We first conducted a short online survey with the double aim of: (a) obtaining statistics from a large sample from the population; and (b) identifying potential study participants who reported to be able to see two percepts simultaneously in an ambiguous image. In the survey, we explained that when seeing ambiguous figures, some people have a preference for seeing one or the other percept, and others have no preference at all. As a cover story, and to avoid biasing responses, we simply stated that we were interested in which “perceptual type” each person was. We did not mention in the survey that we would invite participants based on their responses. In the survey, participants saw six bistable images, and answered two questions for each image. The first question asked directly about the preferred percept. Possible answers were “I see the two figures alternating”; “I see the two figures mostly alternating”; “I see the two figures mostly simultaneously” and “I see the two figures simultaneously”. We also included the alternative “I see only one figure” for cases in which participants could not identify the two alternative interpretations. The second question asked about perceptual effort. Possible answers were: “It is easier for me to see the two figures alternating”; “It is mostly easier to see the two figures alternating”; “It is mostly easier to see the two figures simultaneously” and “It is easier for me to see the two figures simultaneously”. Again, we included the alternative “I see only one figure”.

We distributed a link to the screening survey through the mailing lists of Psychology students of two German universities. We received 282 responses (229 female, 53 male, mean ± SD: 23 ± 5 years) in this screening stage. The majority of women that responded to our survey mirrors the majority of women registered as Psychology students in the Universities where we distributed the link. We invited only those participants that scored at least 7 out of 12 points to participate in the experiment (this meant that they had reported that they saw two figures “simultaneously” or “mostly simultaneously” in more than half of the images presented). Seventeen healthy participants accepted to take part (11 female, age ± SD: 28.5 ± 4 years). All participants were right handed and had no recent history of psychiatric or neurological disease. This study was carried out in accordance with the recommendations of Ethics committee of the German Psychological Society (DGPs) with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki.

For the fMRI scanning, we selected 14 bistable images (consisting in artistic paintings and drawings) in which the alternating percepts corresponded to faces and landscapes (see Figure 1B for examples). None of these images had been presented in the online survey. Further, all participants confirmed in a debriefing after the scanning session that they had never seen the images before.

In the fMRI data analysis (see below), we compared BOLD signal between all three reported percepts, for each participant and irrespective of the image presented. Because the percept dominance was determined by each participant and not experimentally, we could not ensure an even distribution of all three perceptual durations for each image. Thus, and to prevent any systematic errors due to biases by the low-level properties of any particular image that could have dominated any specific percept, we matched the contrast and luminosity of all rivalry images with the SHINE toolbox (Willenbockel et al., 2010a,b). We presented all images using the Psychophysics Toolbox (Brainard, 1997) running in MATLAB 2012b (the Mathworks). We projected the stimuli onto a rear-projection screen mounted inside the scanner bore, at approximately 78 cm from the participants’ eyes. We monitored the manual responses continuously using a Covilex response box (Covilex, Magdeburg, Germany).

We acquired images on a 3 T Magnetom Trio MRI scanner system (Siemens Medical Systems, Erlangen, Germany) using a 12-channel radiofrequency head coil.

We collected structural images using a three-dimensional T1-weighted magnetization prepared gradient-echo sequence (MPRAGE) based on the ADNI protocol¹ (repetition time (TR) = 2500 ms; echo time (TE) = 4.77 ms; TI = 1100 ms, acquisition matrix = 256 × 256 × 176, flip angle = 7°; 1 × 1 × 1 mm voxel size). We asked participants to keep their eyes closed during the structural image collection.

We collected functional images using a T2*-weighted echo planar imaging (EPI) sequence sensitive to BOLD contrast (TR = 2000 ms, TE = 30 ms, image matrix = 64 × 64, FOV = 216 mm, flip angle = 80°, voxel size 3 × 3 × 3 mm, 36 axial slices, interleaved order, gap = 0.6 mm).

To localize FFA, OFA and PPA in each participant, we did two localizer scanning runs. In each run we presented eight series of 12 images. Each series of images contained either neutral faces (Grosbras and Paus, 2006) or landscapes. We fully randomized the order of both the images within each series, and the series within each run. We presented each image for 1.3 s with a fixation cross between them of 0.2 s. Participants did a 1-back task during the localizer run. Between runs we presented a baseline image of either a fixation cross or a phase-scrambled image for 15 s.

Participants completed two 13.5-min scanning runs of a multi-stable perception task, adapted from classical binocular rivalry tasks requiring continuous report (e.g., Carmel et al., 2010).

