Individual variability in perceptual switching behaviour is associated with reversal-related EEG modulations

Clinical Neurophysiology 127 (2016) 479–489

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Individual variability in perceptual switching behaviour is associated with reversal-related EEG modulations Emanuela Russo ⇑, Vilfredo De Pascalis Department of Psychology, ‘‘La Sapienza’’ University of Rome, Italy

a r t i c l e

i n f o

Article history: Accepted 5 June 2015 Available online 11 June 2015 Keywords: Bistable perception Individual differences ERPs Late Positive Component Necker cube

h i g h l i g h t s  Bistable stimuli and event related potentials (ERPs) have been used to evaluate individual differences

in perceptual switching behaviour.  We observed enhancements of Late Positive Component in association with higher number of

reversals.  The present findings could help to understand the contribution of high-order processes in individuals’

perceptual re-organization.

a b s t r a c t Objective: High individual variability is frequently observed in multistable perception, but few ERP studies have considered this factor. The present investigation evaluates the relation between individual perceptual switching and the modulation of reversal-related ERP components. Methods: We used a bistable perception paradigm (Kornmeier and Bach, 2004), consisting of briefly flashed grid of nine Necker cubes, interspersed by a blank screen. The subject’s task was to compare the previous stimulus with the latter one. The number of reversal perceptions was used as a measure of individual perceptual switching behaviour. Results: As in previously reported findings, Reversal Negativity (RN, 180–300 ms) and Late Positive Component (LPC, 350–600 ms) were identified in response to reversal perception. In terms of individual differences, higher reversals were associated with reduced negativity of the RN and enhanced positivity of the LPC. Conclusion: The timing of the present results supports the hypothesis that individual variability in perceptual reversal is associated with different neural activations at later stage of processing, when the neural representation of ambiguous figure must be internalized to produce an appropriate response/behaviour. Significance: Multistable perception can reveal crucial mechanisms underlying individual perceptual re-organization when inconsistent or incoherent stimuli come from the environment. Ó 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction What we perceive in our everyday life is not totally restricted to environmental information, but our brain constantly identifies and organizes sensory inputs through a series of unconscious inferences. In other words, perception represents an active process of ⇑ Corresponding author at: ‘‘Sapienza’’ University of Rome, Department of Psychology, Via dei Marsi 78, 00185 Roma, Italy. Tel.: +39 06 49917643; fax: +39 06 49917711. E-mail address: [email protected] (E. Russo).

disambiguation that allows us to perceive a stable and coherent world. We are not completely aware about this inferential process until it arises to our consciousness whenever the ambiguity is maximized (Leopold and Logothetis, 1999). For example, well-known bistable figures, such as the Necker cube, elicit oscillations between two patterns, equally probable, that correspond to competing state of stability. In these particular cases, perceptual inferences are clearly manifested, since the physical stimulus does not provide enough disambiguating information for our visual system to settle on one perceptual interpretation. Bistable perception represents, therefore, an essential issue to investigate the

http://dx.doi.org/10.1016/j.clinph.2015.06.003 1388-2457/Ó 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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underlying processes that lead to perceptual awareness when the information from the environment is vague or inconsistent (Leopold and Logothetis, 1999; Kornmeier et al., 2004; Braun and Mattia, 2010). This characteristic feature, over the years, yield to a constant interest from researchers, and there is an ongoing debate about the nature of neural mechanisms involved in multistable perception. In particular, one explanation focuses on passive bottom-up processes of neural satiation or fatigue in early visual areas (Babich and Standing, 1981; Toppino and Long, 1987; Long et al., 1992), which eventually lead to a spontaneous alternation of the image, while another supports top-down influences, i.e. integration processes involving cognitive mechanisms, emotions, experiences and expectations (Rock and Mitchener, 1992; Horlitz and O’Leary, 1993; Rock et al., 1994). In a comprehensive and detailed review, Long and Toppino (2004) suggest a hybrid model attempting to overcome this debate. These authors, in fact, advise the investigators to consider interactions between low-level (sensory) and high-level (cognitive) processes in multistable perception. Some studies provided experimental evidence on the interaction of bottom-up and top-down mechanisms (Ilg et al., 2008). In particular, a series of functional magnetic resonance imaging (fMRI) studies have highlighted the neural circuits related to perceptual multistability (Kleinschmidt et al., 1998; Lumer and Rees, 1999; Sterzer et al., 2002; Sterzer and Kleinschmidt, 2007). In these researches, extensive activations of the extrastriate visual cortex and frontal regions were observed. Moreover, parietal activations have also been reported during an interval immediately preceding the perceptual reversion. These results were interpreted as evidence that the onset of the alternation occurs in non-sensory, higher-order associative areas, probably mediated by a shifting in attention. It is also well known that perceptual switching shows a strong inter-individual variability (Borsellino et al., 1972; Strüber et al., 2000), and interestingly, some personality variables and clinical conditions showed specific correlations with the rate of perceptual switching. Lower reversal rate, for instance, was reported in patients with focal frontal lobe (Ricci and Blundo, 1990) and right fronto-parietal damage (Meenan and Miller, 1994) as well as in the presence of depression and anxiety disorders (Ray Li et al., 2000). Studies on individual differences in multistable perception, indeed, have been provided evidences that support the involvement of both sensory and higher order cognitive factors. For example, genetic studies showed that heritable factors contribute to individual differences in perceptual switching rates. However, the influence of heritable factors is restricted to binocular rivalry, while the perception of the Necker cube is less genetically determined (Miller et al., 2010; Shannon et al., 2011). High order processes can influence reversal rate (Liebert and Burk, 1985; Toppino, 2003; Slotnick and Yantis, 2005) as well. Few studies, so far, have investigated the possible relation between the neural correlates of multistability and individual differences in perceptual switching behaviour, as outlined by Kleinschmidt and colleagues (2012). For instance, Strüber and colleagues (2000) reported an enhancement of gamma band activity in high switchers, suggesting that this could be an evidence of higher arousal/vigilance. Nakatani and Leuween (2005) observed a characteristic pattern of sequential occipital alpha and frontal theta band activity in frequent switchers and they assumed that this pattern could be related to a facilitatory attentional effort necessary for perceptual switching. Recently, some functional magnetic resonance imaging (fMRI) studies, using voxel-based morphometry, have reported structural differences in the superior parietal cortex associated with individuals’ perceptual switching rate (Kanai et al., 2010; Kanai and Rees, 2011). Event related potentials (ERPs) are optimal investigation tools for understanding the contribution of bottom-up and top-down

