Open Access
Article  |   July 2016
Spatiotemporal properties of multiple-color channels in the human visual system
Author Affiliations
Journal of Vision July 2016, Vol.16, 14. doi:10.1167/16.9.14
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Daisuke Kondo, Isamu Motoyoshi; Spatiotemporal properties of multiple-color channels in the human visual system. Journal of Vision 2016;16(9):14. doi: 10.1167/16.9.14.

      Download citation file:


      © 2017 Association for Research in Vision and Ophthalmology.

      ×
  • Supplements
Abstract

Psychophysical and neurophysiological evidence argues for neural channels in V1 selectively sensitive to intermediate hues between the cardinal axes of opponent-color space. The present study examined if these multiple-color channels are selective for other visual features analyzed by V1 such as orientation, spatial frequency, and motion direction. Using a conventional masking paradigm, we measured detection thresholds for an isoluminant grating modulated along a particular hue angle either in the presence or absence of a bandpass noise mask that varied in hue angle, orientation, spatial frequency, and motion direction. In line with previous studies, thresholds for the test grating were selectively elevated by noise masks with hue angles similar to that of the test. Hue-selective masking was substantially reduced if test and mask were oriented orthogonally or differed in spatial frequency, but thresholds remained elevated if the mask drifted in the direction opposite to that of the test. Masking also revealed components selective for hue angle, but not for orientation. The results support the notion that multiple-color channels partly involve visual units selective for orientation and spatial frequency but largely nonselective for motion direction.

Introduction
Recent psychophysical and physiological studies in primate color vision suggest that areas of visual cortex such as V1 and beyond have neural channels selectively sensitive to intermediate hues between the cardinal axes (L/M and S) of classical opponent-color space (Eskew, 2009; Gegenfurtner & Kiper, 2003; Krauskopf, Williams, & Heeley, 1982; Krauskopf, Williams, Mandler, & Brown, 1986). These higher order color channels are often referred as multiple-color channels. 
The existence of the multiple-color channels in the human visual system has been demonstrated by psychophysical adaptation and masking experiments (Hansen & Gegenfurtner, 2006; Lindsey & Brown, 2004; Sankeralli & Mullen, 1997; Webster & Mollon, 1991, 1994). For example, Webster and Mollon (1991) showed that adaptation to a disk-shaped stimulus temporally modulated along a particular intermediate hue angle specifically reduces the apparent saturation of a subsequently presented disk with similar hue. Hansen and Gegenfurtner (2006) showed that a rectangular noise-texture target embedded in a noise-texture background is less detectable if target and background were modulated along the same chromatic direction. Single cell recordings in macaque (Shapley & Hawken, 2002, 2011) and functional magnetic resonance imaging (fMRI) measurements in humans (Kuriki, Sun, Ueno, Tanaka, & Cheng, 2015) also provide physiological evidence for neural units in V1 and beyond that respond selectively to stimuli composed of intermediate hues. 
Many neurons in V1 respond selectively to visual stimuli with specific orientations, spatial frequencies, and directions of motion (R. L. De Valois & De Valois, 1988; Hubel & Wiesel, 1962), but whether the above multiple-color channels also exhibit selectivity to these spatiotemporal properties as well as to chromatic hues remains to be investigated. Psychophysical studies have revealed orientation and spatial frequency selectivity of chromatic channels using isoluminant red-green stimuli (Bradley, Switkes, & De Valois, 1988; K. K. De Valois & Switkes, 1983; Losada & Mullen, 1994; Medina & Mullen, 2009). Single-cell recordings have also shown that some color-sensitive neurons in V1 are selective for orientation and spatial frequency (Johnson, Hawken, & Shapley, 2001, 2008) but that other neurons in cytochrome oxidase (CO) blob regions are not (Livingstone & Hubel, 1984; H. D. Lu & Roe, 2008; Yoshioka & Dow, 1996). However, these studies have only examined chromatic stimuli modulated along a particular hue angle as defined by red-green stimuli, and it remains unclear if the same holds true for stimuli modulated along the other intermediate hue angles to which multiple-color channels are specifically sensitive. Indeed, previous psychophysical analyses based on adaptation and masking (Hansen & Gegenfurtner, 2006, 2013; Webster & Mollon, 1991) have examined selectivity to intermediate hues; these studies involved stimuli such as disks (Webster & Mollon, 1991) and block noise (Hansen & Gegenfurtner, 2006, 2013) that, owing to their broadband energy profiles, make it difficult to reveal the joint space–time selectivity of multiple-color channels. Hansen and Gegenfurtner (2006, 2013) showed hue-selective masking for the detection of a rectangular texture region using an orientation discrimination task (vertical vs. horizontal), but the fact that observers can discriminate a particular attribute does not always demonstrate the existence of channels selectively sensitive for that attribute. 
In the present study, we employed spatiotemporally controlled stimuli to examine the selectivity of hue-selective color channels to orientation, spatial frequency, and motion direction. Using a conventional masking technique, we measured detection thresholds for a sinusoidal chromatic grating embedded in a spatially bandpass noise mask of variable hue angle. After having confirmed that thresholds for test gratings were selectively elevated by masks with similar hues (Experiment 1), we found that hue-selective masking was selective for orientation and spatial frequency but largely nonselective for motion direction (Experiments 2, 3, and 4). 
Experiment 1
We first examined whether bandpass noise stimuli could reproduce hue-selective masking effects similar to those obtained with noise texture masking (Hansen & Gegenfurtner, 2006, 2013) for noncardinal hues (i.e., hues whose angles are not aligned with the cardinal axes of classical opponent-color space). 
Methods
Observers
Eight naive paid observers participated in the experiment. All observers had normal trichromatic color vision and normal or corrected-to-normal visual acuity. Informed written consent was obtained from all observers, and all experiments were conducted in accordance with guidelines from the ethical committee of the University of Tokyo and with the Code of Ethics of the World Medical Association (Declaration of Helsinki). 
Apparatus
Visual stimuli were generated on a PC and presented on an organic electroluminescence (OEL) monitor (PVM-2541A, Sony, Tokyo, Japan) with a refresh rate of 60 Hz. The spatial resolution of the monitor was 0.97 min/pixel at a viewing distance of 100 cm. The monitor's CIE xy chromaticity, as measured with a photometer (CRS Optical II, Cambridge Research Systems, Rochester, UK), was (x, y) = (0.646, 0.328) for R, (0.246, 0.639) for G, and (0.143, 0.066) for B, respectively. The luminance of each gun was gamma corrected and controlled on a nearly continuous scale by means of the Noisy-Bit technique (Allard & Faubert, 2008). 
Color space
All chromatic stimuli were defined in the isoluminant plane of the DKL color space (Derrington, Krauskopf, & Lennie, 1984; Kaiser & Boynton, 1996). The neutral white point in the plane had a CIE xy chromaticity of (0.339, 0.348) and a luminance of 6.36 cd/m2. For each observer, we measured detection thresholds for a sinusoidal test grating in the absence of any mask for four hue angles (0°, 45°, 90°, and 135°) and fitted an ellipse to the data. We then defined the scale of the cardinal axes such that the length of the major and minor axes of the ellipse were equal. Rescaling axes in this way conveniently normalizes each observer's detection thresholds regardless of hue angle. 
Stimuli
Test stimuli consisted of a vertically oriented grating pattern within a circular window. The window had a diameter of 3.9 deg and its edges were tapered by a cosine with a 1.9 deg wavelength (Figure 1). The grating was defined by a sinusoidal modulation along a particular hue angle in the isoluminant plane. In order to minimize chromatic aberrations, grating spatial frequency was purposely set to a low value (0.96 c/deg). The grating's spatial phase was fully randomized on each trial. 
Figure 1
 