In total, we presented 14 different bistable images (see Supplementary Information for details, some examples are displayed in Figure 1C), separated by 30-s baseline period displaying either a fixation cross or a phase-scrambled bistable image. Participants viewed each image for a total of 90 s (Figure 1A). Above the images (on the top right and left corners of the screen), the words “FACE” and “LAND” appeared in gray. Participants used their right hand to hold down a key in the button box corresponding to either percept (face or landscape, respectively), and held down both keys if they saw the two percepts simultaneously. One or both of the words (“FACE” and/or “LAND”) were highlighted while participants held down the corresponding keys to provide feedback for their responses. We asked participants to avoid focusing on only one of the percepts, and instead to balance the total amount of time across all three percepts. Importantly, we also instructed participants to report as faithfully as possible the percept that they really perceived in that very moment. To increase the rivalrous aspect of the images, we split the 90-s presentation time into two 45-s periods. During the first 2 s of each of these periods, we presented two attentional cues. These cues could not be taken as emphasizing either the face or the landscape interpretation of the image. Instead, as the example in Figure 1B illustrates, a cue could be pointing to either be an eye or a mountain according to the specific percept. With this instruction, we aimed to prevent participants from simply focussing on different parts of an image (e.g., the sky for landscape viewing and the eye region for face viewing). We asked participants to focus on these regions, and try to interpret them as any one of three options: exclusively part of the face, exclusively part of the landscape, or as part of both simultaneously. Importantly, the attentional instruction cue was only displayed for 2 s and disappeared afterwards to avoid obstructing the images. Participants were asked to continue focussing on the cued feature of the image even after the attentional cue had disappeared.

We defined three conditions based on subjective report, namely face, landscape and simultaneous. Thus, the total duration of each trial was determined by each participant’s behavior.

We analyzed the fMRI data with the SPM8 package² running on Matlab version R2012b. We excluded the first four volumes of each EPI series from the analysis to allow the magnetization to approach a dynamic equilibrium. We applied slice-time correction and realignment to all EPI sets. A mean image for all EPI volumes was created, to which we spatially realigned individual volumes by means of rigid body transformations. We co-registered the structural image with the mean image of the EPI series. We normalized the structural T1 image to the Montreal Neurological Institute (MNI) template and applied the normalization parameters to the EPI images to ensure an anatomically informed normalization. Finally, we smoothed images with a kernel of 8 mm full-width at half maximum (FWHM).

We ran statistical analyses at the subject level using a general linear model (GLM). In the localizer runs, we modeled each stimulus presentation as a discrete event, with a fixed duration given by the duration of each image. In the multi-stable perception task, we modeled each self-reported percept event (each trial) as a discrete event, with a duration given by the total trial duration. The resulting vectors were convolved with a canonical hemodynamic response function (HRF) and its temporal derivatives to form the regressors in a design matrix. We used a high-pass filter of 128 s to remove low-frequency drifts in the time series data. We also included in the GLM realignment parameters in all 6 dimensions to account for variance associated with head motion. Statistical parameter estimates were computed separately for each voxel for all columns in the design matrix at the within-participant level.

We followed the Group-Constrained Subject-Specific (GSS) approach (Julian et al., 2012; Nieto-Castañón and Fedorenko, 2012) to define subject-specific regions of interest. We used the parcels for right and left FFA and PPA derived by Julian et al. (2012). We then obtained participant-specific ROIs by overlaying the parcels to each participant’s corresponding contrast map (i.e., faces > landscapes, for FFA and OFA, and landscapes > faces for PPA), thresholded at p < 0.001 (uncorrected). Finally, we used the Marsbar toolbox (version 0.43³) to extract BOLD signal estimates from the ROIs. We calculated the 10-s time course by subdividing this period into five distinct epochs of 2000 ms (1 TR) and used five finite impulse response regressors to estimate the BOLD signal change for each epoch independently.

Figure 2 shows the percentages of responses for each of the three image categories presented in the online questionnaire (figure-ground, reinterpretation and global vs. local, including two images each). Multiple sources of variability have been related to individual differences in the properties of bistable perception. Albeit in a different paradigm of gender perception in point-like walkers, Schouten et al. (2010) reported gender differences in biases in bistable perception. Because one of the aims of the online screening was to select and invite participants for the fMRI study, we examined responses from men and women separately to explore potential gender differences that would be relevant for fMRI participant recruitment. Across all included images, 39% of men and 41% of women replied that they saw, or that it was easier to see the two possible interpretations simultaneously, whereas 51% of men and 49% of women replied that they saw either percept sequentially. A χ² test revealed no significant gender differences in the proportion of participants that reported seeing the two interpretations simultaneously (χ(1)2 = 0.094, p = 0.759), so we did not consider participants’ gender when selectively inviting them for the fMRI study.