mechanisms in multistable perception. In fact, these measures offer timing information that distinguishes neural responses related to basic sensory features from those associated with mental representation (Eagleman, 2001). Despite this, there are few ERP studies on individual differences in multistable perception. One possible reason for these narrowing evidences probably resides in the inaccurate timing available for endogenous perceptual switching. For example, in previous studies, ERP responses were off-line averaged using the button press (Baå-Eroglu et al., 1993; Isßog˘lu-Alkaç et al., 1998) as a time reference for perceptual reversal. But backward-averaging on the subject’s response introduces a temporal jitter, due to the high variability of the individual’s reaction times (Strüber et al., 2000; Kornmeier and Bach, 2004). In an attempt to bypass this problem, Kornmeier and Bach (2004) adopted a new paradigm using the discontinuous presentation of a bistable figure (Orbach et al., 1966; O’Donnell et al., 1988). Subjects were required to compare, in terms of their perception, any given stimulus with the preceding one, thus allowing averaging ERPs to the stimulus onset. Computing the difference wave (‘‘dERP’’; Reversal perception minus Stability perception), the authors found a negative dERP component that they coined Reversal Negativity (RN). The RN was most prominent at parietal and occipital lobes and occurred in a large time window (180–300 ms). Although several following studies (Kornmeier and Bach, 2005, 2006; Pitts et al., 2007, 2008; Intaite et al., 2010; Britz and Pitts, 2011) have clearly identified the RN in the bistable perception, the functional meaning of this component has not been completely understood. In addition, some reports disclosed a Reversal Positivity on dERPs (RP; about 130 ms), restricted to occipital locations and which was interpreted as an early correlate of the disambiguation process (see Kornmeier and Bach, 2012). The above-mentioned studies have also detected a Late Positive Component (LPC) that showed the same modulation and might reflect the same processes as the P300-like component resulting from backward averaging. Moreover, in a recent work (Pitts et al., 2009), the source generators of RN were identified in ventral occipital-temporal cortex, while generators of LPC in bilateral temporal and left superior parietal cortices. Since the ventral extrastriate regions have been suggested to be the recipients of the attentional-shift biasing signal, these authors hypothesize that RN might reflect the intermediate processing level between low sensory and high-order processes. In contrast, intracranial generators of LPC component were mostly associated with post-perceptual processes, such as novelty detection and stimulus categorization, since they showed similar distribution and functional significance of the visual P3b component. As previously discussed, evidences coming from studies that consider individual differences in multistable perception suggest the involvement of both sensory and cognitive factors. With this in mind, the timing of reversal-related ERP components could provide cues about the stages of individual’s perceptual organization in spontaneous perceptual reversals. Given the limitations in continuous presentation mode, used in previous researches, we have decided to utilize the intermittent paradigm of ambiguous stimuli (Kornmeier et al., 2004) to avoid averaging problems due to the considerable jitter of motor response and inter- and intra-individual variability. Therefore, in line with previous mentioned findings (Kornmeier and Bach, 2004, 2005), we expect to find differences between Reversal and Stability perception in terms of dERPs components. The aim of the present research was to investigate whether reversal-related ERP components may show modulations with the respect of individual perceptual switching rate. In particular, modulations of early occipital dERP components, such as the RP, would support the contribution of early sensory processes, such as neural satiation or adaptation, whereas modulations of the LPC component

