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Article  |   June 2012
Chromatic properties of texture-shape and of texture-surround suppression of contour-shape mechanisms
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Journal of Vision June 2012, Vol.12, 16. doi:10.1167/12.6.16
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      Elena Gheorghiu, Frederick A. A. Kingdom; Chromatic properties of texture-shape and of texture-surround suppression of contour-shape mechanisms. Journal of Vision 2012;12(6):16. doi: 10.1167/12.6.16.

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Abstract
Abstract
Abstract:

Abstract  Contour-shape coding is color selective (Gheorghiu & Kingdom, 2007a) and surround textures inhibit the processing of contour shapes (Gheorghiu & Kingdom, 2011; Kingdom & Prins, 2009). These two findings raise two questions: (1) is texture-surround suppression of contour shape color selective, and (2) is texture-shape processing color selective? To answer these questions, we measured the shape-frequency aftereffect using contours constructed from strings of Gabors defined along the red-green, blue-yellow, and luminance axes of cardinal color space. The stimuli were either single sinusoidal-shaped contours or textures made of sinusoidal-shaped contours arranged in parallel. We measured aftereffects for (A) single-contour adaptors and single-contour tests defined along the same versus different cardinal directions, (B) texture adaptors and single-contour tests in which the central-adaptor contour/single-contour test and surround adaptor contours were defined along the same versus different cardinal directions, and (C) texture adaptors and texture tests defined along same versus different cardinal directions. We found that color selectivity was most prominent for contour-shape processing, weaker for texture-surround suppression of contour-shape processing, and absent for texture-shape processing.

Introduction
The shapes of contours and textured surfaces are important for visual object recognition (Biederman, 1987; Marr, 1982). Studies attest to the idea that contour shapes and texture shapes are processed by different mechanisms (Grigorescu, Petkov, & Westenberg, 2003, 2004; Petkov & Westenberg, 2003), though whether they are processed in different cortical areas is less clear. It is believed that contour shapes are processed in extrastriate areas such as V4 and infero-temporal cortex (IT) (Brincat & Connor, 2004; Ito, Fujita, Tamura, & Tanaka, 1994; Ito, Tamura, Fujita, & Tanaka, 1995; Pasupathy & Connor, 2002), and although some evidence points to textures being processed in primary visual area V1 (Dumoulin, Dakin, & Hess, 2008), most evidence suggests that a variety of extrastriate areas are involved in processing different aspects of and different types of textures (Baker, Mortin, Prins, Kingdom, & Dumoulin, 2006; Cant & Goodale, 2007; Cant, Large, McCall, & Goodale, 2008; Cavina-Pratesi, Kentridge, Heywood, & Milner, 2010; Kastner, De Weerd, & Ungerleider, 2000). 
If contour shapes and texture shapes are processed separately in the brain, one might expect them to exhibit different selectivities to dimensions such as luminance contrast, contrast polarity, and chromaticity. In this communication we consider this question in relation to color direction, which here includes luminance contrast as well as the different directions within the isoluminant plane. With regard to contour-shape processing, Mullen & Beaudot (2002) showed that subjects were able to detect perturbations from circularity at isoluminance and that thresholds depended to some extent on color direction. Gheorghiu and Kingdom (2007a), on the other hand, who showed that contour-shape coding is selective for color direction, using the shape-frequency aftereffect (SFAE) as their tool. The SFAE refers to the phenomenon in which the apparent shape frequency of a contour that is sinusoidally modulated in shape is altered following adaptation to a contour with slightly different shape frequency. Gheorghiu and Kingdom (2007a) found similar-sized SFAEs for the three cardinal color directions (red-green, blue-yellow, luminance), but when adaptors and tests had different color directions, or different poles of a given cardinal direction, the aftereffect was significantly reduced, implying that the SFAE was at least partially selective to color direction and polarity. 
With regard to texture-shape processing, Pearson and Kingdom (2002) used a conventional subthreshold paradigm to test whether the detection of spatial variations in orientation-defined textures was selective for chromatic versus luminance contrast, but found no evidence for selectivity. No studies to our knowledge have considered whether suprathreshold-shape texture variations are selective for color direction. 
A recent piece of psychophysical evidence supporting the idea that contour shapes and texture shapes are processed by different mechanisms is that shape aftereffects using single contours are reduced when the adaptor consists of a series of contours in parallel (i.e., a texture), a phenomenon termed “texture of surround suppression of contour shape” or texture-surround suppression of contour-shape (TSSCS) (Gheorghiu & Kingdom, 2011; Kingdom & Prins, 2009). The remarkable aspect of this finding is that the reduction in contour shape aftereffect occurs in spite of the fact that the multiple-contour adaptor is potentially a stronger adaptor than a single contour. The finding suggests that a multiple-contour adaptor is not processed as a series of individual contours but instead as a separately processed texture. Texture-surround suppression of contour-shape processing is also evident in studies showing that the detectability and shape discriminability of sparsely sampled contours is reduced when embedded in random element noise (Dakin & Baruch, 2009; Schumacher, Quinn, & Olman, 2011). However, the effect of the random element noise in these studies is to reduce the visibility of the contour, and hence its detectability and shape discriminability. In the aforementioned adaptation paradigm, TSSCS manifests itself as a change in the appearance of a fully visible, suprathreshold contour shape. 
Is TSSCS selective for color direction? That is, is the reduction in shape aftereffect with a multiple-contour adaptor eliminated when the central-adaptor/test contour and the surround contours are defined along different color directions? Previous studies have shown that TSSCS is selective for local orientation (Gheorghiu & Kingdom, 2011; Kingdom & Prins, 2009), stereoscopic depth (Gheorghiu, Kingdom, Thai, & Sampasivam, 2009b), and motion direction (Gheorghiu, Kingdom, & Varshney, 2009a; Gheorghiu & Kingdom, in press). On these grounds we might expect TSSCS to be also selective for color direction. Additional support for this conjecture comes from the finding that another surround effect, the tilt illusion, in which the apparent orientation of a stimulus is altered by the presence of a differently oriented surround, is color selective (Clifford, Spehar, Solomon, Martin, & Zaidi, 2003; Forte & Clifford, 2005). 
In this paper, we consider whether TSSCS, as manifested by its impact on a shape aftereffect, is selective for color direction. This has entailed affirming the color selectivity of the contour shape aftereffect itself and also examining whether suprathreshold texture shape processing is color selective. It is worth emphasizing that this study is not primarily concerned with whether contour-shape, texture-shape, and TSSCS mechanisms are sensitive to isoluminant stimuli. Rather, it is to determine whether these mechanisms are tuned to particular colors or ranges of colors. 
We have measured the SFAE for contours and textures comprising strings of Gabor elements. SFAEs have been compared for adaptor and test combinations defined along the same color direction with adaptor and test combinations defined along different color directions. The stimuli are defined along the three cardinal axes of a modified version of the MacLeod-Boynton color space. The axes are often, albeit incorrectly, termed red-green, blue-yellow, and luminance, and are designed to isolate the same-named postreceptoral mechanisms (Cole, Hine, & McIlhagga, 1993; Krauskopf, Williams, & Heeley, 1982). The results of this study have enabled us to refine our understanding of the role of color in TSSCS, contour shape, and texture-shape processing. 
General methods
Subjects
Five subjects participated in this study, the two authors and three subjects who were naive with regard to the experimental aims. All subjects had normal or corrected-to-normal visual acuity. Each subject gave informed consent prior to participation in accordance with the university guidelines. 
Stimuli
Generation and display
The stimuli were generated by a ViSaGe video-graphics card (Cambridge Research Systems Ltd, UK) with 12-bit contrast resolution and presented on a Sony Trinitron monitor at 120 Hz frame rate and 1024 × 768 spatial resolution. The red (R), green (G), and blue (B) outputs of the monitor were gamma-corrected after calibration with an Optical photometer (Cambridge Research Systems). The spectral emission functions of the R, G, and B phosphors were measured using a PR 640 spectral radiometer (Photo Research Inc., USA), with the monitor screen filled with R, G, or B at maximum luminance. The CIE coordinates of the phosphors were as follows: R: x = 0.624, y = 0.341; G: x = 0.293, y = 0.609; and B: x = 0.148, y = 0.075. Long (L), medium (M), and short (S) wave cone excitations were converted to RGB monitor values using the conventional method of multiplying each LMS value by a 3 × 3 matrix of coefficients derived from the RGB spectral emission functions and the Smith & Pokorny (1975) cone sensitivity functions. All stimuli were presented in the center of the monitor on a midgray background with CIE chromaticity x = 0.282 and y = 0.311, and mean luminance of 40 cd/m2. Viewing distance was 55 cm. 
Cardinal axes
Adaptation and test stimuli consisted of pairs of sinusoidal-shaped contours and textures (see Figure 1) defined along the cardinal axes of a modified version of the MacLeod-Boynton color space (MacLeod & Boynton, 1979), shown in Figure 1c. Each axis defines how L, M, and S cone contrasts are combined postreceptorally. The axes are termed LUM, L-M, and S, though in subsequent sections and the figures they are occasionally referred to as luminance, red-green, and blue-yellow, even though these terms are inaccurate as descriptors of the hues involved. The term “cardinal” indicates that each stimulus uniquely stimulates one of the three postreceptoral mechanisms. 
Figure 1
 
