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関連する概念動画

Social Scripts02:10

Social Scripts

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People tend to know what behavior is expected of them in specific, familiar settings. A script is a person’s knowledge about the sequence of events expected in a specific setting (Schank & Abelson, 1977). Essentially, scripts are a particular kind of schema, one containing default values for the features within an event. In the restaurant example, the script's features include the props (e.g., tables, menu, food, and money), the roles to be played (e.g., customer and waiter),...
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Natural Selection and Adaptation01:15

Natural Selection and Adaptation

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Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
Beyond physical adaptations,...
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Steps in the Modeling Process01:14

Steps in the Modeling Process

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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
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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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Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Modeling in Therapy01:26

Modeling in Therapy

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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Updated: May 27, 2025

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking
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ゲームプレイのアイデア化に向けた世界と人間の行動モデル

Anssi Kanervisto1, Dave Bignell1, Linda Yilin Wen1

  • 1Microsoft Research, Cambridge, UK.

Nature
|February 19, 2025
PubMed
まとめ

生成型人工知能 (AI) は 繰り返しデザインをサポートすることで 創造的なアイデアを高めることができます 新しいモデルであるWHAMは,一貫したゲームプレイとユーザー変更を生み出し,AIとクリエイティブな実践を合わせます.

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科学分野:

  • コンピュータ科学
  • 人とコンピュータの相互作用
  • 人工知能

背景:

  • 創造的AIはクリエイティブ産業に 潜在的可能性を秘めているが 繰り返し調整や 異なる思考といった 核心的な創造的実践をサポートする上で 課題に直面している.
  • 現在の生成型AIモデルは,人間の創造的なプロセスを十分にサポートせず,創造的なワークフローへの統合を制限しています.

研究 の 目的:

  • クリエイティブ・プラクティス,特にゲーム開発におけるユーザーニーズと,ジェネラティブ・AIモデル開発を調和させる.
  • イテラティブで異なる創造的プロセスをサポートする制限に対処する新しい生成AIモデルを導入し,評価する.

主な方法:

  • 世界と人間行動モデル (WHAM) を開発し,評価した.創造的なサポートに合わせた生成型AIモデル.
  • ゲーム開発におけるユーザーニーズに焦点を当てて,AIの能力を指引し,ユーザー変更の一貫性,多様性,持続性を強調しています.

主要な成果:

  • WHAMは,一貫した多様なゲームプレイシーケンスを生成する能力を示しています.
  • このモデルは,AIと繰り返し作るワークフローを整合させるための重要な機能であるユーザーによる修正を成功裏に継続します.
  • WHAMはデータから関連する構造を学習し,以前の領域特有のツールと比較してより広範なアプリケーションを可能にします.

結論:

  • WHAMで示された生成AIは,クリエイティブなアイデアと実践をサポートするために,ユーザーのニーズに基づいて効果的に開発および評価することができます.
  • WHAMの実証された機能は,特にゲーム開発のようなダイナミックな領域において,AIによるクリエイティビティサポートツールの重要な進歩を表しています.