Stimuli used in Experiment 1. The test grating is shown in the upper-left quadrant. The hue angle of the test grating is orthogonal to the noise mask in the left panel, and is the same as the noise mask in the right panel. Contrast is exaggerated for display purposes.
Figure 1
 
Stimuli used in Experiment 1. The test grating is shown in the upper-left quadrant. The hue angle of the test grating is orthogonal to the noise mask in the left panel, and is the same as the noise mask in the right panel. Contrast is exaggerated for display purposes.
Mask stimuli consisted of bandpass noise presented in a square field subtending 8.3 × 8.3 deg (Figure 1). We decided against using a grating pattern as a mask because a vivid perception of flicker arises if test and mask gratings drift in opposite directions (see Experiment 4). Noise masks had a center spatial frequency of 0.96 c/deg, a two-octave bandwidth (full-width at half height), a center orientation of 0° (vertical) and an orientation bandwidth of 30°. The mask's RMS contrast remained fixed at 0.1, and test and mask stimuli were linearly combined in the isoluminant plane. 
Procedure
We measured detection thresholds for test gratings embedded in bandpass noise masks by using a four-alternative forced choice (4AFC) method. On each trial, observers gazed binocularly at a black fixation dot (8 × 8 min) in the center of the uniform gray background of 31.3. (W) × 17.5 (H) deg. Stimuli were presented for 500 ms, and onsets and offsets were tapered by a cosine with a 67-ms wavelength. The test grating was presented at one of four possible locations (upper-left, upper-right, lower-left, or lower-right) 1.9 deg away from the fixation dot, and observers indicated the location of the test grating by pressing one of four buttons. The contrast of the test grating was controlled by a staircase procedure whereby contrast was decreased or increased by 0.2 log units if the observer correctly or incorrectly indicated the location of test, respectively. A separate staircase was run for each condition, and staircases were run in parallel such that conditions alternated from trial to trial in a randomly interleaved fashion. In each condition, data were collected for at least 120 trials. Detection threshold, defined as the contrast corresponding to 62.5% correct, was estimated by using a maximum likelihood method, and standard error was estimated using a bootstrap method (400 samples). 
In the present study, we used hue angles of 0° and 90° to define the L/M and S positive axis directions, respectively. In each block, the hue angle of the mask grating was fixed, and detection thresholds were measured for test gratings with four hue angles of 0°, 45°, 90°, and 135°. The block was repeated for different mask hue angles of 0°, 45°, 90°, and 135°. For each condition, we defined mask threshold elevation as the ratio of test threshold in the mask condition to test threshold in the no-mask condition. 
Results
Figure 2a shows log test detection thresholds for test grating of various hue angles plotted in DKL color space. In these polar plots, vertical and horizontal axes represent log contrast in S and L/M, and thresholds in the masked conditions are normalized with respect to test thresholds without masks (see Methods). The dotted black circle represent zero in this space. Each color filled circle represents mask data for different mask hue angles of 0°, 45°, 90°, and 135°, respectively. Black circles represent log thresholds in no-mask conditions. Figure 2b shows mask log threshold elevation plotted as a function of test hue angle. Figure 2d shows log test detection thresholds for observers who participated only in a subset of mask angle conditions. 
Figure 2
 
(a) Log detection threshold for test grating plotted in the DKL space. Red, orange, blue, and purple symbols show thresholds in the with-mask condition for mask hues of 0°, 45°, 90°, and 135°, respectively. Black symbols show log test thresholds in no-mask conditions. (b) Mask log-threshold elevation plotted as a function of test hue angle. Colored triangles denote mask hue angle. Smooth curves are fitted pseudocyclic Gaussian functions. Error bars represent ±1 SEM. Each panel represents one observer. (c) Estimated parameters A, B, and σ of the fitted functions. Circles represent the parameter value (mean across observers) estimated for each mask hue angle, and bars represent their average. (d) Test grating log detection thresholds for observers who participated only in a subset of mask angle conditions. Formats are the same as (a) and (b).
Figure 2
 