Stimuli used in the experiments. One can experience the shape-frequency aftereffect (SFAE) obtained with static single-contour adaptors (a) and static texture adaptors (b) by moving one's eyes back and forth along the markers located midway between the pair of adapting contours (a, left) or textures (b, left) for about 90 s and then shifting one's gaze to the middle of the test contours (right). The two test contours, which have the same shape frequency, should appear different in shape frequency. Thus, adaptation to a contour of a given shape frequency makes a test contour of lower shape frequency appear lower in shape frequency and a test contour of higher shape frequency appear higher in shape frequency. The back-and-forth eye movements along the marker between the pair of static adapting contours (a) and textures (b) on the left prevents the formation of afterimages and minimizes the effects of local orientation adaptation. In the experiments, during the adaptation period, the shape phase of the contour or texture was randomly changed every 1 s to achieve the same ends. Both aftereffects survive shape-phase randomization during adaptation, as can be experienced in the nonstatic contour adaptor (Movie a) and texture adaptor (Movie b) versions shown in the Appendix. (c) Modified version of the MacLeod-Boynton color space used to define the color directions.
Figure 1
 
Stimuli used in the experiments. One can experience the shape-frequency aftereffect (SFAE) obtained with static single-contour adaptors (a) and static texture adaptors (b) by moving one's eyes back and forth along the markers located midway between the pair of adapting contours (a, left) or textures (b, left) for about 90 s and then shifting one's gaze to the middle of the test contours (right). The two test contours, which have the same shape frequency, should appear different in shape frequency. Thus, adaptation to a contour of a given shape frequency makes a test contour of lower shape frequency appear lower in shape frequency and a test contour of higher shape frequency appear higher in shape frequency. The back-and-forth eye movements along the marker between the pair of static adapting contours (a) and textures (b) on the left prevents the formation of afterimages and minimizes the effects of local orientation adaptation. In the experiments, during the adaptation period, the shape phase of the contour or texture was randomly changed every 1 s to achieve the same ends. Both aftereffects survive shape-phase randomization during adaptation, as can be experienced in the nonstatic contour adaptor (Movie a) and texture adaptor (Movie b) versions shown in the Appendix. (c) Modified version of the MacLeod-Boynton color space used to define the color directions.
The cone contributions to the three cardinal axes are as follows. Cone contrast is defined as Lc = ΔL/Lb, Mc = ΔM/Mb, and Sc = ΔS/Sb for the L, M, and S cones, respectively. The numerator in each term represents the difference in cone excitation between the peak of the contour's modulation and the background, while the denominator refers to the background cone excitation. Current estimates of the cone contrast inputs to the three cardinal mechanisms are as follows: kLC + MC for the luminance mechanism, LCMC for the mechanism that differences L and M cone contrasts, and SC – (LC + MC)/2 for the mechanism that differences S from the sum of L plus M cone contrasts (Cole et al., 1993; Sankeralli & Mullen, 1997; Stromeyer, Kronauer, Ryu, Chaparro, & Eskew, 1995). The parameter k determines the relative weighting of the L and M cone-contrast inputs to the luminance mechanism, and it varies between observers. The cone contrasts necessary to make the axes orthogonal are as follows:   (Kingdom, Rangwala, & Hammamji, 2005), where Lc, Mc, and Sc are the contrasts assigned to the L, M, and S cones respectively. Stimulus contrast was defined as follows: for LUM, the contrast assigned to each cone; for L-M, the difference in cone contrasts (preserving the sign of cone contrast); and for S, the contrast assigned to the S cone. 
Textures and contours
Adapting and test stimuli consisted of pairs of sinusoidal-shaped textures or contours presented in the center of the monitor 5° above and below the fixation marker, as shown in Figures 1a and 1b. The test stimuli in Experiment 1 and 2 were pairs of single contours and in Experiment 3 were pairs of textures. The adaptor pair consisted of textures or contours with shape frequencies of 0.1 and 0.3 c/deg, giving a geometric mean shape frequency of 0.173 c/deg. The mean shape frequency of the test contour and texture pair was held constant at 0.173 c/deg. The shape amplitude of the two adaptors and tests was fixed at 0.5°. 
All contours were constructed from strings of odd-symmetric (direct current balanced) Gabor patches with a spatial bandwidth of 1.75 octaves, a center spatial frequency of 1.5 c/deg (low enough to minimize the effects of chromatic aberration), and contrast of 100%. The Gabor patches were positioned along the sinusoidal shape and oriented parallel to the tangent of the shape. The center-to-center spacing between adjacent Gabors was randomly selected from within the range of ±0.33° around a mean of 1°. On average, there were 17 Gabors per contour, but because the Gabor strings were contained within a fixed width window of 15°, the number of Gabors differed between the two adaptors by a factor of 1.125, with 18 and 16 Gabors for the high and low shape-frequency contours, respectively. 
The texture adaptors consisted of a central contour flanked by a surround of 8 parallel contours. The central contour and surround were defined along either the same or different cardinal axes (see Figure 2). 
Figure 2
 
Example contours and textures defined along the S, L-M, and LUM cardinal axis. The textures consisted of a central contour defined along the S (A), L-M (B), and LUM (C) cardinal axes flanked by either S, L-M, or LUM surround.
Figure 2
 
