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 summaryOur 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 jargoninterpretation of this process, whereby the observer chooses the scene configuration with June 8, 20181/19 . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/350082 doi: bioRxiv preprint first posted online Jun. 18, 2018; 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 s...

Perceptual organization is the process of grouping scene elements into whole entities. A classic example is contour integration, in which separate line segments are perceived as continuous contours. Uncertainty in such grouping arises from scene ambiguity and sensory noise. Some classic Gestalt principles of contour integration, and more broadly, of perceptual organization, have been re-framed in terms of Bayesian inference, 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 perceptual uncertainty is one the main signatures of Bayesian decision making. Crucially, trial-by-trial manipulation of sensory uncertainty is a key test to whether humans perform near-optimal Bayesian inference in contour integration, as opposed to using some manifestly non-Bayesian heuristic. We distinguish between these hypotheses in a simplified form of contour integration, 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.

No abstract

Clothing store display is the most important aspect of terminal clothing sales, in the moment "Terminal Winning" marketing era, how to build a science and attract the attention of consumers clothing store environment is an important means of corporate marketing. In today image element is the dominant element and it has incomparable advantages because of its visual image elements, brought by the spread of the appeal, in terms of the desire to inspire customers to buy. This paper argues that in the clothing store display image elements in use is not only a marketing tool, it is the propaganda of brand value and brand culture and it is the development direction of the future show.

No abstract