Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

578
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...
578
Observational Learning01:12

Observational Learning

188
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...
188
Associative Learning01:27

Associative Learning

412
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...
412
Introduction to Learning01:18

Introduction to Learning

446
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...
446
Purposive Learning01:22

Purposive Learning

123
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...
123
Cognitive Learning01:21

Cognitive Learning

249
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...
249

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Diagnostic performance of an artificial intelligence software for diabetic retinopathy organised screening in a pilot study.

Scientific reports·2026
Same author

Correction: A Comprehensive Behavioral Dataset for the Abstraction and Reasoning Corpus.

Scientific data·2025
Same author

A Comprehensive Behavioral Dataset for the Abstraction and Reasoning Corpus.

Scientific data·2025
Same author

Grounded language acquisition through the eyes and ears of a single child.

Science (New York, N.Y.)·2024
Same author

Word meaning in minds and machines.

Psychological review·2021
Same author

Mechanisms for handling nested dependencies in neural-network language models and humans.

Cognition·2021
Same journal

Retraction Note: NSD2 targeting reverses plasticity and drug resistance in prostate cancer.

Nature·2026
Same journal

Enhanced B cell priming induces broadly neutralizing HIV-1 apex antibodies.

Nature·2026
Same journal

Vaccination elicits HIV broadly neutralizing antibodies in primates.

Nature·2026
Same journal

Child online safety needs more than social-media bans.

Nature·2026
Same journal

Ebola preparedness must start with ecosystems and before humans show symptoms.

Nature·2026
Same journal

AI tools can speed up thinking, but evidence still comes from the lab bench.

Nature·2026
関連記事をすべて見る

関連する実験動画

Updated: Jul 12, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K

メタラーニングの神経ネットワークによる人間のような体系的な一般化

Brenden M Lake1, Marco Baroni2,3

  • 1Department of Psychology and Center for Data Science, New York University, New York, NY, USA. brenden@nyu.edu.

Nature
|October 25, 2023
PubMed
まとめ
この要約は機械生成です。

神経ネットワークは 構成能力を最適化することで 言語と思考において人間のような体系性を 達成できるのです 構成性のためのメタラーニング (MLC) アプローチは,ネットワークを柔軟に一般化させ,人工知能における長年の課題に取り組むことができます.

さらに関連する動画

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.4K

関連する実験動画

Last Updated: Jul 12, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.0K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

14.4K

科学分野:

  • 認知科学
  • 人工知能
  • コンピュータ言語学

背景:

  • 人間の認知は体系的な構成性に依存し 既知の要素の新しい組み合わせを可能にします
  • フォドールとピリシンの挑戦は,人工の神経ネットワークにはこの体系性が欠けていて,心のモデルとしての有効性を制限していると仮定しています.
  • 進歩にもかかわらず,ニューラルネットワークにおける体系的な汎用化を達成することは,継続的な課題です.

研究 の 目的:

  • 神経ネットワークが人間のような体系性を 達成できることを示すため
  • 複合性のためのメタラーニング (MLC) の導入と評価
  • MLCの一般化能力を他のモデルと人間のパフォーマンスと比較する.

主な方法:

  • 複合性のためのメタラーニング (MLC) アプローチを開発し,多様な複合的なタスクでニューラルネットワークトレーニングを指導しました.
  • 命令学習のパラダイムを使って 人間の行動に関する実験を行いました
  • MLC,確率的シンボリックモデル,標準的なニューラルネットワークを含む7つの異なるモデルを体系的汎用基準で評価した.

主要な成果:

  • MLCは体系性と柔軟性の両方を達成し,厳格なシンボリックモデルと非体系的なニューラルネットワークを上回りました.
  • MLCは人対人比較で人間のような一般化能力を示した.
  • MLCは複数のベンチマークで 機械学習システムの構成スキルを大幅に向上させました

結論:

  • 構成スキルのためのニューラルネットワークを最適化することで 人間のような体系的な一般化を可能にします
  • MLCアプローチは,より能力があり,人間に似た人工知能を開発するための実用的な方法を提供します.
  • この研究は 人工ニューラルネットワークと 人間の思考と言語の 体系的な性質の間のギャップを埋めるものです