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.
Author summary Our percept of the world is governed not only by the sensory information we have access to, but also by the way we interpret this information. When presented with a visual scene, our visual system undergoes a process of grouping visual elements together to form coherent entities so that we can interpret the scene more readily and meaningfully. For example, when looking at a pile of autumn leaves, one can still perceive and identify a whole leaf even when it is partially covered by another leaf. While Gestalt psychologists have long described perceptual organization with a set of qualitative laws, recent studies offered a statistically-optimal – Bayesian, in statistical jargon – interpretation of this process, whereby the observer chooses the scene configuration with the highest probability given the available sensory inputs. However, these studies drew their conclusions without considering a key actor in this kind of statistically-optimal computations, that is the role of sensory uncertainty. One can easily imagine that our decision on whether two contours belong to the same leaf or different leaves is likely going to change when we move from viewing the pile of leaves at a great distance (high sensory uncertainty), to viewing very closely (low sensory uncertainty). Our study examines whether and how people incorporate uncertainty into perceptual organization, by varying sensory uncertainty from trial to trial in a simple perceptual organization task. We found that people indeed take into account sensory uncertainty, however in a way that subtly deviates from optimal behavior.
