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

Long-term Potentiation01:35

Long-term Potentiation

55.5K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
55.5K
Observational Learning01:12

Observational Learning

255
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...
255
Cognitive Learning01:21

Cognitive Learning

473
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...
473
Learned Behavior II01:20

Learned Behavior II

Learned Behavior IITeaching a dog to sit or learning how to ride a bike are examples of learned behaviors—actions acquired through experience and practice. These behaviors are not instinctive; instead, they develop over time as individuals interact with their environment. While some behaviors are automatic (like blinking), others are learned over time by watching, practicing, or being trained.Animals learn from their parents, their environment, and sometimes from trial and error. Whether a...
Learned Behavior I01:19

Learned Behavior I

Learned Behavior ILearned behaviors are actions that animals develop through experience, observation, or practice rather than being born with them. For example, a dog learning to roll over or a baby bird figuring out how to crack open a seed are both learned behaviors. Unlike instincts, learned behaviors aren’t something you're born knowing. You pick them up through life and experience.Animals, including you, learn in all sorts of ways, such as copying others, solving problems, or remembering...
Associative Learning01:27

Associative Learning

498
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...
498

You might also read

Related Articles

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

Sort by
Same author

Distinct roles of hippocampus and neocortex in symbolic compositional generalization.

Neuron·2026
Same author

Human curriculum learning of a cue combination task.

Nature human behaviour·2026
Same author

Technological <i>folie à deux</i>: feedback loops between AI chatbots and mental health.

Nature. Mental health·2026
Same author

Understanding human metacontrol and its pathologies using deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Hybrid neural-cognitive models reveal how memory shapes human reward learning.

Nature human behaviour·2026
Same author

How malicious AI swarms can threaten democracy.

Science (New York, N.Y.)·2026

Related Experiment Video

Updated: Aug 13, 2025

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

10.7K

Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals.

Timo Flesch1, David G Nagy2, Andrew Saxe3,4

  • 1Department of Experimental Psychology, University of Oxford; Oxford, United Kingdom.

Plos Computational Biology
|January 19, 2023
PubMed
Summary

Artificial neural networks learn tasks differently than humans. This study introduces computational constraints for artificial neural networks, inspired by primate prefrontal cortex, to prevent task interference during sequential learning.

More Related Videos

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.1K
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.5K

Related Experiment Videos

Last Updated: Aug 13, 2025

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments
09:43

A Fully Automated Rodent Conditioning Protocol for Sensorimotor Integration and Cognitive Control Experiments

Published on: April 15, 2014

10.7K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.1K
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.5K

Area of Science:

  • Computational neuroscience
  • Artificial intelligence

Background:

  • Humans exhibit task-specific interference, performing worse on multiple tasks simultaneously compared to sequential learning.
  • Standard deep neural networks show the opposite pattern, often benefiting from simultaneous training.

Purpose of the Study:

  • To develop novel computational constraints for artificial neural networks that mimic human sequential learning costs.
  • To enable artificial neural networks to learn tasks in sequence without forgetting, addressing the limitations of standard models.

Main Methods:

  • Augmented standard stochastic gradient descent with "sluggish" task units and a Hebbian training step.
  • Introduced computational constraints inspired by primate prefrontal cortex gating mechanisms.

Main Results:

  • "Sluggish" units created a training switch-cost, biasing representations towards joint, context-ignoring ones during interleaved training.
  • The Hebbian step fostered orthogonal representations, effectively preventing interference by creating a task-unit gating scheme.

Conclusions:

  • The proposed model successfully replicates human performance differences between blocked and interleaved training curricula.
  • The model's findings suggest that misestimation of category boundaries contributes to performance variations in human learning.