Matches in SemOpenAlex for { <https://semopenalex.org/work/W3103908599> ?p ?o ?g. }
- W3103908599 abstract "Abstract 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. 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 contour integration, an elementary form of perceptual organization, by varying sensory uncertainty from trial to trial in a simple contour integration task. We found that people indeed take into account sensory uncertainty, however in a way that subtly deviates from optimal behavior." @default.
- W3103908599 created "2020-11-23" @default.
- W3103908599 creator A5016592576 @default.
- W3103908599 creator A5032248858 @default.
- W3103908599 creator A5090892103 @default.
- W3103908599 date "2018-06-18" @default.
- W3103908599 modified "2023-09-25" @default.
- W3103908599 title "The role of sensory uncertainty in simple contour integration" @default.
- W3103908599 cites W1456921974 @default.
- W3103908599 cites W1562206072 @default.
- W3103908599 cites W1969090956 @default.
- W3103908599 cites W1986400713 @default.
- W3103908599 cites W1989388297 @default.
- W3103908599 cites W1995897755 @default.
- W3103908599 cites W2006056627 @default.
- W3103908599 cites W2009404864 @default.
- W3103908599 cites W2012427860 @default.
- W3103908599 cites W2017108196 @default.
- W3103908599 cites W2017357931 @default.
- W3103908599 cites W2023688891 @default.
- W3103908599 cites W2031120389 @default.
- W3103908599 cites W2034546031 @default.
- W3103908599 cites W2035550654 @default.
- W3103908599 cites W2040209392 @default.
- W3103908599 cites W2043554950 @default.
- W3103908599 cites W2050878336 @default.
- W3103908599 cites W2062526841 @default.
- W3103908599 cites W2064658612 @default.
- W3103908599 cites W2067822263 @default.
- W3103908599 cites W2072262638 @default.
- W3103908599 cites W2084528670 @default.
- W3103908599 cites W2096016260 @default.
- W3103908599 cites W2106145030 @default.
- W3103908599 cites W2117215414 @default.
- W3103908599 cites W2118354656 @default.
- W3103908599 cites W2125663122 @default.
- W3103908599 cites W2126517582 @default.
- W3103908599 cites W2133410812 @default.
- W3103908599 cites W2138709941 @default.
- W3103908599 cites W2141467654 @default.
- W3103908599 cites W2141516486 @default.
- W3103908599 cites W2147565549 @default.
- W3103908599 cites W2152437364 @default.
- W3103908599 cites W2156707826 @default.
- W3103908599 cites W2157745749 @default.
- W3103908599 cites W2203714058 @default.
- W3103908599 cites W2375262830 @default.
- W3103908599 cites W2579983003 @default.
- W3103908599 cites W2610253745 @default.
- W3103908599 cites W2790916085 @default.
- W3103908599 cites W2896195182 @default.
- W3103908599 cites W2939440148 @default.
- W3103908599 cites W2949885588 @default.
- W3103908599 cites W2949977326 @default.
- W3103908599 cites W2950975704 @default.
- W3103908599 cites W2956814642 @default.
- W3103908599 cites W2963054961 @default.
- W3103908599 cites W32980360 @default.
- W3103908599 cites W4234487882 @default.
- W3103908599 cites W4248681815 @default.
- W3103908599 cites W4252561732 @default.
- W3103908599 cites W4253448410 @default.
- W3103908599 cites W4254404291 @default.
- W3103908599 cites W4288400169 @default.
- W3103908599 cites W4294214781 @default.
- W3103908599 cites W4299551239 @default.
- W3103908599 cites W55848811 @default.
- W3103908599 cites W1989021362 @default.
- W3103908599 doi "https://doi.org/10.1101/350082" @default.
- W3103908599 hasPublicationYear "2018" @default.
- W3103908599 type Work @default.
- W3103908599 sameAs 3103908599 @default.
- W3103908599 citedByCount "2" @default.
- W3103908599 countsByYear W31039085992018 @default.
- W3103908599 countsByYear W31039085992019 @default.
- W3103908599 crossrefType "posted-content" @default.
- W3103908599 hasAuthorship W3103908599A5016592576 @default.
- W3103908599 hasAuthorship W3103908599A5032248858 @default.
- W3103908599 hasAuthorship W3103908599A5090892103 @default.
- W3103908599 hasBestOaLocation W31039085991 @default.
- W3103908599 hasConcept C107673813 @default.
- W3103908599 hasConcept C119857082 @default.
- W3103908599 hasConcept C121332964 @default.
- W3103908599 hasConcept C153180895 @default.
- W3103908599 hasConcept C154945302 @default.
- W3103908599 hasConcept C15744967 @default.
- W3103908599 hasConcept C160234255 @default.
- W3103908599 hasConcept C169760540 @default.
- W3103908599 hasConcept C173801870 @default.
- W3103908599 hasConcept C180747234 @default.
- W3103908599 hasConcept C199360897 @default.
- W3103908599 hasConcept C207201462 @default.
- W3103908599 hasConcept C26760741 @default.
- W3103908599 hasConcept C2776214188 @default.
- W3103908599 hasConcept C2780522230 @default.
- W3103908599 hasConcept C2780704645 @default.
- W3103908599 hasConcept C41008148 @default.
- W3103908599 hasConcept C49937458 @default.
- W3103908599 hasConcept C62520636 @default.
- W3103908599 hasConcept C94487597 @default.