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Emotional Attention: From Eye Tracking to Computational Modeling.

Shaojing Fan, Zhiqi Shen, Ming Jiang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 21, 2022
    PubMed
    Summary

    Emotion-eliciting images capture initial human attention, especially awe-inspiring scenes. A new computational model, CASNet II, effectively simulates this selective visual attention to emotional content.

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    Area of Science:

    • Cognitive Science
    • Computer Vision
    • Neuroscience

    Background:

    • Selective attention to emotion-eliciting stimuli is fundamental to human vision.
    • Understanding how image features influence attention is crucial for visual processing research.

    Purpose of the Study:

    • To investigate the relationship between emotion-elicitation features in images and human selective attention.
    • To develop a computational model that simulates human attention to emotional content.

    Main Methods:

    • Creation of the EMOtional attention dataset (EMOd) with eye-tracking data and object/scene labels.
    • Analysis of human perception data to identify attention patterns.
    • Design and implementation of a deep neural network (CASNet II) incorporating channel weighting and Atrous Spatial Pyramid Pooling (ASPP).

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    Main Results:

    • An emotion prioritization effect was observed: emotional content attracts earlier and stronger attention than neutral content, though this effect is transient.
    • Human attention was notably focused on awe-eliciting, aesthetic scenes featuring vehicles and animals.
    • The CASNet II model demonstrated an ability to computationally simulate human attention behavior, particularly for emotion-eliciting images.

    Conclusions:

    • Emotional salience significantly influences the initial stages of human visual attention.
    • Computational models can effectively replicate human attentional biases towards emotional stimuli.
    • The EMOd dataset provides valuable data for studying the interplay between emotion and attention.