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

The Representativeness Heuristic

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

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...

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...

Observational Learning

Observational Learning

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...

Associative Learning

Associative Learning

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...

Nonconscious Mimicry

Nonconscious Mimicry

Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.

You might also read

Related Articles

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

Sort by

Same author

Protocol for a randomized trial to predict the efficacy of cognitive and behavioral interventions for symptoms of depression.

Frontiers in psychiatry·2026

Same author

Resolution of Hashimoto thyroiditis with Janus kinase inhibitor therapy in a patient with alopecia universalis.

JCEM case reports·2026

Same author

A Resource-Rational Account of Human Eye Movements During Immersive Visual Search.

Open mind : discoveries in cognitive science·2026

Same author

Improving the Reliability of the Pavlovian Go/No-Go Task for Computational Psychiatry Research.

Computational psychiatry (Cambridge, Mass.)·2025

Same author

The adaptive value of stubborn goals.

Trends in cognitive sciences·2025

Same author

Cognitive modeling of real-world behavior for understanding mental health.

Trends in cognitive sciences·2025

Same journal

Body-Brain Integration: The Lower Brainstem in Sleep-Wake Regulation.

Annual review of neuroscience·2026

Same journal

Planning in the Brain: It's Not What You Think It Is.

Annual review of neuroscience·2026

Same journal

The Emerging Neurobiology of Psychedelics: Critical Periods, Metaplasticity, and Extracellular Matrix Remodeling.

Annual review of neuroscience·2026

Same journal

Rethinking Predictive Processing.

Annual review of neuroscience·2026

Same journal

Path Integration in Alzheimer's Disease: Orientation, Movement, and Theta Rhythmicity.

Annual review of neuroscience·2026

Same journal

The Cellular and Circuit Basis of Temperature Sensation in <i>Drosophila</i>.

Annual review of neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 12, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

Human Representation Learning.

Angela Radulescu^1,2, Yeon Soon Shin², Yael Niv^1,2

¹Department of Psychology, Princeton University, Princeton, New Jersey 08544, USA;

Annual Review of Neuroscience

|March 17, 2021

Summary

This summary is machine-generated.

This review explores how attention and memory guide learning by focusing on relevant information. We examine how statistical inference helps us learn efficiently from limited experiences, impacting decision-making.

Keywords:

learning selective attention memory representation learning

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Related Experiment Videos

Last Updated: Nov 12, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Photorealistic Learned Landscapes for Augmented Reality

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Area of Science:

Cognitive Science
Neuroscience
Machine Learning

Background:

Learning involves selecting relevant information from experiences.
Attention and memory are key cognitive functions for information selection.
The process of selecting what to learn is itself learned.

Purpose of the Study:

To review the dynamic interaction between information selection and learning.
To explore how humans learn which features of experiences are most valuable.
To contextualize representation learning as statistical inference.

Main Methods:

Review of recent evidence on attention, memory, and representation learning.
Analysis of statistical inference models for learning.
Discussion of how inference scales to real-world decisions.

Main Results:

Attention and memory shape world representations based on relevance for goals.
These representations are inferred from task-specific experiences.
Statistical inference provides a framework for understanding representation learning.
Belief approximation enables scalable inference from limited data.

Conclusions:

Representation learning can be understood as a process of statistical inference.
Inference from limited experience is crucial for efficient learning and decision-making.
This inference process has significant implications for social decision-making.