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

Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.4K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.4K
Hindsight Biases01:12

Hindsight Biases

4.2K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.2K
Cognitive Learning01:21

Cognitive Learning

950
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...
950
Schemas01:42

Schemas

12.3K
A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
12.3K
Purposive Learning01:22

Purposive Learning

399
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...
399
Observational Learning01:12

Observational Learning

771
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...
771

You might also read

Related Articles

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

Sort by
Same author

Looking to the future: Learning from experience, averting catastrophe.

Neural networks : the official journal of the International Neural Network Society·2019
Same author

ART, cognitive science, and technology transfer.

Wiley interdisciplinary reviews. Cognitive science·2015
Same author

DISCOV (DImensionless Shunting COlor Vision): a neural model for spatial data analysis.

Neural networks : the official journal of the International Neural Network Society·2012
Same author

Self-organizing ARTMAP rule discovery.

Neural networks : the official journal of the International Neural Network Society·2011
Same author

Searching the sky with CONFIGR-STARS.

Neural networks : the official journal of the International Neural Network Society·2010
Same author

Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction.

Neural networks : the official journal of the International Neural Network Society·2009
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Jan 5, 2026

Disrupting Reconsolidation of Fear Memory in Humans by a Noradrenergic β-Blocker
08:32

Disrupting Reconsolidation of Fear Memory in Humans by a Noradrenergic β-Blocker

Published on: December 18, 2014

23.3K

Looking to the future: Learning from experience, averting catastrophe.

Gail A Carpenter1

  • 1Department of Mathematics and Center for Adaptive Systems, Boston University, 677 Beacon Street, Boston, MA 02215, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|October 15, 2019
PubMed
Summary
This summary is machine-generated.

Humans learn from ambiguous contexts, improving expertise despite brain dynamics. The Self-supervised ART neural model acquires new knowledge in unpredictable environments, aiding technology design.

Keywords:
Adaptive Resonance Theory (ART)Artificial Intelligence (AI)Neural networksSelf-supervised (ART)Self-supervised learningSemi-supervised learning

More Related Videos

Extinction Training During the Reconsolidation Window Prevents Recovery of Fear
11:17

Extinction Training During the Reconsolidation Window Prevents Recovery of Fear

Published on: August 24, 2012

36.1K
Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm
09:00

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Published on: October 3, 2020

4.4K

Related Experiment Videos

Last Updated: Jan 5, 2026

Disrupting Reconsolidation of Fear Memory in Humans by a Noradrenergic β-Blocker
08:32

Disrupting Reconsolidation of Fear Memory in Humans by a Noradrenergic β-Blocker

Published on: December 18, 2014

23.3K
Extinction Training During the Reconsolidation Window Prevents Recovery of Fear
11:17

Extinction Training During the Reconsolidation Window Prevents Recovery of Fear

Published on: August 24, 2012

36.1K
Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm
09:00

Investigating Pain-Related Avoidance Behavior using a Robotic Arm-Reaching Paradigm

Published on: October 3, 2020

4.4K

Area of Science:

  • Cognitive Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Human knowledge acquisition evolves beyond structured environments like homes and classrooms.
  • Learning from individual context enhances expert performance but risks altering established neural expectations.
  • Designing adaptive technologies requires addressing systems that autonomously continue learning.

Observation:

  • Fielded systems in the 21st century possess the capability for ongoing, independent learning.
  • The brain's dynamic nature can lead to the transformation of previously reliable expectations.
  • Ambiguous real-world contexts present greater learning challenges than initial, structured environments.

Findings:

  • The Self-supervised ART neural model demonstrates the capacity to acquire substantial new knowledge.
  • This model effectively operates and learns within unpredictable and dynamic contexts.
  • It provides a framework for understanding and developing AI systems that adapt to novel situations.

Implications:

  • Advances in artificial intelligence can be achieved through models capable of continuous, context-aware learning.
  • Understanding adaptive learning is crucial for the responsible development of autonomous technologies.
  • The Self-supervised ART model offers insights into both biological and artificial learning mechanisms.