Example contours and textures defined along the S, L-M, and LUM cardinal axis. The textures consisted of a central contour defined along the S (A), L-M (B), and LUM (C) cardinal axes flanked by either S, L-M, or LUM surround.
Procedure
Isoluminant settings
The relative weightings of the L and M cone inputs to each person's luminance mechanism varies, so it is necessary to behaviorally determine the isoluminant point for L-M. We also wanted to ensure that the S cone-isolating stimuli were isoluminant. Isoluminance was measured using the criterion of minimum perceived motion. We used pairs of S or L-M contours (see the first row of Figure 2) presented in the center of the monitor at 5° above and below the fixation marker, with a shape amplitude of 0.5° and a shape frequency of 0.173 c/deg. The two contours were set to drift in opposite directions at about 1.0 Hz. The contrast of the L-M contours was set to 0.025 and the S contours at 0.25. Subjects added or subtracted equal amounts of L and M cone contrast by pressing a key on the response box until the perceived motion stopped or reached a minimum. Each subject made between 10 and 15 settings per session, and there were five sessions conducted on different days. We calculated the average L+M cone contrast from all measurements. For the L-M defined contours, the L+M contrast needed to produce isoluminance was used to estimate the ratio of L to M cone contrasts in the luminance mechanism (i.e., the parameter k in Equation 2). Parameter k was found to be 0.779 for subject C.D., 2.4 for E.G., 1.7 for F.K., 1.612 for L.C., and 0.745 for N.K. For the S contour, the added L+M contrast was used to define the ratio of luminance to color contrast required to obtain isoluminance. This was found to be 0.031 for subject C.D., 0.029 for E.G., 0.062 for F.K., 0.027 for L.C., and 0.022 for N.K. 
Contrast matching
Contrast-matching experiments were carried out to equate the perceived contrasts of the L-M, LUM, and S textures. We used pairs of textures presented in the center of the monitor at 5° above and below the fixation marker. The matched contrast was determined using an ongoing, two-interval sequential presentation. One interval contained a pair of L-M (or a LUM) textures and the other a pair of S textures of fixed contrast (SC = 0.8). The duration of each interval was 1 s. In order to avoid the occurrence of sharp luminance transients, we presented the stimuli in a raised cosine temporal envelope of 1 s half-period. Subjects used the keys on the response box to adjust the contrast of either the L-M or LUM textures until they matched the perceived contrast of the S textures. There was no time limit for the contrast-matching procedure. Each subject made 10 settings from which the mean value was estimated. This was repeated five times on different days. The mean value of L-M contrast was 0.134 for subject C.D., 0.11 for E.G., 0.172 for F.K., 0.1373 for L.C., and 0.174 for N.K. The mean value of LUM contrast was 0.066 for subject C.D., 0.07 for E.G., 0.07 for F.K., 0.053 for L.C., and 0.086 for N.K. 
Shape-frequency aftereffect
Adapting stimuli consisted of pairs of sinusoidal-shaped textures or contours, as shown in Figures 1a and 1b. The test stimuli were pairs of single contours in Experiment 1 and 2 and pairs of textures in Experiment 3. In all experiments, the adaptor pair consisted of textures or contours with shape frequencies of 0.1 and 0.3 c/deg, giving a geometric mean shape frequency of 0.173 c/deg. The mean shape frequency of the test contour or texture pair was always maintained constant at 0.173 c/deg. The shape amplitude of the two adaptors and tests was fixed at 0.5°. 
Each session began with an initial adaptation period of 90 s, followed by a repeated test of 1 s duration interspersed with top-up adaptation of 3 s. During the adaptation period, the shape phase of the contour or texture was randomly changed every 1 s in order to prevent the formation of afterimages and to minimize the effects of local orientation adaptation. An important property of SFAE is that the aftereffect survives shape-phase randomization during adaptation, as can be experienced in the nonstatic contour adaptor (Movie a) and texture adaptor (Movie b) examples shown in the Appendix section. In order to minimize sharp luminance transients, the contours and textures were presented in a raised cosine temporal envelope of 1 s half-period. The presentation of each test contour or test texture was signaled by a tone. The display was viewed in a dimly lit room at a viewing distance of 55 cm. Subjects were required to fixate on the marker placed between each pair of contours for the entire session. 
A staircase method was used to estimate the point of subjective equality (PSE). The geometric mean shape frequency of the two test contours (Experiment 1 and 2) or test textures (Experiment 3) was held constant at 0.1732 c/deg during the test period, while the computer varied the relative shape frequencies of the two test contours or test textures in accordance with the subject's response. At the start of the test period the ratio of the two test shape frequencies was set to a random number between 0.7 and 1.44. On each trial subjects indicated via a button press whether the upper or lower test contour (Experiment 1 and 2) or test textures (Experiment 3) had the higher perceived shape frequency. The computer then changed the ratio of test shape frequencies by a factor of 1.06 for the first five trials and 1.015 thereafter, in a direction opposite to that of the response (i.e., toward the PSE). The session was terminated after 25 trials. In order that the total amount of adaptation for each condition was the same, we used a staircase method that was terminated after a fixed number (25) of trials rather than a fixed number of reversals. We found in previous studies (Gheorghiu & Kingdom, 2006, 2007b, 2008, 2009; Gheorghiu, Kingdom, Bell, & Gurnsey, 2011; Gheorghiu, Kingdom, & Witney, 2010) that a step size of 1.015 was sufficient to produce a visible change in the shape frequency on each trial while ensuring a stable convergence over the last 20 trials and hence an accurate estimate of the PSE. The shape-frequency ratio at the PSE was calculated as the geometric mean shape-frequency ratio of the two tests (in the ratio, the numerator was the test at the position of the lower shape-frequency adaptor and the denominator was the test at the position of the higher shape-frequency adaptor), averaged across the last 20 trials. 
