When a white annulus was flickered on/off on a black background (~2–30 Hz), we noticed that light grey blobs appeared and rotated around the annulus, first in one direction then the other (Figure 1A; see Videos 1 and 2). Unlike full field luminance flicker stimulation whose content changes as a function of flicker frequency (Allefeld et al., 2011; Mauro et al., 2015), the light grey blobs remained clearly observable across the range of oscillation frequencies tested. This reduces the visual feature dimensions and overcomes many of the prior limitations set by the multi-feature heterogeneous content in pathological, spontaneous and full field flicker induced hallucinations.

Video 1 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg An animated movie representation of one of our stimuli. Under the right viewing conditions, you may experience light grey blobs (that are not physically presented in the movie) appearing around the flickering annulus. This video contains flashing and alternating images, and therefore might not be suitable for readers with photosensitive epilepsy. https://doi.org/10.7554/eLife.17072.004

Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as Ogg A perceptual, retina-based representation approximating what some see in the otherwise empty flickering white annulus of Video 1. Note, this is only one interpretation, the individual hallucination experience may vary from individual to individual. This video contains flashing and alternating images, and therefore might not be suitable for readers with photosensitive epilepsy. https://doi.org/10.7554/eLife.17072.005

To measure the strength of this hallucination, we devised a technique allowing us to assess the effective contrast with a two alternative forced choice procedure. We presented an interior annulus housing physical sinusoidal luminance modulation simultaneously with the flickering annulus (Figure 1B). We presented this physical retina-sourced annulus at a range of different contrasts and participants reported whether it was higher or lower in contrast than any content in the flickering empty annulus. Across two experiments, data from 28 and 24 subjects were fit with cumulative Gaussian functions to give an estimate of the point of subjective contrast equivalence. This gave us a proxy of the effective contrast of the hallucinated blob structures across different flicker frequencies. Figure 1D shows the effective contrast of the hallucinations as a function of flicker frequency. Contrast estimates peaked around 11 Hz, then continued to decline slowly as a function of frequency (main effect frequency: exp. 1A F(3,81) = 3.42, p=0.021; exp. 1B: F(3,69) = 5.81, p=0.001).

One proposition is that flicker induced hallucinations might be largely due to an interaction between perception and retinal after effects (Bidwell, 1897). To test for a cortical contribution to the hallucinated blobs, we devised a version of the hallucination-contrast experiment that depended on cortical interocular cross-talk. We presented two small annuli, one to each eye using a mirror stereoscope, and flickered both rings either synchronously or asynchronously at 2.5 Hz to 20 new participants. In the asynchronous condition binocular neurons should receive flicker stimulation at ~5 Hz (rather than 2.5 Hz). Accordingly, if the hallucinated content is the product of binocular neurons, viewers should experience higher contrast in the asynchronous condition as 5 Hz is closer to the 11 Hz peak in contrast, we found in the first experiment. Figure 1E shows exactly this; hallucinations in the asynchronous 2.5 Hz condition were perceived at a higher contrast than in the synchronous condition (t(19) = 2.60, p=0.018). Likewise, the same experiment performed at 21 Hz yielded the opposite pattern of results, the synchronous condition gave higher contrast values (t(19) = 2.28, p=0.034; interaction: F(1, 19) = 8.06, p=0.011), as hallucinations produced by >21 Hz flicker were perceived as lower in contrast in our first experiment (Figure 1D). Together these data suggest that flicker induced hallucinations transpire at or beyond binocular neurons and cannot be the sole product of retinal afterimages.

To measure the motion dynamics of rotation in this hallucination, we devised a technique allowing us to quantify the speed of rotation similar to that used in (Wilson et al., 2001). Observers depressed and held a key when a monitored section of the rotating hallucinatory pattern passed a nonius line at the top of the annulus (Figure 1C). Observers then released the key only when the monitored section of the pattern reached a bottom nonius line, marking a travelled distance of half a rotation. This technique gave us propagation times for a fixed annulus distance, hence the speed of the travelling hallucination. The rotational speed was dependent on the annulus flicker rate (Figure 1F), with higher frequencies giving faster rotation speeds (F(2,10) = 24.75, p<0.001).

To learn how rotation speed varies with eccentricity, we scaled our entire stimulus across three different mean annular radii and using the same timing procedure mapped speed in 6 observers. Using the same speed procedure as above, the mean speed for eccentricities of 6.19°, 7.96°, and 9.67° (mean radii) were 34, 33 and 33° s−1. Based on the hypothesis that the hallucinated structure and motion originates in primary visual cortex we converted visual distance into physical distance on the cortex in cm using the detailed surface map of human V1 (Horton and Hoyt, 1991). Figure 1G shows longer propagation times as a function of greater neural distance, corresponding to a mean propagation speed of 5.6 cm s−1 over V1 surface.

