Approaching human visual perception through AI-based representation of figure-ground segregation

Understanding how the visual system assigns borders to foreground objects is central to figure-ground perception, yet the computational principles underlying this process are still under investigation.

We trained multiple convolutional neural network (CNN) architectures on simple overlapping/occlusion stimuli and tested them on systematically degraded contours to probe how border-ownership (BOS) inference depends on available border context.

Across networks, BOS could be inferred from feedforward computations even under degraded conditions, but performance showed a strong dependence on junction-like configurations, indicating that geometric context contributes more than isolated edges. Accuracy increased approximately linearly with the amount of contextual information provided by fragmented borders, and representation analyses revealed a hierarchical progression from local edge responses to more spatially coherent, BOS-specific features.

Together, these results delineate which aspects of BOS can emerge from hierarchical feedforward processing and suggest that additional mechanisms such as horizontal and feedback interactions may reduce the visual information required for robust figure-ground segregation.