Natural scene segmentation dynamics reveal iterative Bayesian inference

02 Feb 2026 in Publications

with T. K. Biswas, S. Molholm, P. Mamassian and R. Coen-Cagli.

bioRxiv

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This work extends our segmentation line to reaction times and natural images. We reconstruct subjective segmentation maps from human responses and use the temporal dynamics to test an iterative Bayesian inference account of visual segmentation.

1. Biswas, T. K., Vacher, J., Molholm, S., Mamassian, P. & Coen-Cagli, R. Natural scene segmentation dynamics reveal iterative Bayesian inference. bioRxiv 2026–01 (2026).

   @article{biswas2026natural,
     title = {Natural scene segmentation dynamics reveal iterative Bayesian inference},
     author = {Biswas, Tridib K and Vacher, Jonathan and Molholm, Sophie and Mamassian, Pascal and Coen-Cagli, Ruben},
     journal = {bioRxiv},
     pages = {2026--01},
     year = {2026},
     publisher = {Cold Spring Harbor Laboratory}
   }
   

Overview

When image evidence agrees with spatial priors, inference should settle faster. This prediction explains why reaction times can increase with distance for same-segment judgments yet decrease with distance for different-segment judgments, and it also captures individual differences in spatial biases. More broadly, the paper argues that the tempo of segmentation in natural scenes is shaped by recurrent probabilistic inference rather than by a purely feedforward grouping rule.