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would disclose the role of later stages of processing, mainly linked to the evaluation of perceptual outcome. 2. Methods 2.1. Subjects Fifty subjects (11 males; mean age = 23.7 years, SD = 3.1), naive to the aim of the experiment, took part in the study. Every subject had normal or corrected-to-normal vision and reported to be free from neurological or psychiatric diseases. Since menstrual cycle may affect EEG activity (Glass, 1968), female subjects who were in their menstrual period were invited for electrophysiological and behavioural recordings on another occasion. The local ethical committee formally approved the study and written informed consent was obtained from each participant. 2.2. Stimulus The stimulus was a ‘‘Necker lattice’’, a figure composed of nine Necker cubes in a 3  3 lattice. As in a previous research (Kornmeier and Bach, 2005), perceptually ambiguous and non– ambiguous versions of Necker lattice were used. Unambiguous versions of the stimulus were obtained adding depth cues to the figure in order to disambiguate the cube orientation. All the stimuli subtended a viewing angle of 7.5°  7.5° and were presented on a monitor with a refresh rate of 75 Hz (luminance of 200 cd/m2). We also used different variants of Necker lattices, between the experimental trials, in order to avoid afterimages and trivial local cues (Kornmeier and Bach, 2005, 2006). These variants were obtained by randomly repositioning the stimulus in space over ±8° in both horizontal and vertical directions, resulting in total five variants. A small cross in the centre of the screen was used as a fixation target and was present during all inter-stimulus intervals. 2.3. Procedure The experiment was conducted in an electromagnetically shielded room, where subjects seated 110 cm from the screen. Before EEG recordings, a static version of the Necker lattice stimulus was presented to the subjects. The experimenter, when necessary, helped the observer to easily perceive both configurations of the figure. Participants performed two separate experiments, one with unambiguous versions of Necker lattices (exogenous perception) and the other with ambiguous Necker lattices (endogenous perception). The employment of unambiguous stimuli allowed to obtain a control condition to test if the dERP signatures are specific to endogenous reversal perception and assured that participants became familiar with the task requirements and the button press. Both ambiguous and unambiguous lattices were presented in a discontinuous presentation mode (onset/offset). Two briefly successive stimulus presentations of 800 ms, interspersed by a blank screen of 400 ms, constituted a trial. For every trial, subjects were asked to compare, in terms of their perceived front-back orientation, the previous stimulus with the latter by pressing a button, according to different go/nogo instructions in two separated experimental runs. In the Reversal Condition, they had to respond only if they perceived a difference in the front-back orientation of the lattice within a trial, while in the Stability Condition they had to press the button if the two successive Necker lattices were perceived as identical (Fig. 1). Participants were instructed to gaze at the fixation cross and to not press the button during the stimulus presentation, otherwise the epoch was excluded from the analysis. Following each button press, the inter-stimulus-interval was extended by 1 s, in order to avoid the interference of the motor task

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on successive stimulus. Prior to the experimental session subjects performed a trial session in which they compared unambiguous stimuli in order to become familiar with the task requirements and the button press. Overall, the experimental session lasted about 35 min. Given that our study focuses on individual differences in multistable perception, intentional effects were possibly controlled by specific instructions that intended to reproduce a spontaneously initiated behaviour. Participants were instructed not to provoke volitionally perceptual reversal and to respond only if they were certain about their percept. Subjects were also informed that reversal and stability were equally important, and that there were no good or bad performances. 2.4. Electrophysiological recordings and analysis EEG data were recorded continuously using Neuroscan Acquire 4.3 with tiny electrodes located over 30 scalp sites (Fp1, Fp2, F7, F3, Fz, F4, F8, FT7, FC3, FCz, FC4, FT8, T3, C3, CZ, T4, C4, T5, CP3, CPz, CP4, T6, P3, Pz, P4, TP7, TP8, O1, Oz, O2) and grounded using a midline electrode positioned 10 mm anteriorly to Fz lead. Averaged ear lobes [(A1 + A2)/2] were used as a reference electrode. EEG and electrooculogram (EOG) were acquired continuously and simultaneously with the performance by a 40-channel NuAmps DC amplifier system (Neuroscan Inc.), set at a gain of 200, sampling rate of 1024 Hz, low-band filtered under 100 Hz. In addition, a 50 Hz notch filter was applied. Electrode impedance was kept below 5 kO. The horizontal and vertical EOG were monitored via a pair of tin electrodes placed 1 cm lateral to the outer canthus of each eye and the vertical EOG was monitored via bipolar montage using two electrodes placed above and below the center of the left eye. The EEG was later processed using Brain Vision Analyzer 2.0 system (Brain Product). The EEG was reconstructed into discrete, single-trial epochs. ERPs were time-locked to stimulus onset, with a 150-ms pre-stimulus baseline. Trials that contained an eye blink or eye movement artifacts (EOG > 70 lV) were rejected and discarded from analyses. In addition, slow eye movements were corrected using the procedure of Gratton et al. (1983). Behavioural responses were used to classify EEG epochs as either Reversal or Stable trials. EEG epochs were obtained for unambiguous and ambiguous trials. To ensure an acceptable signal-to-noise ratio in the averaged ERP waveforms, only subject’s data including no less than 30 artifact-free epoched trials per condition were included. Based on this criterion, from the initial 60, only 50 participants were included in the analysis. All average files were then digitally filtered (25-Hz low pass, 48 dB/octave). In previous studies, a chain of reversal-related ERPs have been observed after the onset of the second display (see Kornmeier and Bach, 2012 for a review). We computed the difference wave (Reversion minus Stability) and looked for significant deviation in the dERP. Two components were identified: a Reversal Negativity (RN, 180–300 ms), that was maximal at posterior-occipital leads, and a Late Positivity (LPC, 350–550 ms), that was distributed across central, central-parietal, and parietal locations. An early positive ongoing (i.e., Reversal Positivity, 90–130 ms) was also present and it was restricted to occipital locations. For each reversion and stability condition, mean amplitudes for RN (180–300 ms), and LPC (350–550 ms) time windows, were calculated. This computation was chosen since, according to Pitts et al. (2007), these measures are more accurate and conservative when long-duration ERP components are analyzed. Since the difference wave has evidenced different scalp distributions, for successive statistical analysis, we averaged the electrodes and we obtained separate regions of interest (Fig. 2) for the RN: leftparieto-occipital (P3,O1), middle-parieto-occipital (Oz, Pz), rightparieto-occipital (P4,O2); and for the LPC: left-central-parietal

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Fig. 1. Experimental paradigm design from Kornmeier and Bach (2004). Unambiguous lattices or Necker variants are administered to participants in separate blocks. Stimuli are presented in an onset/offset mode with a 800 ms presentation time, followed by a blank screen for 400 ms Subjects compared the front–back orientation of any given stimulus with the preceding one (observation sequence), and to respond accordingly to different instructions: Reversion and Stability. The unambiguous stimuli reversed randomly in 50% of the trials.

