Abstract
Perceptual organization is the process of grouping scene elements into whole entities, for example line segments into contours. Uncertainty in grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of perceptual organization have been re-framed in terms of Bayesian models, whereby the observer computes the probability that the whole entity is present. Previous studies that proposed a Bayesian interpretation of perceptual organization, however, have ignored sensory uncertainty, despite the fact that accounting for the current level of uncertainty is the main signature of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is necessary to test whether humans perform optimal Bayesian inference in perceptual organization, as opposed to using some non-Bayesian heuristic. We distinguish between these hypotheses in an elementary form of perceptual organization, namely judging whether two line segments separated by an occluder are collinear. We manipulate sensory uncertainty by varying retinal eccentricity. A Bayes-optimal observer would take the level of sensory uncertainty into account - in a very specific way - in deciding whether a measured offset between the line segments is due to non-collinearity or to sensory noise. We find that people deviate slightly but systematically from Bayesian optimality, while still performing "probabilistic computation" in the sense that they take into account sensory uncertainty via a heuristic rule. Our work contributes to an understanding of the role of sensory uncertainty in higher-order perception.
