The neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center–surround organization of receptive fields in early vision.

The retinas of birds receive a substantial efferent, or centrifugal, input from a midbrain nucleus. The function of this input is presently unclear, but previous work in the pigeon has shown that efferent input is excluded from the area centralis, suggesting that the functions of the area centralis and the efferent system are incompatible. Using an antibody specific to rods, we have identified the area centralis in another species, the chicken, and mapped the distribution of the unique amacrine cells that are the postsynaptic partners of efferent fibers. Efferent target amacrine cells are found within the chicken area centralis and their density is continuous across the border of the area centralis. In contrast to the pigeon retina then, we conclude that the chicken area centralis receives efferent input. We suggest that the difference between the two species is attributable to the presence of a fovea within the area centralis of the pigeon and its absence from that of the chicken.

In the avian brain, the optokinetic response is controlled by two retinal-recipient nuclei: the nucleus of the basal optic root (nBOR) of the accessory optic system and the pretectal nucleus lentiformis mesencephali (LM). Although considered sister nuclei because of their similar response properties and function, there are both similarities and differences with respect to efferent projections and neurochemistry. Both nBOR and LM project to the cerebellum (Cb) directly as mossy fibers but also indirectly via the inferior olive (IO). In a previous report, we showed that the cerebellar- and inferior olivary-projecting neurons in nBOR of pigeons differentially express the calcium-binding proteins calretinin (CR) and parvalbumin (PV). Both CR and PV are expressed in the somata of LM neurons, although the latter is not as prevalent, and whether expression of CR and PV reflects cerebellar and IO projections is not known. In this report, by combining retrograde neuronal tracing from the Cb and IO with fluorescent immunohistochemistry, we examined the expression of these calcium-binding proteins in the pigeon LM. Half (52%) of the cerebellar-projecting neurons were CR+ve, but only 15% were PV+ve. Almost all (>95%) these PV+ve cells also expressed CR. In contrast, few of the IO-projecting neurons expressed CR or PV (≤5%). This is strikingly similar to what we observed in nBOR and reveals that calcium-binding protein expression is concordant with projection patterns in two nuclei that share similar functions.

Correctly interpreting a natural image requires dealing properly with the effects of occlusion, and hence, contour grouping across occlusions is a major component of many natural visual tasks. To better understand the mechanisms of contour grouping across occlusions, we (a) measured the pair-wise statistics of edge elements from contours in natural images, as a function of edge element geometry and contrast polarity, (b) derived the ideal Bayesian observer for a contour occlusion task where the stimuli were extracted directly from natural images, and then (c) measured human performance in the same contour occlusion task. In addition to discovering new statistical properties of natural contours, we found that naïve human observers closely parallel ideal performance in our contour occlusion task. In fact, there was no region of the four-dimensional stimulus space (three geometry dimensions and one contrast dimension) where humans did not closely parallel the performance of the ideal observer (i.e., efficiency was approximately constant over the entire space). These results reject many other contour grouping hypotheses and strongly suggest that the neural mechanisms of contour grouping are tightly related to the statistical properties of contours in natural images.

The human visual system is remarkably tolerant to degradation in image resolution: human performance in scene categorization remains high no matter whether low-resolution images or multimegapixel images are used. This observation raises the question of how many pixels are required to form a meaningful representation of an image and identify the objects it contains. In this article, we show that very small thumbnail images at the spatial resolution of 32 × 32 color pixels provide enough information to identify the semantic category of real-world scenes. Most strikingly, this low resolution permits observers to report, with 80% accuracy, four to five of the objects that the scene contains, despite the fact that some of these objects are unrecognizable in isolation. The robustness of the information available at very low resolution for describing semantic content of natural images could be an important asset to explain the speed and efficiently at which the human brain comprehends the gist of visual scenes.

Modern accounts of color appearance differ in whether they assume that the perceptual primaries (e.g., white and the unique hues of red, green, blue, and yellow) correspond to unique states determined by the spectral sensitivities of the observer or by the spectral statistics of the environment. We examined the interaction between observers and their environments by asking how color perception should vary if appearance depends on fixed responses in a set of color channels, when the sensitivities of these channels are adapted in plausible ways to different environments. Adaptation was modeled as gain changes in the cones and in multiple postreceptoral channels tuned to different directions in color–luminance space. Gains were adjusted so that the average channel responses were equated across two environments or for the same environment during different seasons, based on sets of natural outdoor scenes (Webster et al., 2007). Because of adaptation, even observers with a shared underlying physiology should perceive color in significantly and systematically different ways when they are exposed to and thus adapted by different contexts. These include differences in achromatic settings (owing to variations in the average chromaticity of locations) and differences in perceived hue (because of differences in scene contrasts). Modeling these changes provides a way of simulating how colors might be experienced by individuals in different color environments and provides a measure of how much color appearance might be modulated for a given observer by variations in the environment.

Bayesian decision theory (BDT) is a mathematical framework that allows the experimenter to model ideal performance in a wide variety of visuomotor tasks. The experimenter can use BDT to compute benchmarks for ideal performance in such tasks and compare human performance to ideal. Recently, researchers have asked whether BDT can also be treated as a process model of visuomotor processing. It is unclear what sorts of experiments are appropriate to testing such claims and whether such claims are even meaningful. Any such claim presupposes that observers’ performance is close to ideal, and typical experimental tests involve comparison of human performance to ideal. We argue that this experimental criterion, while necessary, is weak. We illustrate how to achieve near-optimal performance in combining perceptual cues with a process model bearing little resemblance to BDT. We then propose experimental criteria termed transfer criteria that constitute more powerful tests of BDT as a model of perception and action. We describe how recent work in motor control can be viewed as tests of transfer properties of BDT. The transfer properties discussed here comprise the beginning of an operationalization (Bridgman, 1927) of what it means to claim that perception is or is not Bayesian inference (Knill & Richards, 1996). They are particularly relevant to research concerning natural scenes since they assess the ability of the organism to rapidly adapt to novel tasks in familiar environments or carry out familiar tasks in novel environments without learning.