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相关概念视频

The Representativeness Heuristic02:13

The Representativeness Heuristic

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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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Self-Discrepancy Theory02:45

Self-Discrepancy Theory

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One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.  
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Test for Homogeneity01:23

Test for Homogeneity

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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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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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Nonconscious Mimicry01:13

Nonconscious Mimicry

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Nonconscious mimicry occurs when individuals alter their mannerisms to match the behaviors and expressions of those nearby, without intention.
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Updated: May 13, 2025

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

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试验级别的代表性相似性分析.

Shenyang Huang, Cortney M Howard, Paul C Bogdan

    bioRxiv : the preprint server for biology
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    此摘要是机器生成的。

    试验级表示相似性分析 (tRSA) 为研究神经表示提供了一种比经典RSA (cRSA) 更强大的神经表示方法. 这种新的框架通过考虑个别试验来增强大脑活动的分析,从而在认知神经科学中获得更敏感和更准确的发现.

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    相关实验视频

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    科学领域:

    • 认知神经科学 认知神经科学
    • 神经成像是一种神经成像.
    • 计算神经科学是一种神经科学.

    背景情况:

    • 神经表现是理解认知经验的关键.
    • 经典的表示相似性分析 (cRSA) 使用相似性矩阵评估表示质量,但不能建模试验级差异.
    • 由于cRSA的局限性,因此难以评估受试者,刺激和试验对神经表征的影响.

    研究的目的:

    • 介绍试验级表示相似性分析 (tRSA),这是分析神经表示的新框架.
    • 与cRSA相比,评估tRSA的性能和优势.
    • 使用模拟和真实神经成像数据演示tRSA的应用和好处.

    主要方法:

    • 正式引入试验级别代表性相似性分析 (tRSA) 框架.
    • 使用多级模型来估计个体试验级别的神经表现强度.
    • 使用模拟数据和真实fMRI数据集对tRSA与cRSA进行验证和比较.

    主要成果:

    • 在量化整体表示强度方面,tRSA与cRSA有很强的对应性.
    • 与cRSA相比,tRSA的多层次方法在理论上更合理,对影响更敏感.
    • 在真正的fMRI数据中,tRSA显示出对cRSA遇到的问题更强大的稳定性.
    • 关于神经表征的新发现仅通过tRSA进行识别.

    结论:

    • tRSA 是一种用于认知神经科学的多功能和强大的分析框架.
    • tRSA克服了cRSA的局限性,通过使试验级分析成为可能.
    • tRSA 方法有助于对神经表征及其潜在变异有更细致的理解.