Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Generalization, Discrimination, and Extinction

Generalization, Discrimination, and Extinction

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...

Purposive Learning

Purposive Learning

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...

Cognitive Learning

Cognitive Learning

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...

Introduction to Learning

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

Inductive Reasoning

Inductive Reasoning

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...

Higher Mental Functions of Brain: Learning and Memory

Higher Mental Functions of Brain: Learning and Memory

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...

You might also read

Related Articles

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

Sort by

Same author

Human curriculum learning of a cue combination task.

Nature human behaviour·2026

Same author

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

Nature human behaviour·2026

Same author

Shared sensitivity to data distribution during learning in humans and transformer networks.

Nature human behaviour·2025

Same author

The levers of political persuasion with conversational artificial intelligence.

Science (New York, N.Y.)·2025

Same author

Humans and neural networks show similar patterns of transfer and interference during continual learning.

Nature human behaviour·2025

Same author

The impact of advanced AI systems on democracy.

Nature human behaviour·2025

Same journal

The TaMYB55-TaSnRK1α1-TabZIP9 module confers heat stress tolerance in wheat.

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

Same journal

Superstatistics approach to turbulent circulation fluctuations.

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

Same journal

A molecular timescale for evolution of cobamide biosynthesis.

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

Same journal

Pierre Chambon, a pioneer of molecular biology and gene regulation in eukaryotes.

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

Same journal

Granulosa cell glycogen fuels the avascular corpus luteum.

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

Same journal

Synthetic essentiality of TRAIL/TNFSF10 in VHL-deficient renal cell carcinoma.

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

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 26, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Curriculum learning for human compositional generalization.

Ronald B Dekker¹, Fabian Otto¹, Christopher Summerfield¹

¹Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America

|October 3, 2022

Summary

This summary is machine-generated.

Human generalization excels at compositional tasks, outperforming standard neural networks. This ability is enhanced by specific training methods and shows asynchronous learning patterns, unlike current AI models.

Keywords:

compositionality decision-making generalization learning neural network

More Related Videos

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Published on: August 23, 2017

Related Experiment Videos

Last Updated: Aug 26, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

Published on: February 8, 2019

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Published on: August 23, 2017

Area of Science:

Cognitive Science
Artificial Intelligence
Neuroscience

Background:

Generalization, or transfer learning, is crucial for intelligence but its mechanisms remain debated.
Understanding how humans generalize is key to developing more advanced artificial intelligence.

Purpose of the Study:

To investigate human compositional generalization abilities.
To compare human generalization with standard neural network capabilities.
To identify factors influencing human generalization and model them computationally.

Main Methods:

Formalized transfer learning as function recomposition for novel problem-solving.
Developed a neural network model with a Hebbian gating process.
Analyzed human learning patterns under different training curricula.

Main Results:

Humans demonstrate superior compositional generalization compared to standard neural networks.
Human generalization improves with axis-aligned and temporally correlated training data.
A Hebbian gating model successfully captured human learning benefits from specific training.

Conclusions:

Human generalization is distinct and more flexible than current AI.
Training data structure significantly impacts human generalization.
Human learning of composable functions is asynchronous, mirroring developmental patterns.