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

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
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

2.3K
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...
2.3K
Classical Conditioning01:18

Classical Conditioning

14.1K
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...
14.1K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.8K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.8K
Observational Learning01:12

Observational Learning

1.2K
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...
1.2K
Principles of Classical Conditioning01:23

Principles of Classical Conditioning

3.5K
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...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Bridging Computation and Representation in Associative Learning.

Computational brain & behavior·2026
Same author

Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks.

Nature communications·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

Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules.

Advances in neural information processing systems·2026
Same author

Probabilistic forecasting guides dynamic decisions.

Psychological review·2026
Same author

Phasic dopamine drives conditioned responding beyond its role in learning.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Mar 30, 2026

Appetitive Associative Olfactory Learning in Drosophila Larvae
09:22

Appetitive Associative Olfactory Learning in Drosophila Larvae

Published on: February 18, 2013

19.9K

A Unifying Probabilistic View of Associative Learning.

Samuel J Gershman1

  • 1Department of Psychology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America.

Plos Computational Biology
|November 5, 2015
PubMed
Summary

Animals learn associations using Bayesian principles and reinforcement learning. This unifying framework explains complex learning behaviors that previous theories could not.

Area of Science:

  • Cognitive Science
  • Neuroscience
  • Behavioral Psychology

Background:

  • Recent decades have seen two major theories of associative learning: Bayesian learning and reinforcement learning.
  • Both theories are normative, based on rational principles, and descriptive, explaining empirical phenomena.
  • Earlier theories struggled to account for various associative learning behaviors.

Purpose of the Study:

  • To present a unifying framework for Bayesian and reinforcement learning in associative learning.
  • To integrate these two perspectives to explain phenomena not covered by individual theories.

Main Methods:

  • Conceptual synthesis of Bayesian learning and reinforcement learning models.
  • Analysis of empirical phenomena through the lens of the unified framework.

More Related Videos

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans
07:17

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans

Published on: June 23, 2022

3.1K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Related Experiment Videos

Last Updated: Mar 30, 2026

Appetitive Associative Olfactory Learning in Drosophila Larvae
09:22

Appetitive Associative Olfactory Learning in Drosophila Larvae

Published on: February 18, 2013

19.9K
Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans
07:17

Aversive Associative Learning and Memory Formation by Pairing Two Chemicals in Caenorhabditis elegans

Published on: June 23, 2022

3.1K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

Main Results:

  • The unified framework integrates Bayesian uncertainty tracking and reinforcement learning reward predictions.
  • This synthesis provides a more comprehensive explanation of associative learning.

Conclusions:

  • Bayesian and reinforcement learning capture distinct but complementary aspects of associative learning.
  • The integration of these theories offers deeper insights into animal cognition and learning mechanisms.