The role of sensory uncertainty in simple contour integration

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.

Uncertainty-based inference of a common cause for body ownership

“…As a consequence, the decision criterion changes as a function of the sensory noise affecting the observer’s measurement ( Figure 5 ). This is a crucial property of BCI and indeed a property shared by Bayesian models used in previous work on multisensory synchrony judgments ( Magnotti et al, 2013 ), audiavisual spatial localization ( Körding et al, 2007 ), visual searching ( Stengård and van den Berg, 2019 ), change detection ( Keshvari et al, 2012 ), collinearity judgment ( Zhou et al, 2020 ), and categorization ( Qamar et al, 2013 ). The output of the BCI model is the probability of the observer reporting the visual and tactile inputs as emerging from the same source when presented with a specific asynchrony value : …”

“…Some of these deviations from optimality can be explained by a contribution of sensory uncertainty to the perception that differs from that assumed under a Bayesian-optimal inference ( Drugowitsch et al, 2016 ). Challenging the Bayesian-optimal assumption is thus a necessary good practice in computational studies ( Jones and Love, 2011 ), and this is often done in studies of the perception of external sensory events, such as visual stimuli ( Qamar et al, 2013 ; Stengård and van den Berg, 2019 ; Zhou et al, 2020 ). However, very few studies have investigated the role of sensory uncertainty in perceiving one’s own limbs from a computational perspective.…”

“…A second key aim was to test whether sensory uncertainty influences the inference of a common cause for the rubber hand illusion, which is a critical prediction of the BCI models not tested in earlier studies ( Fang et al, 2019 ; Samad et al, 2015 ). Specifically, a Bayesian observer would take into account trial-to-trial fluctuations in sensory uncertainty when making perceptual decisions, changing their decision criterion in a specific way as a function of the sensory noise level of the current trial ( Keshvari et al, 2012 ; Körding et al, 2007 ; Magnotti et al, 2013 ; Qamar et al, 2013 ; Zhou et al, 2020 ). Alternatively, the observer might incorrectly assume that sensory noise does not change or might ignore variations in sensory uncertainty.…”

“…Such an observer would make a decision regarding whether the rubber hand is theirs or not based on a fixed criterion (FC) that does not depend on sensory uncertainty. Suboptimal but potentially ‘easy-to-implement’ observer models using a FC decision rule have often been used to challenge Bayesian models of perception ( Badde et al, 2020 ; Qamar et al, 2013 ; Rahnev et al, 2011 ; Stengård and van den Berg, 2019 ; Zhou et al, 2020 ). To address whether humans optimally adjust the perceptual decision made to the level of sensory uncertainty when inferring a common cause for body ownership, we varied the level of sensory noise from trial to trial and determined how well was the data fit from our BCI model compared to a FC model.…”

Change-detection training and its effects on visual processing skills

“…Visual grouping has been found to benefit visual working memory, as illustrated by Li et al 34 . Their study focused on the grouping effect of illusory contours, which is a phenomenon in contour integration in which a person observes contours that are not physically present 35 . Similar to the contrast sensitivity task, our implementation here did not include a working memory component and consequently, any observed transfer to this untrained task would more likely be attributed to improved perceptual processes.…”