For each “with-adaptor” condition we made six measurements, three in which the upper adaptor had the higher shape frequency (0.3 c/deg) and three in which the lower adaptor had the higher shape frequency. In addition, we measured for each condition the shape-frequency ratio at the PSE in the absence of the adapting stimulus (the “no-adaptor” condition. To obtain an estimate of the size of the SFAE we first calculated the difference between the logarithm of each with-adaptor shape-frequency ratio at the PSE and the mean of the logarithms of the no-adaptor shape-frequency ratios at the PSE. We then calculated the mean and standard error of these differences across the six measurements. These standard errors are the ones shown in the graphs. Note that the magnitude of the aftereffect is defined as the ratio of shape frequencies at the PSE, and thus its units are dimensionless. 
Experiments and results
Experiment 1: is contour-shape selective to color?
We have previously shown using continuous sinusoidal-shaped contours that the SFAE is selective for cardinal axis and within-cardinal-axis polarity (Gheorghiu & Kingdom, 2007a). In this experiment, we confirm the selectivity to cardinal axis using contours made of Gabor strings, as these will be used in all subsequent experiments. To simplify what follows we refer to a cardinal axis simply as a color from now on. 
In this experiment we compared aftereffects obtained using single-contour adaptors/tests with the same colors with aftereffects obtained using single-contour adaptors/tests with different colors. All nine combinations of L-M, S, and LUM adaptors and tests were tested. Three subjects participated in this experiment. Figure 3 shows the results for same (light gray bars) and different (black bars) colors for S (Figure 3a), L-M (Figure 3b), and LUM (Figure 3c) test contours. It can be seen that the different-color conditions produced significantly smaller after effects than the same-color conditions (compare black and light gray bars). This is similar to our previous findings obtained using continuous contours (Gheorghiu & Kingdom, 2007a) and indicates that contour-shape coding is partially selective for color. 
Figure 3
 
Results of Experiment 1. SFAEs obtained with single-contour adaptor and test combinations defined along the same (light gray bars) and different (black bars) cardinal axes for the S-defined (A), L-M-defined (B), and LUM-defined (C) test contours.
Figure 3
 
Results of Experiment 1. SFAEs obtained with single-contour adaptor and test combinations defined along the same (light gray bars) and different (black bars) cardinal axes for the S-defined (A), L-M-defined (B), and LUM-defined (C) test contours.
To determine whether the color selectivity was significant we performed a two-way within-subjects repeated measures analysis of variance (ANOVA), with factors “combination” (same versus different) and “color” of test (L-M versus S versus LUM), Bonferroni correcting the p-values to allow for multiple comparisons. For the different-color conditions, the two adaptor colors for each test color were averaged (e.g., for an S test, we averaged the results for L-M and LUM adaptors). The factor combination was significant (F[1,2] = 108.87, p = 0.009, ηp2 = 0.982), indicating that the aftereffect is selective to color. The main effect of color of test was not significant (F[2,4] = 1.75, p = 0.283, ηp2 = 0.467). 
Experiment 2: is TSSCS selective to color?
In this experiment we examined whether TSSCS occurs with isoluminant texture adaptors and, if it does, whether it is also selective to color. We used four adapting conditions: single contour (no surround), contour flanked by surround of the same color, and contour flanked by surround of the other two different colors. The test stimuli were single contours of the same color as the central-contour adaptor (see Figure 2). We measured the SFAE for all 12 adaptor and test combinations (3 colors × 4 center-surround adaptor combinations). Five subjects participated in this experiment. 
Figure 4a shows SFAEs obtained using an S adaptor and test contour, in which the adaptor contour was flanked by same-color surround contours (light gray bars), different-color surround contours (black bars), or no surround (blue dashed line). Figures 4b and 4c show the corresponding results for the L-M and LUM adaptor contours. The results show that (1) the effect of both chromatic and achromatic surround contours is to reduce the SFAE from its no-surround baseline (compare bars with dashed line), (2) for S and L-M adaptor and test contours, the reduction in SFAE is greater when the center and surround contours have the same compared with different colors (compare light gray and black bars in Figures 4a and 4b), and (3) for a LUM adaptor and test contour all colors of surround have more or less the same suppressive effect (compare light gray and black bars in Figure 4c). These results indicate that suppressive texture surrounds show weak color selectivity for S and L-M adaptor and test contours but no color selectivity for LUM adaptor and test contours. 
Figure 4
 
