Recent behavioral and electrophysiological evidence suggests that despite our seamless visual experience, incoming visual information is periodically enhanced for analysis in the visual system (VanRullen, 2016a; VanRullen, 2016b; Zoefel and VanRullen, 2017). This periodic sampling mechanism is proposed to result from the allocation of visual attention (Busch and VanRullen, 2010; Dugué et al., 2016; Dugué and VanRullen, 2017a; VanRullen et al., 2007; Zoefel and VanRullen, 2017), wherein alternating windows of high- and low-attentional resources operate to parcel incoming visual information, similar to the sequential frames that capture film within a video camera (Chakravarthi and Vanrullen, 2012; Vanrullen and Dubois, 2011). Whether stimuli are presented at the appropriate phase (Busch et al., 2009; Mathewson et al., 2009; VanRullen et al., 2007) or location (Dugué et al., 2015; Dugué et al., 2016; Dugué and Vanrullen, 2014; Dugué and VanRullen, 2017a; Dugué et al., 2017b; Huang et al., 2015; Landau and Fries, 2012; Song et al., 2014) of this sampling mechanism has been shown to modulate the accurate detection of a visual stimulus, in stark contrast to our experience of an uninterrupted visual environment.

To date, primary neural evidence for the rhythmic gating of visual processing stems from the dependence of target detection on the pre-target phase of neural oscillations at approximately 7–8 Hz (Busch et al., 2009; Busch and VanRullen, 2010). These spontaneous fluctuations in detection may result from the allocation of visual attention toward a single location (Busch and VanRullen, 2010; Dugué et al., 2015; Spaak et al., 2014; VanRullen, 2016b; Zoefel and VanRullen, 2017), and support the assumption that neural excitability cycles gate and filter incoming information for further processing (Schroeder and Lakatos, 2009; VanRullen, 2013; Zoefel and VanRullen, 2017).

This periodic gating of visual perception is also prominent behaviorally in the time-course of detection accuracy. Spectral analyses applied to high temporal resolution behavioral measures reveal 7–8 Hz modulations in performance following cues to reorient attention (Fiebelkorn et al., 2013), which slow proportionately when attention is divided between two or more locations (e.g. Chen et al., 2017; Holcombe and Chen, 2013; Huang et al., 2015; Landau and Fries, 2012; Landau et al., 2015; VanRullen, 2013). For example, Landau and Fries, 2012 observed that following a cue to reorient attention to either the left or right visual hemifield, target detection oscillated at a 4 Hz counterphase rhythm depending on whether cues were congruent or incongruent with the target location. Critically, this counterphase sampling of visual information persisted at ~4 Hz when attention was directed to two locations on a single object (Fiebelkorn et al., 2013), and when cues to reorient attention were incongruent with target location – requiring a subsequent shift in the allocation of attention to a second location (Huang et al., 2015). These successive fluctuations in target detection and counterphase sampling between locations have led to the suggestion that an intrinsic ~7–8 Hz attentional rhythm can be allocated over space and time in a sequential manner (Dugué et al., 2016; Dugué and VanRullen, 2017a; Fiebelkorn et al., 2013; Holcombe and Chen, 2013; Landau and Fries, 2012; VanRullen, 2013; Zoefel and VanRullen, 2017).

Here, we tested if rhythmic attentional sampling is at play during binocular rivalry. During binocular rivalry, incompatible images are presented to each eye which results in stochastic perceptual alternations, with one image visible at a time while the other is suppressed (Alais, 2012; Alais and Blake, 2005; Maier et al., 2012). In an experiment designed to induce or delay these transitions using auditory and tactile cues, we found that changes in consciousness were occurring rhythmically after the reorientation of attention. These fluctuations occurred depending on whether the crossmodal cue directed attention toward either the dominant or suppressed visual image, resulting in ~8 Hz and ~3.5 Hz oscillations, respectively. Critically, these rhythms were observed in both behavior and the electroencephalogram (EEG), and were absent when cues were not task-relevant. This approximate halving of frequency suggests that when non-visual input is inconsistent with the ongoing visual percept, attentional sampling can flexibly orient away from a consciously perceived image, seemingly ‘searching for’ alternative sensory information to resolve the conflict.