(a) Log detection threshold for test grating plotted in the DKL space. Red, orange, blue, and purple symbols show thresholds in the with-mask condition for mask hues of 0°, 45°, 90°, and 135°, respectively. Black symbols show log test thresholds in no-mask conditions. (b) Mask log-threshold elevation plotted as a function of test hue angle. Colored triangles denote mask hue angle. Smooth curves are fitted pseudocyclic Gaussian functions. Error bars represent ±1 SEM. Each panel represents one observer. (c) Estimated parameters A, B, and σ of the fitted functions. Circles represent the parameter value (mean across observers) estimated for each mask hue angle, and bars represent their average. (d) Test grating log detection thresholds for observers who participated only in a subset of mask angle conditions. Formats are the same as (a) and (b).
In all cases, detection thresholds reach peak elevation if test and mask hues are identical. Importantly, hue-selective threshold elevations are observed not only in cases where test and mask hues align with cardinal opponent-color axes (0° and 90°) but also in cases where test and mask line up with intermediate hue angles (45° and 135°). Two important conclusions stem from this experiment. First, results exemplify the defining property of multiple-color channels, namely that such channels are equally selective for (and sensitive to) intermediate hues as they are for hues aligned with cardinal color-opponent axes (e.g., Hansen & Gegenfurtner, 2006, 2013). Second, bandpass noise produces hue-selective threshold elevations comparable to those obtained with other masks (e.g., noise texture), and can be regarded as a good psychophysical tool for investigating spatiotemporal selectivity. It should be noted, however, that such stimuli could involve potential artefacts due to pixel-wise spatial misalignment between test and mask (see Stromeyer, Thabet, Chaparro, & Kronauer, 1999). 
In order to characterize the effect of masking more quantitatively, we fitted the following pseudocyclic Gaussian function to the threshold elevation data:  where θ and θ0 correspond to test and mask hue angles, respectively. A defines the amplitude of the hue-selective masking component, B is the amplitude of the masking component that is invariant with hue angle, and σ is the standard deviation that defines the bandwidth (in radians) of the hue-selective masking component. Fitting of this pseudocyclic Gaussian function was constrained such that 0 < σ < 0.7 in order to avoid extreme values at local minima. Fitted functions are drawn as smooth curves in each plot, and estimated fit parameters are shown in Figure 2c (observer averages). Fit parameters revealed that threshold elevations are accounted for by a large amount of hue-selective masking (A) and some degree of hue-independent masking (B). The estimated bandwidth of hue-selective component (σ) is ∼ 30° (∼0.5 rad)—a value roughly consistent with results from previous studies (Hansen & Gegenfurtner, 2006, 2013). It should be noted, however, that this bandwidth estimate does not necessarily reflect the tuning width of underlying mechanisms given that we cannot exclude the possibility of off-axis looking in target detection (Hansen & Gegenfurtner, 2006; Lindsey & Brown, 2004).  
Experiment 2
In this experiment, we investigated the spatial orientation selectivity of hue-selective masking. 
Methods
In Experiment 1, the spatial orientation of both tests and masks remained vertical across all conditions. In the current experiment, we compared hue-masking threshold elevation for tests and masks with either parallel or orthogonal spatial orientations (Figure 3). Detection thresholds were measured for masks with diagonal hue angles (45° and 135°). Six observers participated in the experiment. All other experimental aspects remained unchanged from Experiment 1
Figure 3
 
Examples of stimuli used in Experiment 2. Test grating remains vertical, and mask noise was (a) vertical (parallel), or (b) horizontal (orthogonal). Contrast is exaggerated for display purposes.
Figure 3
 
Examples of stimuli used in Experiment 2. Test grating remains vertical, and mask noise was (a) vertical (parallel), or (b) horizontal (orthogonal). Contrast is exaggerated for display purposes.
Results
Figure 4 shows threshold curves and threshold-elevation curves obtained for 45° (a), 135° (b), and 0° and 90° (c) mask hue angles, respectively (mask conditions). Purple symbols show data for conditions in which tests and mask had parallel spatial orientations (vertical test with vertical mask, replot of Figure 2), while orange symbols show data for conditions in which tests and masks had orthogonal spatial orientations (vertical test with horizontal mask). 
Figure 4
 
Orientation specificity of hue masking. Masking data are shown for mask hue angles of 45° (a), 135° (b), and cardinal axes (0° in the right panels and 90° in the left panels; [c]). In each subfigure, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hue angles in the lower panels. Colored triangles denote mask hue angle. Purple and orange circles represent the results for the parallel and orthogonal masks, respectively. Black curves represent log test threshold in no-mask conditions. Error bars represent ±1 SEM. Upper-right panels show the estimated parameters A, B, and σ of fitted Gaussian function. In (a) and (b), circles represent parameter values estimated for each observer, and bars represent their average.
Figure 4
 
Orientation specificity of hue masking. Masking data are shown for mask hue angles of 45° (a), 135° (b), and cardinal axes (0° in the right panels and 90° in the left panels; [c]). In each subfigure, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hue angles in the lower panels. Colored triangles denote mask hue angle. Purple and orange circles represent the results for the parallel and orthogonal masks, respectively. Black curves represent log test threshold in no-mask conditions. Error bars represent ±1 SEM. Upper-right panels show the estimated parameters A, B, and σ of fitted Gaussian function. In (a) and (b), circles represent parameter values estimated for each observer, and bars represent their average.
For both diagonal mask hue angles (Figure 4a, b), threshold elevations were significantly reduced in cases where test and mask had orthogonal spatial orientations. In particular, threshold elevation in conditions where test and mask hues align (i.e., the hue selective component of masking; the estimated parameter A) was smaller for spatially orthogonal masks with the exception of observer KN (left panel in Figure 4b). We ran one observer (SM) for mask hue angles of 0° and 90° and again found reduced thresholds in the orthogonal mask condition as well as orientation selectivity in the parallel and orthogonal mask conditions (Figure 4c). 
An analysis of the fitted curves revealed that, as shown in the bar plots, the amount of hue-selective threshold elevation (A in Equation 1) decreases in conditions where test and mask orientations are orthogonal. This indicates that hue-selective masking is also selective for orientation. The analysis also revealed that hue-selective threshold elevation nonetheless remains evident in the orthogonal mask condition, a finding that suggests an additional masking component selective for hue but not for orientation. With respect to the hue-independent threshold-elevation component (B in Equation 1), its value decreases to zero (or even drops below zero in some cases) in conditions where test and mask orientation are orthogonal. This trend appears consistent with the notion that the hue-independent component of masking is orientation selective, yet it remains unclear why detection thresholds decrease for some observers if tests and masks are orthogonal in both orientation and hue angle. Taken together, these patterns of results suggest that threshold is determined by a set of channels corresponding to each masking component and whereby each masking component is selective for the both hue and orientation, only for hue, and only for orientation. However, it is also possible that the first component selective for both hue and orientation is simply a product combination of the latter two components selective for hue or orientation (see also Discussion). 
Experiment 3
In this experiment, we examined the spatial frequency selectivity of hue-selective masking. 
Methods
We defined three conditions in which the spatial frequency of the bandpass noise mask (a) remained the same as that of the test grating (0.96 c/deg; Figure 5a), (b) was raised by one octave (1.9 c/deg; Figure 5b), and (c) was raised by two octaves (3.8 c/deg; Figure 5c). The spatial-frequency bandwidth (full-width at half height) was 2.0 octaves for all conditions. Detection thresholds were measured for two diagonal mask hues (45° and 135°). We did not investigate lower mask spatial frequencies because the test region would have been too small to accommodate a minimum number of spatial cycles. Six observers participated in the experiment. All other experimental aspects remained unchanged from Experiment 1
Figure 5
 