Next we tested whether the hallucinations could propagate across gaps in the flickering annulus stimulus. We added 4, 8, and 12 permanent gaps (0.59° width) into the flickering annulus at cardinal locations and sub-cardinal divisions (Figure 1H). Eight observers monitored the direction of motion of the hallucinated blobs by holding one of the designated keys down or reported no clear direction by releasing keys for multiple 60-second durations. Figure 1H shows the percentage of time observers reported rotational motion vs. stationary patterns as a function of gap number. There was a clear trend of less rotation with a greater number of gaps (F(2,6) = 14.68, p=0.005). Next we held the number of gaps constant at four, and manipulated gap size across three different values (0.59°, 1.78°, 2.97°), while observers again tracked hallucinatory motion or its absence. Again, the percentage of time rotational motion was perceived went down as a function of gap size (F(2,6) = 15.93, p=0.004) (Figure 1I).

To learn if these hallucinations are specific to luminance flicker or generalize to isoluminant color flicker, we ran a new experiment with five observers who tracked static shapes, moving shapes, or no structure at all, for both the standard luminance flicker and subjective isoluminant red and green annuli. We first assessed subjective isoluminance in each observer using the flicker fusion technique (Wagner and Boynton, 1972). Using these color values we flickered annuli at 6 or 12 Hz for 30 s. Figure 1J shows the percentage of time reported for each category (no shape, shape only, and shape + motion) for luminance and isoluminance stimuli collapsed across flicker frequency (main effect and all interactions for flicker frequency: all ps > 0.29). Strikingly, unlike luminance stimuli, observers reported no pattern for over 60% of the time at isoluminance (F(2,8) = 39.67, p<0.001). These data suggest a neural locus sensitive to luminance, but not color flicker, likely the dorsal visual processing stream, which has a higher proportion of cells blind to isoluminance (Shapley, 1990). However, we only tested isoluminant red/green, so it remains unknown if this pattern would extend to blue/yellow stimuli.

Over the past several decades researchers have been fascinated by perceptual bistability as a method to study the neural correlates of consciousness and how the brain makes decisions (Blake and Logothetis, 2002). Accordingly, we wondered if the motion in this hallucination might indeed be bistable and hence open a new window into the study of how the brain makes choices for conscious experience. First, to investigate if people hallucinated clockwise and counter-clockwise motion equal proportions of time, 9 observers tracked rotation direction for 10 min. Figure 2A shows equivalent percentages of time reported for each motion direction (CW vs CCW: t(8) < 1, p=0.53). One classic hallmark of bistability is that the distribution of dominance durations exhibit a long tail (occasional long durations), forming a gamma-like distribution (Blake and Logothetis, 2002). Figure 2B shows a clear long tail for dominance durations for the hallucinated bistability, consistent with the core characteristic of other bistable perceptual phenomena.

Figure 2 Download asset Open asset Bistability of the hallucination. (A) Data showing that subjects experience the hallucination rotation equally in clockwise and counter-clockwise directions (Exp. 6; N = 9, 10 min of tracking). (B) A histogram of dominance durations, showing the long tail and fit with a gamma function, a hallmark of perceptual bistability (Exp. 6; from the gamma fit: a = 2.49; b = 0.40). (C) Depiction of the motion probe stimulus. Motion probes were presented moving clockwise or counter-clockwise, while subjects tracked hallucination alternations. (D) Contrast thresholds from the probe stimulus for congruent and incongruent probes (Exp. 7; N = 13, 384 trials). (E) Longer dominance durations over viewing time suggest a form of adaptation that is local in visual space, the second ring ‘resets’ the adaptation (Exp. 8A; N = 7, 10 min tracking per stimulus order). (F) Dominance durations over time for three different flicker frequencies (Exp. 8B; N = 6, 10 trials per frequency). (G) 8 individual subjects and the mean stability for intermittent physical presentations (Exp. 9; 100 trials). (H) Comparing dominance durations in binocular rivalry, rotation globe-stimulus and flicker hallucinations (Exp. 10; N = 84, 2 trials of two minutes tracking). Retest reliability: flicker: r =0.736; globe: r =0.741; rivalry: r =0.744; all ps <0.0001. All error bars show ± SEM. https://doi.org/10.7554/eLife.17072.006