Our findings provide novel evidence that attentional sampling exists during binocular rivalry, demonstrated in both behavior and the electroencephalogram (EEG). Behaviorally, we replicated previous evidence that stimulus-driven cues can cause a switch to previously suppressed visual stimuli when mismatched with the current percept (to bring about congruence), as well as increase the maintenance of a dominant visual image if cues matched perception (Figure 2; Dieter et al., 2015; Lunghi and Alais, 2015; Lunghi et al., 2014). Critically, we found distinct attentional sampling frequencies evident in the time-course of first switches during these cues (Figure 3). When crossmodal cues were incongruent in temporal frequency with the dominant visual stimulus, switches in visual consciousness occurred earlier, and within a distinct ~3.5 Hz rhythm. This 3.5 Hz rhythm is consistent with previous reports of divided attentional sampling between two locations (Fiebelkorn et al., 2013; Landau and Fries, 2012; Landau et al., 2015). However, when crossmodal cues were matched in temporal frequency to the dominant visual stimulus, changes in visual consciousness demonstrated an ~8 Hz rhythm, consistent with periodicities in behavioral measures observed when attending to a single visual location (Fiebelkorn et al., 2013), and suggestions that a cortical 7–8 Hz attentional rhythm may gate visual processing (Busch and VanRullen, 2010; Dugué and VanRullen, 2017a; Fries, 2015). In the EEG (Figures 4 and 5), distinct correlates of these divided and focused attentional sampling frequencies emerged over right fronto-temporal and right parieto-occipital sites, respectively, with ITPC strength at these frequencies correlating with the behaviorally reported change in consciousness across subjects.

Traditionally, top-down, voluntary attention has been thought to have limited control over perceptual dynamics during binocular rivalry; attention may alter dominance durations, but cannot halt the process of perceptual reversals entirely (Chong and Blake, 2006; Chong et al., 2005; Chopin and Mamassian, 2010; Dieter et al., 2016b; Dieter et al., 2015; Dieter et al., 2016b; Mitchell et al., 2004; Paffen and Alais, 2011; for bottom-up control, including crossmodal stimulation, see Conrad et al., 2010; Deroy et al., 2014; Guzman-Martinez et al., 2012; Kang and Blake, 2005; Lunghi and Alais, 2013; Lunghi et al., 2010; Lunghi et al., 2014; van Ee et al., 2009). Our results clearly show additional dependence on the top-down deployment of attention, as without explicit instruction to attend to crossmodal signals, no facilitatory crossmodal effects emerged (see also Jack and Hacker, 2014; Talsma et al., 2010; van Ee et al., 2009). This interaction between low-level stimulus features (temporal frequency) and the allocation of attention indicates the facilitative role of both crossmodal stimuli (Deroy et al., 2014; Deroy et al., 2016) and attention for perceptual transitions during binocular rivalry (Dieter et al., 2016a; Dieter et al., 2015; Dieter and Tadin, 2011; Paffen and Alais, 2011; Zhang et al., 2011b). Our results are consistent with previous research, which has shown that exogenous feature-based cues can bias rivalry dynamics (Dieter et al., 2015; van Ee et al., 2009) and extend these reports by revealing an oscillatory basis to these changes in visual perception.

Our demonstration of these oscillatory changes in visual consciousness, which have been evoked by attended crossmodal cues, are also relevant to current computational models of binocular rivalry (e.g. Laing and Chow, 2002; Li et al., 2017; Wilson, 2003). No current model has accounted for the interaction we observe between attentional allocation and crossmodal stimuli, nor attention as an oscillatory process. Most recently, Li et al., 2017 have described a model for binocular rivalry incorporating attention and mutual inhibition. In their model, attentional modulation is dealt to the sensory representation that has the stronger sensory responses, by providing feedback to monocular-excitatory drives that otherwise increase monotonically with stimulus contrast. Building on our findings, future models could incorporate an oscillatory increase in excitatory drive as a result of periodic, rather than sustained attentional modulation (Fiebelkorn et al., 2018; Helfrich et al., 2018; VanRullen, 2018).

