Jove
Visualize
Contact Us

Related Concept Videos

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

117
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
117
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

396
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...
396
Cognitive Learning01:21

Cognitive Learning

845
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
845
Observational Learning01:12

Observational Learning

638
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...
638
Purposive Learning01:22

Purposive Learning

300
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
300
Introduction to Learning01:18

Introduction to Learning

706
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
706

You might also read

Related Articles

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

Sort by
Same author

Copyright protection of deep neural network models using digital watermarking: a comparative study.

Multimedia tools and applications·2022
Same author

An IoT enabled system for enhanced air quality monitoring and prediction on the edge.

Complex & intelligent systems·2021
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: Nov 22, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.8K

Intelligence Is beyond Learning: A Context-Aware Artificial Intelligent System for Video Understanding.

Ahmed Ghozia1, Gamal Attiya1, Emad Adly1

  • 1Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Shibin El Kom, Menofia Governorate, Egypt.

Computational Intelligence and Neuroscience
|January 11, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a context-aware artificial intelligence (AI) technique for video understanding, overcoming deep learning limitations. The new method extracts meaningful concepts from video context for more accurate, efficient video message interpretation.

More Related Videos

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

9.3K

Related Experiment Videos

Last Updated: Nov 22, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.8K
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

9.3K

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Multimedia Analysis

Background:

  • Current deep learning (DL) video understanding methods focus on pattern recognition, leading to feature abstraction rather than true semantic understanding.
  • Video content analysis alone is insufficient; event meaning is derived from context, which DL models often fail to capture.
  • Artificial intelligence (AI) is a multifaceted process involving innate knowledge, approximations, and context awareness, extending beyond simple learning.

Purpose of the Study:

  • To address the limitations of DL in video understanding by proposing a novel context-aware technique.
  • To enable machines to comprehend the underlying message within video streams by extracting meaningful concepts, emotions, and spatio-temporal data.
  • To enhance AI's capability in video analysis beyond mere pattern recognition.

Main Methods:

  • Development of a context-aware video understanding framework that integrates heterogeneous data patterns.
  • Extraction of meaningful concepts, emotions, temporal, and spatial data from the video context.
  • Comparative analysis against deep learning approaches using objective and subjective metrics.

Main Results:

  • The proposed context-aware technique demonstrates superior accuracy in understanding video messages compared to traditional deep learning methods.
  • Significant improvements in resource utilization, including retrieval time, computing time, and data size, were observed.
  • The system proves suitable for real-time video analysis scenarios due to its efficient resource management.

Conclusions:

  • Context-aware AI offers a more profound and accurate approach to video understanding than content-based deep learning.
  • The developed technique provides a more intelligent and resource-efficient solution for real-time video analysis.
  • Further discussion on the advantages and disadvantages of deep learning architectures in AI is provided.