Results of Experiment 2. SFAEs obtained using texture adaptor and contour test in which the central-contour adaptor was flanked by same-color surround contours (light gray bars), different-color surround contours (black bars), or no surround (dashed line) for S-defined (A), L-M-defined (B), and LUM-defined (C) central-contour adaptor. The dashed line indicates the no surround condition for the S (blue), L-M (red), and LUM (black) central-contour adaptor.
Figure 4
 
Results of Experiment 2. SFAEs obtained using texture adaptor and contour test in which the central-contour adaptor was flanked by same-color surround contours (light gray bars), different-color surround contours (black bars), or no surround (dashed line) for S-defined (A), L-M-defined (B), and LUM-defined (C) central-contour adaptor. The dashed line indicates the no surround condition for the S (blue), L-M (red), and LUM (black) central-contour adaptor.
To express the results in terms of the magnitude of surround suppression we calculated a surround suppression index (SSI) as follows: SSI = 1 − SFAEWITH-SURROUND/SFAENO-SURROUND, where SFAEWITH-SURROUND and SFAENO-SURROUND are the shape aftereffects obtained with and without the surround texture, respectively. Note that SSI is inversely related to the magnitude of the SFAE. Thus, a surround suppression index of 1 indicates complete suppression of the SFAE, while 0 indicates no suppression. Figure 5 plots the across-subjects average SSI for the S (Figure 5a), L-M (Figure 5b), and LUM (Figure 5c) adaptor and test contours. The results show that for S and L-M adaptor and test contours, TSSCS is only weakly selective for surround color, with an average SSI of approximately 0.68 for same-color surrounds and approximately 0.42 for different-color surrounds, a difference of about 25%. Moreover, with a LUM adaptor and test contour, the SSI was on average 0.57 and was nonselective to surround color. 
Figure 5
 
The across-subjects average SSI for the S (A), L-M (B), and LUM (C) central-contour adaptor and test conditions. Note that SSI is inversely related to the magnitude of SFAE. A SSI of 1 indicates complete suppression, while 0 indicates complete lack of suppression.
Figure 5
 
The across-subjects average SSI for the S (A), L-M (B), and LUM (C) central-contour adaptor and test conditions. Note that SSI is inversely related to the magnitude of SFAE. A SSI of 1 indicates complete suppression, while 0 indicates complete lack of suppression.
To determine whether the color selectivity of TSSCS was significant we performed a two-way, within-subjects repeated measures ANOVA on the data shown in Figure 4, with factors “combination of center and surround contours” (same versus different) and “color of central-contour adaptor and test” (L-M versus S versus LUM). The p-values were Bonferroni corrected to allow for multiple comparisons. For the different-color conditions, the surround colors for each adaptor and test color were averaged (e.g., for an S adaptor and test, both L-M and LUM surrounds were averaged). The main effect of combination was significant (F[1,4] = 11.147, p = 0.029, ηp2 = 0.736), indicating that the aftereffect is selective for surround color. The main effect of adaptor and test contour color was not significant, however (F[2,8] = 1.875, p = 0.215, ηp2 = 0.319), indicating that the aftereffect was not affected by the adaptor and test color when averaged across combination. Finally, there was a significant interaction of combination and color of adaptor and test (F[2,8] = 8.124, p = 0.012, ηp2 = 0.670), indicating that the effect of combination changed as a function of color of adaptor and test. 
To investigate this interaction, paired (within-subject) t-tests were conducted on the non-normalized data to test for significant differences of combination for each adaptor and test color. All conditions were significantly different (p < 0.01) except the LUM adaptor and test condition (t[4] = 0.1087, p = 0.9187). 
Experiment 3: is texture shape selective to color?
In this experiment we measured the SFAE for texture adaptors and texture tests. All combinations of L-M, S, and LUM adaptors and tests were tested. Three subjects participated in this experiment. 
Figure 6 shows SFAEs obtained with the various texture adaptor and test color combinations. Figures 6a, 6b, and 6C show results for test S, L-M, and LUM textures, respectively. Results show that in almost all instances, the aftereffects obtained with same-color and different-color adaptors and tests are of comparable magnitude (compare light gray and dark bars in Figure 6). These results indicate that texture-shape processing is nonselective for color. 
Figure 6
 
Experiment 3: Example texture adaptor and test stimuli, and (A) SFAEs obtained with texture adaptor and test combinations defined along the same S (light gray bars) and along different, L-M, or LUM (dark bars) cardinal axes for S test textures. (B) Corresponding results for the L-M test textures. (C) Corresponding results for the LUM test textures.
Figure 6
 
Experiment 3: Example texture adaptor and test stimuli, and (A) SFAEs obtained with texture adaptor and test combinations defined along the same S (light gray bars) and along different, L-M, or LUM (dark bars) cardinal axes for S test textures. (B) Corresponding results for the L-M test textures. (C) Corresponding results for the LUM test textures.
To determine whether the color selectivity was significant we performed a two-way, within-subjects repeated measures ANOVA, with factors “combination” (same versus different) and color of test (L-M versus S versus LUM), Bonferroni correcting the p-values to allow for multiple comparisons. For the different-color conditions, the two adaptor colors for each test color were averaged. The main effect of combination was not significant (F[1,2] = 0.016, p = 0.911, ηp2 = 0.0079), indicating that the aftereffect is not selective to color. The main effect of color of test was also not significant (F[2,4] = 2.153, p = 0.2318, ηp2 = 0.5184). 
Discussion
The findings of this study are summarized in Figure 7a for SFAEs using single-contour shapes, Figure 7b for contour shapes with texture surrounds, and Figure 7c for texture shapes. In Figures 7a and 7c the results are normalized to the same-color adaptor and test conditions (light gray bars in Figure 3 and Figure 6, respectively), whereas in Figure 7b the results are normalized to the single-contour adaptor condition (dashed line in Figure 4). One can think of the measures used in these figures as the amount of transfer of the aftereffect across different colors. For single contours, different-color adaptors and tests produced significantly smaller aftereffects than same-color adaptors and tests (compare black and light gray bars in Figure 7a). On the other hand with textures, same-color and different-color conditions produced comparable-sized aftereffects (compare light gray and dark bars in Figure 7c). 
Figure 7
 
