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Meaning Guides Attention during Real-World Scene Description.

John M Henderson^1,2, Taylor R Hayes³, Gwendolyn Rehrig⁴

¹Center for Mind and Brain, University of California, Davis, USA. johnhenderson@ucdavis.edu.

Scientific Reports

|September 12, 2018

Summary

Meaning, not image salience, guides visual attention in real-world scenes. Cognitive relevance drives attentional selection, impacting AI image labeling.

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Area of Science:

Cognitive Psychology
Computer Vision
Neuroscience

Background:

Intelligent visual scene analysis requires prioritizing important regions for processing.
Understanding the basis of attentional selection in real-world scenes is crucial for both human cognition and artificial intelligence.

Purpose of the Study:

To compare the influence of semantic meaning and image salience on attentional guidance during scene description tasks.
To determine whether meaning or salience uniquely predicts attention allocation in real-world scenes.

Main Methods:

Utilized free-viewing scene description tasks with real-world images.
Created meaning maps (semantic features) and saliency maps (image features).
Compared these maps to eye-fixation data to derive attention maps.

Main Results:

Both meaning and image salience influenced attention distribution.
After controlling for the correlation between meaning and salience, only meaning uniquely predicted attention.
Cognitive relevance emerged as the dominant factor in attentional guidance.

Conclusions:

Semantic meaning, representing cognitive relevance, is the primary driver of human attentional selection in scenes.
Findings have implications for developing more sophisticated AI systems for image understanding and labeling.