Taken at face value, these results show that an important part of the student population that we surveyed reported to be able to entertain two percepts simultaneously, especially in the case of those images that we included in the “Figure-ground” and in the “Global vs. Local” categories. This was less strongly so when the alternation between the two percepts required a complete reinterpretation of the image.

The results of this short online survey do not provide any details about the phenomenology of the simultaneous percept so, to better understand this alleged simultaneous perceptual state, we measured BOLD signal activity in a selected sample of participants while they viewed ambiguous images. In line with the survey results, the ambiguous images that we selected as stimuli depicted one stimulus (a face) when processed globally and a different stimulus (a landscape) when processed locally.

In the fMRI scanner, participants reported continuously which was the dominant percept (face, landscape, or simultaneous). We first considered the behavioral durations of each percept.

Figure 3A shows the summed duration of each percept, for each participant. We included all participants in the behavioral analyses, but excluded four participants (marked with a + symbol in Figure 3A) from the subsequent fMRI analyses because the summed duration of at least one of the percepts represented less than 10% of the summed experiment duration, which could lead to biased estimates (Wager et al., 2005). Figure 3B shows the distribution of durations for each percept, aggregated for all participants. The distribution of the simultaneous percept is very similar to the distribution of the face and landscape percepts, indicating that it is not just a short-lived transition state, but that, on the contrary, it is at least as stable as the other two percepts.

Because percept durations have often shown to be stable and trait-like (Kleinschmidt et al., 2012), we examined the mean duration of each percept: on average, participants indicated to have seen the simultaneous percept longer (M = 12.29 s, SD = 7.26 s) than only the landscape (M = 9.40 s, SD = 7.97 s) or only the face (M = 8.74 s, SD = 5.04 s) in the pictures. These differences were not statistically significant (one-way analysis of variance (ANOVA); (F_(1.27,20.31) = 1.38, p = 0.262, partial η² = 0.08, Greenhouse-Geisser corrected). We also examined the number of occurrences of each percept. Here, participants showed more occurrences of the simultaneous percept (M = 60.0, SD = 27.89) than only the landscape (M = 40.88, SD = 15.78) or the face percepts (M = 38.24, SD = 15.90). A one-way ANOVA revealed that the differences were statistically significant (F_(1.98,31.65) = 16.63, p < 0.001, partial η² = 0.51). Follow-up t-tests revealed that this difference was driven by the greater number of occurrences of the simultaneous percept (simultaneous vs. landscape: t₍₁₆₎ = 4.49, p < 0.001, d = 1.09; simultaneous vs. face: t₍₁₆₎ = 5.20, p < 0.001, d = 1.26), but not between the face and landscape percepts (t₍₁₆₎ = 0.68, p = 0.51, d = 0.16).

We then asked if simultaneous percepts are merely transitional. Concretely, we calculated for each participant the proportion of simultaneous percept flanked by two different percepts, relative to all simultaneous percepts flanked by any two percepts. We compared this to the proportion corresponding to chance (1/2). We found that the proportion was slightly but reliably above chance level (M = 0.611, SD = 0.134, one-sample t-test t₍₁₆₎ = 3.415, p = 0.004, d = 0.828). We note however that very short percept durations (i.e., 0.2 s) were relatively overrepresented in the data (Figure 3B). We therefore repeated the analysis, this time excluding trials under 0.2 s Here, we found no evidence to suggest that the proportion was significantly different from chance level (M = 0.467, SD = 0.226, one-sample t-test t₍₁₆₎ = −0.551, p = 0.589, d = −0.134). This suggests that while short simultaneous percepts were often transitional, this was not necessarily the case for the longer-lasting simultaneous percepts.

When examining the relationship between the percept durations with a Pearson correlation test, there was a clear correlation between the face and landscape conditions (r = 0.752, p < 0.001) but we found no evidence for a correlation between the simultaneous and face (r = −0.160, p = 0.540) or simultaneous and landscape conditions (r = −0.228, p = 0.379). Further, we did a Fisher r-to-z transformation to statistically test the differences in correlations. We found that the correlation between the mean face and landscape durations differed significantly from both the correlation between face and simultaneous conditions, (z = 3.01, p = 0.003) and from the correlation between the landscape and simultaneous conditions (z = 3.2, p = 0.001).