Examples of stimuli used in Experiment 3. The spatial frequency of the test grating was 0.96 c/deg, and that of the mask noise was (a) 0.96 c/deg (same as test), (b) 1.9 c/deg (one octave higher than test), or (c) 3.8 c/deg (two octaves higher than test). Contrast is exaggerated for display purposes.
Figure 5
 
Examples of stimuli used in Experiment 3. The spatial frequency of the test grating was 0.96 c/deg, and that of the mask noise was (a) 0.96 c/deg (same as test), (b) 1.9 c/deg (one octave higher than test), or (c) 3.8 c/deg (two octaves higher than test). Contrast is exaggerated for display purposes.
Results
Figure 6 shows results obtained for mask hues of 45° (a) and 135° (b), respectively. Purple symbols show data in conditions in which test and mask had identical spatial frequencies (replot of Figure 2), while orange and red symbols show the results for conditions in which mask spatial frequency was higher than the test grating by one octave (1.9 c/deg) and two octaves (3.8 c/deg), respectively. 
Figure 6
 
Spatial-frequency specificity of hue masking. Masking data are shown for mask hue angles of 45° (a) and 135° (b). For each mask hue angle, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hue angles in the lower panels. Colored triangles denote mask hue angle. Purple, orange, and red circles represent data in conditions where mask spatial frequency was the same as test grating (0.96 c/deg), or higher than the test by one octave (1.9 c/deg) or two octaves (3.8 c/deg), respectively. Black curves represent log test threshold in no-mask conditions. Error bars represent ±1 SEM. The upper-right panel shows the estimated parameters A, B, and σ of the fitted Gaussian function. Circles represent parameter values estimated for each observer, and bars represent their average.
Figure 6
 
Spatial-frequency specificity of hue masking. Masking data are shown for mask hue angles of 45° (a) and 135° (b). For each mask hue angle, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hue angles in the lower panels. Colored triangles denote mask hue angle. Purple, orange, and red circles represent data in conditions where mask spatial frequency was the same as test grating (0.96 c/deg), or higher than the test by one octave (1.9 c/deg) or two octaves (3.8 c/deg), respectively. Black curves represent log test threshold in no-mask conditions. Error bars represent ±1 SEM. The upper-right panel shows the estimated parameters A, B, and σ of the fitted Gaussian function. Circles represent parameter values estimated for each observer, and bars represent their average.
We found that threshold elevation decreases moderately if mask frequency was one octave higher than that of test (1.9 c/deg) and is altogether nonexistent if mask frequency was two octaves higher than the test (3.8 c/deg). An analysis of the fitted curves (bar plots) revealed that the amount of hue-selective threshold elevation (A in Equation 1) decreased to near zero in the case where test and mask spatial frequencies were two octaves apart, a finding highly suggestive of spatial-frequency selectivity in hue-selective masking. Hue-independent threshold elevation (B in Equation 1) appears to decrease with spatial frequency separation between test and mask, thereby indicating that this component is also selective for spatial frequency. If test and mask spatial frequencies are two octaves apart, the hue-invariant component drops below zero in many instances (p = 0.22 for 45° mask and p = 0.04 for 135° mask; t test for difference between parameter B and 0). 
Experiment 4
In this experiment, we examined the selectivity of hue-selective masking for direction of motion. 
Method
Unlike other experiments in this study, the current experiment employed dynamic stimuli. The noise mask drifted horizontally either at 2 or 4 pixels per frame on the monitor, thereby resulting in drift temporal frequencies of 1.9 or 3.8 Hz, respectively. Pixels at stimuli edges were spatially wrapped around to the corresponding opposite side. The test grating also drifted at the same speed as the mask within its circular window. Test gratings and noise masks drifted either in the same direction or in the opposite direction. As we mentioned earlier, if the mask was a grating instead of bandpass noise, a vivid perception of flicker arises in conditions in which test and mask gratings drift in opposite directions. Indeed, this was the very reason why we used bandpass noise as mask stimuli. The direction of the test (leftward or rightward) was randomly determined on each trial. Since the noise mask had a relatively large spatial frequency bandwidth, no flicker was visible if test and mask drifted in the opposite direction. We measured thresholds for mask hue angles of 45° and 135°. Six observers participated in the experiment. All other experimental aspects remained unchanged from Experiment 1
Results
Figure 7 shows results obtained for mask hues of 45° (a, b), 135° (c, d), and 0° and 90° (e, f), respectively. Results for 1.9 Hz stimuli are shown in Panels a, c, and e, and results for 3.8 Hz stimuli are shown in Panels b, d, and f. In each plot, red symbols show data for conditions in which test and mask drifted in the same direction, while orange symbols show data for test and mask drifted in the opposite direction. For the sake of comparison, data obtained for static stimuli in Experiment 1 are replotted as purple symbols. 
Figure 7
 
Direction specificity of hue masking. Masking data are shown for mask hues of 45° (a, b), 135° (c, d), and cardinal axes (0° in the right panels and 90 deg in the left panels; [e, f]), and for drift frequencies of 1.9 Hz (a, c, e) and 3.8 Hz (b, d, f). In each subfigure, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hues in the lower panels. Colored triangles denote mask hue angle. Data for conditions in which test and mask stimuli drifted either in same or opposite directions are represented by red and orange symbols, respectively. Purple symbols show data for static stimuli. Black curves represent log test thresholds in no-mask conditions. Error bars represent ±1 SEM. Upper-right panels show estimated parameters A, B, and σ of the corresponding fitted Gaussian function. In (a–d), circles represent parameter values estimated for each observer, and bars represent their average.
Figure 7
 
