Differential roles of contrast polarity reveal two streams of second-order visual processing

Task-dependent contribution to edge-based versus region-based texture perception

Texture segregation studies indicate that some types of textures are processed by edge-based and others by region-based mechanisms. However, studies employing nominally edge-based textures have found evidence for region-based processing mechanisms when the task was to detect rather than segregate the textures. Here we investigate directly whether the nature of the task determines if region-based or edge-based mechanisms are involved in texture perception. Stimuli consisted of randomly positioned Gabor micropattern texture arrays with five types of modulation: orientation modulation, orientation variance modulation, luminance modulation, contrast modulation and contrast variance modulation (CVM). There were four modulation frequencies: 0.1, 0.2, 0.4 and 0.8 cpd. Each modulation type was defined by three types of waveforms: sinewave (SN) with its smooth variations, square-wave (SQ) and cusp-wave (CS) with its sharp texture edges. The CS waveform was constructed by removing a sinewave from an equal amplitude square-wave. Participants performed two tasks: detection in which participants selected which of two stimuli contained the modulation and discrimination in which participants indicated which of two textures had a different modulation orientation. Our results indicate that threshold amplitudes in the detection task followed the ordering SQ < SN < CS across all spatial frequencies, consistent with detection being mediated by the overall energy in the stimulus and hence region based. With the discrimination task at low texture spatial frequencies and with CVM textures at all spatial frequencies the order was CS ≤ SQ with both < SN, consistent with being edge-based. We modeled the data by estimating the spatial frequency of a Difference of Gaussian filter that gave the largest peak amplitude response to the data. We found that the peak amplitude was lower for detection than discrimination across all texture types except for the CVM texture. We conclude that task requirements are critical to whether edges or regions underpin texture processing.

Second-order boundaries segment more easily when they are density-defined rather than feature-defined

Previous studies have demonstrated that density is an important perceptual aspect of textural appearance to which the visual system is highly attuned. Furthermore, it is known that density cues not only influence texture segmentation, but can enable segmentation by themselves, in the absence of other cues. A popular computational model of texture segmentation known as the "Filter-Rectify-Filter" (FRF) model predicts that density should be a second-order cue enabling segmentation. For a compound texture boundary defined by superimposing two single-micropattern density boundaries, a version of the FRF model in which different micropattern-specific channels are analyzed separately by different second-stage filters makes the prediction that segmentation thresholds should be identical in two cases: (1) Compound boundaries with an equal number of micropatterns on each side but different relative proportions of each variety (compound feature boundaries) and (2) Compound boundaries with different numbers of micropatterns on each side, but with each side having an identical number of each variety (compound density boundaries). We directly tested this prediction by comparing segmentation thresholds for second-order compound feature and density boundaries, comprised of two superimposed single-micropattern density boundaries comprised of complementary micropattern pairs differing either in orientation or contrast polarity. In both cases, we observed lower segmentation thresholds for compound density boundaries than compound feature boundaries, with identical results when the compound density boundaries were equated for RMS contrast. In a second experiment, we considered how two varieties of micropatterns summate for compound boundary segmentation. In the case where two single micro-pattern density boundaries are superimposed to form a compound density boundary, we find that the two channels combine via probability summation. By contrast, when they are superimposed to form a compound feature boundary, segmentation performance is worse than for either channel alone. From these findings, we conclude that density segmentation may rely on neural mechanisms different from those which underlie feature segmentation, consistent with recent findings suggesting that density comprises a separate psychophysical 'channel'.

Varying test-pattern duration to explore the dynamics of contrast-comparison and contrast-normalization processes

In this paper, we examine the dynamics of contrast-comparison and contrast-normalization processes. Observers adapted (for 1 second) to a grid of Gabor patches at one contrast; then a test pattern (which varied in duration from 12 ms to 3012 ms) was shown; and then the adapt pattern was shown again (1 second). All the Gabor patches in all the adapt patterns had 50% contrast. The test pattern was the same as the adapt pattern except that the Gabor patches in the test pattern had two different contrasts; the test contrasts varied from row to row (horizontal test pattern) or column to column (vertical test pattern). The task was to identify the orientation of the contrast variation in the test pattern (in other words, the observer performed a second-order orientation identification task). The two contrasts in each test pattern were varied while keeping the difference between the two contrasts constant. We have previously found that the observer's performance is poor for test patterns containing contrasts both above and below the adapt patterns' contrast (what we have called the "straddle effect") when the test duration is approximately 100 ms. Here, we find the straddle effect persists at all test durations we used. Other features of the results varied dramatically with test duration. We find that a simple model containing contrast-comparison and contrast-normalization processes provides a good explanation for the psychophysical results. The results provide some insight into the dynamics of these processes.