Summary of normalized SFAEs for the three experiments averaged across subjects. (A) Experiment 1: SFAEs normalized to the same-color contour adaptor and contour test condition for the S (left), L-M (middle), and LUM (right) contour test conditions. (B) Experiment 2: SFAEs normalized to the no-surround adaptor condition for the S (left), L-M (middle), and LUM (right) central-contour adaptor and test conditions. (C) Experiment 3: SFAEs normalized to the same-color texture adaptor and test condition for the S (left), L-M (middle), and LUM (right) texture test conditions.
Figure 7
 
Summary of normalized SFAEs for the three experiments averaged across subjects. (A) Experiment 1: SFAEs normalized to the same-color contour adaptor and contour test condition for the S (left), L-M (middle), and LUM (right) contour test conditions. (B) Experiment 2: SFAEs normalized to the no-surround adaptor condition for the S (left), L-M (middle), and LUM (right) central-contour adaptor and test conditions. (C) Experiment 3: SFAEs normalized to the same-color texture adaptor and test condition for the S (left), L-M (middle), and LUM (right) texture test conditions.
The results with texture surrounds are shown in Figure 7b. It is clear that all types of surround reduced the aftereffect (compare bars with dashed line). For the S and L-M contours, the reduction in aftereffect was greater for same-color surrounds than for different-color surrounds (compare light gray and black bars), with surround suppression indices of on average 0.68 and 0.42, respectively, a difference of around 25%. However, the reduction in the aftereffect for a LUM contour adaptor was the same for all surround colors at around 0.57 (compare light gray and black bars in the right panel). 
The significant color selectivity of the SFAE using Gabor-string contours confirms our previous finding using continuous contours (Gheorghiu & Kingdom, 2007a). The new findings are (1) that TSSCS, at least as manifested by the effect of surround texture on the SFAE, appears to be partially selective for color, while (2) the texture-shape aftereffect is not selective for color. Therefore, there would appear to be a hierarchy with regard to color selectivity of the SFAE: contour shape > texture-surround suppression of contour shape > texture shape. 
The one exception to this trend is that the suppressive effect of a surround on a luminance central contour appears nonselective to color (compare Figure 5c with Figures 5a and 5b). We do not have an explanation for this apparently anomalous result, but it might reflect the relative contribution of monocular and binocular neurons to the SFAE. Forte and Clifford (2005) found with the tilt illusion that if the color difference between test and surround was along the chromatic luminance dimension, the illusion was reduced significantly only if mediated by monocular mechanisms. So perhaps binocular spatial channels are in general less color selective than monocular ones. We have previously shown that the SFAE is mediated by binocular curvature-selective neurons that, while not disparity selective in themselves, are nevertheless subject to disparity-selective texture-surround suppression (Gheorghiu et al., 2009b). Thus, the weak surround-color selectivity of TSSCS observed here with L-M and S contours may implicate the involvement of monocular neurons, whereas the absence of surround-color selectivity for LUM contours may implicate the involvement of binocular neurons. It follows also from this argument that the absence of color selectivity for the texture-shape aftereffect that we found may be because texture coding is purely binocular, consistent with the finding that texture-density encoding is primarily binocular (Durgin, 2001). 
One might suppose that a contour adaptor flanked by different-color surround contours would be released from “iso-orientation surround suppression,” or, one might say, become ungrouped from the surround and as a result become more salient (i.e., pop out). Color pop-out has been shown to play an important role in a variety of visual tasks such as singleton search (Gheri, Morgan, & Solomon, 2007; Koene & Zhaoping, 2007), letter recognition (Poder, 2007), orientation identification (Kennedy & Whitaker, 2010), and texture segmentation (Zhaoping & May, 2007). Color pop-out is also implicated in the well-known finding that crowding is diminished when the target and flanker are of a different color, as in some of the above-mentioned studies (Gheri et al., 2007; Kennedy & Whitaker, 2010; Koene & Zhaoping, 2007; Poder, 2007). For example, Kennedy and Whitaker (2010) found that crowding of a Gabor target surrounded by an annular plaid flanker was strongest when target and flanker had the same chromaticity and was almost absent when they differed in chromaticity, implying that crowding is strongly selective for color direction. Our results show that for colors defined along the three cardinal axes, surround suppression is significant for both same-color and different-color surrounds, with a 25% difference between the two. The weak color selectivity to surrounds means that one cannot rule out altogether a contribution of color pop-out for the L-M and S color contours; however, for the LUM contours, which showed no difference, one can. Furthermore, the weak selectivity for L-M and S colors and the lack of color selectivity for the LUM contours is not consistent with TSSCS being an instance of crowding. 
Our finding that texture-shape processing is nonselective for color (Experiment 3, Figure 7c) is in keeping with findings from a conventional subthreshold detection paradigm that found that detection of orientation-modulated textures is agnostic to whether the texture elements are defined by luminance or chromaticity (Pearson & Kingdom, 2002). Thus, the absence of color selectivity for both near-threshold (Pearson & Kingdom, 2002) and suprathreshold texture-shape variations found in the present study indicates that texture-shape mechanisms pool information from both the luminance and chromatic postreceptoral mechanisms. 
Significance for the relationship between color and form
