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関連する概念動画

Reason and Intuition01:37

Reason and Intuition

The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the brain can only use...
Schemas01:42

Schemas

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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...
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint Vincent in...
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
Intelligence01:27

Intelligence

The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember detailed...

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A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies
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Published on: August 4, 2023

人工知能のメンタリティの3つの枠組み

Henry Shevlin1

  • 1Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, United Kingdom.

Frontiers in psychology
|February 20, 2026
PubMed
まとめ
この要約は機械生成です。

大型言語モデル (LLM) は,ユーザーがAIにメンタリティを代入するよう促します. この論文では,AIメンタリティの3つのフレームワークを分析し,段階的な信念と願望の属性に対する"最小の認知エージェント"の見解を提案しています.

キーワード:
アントロポモルフィズム (anthropomorphism) とはフォーク心理学 フォーク心理学人間とAIの関係大規模な言語モデルがあります.AIの哲学とは,AIの哲学である.ソーシャルAI ソーシャルAI理論,理論,理論,理論などです.

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関連する実験動画

Last Updated: Jun 26, 2026

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科学分野:

  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • コグニティブ・サイエンス コグニティブ・サイエンス
  • 心の哲学 心の哲学

背景:

  • 大型言語モデル (LLM) の急速な進歩により,AIに対するメンタリティの公的およびユーザの帰属が増加しました.
  • 人工知能のメンタリティを理解するための既存の枠組みには",無意識の機械"と"単なるロールプレイ"の見解が含まれています.
  • これらのフレームワークは,ユーザー属性とAI認知の性質を説明する上で課題に直面しています.

研究 の 目的:

  • 人工知能のメンタリティの属性に関する構造的な分析を提供すること.
  • AI認知を理解するための既存のフレームワークを批判的に評価する.
  • LLMに精神状態を割り当てるための新しい枠組みを提案する.

主な方法:

  • Marrの分析レベルを用いた建築的反証論の分析.
  • 人工知能の相互作用に対する"単なるロールプレイ"の類似点の評価.
  • "最小限の認知エージェント"の枠組みの開発.

主要な成果:

  • 建築的説明は,実装に敏感な概念と無関心な概念を区別する民俗心理学的説明の必要性を完全に否定しません.
  • "単なるロールプレイ"の見解は,心理的に不安定で,理論的に人類型AIには不完全である.
  • LLMは",最小限の認知エージェント"の枠組みの下で,信念や欲望のような状態の限られた,段階的な属性を保証することができます.

結論:

  • 信念のバイナリー概念を超えて,多次元的で連続的なモデルに移行することが必要である.
  • このアプローチは,AIに関する現在の解釈の慣行をよりよく捉えることができます.
  • それは,人間,LLM,およびより単純なシステム認知の間の区別を維持するのに役立ちます.