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

Introduction to Learning01:18

Introduction to Learning

1.6K
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...
1.6K
Associative Learning01:27

Associative Learning

1.9K
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.
Classical conditioning, also known...
1.9K
Purposive Learning01:22

Purposive Learning

575
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...
575
Concepts and Prototypes01:24

Concepts and Prototypes

631
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
631
Cognitive Learning01:21

Cognitive Learning

1.5K
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...
1.5K
Inductive Reasoning00:59

Inductive Reasoning

69.3K
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
69.3K

You might also read

Related Articles

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

Sort by
Same author

Conniving With Continuations: Representing Goals in a Domain-Specific Language of Thought.

Topics in cognitive science·2026
Same author

Neural representation of action symbols in primate frontal cortex.

Nature·2026
Same author

Human-level learning of complex novel tasks as theory-based modelling, exploration and planning.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2026
Same author

Reverse engineering the centered self.

Psychological review·2026
Same author

Whither symbols in the era of advanced neural networks?

Trends in cognitive sciences·2026
Same author

Correction: A Comprehensive Behavioral Dataset for the Abstraction and Reasoning Corpus.

Scientific data·2025
Same journal

A native sulfur deposit in Gale crater, Mars.

Science (New York, N.Y.)·2026
Same journal

Coordinated demise of harmful algal blooms.

Science (New York, N.Y.)·2026
Same journal

Genetic effects put into context.

Science (New York, N.Y.)·2026
Same journal

Bacteria share proteins to survive antibiotics.

Science (New York, N.Y.)·2026
Same journal

Impacts shaped Earth's first continents.

Science (New York, N.Y.)·2026
Same journal

Erratum for the Report "Covalently bonded single-molecule junctions with stable and reversible photoswitched conductivity" by C. Jia <i>et al</i>.

Science (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Mar 28, 2026

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.6K

Human-level concept learning through probabilistic program induction.

Brenden M Lake1, Ruslan Salakhutdinov2, Joshua B Tenenbaum3

  • 1Center for Data Science, New York University, 726 Broadway, New York, NY 10003, USA. brenden@nyu.edu.

Science (New York, N.Y.)
|December 15, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new computational model for machine learning that mimics human ability to learn from single examples. This model achieves human-level performance in one-shot learning and demonstrates creative generalization.

More Related Videos

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

9.3K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.2K

Related Experiment Videos

Last Updated: Mar 28, 2026

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.6K
Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

9.3K
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

8.2K

Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Machine Learning

Background:

  • Conventional machine learning requires numerous examples for accuracy, unlike human learning which excels with single instances.
  • Humans utilize learned concepts flexibly for diverse applications, a capability often lacking in current algorithms.

Purpose of the Study:

  • To develop a computational model that replicates human-like generalization and creative concept usage.
  • To achieve human-level performance on one-shot learning tasks for visual concepts, specifically handwritten characters.

Main Methods:

  • A Bayesian approach was used to represent concepts as simple programs that best explain observed data.
  • The model was evaluated on a challenging one-shot classification task involving handwritten alphabetic characters.
  • Creative generalization was assessed through "visual Turing tests" comparing model and human behavior.

Main Results:

  • The model achieved human-level performance on the one-shot classification task, surpassing recent deep learning methods.
  • The model demonstrated robust generalization capabilities, performing comparably to humans in visual Turing tests.
  • The developed model effectively captures human-like learning from single examples.

Conclusions:

  • The proposed model offers a novel approach to machine learning, bridging the gap between human and artificial learning efficiency.
  • This research highlights the potential of program-based concept representation for achieving human-level AI.
  • The findings suggest new directions for developing more adaptable and creative artificial intelligence systems.