Previous behavioral investigations of attentional sampling have relied upon a brief cue to reorient attention, before estimating the time-course of target detection by densely sampling subject responses over closely spaced target-presentation intervals. Our design is unique in that ‘target-detection’ here is operationalized as the first reported change in visual consciousness for a continuously presented stimulus, resolved at 16.7 ms (or 60 Hz) from 500 ms to 2000 ms following cue-onset.

Past researchers have demonstrated approximately 7–8 Hz fluctuations in perceptual performance following the allocation of visual attention (Dugué et al., 2015; Fiebelkorn et al., 2013; VanRullen, 2013; VanRullen et al., 2007; Zoefel and VanRullen, 2017), commensurate with suggestions that cortical oscillations at approximately 7–8 Hz gate the content of visual perception (Busch and VanRullen, 2010; Dugué and VanRullen, 2017a; Hanslmayr et al., 2013). In our binocular rivalry paradigm, we also observed changes in visual consciousness occurring within an 8 Hz rhythm, yet unique to when cues were congruent with the dominant visual percept at cue onset. By contrast, perceptual sampling has previously been observed at ~4 Hz when cues have encouraged dividing attention between two objects or locations (Dugué et al., 2016; Fiebelkorn et al., 2013; Huang et al., 2015; Landau and Fries, 2012; Landau et al., 2015; Song et al., 2014). As such, the ~3.5 Hz rhythm we observed when crossmodal cues mismatched with the conscious visual percept extends the evidence for divided attentional sampling to binocular rivalry.

From our data alone, we cannot infer whether during conventional binocular rivalry, attention samples at 8 or 4 Hz. We surmise that increased attention to stimulus competition may be required to observe the attentional sampling rhythms we report here. Indeed outside of attended cue periods we did not observe periodic behavioral responses (Figure 3—figure supplement 1), suggesting that attentional sampling is commensurate with sustained and goal-directed attention, instead of persisting throughout rivalry. We also note that the issue of trial-to-trial variability when reporting on perceptual changes cannot be completely avoided in binocular rivalry research, and is important to consider. Here, one might argue that variable timing in perceptual reports may blur any effects of temporal periodicity. However, our results clearly demonstrate that a change in perceptual state occurred for attended low-frequency cues that were unique in changing visual consciousness compared to visual-only baseline, at or near frequencies of attentional sampling that have been reported in previous literature. By comparison, the time-course of visual switches during visual-only periods did not exhibit periodic sampling, nor did the first-switches during non-attended, or high-frequency cues.

Distinct neural correlates of these attentional sampling rhythms were also found in the EEG. We found significantly greater 3.5 Hz ITPC strength for mismatched compared to matched cue types over right fronto-centro-temporal electrodes [FT8 and C6], suggesting this region may be a candidate neural correlate for divided periodic attentional sampling (Figure 4a). Accordingly, following both mismatched and matched cues, increased 3.5 Hz ITPC in this region also positively correlated with the likelihood of switching to the previously suppressed visual image across subjects (Figure 4c–d). Using visual-only stimulation, previous research has identified a pre-target ~4 Hz phase-dependency for peri-threshold perception when attention is divided across visual hemifields (Landau et al., 2015). While our right fronto-temporal region is different to those previously implicated in attentional-sampling (e.g. Landau et al., 2015), we note that in our paradigm, attention was not divided between visual hemifields, but between competing stimuli during binocular rivalry. Right fronto-temporal regions have previously been implicated in the reorientation of attention to unattended locations (Corbetta and Shulman, 2002; Downar et al., 2000; Proskovec et al., 2018), and most recently, Helfrich et al., 2018 have demonstrated that the phase of ~4 Hz dependent sampling in frontal and parietal areas determines visual perception. Taken together, our results support previous research indicating that a periodic attentional sampling mechanism modulates visual perception, here extending this finding into binocular rivalry when visual stimuli spatially overlap and compete for perceptual dominance.

