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 Experiment Videos

Contextual quick-learning and generalization by humans and machines

J Bernasconi1, K Gustafson

  • 1ABB Corporate Research Ltd, Baden-Dättwil, Switzerland. jakob.bernasconi@chcrc.abb.ch

Network (Bristol, England)
|December 23, 1998
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

The control of diabetes and other Non-communicable Diseases is an urgent health priority in Africa: Grenoble declaration.

Medecine et sante tropicales·2019
Same author

Nondiffusive transport regimes for suprathermal ions in turbulent plasmas.

Physical review. E, Statistical, nonlinear, and soft matter physics·2015
Same author

Nondiffusive suprathermal ion transport in simple magnetized toroidal plasmas.

Physical review letters·2012
Same author

AMPA receptor subunit GluR1 downstream of D-1 dopamine receptor stimulation in nucleus accumbens shell mediates increased drug reward magnitude in food-restricted rats.

Neuroscience·2009
Same author

Development and commercial use of Bollgard cotton in the USA--early promises versus today's reality.

The Plant journal : for cell and molecular biology·2001
Same author

The emergency medicine training needs of rural general practitioners.

The Australian journal of rural health·2000
Same journal

Enhancing IoT security: A Creative Swagger Optimization algorithm for DDoS defence.

Network (Bristol, England)·2026
Same journal

Parametric optimization for electrical discharge diamond grinding (EDDG) system using dual approach.

Network (Bristol, England)·2025
Same journal

A novel lung cancer diagnosis model using hybrid convolution (2D/3D)-based adaptive DenseUnet with attention mechanism.

Network (Bristol, England)·2025
Same journal

Hybrid optimization enabled Eff-FDMNet for Parkinson's disease detection and classification in federated learning.

Network (Bristol, England)·2025
Same journal

AI-driven plant disease detection with tailored convolutional neural network.

Network (Bristol, England)·2025
Same journal

Layer modified residual Unet++ for speech enhancement using Aquila Black widow optimizer algorithm.

Network (Bristol, England)·2025
See all related articles

Humans excel at quick learning by actively seeking or creating context, even in abstract or incomplete situations. This contextualization is key to understanding and rapid modeling.

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Previous research compared human quick learning and generalization to neural networks, symbolic algorithms, and pattern classifiers.
  • The prior studies highlighted the potential role of context in the speed and nature of human learning.

Purpose of the Study:

  • To investigate the role of context in human learning using the Quinlan classification problem.
  • To understand how humans handle abstract, incomplete, or contradictory information during learning.

Main Methods:

  • Replication of a previous experimental setup (Quinlan classification problem).
  • Comparative analysis of human learning strategies against computational models.
  • Qualitative assessment of human approaches to ambiguous data.

Related Experiment Videos

Main Results:

  • Humans demonstrated a strong tendency to actively seek, create, or imagine context.
  • Contextualization was employed to derive meaning from abstract or untenable situations.
  • This active contextualization significantly aids human quick learning and generalization.

Conclusions:

  • Human learning is characterized by an inherent drive to establish context.
  • The ability to generate context is a critical factor in rapid human modeling and problem-solving.
  • Understanding human contextualization strategies offers insights for developing more adaptive AI.