(C3, CP3, P3), middle-central-parietal (Cz, CPz, Pz) right-centralparietal (C4, CP4, P4). 2.5. Statistical analysis It has been reported that the duration of reversal intervals can be approximated by either Gamma (Leopold and Logothetis, 1999) or lognormal (Lehky, 1995; Zhou et al., 2004) distributions. In our study participants have to consecutively compare stimuli within a trial, which lasted about 2 s. Some perceptual reversals could also be occurred between trials, but we do not considered them because different repositioned variants of Necker lattices between the experimental trials were used. We considered a trial as a temporal unit and obtained a measure of the reversal interval computing how many trials precede or follow a change in the subject’s report (reversal/stability). In order to test if our duration of reversal intervals showed these statistical properties, we computed parameter estimation by LSE from the cumulative probability distribution, both of the a and b parameters of the gamma distribution, and of the r and l parameters of the lognormal distribution. We performed statistical goodness-of-fit analysis employing the Kolmogorov–Smirnov test. For RN mean amplitude, a 2 Perception (Reversal, Stability)  3 Scalp Region (left-parieto-occipital, middle-parieto-occipital, and right-parieto-occipital) statistical design was applied. For LPC amplitude we used a similar design with the exception of location factor that includes 3 different regions of the scalp (left-centralparietal, middle-central-parietal, and right-central-parietal). For the early RP peak amplitude, the statistical design applied was a 2 Perception (Reversal, Stability)  3 Location (O1, Oz and O2). The relationship between individual switching behaviour and the reversal-related ERPs components was investigated computing zero-order correlations. The same statistical analyses were also applied to the data from unambiguous stimuli presentation.

To prevent the risk of type-I errors, as may happen using repeated measures analysis if the sphericity assumption has been violated (Vasey and Thayer, 1987), the Huynh-Feldt epsilon correction of significance levels was applied when necessary. 3. Results 3.1. Behavioural results In the reversion blocks subjects reported, on average, the 35% of clear reversal perceptions, while in the stability blocks the 50% of stable perceptions. Reaction times varied neither within nor between subjects or inter-blocks. Participants reported a mean total number of reversals (nREV) of 70.12 ± 25.6 out of 200 possible trial interpretations. In Fig. 3 are plotted the histograms of reversal intervals and the density function for the two theoretical distributions. The Kolmogorov–Smirnov test confirmed that the reversal intervals followed both a gamma (a = 1.60; b = 3.49; P < 0.001) and a lognormal distribution (l = 8.37; r = 0.71; P < 0.010). The fitting analyses were also repeated for two subgroups of High Switchers (‘‘HS’’ n = 13, nREV > 75° percentile) and Low Switchers (‘‘LS’’ n = 14, nREV < 25° percentile). As can be seen in Fig. 3, in both cases the Kolmogorov–Smirnov test revealed that perceptual durations could be fitted to a gamma or lognormal distribution. 3.2. Electrophysiological results 3.2.1. Unambiguous Lattice The RN mean amplitude showed a significant main effect of Perception, F(1,49) = 25.79, p > 0.001, indicating an enhancements of this component in Reversal perception compared to Stable. The LPC showed no significant main effect of Perception, F(1,49) = 0.09, p = 0.76. Neither the RN nor the LPC showed significant correlation with the reversal rate.

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Fig. 2. Grand mean ERPs across all subjects for: (a) unambiguous Necker lattices and (b) ambiguous Necker lattices at central, parietal and occipital locations. The black solid lines represent reversal waveforms and gray solid lines represent stable waveforms. Difference waves (reversal minus stable) are plotted in black dashed lines. An amplitude x time scale is provided in the Oz lead (negative is plotted up). Reversal Negativity (RN) and Late Positive Component (LPC) are indicated by labels and arrows.

3.2.2. Ambiguous Lattice The RN mean amplitude showed a significant main effect of Perception, F(1,49) = 5.55, p = 0.02, indicating an enhancements of this component in Reversal perception compared to Stable. For the LPC component the main effect of Perception was also significant, F(1,49) = 14.91, p = 0.003, showing larger amplitudes

in the reversal compared to the stable perception. In addition, there was a significant interaction of Perception  Scalp Region, F(2,98) = 16.98, p > 0.001. This interaction, investigated by post hoc comparison, showed higher enhancement of the LPC, in the reversion perception compared to the stability one, over the left and central scalp leads.

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Fig. 3. Statistical properties of the reversal intervals: histograms of the probability density function of the duration of reversal intervals for the entire sample and for two representative subgroups. The dashed line displays the fitted lognormal probability distribution; the solid line displays the fitted gamma distribution.

Table 1 Correlation between RN amplitudes and number of reversals. Scalp region

Ambiguous perception

nREV ⁄

Note. p < 0.05,

⁄⁄

Unambiguous perception

PO left

PO central

PO right

PO left

PO central

PO right

0.30⁄

0.29⁄

0.17

0.24

0.22

0.16

p < 0.01.