Human Texture Vision as Multi-Order Spectral Analysis

Texture information plays a critical role in the rapid perception of scenes, objects, and materials. Here, we propose a novel model in which visual texture perception is essentially determined by the 1st-order (2D-luminance) and 2nd-order (4D-energy) spectra. This model is an extension of the dimensionality of the Filter-Rectify-Filter (FRF) model, and it also corresponds to the frequency representation of the Portilla-Simoncelli (PS) statistics. We show that preserving two spectra and randomizing phases of a natural texture image result in a perceptually similar texture, strongly supporting the model. Based on only two single spectral spaces, this model provides a simpler framework to describe and predict texture representations in the primate visual system. The idea of multi-order spectral analysis is consistent with the hierarchical processing principle of the visual cortex, which is approximated by a multi-layer convolutional network.

Segmenting surface boundaries using luminance cues

Segmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter–Rectify–Filter model positing two cascaded stages of filtering, which fits our data well, and explains observers' ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.

Perception and decision mechanisms involved in average estimation of spatiotemporal ensembles

A number of studies on texture and ensemble perception have shown that humans can immediately estimate the average of spatially distributed visual information. The present study characterized mechanisms involved in estimating averages for information distributed over both space and time. Observers viewed a rapid sequence of texture patterns in which elements' orientation were determined by dynamic Gaussian noise with variable spatial and temporal standard deviations (SDs). We found that discrimination thresholds increased beyond a certain spatial SD if temporal SD was small, but if temporal SD was large, thresholds remained nearly constant regardless of spatial SD. These data are at odds with predictions that threshold is uniquely determined by spatiotemporal SD. Moreover, a reverse correlation analysis revealed that observers judged the spatiotemporal average orientation largely depending on the spatial average orientation over the last few frames of the texture sequence - a recency effect widely observed in studies of perceptual decision making. Results are consistent with the notion that the visual system rapidly computes spatial ensembles and adaptively accumulates information over time to make a decision on spatiotemporal average. A simple computational model based on this notion successfully replicated observed data.

Role of Population Receptive Field Size in Complex Visual Dysfunctions: A Posterior Cortical Atrophy Model

Importance

The neuronal mechanism of visual agnosia and foveal crowding that underlies the behavioral symptoms of several classic neurodegenerative diseases, including impaired holistic perception, navigation, and reading, is still unclear. A better understanding of this mechanism is expected to lead to better treatment and rehabilitation.

Objective

To use state-of-the-art neuroimaging protocols to assess a hypothesis that abnormal population receptive fields (pRF) in the visual cortex underlie high-order visual impairments.

Design, Setting, and Participants

Between April 26 and November 21, 2016, patients and controls were recruited from the Hadassah-Hebrew University medical center in a cross-sectional manner. Six patients with posterior cortical atrophy (PCA) were approached and 1 was excluded because of an inability to perform the task. Participants underwent functional magnetic resonance imaging-based cortical visual field mapping and pRF evaluation and performed a masked repetition priming task to evaluate visuospatial perception along the eccentricity axis. The association between pRF sizes and behavioral impairments was assessed to evaluate the role of abnormal pRF sizes in impaired visual perception. Posterior cortical atrophy is a visual variant of Alzheimer disease that is characterized by progressive visual agnosia despite almost 20/20 visual acuity. Patients with PCA are rare but invaluable for studying visual processing abnormalities following neurodegeneration, as atrophy begins in visual cortices but initially spares other brain regions involved in memory and verbal communication.

Exposures

Participants underwent a magnetic resonance imaging scan.

Main Outcomes and Measures

Population receptive field sizes and their association with visual processing along the fovea-to-periphery gradient.