Direction specificity of hue masking. Masking data are shown for mask hues of 45° (a, b), 135° (c, d), and cardinal axes (0° in the right panels and 90 deg in the left panels; [e, f]), and for drift frequencies of 1.9 Hz (a, c, e) and 3.8 Hz (b, d, f). In each subfigure, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hues in the lower panels. Colored triangles denote mask hue angle. Data for conditions in which test and mask stimuli drifted either in same or opposite directions are represented by red and orange symbols, respectively. Purple symbols show data for static stimuli. Black curves represent log test thresholds in no-mask conditions. Error bars represent ±1 SEM. Upper-right panels show estimated parameters A, B, and σ of the corresponding fitted Gaussian function. In (a–d), circles represent parameter values estimated for each observer, and bars represent their average.
In contrast to the obvious hue masking selectivity for orientation and spatial frequency observed in the preceding experiments, we found that hue masking largely invariant to the relative direction of motion between test and mask. For some observers, detection thresholds for test gratings is elevated by similar amount regardless of whether noise masks drifted either in the same or opposite direction. For other observers, threshold elevations are less pronounced for opposite directions of motion, but a considerable amount of hue-selective threshold elevation is still found for the opposite direction. We ran one observer (SM) for mask hue angles of 0° and 90° and found little evidence of directionally selective hue masking (Figure 7e, f). 
Variations in observer performance may be attributable in part to individual differences in spectral sensitivity or to the point of isoluminance—two factors that were not considered in the present experiments. We also note that, for most observers, threshold elevation curves are nearly identical between drifting stimuli (red symbols) and static stimuli (purple symbols, replot of the data in Experiment 1). An analysis of the fitted curves (bar plots) revealed that the magnitude of hue-selective threshold elevation (A in Equation 1) decreased somewhat in the opposite-direction condition as compared to the same-direction condition, but this is not always true across all conditions or observers. The magnitude of the decrease is visibly small compared to that for orientation selectivity (Experiment 2) and spatial-frequency selectivity (Experiment 3) except for data of SM at 90° and 3.8 Hz (left panel in Figure 7f). Despite some variability in the data, it appears that hue-selective masking is poorly selective for, or invariant with, the relative direction of motion between test and mask. Hue-independent threshold elevation (B in Equation 1) does not change, or increases slightly, in the opponent-direction condition, thereby indicating that the hue-nonselective component exhibits little or no selectivity for the relative direction of motion between test and mask. 
Discussion
The present study carried out psychophysical masking experiments to investigate the orientation, spatial frequency, and motion-direction selectivity of hue-selective mechanisms. In agreement with previous masking studies (Hansen & Gegenfurtner, 2006, 2013; Lindsey & Brown, 2004), we showed that detection thresholds for test gratings with noncardinal (i.e., intermediate) hue angles were elevated only by masks with hue angles similar to those of the test. In a series of subsequent experiments, we showed that hue-selective threshold elevations decreased if test and mask were widely separated in either orientation or spatial frequency. Interestingly, however, threshold elevations did not decrease significantly across conditions in which a dynamic test drifted in the same or opposite direction as the mask, except for data of SM at 90° and 3.8 Hz (left panel in Figure 7f). Results suggest that, at least near threshold level, hue-selective mechanisms in the human visual system are selective for orientation and spatial frequency but exhibit little selectivity for motion direction. As we argue below, hue-selective mechanisms may even be insensitive to motion altogether. 
Previous studies on chromatic adaptation and chromatic masking have demonstrated orientation and spatial frequency selectivity using test stimuli limited to the isoluminant red-green axis (Bradley et al., 1988; K. K. De Valois & Switkes, 1983, Losada & Mullen, 1994; Medina & Mullen, 2009). These findings have been considered as indicating the selectivity of early color-opponent mechanisms to these pattern attributes. The present study now demonstrated a similar pattern selectivity for stimuli modulated along intermediate hue angles between the cardinal axes, thereby suggesting pattern selectivity in hue-selective mechanisms. Physiological evidence suggests that color-opponent neurons in lateral geniculate nucleus (LGN) are primarily insensitive to orientation (R. L. De Valois & De Valois, 1988; Ferster, Chung, & Wheat, 1996), whereas color sensitive neurons in V1 interblob are narrowly tuned to orientation and spatial frequency (Johnson et al., 2001; Shapley & Hawken, 2002). Therefore, given that behavioral thresholds are a product of multiple stages of visual processing, it is possible that pattern selectivity obtained in the previous masking and adaptation studies using red-green stimuli (Bradley et al., 1988; K. K. De Valois & Switkes, 1983; Losada & Mullen, 1994; Medina & Mullen, 2009) also reflect the properties of cortical hue-selective mechanisms rather than that of low-level color-opponent mechanisms. 
We found that hue-selective mechanisms, while exhibiting clear selectivity for orientation and spatial frequency, showed little evidence of directional selectivity with dynamic stimuli. In line with the classical notion that early motion detectors hardly respond to isoluminant stimuli (e.g., Livingstone & Hubel, 1987), our data indicate that the detection of chromatic test gratings was mainly determined by visual sensors insensitive to motion. The idea that motion detectors are silenced under isoluminant conditions has been challenged for some time by findings that smooth motion can be perceived with isoluminant stimuli (Z. L. Lu, Lesmes, & Sperling, 1999; Mullen & Boulton, 1992) and that cross-directional integration of motion signals depends on hue similarity between motion components (Cropper, Mullen, & Badcock, 1996; Krauskopf & Farell, 1990). Alternatively, it has also been claimed that suprathreshold motion perception with chromatic stimuli (Metha & Mullen, 1998) and transparent motion (McOwan & Johnston, 1996; Snowden & Verstraten, 1999; Stoner & Albright, 1996) is essentially mediated by high-level motion mechanisms involving attentional tracking (Burr & Thompson, 2011; Z. L. Lu et al., 1999; Verstraten, Cavanagh, & Labianca, 2000). The limited motion selectivity observed for our near-threshold stimuli appears consistent with the notion that linear motion detectors are largely insensitive to isoluminant chromatic stimuli. 
Our masking data can be primarily explained by a simple bank of color-sensitive visual sensors with Gabor-like receptive fields that detect image components differing along three dimensions—orientation, spatial frequency, and hue. The model predicts that detection of test grating is impaired when mask also activates the sensors responding to test grating. However, this simple model cannot explain some of our findings that revealed small but significant nonselective components to hue and orientation masking. Such nonselective components are inconsistent with the relatively narrow bandwidths reported for orientation (20°–30°; R. L. De Valois & De Valois, 1988) and hue angle (10°–30°; Hansen & Gegenfurtner, 2006). Additional assumptions must be made in order to incorporate these nonselective components of masking into our sensor model. One explanation for our results is that, in addition to Gabor-like units, other types of units not selective for orientation or hue are involved in grating detection. Another simple explanation is that nonselective masking is a byproduct of contrast-gain control, as it is widely suggested that responses of orientation-selective neurons in V1 are normalized, or divided, by the responses of other neurons tuned to various orientations (Carandini & Heeger, 1994; Heeger, 1992). Such a computation is known to be suitable for the efficient coding of orientation signals in natural images (Schwartz & Simoncelli, 2001). Moreover, contrast gain control models have been applied to a large and diverse body of masking data that also involves nonselective components (Foley, 1994; Foley & Boynton, 1993; Foley & Chen, 1997). Similarly, our masking data could be at least qualitatively explained by assuming that the response of each channel is inhibited by the response of other channels tuned to various combinations of orientation and hue. 
