Natural scene segmentation dynamics reveal iterative Bayesian inference
Tridib K Biswas1, Jonathan Vacher2, Sophie Molholm1,3
1Albert Einstein College of Medicine, Dept. of Neuroscience.
Biorxiv : the Preprint Server for Biology
|February 12, 2026
Summary
Human visual segmentation dynamics for natural images were measured. Iterative Bayesian inference algorithms explain these dynamics, showing faster processing when visual inputs align with spatial expectations.
Area of Science:
- Neuroscience
- Cognitive Science
- Computer Vision
Background:
- The visual system segments input into perceptual objects.
- Segmentation dynamics are observable in perceptual choices and neural activity.
- Dynamics for natural stimuli are poorly understood due to ambiguity.
Purpose of the Study:
- To measure subjective human segmentation maps for natural images.
- To uncover richer spatiotemporal dynamics than current theories predict.
- To explain observed dynamics using computational models.
Main Methods:
- Developed an innovative paradigm to measure subjective human segmentation maps.
- Introduced Iterative Bayesian Inference algorithms for segmentation.
- Integrated visual inputs with spatial compactness priors.
Main Results:
- Uncovered richer spatiotemporal dynamics than predicted by existing theories.
- Demonstrated that iterative inference is faster when visual inputs match spatial priors.
- Confirmed that this relationship reflects individual participants' spatial biases.
Conclusions:
- Iterative Bayesian inference is a key mechanism for natural vision segmentation.
- This process sets the tempo for fundamental visual functions.
- Computational models can explain complex perceptual dynamics.
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