Results

Five patients with PCA (4 men [80%]; mean [SEM] age, 62.9 [3.5] years) were compared with 8 age-matched controls (1 man [25%]; mean [SEM] age, 63.7 [3.7] years) and demonstrated an atypical pRF mapping that varied along the eccentricity axis, which presented as abnormally small peripheral and large foveal pRFs sizes. Abnormality was seen in V1 (peripheral, 4.4° and 5.5°; foveal, 5.5° and 4.5° in patients and controls, respectively; P < .05) as well as in higher visual regions, but not in intermediate ones. Behaviorally, an atypical fovea-to-periphery gradient in visual processing was found that correlated with their pRF properties (r = 0.8; P < .01 for the correlation between pRF and behavioral fovea-to-periphery slopes).

Conclusions and Relevance

High-order visuocognitive functions may depend on abnormalities in basic cortical characteristics. These results may fundamentally change approaches to rehabilitation in such conditions, emphasizing the potential of low-level visual interventions.

Luminance-contrast properties of texture-shape and texture-surround suppression of contour shape

Studies have revealed that textures suppress the processing of the shapes of contours they surround. One manifestation of texture-surround suppression is the reduction in the magnitude of adaptation-induced contour-shape aftereffects when the adaptor contour is surrounded by a texture. Here we utilize this phenomenon to investigate the nature of the first-order inputs to texture-surround suppression of contour shape by examining its selectivity to luminance polarity and the magnitude of luminance contrast. Stimuli were constructed from sinusoidal-shaped strings of either "bright" or "dark" elongated Gaussians. Observers adapted to pairs of contours, and the aftereffect was measured as the shift in the apparent shape frequency of subsequently presented test contours. We found that the suppression of the contour-shape aftereffect by a surround texture made of similar contours was maximal when the adaptor's center and surround contours were of the same polarity, revealing polarity specificity of the surround-suppression effect. We also measured the effect of varying the relative contrasts of the adaptor's center and surround and found that the reduction in the contour-shape aftereffect was determined by the surround-to-center contrast ratio. Finally, we measured the selectivity to luminance polarity of the texture-shape aftereffect itself and found that it was reduced when the adaptors and tests were of opposite luminance polarity. We conclude that texture-surround suppression of contour-shape as well as texture-shape processing itself depend on "on-off" luminance-polarity channel interactions. These selectivities may constitute an important neural substrate underlying efficient figure-ground segregation and image segmentation.

Modeling second-order boundary perception: A machine learning approach

Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defined by spatial variations in pixel intensities. However, such methods are poorly suited to understanding sensory processing mechanisms for complex visual stimuli such as second-order boundaries defined by spatial differences in contrast or texture. We introduce a novel machine learning framework for modeling human perception of second-order visual stimuli, using image-computable hierarchical neural network models fit directly to psychophysical trial data. This framework is applied to modeling visual processing of boundaries defined by differences in the contrast of a carrier texture pattern, in two different psychophysical tasks: (1) boundary orientation identification, and (2) fine orientation discrimination. Cross-validation analysis is employed to optimize model hyper-parameters, and demonstrate that these models are able to accurately predict human performance on novel stimulus sets not used for fitting model parameters. We find that, like the ideal observer, human observers take a region-based approach to the orientation identification task, while taking an edge-based approach to the fine orientation discrimination task. How observers integrate contrast modulation across orientation channels is investigated by fitting psychophysical data with two models representing competing hypotheses, revealing a preference for a model which combines multiple orientations at the earliest possible stage. Our results suggest that this machine learning approach has much potential to advance the study of second-order visual processing, and we outline future steps towards generalizing the method to modeling visual segmentation of natural texture boundaries. This study demonstrates how machine learning methodology can be fruitfully applied to psychophysical studies of second-order visual processing.

Many naturally occurring visual boundaries are defined by spatial differences in features other than luminance, for example by differences in texture or contrast. Quantitative models of such “second-order” boundary perception cannot be estimated using the standard regression techniques (known as “classification images”) commonly applied to “first-order”, luminance-defined stimuli. Here we present a novel machine learning approach to modeling second-order boundary perception using hierarchical neural networks. In contrast to previous quantitative studies of second-order boundary perception, we directly estimate network model parameters using psychophysical trial data. We demonstrate that our method can reveal different spatial summation strategies that human observers utilize for different kinds of second-order boundary perception tasks, and can be used to compare competing hypotheses of how contrast modulation is integrated across orientation channels. We outline extensions of the methodology to other kinds of second-order boundaries, including those in natural images.