Finally, we quantified BOLD signal levels in FFA, OFA and PPA, areas known to show selectively stronger BOLD activity with attention to faces or landscapes. We extracted the BOLD signal in each condition relative to baseline in the three ROIs. Figure 4 shows the relative signal change estimates. (Note that, to avoid computing biased estimates, we discarded four participants—see Figure 3A. The following results are therefore based on 13 participants). We show in Figure 4A the time courses of the BOLD signal corresponding to each of the three reported percepts (face, landscape and simultaneous) for each of the three ROIs.

In a planned comparison and as a positive control, we first examined BOLD signal levels in PPA and FFA associated with the face and landscape conditions. We ran our statistical analyses on the mean percent signal change over a window of 4–6 s as a summary measure (Figure 4B). The PPA ROI showed the expected pattern. Namely, the landscape percept was associated with stronger BOLD signal levels than the face percept (t₍₁₂₎ = 3.148, p = 0.008, d = 0.872). This suggests that participants were able to accurately identify and report their subjective perceptual state. Turning to our main condition of interest, we examined the BOLD signal level for the simultaneous condition. We found that it was significantly lower than for landscape (t₍₁₂₎ = −2.511, p = 0.027, d = −0.696) though not different than the face condition (t₍₁₂₎ = 1.554, p = 0.146, d = 0.431).

FFA and OFA, instead, did not show the expected pattern. We expected the face percept to show the strongest levels of BOLD signal in these face-selective regions. Instead, we found no differences between face and landscape conditions (with t₍₁₂₎ = −1.884, p = 0.084, d = −0.523 for FFA and t₍₁₂₎ = −1.626, p = 0.130, d = −0.451 for OFA). When we compared BOLD signal for simultaneous and face percepts in these regions, we found no differences relative to the face condition (FFA: t₍₁₂₎ = −1.169, p = 0.265, d = −0.324; OFA: t₍₁₂₎ = −1.686, p = 0.118, d = −0.467); but significant differences between the simultaneous and landscape conditions (FFA: t₍₁₂₎ = −4.522, p < 0.001, d = −1.254; OFA: t₍₁₂₎ = −2.898, p = 0.013, d = −0.804).

We argue that the simultaneous state allegedly experienced by our participants is phenomenologically and physiological different from the mixed states or piecemeal perception typically observed in many cases of bistable perception (Knapen et al., 2011). These mixed states, consistently reported by experimental participants, are clearly transition states that are flanked by two “simple” percepts. We found no evidence that this was the case for the simultaneous percept. Also tellingly, mixed percepts are often described as a state of “mixed dominance” (Brascamp et al., 2015). In our view, this description underscores the notion of competition because while no single stimulus dominates the entire visual field, the two alternative stimuli share the space and are alternatively dominant in separate local regions of the visual field. In contrast, according to our participants’ reports—or, perhaps fairly, according to our best understanding of or participants’ reports—, dominance was not exclusive. Participants were allegedly able to interpret the same point in space within the visual field as belonging to both a face and a landscape. No participant spontaneously reported any piecemeal perception as it has been described in, for example, traveling waves of piecemeal perception in binocular rivalry paradigms. We thus take the strong view that the phenomenology that we describe here is different from that reported before in mixed states. Admittedly however, stronger evidence for this view could only come from specifically probing participants to describe the differences between the two ways of simultaneous perception.

The online survey that we first conducted revealed that there is natural variability within the normal population on the properties of bistable perception. This observation goes in line with the literature highlighting differences between individuals in the dynamics of bistability. Inter-individual differences have been related to molecular, structural and functional factors (for reviews see Kleinschmidt et al., 2012; and Scocchia et al., 2014). For instance, at the molecular level differences between mean perceptual duration (or switch rate) in auditory and visual rivalry have been associated with genetic differences in the dopamine and serotonin receptors (Kondo et al., 2012) and with differences in GABA concentration measured with MR spectroscopy (van Loon et al., 2013, but see Sandberg et al., 2016 for Bayesian analyses of the same effects, suggesting that the some of the correlations reported were overestimated or even false positives). Individual differences in brain structure have been found to correlate with multiple cognitive functions (Kanai and Rees, 2011), and bistable perception is no exception. For example, the speed of visual traveling waves has been associated with cortical surface area in visual areas V1 and V2 (Genç et al., 2015); and biases in bistable motion have been related with microstructure of callosal segments (namely, radial diffusivity, in this case presumably reflecting axon diameter) connecting motion-sensitive areas of the human MT/V5 complex (Genç et al., 2011). Further, structural characteristics of the superior parietal lobe (SPL) have been causally related to the rate of perceptual alternations in a rotating structure-from-motion stimulus (Kanai et al., 2010). Finally, the characteristics of bistable perception have also been described and manipulated at the functional level. Importantly, this level of analysis has revealed that perceptual switch rate and other characteristics cannot always be considered as a stable individual trait, as the relationships to brain structure might suggest. For example, tACS-induced gamma (but not alpha) activity increased the rate of perceptual switches in a structure-from-motion task (Cabral-Calderin et al., 2015), as did intoxicating levels of alcohol intake on the duration of piecemeal perception during a binocular rivalry task (Cao et al., 2016). Attentional effects can also have a profound impact on biases in binocular rivalry (Pearson et al., 2008; Dieter et al., 2016).