Table 2 Correlation between LPC amplitudes and number of reversals. Scalp region

Ambiguous perception CP left

CP central

CP right

CP left

CP central

CP right

nREV

0.41⁄⁄

0.42⁄⁄

0.42⁄⁄

0.20

0.22

0.25

⁄

Note. p < 0.05,

⁄⁄

p < 0.01.

Unambiguous perception

The ongoing positivity of the early RP was also tested using ANOVA, which revealed a barely significant effect F(1,49) = 3.61, p = 0.063. For RN mean amplitude, we found significant positive correlations with the number of reversals, in the left and central regions (Table 1). These correlations indicate that in the ambiguous condition increased reversals were associated with less negative RN amplitudes, in left and middle scalp regions. The LPC component showed an opposite and stronger pattern of brain activation, i.e. more positive amplitudes of LPC for higher nREV (Table 2 and Fig. 4). Even though our RP was roughly significant we looked for similar association with the nREV, but no correlations were found. Only for graphical purposes, subjects were divided into two representative groups of High Switchers (‘‘HS’’ n = 13, nREV > 75° percentile) and Low Switchers (‘‘LS’’ n = 14, nREV < 25° percentile), respectively. Fig. 5 shows ERP traces from Reversal and Stability conditions and dERPs for HS and LS groups.

Fig. 4. A scatterplot showing the relationship between the total number of reversals (nREV) for each subject and LPC amplitude at left (rhombus), central (square) and right (triangle) centro-parietal locations.

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Fig. 5. Grand average ERPs for reversion and stability in High and Low Switchers plotted separately for left and right scalp regions. Black lines represent reversion, stability and difference waves for High Switchers, whereas gray lines represent reversal, stable and difference waves for Low Switchers. An amplitude x time scale is provided in CPz lead (negative is plotted up). Reversal Negativity (RN) and Late Positive Component (LPC) are indicated by labels and arrows.

4. Discussion The aim of the present investigation was to explore whether modulations of the reversal-related ERP components are associated to individual variability in bistable perception. We used an intermittent presentation of ambiguous stimuli following the paradigm refined by Kornmeier and Bach (2004). Whether neural mechanisms involved in multistable perception under continuous and discontinuous presentation mode are the same, is so far under debate (Noest et al., 2007). Nonetheless, some evidences showed that the intermittent paradigm with short inter-stimulus-intervals (<400 ms) may be compared to the continuous case, in which there are physiological disruptions due to normal eye-blinks. In particular, an eye-blink occurs on average every 4s and lasts about 200 ms (see Kornmeier and Bach, 2012), thus short interstimulus interval may likely reproduce continuous viewing. The distribution of reversal rate for the entire sample and for the two representative subgroups (HS; LS) showed a gamma and lognormal distribution, confirming the stochasticity of perceptual dominance. This result provides further evidence that the intermittent presentation paradigm produces spontaneous perceptual reversal rates which are similar to those under continuous viewing (Britz et al., 2009, 2011). So far, this paradigm offers a roughly precise time reference for the perceptual reversal since it has been demonstrated that instantaneous reversal of the figure occurred very closely (about 30 ms) to the onset of the stimulus (Kornmeier and Bach, 2012). As in previous studies, we asked the

participants to report, between successive ambiguous stimuli, their interpretation in separate go/nogo conditions (reversal and stability). Given that the stimulus (Necker lattice), the task (compare) and the response (go), were the same across the two conditions, what may possibly emerge from the difference wave (reversal minus stability trials) should be then related to perceptual reversals (Kornmeier and Bach, 2005). Electrophysiological responses revealed, as expected, the Reversal Negativity (180–300 ms post-stimulus onset) and the Late Positive Component (350–550 ms post-stimulus onset) related to endogenous reversal perception (Fig. 2b). These results are consistent with previously reported findings (Britz and Pitts, 2011; Kornmeier and Bach, 2004, 2005, 2006; Pitts et al., 2008, 2009, 2007) in terms of activations and scalp distribution of the dERPs components. In contrast with some of the studies mentioned above, we found only a barely significant difference for the early Reversal Positivity (90–130 ms post-stimulus onset), perhaps because this small component needs a larger number of epochs to be clearly detected (Kornmeier and Bach, 2012). We considered using, as in previous studies, also unambiguous variants of Necker lattice in a separate experiment, in order to obtain exogenous ERP traces to perceptual reversals. In this case, only the RN was detected in the difference wave whereas the LPC was absent (Fig. 2a). It has been suggested that this chain of dERP components may reflect differential stages of processing during ambiguous figure’s perception, where the RP reflects the fast disambiguation after the destabilization due to the experienced ambiguity. The