We also found behavioral and neural correlates of focused attentional sampling during binocular rivalry when cues were consistent with the prevailing visual percept. Specifically, 8 Hz ITPC over parieto-occipital electrodes was negatively correlated with the likelihood of switching to the incongruent perceptual outcome (Figure 5d). Previously, phase-dependent peri-threshold perception has been reported for focused attention tasks in the visual domain (Busch et al., 2009; Busch and VanRullen, 2010; Hanslmayr et al., 2013; Mathewson et al., 2009), and has primarily implicated an approximately 7 Hz component located over fronto-central electrodes (Busch et al., 2009; Busch and VanRullen, 2010). Given the differences between paradigms, it is unsurprising that our identified region for focused attentional sampling does not coincide with those reported in previous research regarding phase-dependent perception. Particularly as our right-lateralized response may be due to the left-lateralized tactile input used to investigate crossmodal attentional sampling (though ITPC was not different among the three crossmodal stimulation types, data not shown). While promising, future experiments that control for this lateralization are needed to characterize the contributions of fronto-centro-temporal and parieto-occipital regions to this effect, particularly as activity over each of these regions has previously been implicated in the reorienting of visuo-spatial attention (Corbetta and Shulman, 2002; Downar et al., 2000; Dugué et al., 2018; Proskovec et al., 2018), and for the integration of multisensory stimuli into a coherent percept (Beauchamp, 2005; Bushara et al., 2003; Calvert and Thesen, 2004; Driver and Noesselt, 2008; Zhang et al., 2011a). Increases in right parieto-occipital theta power (4–8 Hz) have also been shown when attending to visual stimuli in the presence of auditory distractors (van Driel et al., 2014), with the phase of right parieto-occipital alpha (8–10 Hz) or theta (6–7 Hz) oscillations determining the perceptual outcome of bistable stimuli (Ronconi et al., 2017). As such, the present modulation for 8 Hz parieto-occipital ITPC is consistent with the idea that right-parietal networks may preferentially represent temporal information in the visual modality (Battelli et al., 2007; Guggisberg et al., 2011).

Reporting binocular rivalry switches involves both a change in perception, and a decision to press the button. Accordingly, the attentional sampling in binocular rivalry we report here may reflect the fluctuation of perception or of decision criterion. Recent studies of behavioral oscillations that have employed signal detection theory have reported that sensitivity and response criterion both exhibit oscillations (at distinct frequencies) in the high theta/low alpha band, for both vision (Zhang et al., 2018) and audition (Ho et al., 2017). Consequently, whether our oscillations reflect perceptual or decision-level effects must be clarified (Ho et al., 2017; Iemi and Busch, 2018; Iemi et al., 2017; Limbach and Corballis, 2016). Our paradigm cannot resolve this as it is, although a future investigation using our paradigm combined with signal detection theory could do so.

Our analysis has so far revealed that when crossmodal cues mismatched with the dominant binocular rivalry stimulus, that rates of attentional sampling slowed to ~3.5 Hz – implicating the division of attention over multiple locations. However, our exogenous cues oriented attention toward the congruency of visual and crossmodal stimuli, prompting the question: between what was attentional sampling divided? One possibility is that attentional sampling during mismatched cues was divided between two sensory modalities, as the brain tried to resolve a conflict between concurrent auditory/tactile and visual information. Figure 6a provides a schematic of this multisensory interpretation. If the neural activity in our identified region is representative of divided sampling between modalities, it constitutes the first evidence that an attentional sampling mechanism can flexibly orient between temporally co-modulating crossmodal stimuli. Although the facilitative role of attention in multisensory integration remains controversial (Hartcher-O’Brien et al., 2016; Talsma et al., 2010), we see it as a viable possibility that this mechanism resolved perceptual ambiguity through a visual perceptual switch to the competing image, rendering the multisensory stimuli congruent.

