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Approaching human visual perception through AI-based representation of figure-ground segregation
Chakkai Yip1, Ezekiel Moroze1, Shigeaki Nishina2
1Computational Neuroscience and Vision Laboratory, Department of Psychological and Brain Sciences, Boston University, Boston, MA, United States.
Frontiers in Psychology
|March 16, 2026
Summary
Convolutional neural networks (CNNs) can infer border-ownership (BOS) for figure-ground perception from visual context. Geometric configurations, not just edges, are crucial for this process, even with degraded visual input.
Area of Science:
- Computational neuroscience
- Computer vision
- Visual perception
Background:
- Figure-ground perception is essential for object recognition.
- The computational basis of border-ownership assignment (BOS) remains unclear.
- Understanding BOS aids in developing artificial visual systems.
Purpose of the Study:
- To investigate how convolutional neural network (CNN) architectures infer border-ownership (BOS).
- To determine the role of contextual information in BOS inference under degraded conditions.
- To explore the hierarchical processing of visual features in CNNs related to BOS.
Main Methods:
- Trained multiple CNN architectures on overlapping/occlusion stimuli.
- Tested CNN performance on systematically degraded contours.
- Analyzed network representations to understand feature hierarchies.
Main Results:
- CNNs inferred BOS from feedforward computations even with degraded contours.
- Performance strongly depended on junction-like configurations, highlighting geometric context.
- Accuracy scaled linearly with contextual information from fragmented borders.
- Hierarchical progression from local edges to BOS-specific features was observed.
Conclusions:
- Hierarchical feedforward processing can explain certain aspects of BOS inference.
- Geometric context is more influential than isolated edges for BOS.
- Additional mechanisms like feedback may be necessary for robust figure-ground segregation.
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