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Reversal Negativity has been interpreted as the upper limit of the time window of disambiguation in Necker cube reversals. The LPC, instead, has been observed both in studies using intermittent presentation as well as studies using continuous presentation (O’Donnell et al., 1988; Baå-Eroglu et al., 1993; Isßog˘lu-Alkaç et al., 1998; Kornmeier et al., 2004; Kornmeier and Bach, 2006, 2009; Pitts et al., 2009; Britz and Pitts, 2011) and its morphology and latency indicates that this component belongs to the P300 family. In particular, the LPC shows very close similarity to the parietally distributed P3b, suggesting to reflect context-updating or conscious appraisal of the just perceived shift (Donchin, 1981; Donchin and Coles, 1988). In terms of individual differences in perceptual switching behaviour we found a remarkable association to the nREV only for RN and LPC measures. In particular, less negative RN and more positive LPC were associated with higher number of perceptual reversals. Furthermore, analyses performed on dERP measures from unambiguous stimuli did not reveal any significant correlations with the number of reversals, corroborating that RN and LPC modulations, in the present study, are specific to endogenous reversal perception. This is most valid for the Late Positive Component which reveals stronger results (Fig. 5) and which is absent in the exogenous conditions. 4.1. Reversal Negativity We have already mentioned that the functional meaning of the RN is still not completely understood. Some authors have suggested that the RN may reflect specific aspects of visual awareness since it shares some characteristics with the visual awareness negativity (VAN; Koivisto and Revonsuo, 2010), even though recent studies did not confirm that the RN exhibit the same features (Intaite˙ et al., 2013). Pitts and colleagues (2008) inferred that RN may share common mechanisms with attention shifting, because this component exhibits temporal and scalp distributions similarities with another well known component that is the Selection Negativity (SeN; Hillyard and Anllo-Vento, 1998). In particular, these authors reported enhancements in RN amplitude under volitional control of perceptual reversal in comparison with a passive condition. Thus, the modulation of the RN component was interpreted, by the authors, as an expression of top-down processes, probably related to the critical role of selective attention in perceptual reversals. Kornmeier and Bach (2009) pointed out that in their specific discontinuous paradigm SeNs should be excluded since the RN is derived from the difference between two ERPs to target stimuli (response to reversals minus response to stability). This latter consideration, however, does not exclude that the RN and the SeN may share similar neural mechanisms, given that a perceptual reversal could still catch more attention than a stable/unchanged percept (Pitts et al., 2007). Related to this, source generators of the RN have been estimated in the inferior temporal cortex, suggesting that this component could better reflect brain activation in response to a fronto-parietal attention-shift rather than the attention-shift per se (Slotnick and Yantis, 2005; Pitts et al., 2008). Moreover, the RN is usually observed within a time window enclosing P2 and N2 waves (180–300 ms post-stimulus onset) and it is plausible to consider that this reversal-related negativity may be partially due to the larger negativity of the posterior N2, which is specific to the visual modality and related to stimulus’ change detection. 4.2. The RN and its relationship with mismatch responses Since change detection requires the comparison between a previous sensory memory trace and the actual percept, the RN could also mimic the visual Mismatch Negativity (vMMN; Tales et al.,

1999; Stagg et al., 2004; Czigler et al., 2006), a component typically elicited by stimuli that deviate from the previous one. Recently, Davidson and Pitts (2014) suggested a similar overlap between a possible auditory analog of the RN (aRN) and the MMN. Since perceptual reversals occur less frequently than stable perceptions, they assumed, in terms of stimulus occurrence, that perceptual reversal might be seen as a deviant event and stable perception as a standard one. They expected to find higher amplitudes of the aRN in participants who experienced perceptual reversals less frequently, but their data do not show any significant relationship. Here, we reported less negative RN in participants who experienced more perceptual reversals, i.e. in which the deviant/reversal was less rare, thus suggesting a partial functional overlap between the RN and vMMN. However, this should imply the reduction or absence of the RN in the exogenous condition, in which reversal and stability perceptions occurred with the same probability, while our exogenous RN has been more strongly elicited than the endogenous one (Fig. 2a). Indeed, mismatch negativities have been often observed even with equiprobable stimuli. In our case, this result could confirm that the same neural networks are involved in endogenous and exogenous perceptual switching (Leopold and Logothetis, 1999), potentially associated with the detection that ‘‘something has changed’’ in the environment. Recently, the predictive coding framework has been applied to explain the functional significance of mismatch responses (Winkler and Czigler, 2012; Stefanics et al., 2014). According to this account, stimulus deviations are based on recurrent and iterative computations between top-down predictions and bottom-up sensory inputs. If the RN shares functional commonalities with the vMMN, then its modulations could be not merely due to the neural adaptation or refractoriness but, in a predictive coding account, also to the active memory trace of the previous stimulus. Undeniably, perceptual memory is present in intermittent bistable perception and the way in which the previous stimulus is disambiguated might likely bias inferences or predictions about the subsequent stimulus. The comparison between individuals’ predictions and the actual percept could, thus, produce different ‘‘prediction-error signals’’, i.e. mismatch responses. Related to this, Kleinschmidt et al. (2012) have underlined that during intermittent viewing: ‘‘it could be that the fate of a given trial to be reported as a reversal is already determined by neural activity preceding the actual stimulus onset that will result in this report’’. In fact, there are some studies wherein a reversal-related brain activity in inferior parietal cortices was observed before stimulus onset in a bistable perception (Britz et al., 2009, 2011), as well as Ehm et al. (2011) found that gamma band activity modulations, 200 ms prior the onset of a Necker cube, predicted a successive perceptual reversal. If our speculations in terms of predictive coding theory are correct, then it would be interesting to inspect and compare, between high and low switchers, the neural events preceding the switch in comparison with the reversal-related ERPs. 4.3. Late Positive Component From early ERP studies on multistable perception, an increased positivity, in the P3 time window, related to perceptual reversal has been identified. Both this slow wave potential and the LPC have been interpreted in terms of an enhanced amplitude of the parietal P3b in response to the novel pattern configuration, i.e. conscious appraisal of the just perceived shift (Donchin, 1981; Donchin and Coles, 1988; Kornmeier and Bach, 2006). As the RN component, the LPC could reflect, therefore, a brain response to lower probability stimuli represented, in general, by perceptual reversals. Davidson and Pitts (2014) reported a statistical trend toward significance in the direction of larger amplitudes for the auditory counterpart of the LPC (aLPC) in subjects: ‘‘in which reversal were