Figure 6

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As only attended, low-frequency modulated cues enabled a change in visual consciousness, we must consider whether the lack of a high-frequency effect reflects an upper limit in temporal frequency on crossmodal interactions or attention. Such a limit on crossmodal interactions may explain why we observed low- but not high-frequency behavioral effects in the present task, and is supported by previous investigations regarding the binding of multisensory stimulus attributes (Fujisaki and Nishida, 2005; Fujisaki and Nishida, 2010; Lunghi et al., 2014; Vroomen and Keetels, 2010), and the limits of crossmodal temporal judgments (Fujisaki and Nishida, 2005; Fujisaki and Nishida, 2010; Holcombe, 2009; Vroomen and Keetels, 2010). For example, Fujisaki and Nishida, 2005 have shown that judgments of temporal synchrony between rhythmic sensory streams degrade above ~4 Hz. It is plausible that the ineffective crossmodal cueing that we found is related to the above-mentioned findings, reflecting a limit on crossmodal integration processes, rather than attention.

Having said that, one previous study using a similar design to ours was successful in eliciting a high-frequency crossmodal effect (15–20 Hz; Lunghi et al., 2014), and notably, in the absence of explicit attentional demands. However, these differences are not wholly unexpected, as to optimize the present task for EEG recordings we used larger (6.5° visual angle) luminance-modulated sinusoidal gratings to facilitate subsequent steady-state visually evoked potential analyses. While in comparison, Lunghi et al. succeeded in showing a high-frequency effect with rivalry stimuli that were contrast-modulated narrow-band random noise patterns (3.2° visual angle), and did so under conditions analogous to our non-attend conditions. This difference in the composition of visual stimuli is noteworthy, as stimulus size is known to strongly affect rivalry dynamics (Blake et al., 1992). To our knowledge, whether stimulus size impacts upon crossmodal effects during binocular rivalry is unknown. However, given the strength of our results for attended low-frequency flicker (Figure 2a), we note that the low- and high-frequency effects observed by Lunghi et al., 2014 are not generalizable to the larger and attended-rivalry stimuli employed here. Similarly, whether the type of stimuli (e.g., gratings vs random noise patterns) also impacts upon crossmodal effects during rivalry represents a fruitful endeavor for research, particularly given the novel possibility of distinguishing between crossmodal and attentional limits on attentional sampling.

An alternate possibility to crossmodal attentional sampling is that the 3.5 Hz rhythm in our paradigm reflects divided attentional sampling between dominant and suppressed visual images during binocular rivalry (Figure 6b). The frequency of divided attentional sampling that we observed is consistent with those obtained when visual attention has been divided between two objects or locations (Fiebelkorn et al., 2013; Landau and Fries, 2012). As our binocular rivalry stimuli necessarily occupied the same spatial location, attention in our paradigm was likely divided between either features or objects, instead of locations. Indeed, feature-based attention has already been shown to modulate neural processes when an attended target is suppressed during continuous flash suppression (Kanai et al., 2006). During binocular rivalry, perceptual dominance is also influenced by object-based attention (Mitchell et al., 2004), with unconscious selection mechanisms argued to mediate perceptual transitions (Lin and He, 2009). This second alternative is also indirectly supported by the temporal limits of binocular rivalry when conflicting visual stimuli are presented asynchronously, without temporal overlap between the two eyes (O'Shea and Blake, 1986; van Boxtel et al., 2008b; van Boxtel et al., 2007). The maximum stimulus onset asynchrony that can sustain this type of rivalry is approximately 350 ± 50 ms, beyond which alternating stimuli introduced to one eye are perceived immediately, without rivalry occurring (van Boxtel et al., 2008a). This limit is consistent with a 7–8 Hz attentional sampling rhythm distributed between the two conflicting stimuli (each sampled at ~3–4 Hz). When stimuli are presented rapidly enough they are temporally bound together and can engage in ongoing rivalry; when stimuli are presented slower than at 3–4 Hz, they are temporally individuated by attention, and rivalry ceases. A recent computational model that explicitly modeled time-varying attention could indeed reproduce this finding (Li et al., 2017), suggesting that attention is the process that temporally binds the successive stimulus presentations together. Based on our findings, we propose the persistence of percepts may also be modeled using an oscillatory, and feature-selective attentional mechanism (Li et al., 2015; Li et al., 2017).

