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Related Concept Videos

Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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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...
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Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Visual System01:26

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Related Experiment Video

Updated: Mar 7, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

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Insights into knowledge evolution based on semantic representation and dynamic visual analytics.

Jieyang Peng1,2, Jianing Li1, Zhibin Niu3

  • 1Department of Electronic Engineering, Tsinghua University, Beijing 100084, P.R. China.

Innovation (Cambridge (Mass.))
|March 6, 2026
PubMed
Summary
This summary is machine-generated.

New methods for knowledge science reveal that established fields support emerging ones. This preferential attachment accelerates newcomer growth, highlighting the need for equitable knowledge networks and supportive policies for new disciplines.

Keywords:
knowledge networkknowledge sciencesemantic embeddingstructured knowledge representationvisual analytic

Related Experiment Videos

Last Updated: Mar 7, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K

Area of Science:

  • Knowledge Science
  • Information Science
  • Computational Social Science

Background:

  • Current knowledge science lacks a unified semantic framework for structured knowledge representation.
  • This limitation impedes quantitative analysis of knowledge evolution and uncovering complex relationships.

Purpose of the Study:

  • Propose a structured knowledge representation method using semantic embedding.
  • Introduce knowledge transfer flow for quantitative analysis of knowledge hotspot evolution.
  • Develop the KnowFlowViz system for interactive visual analytics of knowledge systems.

Main Methods:

  • Semantic embedding for structured knowledge representation.
  • Knowledge transfer flow analysis for dynamic evolution.
  • Interactive visual analytics via the KnowFlowViz system.

Main Results:

  • Established knowledge domains maintain dominance.
  • Emerging knowledge entities preferentially attach to established domains, forming interdisciplinary links.
  • Advantage accumulation and preferential attachment accelerate newcomer growth and recognition.

Conclusions:

  • Findings reveal mechanisms driving knowledge transformation and interdisciplinary linkages.
  • Emphasize the importance of equitable and inclusive knowledge networks.
  • Support policies for nurturing emerging disciplines and sustaining a diverse knowledge ecosystem.