However, one should also note that the present masking data do not solely demonstrate the existence of Gabor-like channels that are jointly selective for both hue and spatial pattern attributes. Regarding the data of Experiment 2, for example, the masking component selective for both hue and orientation could result from the combined product of two masking components selective for either hue or orientation. According to classical functional segregation between color and form processing in visual cortex (e.g., Livingstone & Hubel, 1987), it is possible that detection threshold for our chromatic stimuli were essentially determined via a certain form of signal summation between channels sensitive to color only (e.g., V1 blobs) and channels sensitive to pattern only (e.g., V1 interblobs). Signal summation may take place at later processing stages including that of perceptual decision making. 
In some instances, our data also showed that detection thresholds decreased rather than increased if test and mask differed widely in orientation or spatial frequency. This threshold reduction, which was illustrated by negative values for the nonselective component (B in Equation 1) of our descriptive Gaussian model, implies that masks facilitated the detection of the test compared to condition in which the mask was altogether absent. This effect may be indicative of disinhibitory interactions between channels that have been observed in some adaptation studies (K. K. De Valois, 1977). It is also possible that the disinhibitory effect is simply an artefact resulting from the order in which we presented various conditions. We first measured detection threshold for test gratings in the no-mask condition before proceeding with masked conditions in subsequent sessions, and a reduction in absolute thresholds between the mask and no-mask conditions is subject to factors such as practice. It would be worthwhile for future investigations to examine whether instances of mask facilitation observed in the current study would hold in a paradigm in which mask and no-mask conditions are more carefully counterbalanced. 
Acknowledgments
This study was supported by KAKENHI Grant Numbers 15H05916 and 15H03461 to IM. A portion of the study was presented at Vision Society of Japan 2016 winter meeting. 
Commercial relationships: none. 
Corresponding author: Daisuke Kondo. 
Email: dkondo.cbs@gmail.com. 
Address: Department of Integrated Sciences, College of Arts and Sciences, The University of Tokyo, Tokyo, Japan. 
References
Allard R., Faubert J. (2008). The noisy-bit method for digital displays: Converting a 256 luminance resolution into a continuous resolution. Behavior Research Methods, 40 (3), 735–743.
Bradley A., Switkes E., De Valois K. (1988). Orientation and spatial frequency selectivity of adaptation to color and luminance gratings. Vision Research, 28 (7), 841–856.
Burr D., Thompson P. (2011). Motion psychophysics: 1985–2010. Vision Research, 51 (13), 1431–1456.
Carandini M., Heeger D. J. (1994). Summation and division by neurons in primate visual cortex. Science, 264 (5163), 1333–1336.
Cropper S. J., Mullen K. T., Badcock D. R. (1996). Motion coherence across different chromatic axes. Vision Research, 36 (16), 2475–2488.
De Valois K. K. (1977). Spatial frequency adaptation can enhance contrast sensitivity. Vision Research, 17 (9), 1057–1065.
De Valois K. K., Switkes E. (1983). Simultaneous masking interactions between chromatic and luminance gratings. Journal of the Optical Society of America A, 73 (1), 11–18.
De Valois R. L., De Valois K. K. (1988). Spatial vision (No. 14). New York: Oxford University Press.
Derrington A. M., Krauskopf J., Lennie P. (1984). Chromatic mechanisms in lateral geniculate nucleus of macaque. Journal of Physiology, 357 (1), 241–265.
Eskew R. T. (2009). Higher order color mechanisms: A critical review. Vision Research, 49 (22), 2686–2704.
Ferster D., Chung S., Wheat H. (1996). Orientation selectivity of thalamic input to simple cells of cat visual cortex. Nature, 380 (6571), 249–252.
Foley J. M. (1994). Human luminance pattern-vision mechanisms: masking experiments require a new model. Journal of the Optical Society of America A, 11 (6), 1710–1719.
Foley J. M., Boynton G. M. (1993). Forward pattern masking and adaptation: Effects of duration, interstimulus interval, contrast, and spatial and temporal frequency. Vision Research, 33 (7), 959–980.
Foley J. M., Chen C. C. (1997). Analysis of the effect of pattern adaptation on pattern pedestal effects: A two-process model. Vision Research, 37 (19), 2779–2788.
Gegenfurtner K. R., Kiper D. C. (2003). Color vision. Annual Review of Neuroscience, 26 (1), 181–206.
Hansen T., Gegenfurtner K. R. (2006). Higher level chromatic mechanisms for image segmentation. Journal of Vision, 6 (3): 5, 239–259, doi:10.1167/6.3.5. [PubMed] [Article]
Hansen T., Gegenfurtner K. R. (2013). Higher order color mechanisms: Evidence from noise-masking experiments in cone contrast space. Journal of Vision, 13 (1): 26, 1–21, doi:10.1167/13.1.26. [PubMed] [Article]
Heeger D. J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9, 181–197.
Hubel D. H., Wiesel T. N. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology, 160 (1), 106–154.
Johnson E. N., Hawken M. J., Shapley R. (2001). The spatial transformation of color in the primary visual cortex of the macaque monkey. Nature Neuroscience, 4 (4), 409–416.
Johnson E. N., Hawken M. J., Shapley R. (2008). The orientation selectivity of color-responsive neurons in macaque V1. Journal of Neuroscience, 28 (32), 8096–8106.
Kaiser P. K., Boynton R. M. (1996). Human color vision. Washington, DC: Optical Society of America.
Krauskopf J., Farell B. (1990). Influence of colour on the perception of coherent motion. Nature, 348 (6299), 328–331.
Krauskopf J., Williams D. R., Heeley D. W. (1982). Cardinal directions of color space. Vision Research, 22 (9), 1123–1131.
Krauskopf J., Williams D. R., Mandler M. B., Brown A. M. (1986). Higher order color mechanisms. Vision Research, 26 (1), 23–32.
Kuriki I., Sun P., Ueno K., Tanaka K., Cheng K. (2015). Hue selectivity in human visual cortex revealed by functional magnetic resonance imaging. Cerebral Cortex, 25 (12), 4869–4884.
Lindsey D. T., Brown A. M. (2004). Masking of grating detection in the isoluminant plane of DKL color space. Visual Neuroscience, 21 (3), 269–273.
Livingstone M. S., Hubel D. H. (1984). Anatomy and physiology of a color system in the primate visual cortex. Journal of Neuroscience, 4 (1), 309–356.
Livingstone M. S., Hubel D. H. (1987). Psychophysical evidence for separate channels for the perception of form, color, movement and depth. Journal of Neuroscience, 7 (11), 3416–3468.
Losada M. A., Mullen K. T. (1994). The spatial tuning of chromatic mechanisms identified by simultaneous masking. Vision Research, 34 (3), 331–341.
Lu H. D., Roe A. W. (2008). Functional organization of color domains in V1 and V2 of macaque monkey revealed by optical imaging. Cerebral Cortex, 18 (3), 516–533.
Lu Z. L., Lesmes L. A., Sperling G. (1999). The mechanisms of isoluminant chromatic motion perception. Proceedings of the National Academy of Sciences, 96 (14), 8289–8294.
McOwan P. W., Johnston A. (1996). Motion transparency arises from perceptual grouping: evidence from luminance and contrast modulation motion displays. Current Biology, 6 (10), 1343–1346.
Medina J. M., Mullen K. T. (2009). Cross-orientation masking in human color vision. Journal of Vision, 9 (3): 20, 1–16, doi:10.1167/9.3.20. [PubMed] [Article]
Metha A. B., Mullen K. T. (1998). Failure of direction discrimination at detection threshold for both fast and slow chromatic motion. Journal of the Optical Society of America A, 15 (12), 2945–2950.
Mullen K. T., Boulton J. C. (1992). Interactions between colour and luminance contrast in the perception of motion. Ophthalmic and Physiological Optics, 12 (2), 201–205.
Sankeralli M. J., Mullen K. T. (1997). Postreceptoral chromatic detection mechanisms revealed by noise masking in three-dimensional cone contrast space. Journal of the Optical Society of America A, 14 (10), 2633–2646.
Schwartz O., Simoncelli E. P. (2001). Natural signal statistics and sensory gain control. Nature Neuroscience, 4 (8), 819–825.
Shapley R., Hawken M. J. (2002). Neural mechanisms for color perception in the primary visual cortex. Current Opinion in Neurobiology, 12 (4), 426–432.
Shapley R., Hawken M. J. (2011). Color in the cortex: Single- and double-opponent cells. Vision Research, 51 (7), 701–717.
Snowden R. J., Verstraten F. A. (1999). Motion transparency: Making models of motion perception transparent. Trends in Cognitive Sciences, 3 (10), 369–377.
Stoner G. R., Albright T. D. (1996). The interpretation of visual motion: Evidence for surface segmentation mechanisms. Vision Research, 36 (9), 1291–1310.
Stromeyer C. F., Thabet R., Chaparro A., Kronauer R. E. (1999). Spatial masking does not reveal mechanisms selective for combined luminance and red-green color. Vision Research, 39 (12), 2099–2112.
Verstraten F. A., Cavanagh P., Labianca A. T. (2000). Limits of attentive tracking reveal temporal properties of attention. Vision Research, 40 (26), 3651–3664.
Webster M. A., Mollon J. D. (1991). Changes in colour appearance following post-receptoral adaptation. Nature, 349 (6306), 235–238.
Webster M. A., Mollon J. D. (1994). The influence of contrast adaptation on color appearance. Vision Research, 34 (15), 1993–2020.
Yoshioka T., Dow B. M. (1996). Color, orientation and cytochrome oxidase reactivity in areas V1, V2 and V4 of macaque monkey visual cortex. Behavioural Brain Research, 76 (1), 71–88.
Figure 1
 