To obtain more objective performance based measures of this rotational bistability we tested whether sensitivity to retina-sourced physical motion stimuli presented within the annulus might differ when presented congruently or incongruently with the hallucinated motion. This would demonstrate an interaction between hallucinated content and physical discrimination – supporting a common mechanism. A new set of 10 participants tracked hallucinated motion alternations for periods of 10 s. At a random time-point during the final 5 s of tracking a physical motion probe (Figure 2C; see Materials and methods) was presented in the annulus at either left or right of fixation and participants had to report on which side the probe was presented (a two alternative forced choice task). The probe was presented at one of six different contrasts (9.5%, 10%, 11%, 13%, 17%, 25%), set during pilot tests. Probe data was then separated based on probe direction, congruent or incongruent with the concurrent hallucination direction, and probe accuracy was fit with a non-linear function to give a threshold estimate of 70% accuracy (see Materials and methods). Figure 2D shows a significant difference between mean contrast thresholds for congruent and incongruent trials, with greater sensitivity to the probe stimulus when it was rotating in a congruent direction with the hallucinatory percept (t(9) = 2.8, p=0.02). This suggests that the hallucinatory motion percept (or tracking it) weakly suppresses or boosts retina-based motion depending on the motion congruity.

Next we wondered if hallucinated bistability, like perceptual retina-sourced bistability such as binocular rivalry, undergoes a local form of adaptation resulting in longer dominance durations over time (Suzuki and Grabowecky, 2007). For example, binocular rivalry alternations slow during viewing or when the stimulus is moved through visual space (Blake et al., 2003). Participants continuously tracked hallucinated alternations for a 5-min period in a small annulus, immediately followed by a further 5 min of tracking in a larger annulus, that did not spatially overlap with the prior stimulus (order was counter-balanced). Figure 2E shows that dominance durations increased over time during a session of same-sized stimulus. Further, in the second period of hallucination tracking, the new different-sized and non-overlapping annulus did not ‘follow on’ from the slower dynamics, but returned to the original shorter durations. These data suggest that hallucinated bistability can undergo a form of sensory adaptation that is local in visual space. To test if these changes in alternation rate were due to adaptation to the hallucinated content or due to adaptation to the actual perceptual flicker, six participants tracked alternations for 10 consecutive 30 s periods at three different flicker rates (8.5, 10.6 and 14.2 Hz). Figure 2F again shows adaptation, with an increase in dominance durations over time, however across the whole period the alternation rate was not statistically different between the three flicker rates (F(2,24) = 1.76, p=0.19), suggesting the change in alternation rate was not due to flicker adaptation, but most probably due to neural fatigue in neurons representing the hallucinatory bistable structure.

Another hallmark of perceptual bistability is the striking stabilization of the normal continuous stochastic dynamics by a sensory memory between intermittent presentations (Pearson and Brascamp, 2008). To learn if these bistable hallucinations show a similar sensory memory we presented the flickering annulus to eight participants for 2 s followed by 4 s without flicker (repeating intermittent presentation). Participants reported the dominant rotation direction on each 2 s presentation for 100 trials. Figure 2G shows the hallucination motion stability as the percentage of reported motion direction consecutively the same (e.g. clockwise, clockwise), such that reporting the same percept on every trial would result in 100% stability (Pearson and Brascamp, 2008). All individual subjects show a stability measure above the chance score of 50% (two rotation directions), with the mean significantly above chance (75%; compared to 50%: p<0.001), suggesting that hallucinated bistability can be stabilized by a novel form of memory across intermittent presentations.

Finally, to probe for potential overlapping mechanisms between hallucinated and perceptual or retina-sourced bistability 74 new participants tracked alternations in binocular rivalry, a bistable rotating sphere (see Materials and methods) and the flicker-induced hallucinations. Figure 2H shows a scatter plot of the data, the rotating sphere (red) significantly predicted hallucinated alternation rates (r = 0.312; p=0.006), while binocular rivalry (blue) did not (r = 0.133; p=0.228). We propose that the predictive relationship between the rotating sphere and hallucinated bistability, but not rivalry, might be due to the former two both involving a common neurophysiological mechanism for motion.

Next we extend a model developed for full field flicker hallucination to explain both the constrained hallucinated content and its bistability (Rule et al., 2011). This model is based on the idea that uniform luminance flicker stimulation resonates with the natural frequency of cortical cells to evoke standing waves of activity in primary visual cortex that induce the conscious experience of the hallucination (Billock and Tsou, 2012; Rule et al., 2011; Ermentrout and Cowan, 1979).

We modeled the region of visual cortex that was stimulated by the flickering annulus as a ring of tissue in one spatial dimension (Figure 3A). The neural tissue was modelled using an established neural field model (see Appendix 1: Equations 1–3) comprising spatially-coupled populations of excitatory (E) and inhibitory (I) cells (Figure 3B, top). The standing waves/hallucinations arise when flicker frequencies are approximately twice the natural frequency of the damped oscillations in the neural dynamics (Rule et al., 2011). The resonant frequency of simple cells in the primary visual cortex of cat typically peaks near 5 Hz (Movshon et al., 1978). Here we tuned the natural frequency of the model to 5.5 Hz so that it elicited prominent standing waves with 11 Hz flicker to match our behavioral data (Figure 1D).

