Scaling effects in the perception of higher-order spatial correlations

Sensitivity to naturalistic texture relies primarily on high spatial frequencies

Natural images contain information at multiple spatial scales. Though we understand how early visual mechanisms split multiscale images into distinct spatial frequency channels, we do not know how the outputs of these channels are processed further by mid-level visual mechanisms. We have recently developed a texture discrimination task that uses synthetic, multi-scale, "naturalistic" textures to isolate these mid-level mechanisms. Here, we use three experimental manipulations (image blur, image rescaling, and eccentric viewing) to show that perceptual sensitivity to naturalistic structure is strongly dependent on features at high object spatial frequencies (measured in cycles/image). As a result, sensitivity depends on a texture acuity limit, a property of the visual system that sets the highest retinal spatial frequency (measured in cycles/degree) at which observers can detect naturalistic features. Analysis of the texture images using a model observer analysis shows that naturalistic image features at high object spatial frequencies carry more task-relevant information than those at low object spatial frequencies. That is, the dependence of sensitivity on high object spatial frequencies is a property of the texture images, rather than a property of the visual system. Accordingly, we find human observers' ability to extract naturalistic information (their efficiency) is similar for all object spatial frequencies. We conclude that the mid-level mechanisms that underlie perceptual sensitivity effectively extract information from all image features below the texture acuity limit, regardless of their retinal and object spatial frequency.

Integration across Time Determines Path Deviation Discrimination for Moving Objects

BACKGROUND

Human vision is vital in determining our interaction with the outside world. In this study we characterize our ability to judge changes in the direction of motion of objects-a common task which can allow us either to intercept moving objects, or else avoid them if they pose a threat.

METHODOLOGY/PRINCIPAL FINDINGS

Observers were presented with objects which moved across a computer monitor on a linear path until the midline, at which point they changed their direction of motion, and observers were required to judge the direction of change. In keeping with the variety of objects we encounter in the real world, we varied characteristics of the moving stimuli such as velocity, extent of motion path and the object size. Furthermore, we compared performance for moving objects with the ability of observers to detect a deviation in a line which formed the static trace of the motion path, since it has been suggested that a form of static memory trace may form the basis for these types of judgment. The static line judgments were well described by a 'scale invariant' model in which any two stimuli which possess the same two-dimensional geometry (length/width) result in the same level of performance. Performance for the moving objects was entirely different. Irrespective of the path length, object size or velocity of motion, path deviation thresholds depended simply upon the duration of the motion path in seconds.

CONCLUSIONS/SIGNIFICANCE

Human vision has long been known to integrate information across space in order to solve spatial tasks such as judgment of orientation or position. Here we demonstrate an intriguing mechanism which integrates direction information across time in order to optimize the judgment of path deviation for moving objects.

VEPs elicited by local correlations and global symmetry: characteristics and interactions

Psychophysical and fMRI studies have indicated that visual processing of global symmetry has distinctive scaling properties, and proceeds more slowly than analysis of contrast, spatial frequency, and texture. We therefore undertook a visual evoked potential (VEP) study to directly compare the dynamics of symmetry and texture processing, and to determine the extent to which they interact. Stimuli consisted of interchange between structured and random black-and-white checkerboard stimuli. For symmetry, structured stimuli were colored with 2-fold symmetry (horizontal or vertical mirror), 4-fold symmetry (both mirror axes), and 8-fold symmetry (oblique mirror axes added). For texture, structured stimuli were colored according to the "even" isodipole texture [Julesz, B., Gilbert, E. N., & Victor, J. D. (1978). Visual discrimination of textures with identical third-order statistics. Biological Cybernetics, 31, 137-140]. Thus, all stimuli had the same contrast, and check size, but differed substantially in correlation structure. To separate components of the VEP related to symmetry and texture from components that could be generated by local luminance and contrast changes, we extracted the odd-harmonic components of the VEP (recorded at C(z)-O(z), C(z)-O(1), C(z)-O(2), C(z)-P(z)) elicited by structured-random interchange. Responses to symmetry were largest for the 8-fold patterns, and progressively smaller for 4-fold, vertical, and horizontal symmetry patterns. Eightfold patterns were therefore used in the remainder of the study. The symmetry response is shifted to larger checks and lower temporal frequencies compared to the response to texture, and its temporal tuning is broader. Processing of symmetry makes use of neural mechanisms with larger receptive fields, and slower, more sustained temporal tuning characteristics than those involved in the analysis of texture. Sparse stimuli were used to dissociate check size and check density. VEP responses to sparse symmetry stimuli showed that there is no difference between first- and second-order symmetry for densities less than 12.5%. We discuss these findings in relation to local and global visual processes.

