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

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

850
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...
850
Elaborative Rehearsals01:07

Elaborative Rehearsals

142
Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
142
Observational Learning01:12

Observational Learning

348
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...
348
Associative Learning01:27

Associative Learning

628
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...
628
Purposive Learning01:22

Purposive Learning

213
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...
213
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

985
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
985

You might also read

Related Articles

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

Sort by
Same author

A brain-inspired agentic architecture to improve planning with LLMs.

Nature communications·2025
Same author

A rubric for human-like agents and NeuroAI.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2022
Same author

Representations of Temporal Community Structure in Hippocampus and Precuneus Predict Inductive Reasoning Decisions.

Journal of cognitive neuroscience·2022
Same author

Collective minds: social network topology shapes collective cognition.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2021
Same author

Predictive Representations in Hippocampal and Prefrontal Hierarchies.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2021
Same author

The Learning Salon: Toward a new participatory science.

Neuron·2021

Related Experiment Video

Updated: Sep 27, 2025

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

7.7K

Learning Structures: Predictive Representations, Replay, and Generalization.

Ida Momennejad1

  • 1Columbia University.

Current Opinion in Behavioral Sciences
|April 14, 2022
PubMed
Summary
This summary is machine-generated.

This review explores how reinforcement learning (RL) models, using predictive representations and replay, explain cognitive maps for memory and planning. These findings offer insights into how brains learn and generalize relational structures.

Keywords:
DynaReinforcement learninghierarchical reinforcement learninghippocampusmemorymodel-basedmodel-freeplanningpredictionprefrontal cortexprioritizedreplaysuccessor representation

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
Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.7K

Related Experiment Videos

Last Updated: Sep 27, 2025

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

7.7K
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
Automated Interactive Video Playback for Studies of Animal Communication
07:21

Automated Interactive Video Playback for Studies of Animal Communication

Published on: February 9, 2011

13.7K

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Understanding how the brain learns and generalizes relational structures is crucial for explaining memory and planning.
  • Cognitive maps, proposed a century ago, remain a key area of research in neuroscience.
  • Flexible behavior in humans and animals depends on compact representations of experience structures.

Purpose of the Study:

  • To review reinforcement learning (RL) approaches for learning compact state representations.
  • To explore how predictive representations updated via replay offer a neurally plausible account of cognitive maps.
  • To highlight key advances such as multi-scale successor representations, prioritized replay, and policy-dependence.

Main Methods:

  • Review of existing literature on reinforcement learning and cognitive maps.
  • Analysis of evidence supporting predictive representations updated via replay.
  • Examination of concepts like multi-scale successor representations, prioritized replay, and policy-dependence.

Main Results:

  • Reinforcement learning models with predictive representations and replay provide a plausible account of cognitive maps.
  • These models explain how the brain learns and generalizes relational structures.
  • Advances in RL offer new perspectives on the interplay of learning, memory, prediction, and planning.

Conclusions:

  • Predictive representations updated via replay offer a neurally plausible framework for understanding cognitive maps.
  • The reviewed RL approach advances our understanding of how brains learn and generalize relational structures.
  • Future research should explore the entanglement of learning, memory, prediction, and planning.