Shape discrimination along morph-spaces

Superordinate Categorization Based on the Perceptual Organization of Parts

Plants and animals are among the most behaviorally significant superordinate categories for humans. Visually assigning objects to such high-level classes is challenging because highly distinct items must be grouped together (e.g., chimpanzees and geckos) while more similar items must sometimes be separated (e.g., stick insects and twigs). As both animals and plants typically possess complex multi-limbed shapes, the perceptual organization of shape into parts likely plays a crucial rule in identifying them. Here, we identify a number of distinctive growth characteristics that affect the spatial arrangement and properties of limbs, yielding useful cues for differentiating plants from animals. We developed a novel algorithm based on shape skeletons to create many novel object pairs that differ in their part structure but are otherwise very similar. We found that particular part organizations cause stimuli to look systematically more like plants or animals. We then generated other 110 sequences of shapes morphing from animal- to plant-like appearance by modifying three aspects of part structure: sprouting parts, curvedness of parts, and symmetry of part pairs. We found that all three parameters correlated strongly with human animal/plant judgments. Together our findings suggest that subtle changes in the properties and organization of parts can provide powerful cues in superordinate categorization.

One-shot generalization in humans revealed through a drawing task

Humans have the amazing ability to learn new visual concepts from just a single exemplar. How we achieve this remains mysterious. State-of-the-art theories suggest observers rely on internal 'generative models', which not only describe observed objects, but can also synthesize novel variations. However, compelling evidence for generative models in human one-shot learning remains sparse. In most studies, participants merely compare candidate objects created by the experimenters, rather than generating their own ideas. Here, we overcame this key limitation by presenting participants with 2D 'Exemplar' shapes and asking them to draw their own 'Variations' belonging to the same class. The drawings reveal that participants inferred-and synthesized-genuine novel categories that were far more varied than mere copies. Yet, there was striking agreement between participants about which shape features were most distinctive, and these tended to be preserved in the drawn Variations. Indeed, swapping distinctive parts caused objects to swap apparent category. Our findings suggest that internal generative models are key to how humans generalize from single exemplars. When observers see a novel object for the first time, they identify its most distinctive features and infer a generative model of its shape, allowing them to mentally synthesize plausible variants.

Skeletal representations of shape in the human visual cortex

Shape perception is crucial for object recognition. However, it remains unknown exactly how shape information is represented and used by the visual system. Here, we tested the hypothesis that the visual system represents object shape via a skeletal structure. Using functional magnetic resonance imaging (fMRI) and representational similarity analysis (RSA), we found that a model of skeletal similarity explained significant unique variance in the response profiles of V3 and LO. Moreover, the skeletal model remained predictive in these regions even when controlling for other models of visual similarity that approximate low-to high-level visual features (i.e., Gabor-jet, GIST, HMAX, and AlexNet), and across different surface forms, a manipulation that altered object contours while preserving the underlying skeleton. Together, these findings shed light on shape processing in human vision, as well as the computational properties of V3 and LO. We discuss how these regions may support two putative roles of shape skeletons: namely, perceptual organization and object recognition.

Mutual Information and Categorical Perception

Categorical perception refers to the enhancement of perceptual sensitivity near category boundaries, generally along dimensions that are informative about category membership. However, it remains unclear exactly which dimensions are treated as informative and why. This article reports a series of experiments in which subjects were asked to learn statistically defined categories in a novel, unfamiliar 2D perceptual space of shapes. Perceptual discrimination was tested before and after category learning of various features in the space, each defined by its position and orientation relative to the maximally informative dimension. The results support a remarkably simple generalization: The magnitude of improvement in perceptual discrimination of each feature is proportional to the mutual information between the feature and the category variable. This finding suggests a rational basis for categorical perception in which the precision of perceptual discrimination is tuned to the statistical structure of the environment.

An image-computable model of human visual shape similarity

Shape is a defining feature of objects, and human observers can effortlessly compare shapes to determine how similar they are. Yet, to date, no image-computable model can predict how visually similar or different shapes appear. Such a model would be an invaluable tool for neuroscientists and could provide insights into computations underlying human shape perception. To address this need, we developed a model (‘ShapeComp’), based on over 100 shape features (e.g., area, compactness, Fourier descriptors). When trained to capture the variance in a database of >25,000 animal silhouettes, ShapeComp accurately predicts human shape similarity judgments between pairs of shapes without fitting any parameters to human data. To test the model, we created carefully selected arrays of complex novel shapes using a Generative Adversarial Network trained on the animal silhouettes, which we presented to observers in a wide range of tasks. Our findings show that incorporating multiple ShapeComp dimensions facilitates the prediction of human shape similarity across a small number of shapes, and also captures much of the variance in the multiple arrangements of many shapes. ShapeComp outperforms both conventional pixel-based metrics and state-of-the-art convolutional neural networks, and can also be used to generate perceptually uniform stimulus sets, making it a powerful tool for investigating shape and object representations in the human brain.

The ability to describe and compare shapes is crucial in many scientific domains from visual object recognition to computational morphology and computer graphics. Across disciplines, considerable effort has been devoted to the study of shape and its influence on object recognition, yet an important stumbling block is the quantitative characterization of shape similarity. Here we develop a psychophysically validated model that takes as input an object’s shape boundary and provides a high-dimensional output that can be used for predicting visual shape similarity. With this precise control of shape similarity, the model’s description of shape is a powerful tool that can be used across the neurosciences and artificial intelligence to test role of shape in perception and the brain.

One-shot categorization of novel object classes in humans

One aspect of human vision unmatched by machines is the capacity to generalize from few samples. Observers tend to know when novel objects are in the same class despite large differences in shape, material or viewpoint. A major challenge in studying such generalization is that participants can see each novel sample only once. To overcome this, we used crowdsourcing to obtain responses from 500 human observers on 20 novel object classes, with each stimulus compared to 1 or 16 related objects. The results reveal that humans generalize from sparse data in highly systematic ways with the number and variance of the samples. We compared human responses to 'ShapeComp', an image-computable model based on >100 shape descriptors, and 'AlexNet', a convolution neural network that roughly matches humans at recognizing 1000 categories of real-world objects. With 16 samples, the models were consistent with human responses without free parameters. Thus, when there are a sufficient number of samples, observers rely on shallow but efficient processes based on a fixed set of features. With 1 sample, however, the models required different feature weights for each object. This suggests that one-shot categorization involves more sophisticated processes that actively identify the unique characteristics underlying each object class.