Stimuli used in Experiment 1. The test grating is shown in the upper-left quadrant. The hue angle of the test grating is orthogonal to the noise mask in the left panel, and is the same as the noise mask in the right panel. Contrast is exaggerated for display purposes.
Figure 1
 
Stimuli used in Experiment 1. The test grating is shown in the upper-left quadrant. The hue angle of the test grating is orthogonal to the noise mask in the left panel, and is the same as the noise mask in the right panel. Contrast is exaggerated for display purposes.
Figure 2
 
(a) Log detection threshold for test grating plotted in the DKL space. Red, orange, blue, and purple symbols show thresholds in the with-mask condition for mask hues of 0°, 45°, 90°, and 135°, respectively. Black symbols show log test thresholds in no-mask conditions. (b) Mask log-threshold elevation plotted as a function of test hue angle. Colored triangles denote mask hue angle. Smooth curves are fitted pseudocyclic Gaussian functions. Error bars represent ±1 SEM. Each panel represents one observer. (c) Estimated parameters A, B, and σ of the fitted functions. Circles represent the parameter value (mean across observers) estimated for each mask hue angle, and bars represent their average. (d) Test grating log detection thresholds for observers who participated only in a subset of mask angle conditions. Formats are the same as (a) and (b).
Figure 2
 
