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Accretion-deletion is widely considered a decisive cue to surface depth ordering, with the accreting or deleting surface interpreted as behind an adjoining surface. However, Froyen, Feldman, and Singh (2013) have shown that when accretion-deletion occurs on both sides of a contour, accreting-deleting regions can also be perceived as in front and as self-occluding due to rotation in three dimensions. In this study we ask whether geometric figure-ground cues can override the traditional "depth from accretion-deletion" interpretation even when accretion-deletion takes place only on one side of a contour. We used two tasks: a relative-depth task (front/back), and a motion-classification task (translation/rotation). We conducted two experiments, in which texture in only one set of alternating regions was moving; the other set was static. Contrary to the traditional interpretation of accretion-deletion, the moving convex and symmetric regions were perceived as figural and rotating in three dimensions in roughly half of the trials. In the second experiment, giving different motion directions to the moving regions (thereby weakening motion-based grouping) further weakened the traditional accretion-deletion interpretation. Our results show that the standard "depth from accretion-deletion" interpretation is overridden by static geometric cues to figure-ground. Overall, the results demonstrate a rich interaction between accretion-deletion, figure-ground, and structure from motion that is not captured by existing models of depth from motion.

Humans can judge from vision alone whether an object is physically stable or not. Such judgments allow observers to predict the physical behavior of objects, and hence to guide their motor actions. We investigated the visual estimation of physical stability of 3-D objects (shown in stereoscopically viewed rendered scenes) and how it relates to visual estimates of their center of mass (COM). In Experiment 1, observers viewed an object near the edge of a table and adjusted its tilt to the perceived critical angle, i.e., the tilt angle at which the object was seen as equally likely to fall or return to its upright stable position. In Experiment 2, observers visually localized the COM of the same set of objects. In both experiments, observers' settings were compared to physical predictions based on the objects' geometry. In both tasks, deviations from physical predictions were, on average, relatively small. More detailed analyses of individual observers' settings in the two tasks, however, revealed mutual inconsistencies between observers' critical-angle and COM settings. The results suggest that observers did not use their COM estimates in a physically correct manner when making visual judgments of physical stability.

It is still an unresolved question how the visual system perceives surface lightness given the ambiguity of the sensory input signal. We studied lightness perception using two-dimensional images of variegated checkerboards shown as perspective projections of three-dimensional objects. We manipulated the contrast of a target check relative to its surround either by rendering the image under different viewing conditions or by introducing noncoincidental changes of the reflectance of the surfaces adjacent to the target. We examined the predictive power of the normalized contrast model (Zeiner & Maertens, 2014) for the different viewing conditions (plain view vs. dark and light transparency) as well as for the noncoincidental surround changes (only high or only low reflectances in the surround). The model accounted for lightness matches across different viewing conditions but not for the surround changes. The observed simultaneous contrast effects were smaller than what would be predicted by the model. We evaluated two model extensions that-both relying on contrast-predicted the observed data well. Both model extensions point to the importance of contrast statistics across space and/or time for the computation of lightness, but it awaits future testing to evaluate whether and how the visual system could represent such statistics.

We developed a method analogous to classification images that allowed us to measure the influence that each dot in a dot cluster had on observers' estimates of the center of the cluster. In Experiment 1, we investigated whether observers employ a robust estimator when estimating the centers of dot clusters that were drawn from a single distribution. Most observers' fitted influences did not differ significantly from that predicted by a center-of-gravity (COG) estimator. Such an estimator is not robust. In Experiments 2 and 3, we considered an alternative approach to the problem of robust estimation, based on source separation, that makes use of the visual system's ability to segment visual data. The observers' task was to estimate the center of one distribution when viewing complex dot clusters that were drawn from a mixture of two distributions. We compared human performance to that of an ideal observer that separated the cluster into two sources through a maximum likelihood algorithm and based its estimates of location using the dots it assigned to just one of the two sources. The results suggest that robust methods employed by the visual system are closely tied to mechanisms of perceptual segmentation.

It is well-known that “smooth” chains of oriented elements—contours—are more easily detected amid background noise than more undulating (i.e., “less smooth”) chains. Here, we develop a Bayesian framework for contour detection and show that it predicts that contour detection performance should decrease with the contour's complexity, quantified as the description length (DL; i.e., the negative logarithm of probability integrated along the contour). We tested this prediction in two experiments in which subjects were asked to detect simple open contours amid pixel noise. In Experiment 1, we demonstrate a consistent decline in performance with increasingly complex contours, as predicted by the Bayesian model. In Experiment 2, we confirmed that this effect is due to integrated complexity along the contour, and does not seem to depend on local stretches of linear structure. The results corroborate the probabilistic model of contours, and show how contour detection can be understood as a special case of a more general process—the identification of organized patterns in the environment.

Many recent findings suggest that human observers are surprisingly "blind" to changes in visual displays, failing to notice when substantial scene elements are added, subtracted, or altered in successive presentations of the scene. But observers are far more sensitive to certain visual changes than others, and we suggest that which types of changes enjoy differential sensitivity can reveal a great deal about the underlying visual representations. In this study, we investigate how the human visual system represents the shape of objects by demonstrating a previously unknown influence on detection of changes in shape: the sign of contour curvature. We show that subjects are substantially more sensitive to changes in concave regions of a shape's contour than to changes in convex regions, even when these changes do not alter the number or location of parts. Further, we show that this effect is modulated by figure-ground assignment, so that changes to the same physical contour are more or less detectable, depending on the contour's perceived figural status, which determines whether the change falls in a concave or convex region. The results demonstrate a heightened sensitivity for changes at concavities that is not reducible to a sensitivity to changes in gross part structure.

We present a novel phenomenon involving an interaction between accretion deletion, figure-ground interpretation, and structure-from-motion. Our displays contain alternating light and dark vertical regions in which random-dot textures moved horizontally at constant speed but in opposite directions in alternating regions. This motion is consistent with all the light regions in front, with the dark regions completing amodally into a single large surface moving in the background, or vice versa. Surprisingly, the regions that are perceived as figural are also perceived as 3-D volumes rotating in depth (like rotating columns)—despite the fact that dot motion is not consistent with 3-D rotation. In a series of experiments, we found we could manipulate which set of regions is perceived as rotating volumes simply by varying known geometric cues to figure ground, including convexity, parallelism, symmetry, and relative area. Subjects indicated which colored regions they perceived as rotating. For our displays we found convexity to be a stronger cue than either symmetry or parallelism. We furthermore found a smooth monotonic decay of the proportion by which subjects perceive symmetric regions as figural, as a function of their relative area. Our results reveal an intriguing new interaction between accretion-deletion, figure-ground, and 3-D motion that is not captured by existing models. They also provide an effective tool for measuring figure-ground perception.