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rarer’’. These authors, therefore, suggested a partial functional overlap of the aLPC with the P3b because of its sensitivity to stimulus probability. Our data, however, evidenced an opposite pattern in the direction of a more positive LPC for frequent perceptual reversals. A number of aspects ranging from statistical differences related to different sample sizes or different tasks (auditory vs. visual stimuli) may explain this inconsistency between ours and their results, but notably the aLPC was observed at fronto-central electrode sites, at least suggesting a closer similarity with the P3a, which is characterized by shorter latencies and a more anterior topography. It has been proposed that the P3a and the P3b reflect different functional correlates, where the frontally P3a represents a sort of comparator that drive the attention towards an unexpected or relevant event, while the P3b reflects, more properly, the updating of working memory (Patel and Azzam, 2005). In particular, P3b seems to respond more to stimulus categorization, rather than probability itself, i.e. it is probability-dependent only when the stimulus is easy to assign to a category. Thus, the LPC may likely be interpreted as the result of P3b enhancements to less frequent event but, in the case of LS, stimulus occurrence could be inflated by stimulus categorization. In other words, LS have to allocate more cognitive resources for stimulus categorization in perceptual reversals. This interpretation also fits well with the Johnson’s triarchic model (1985) of P3 amplitude, suggesting that there are three factors that affect the amplitude of this component: subjective probability, stimulus meaning, and information transmission. Subjective probability includes the global probability and sequential expectancies, while stimulus meaning is defined by the sum of task complexity, stimulus complexity and stimulus value. These two factors are further modulated by the proportion of the stimulus’ information transmission, which can be reduced by uncertainty about the perceptual outcome or equivocation. Different degrees of equivocation are likely to be present when external or internal factors reduce the stimulus discriminability. External reasons can be addressed to the characteristics of the stimulus or the task, while internal ones are related to individual factors. Positive correlation between LPC amplitudes and nREV (Fig. 4) could hence reflect that HS would more efficiently operate the conscious processing of the perceptual outcome, maybe because it is close to their internal expectations/predictions (reduced RN’s amplitude). According to this, Eimer and Mazza (2005) reported similar modulations of P3 related to variations in observers’ confidence about their perceptual judgments. This is also in agreement with the proposed functional meaning of P3 as a decision-related process between stimulus perception and the response to that stimulus (Verleger et al., 2005). 4.4. Individual differences in the processing of perceptual reversal Given their temporal occurrence, the RN and LPC disclosed different levels of cortical engagement during ambiguous figure’s change detection and the successive appraisal/update of the mental representation. It is noteworthy to mention that, even though the early RP component was barely significant, we still looked for possible modulations associated with nREV. The lack of findings in this case, can only allow us to speculate that individual variability in Necker cube reversals is not associated with differences in the early disambiguation processes. In fact, if the RP is the correlate of early sensory processes, then it is plausible that the same processes are similar between individuals, wherein successive cognitive operations, indexed by the LPC, may likely exploit differences among individuals in post-perceptual and decision-related processes. The RN, instead, could reflect shared neural mechanisms underpinning exogenous and endogenous perceptual switching. In particular, those shared neural networks could be involved in what is referred as phenomenal consciousness (Block, 2005) which is

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proposed to reside in the extrastriate visual areas and to resemble a sort of iconic memory (Koivisto and Revonsuo, 2010; Pitts and Britz, 2011). Phenomenal consciousness temporally precedes the access consciousness that is distinguished from the former because it is constituted of specific contents of phenomenal consciousness that could be selected to make subjective reports, motor outputs or long-term encoding of stimuli (Koivisto and Revonsuo, 2010). The LPC may represent the electrophysiological correlate of access/reflective consciousness, since its temporal occurrence and wide-spread activation are similar to the P3b and therefore it likely represents the maintenance of the percept in working memory for successive conscious report (Koivisto and Revonsuo, 2010; Pitts and Britz, 2011). If these interpretations are correct, there could be also room for explaining the flattening of the LPC in the exogenous conditions and the more positive LPC in participants who reported more perceptual reversals. In fact, in the exogenous conditions, where the reversal and stability are driven by the physical properties of the stimulus, the contents of access/reflective consciousness might likely arise with the same strength/activation. On the other hand, when the perceptual reversal is represented by an internally generated interpretation, higher-order mechanisms related to subjective experience could more strongly emerge and better substantiate individual differences. 4.5. Variability in perceptual switching behaviour: a matter of interpreting what you perceive Estimated source generators of the RN were found in inferior occipital–temporal cortex while those of the LPC have been reported in inferior temporal and superior parietal cortices. Several studies, employing different neurophysiological techniques, have described the involvement of these cortical regions, outside the striate cortex, in multistable perception (Kleinschmidt et al., 1998; Leopold and Logothetis, 1999; Lumer and Rees, 1999; Sterzer et al., 2009). For example, recordings of isolated neurons in the visual pathway of the monkey during binocular rivalry have revealed that the activity of a subset of neurons in inferotemporal cortex strongly correlates with the monkey’s state of awareness, i.e. the reported perceptual change. The inferotemporal cortex of the primate is thought to play an essential role in object discrimination and recognition, therefore this brain activations could reflect the internal representation of the visual stimulus (Sheinberg and Logothetis, 1997; Leopold and Logothetis, 1999). Interestingly, an EEG source imaging study during binocular rivalry in humans (Britz and Pitts, 2011) suggested that the RN could reflect a decreased activity of the ventral stream in response to a less stable object representation. In particular, they reported increased amplitude in the stable trials, as compared to reversal ones, during the P2 time window and this increased activity was located in lateral occipital and inferior temporal areas, which are associated to percept stabilization during binocular rivalry in the primate model. Structural brain differences in the bilateral superior parietal lobules (SPL) associated with individual variability in perceptual switch rates have been also recently reported (Kanai et al., 2010; Kanai and Rees, 2011). In these studies, participants who reported fast switch rate are characterized by thicker and larger volume of SPLs than those with a slow switch rate. One possible explanation of this relationship, for the authors, could be that larger SPLs involve more strong feedback signals to early sensory areas and therefore bias the neuronal activity supporting the current percept. Parietal cortical areas are frequently found as source generators of the P3b (see Polich, 2007) and the parietal lobe volume has been reported to be significantly correlated to P3b amplitude (Ford et al., 1994). According to this, it is plausible that the same parietal regions are involved both in generate a feedback signal to the sensory cortex and to activate a neural response to a