Bidirectional aftereffects in perceived contrast

It is well known that prolonged observation of a high-contrast stimulus alters the perception of a subsequent test stimulus. Previous studies of perceived contrast shifts only reported perceived contrast reductions. Here, we used successive presentations of test and reference stimuli and found that perceived contrast was reduced if tests had a lower contrast than adaptors but was significantly enhanced when tests had a higher contrast than adaptors. Such bidirectional contrast aftereffects were not observed for single adaptor flashes but became increasingly pronounced for repeated adaptor presentations, thereby suggesting that the aftereffect is a consequence of adaptation rather than of attentional cuing or temporal repulsion. In addition, perceived contrast reduction weakened as we increasingly jittered the spatial position of the adaptor, but perceived contrast enhancement was observed for large spatial range of jittered adaptor positions. We conclude that aftereffects involve adaptation in distinct mechanisms with narrow and broad spatial tunings. Results suggest that the visual system not only possesses low-level contrast encoding units, which monotonically increase their responses as physical contrast increases, but is also equipped with high-level channels selectively tuned for particular contrast ranges.

Binocular functional architecture for detection of contrast-modulated gratings

Combination of signals from the two eyes is the gateway to stereo vision. To gain insight into binocular signal processing, we studied binocular summation for luminance-modulated gratings (L or LM) and contrast-modulated gratings (CM). We measured 2AFC detection thresholds for a signal grating (0.75c/deg, 216ms) shown to one eye, both eyes, or both eyes out-of-phase. For LM and CM, the carrier noise was in both eyes, even when the signal was monocular. Mean binocular thresholds for luminance gratings (L) were 5.4dB better than monocular thresholds - close to perfect linear summation (6dB). For LM and CM the binocular advantage was again 5-6dB, even when the carrier noise was uncorrelated, anti-correlated, or at orthogonal orientations in the two eyes. Binocular combination for CM probably arises from summation of envelope responses, and not from summation of these conflicting carrier patterns. Antiphase signals produced no binocular advantage, but thresholds were about 1-3dB higher than monocular ones. This is not consistent with simple linear summation, which should give complete cancellation and unmeasurably high thresholds. We propose a three-channel model in which noisy monocular responses to the envelope are binocularly combined in a contrast-weighted sum, but also remain separately available to perception via a max operator. Vision selects the largest of the three responses. With in-phase gratings the binocular channel dominates, but antiphase gratings cancel in the binocular channel and the monocular channels mediate detection. The small antiphase disadvantage might be explained by a subtle influence of background responses on binocular and monocular detection.

A perceptual space of local image statistics

Local image statistics are important for visual analysis of textures, surfaces, and form. There are many kinds of local statistics, including those that capture luminance distributions, spatial contrast, oriented segments, and corners. While sensitivity to each of these kinds of statistics have been well-studied, much less is known about visual processing when multiple kinds of statistics are relevant, in large part because the dimensionality of the problem is high and different kinds of statistics interact. To approach this problem, we focused on binary images on a square lattice - a reduced set of stimuli which nevertheless taps many kinds of local statistics. In this 10-parameter space, we determined psychophysical thresholds to each kind of statistic (16 observers) and all of their pairwise combinations (4 observers). Sensitivities and isodiscrimination contours were consistent across observers. Isodiscrimination contours were elliptical, implying a quadratic interaction rule, which in turn determined ellipsoidal isodiscrimination surfaces in the full 10-dimensional space, and made predictions for sensitivities to complex combinations of statistics. These predictions, including the prediction of a combination of statistics that was metameric to random, were verified experimentally. Finally, check size had only a mild effect on sensitivities over the range from 2.8 to 14min, but sensitivities to second- and higher-order statistics was substantially lower at 1.4min. In sum, local image statistics form a perceptual space that is highly stereotyped across observers, in which different kinds of statistics interact according to simple rules.

On-Off asymmetry in the perception of blur

Natural images appear blurred when imperfect lens focus reduces contrast energy at higher spatial frequencies. Here, we present evidence that perceived blur also depends on asymmetries between On (positive contrast polarities) and Off (negative contrast polarities) image signals. Psychophysical matching experiments involving natural and artificial stimuli suggest that attenuating Off signals at high spatial frequencies results in increased perceptual blur relative to similar attenuations of On signals. Results support the notion that Off image signals play an important role in blur perception.

