Infants and toddlers show enlarged visual sensitivity to nonaccidental compared with metric shape changes

Core knowledge of geometry can develop independently of visual experience

Geometrical intuitions spontaneously drive visuo-spatial reasoning in human adults, children and animals. Is their emergence intrinsically linked to visual experience, or does it reflect a core property of cognition shared across sensory modalities? To address this question, we tested the sensitivity of blind-from-birth adults to geometrical-invariants using a haptic deviant-figure detection task. Blind participants spontaneously used many geometric concepts such as parallelism, right angles and geometrical shapes to detect intruders in haptic displays, but experienced difficulties with symmetry and complex spatial transformations. Across items, their performance was highly correlated with that of sighted adults performing the same task in touch (blindfolded) and in vision, as well as with the performances of uneducated preschoolers and Amazonian adults. Our results support the existence of an amodal core-system of geometry that arises independently of visual experience. However, performance at selecting geometric intruders was generally higher in the visual compared to the haptic modality, suggesting that sensory-specific spatial experience may play a role in refining the properties of this core-system of geometry.

Sensitivity to Nonaccidental Configurations of Two-Line Stimuli

According to Recognition-By-Components theory, object recognition relies on a specific subset of three-dimensional shapes called geons. In particular, these configurations constitute a powerful cue to three-dimensional object reconstruction because their two-dimensional projection remains viewpoint-invariant. While a large body of literature has demonstrated sensitivity to changes in these so-called nonaccidental configurations, it remains unclear what information is used in establishing such sensitivity. In this study, we explored the possibility that nonaccidental configurations can already be inferred from the basic constituents of objects, namely, their edges. We constructed a set of stimuli composed of two lines corresponding to various nonaccidental properties and configurations underlying the distinction between geons, including collinearity, alignment, curvature of contours, curvature of configuration axis, expansion, cotermination, and junction type. Using a simple visual search paradigm, we demonstrated that participants were faster at detecting targets that differed from distractors in a nonaccidental property than in a metric property. We also found that only some but not all of the observed sensitivity could have resulted from simple low-level properties of our stimuli. Given that such sensitivity emerged from a configuration of only two lines, our results support the view that nonaccidental configurations could be encoded throughout the visual processing hierarchy even in the absence of object context.

Deep Neural Networks as a Computational Model for Human Shape Sensitivity

Theories of object recognition agree that shape is of primordial importance, but there is no consensus about how shape might be represented, and so far attempts to implement a model of shape perception that would work with realistic stimuli have largely failed. Recent studies suggest that state-of-the-art convolutional ‘deep’ neural networks (DNNs) capture important aspects of human object perception. We hypothesized that these successes might be partially related to a human-like representation of object shape. Here we demonstrate that sensitivity for shape features, characteristic to human and primate vision, emerges in DNNs when trained for generic object recognition from natural photographs. We show that these models explain human shape judgments for several benchmark behavioral and neural stimulus sets on which earlier models mostly failed. In particular, although never explicitly trained for such stimuli, DNNs develop acute sensitivity to minute variations in shape and to non-accidental properties that have long been implicated to form the basis for object recognition. Even more strikingly, when tested with a challenging stimulus set in which shape and category membership are dissociated, the most complex model architectures capture human shape sensitivity as well as some aspects of the category structure that emerges from human judgments. As a whole, these results indicate that convolutional neural networks not only learn physically correct representations of object categories but also develop perceptually accurate representational spaces of shapes. An even more complete model of human object representations might be in sight by training deep architectures for multiple tasks, which is so characteristic in human development.

Shape plays an important role in object recognition. Despite years of research, no models of vision could account for shape understanding as found in human vision of natural images. Given recent successes of deep neural networks (DNNs) in object recognition, we hypothesized that DNNs might in fact learn to capture perceptually salient shape dimensions. Using a variety of stimulus sets, we demonstrate here that the output layers of several DNNs develop representations that relate closely to human perceptual shape judgments. Surprisingly, such sensitivity to shape develops in these models even though they were never explicitly trained for shape processing. Moreover, we show that these models also represent categorical object similarity that follows human semantic judgments, albeit to a lesser extent. Taken together, our results bring forward the exciting idea that DNNs capture not only objective dimensions of stimuli, such as their category, but also their subjective, or perceptual, aspects, such as shape and semantic similarity as judged by humans.

