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Alan Z Lu^1,2, Aditya Upadhyayula³, John M Henderson^1,2

¹Center for Mind and Brain, University of California, Davis.

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|August 28, 2025

Summary

This summary is machine-generated.

Detecting visual changes relies on scene meaning. Highly meaningful regions in naturalistic scenes are more easily recognized for changes, demonstrating the visual system

Keywords:

change detection meaning maps scene inversion scene perception scene semantics

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Area of Science:

Cognitive Neuroscience
Visual Perception
Computational Vision

Background:

Change detection is crucial for adapting to dynamic environments.
Prior studies suggest high-level semantic information guides change detection in natural scenes.
Working memory actively maintains representations for comparison.

Purpose of the Study:

To investigate the role of semantic features in visual change detection.
To determine if semantic information predicts change detection accuracy in a flicker paradigm.
To explore the influence of scene meaning on visual processing.

Main Methods:

Utilized meaning maps, a data-driven method, to quantify semantic information in naturalistic scenes.
Employed a flicker paradigm to present visual stimuli and record change detection performance.
Conducted two experiments manipulating scene meaning and orientation (Experiment 2 used scene inversion).

Main Results:

Changes in highly meaningful regions were detected more readily than in non-meaningful regions, even when controlling for low-level saliency.
Scene inversion significantly reduced the advantage for detecting changes in meaningful regions.
Semantic information demonstrably influences visual change detection.

Conclusions:

The visual system actively leverages semantic features for effective change detection.
Meaningful content plays a significant role in how we perceive and react to changes in our environment.
These findings highlight the importance of semantic processing in visual working memory and scene understanding.