Numerous studies have demonstrated that many types of form task are possible at isoluminance (Kingdom, Moulden, & Collyer, 1992; McIlhagga & Mullen, 1996; Mullen, Beaudot, & McIlhagga, 2000). Typically, however, more color contrast than luminance contrast is needed to achieve the same level of performance, and this may in part be due to greater internal noise in the chromatic form signal (Shevell & Kingdom, 2008). It is worth emphasizing that in both our previous (Gheorghiu & Kingdom, 2007a) and present studies we used an appearance not a performance task, and therefore a task presumably unaffected by internal noise and one that taps directly how curvature is represented in the brain (Gheorghiu & Kingdom, 2007b, 2009). Thus, our current findings are important because they show a graded color selectivity for three levels of curvature representation: for single contours, for single contours with texture surrounds, and for textures. Our findings are in agreement with other studies showing that color and orientation are conjointly represented, as evidenced by the tilt aftereffect (Elsner, 1978; Held & Shattuck, 1971; Lovegrove & Mapperson, 1981; Lovegrove & Over, 1973) and the McCollough aftereffect (Houck & Hoffman, 1986; McCollough, 1965). 
Relationship to neurophysiology
Color and contour shape
Our earlier studies showed that the SFAE is mediated by mechanisms selective for local curvature, specifically to the half-cycle, cosine-shape parts of the contour (Gheorghiu & Kingdom, 2007b, 2009). Single-unit recordings in primates indicate a key role of areas V4 and IT in contour-shape processing. Neurons in area V4 are selective for parts of shapes (curves, angles) with constant sign of curvature (Pasupathy & Connor, 1999, 2001, 2002). V4 neurons are also color selective (Bushnell, Harding, Kosai, Bair, & Pasupathy, 2011; Desimone & Schein, 1987; Schein & Desimone, 1990; Schein, Marrocco, & de Monasterio, 1982; Zeki, 1983a, 1983b) and receive inputs from color-opponent neurons (Schein & Desimone, 1990). Using similar shape stimuli as those previously used by Pasupathy and Connor (2001), Bushnell et al. (2011) recently found a subpopulation of V4 neurons (∼22%) that respond selectively to equiluminant-colored shapes; their responses were largest near zero-luminance contrast and decreased as contrast increased. Some of the equiluminance cells were found to exhibit strong color tuning while others showed little or no color tuning. Area IT neurons are also involved in coding curves, angles, and shapes (Desimone, Schein, Moran, & Ungerleider, 1985) and are also selective to color and preserve information about luminance contrast polarity (Ito et al., 1994). Therefore the human homologs of primate area V4 or IT are the most likely cortical areas involved in the contour shape aftereffects reported here and elsewhere. 
Color and texture-surround suppression of contour shape
In V1 around 90% of neurons that are sensitive to oriented lines or grating patches are suppressed by stimuli falling outside of their classical receptive field (CRF), with maximal suppression when the surround orientations are the same as those preferred by the CRF (Blakemore & Tobin, 1972; Cavanaugh, Bair, & Movshon, 2002; Jones, Grieve, Wang, & Sillito, 2001; Knierim & van Essen, 1992; Levitt & Lund, 1997; Nelson & Frost, 1985; Nothdurft, Gallant, & Van Essen, 1999; Yao & Li, 2002). These neurons are said to exhibit iso-orientation surround suppression. We have suggested that these neurons might mediate the suppression of the contour-shape aftereffects evidenced here and in previous studies, specifically by feeding their responses into high-level visual areas such as V4 that are directly involved in processing contour shape (Gheorghiu et al., 2009b; Gheorghiu & Kingdom, 2011; Kingdom & Prins, 2009). 
Most neurons in areas V1 and V2 are tuned to both color and luminance contrast (Derrington, Krauskopf, & Lennie, 1984; Johnson, Hawken, & Shapley, 2001; Lennie, Krauskopf, & Sclar, 1990; Thorell, De Valois, & Albrecht, 1984). Neurophysiological studies have also found color-selective V1 neurons with suppressive or facilitatory surrounds (Ts'o & Gilbert, 1988; Wachtler, Sejnowski, & Albright, 2003). For example, using homogeneous color patches, Wachtler et al. (2003) found that when a stimulus of a certain chromatic direction is presented on a background of the same chromatic direction (but weaker saturation), its response is reduced, whereas an opponent-color background had little or no effect. However, no neurophysiological studies have to our knowledge examined surround suppression using stimuli made up of narrowband-oriented elements that isolate the cardinal axes, so it will be interesting to see if the color selectivity found here for TSSCS will also be found for V1 neurons responding to such stimuli. It is possible that the TSSCS manifested here is instantiated in the higher visual areas mediating contour shape perception per se, but to our knowledge there is as yet no evidence for iso-orientation surround suppression in V4 and IT. 
Finally, it is worth emphasizing that model simulations of the responses of neurons exhibiting iso-orientation surround suppression reveal that they are sensitive to isolated contours, such as the edges of objects, but relatively unresponsive to lines or contours that form part of dense textures (Grigorescu et al., 2003, 2004; Huang, Jiao, & Jia, 2008; Petkov & Westenberg, 2003; Ursino & La Cara, 2004). The effect of iso-orientation surround suppression in these models is thus to separate the processing of contours from that of textures. However, to our knowledge no model has incorporated the weak color selectivity for iso-orientation surround suppression evidenced here, so it remains to be determined what consequences this might have for contour versus texture processing in images of natural scenes. 
Supplementary Materials
Acknowledgments
This research was supported by a Research Foundation Flanders (Fonds Wetenschappelijk Onderzoek -Vlaanderen) fellowship and a Kredit aan Navorsers research grant given to E. G. and by a Canadian Institute of Health Research (CIHR) grant (#MOP-11554) given to F. K. 
Commercial relationships: none. 
Corresponding author: Elena Gheorghiu. 
Email: elena.gheorghiu@psy.kuleuven.be. 
Address: Laboratory of Experimental Psychology, University of Leuven, Leuven, Belgium. 
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Appendix
Example SFAE obtained with nonstatic contour-shape adaptor and test (Movie a), texture-shape adaptor and contour-shape test (Movie b), and texture-shape adaptor and test (Movie c). Note that because of the limitations of electronic reproduction, the stimuli may be different from their appearance on the display. 
Movie a: SFAE with contour-shape adaptor and test 
Movie b: SFAE with texture-shape adaptor and contour-shape test 
Movie c: SFAE with texture-shape adaptor and test 
Figure 1
 