Given the multiple levels at which differences in brain structure and function can be translated into overt differences in the properties of bistable perception, it is possible that the ability to entertain two percepts simultaneously is related to measurable properties of brain structure. With this study we highlight the possibility that the perception of simultaneous states might serve as an additional parameter of interest in the understanding of bistable perception.

It has been argued that introspection cannot be trusted as a reliable source of information: subjective report can be incomplete and inaccurate (Nisbett and Wilson, 1977; Schwitzgebel, 2008), alter the object of introspection (Seli et al., 2013) and may add spurious monitoring mechanisms on top of the first order cognitive processes of interest (Guggisberg et al., 2011). Many of these points were compellingly illustrated in a recent experiment of binocular rivalry (Frässle et al., 2014). Here, we present the flip side of the argument. Despite the clear limitations of introspection as a method, ignoring the subjective experience of participants altogether, and favoring a strictly behaviorist approach might lead to overlooking certain fundamental aspects of cognitive functioning, as we illustrate with the results we present here. We started from participants’ subjective report, and explored the objective evidence that supported its veracity. We found that the behavioral and neurophysiological correlates of the reported subjective states gave first evidence to the notion that entertaining two percepts simultaneously can be a distinct state. Therefore, we highlight the importance of a balanced contribution of different experimental approaches to the literature (Callard and Fitzgerald, 2014).

We should point to several important limitations of these data. Perhaps the strongest limitation is that they do not suffice to distinguish a true simultaneous percept from a quick alternation between the two single percepts. In other words, it is in principle possible that what participants perceive to be the coexistence of two percepts was in fact a very rapid alternation between two single percepts. When asked this question explicitly, our participants consistently maintained that the term “simultaneous” was a more accurate description of their subjective experience. Conducting interviews with participants trained in describing their pristine experience (Hurlburt and Heavey, 2006), through which participants acquire tools to observe and describe their subjective states might be a way to further understand the phenomenology of the simultaneous perception states that we identified.

Our data are specific in two important ways that limit their generalizability. First, we tested only one kind of bistable stimuli where the two percepts resulted from either local or global processing. We selected these stimuli because they contained faces and landscapes as alternating percepts, both of which have known brain correlates. These stimuli would then allow us to identify objective correlates of the subjectively reported percepts, thus validating the subjective report. Importantly however, these stimuli are not representative of all kinds of bistable perception (as discussed further above, see Hasson et al., 2001). Our conclusions may also not hold true for other, also widely used stimuli where rivalry does strictly happen, as is perhaps the case for the Necker cube (Necker, 1832), the structure-from-motion illusion (Nawrot and Blake, 1989) or the kinetic depth effect (Wallach and O’Connell, 1953). Further studies could extend this approach to other forms of stability and ask the important question of generalizability of these results.

Second, because in this study we were specifically interested in studying the simultaneous percept, we investigated their behavioral and brain correlates only in participants that reported to experience it and did not include a “control group” of participants that did not experience the simultaneous percept. While this comparison would not have been informative in understanding the nature of simultaneous percept alone, it could have informed the interpretation of the fMRI results. As we described above, several studies have reported inter-individual differences in perceptual exclusivity. Hence, it is in principle possible that the putative automatic face processing and accompanying enhanced BOLD activation in the face-selective areas FFA and OFA was not a consequence of the stimulus material but an idiosyncrasy of this preselected subgroup. Finally, our sample of participants, explicitly selected for high levels of simultaneous perception, reported similar durations of the simultaneous and the alternative “simple” percepts. It is possible that a different sample of participants for whom entertaining two percepts simultaneously is possible but difficult would have revealed a qualitatively different pattern of the duration subjective reports. Thus, these results could be extended in interesting ways by including a comparison to a control group to explore, for example, whether there are any differences in brain structure that could account for this difference in the available perceptual states.