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.

We rarely experience difficulty picking up objects, yet of all potential contact points on the surface, only a small proportion yield effective grasps. Here, we present extensive behavioral data alongside a normative model that correctly predicts human precision grasping of unfamiliar 3D objects. We tracked participants' forefinger and thumb as they picked up objects of 10 wood and brass cubes configured to tease apart effects of shape, weight, orientation, and mass distribution. Grasps were highly systematic and consistent across repetitions and participants. We employed these data to construct a model which combines five cost functions related to force closure, torque, natural grasp axis, grasp aperture, and visibility. Even without free parameters, the model predicts individual grasps almost as well as different individuals predict one another's, but fitting weights reveals the relative importance of the different constraints. The model also accurately predicts human grasps on novel 3D-printed objects with more naturalistic geometries and is robust to perturbations in its key parameters. Together, the findings provide a unified account of how we successfully grasp objects of different 3D shape, orientation, mass, and mass distribution.

The human visual system is foveated: we can see fine spatial details in central vision, whereas resolution is poor in our peripheral visual field, and this loss of resolution follows an approximately logarithmic decrease. Additionally, our brain organizes visual input in polar coordinates. Therefore, the image projection occurring between retina and primary visual cortex can be mathematically described by the log-polar transform. Here, we test and model how this space-variant visual processing affects how we process binocular disparity, a key component of human depth perception. We observe that the fovea preferentially processes disparities at fine spatial scales, whereas the visual periphery is tuned for coarse spatial scales, in line with the naturally occurring distributions of depths and disparities in the real-world. We further show that the visual system integrates disparity information across the visual field, in a near-optimal fashion. We develop a foveated, log-polar model that mimics the processing of depth information in primary visual cortex and that can process disparity directly in the cortical domain representation. This model takes real images as input and recreates the observed topography of human disparity sensitivity. Our findings support the notion that our foveated, binocular visual system has been moulded by the statistics of our visual environment.

Grasping is one of the fundamental actions we perform to interact with objects in real environments, and in the real world we rarely experience difficulty picking up objects. Grasping plays a fundamental role for interactive virtual reality (VR) systems that are increasingly employed not only for recreational purposes, but also for training in industrial contexts, in medical tasks, and for rehabilitation protocols. To ensure the effectiveness of such VR applications, we must understand whether the same grasping behaviors and strategies employed in the real world are adopted when interacting with objects in VR. To this aim, we replicated in VR an experimental paradigm employed to investigate grasping behavior in the real world. We tracked participants' forefinger and thumb as they picked up, in a VR environment, unfamiliar objects presented at different orientations, and exhibiting the same physics behavior of their real counterparts. We compared grasping behavior within and across participants, in VR and in the corresponding real world situation. Our findings highlight the similarities and differences in grasping behavior in real and virtual environments.

The recent release of the Oculus Rift, originally developed for entertainment applications, has reignited the interest of researchers and clinicians toward the use of head-mounted-displays in basic behavioral research and physical and psychological rehabilitation. However, careful evaluation of the Oculus Rift is necessary to determine whether it can be effectively used in these novel applications. In this article we address two issues concerning the perceptual quality of the Oculus Rift. (a) Is the Oculus able to generate an acceptable degree of immersivity? In particular, is it possible to elicit the sensation of presence via the virtual stimuli rendered by the device? (b) Does the Virtual Reality experienced through the Oculus Rift induce physical discomfort? To answer these questions, we employed four virtual scenarios in three separate experiments and evaluated performance with objective and subjective outcomes. In Experiment 1 we monitored observers’ heart rate and asked them to rate their Virtual Reality experience via a custom questionnaire. In Experiment 2 we monitored observers’ head movements in reaction to virtual obstacles and asked them to fill out the Simulator Sickness Questionnaire (Kennedy et al., 1993) both before and after experiencing Virtual Reality. In Experiment 3 we compared the Oculus Rift against two other low-cost devices used in immersive Virtual Reality: the Google cardboard and a standard 3DTV monitor. Observers’ heart rate increased during exposure to Virtual Reality, and they subjectively reported the experience to be immersive and realistic. We found a strong relationship between observers’ fear of heights and vertigo experienced during one of the virtual scenarios involving heights, suggesting that observers felt a strong sensation of presence within the virtual worlds. Subjects reacted to virtual obstacles by moving to avoid them, suggesting that the obstacles were perceived as real threats. Observers did not experience simulator sickness when the exposure to Virtual Reality was short and did not induce excessive amounts of vection. Compared to the other devices the Oculus Rift elicited a greater degree of immersivity. Thus our investigation suggests that the Oculus Rift head-mounted-display is a potentially powerful tool for a wide array of basic research and clinical applications.