review of vision science·2025
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
Same journal

Spatial-temporal Relation guided Motion Transfer via Diffusion Model.

IEEE transactions on visualization and computer graphics·2026
See all related articles
  1. Home
  3. Affective Color Scales For Colormap Data Visualizations.
  1. Home
  3. Affective Color Scales For Colormap Data Visualizations.

Related Experiment Video

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
08:25

Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

Published on: December 6, 2024

825

Affective Color Scales for Colormap Data Visualizations.

Halle C Braun, Kushin Mukherjee, Seth R Gorelik

    IEEE Transactions on Visualization and Computer Graphics
    |December 2, 2025

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Color in data visualizations can convey emotions, but must balance affective design with data clarity. This study shows colormaps can achieve both, considering color scales and data patterns for effective visualization.

    More Related Videos

    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    9.5K
    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

    2.8K

    Related Experiment Videos

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
    08:25

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

    Published on: December 6, 2024

    825
    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    9.5K
    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

    2.8K

    Area of Science:

    • Information Visualization
    • Perception Psychology

    Background:

    • Color significantly influences the emotional perception of visualizations.
    • Lightness of color is a primary driver of color-emotion associations (light=positive, dark=negative).
    • Designing for pure light or dark colors can hinder spatial detail in data visualizations using color to represent patterns.

    Purpose of the Study:

    • To investigate if colormaps can maintain affective connotation while preserving lightness contrast for spatial vision.
    • To explore the influence of color scale and data-dependent color frequency on affective connotation.
    • To highlight the importance of data-aware design in affective visualization.

    Main Methods:

    • Developed and evaluated colormaps designed for both lightness contrast and affective communication.
  • Assessed how color scale choices and the frequency of color appearance (data-dependence) impact perceived emotion.
  • Utilized principles of visual perception and affective design.

    • Main Results:

    • It is feasible to create colormaps with strong lightness contrast that also convey distinct affective connotations.
    • Affective connotation is influenced by both the chosen color scales and the underlying data's distribution of colors.
    • The data-dependence hypothesis was supported, showing data characteristics impact perceived emotion.

    Conclusions:

    • Affective colormap design is achievable, balancing visual clarity with emotional impact.
    • Effective affective visualization requires considering how data characteristics interact with design elements like color.
    • Data-aware design is crucial for creating visualizations that are both informative and emotionally resonant.