Figure 3 with 1 supplement with 1 supplement see all Download asset Open asset A neural field model of flicker-induced bistable motion. (A) The log-polar retinotopic map of human visual cortex. The fovea is located at the centre. The annulus stimulus maps onto a thin strip of tissue (shaded) that spans both hemispheres. Inter-hemispheric fibres (dotted lines) connect the strips to form a contiguous ring. (B) Schematic of the model. The ring of tissue is modelled in one spatial dimension using an established neural field model (Rule et al., 2011). that comprises local populations of excitatory (E) and inhibitory (I) cells. Flicker and counter-phase stimulation both induce counter-phase responses in this model. Motion within the cortical response patterns was detected by banks of Gabor filters (large arrows) following the motion-energy model. The motion-energy signals (boxes) represented the percepts of LEFT (anti-clockwise) and RIGHT (clockwise) motion. Those percepts were subject to rivalry through mutual inhibition and firing rate adaptation. (C) Space-time plots of flicker. (D) Cortical responses to flicker. (E) Time-averaged LEFT and RIGHT motion-energy responses to flicker stimulation. Error bars are ±1 standard deviation. (F) Time course of the perceptual decisions evoked by flicker. (G) Histogram of dominance durations for switching between left and right motion percepts fit with a gamma function. The variation in switch times is due to injected noise in perceptual rivalry model. (H–L) The analogous representations for the counter-phase retina-sourced stimulation. https://doi.org/10.7554/eLife.17072.007

Motion signals were then extracted from the space-time signatures of the standing waves in cortex using two banks of direction-selective motion detectors (Figure 3B, middle). These detectors (see Appendix 1: Equations 8–10) were implemented using Gabor filters (Jones and Palmer, 1987) according to the classic motion-energy model (Adelson and Bergen, 1985). Given that our interest was in hallucinated motion, we applied the motion-energy model directly to the cortical activity patterns rather than to the stimulus pattern. The output of the motion detectors within each bank were linearly combined to form a net motion-energy signal that represented the valence of motion in the detector’s preferred direction. These net motion signals were then fed into distinct neural populations that encoded the perceptual states of Left (counter-clockwise) and Right (clockwise) motion respectively (Figure 3B, bottom). Perceptual rivalry between these two neural populations was achieved through mutual inhibition and firing rate adaptation (see Appendix 1: Equations 11–13). Models of this class replicate spontaneous perceptual switching between ambiguous stimuli and the association of higher switching rates with higher stimulus contrast/energy (Laing and Chow, 2002; Shpiro et al., 2007; Wilson, 2003). Independent noise was injected into the LEFT and RIGHT neural populations to induce variability in the dominance times of the rival motion percepts.

When stimulated with 11 Hz uniform flicker (the stimulus parameters that gave the highest hallucinated contrast; Figures 1D and 3C), the model generated ‘cortical’ standing waves, that is, oscillations in modelled cortex, at half the flicker rate (Figure 3D), in accordance with previous findings. Standing waves were observed for flicker frequencies in the range 8–18 Hz, which we interpret as hallucinatory. Further, it was also possible to induce spatially irregular patterns by adding in weak random connections between the E cells shown in Figure 3B (data not shown). Beyond those frequencies the cortical model produced spatially uniform responses (Figure 3—figure supplement 1H) which we interpreted as non-hallucinatory. The space-time signatures of the flicker-induced standing waves evoked identical responses from both the Left and Right motion detectors (Figure 3E). Consequently, those ambiguous motion signals induced spontaneous switching between the two populations (Figure 3F). The dominance times of each perceptual decision (Figure 3G) exhibited a long-tailed distribution (M = 5.1 s, SD = 1.6 s) that was qualitatively similar to those observed experimentally (Figure 2B). However, it is interesting to note the differences in the shape-parameter (a) of the gamma fits, between the behavioral and model data. This difference could be summed up by describing the model data as being closer to a normal distribution than the behavioral data. One possible reason for the difference could be our choice of Gaussian noise in the rivalry model. Future work could probe different noise distributions to fine tune a model of bistable hallucinations.

When the same model is presented with a counter-phase retina-sourced physical patterned stimulus (Figure 3H), with identical spatial (fx = 0.11 cycles/mm) and temporal (ft = 5.5 Hz) frequencies to the flicker-induced standing waves (hallucinations), standing waves were also produced in model-cortex (Figure 3I; see Appendix 2). The net motion signals were stronger than those induced by the blank flicker stimulation (Figure 3E and J), hence the spontaneous switching between LEFT and RIGHT perceptual decisions was somewhat faster (M = 4.3, SD = 1.5, Figure 3K and L).