Greater sensitivity to nonaccidental than metric shape properties in preschool children

Nonaccidental properties (NAPs) are image properties that are invariant over orientation in depth and allow facile recognition of objects at varied orientations. NAPs are distinguished from metric properties (MPs) that generally vary continuously with changes in orientation in depth. While a number of studies have demonstrated greater sensitivity to NAPs in human adults, pigeons, and macaque IT cells, the few studies that investigated sensitivities in preschool children did not find significantly greater sensitivity to NAPs. However, these studies did not provide a principled measure of the physical image differences for the MP and NAP variations. We assessed sensitivity to NAP vs. MP differences in a nonmatch-to-sample task in which 14 preschool children were instructed to choose which of two shapes was different from a sample shape in a triangular display. Importantly, we scaled the shape differences so that MP and NAP differences were roughly equal (although the MP differences were slightly larger), using the Gabor-Jet model of V1 similarity (Lades & et al., 1993). Mean reaction times (RTs) for every child were shorter when the target shape differed from the sample in a NAP than an MP. The results suggest that preschoolers, like adults, are more sensitive to NAPs, which could explain their ability to rapidly learn new objects, even without observing them from every possible orientation.

Sensitivity to nonaccidental properties across various shape dimensions

Nonaccidental properties (NAPs) are image properties that are invariant over orientation in depth and are distinguished from metric properties (MPs) that can change continuously with variations over depth orientation. To a large extent NAPs allow facile recognition of objects at novel viewpoints. Two match-to-sample experiments with 2D or 3D appearing geons assessed sensitivity to NAP vs. MP differences. A matching geon was always identical to the sample and the distractor differed from the matching geon in either a NAP or an MP on a single generalized cone dimension. For example, if the sample was a cylinder with a slightly curved axis, the NAP distractor would have a straight axis and the MP distractor would have an axis of greater curvature than the sample. Critically, the NAP and MP differences were scaled so that the MP differences were slightly greater according to pixel energy and Gabor wavelet measures of dissimilarity. Exp. 1 used a staircase procedure to determine the threshold presentation time required to achieve 75% accuracy. Exp. 2 used a constant, brief display presentation time with reaction times and error rates as dependent measures. Both experiments revealed markedly greater sensitivity to NAP over MP differences, and this was generally true for the individual dimensions. The NAP advantage was not reflected in the similarity computations of the C2 stage of HMAX, a widely cited model of later stage cortical ventral stream processing.

Towards a new kind of experimental psycho-aesthetics? Reflections on the Parallellepipeda project

Experimental psycho-aesthetics-the science aimed at understanding the factors that determine aesthetic experience-is reviewed briefly as background to describe the Parallellepipeda project, a cross-over project between artists and scientists in Leuven. In particular, I sketch how it started and developed further, with close interactions between the participating artists and scientists. A few examples of specific research projects are mentioned to illustrate the kind of research questions we address and the methodological approach we have taken. We often found an effect of providing participants with additional information, a difference between novice and expert participants, and a shift with increasing experience with an artwork, in the direction of tolerating more complexity and acquiring more order from it. By establishing more connections between parts of an artwork and more associations to the artwork, it becomes a stronger Gestalt, which is more easily mastered by the viewer and leads to increased appreciation. In the final part of the paper, I extract some general lessons from the project regarding a possible new way of doing psycho-aesthetics research, which is able to solve some of the problems of traditional experimental psycho-aesthetics (eg, trade-off between experimental control and ecological validity).

Development of differential sensitivity for shape changes resulting from linear and nonlinear planar transformations

A shape bias for extending names to objects that look visually similar has been commonly accepted but it is hard to define which kind of shape dissimilarities are diagnostic for the identity of an object. Here, we present a transformational approach to describe shape differences that can incorporate many significant shape features. We introduce two kinds of transformations: one kind concerns linear transformations of the image plane (affine transformations), generally limiting shape variations within the borders of basic-level categories; the other kind concerns nonlinear continuous transformations of the image plane (topological transformations), allowing all kinds of shape variation crossing and not crossing the borders of basic-level categories. We administered stimulus pairs differing in these shape transformations to children of 3 years to 7 years old in a delayed match-to-sample task. With increasing age, especially between 5 years and 6 years, children became more sensitive to the topological deformations that are relevant for between-category distinctions, indicating that acquired categorical knowledge in early years induces perceptual learning of the relevant generic shape differences between categories.