Determining distances to objects is one of the most ubiquitous perceptual tasks in everyday life. Nevertheless, it is challenging because the information from a single image confounds object size and distance. Though our brains frequently judge distances accurately, the underlying computations employed by the brain are not well understood. Our work illuminates these computions by formulating a family of probabilistic models that encompass a variety of distinct hypotheses about distance and size perception. We compare these models' predictions to a set of human distance judgments in an interception experiment and use Bayesian analysis tools to quantitatively select the best hypothesis on the basis of its explanatory power and robustness over experimental data. The central question is: whether, and how, human distance perception incorporates size cues to improve accuracy. Our conclusions are: 1) humans incorporate haptic object size sensations for distance perception, 2) the incorporation of haptic sensations is suboptimal given their reliability, 3) humans use environmentally accurate size and distance priors, 4) distance judgments are produced by perceptual "posterior sampling". In addition, we compared our model's estimated sensory and motor noise parameters with previously reported measurements in the perceptual literature and found good correspondence between them. Taken together, these results represent a major step forward in establishing the computational underpinnings of human distance perception and the role of size information.

Perception is fundamentally underconstrained because different combinations of object properties can generate the same sensory information. To disambiguate sensory information into estimates of scene properties, our brains incorporate prior knowledge and additional "auxiliary" (i.e., not directly relevant to desired scene property) sensory information to constrain perceptual interpretations. For example, knowing the distance to an object helps in perceiving its size. The literature contains few demonstrations of the use of prior knowledge and auxiliary information in combined visual and haptic disambiguation and almost no examination of haptic disambiguation of vision beyond "bistable" stimuli. Previous studies have reported humans integrate multiple unambiguous sensations to perceive single, continuous object properties, like size or position. Here we test whether humans use visual and haptic information, individually and jointly, to disambiguate size from distance. We presented participants with a ball moving in depth with a changing diameter. Because no unambiguous distance information is available under monocular viewing, participants rely on prior assumptions about the ball's distance to disambiguate their -size percept. Presenting auxiliary binocular and/or haptic distance information augments participants' prior distance assumptions and improves their size judgment accuracy-though binocular cues were trusted more than haptic. Our results suggest both visual and haptic distance information disambiguate size perception, and we interpret these results in the context of probabilistic perceptual reasoning.

This paper compares the ability of human observers to detect target im- age curves with that of an ideal observer. The target curves are sam- pled from a generative model which specifies (probabilistically) the ge- ometry and local intensity properties of the curve. The ideal observer performs Bayesian inference on the generative model using MAP esti- mation. Varying the probability model for the curve geometry enables us investigate whether human performance is best for target curves that obey specific shape statistics, in particular those observed on natural shapes. Experiments are performed with data on both rectangular and hexagonal lattices. Our results show that human observers’ performance approaches that of the ideal observer and are, in general, closest to the ideal for con- ditions where the target curve tends to be straight or similar to natural statistics on curves. This suggests a bias of human observers towards straight curves and natural statistics.

Converging evidence has shown that human object recognition depends on observers' familiarity with objects' appearance. The more similar the objects are, the stronger this dependence will be, and the more important two-dimensional (2D) image information will be. The degree to which 3D structural information is used, however, remains an area of strong debate. Previously, we showed that all models that allow rotations in the image plane of independent 2D templates could not account for human performance in discriminating novel object views. We now present results from models of generalized radial basis functions (GRBF), 2D nearest neighbor matching that allows 2D affine transformations, and a Bayesian statistical estimator that integrates over all possible 2D affine transformations. The performance of the human observers relative to each of the models is better for the novel views than for the template views, suggesting that humans generalize better to novel views from template views. The Bayesian estimator yields the optimal performance with 2D affine transformations and independent 2D templates. Therefore, no models of 2D affine operations with independent 2D templates account for the human performance.

A multilayer, depth planes approach to image segmentation is proposed, where pixels may have arisen from a single smooth surface in the scene are represented in a common layer. Two types of output are produced at each pixel, i.e., a layer number and a vector of depth values, one value for each layer. The layer assignment performs image partitioning based on surface properties. The depth value assignment for each layer either represents the input data with the noise removed or interpolates between data values to fill-in nonvisible parts of the scene. The disjoint surfaces due to occlusion or transparency are also grouped together if they form a smooth surface.>

It is well known that the human visual system can reconstruct depth from simple random-dot displays given binocular disparity or motion information. This fact has lent support to the notion that stereo and structure from motion systems rely on low-level primitives derived from image intensities. In contrast, the judgment of surface transparency is often considered to be a higher-level visual process that, in addition to pictorial cues, utilizes stereo and motion information to separate the transparent from the opaque parts. We describe a new illusion and present psychophysical results that question this sequential view by showing that depth from transparency and opacity can override the bias to see rigid motion. The brain's computation of transparency may involve a two-way interaction with the computation of structure from motion.