Prominent effects and neural correlates of visual crowding in a neurodegenerative disease population

Crowding is a breakdown in the ability to identify objects in clutter, and is a major constraint on object recognition. Crowding particularly impairs object perception in peripheral, amblyopic and possibly developing vision. Here we argue that crowding is also a critical factor limiting object perception in central vision of individuals with neurodegeneration of the occipital cortices. In the current study, individuals with posterior cortical atrophy (n=26), typical Alzheimer's disease (n=17) and healthy control subjects (n=14) completed centrally-presented tests of letter identification under six different flanking conditions (unflanked, and with letter, shape, number, same polarity and reverse polarity flankers) with two different target-flanker spacings (condensed, spaced). Patients with posterior cortical atrophy were significantly less accurate and slower to identify targets in the condensed than spaced condition even when the target letters were surrounded by flankers of a different category. Importantly, this spacing effect was observed for same, but not reverse, polarity flankers. The difference in accuracy between spaced and condensed stimuli was significantly associated with lower grey matter volume in the right collateral sulcus, in a region lying between the fusiform and lingual gyri. Detailed error analysis also revealed that similarity between the error response and the averaged target and flanker stimuli (but not individual target or flanker stimuli) was a significant predictor of error rate, more consistent with averaging than substitution accounts of crowding. Our findings suggest that crowding in posterior cortical atrophy can be regarded as a pre-attentive process that uses averaging to regularize the pathologically noisy representation of letter feature position in central vision. These results also help to clarify the cortical localization of feature integration components of crowding. More broadly, we suggest that posterior cortical atrophy provides a neurodegenerative disease model for exploring the basis of crowding. These data have significant implications for patients with, or who will go on to develop, dementia-related visual impairment, in whom acquired excessive crowding likely contributes to deficits in word, object, face and scene perception.

Pattern randomness aftereffect

Humans can easily discriminate a randomly spaced from a regularly spaced visual pattern. Here, we demonstrate that observers can adapt to pattern randomness. Following their adaption to prolonged exposure to two-dimensional patterns with varying levels of physical randomness, observers judged the randomness of the pattern. Perceived randomness decreased (increased) following adaptation to high (low) physical randomness (Experiment 1). Adaptation to 22.5°-rotated adaptor stimuli did not cause a randomness aftereffect (Experiment 2), suggesting that positional variation is unlikely to be responsible for the pattern randomness perception. Moreover, the aftereffect was not selective to contrast polarity (Experiment 3) and was not affected by spatial jitter (Experiment 4). Last, the aftereffect was not affected by adaptor configuration (Experiment 5). Our data were consistent with a model assuming filter-rectify-filter processing for orientation inputs. Thus, we infer that neural processing for orientation grouping/segregation underlies the perception of pattern randomness.

Line orientation adaptation: local or global?

Prolonged exposure to an oriented line shifts the perceived orientation of a subsequently observed line in the opposite direction, a phenomenon known as the tilt aftereffect (TAE). Here we consider whether the TAE for line stimuli is mediated by a mechanism that integrates the local parts of the line into a single global entity prior to the site of adaptation, or the result of the sum of local TAEs acting separately on the parts of the line. To test between these two alternatives we used the fact the TAE transfers almost completely across luminance contrast polarity [1]. We measured the TAE using adaptor and test lines that (1) either alternated in luminance polarity or were of a single polarity, and (2) either alternated in local orientation or were of a single orientation. We reasoned that if the TAE was agnostic to luminance polarity and was parts-based, we should obtain large TAEs using alternating-polarity adaptors with single-polarity tests. However we found that (i) TAEs using one-alternating-polarity adaptors with all-white tests were relatively small, increased slightly for two-alternating-polarity adaptors, and were largest with all-white or all-black adaptors. (ii) however TAEs were relatively large when the test was one-alternating polarity, irrespective of the adaptor type. (iii) The results with orientation closely mirrored those obtained with polarity with the difference that the TAE transfer across orthogonal orientations was weak. Taken together, our results demonstrate that the TAE for lines is mediated by a global shape mechanism that integrates the parts of lines into whole prior to the site of orientation adaptation. The asymmetry in the magnitude of TAE depending on whether the alternating-polarity lines was the adaptor or test can be explained by an imbalance in the population of neurons sensitive to 1^st-and 2^nd-order lines, with the 2^nd-order lines being encoded by a subset of the mechanisms sensitive to 1^st-order lines.

