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相关概念视频

Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

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The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
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The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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使用决策变量关联量化与任务相关的表示相似性.

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    我们引入决策变量相关性 (DVC) 来比较大脑和人工智能模型如何做出决策. 人工智能模型显示,与子大脑的决策策略相似性较低,这表明与任务相关的表示分歧.

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    科学领域:

    • 神经科学是一个神经科学.
    • 人工智能的人工智能
    • 计算机视觉 计算机视觉

    背景情况:

    • 将视觉皮层中的神经表示与深度神经网络 (DNN) 的比较对于理解生物和人工视觉至关重要.
    • 之前的研究表明,关于神经活动和DNN表示之间的相似性,结果各不相同.
    • 需要一种新的方法来具体评估与任务相关的决策策略,而不仅仅是一般的代表性调整.

    研究的目的:

    • 提出和评估决策变量相关性 (DVC) 作为一种新的方法来量化观察者 (大脑或模型) 之间的决策策略的相似性.
    • 为了比较子视觉皮层 (V4/IT) 与在图像分类上受过训练的DNN的任务相关表示.

    主要方法:

    • 开发了决策变量相关性 (DVC) 来测量从内部表示中解码的决策的图像对图像相关性.
    • 在分类任务中从子V4/IT收集了神经记录.
    • 利用了在图像分类任务上训练的各种DNN,包括那些具有对抗性训练和大数据集预训练的DNN.

    主要成果:

    • 模型与模型和子与子的相似性是可比的,但模型与子的相似性始终较低.
    • 决策变量相关性 (DVC) 随着ImageNet-1k网络性能的提高而下降.
    • 对抗性训练和大数据集预训练并没有改善与任务相关维度的模型和子相似性.

    结论:

    • 决策变量相关性 (DVC) 有效地捕获与任务相关的信息,揭示决策策略的差异.
    • 子V4/IT中与任务相关的表示与通过标准图像分类DNNs学到的不同.
    • 目前的DNN培训方法不能完全弥合与生物视觉相比决策策略的差距.