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関連する概念動画

Concepts and Prototypes01:24

Concepts and Prototypes

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
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Piaget's Stage 2 of Cognitive Development01:14

Piaget's Stage 2 of Cognitive Development

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The preoperational stage, the second of Jean Piaget's four stages of cognitive development, spans approximately ages 2 to 7 and is characterized by the emergence of symbolic thinking. During this stage, children use language, images, and symbols to represent objects and concepts, enabling them to engage in imaginative and pretend play. This symbolic thinking supports children's ability to perform make-believe actions, such as imagining a broom as a horse or their hand as a phone, blending...
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Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Piaget's Stage 1 of Cognitive Development01:14

Piaget's Stage 1 of Cognitive Development

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The sensorimotor stage, the initial phase of Jean Piaget's theory of cognitive development, spans the first two years of a child's life. During this period, infants actively engage with their surroundings, building cognitive awareness through direct interaction with the world. This interaction is primarily based on sensory perception and motor actions, allowing infants to gradually understand basic physical properties and predict how objects interact within their environment.
Exploration...
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Cognitivism01:17

Cognitivism

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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
Previously dominated by behaviorism, which prioritized observable behaviors and largely ignored mental processes, psychology transformed in the 1950s. Cognitive psychologists argue that understanding how we think and process...
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Behaviorism01:28

Behaviorism

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The field of behaviorism was pioneered by figures such as Ivan Pavlov, John B. Watson, and B.F. Skinner fundamentally shifted the focus of psychology to the observable and controllable aspects of human and animal behavior. This shift marked a critical evolution in the discipline, emphasizing scientific rigor and experimental methodology.
The core premise of behaviorism is its focus on observable behavior rather than internal thoughts or feelings. This approach argues that true scientific...
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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ピクセルからのオブジェクト中心のプロトシンボリック行動推論

Ruben van Bergen1, Justus Hübotter1, Alma Lago2

  • 1Donders Institute, Radboud University, Nijmegen, the Netherlands.

Neural networks : the official journal of the International Neural Network Society
|December 20, 2025
PubMed
まとめ

この研究は、自律エージェント向けのオブジェクト中心の表現を使用した脳着想のディープラーニングモデルを紹介します。このモデルは、人間の監督なしで環境を推論および制御することを学習し、創発的な論理推論能力を示します。

キーワード:
脳着想の知覚と制御ディープラーニングアーキテクチャオブジェクト中心の推論

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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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科学分野:

  • 人工知能
  • 認知科学
  • ロボット工学

背景:

  • 自律エージェントは、低レベルの感覚入力と高レベルの推論を橋渡しする必要があります。
  • 教師なし学習方法は、コストのかかるデータ注釈を回避するために必要です。
  • オブジェクト中心の表現は、知覚、行動、推論の間の有望なインターフェースを提供します。

研究 の 目的:

  • 自律エージェント向けの新しい脳着想のディープラーニングアーキテクチャを提示すること。
  • エージェントがピクセルから学習し、オブジェクト中心の表現を使用して解釈、制御、推論できるようにすること。
  • 論理的推論と連続制御を必要とする合成環境でこのアプローチの有用性を示すこと。

主な方法:

  • 脳にヒントを得た新しいディープラーニングアーキテクチャを開発しました。
  • ピクセルデータから学習したオブジェクト中心の表現を採用しました。
  • タスク評価のために合成環境（2Dおよび3D dSprites）を利用しました。

主要な成果:

  • エージェントは創発的な条件付き行動推論と論理的合成を学習しました。
  • エージェントは、推論された論理規則に基づいて環境を正常に制御しました。
  • エージェントは、環境変化へのオンライン適応と、ワールドモデル違反に対する堅牢性を示しました。

結論:

  • オブジェクト中心の表現は、自律エージェントにおける教師なし学習のための主要な帰納的バイアスとして機能します。
  • 提案されたアーキテクチャは、接地されたオブジェクト表現を操作することによって行動推論を促進します。
  • 将来の研究では、このアプローチをより複雑な実世界のシナリオに拡張できます。