Humans are known to be good at rapidly detecting animals in natural scenes. Evoked potential studies indicate that the corresponding neural signals can emerge in the brain within 150 msec of stimulus onset (S. Thorpe, D. Fize, & C. Marlot, 1996) and eye movements toward animal targets can be initiated in roughly the same timeframe (H. Kirchner & S. J. Thorpe, 2006). Given the speed of this discrimination, it has been suggested that the underlying visual mechanisms must be relatively simple and feedforward, but in fact little is known about these mechanisms. A key step is to understand the visual cues upon which these mechanisms rely. Here we investigate the role and dynamics of four potential cues: two-dimensional boundary shape, texture, luminance, and color. Results suggest that the fastest mechanisms underlying animal detection in natural scenes use shape as a principal discriminative cue, while somewhat slower mechanisms integrate these rapidly computed shape cues with image texture cues. Consistent with prior studies, we find little role for luminance and color cues throughout the time course of visual processing, even though information relevant to the task is available in these signals.

Prior studies of the perception of surface shape and attitude from texture have focused on measuring the sensitivity of the visual system to the various geometric deformations induced by projection. Studies that examine variations in accuracy caused by spatial properties of the texture itself are fewer, and often confound multiple, potentially important properties. Here we examine the perception of surface attitude for a broad range of synthetic textures that may represent the types of structure encountered in the natural world. These stimuli allow us to isolate the respective roles of texels, spatial scale structure, discrete symmetries and regularity in the judgement of both the slant and tilt of textured surfaces. Texels, spatial scale structure and discrete symmetries were all found to play a role. Discrete rotational symmetries were found to be particularly important for accurate tilt estimation, likely mediated by skew symmetry and/or linear perspective cues. The operational range of viewing distances over which accurate attitude judgements can be made is greatly extended when texture structure is distributed over multiple scales. Small biases caused by variations in the spin of symmetric textures are observed and are consistent, at least qualitatively, with a Bayesian cue combination model previously proposed by .

It is well established that a range of basic visual acuities and sensitivities decline with retinal eccentricity due in part to a decline in spatial sampling in the retina. However, it is also known that not all peripheral deficits can be explained entirely by such low-level factors, suggesting a specialization of central vision for certain visual tasks. Here, we examine visual short-term memory for natural scenes and ask whether low-level factors can fully account for variations in performance across the visual field. We measure local recognition performance as a function of eccentricity for both coherent and scrambled natural scenes. We find that while spatial coherence substantially increases recognition rates for targets near fixation, the benefit of spatial coherence vanishes in the periphery. These results suggest that low-level factors cannot fully explain the decline in visual short-term memory for natural scenes in the periphery and that mechanisms selective for global configuration are largely confined to the central visual field.

Typical visual environments contain a rich array of colors, textures, surfaces, and objects, but it is well established that observers do not have access to all of these visual details, even over short intervals (R. A. Rensink, J. K. O'Regan, & J. J. Clark, 1997). Rather, it seems that human vision extracts only partial information from every glance. What is the nature of this selective encoding of the scene? Although there is considerable research on short-term coding of individual objects, much less is known about the representation of a natural scene in visual short-term memory (VSTM). Here, we examine the VSTM of natural scenes using a local recognition task. A major finding is that local recognition performance is better when image segments are viewed in the context of coherent rather than scrambled scenes, suggesting that observers rely on an encoding of a global 'gist' of the scene. Variations on this experiment allow quantification of the role of multiple factors in local recognition. Color statistics and the global configural context are found to be more important than local features of the target, even for a local recognition task.

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