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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Lateralization01:28

Lateralization

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Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
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Modeling and Similitude01:12

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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相关实验视频

Updated: Sep 16, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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语言模型代理在评估可视化时是否与人类保持一致? 一个实证研究

Zekai Shao, Yi Shan, Yixuan He

    IEEE computer graphics and applications
    |July 9, 2025
    PubMed
    概括

    大型语言模型 (LLM) 可以模拟可视化设计任务的人类评级,但只有在专家信心的指导下. 这些人工智能代理可以补充,但不能取代传统的用户研究.

    科学领域:

    • 人与计算机的互动.
    • 人工智能的人工智能是人工智能.
    • 数据可视化数据可视化

    背景情况:

    • 大型语言模型 (LLM) 在解释视觉数据和设计原则方面展示了潜力.
    • 关于LLM准确预测人类对可视化设计的反的能力在很大程度上仍未被探索.

    研究的目的:

    • 在可视化任务中评估基于LLM的代理评级和人类判断之间的对齐.
    • 调查影响人类反LLM预测准确性的因素.
    • 探索LLM代理在快速原型评估中的实用性.

    主要方法:

    • 进行了三项研究:复制了对人进行的研究,模拟了使用LLM剂的六项先前研究,并测试了增强技术.
    • 雇佣了LLM代理来提供可视化任务的评级,并将他们的表现与人类受试者数据和专家信心水平进行了比较.
    • 测试了输入预处理和知识注入等技术,以评估它们对代理强度和偏差的影响.

    主要成果:

    • 在一项复制的研究中,LLM代理在模仿类似人类推理和评级方面表现出有希望的表现.
    • 在模拟研究中,代理人与人类的对齐与专家在实验前的信心有积极的相关性.
    • 增强技术揭示了LLM代理的稳定性和潜在偏差的局限性.

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    Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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    结论:

    • 基于LLM的代理可以有效地模拟可视化任务的人类评级,当由高可信度专家假设指导时.
    • 在可视化设计中,LLM代理显示出作为快速原型评估的补充工具的潜力.
    • 法学士模拟是有价值的,但不能取代全面的用户研究.