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

Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

121
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
121
Quantifying Work02:30

Quantifying Work

19.3K
As a system undergoes a change, its internal energy can change, and energy can be transferred from the system to the surroundings, or from the surroundings to the system. 
19.3K
Singularity Functions for Shear01:26

Singularity Functions for Shear

128
In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous  variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the...
128

You might also read

Related Articles

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

Sort by
Same author

Second-order facial features are processed analytically in composite faces.

Attention, perception & psychophysics·2025
Same author

How should the advancement of large language models affect the practice of science?

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Dialogues about the practice of science.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Non-dominant hand contractions do not facilitate performance under pressure in common desktop tasks.

PloS one·2025
Same author

Modeling collaborative memory with SAM.

Memory & cognition·2024
Same author

Pride and moral disengagement: associations among comparison-based pride, moral disengagement, and unethical decision-making.

Cognition & emotion·2024
Same journal

Limited protective effects of multilingualism against age-related cognitive decline.

Memory & cognition·2026
Same journal

Validation of illustrated texts: Can pictures raise awareness of inconsistencies?

Memory & cognition·2026
Same journal

4I remember (and forget) your happy smiling face: Directed forgetting of emotionally expressive faces of in-group and out-group members.

Memory & cognition·2026
Same journal

Identity in the spotlight: Matching faces without overlapping features.

Memory & cognition·2026
Same journal

Test delay and change awareness moderate retroactive and proactive memory effects.

Memory & cognition·2026
Same journal

The Deese/Roediger-McDermott (DRM) illusion in short-term memory: Opposite effects of retention interval on true and false recognition.

Memory & cognition·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.2K

Function estimation: Quantifying individual differences of hand-drawn functions.

Daniel R Little1, Richard M Shiffrin2, Simon M Laham3

  • 1Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia. daniel.little@unimelb.edu.au.

Memory & Cognition
|June 29, 2024
PubMed
Summary
This summary is machine-generated.

Understanding graphical perception is key. People interpret data graphs differently, either focusing on overall trends or local details, influencing how they see scientific information.

Keywords:
Gaussian processesGraphical perceptionIndividual differences

More Related Videos

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
07:25

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

6.9K
Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy
07:53

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Published on: August 5, 2022

2.0K

Related Experiment Videos

Last Updated: Jun 22, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.2K
An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
07:25

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

6.9K
Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy
07:53

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Published on: August 5, 2022

2.0K

Area of Science:

  • Cognitive Psychology
  • Data Visualization
  • Scientific Communication

Background:

  • Graphical perception is crucial for interpreting scientific data and media representations.
  • Graphs contain both global trends and local variations, posing interpretation challenges.
  • Understanding how individuals perceive and reconstruct data from graphs is vital.

Purpose of the Study:

  • To investigate how individuals perceive and reconstruct functions from noisy scatterplots.
  • To explore the influence of graph characteristics (data points, scale) on function estimation.
  • To identify individual differences in graphical data interpretation strategies.

Main Methods:

  • A novel function estimation task was designed using scatterplots of noisy data.
  • Participants (170 psychology undergraduates) were asked to draw the generating function.
  • Data varied in number of points, scale, and underlying true function.

Main Results:

  • Graph properties significantly influenced perceived functions; more data points led to smoother estimations.
  • Clear individual differences emerged: some tracked local data fluctuations, others focused on global trends.
  • Observer experience with mathematical functions showed mixed influence.

Conclusions:

  • Graphical perception is influenced by both data presentation and individual interpretation strategies.
  • Some individuals prioritize global patterns, while others focus on local details in data visualization.
  • Further research is needed to understand the cognitive mechanisms behind these interpretation differences.