Texture sparseness, but not local phase structure, impairs second-order segmentation

Texture boundary segmentation is typically thought to reflect a comparison of differences in Fourier energy (i.e. low-order texture statistics) on either side of a boundary. However in a previous study (Arsenault, Yoonessi, & Baker, 2011) we showed that the distribution of energy within a natural texture (i.e. its higher-order statistical structure) also influences segmentation of contrast boundaries. Here we examine the influence of specific higher-order texture statistics on segmentation of contrast- and orientation-defined boundaries. Using naturalistic synthetic textures to manipulate the sparseness, global phase structure, and local phase alignments of carrier textures, we measure segmentation thresholds based on forced-choice judgments of boundary orientation. We find a similar pattern of results for both contrast and orientation boundaries: (1) randomizing all structure by globally phase scrambling the texture reduces segmentation thresholds substantially, (2) decreasing sparseness also reduces thresholds, and (3) removing local phase alignments has little or no effect on segmentation thresholds. We show that a two-stage filter model with an intermediate compressive nonlinearity and expansive output nonlinearity can account for these data using synthetic textures. Furthermore, the model parameter fits obtained using synthetic textures also predict the segmentation thresholds presented in Arsenault, Yoonessi, and Baker (2011) for natural and phase-scrambled natural texture carriers.

A unified account of gloss and lightness perception in terms of gamut relativity

A recently introduced computational theory of visual surface representation, termed gamut relativity, overturns the classical assumption that brightness, lightness, and transparency constitute perceptual dimensions corresponding to the physical dimensions of luminance, diffuse reflectance, and transmittance, respectively. Here I extend the theory to show how surface gloss and lightness can be understood in a unified manner in terms of the vector computation of "layered representations" of surface and illumination properties, rather than as perceptual dimensions corresponding to diffuse and specular reflectance, respectively. The theory simulates the effects of image histogram skewness on surface gloss/lightness and lightness constancy as a function of specular highlight intensity. More generally, gamut relativity clarifies, unifies, and generalizes a wide body of previous theoretical and experimental work aimed at understanding how the visual system parses the retinal image into layered representations of surface and illumination properties.

Contrast negation and texture synthesis differentially disrupt natural texture appearance

Natural textures have characteristic image statistics that make them discriminable from unnatural textures. For example, both contrast negation and texture synthesis alter the appearance of natural textures even though each manipulation preserves some features while disrupting others. Here, we examined the extent to which contrast negation and texture synthesis each introduce or remove critical perceptual features for discriminating unnatural textures from natural textures. We find that both manipulations remove information that observers use for distinguishing natural textures from transformed versions of the same patterns, but do so in different ways. Texture synthesis removes information that is relevant for discrimination in both abstract patterns and ecologically valid textures, and we also observe a category-dependent asymmetry for identifying an “oddball” real texture among synthetic distractors. Contrast negation exhibits no such asymmetry, and also does not impact discrimination performance in abstract patterns. We discuss our results in the context of the visual system’s tuning to ecologically relevant patterns and other results describing sensitivity to higher-order statistics in texture patterns.

The integration of straight contours (snakes and ladders): The role of spatial arrangement, spatial frequency and spatial phase

In the present study we addressed the issue of whether the Gestalt principle of grouping by similarity (iso-orientation) subtends extraction of straight contours made up of disconnected, iso-oriented Gabor elements, whether collinear (snakes) or parallel (ladders). To prevent the use of the most obvious grouping principle of good continuation, which allows us to perceive the relation between local and global orientation along the contour, we manipulated the spatial arrangement of randomly oriented Gabors in the background: they were positioned on an ordered grid, and grouped on the basis of good continuation, or randomly positioned and not grouped. Grid-positioned backgrounds exert a suppressive contextual influence on detection of good continuation along the contour path. Results obtained in a two-interval forced choice task showed that the orderly-positioned background did not completely prevent detection of snakes and ladders. Detection of snakes was hampered at low spatial frequency whereas detection of ladders was improved by the randomly-positioned background at high spatial frequency. These contextual influences support the suggestion that both iso-orientation and good continuation rules are employed by the association field underlying the binding of straight contours. In addition, they are not compatible with integration of snakes and ladders elements within a single receptive field. In support of this suggestion we found that phase constancy within contour elements (as opposed to phase randomization) improved snake detectability at low spatial frequency, and, unexpectedly, impaired ladder detectability at high spatial frequency. This suggests that a low-level mechanism based on the balance between excitatory and inhibitory lateral interactions at a first stage may account for the detection of both straight contours.

