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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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Theory of Attribution II: Kelley's Covariation Theory01:29

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Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
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Related Experiment Video

Updated: Nov 1, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Learning From a Complementary-Label Source Domain: Theory and Algorithms.

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    This study introduces complementary labels for unsupervised domain adaptation (UDA), reducing data collection costs. A novel method, CLARINET, effectively uses complementary-label data for UDA tasks.

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    Area of Science:

    • Machine Learning
    • Computer Vision

    Background:

    • Unsupervised Domain Adaptation (UDA) typically requires extensive true-label data from the source domain, which is often costly and impractical.
    • Complementary labels (CLs), indicating classes a data point does *not* belong to, offer a less laborious alternative to true labels (TLs).

    Purpose of the Study:

    • To propose a novel UDA setting utilizing complementary-label data from the source domain.
    • To introduce and evaluate a method for solving UDA problems with complementary-label data, addressing both completely and partly complementary scenarios.

    Main Methods:

    • Developed a theoretical bound for the proposed complementary-label UDA setting.
    • Proposed the Complementary Label Adversarial Network (CLARINET), a dual-network architecture for UDA.
    • CLARINET simultaneously classifies complementary-label source data and performs source-to-target domain adaptation.

    Main Results:

    • CLARINET effectively addresses both completely complementary UDA (CC-UDA) and partly complementary UDA (PC-UDA).
    • The proposed method significantly outperforms existing baselines in experimental evaluations.
    • Demonstrated strong performance on handwritten digit and object recognition tasks.

    Conclusions:

    • Complementary labels offer a viable and cost-effective alternative for UDA data collection.
    • CLARINET provides an effective solution for UDA problems leveraging complementary-label data.
    • The findings open new avenues for UDA research with less burdensome data requirements.