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A fundamental natural visual task is the identification of specific target objects in the environments that surround us. It has long been known that some properties of the background have strong effects on target visibility. The most well-known properties are the luminance, contrast, and similarity of the background to the target. In previous studies, we found that these properties have highly lawful effects on detection in natural backgrounds. However, there is another important factor affecting detection in natural backgrounds that has received little or no attention in the masking literature, which has been concerned with detection in simpler backgrounds. Namely, in natural backgrounds the properties of the background often vary under the target, and hence some parts of the target are masked more than others. We began studying this factor, which we call the "partial masking factor," by measuring detection thresholds in backgrounds of contrastmodulated white noise that was constructed so that the standard template-matching (TM) observer performs equally well whether or not the noise contrast modulates in the target region. If noise contrast is uniform in the target region, then this TM observer is the Bayesian optimal observer. However, when the noise contrast modulates then the Bayesian optimal observer weights the template at each pixel location by the estimated reliability at that location. We find that human performance for modulated noise backgrounds is predicted by this reliability-weighted TM (RTM) observer. More surprisingly, we find that human performance for natural backgrounds is also predicted by the RTM observer. [ABSTRACT FROM AUTHOR]

A fundamental everyday visual task is to detect target objects within a background scene. Using relatively simple stimuli, vision science has identified several major factors that affect detection thresholds, including the luminance of the background, the contrast of the background, the spatial similarity of the background to the target, and uncertainty due to random variations in the properties of the background and in the amplitude of the target. Here we use an experimental approach based on constrained sampling frommultidimensional histograms of natural stimuli, together with a theoretical analysis based on signal detection theory, to discover how these factors affect detection in natural scenes. We sorted a large collection of natural image backgrounds into multidimensional histograms, where each bin corresponds to a particular luminance, contrast, and similarity. Detection thresholds were measured for a subset of bins spanning the space, where a natural background was randomly sampled from a bin on each trial. In low-uncertainty conditions, both the background bin and the amplitude of the target were fixed, and, in high-uncertainty conditions, they varied randomly on each trial. We found that thresholds increase approximately linearly along all three dimensions and that detection accuracy is unaffected by background bin and target amplitude uncertainty. The results are predicted from first principles by a normalized matched-template detector, where the dynamic normalizing gain factor follows directly from the statistical properties of the natural backgrounds. The results provide an explanation for classic laws of psychophysics and their underlying neural mechanisms. [ABSTRACT FROM AUTHOR]

In everyday listening, sound reaches our ears directly from a source as well as indirectly via reflections known as reverberation. Reverberation profoundly distorts the sound from a source, yet humans can both identify sound sources and distinguish environments from the resulting sound, via mechanisms that remain unclear. The core computational challenge is that the acoustic signatures of the source and environment are combined in a single signal received by the ear. Here we ask whether our recognition of sound sources and spaces reflects an ability to separate their effects and whether any such separation is enabled by statistical regularities of real-world reverberation. To first determine whether such statistical regularities exist, we measured impulse responses (IRs) of 271 spaces sampled from the distribution encountered by humans during daily life. The sampled spaces were diverse, but their IRs were tightly constrained, exhibiting exponential decay at frequency-dependent rates: Mid frequencies reverberated longest whereas higher and lower frequencies decayed more rapidly, presumably due to absorptive properties of materials and air. To test whether humans leverage these regularities, we manipulated IR decay characteristics in simulated reverberant audio. Listeners could discriminate sound sources and environments from these signals, but their abilities degraded when reverberation characteristics deviated from those of real-world environments. Subjectively, atypical IRs were mistaken for sound sources. The results suggest the brain separates sound into contributions from the source and the environment, constrained by a prior on natural reverberation. This separation process may contribute to robust recognition while providing information about spaces around us. [ABSTRACT FROM AUTHOR]

One of the most intriguing of the many discrepancies between perceived spatial relationships and the physical structure of visual stimuli is the Poggendorffillusion, when an obliquely oriented line that is interrupted no longer appears collinear. Although many different theories have been proposed to explain this effect, there has been no consensus about its cause. Here, we use a database of range images (i.e., images that include the distance from the image plane of every pixel in the scene) to show that the probability distribution of the possible locations of line segments across an interval in natural environments can fully account for all of the behavior of this otherwise puzzling phenomenon. [ABSTRACT FROM AUTHOR]

The Müller-Lyer effect, the apparent difference in the length of a line as the result of its adornment with arrowheads or arrow tails, is the best known and most controversial of the classical geometrical illusions. By sampling a range-image database of natural scenes, we show that the perceptual effects elicited by the Müller-Lyer stimulus and its major variants are correctly predicted by the probability distributions of the possible physical sources underlying the relevant retinal images. These results support the conclusion that the MülIer-Lyer illusion is a manifestation of the probabilistic strategy of visual processing that has evolved to contend with the uncertain provenance of retinal stimuli. [ABSTRACT FROM AUTHOR]