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feed-forward signal from the sensory cortex in bistable perception. In other words, our observed modulations in the reversal-related ERPs may be related to individual differences in brain structure and connectivity that may produce different engagement in perceptual reversal and different evaluation of the perceptual outcome. As Leopold and Logothetis (1999) pointed out: ‘‘while different perceptions of ambiguous stimuli ultimately depend upon activity in the ‘sensory’ visual areas, this activity is continually steered and modified by central brain structures involved in planning and generating behavioural actions’’. 5. Conclusion Our results showed that variability in perceptual switching behaviour is associated with different activations of the RN and the LPC component. These two dERPs may reflect intermediate and late stages of information processing mainly linked with perceptual awareness and post-perceptual processes related to identification of perceptual reversal. In accordance with Kleinschmidt et al. (2012), we agree that consciousness can clearly provide a different context in which appropriate inferences are constructed when inconsistent or ambiguous information comes from the environment. Albeit speculative, it is important to underline that some mood disorders and personality variables showed relationship with the individuals’ perceptual switching. For example, patients with psychotic symptoms and obsessive compulsive disorders showed an increased switching rate, whereas depressed patients exhibited opposite patterns (Ray Li et al., 2000; Tschacher et al., 2006). Acknowledgments The authors would like to thank Dr. Jürgen Kornmeier for helpful comments and suggestions on the earlier versions of the manuscript. We also thank all our participants for being part of the study and Francesca Fracasso for assistance in collecting data. Conflict of interest: None. References Baå-Eroglu C, Strüber D, Stadler M, Kruse P, Baå E. Multistable visual perception induces a slow positive EEG wave. Int J Neurosci 1993;73(1–2):139–51. Babich S, Standing L. Satiation effects with reversible figures. Percept Mot Skills 1981;52(1):203–10. Block N. Two neural correlates of consciousness. Trends Cogn Sci 2005;9(2):46–52. Borsellino A, De Marco A, Allazetta A. Reversal time distribution in the perception of visual ambiguous stimuli. Kybernetik 1972:139–44. Braun J, Mattia M. Attractors and noise: twin drivers of decisions and multistability. Neuroimage 2010;52(3):740–51. Britz J, Pitts MA. Perceptual reversals during binocular rivalry: ERP components and their concomitant source differences. Psychophysiology 2011;48(11):1490–9. Britz J, Landis T, Michel CM. Right parietal brain activity precedes perceptual alternation of bistable stimuli. Cereb Cortex 2009;19(1):55–65. Britz J, Pitts MA, Michel CM. Right parietal brain activity precedes perceptual alternation during binocular rivalry. Hum Brain Mapp 2011;32(9):1432–42. Czigler I, Weisz J, Winkler I. ERPs and deviance detection: visual mismatch negativity to repeated visual stimuli. Neurosci Lett 2006;401(1–2):178–82. Davidson GD, Pitts MA. Auditory event-related potentials associated with perceptual reversals of bistable pitch motion. Front Hum Neurosci 2014;8:572. Donchin E. Surprise!. . . Surprise? Psychophysiology. Blackwell Publishing Ltd; 1981;1;18(5):493–513 Donchin E, Coles MGH. Is the P300 component a manifestation of context updating? Behav Brain Sci 1988;11(03):357–74. Eagleman DM. Visual illusions and neurobiology. Nat Rev Neurosci 2001;2(12): 920–6. Ehm W, Bach M, Kornmeier J. Ambiguous figures and binding: EEG frequency modulations during ltistable perception. Psychophysiology 2011;48(4):547–58. Eimer M, Mazza V. Electrophysiological correlates of change detection. Psychophysiology 2005;42(3):328–42. Ford JM, Sullivan EV, Marsh L, White PM, Lim KO, Pfefferbaum A. The relationship between P300 amplitude and regional gray matter volumes depends upon the attentional system engaged. Electroencephalogr Clin Neurophysiol 1994;90:214–28.

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