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Related Concept Videos

Correspondence Bias01:17

Correspondence Bias

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Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
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Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Theory of Attribution I: Correspondent Inference Theory01:15

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Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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Basics of Multivariate Analysis in Neuroimaging Data
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Generalizing Correspondence Analysis for Applications in Machine Learning.

Hsiang Hsu, Salman Salamatian, Flavio P Calmon

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    This summary is machine-generated.

    Correspondence analysis (CA) can now scale to large datasets by interpreting it through principal inertia components. Deep neural networks approximate these components, enabling efficient CA for complex data analysis.

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    Area of Science:

    • Multivariate statistics
    • Data visualization
    • Machine learning

    Background:

    • Correspondence analysis (CA) is a statistical method for visualizing data dependencies.
    • Existing CA methods struggle with large, high-dimensional datasets.
    • Applications span epidemiology, social sciences, and beyond.

    Purpose of the Study:

    • To develop scalable Correspondence Analysis (CA) methods.
    • To introduce a novel interpretation of CA using principal inertia components.
    • To leverage deep learning for efficient CA implementation.

    Main Methods:

    • Novel interpretation of CA via principal inertia components.
    • Functional optimization problem formulation for principal inertia components.
    • Deep neural networks for approximating principal inertia components from data.

    Main Results:

    • Equivalence established between estimating principal inertia components and performing CA.
    • Development of algorithms for scalable CA.
    • Demonstrated reliable approximation of principal inertia components using deep neural networks.

    Conclusions:

    • The novel interpretation enables scalable CA for high-dimensional data.
    • Deep learning provides an effective tool for approximating principal inertia components.
    • Maximal correlation embeddings are crucial for multi-view/multi-modal learning and visualization.