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

Classical Conditioning01:18

Classical Conditioning

603
Associative learning, a core principle in behavioral psychology, involves forming connections between events and facilitating learned responses. This concept is vividly illustrated by classical conditioning, a process extensively studied by the Russian physiologist Ivan Pavlov. Pavlov's pioneering research on dogs' digestive systems led to the discovery that behaviors can be learned through association, laying the groundwork for classical conditioning.
Ivan Pavlov observed that dogs...
603
Behaviorism01:28

Behaviorism

2.4K
The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
2.4K
Associative Learning01:27

Associative Learning

484
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...
484
Principles of Classical Conditioning01:23

Principles of Classical Conditioning

791
Classical conditioning, as described by Ivan Pavlov, is a foundational concept in associative learning, where a neutral stimulus becomes capable of eliciting a conditioned response through association with an unconditioned stimulus. The process of acquisition, where this learning occurs, and the subsequent phenomena of contiguity, contingency, generalization, discrimination, extinction, and spontaneous recovery are crucial for a comprehensive understanding of classical conditioning.
During the...
791
Cognitive Learning01:21

Cognitive Learning

464
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...
464
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

666
Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
666

You might also read

Related Articles

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

Sort by
Same author

ADAR2 induces the differentiation of osteosarcoma cells by editing activity on IGFBP7: new implications for therapy.

Bone research·2026
Same author

Networks of Hebbian networks: more is different.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Cannabidiol attenuates epileptic phenotype and increases survival in a mouse model of developmental and epileptic encephalopathy type 1.

Epilepsia·2025
Same author

Altered Ca2+ responses and antioxidant properties in Friedreich's ataxia-like cerebellar astrocytes.

Journal of cell science·2024
Same author

Learning in Associative Networks Through Pavlovian Dynamics.

Neural computation·2024
Same author

Inverse modeling of time-delayed interactions via the dynamic-entropy formalism.

Physical review. E·2024
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Aug 6, 2025

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

From Pavlov Conditioning to Hebb Learning.

Elena Agliari1,2, Miriam Aquaro1,3, Adriano Barra4,5

  • 1Sapienza University of Rome, Department of Mathematics, 00185, Rome, Italy.

Neural Computation
|March 21, 2023
PubMed
Summary
This summary is machine-generated.

This study bridges Pavlovian conditioning and Hebbian learning using stochastic process theory. It demonstrates how distinct neural and synaptic timescales naturally lead to Hebbian learning from Pavlovian mechanisms.

More Related Videos

Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.0K
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 6, 2025

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
Pavlovian Conditioned Approach Training in Rats
06:57

Pavlovian Conditioned Approach Training in Rats

Published on: February 4, 2016

11.0K
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
  • Learning Theory
  • Mathematical Biology

Background:

  • Hebbian learning, "neurons that fire together wire together," is widely modeled.
  • Pavlovian conditioning, based on conceptual correlations, lacks extensive computational models.
  • A mathematical link between these two learning paradigms is missing.

Purpose of the Study:

  • To establish a mathematical connection between Pavlovian conditioning and Hebbian learning.
  • To address the lack of computational models for Pavlovian conditioning.
  • To explain how Pavlovian mechanisms can lead to Hebbian synaptic weight changes.

Main Methods:

  • Utilized stochastic process theory to model neural and synaptic dynamics.
  • Maintained a clear separation between neuronal and synaptic timescales.
  • Analyzed the emergent properties of the system under these conditions.

Main Results:

  • Demonstrated that Pavlovian conditioning mechanisms spontaneously emerge.
  • Showed that synaptic weights recover the Hebbian kernel under specific timescale conditions.
  • Provided a theoretical framework connecting conceptual and neuronal correlation learning.

Conclusions:

  • The study successfully bridges Pavlovian and Hebbian learning through a unified mathematical framework.
  • Distinct timescales are crucial for Pavlovian mechanisms to yield Hebbian synaptic plasticity.
  • This work offers new insights into the fundamental principles of associative learning in neural systems.