The suggestion that attention can sample between conscious and nonconscious vision is also consistent with a view that the underlying neuronal processes for attention and consciousness are supported by distinct neural mechanisms (Bahrami et al., 2007; Watanabe et al., 2011); for review see Koch and Tsuchiya, 2007). We note that while attentional sampling of a suppressed image suggests that attention is not sufficient for consciousness (Dehaene et al., 2006; Koch and Tsuchiya, 2007; Lamme, 2003; van Boxtel et al., 2010a; van Boxtel et al., 2010b), this interpretation remains consistent with a view that attention may still be necessary for conscious perception (Chica and Bartolomeo, 2012; Cohen and Dennett, 2011; Merikle and Joordens, 1997; O'Regan and Noë, 2001; Posner, 1994; Posner, 2012).

Whether attributable to conscious-nonconscious, or visual-crossmodal attentional sampling, the present results also complement the ‘active-sensing’ hypothesis (Schroeder et al., 2010), whereby perceptual selection is determined by routine exploratory behaviors. According to the active-sensing hypothesis, attention is critical to search for task-relevant information from the environment (Schroeder et al., 2010), particularly via the rhythmic coordination of multisensory information (Schroeder et al., 2010; Thorne and Debener, 2014). Intriguingly, early contributions from multi-sensory (non-visual) information have been shown to modulate perception (Morillon et al., 2014; Schroeder et al., 2010; van Atteveldt et al., 2014). The rhythmic modulation of visual performance has also been demonstrated to follow the onset of both voluntary (Hogendoorn, 2016), and preparatory motor behaviors (Tomassini et al., 2017; Tomassini et al., 2015). Here, in further support of the active-sensing hypothesis, we have shown that task-relevant crossmodal information can change the rhythmic modulations of perceptual selection during competition for perceptual dominance.

In summary, here we have provided novel evidence in support of attentional sampling during binocular rivalry through the use of crossmodal cues matched to either a conscious or nonconscious visual stimulus. As the attention sampling hypothesis continues to garner traction from various psychophysical and neuronal paradigms (Fiebelkorn et al., 2018; Helfrich et al., 2018; VanRullen, 2016a; VanRullen, 2016b; VanRullen, 2018), future targeted experimentation can confirm whether attention can indeed sample across modalities (Figure 6a), as well as if attention can sample between conscious and nonconscious neural representations during binocular rivalry (Figure 6b). The interactions between crossmodal stimuli and conscious perception represent a fruitful avenue for experimentation (Faivre et al., 2017), here uncovering the previously unknown dependence of attention and consciousness on rhythmic neural dynamics of the human brain.

The raw data in this study are available via the Monash University Figshare repository (https://figshare.com/projects/Crossmodal_binocular_rivalry_attention_sampling_project/56252). Analysis code is available via GitHub (https://github.com/Davidson-MJ/BRproject-attentionsampling; copy archived at https://github.com/elifesciences-publications/BRproject-attentionsampling).

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Author details

1. Matthew J Davidson

   1. School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Australia
   2. Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   mjd070@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2088-040X

2. David Alais

   School of Psychology, The University of Sydney, Camperdown, Australia

   Contribution

   Conceptualization, Resources, Software, Supervision, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0411-940X

3. Jeroen JA van Boxtel

   1. School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Australia
   2. Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Australia
   3. School of Psychology, Faculty of Health, University of Canberra, Canberra, Australia

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing—review and editing

   Contributed equally with

   Naotsugu Tsuchiya

   For correspondence

   jeroen.van.boxtel@monash.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2643-0474

4. Naotsugu Tsuchiya

   1. School of Psychological Sciences, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Melbourne, Australia
   2. Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Melbourne, Australia

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing—review and editing

   Contributed equally with

   Jeroen JA van Boxtel

   For correspondence

   Naotsugu.Tsuchiya@monash.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4216-8701

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