Jove
Visualize
Contact Us

Related Concept Videos

The Representativeness Heuristic02:13

The Representativeness Heuristic

15.9K
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.
15.9K
SBAR I: Understanding the Concept01:29

SBAR I: Understanding the Concept

4.7K
Effective communication among healthcare professionals during hand-off reporting is essential to delivering safe and continuous patient care. Common professional interactions include reports to healthcare team members, hand-off, and transfer reports. Nurses routinely report information to other healthcare team members and also urgently contact healthcare providers to report changes in patient status.
Standardized methods of communication have been developed to ensure that information is...
4.7K
State Space Representation01:27

State Space Representation

273
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
273
Gestalt Psychology01:14

Gestalt Psychology

714
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,...
714
Stereotype Content Model02:16

Stereotype Content Model

14.8K
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...
14.8K
Nonconscious Mimicry01:13

Nonconscious Mimicry

4.6K
Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
4.6K

You might also read

Related Articles

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

Sort by
Same author

Empirical Nonnegative Finite-Resolution MAR-PID for Continuous Variables.

Entropy (Basel, Switzerland)·2026
Same author

Adaptive law-based feature representation for time series classification.

Scientific reports·2025
Same author

Unified causality analysis based on the degrees of freedom.

Physical review. E·2025
Same author

Analogies and Relations between Non-Additive Entropy Formulas and Gintropy.

Entropy (Basel, Switzerland)·2024
Same author

Facilitating time series classification by linear law-based feature space transformation.

Scientific reports·2022
Same author

f-Gintropy: An Entropic Distance Ranking Based on the Gini Index.

Entropy (Basel, Switzerland)·2022
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
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 Experiment Video

Updated: Aug 28, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.3K

A Note on Representational Understanding.

Antal Jakovác1, András Telcs1,2,3

  • 1Department of Computational Sciences, Wigner Research Centre for Physics, H-1121 Budapest, Hungary.

Entropy (Basel, Switzerland)
|September 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel set representation approach to understanding data. We demonstrate that optimal data representation minimizes representational entropy, offering a new computational framework for machine learning.

Keywords:
coordinate systemsdata representationentropyfirst and second error

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

26.0K

Related Experiment Videos

Last Updated: Aug 28, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.3K
Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K
Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

26.0K

Area of Science:

  • Information Theory
  • Machine Learning
  • Computational Statistics

Background:

  • Current methods for data representation often lack a rigorous theoretical framework.
  • Defining and quantifying 'understanding' in computational systems remains a challenge.
  • Existing loss functions may not adequately balance representational accuracy with error minimization.

Purpose of the Study:

  • To propose a novel framework for understanding data through set representation.
  • To establish the mathematical equivalence between data understanding and minimizing representational entropy.
  • To introduce a new loss function for guiding the search for optimal data representations.

Main Methods:

  • Interpreting understanding as a set representation problem.
  • Proving the equivalence of optimal representation to minimizing representational entropy.
  • Developing a loss function combining representational entropy, Type I, and Type II errors.

Main Results:

  • Demonstrated that finding the appropriate data coordination is equivalent to minimizing representational entropy.
  • Proposed a novel loss function for controlling the search for optimal representations.
  • Provided computational complexity estimates for the understanding and representation process.

Conclusions:

  • The proposed set representation framework offers a mathematically grounded approach to data understanding.
  • Minimizing representational entropy is a key objective for effective data representation.
  • The novel loss function provides a practical tool for optimizing data representation in computational systems.