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

Gestalt Principles of Perception01:21

Gestalt Principles of Perception

284
Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
284
Role of Shaping in Operant Conditioning01:19

Role of Shaping in Operant Conditioning

276
Shaping is a technique used in operant conditioning to train complex behaviors by rewarding successive approximations toward the target behavior. This method is necessary because organisms are unlikely to perform complex behaviors spontaneously. Instead, shaping breaks down the desired behavior into small, manageable steps.
The steps involved in shaping begin with reinforcing any response that resembles the desired behavior. For example, parents might praise a child for picking up one toy. As...
276
Gestalt Psychology01:14

Gestalt Psychology

528
Gestalt psychology, founded by Max Wertheimer, Kurt Koffka, and Wolfgang Kohler, emphasizes the importance of understanding perception as an organized whole. Developed as a counter to Wilhelm Wundt's structuralism, this approach posits that our perceptions are more than just the sum of sensory parts; they are comprehensive wholes where the relationships between parts define the perception. The principle "The whole is greater than the sum of its parts" encapsulates this view,...
528
The Representativeness Heuristic02:13

The Representativeness Heuristic

15.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.8K
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

135
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
135
Concepts and Prototypes01:24

Concepts and Prototypes

119
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
119

You might also read

Related Articles

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

Sort by
Same author

Contextualization or Rationalization? The Effect of Causal Priors on Data Visualization Interpretation.

IEEE transactions on visualization and computer graphics·2026
Same author

Graphical Perception of Icon Arrays versus Bar Charts for Value Comparisons in Health Risk Communication.

IEEE transactions on visualization and computer graphics·2025
Same author

Characterizing Visualization Perception with Psychological Phenomena: Uncovering the Role of Subitizing in Data Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

Visual Stenography: Feature Recreation and Preservation in Sketches of Noisy Line Charts.

IEEE transactions on visualization and computer graphics·2025
Same author

Distortion-Aware Brushing for Reliable Cluster Analysis in Multidimensional Projections.

IEEE transactions on visualization and computer graphics·2025
Same author

A Survey on Annotations in Information Visualization: Empirical Studies, Applications and Challenges.

IEEE transactions on visualization and computer graphics·2025
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
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
See all related articles

Related Experiment Video

Updated: Jun 13, 2025

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

11.8K

Shape It Up: An Empirically Grounded Approach for Designing Shape Palettes.

Chin Tseng, Arran Zeyu Wang, Ghulam Jilani Quadri

    IEEE Transactions on Visualization and Computer Graphics
    |September 16, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Choosing effective shapes for data visualization is challenging. This study found traditional methods insufficient and developed a model to guide shape selection for better categorical data encoding.

    More Related Videos

    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

    2.4K
    Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
    06:30

    Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

    Published on: May 24, 2019

    5.2K

    Related Experiment Videos

    Last Updated: Jun 13, 2025

    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

    11.8K
    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

    2.4K
    Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
    06:30

    Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

    Published on: May 24, 2019

    5.2K

    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Perception Science

    Background:

    • Shape is crucial for distinguishing categories in scatterplots.
    • Current shape palette guidelines lack empirical support and scalability.
    • The non-numerical nature of shapes complicates design heuristics.

    Purpose of the Study:

    • Evaluate the perceptual efficiency of 39 shapes across various visualization tasks.
    • Identify effective shape characteristics for categorical data encoding.
    • Develop a data-driven model to aid shape palette design.

    Main Methods:

    • Conducted four experiments assessing shape efficiency in relative mean judgment, expert preference, and correlation estimation tasks.
    • Analyzed perceptual performance and pairwise shape relations.
    • Developed a predictive model for shape palette effectiveness.

    Main Results:

    • Conventional shape attributes (e.g., filled vs. unfilled) are inadequate for effective palette design.
    • Expert-selected shape palettes show significant variation in effectiveness.
    • Empirical data reveals key factors influencing shape perception in visualizations.

    Conclusions:

    • Existing shape selection heuristics are insufficient for complex categorical data.
    • The developed model and design tool offer data-driven guidance for shape palette creation.
    • This research advances understanding of shape perception and improves categorical data encoding in visualizations.