Abstract
Mammalian visual cortex is known to have various neuronal response properties that depend on stimuli outside classical receptive fields. In this article, we give a probabilistic explanation to one such property called border-ownership signals, by interpreting them as posterior joint probabilities of a low-level edge property and a high-level figure property. We show that such joint probabilities can be found in a hierarchical Bayesian network mimicking visual cortex, and indeed they exhibit simulational responses qualitatively similar to physiological data.
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