(a) Log detection threshold for test grating plotted in the DKL space. Red, orange, blue, and purple symbols show thresholds in the with-mask condition for mask hues of 0°, 45°, 90°, and 135°, respectively. Black symbols show log test thresholds in no-mask conditions. (b) Mask log-threshold elevation plotted as a function of test hue angle. Colored triangles denote mask hue angle. Smooth curves are fitted pseudocyclic Gaussian functions. Error bars represent ±1 SEM. Each panel represents one observer. (c) Estimated parameters A, B, and σ of the fitted functions. Circles represent the parameter value (mean across observers) estimated for each mask hue angle, and bars represent their average. (d) Test grating log detection thresholds for observers who participated only in a subset of mask angle conditions. Formats are the same as (a) and (b).
Figure 3
 
Examples of stimuli used in Experiment 2. Test grating remains vertical, and mask noise was (a) vertical (parallel), or (b) horizontal (orthogonal). Contrast is exaggerated for display purposes.
Figure 3
 
Examples of stimuli used in Experiment 2. Test grating remains vertical, and mask noise was (a) vertical (parallel), or (b) horizontal (orthogonal). Contrast is exaggerated for display purposes.
Figure 4
 
Orientation specificity of hue masking. Masking data are shown for mask hue angles of 45° (a), 135° (b), and cardinal axes (0° in the right panels and 90° in the left panels; [c]). In each subfigure, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hue angles in the lower panels. Colored triangles denote mask hue angle. Purple and orange circles represent the results for the parallel and orthogonal masks, respectively. Black curves represent log test threshold in no-mask conditions. Error bars represent ±1 SEM. Upper-right panels show the estimated parameters A, B, and σ of fitted Gaussian function. In (a) and (b), circles represent parameter values estimated for each observer, and bars represent their average.
Figure 4
 
Orientation specificity of hue masking. Masking data are shown for mask hue angles of 45° (a), 135° (b), and cardinal axes (0° in the right panels and 90° in the left panels; [c]). In each subfigure, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hue angles in the lower panels. Colored triangles denote mask hue angle. Purple and orange circles represent the results for the parallel and orthogonal masks, respectively. Black curves represent log test threshold in no-mask conditions. Error bars represent ±1 SEM. Upper-right panels show the estimated parameters A, B, and σ of fitted Gaussian function. In (a) and (b), circles represent parameter values estimated for each observer, and bars represent their average.
Figure 5
 
Examples of stimuli used in Experiment 3. The spatial frequency of the test grating was 0.96 c/deg, and that of the mask noise was (a) 0.96 c/deg (same as test), (b) 1.9 c/deg (one octave higher than test), or (c) 3.8 c/deg (two octaves higher than test). Contrast is exaggerated for display purposes.
Figure 5
 
Examples of stimuli used in Experiment 3. The spatial frequency of the test grating was 0.96 c/deg, and that of the mask noise was (a) 0.96 c/deg (same as test), (b) 1.9 c/deg (one octave higher than test), or (c) 3.8 c/deg (two octaves higher than test). Contrast is exaggerated for display purposes.
Figure 6
 
Spatial-frequency specificity of hue masking. Masking data are shown for mask hue angles of 45° (a) and 135° (b). For each mask hue angle, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hue angles in the lower panels. Colored triangles denote mask hue angle. Purple, orange, and red circles represent data in conditions where mask spatial frequency was the same as test grating (0.96 c/deg), or higher than the test by one octave (1.9 c/deg) or two octaves (3.8 c/deg), respectively. Black curves represent log test threshold in no-mask conditions. Error bars represent ±1 SEM. The upper-right panel shows the estimated parameters A, B, and σ of the fitted Gaussian function. Circles represent parameter values estimated for each observer, and bars represent their average.
Figure 6
 
Spatial-frequency specificity of hue masking. Masking data are shown for mask hue angles of 45° (a) and 135° (b). For each mask hue angle, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hue angles in the lower panels. Colored triangles denote mask hue angle. Purple, orange, and red circles represent data in conditions where mask spatial frequency was the same as test grating (0.96 c/deg), or higher than the test by one octave (1.9 c/deg) or two octaves (3.8 c/deg), respectively. Black curves represent log test threshold in no-mask conditions. Error bars represent ±1 SEM. The upper-right panel shows the estimated parameters A, B, and σ of the fitted Gaussian function. Circles represent parameter values estimated for each observer, and bars represent their average.
Figure 7
 
Direction specificity of hue masking. Masking data are shown for mask hues of 45° (a, b), 135° (c, d), and cardinal axes (0° in the right panels and 90 deg in the left panels; [e, f]), and for drift frequencies of 1.9 Hz (a, c, e) and 3.8 Hz (b, d, f). In each subfigure, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hues in the lower panels. Colored triangles denote mask hue angle. Data for conditions in which test and mask stimuli drifted either in same or opposite directions are represented by red and orange symbols, respectively. Purple symbols show data for static stimuli. Black curves represent log test thresholds in no-mask conditions. Error bars represent ±1 SEM. Upper-right panels show estimated parameters A, B, and σ of the corresponding fitted Gaussian function. In (a–d), circles represent parameter values estimated for each observer, and bars represent their average.
Figure 7
 
Direction specificity of hue masking. Masking data are shown for mask hues of 45° (a, b), 135° (c, d), and cardinal axes (0° in the right panels and 90 deg in the left panels; [e, f]), and for drift frequencies of 1.9 Hz (a, c, e) and 3.8 Hz (b, d, f). In each subfigure, log test thresholds are plotted in the upper-left panels, and log threshold elevations are plotted as a function of test hues in the lower panels. Colored triangles denote mask hue angle. Data for conditions in which test and mask stimuli drifted either in same or opposite directions are represented by red and orange symbols, respectively. Purple symbols show data for static stimuli. Black curves represent log test thresholds in no-mask conditions. Error bars represent ±1 SEM. Upper-right panels show estimated parameters A, B, and σ of the corresponding fitted Gaussian function. In (a–d), circles represent parameter values estimated for each observer, and bars represent their average.
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×