Stimuli used in the experiments. One can experience the shape-frequency aftereffect (SFAE) obtained with static single-contour adaptors (a) and static texture adaptors (b) by moving one's eyes back and forth along the markers located midway between the pair of adapting contours (a, left) or textures (b, left) for about 90 s and then shifting one's gaze to the middle of the test contours (right). The two test contours, which have the same shape frequency, should appear different in shape frequency. Thus, adaptation to a contour of a given shape frequency makes a test contour of lower shape frequency appear lower in shape frequency and a test contour of higher shape frequency appear higher in shape frequency. The back-and-forth eye movements along the marker between the pair of static adapting contours (a) and textures (b) on the left prevents the formation of afterimages and minimizes the effects of local orientation adaptation. In the experiments, during the adaptation period, the shape phase of the contour or texture was randomly changed every 1 s to achieve the same ends. Both aftereffects survive shape-phase randomization during adaptation, as can be experienced in the nonstatic contour adaptor (Movie a) and texture adaptor (Movie b) versions shown in the Appendix. (c) Modified version of the MacLeod-Boynton color space used to define the color directions.
Figure 1
 
Stimuli used in the experiments. One can experience the shape-frequency aftereffect (SFAE) obtained with static single-contour adaptors (a) and static texture adaptors (b) by moving one's eyes back and forth along the markers located midway between the pair of adapting contours (a, left) or textures (b, left) for about 90 s and then shifting one's gaze to the middle of the test contours (right). The two test contours, which have the same shape frequency, should appear different in shape frequency. Thus, adaptation to a contour of a given shape frequency makes a test contour of lower shape frequency appear lower in shape frequency and a test contour of higher shape frequency appear higher in shape frequency. The back-and-forth eye movements along the marker between the pair of static adapting contours (a) and textures (b) on the left prevents the formation of afterimages and minimizes the effects of local orientation adaptation. In the experiments, during the adaptation period, the shape phase of the contour or texture was randomly changed every 1 s to achieve the same ends. Both aftereffects survive shape-phase randomization during adaptation, as can be experienced in the nonstatic contour adaptor (Movie a) and texture adaptor (Movie b) versions shown in the Appendix. (c) Modified version of the MacLeod-Boynton color space used to define the color directions.
Figure 2
 
Example contours and textures defined along the S, L-M, and LUM cardinal axis. The textures consisted of a central contour defined along the S (A), L-M (B), and LUM (C) cardinal axes flanked by either S, L-M, or LUM surround.
Figure 2
 
Example contours and textures defined along the S, L-M, and LUM cardinal axis. The textures consisted of a central contour defined along the S (A), L-M (B), and LUM (C) cardinal axes flanked by either S, L-M, or LUM surround.
Figure 3
 
Results of Experiment 1. SFAEs obtained with single-contour adaptor and test combinations defined along the same (light gray bars) and different (black bars) cardinal axes for the S-defined (A), L-M-defined (B), and LUM-defined (C) test contours.
Figure 3
 
Results of Experiment 1. SFAEs obtained with single-contour adaptor and test combinations defined along the same (light gray bars) and different (black bars) cardinal axes for the S-defined (A), L-M-defined (B), and LUM-defined (C) test contours.
Figure 4
 
Results of Experiment 2. SFAEs obtained using texture adaptor and contour test in which the central-contour adaptor was flanked by same-color surround contours (light gray bars), different-color surround contours (black bars), or no surround (dashed line) for S-defined (A), L-M-defined (B), and LUM-defined (C) central-contour adaptor. The dashed line indicates the no surround condition for the S (blue), L-M (red), and LUM (black) central-contour adaptor.
Figure 4
 
Results of Experiment 2. SFAEs obtained using texture adaptor and contour test in which the central-contour adaptor was flanked by same-color surround contours (light gray bars), different-color surround contours (black bars), or no surround (dashed line) for S-defined (A), L-M-defined (B), and LUM-defined (C) central-contour adaptor. The dashed line indicates the no surround condition for the S (blue), L-M (red), and LUM (black) central-contour adaptor.
Figure 5
 
The across-subjects average SSI for the S (A), L-M (B), and LUM (C) central-contour adaptor and test conditions. Note that SSI is inversely related to the magnitude of SFAE. A SSI of 1 indicates complete suppression, while 0 indicates complete lack of suppression.
Figure 5
 
The across-subjects average SSI for the S (A), L-M (B), and LUM (C) central-contour adaptor and test conditions. Note that SSI is inversely related to the magnitude of SFAE. A SSI of 1 indicates complete suppression, while 0 indicates complete lack of suppression.
Figure 6
 
Experiment 3: Example texture adaptor and test stimuli, and (A) SFAEs obtained with texture adaptor and test combinations defined along the same S (light gray bars) and along different, L-M, or LUM (dark bars) cardinal axes for S test textures. (B) Corresponding results for the L-M test textures. (C) Corresponding results for the LUM test textures.
Figure 6
 
Experiment 3: Example texture adaptor and test stimuli, and (A) SFAEs obtained with texture adaptor and test combinations defined along the same S (light gray bars) and along different, L-M, or LUM (dark bars) cardinal axes for S test textures. (B) Corresponding results for the L-M test textures. (C) Corresponding results for the LUM test textures.
Figure 7
 
Summary of normalized SFAEs for the three experiments averaged across subjects. (A) Experiment 1: SFAEs normalized to the same-color contour adaptor and contour test condition for the S (left), L-M (middle), and LUM (right) contour test conditions. (B) Experiment 2: SFAEs normalized to the no-surround adaptor condition for the S (left), L-M (middle), and LUM (right) central-contour adaptor and test conditions. (C) Experiment 3: SFAEs normalized to the same-color texture adaptor and test condition for the S (left), L-M (middle), and LUM (right) texture test conditions.
Figure 7
 
Summary of normalized SFAEs for the three experiments averaged across subjects. (A) Experiment 1: SFAEs normalized to the same-color contour adaptor and contour test condition for the S (left), L-M (middle), and LUM (right) contour test conditions. (B) Experiment 2: SFAEs normalized to the no-surround adaptor condition for the S (left), L-M (middle), and LUM (right) central-contour adaptor and test conditions. (C) Experiment 3: SFAEs normalized to the same-color texture adaptor and test condition for the S (left), L-M (middle), and LUM (right) texture test conditions.
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