Encoding and stability of image statistics in working memory

Visual working memory contains a representation of certain image statistics (Victor & Conte, 2004), in addition to a pixel-by-pixel representation. Here, we show that the representation of statistics is more stable in time (up to 3000 ms) than the pixel-by-pixel representation, especially for changes in luminance and local high-order statistics, and is not affected by visual masking. Bilaterally symmetric arrays and arrays with local correlations are more readily encoded than random ones, but a change in the presence of bilateral symmetry, per se, contributes only modestly to the ability to detect that an array has changed.

Local processes and spatial pooling in texture and symmetry detection

We examined the ability of human observers to detect three kinds of statistical structure in binary arrays: first-order statistics (luminance), local fourth-order statistics (isodipole textures), and long-range statistics (bilateral symmetry). Performance was closest to ideal on the luminance task and furthest from ideal on the symmetry task. For each kind of statistic, the dependence of performance on the degree of structure was well described by a model consisting of an initial stage of multiple independent detectors, followed by a pooling stage. For the luminance task and the isodipole task, performance was well-modeled by local processing followed by extensive spatial pooling. For the symmetry task, limitations at the local detection stage and a near-absence of spatial pooling were needed to model for performance.

Scale invariance is driven by stimulus density

Scale invariance refers to aspects of visual perception that remain constant with changes in viewing distance. Previously, Dakin and Herbert [Proc. Roy. Soc. B. 265 (1397) (1998) 659] reported that the spatial integration region (IR) for mirror symmetry in bandpass noise is scale invariant because its dimensions scale with the inverse of peak spatial frequency. In bandpass noise, however, peak spatial frequency covaries with stimulus numerosity (i.e. the total number of information samples) and density (i.e. the total number of information samples per unit area). In this study, we report four experiments that decoupled properties of the retinal image affected by viewing distance--spatial frequency, numerosity, size, and density--and measured their effect on IR size. Stimuli consisted of bandpass microelements with vertically mirror-symmetric but otherwise random positions, and we measured observer resistance to random jitter imposed on microelement position. Results show that jitter resistance and IR size vary with the inverse of stimulus density but are unaffected by changes in stimulus spatial frequency, numerosity, or size. We found the IR has a 2:1 height-to-width aspect ratio and integrates information from approximately 18 microelements regardless of their spatial separation. Our results reveal that stimulus density plays a central role in the visual system's implementation of scale invariance. Using an ideal-observer, we demonstrate that scale invariance reflects genuine neural scale selection rather than a physical limitation on the stimulus' information content. Our findings that jitter resistance and IR size vary with the inverse of density challenge current models of spatial vision but can be reconciled with a model that compares the output of bandpass non-Fourier mechanisms to select spatial scales that match stimulus density.

Geometric representation of the mechanisms underlying human curvature detection

Combined manipulation of blur, line length and contrast reveal two distinct processes involved in curvature detection. When line length is small relative to blur, thresholds are almost directly proportional to blur and independent of line length. When line length is large relative to blur thresholds are directly proportional to line length and independent of blur. The aspect ratio (line length/blur) of curved contours represents a scale-invariant metric which forms the decisive factor in determining curvature performance.