Contrast polarity preservation's role in perception: explained and unexplained stimuli

Roncato and Casco (2003, Perception & psychophysics 65 1252-1272) had shown that in situations where the Gestalt principle of good continuity is put into conflict with preservation of contrast polarity (CP) the perception that preserves CP prevails. Parlangeli and Roncato (2010, Perception 39 255-259) have studied this question of preservation of CP more closely and have added an addendum to the rule. They have used stimuli consisting of a checkerboard of perpendicularly arranged rectangular bricks (white, gray, or black) and draughtsmen white, gray, or black disks placed at the corners of the bricks. This study has caused them to add an addendum to the rule of CP-preserved path-conjunction binding: if there are two contour completions that preserve the CP, the one with the higher contrast will prevail. Parlangeli and Roncato find that, for certain shades of the disks and bricks, the perpendicular lines of the checkerboard appear strikingly to be slanted or undulating. Here we consider all possible arrangements of relative magnitudes of checkerboards consisting of bricks of two different shades and disks of two shades as well, as such arrangements with widely varying differences in the magnitude of brightness. We have found a number of cases where the perception is not explained by the rule and addendum of Roncato and Casco, and Parlangeli and Roncato, and a case where preservation of "distant" as well as local CP plays a role in perception. The previously known cases, and the new exceptional unexplained stimuli we have found, warrant further study.

Beyond multiple pattern analyzers modeled as linear filters (as classical V1 simple cells): useful additions of the last 25 years

This review briefly discusses processes that have been suggested in the last 25 years as important to the intermediate stages of visual processing of patterns. Five categories of processes are presented: (1) Higher-order processes including FRF structures; (2) Divisive contrast nonlinearities including contrast normalization; (3) Subtractive contrast nonlinearities including contrast comparison; (4) Non-classical receptive fields (surround suppression, cross-orientation inhibition); (5) Contour integration.

Curvature coding in illusory contours

We have employed the shape frequency and shape-amplitude after-effects (SFAE and SAAE) to investigate: (i) whether the shapes of illusory and real curves are processed by the same or different mechanisms, and (ii) the carrier-tuning properties of illusory curvature mechanisms. The SFAE and SAAE are the phenomena in which adaptation to a sinusoidal-shaped contour results in a shift in, respectively, the perceived shape-frequency and perceived shape-amplitude of a test contour in a direction away from that of the adapting stimulus. Both after-effects are believed to be mediated by mechanisms sensitive to curvature (Gheorghiu & Kingdom, 2007a, 2009; see also Hancock & Peirce, 2008). We observed both shape after-effects in sinusoidally-shaped illusory contours defined by phase-shifted line-grating carriers. We tested whether illusory and real contours were mediated by the same or different mechanisms by comparing same adaptor-and-test with different adaptor-and-test combinations of real and illusory contours. Real contour adaptors produced after-effects in illusory contour tests that were as great as, or even greater than those produced by illusory contour adaptors. However, illusory contour adaptors produced much weaker after-effects in real contour tests than did real contour adaptors. This asymmetry suggests that illusory contour curves are encoded by a sub-set of mechanisms sensitive to real contour curves. We also examined the carrier-tuning properties of illusory-contour curvature processing using adaptor and test illusory contours that differed in the luminance contrast-polarity, luminance scale and orientation of the carriers. We found no selectivity to any of these dimensions for either even-symmetric or odd-symmetric line-gratings carriers, even though selectivity to these dimensions was found for real contours.

Texture modulation detection by probability summation among orientation-selective and isotropic mechanisms

Substantial evidence has accumulated for the notion that modulations of second-order properties in the visual scene are processed by mechanisms which detect contrast variations within narrow orientation/spatial frequency channels. It has also been suggested that mechanisms exist which detect contrast modulations across all orientations. Many naturally occurring texture variations (e.g., modulations in orientation and/or spatial frequency) involve simultaneous contrast modulations in multiple channels. Contrasting conclusions have been drawn regarding the manner in which the information carried in multiple channels is combined. In a series of two experiments it is shown that simultaneous contrast modulations in two narrow orientation bands are detected by three mechanisms, two of which detect contrast modulations within the modulated bands only, the third of which integrates contrast across orientations in order to detect modulations of overall contrast. The three mechanisms combine their efforts by probability summation.