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Tsuchiya, N.

Publications and source records attributed to Tsuchiya, N..

At least 19 recordsLinked to original sources

Robust individual alignment of color qualia structures: toward a structure-based taxonomy of divergent color experiences

Whether qualitative aspects of consciousness, or qualia in short, are equivalent across individuals is a foundational scientific question. Testing this is challenging because one cannot assume a shared mapping between stimuli and private experience (my "red" may be your "green") [1-3]. Previously, we proposed a structural characterization of qualia [4, 5] and the quantitative assessment of structural correspondences through an unsupervised alignment method [4, 6], which does not presuppose such correspondence. Using this approach, our previous work focused on identifying optimal mappings between relational structures of color qualia at the group level [4]. Given known perceptual diversities [7], however, it remained unknown whether any two individuals structures could be empirically aligned. Here, we resolve this by collecting 4,371 pairwise similarity ratings for 93 colors-from 11 individuals, enabling direct individual-to-individual alignment. We reveal two fundamental, coexisting features. First, we identified two clusters of individuals showing robust within-cluster alignment, corresponding to color-neurotypicals and atypicals. Second, we uncovered a continuous spectrum of diversity: some participants who showed normal color discrimination ability in terms of the Total Error Score (TES) on Farnsworth-Munsell 100 hue test nevertheless failed to align with either cluster, revealing idiosyncratic structures that defy simple categorization. Together, these findings suggest a novel structure-based taxonomy of divergent color qualia that complements conventional performance-based classification. Our method is generalizable to other sensory modalities, and opens a path to the scientific investigation of both shared and idiosyncratic qualitative aspects of consciousness.

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Distinct cortical profiles underlie the common reportability of thought-free experiences

Mind blanking (MB) refers to moments of seemingly no reportable thought content, challenging the view of a constant thought-full mind. What remains unclear is which brain modes can lead to MB and why. Here, we aimed at delineating the nuances of MB by combining EEG-fMRI with experience-sampling of thought content and self-reported vigilance. Using a dynamic framework, we show that MB is linked to a "rich" fMRI connectivity pattern of regional anticorrelations, while low vigilance is linked to a "simple" pattern of overall positive connectivity. Put together, an interaction appears: when people are alert, connectivity around MB resembles the hyperconnected pattern, indicating that the neural correlates of MB depend on vigilance levels. This hyperconnected pattern further correlated with EEG slow-wave-like activity, tying MBs topology to sleep-like electrophysiology. We conclude that MBs neurophysiological correlates vary with vigilance, and a more refined characterisation of the neural correlates of thought-free states exists.

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Common Phenomenal and Neural Substrate Geometry in Visual Motion Perception.

What is a possible physical substrate of the qualitative aspects of consciousness (qualia)? Answering this question is a central goal of consciousness research. Due to their subjective and ineffable nature, finding a quantitative way to characterise qualia from verbal description has thus far proven elusive. To overcome the challenge of expressing subjective experience, recent structural and relational approaches have been proposed from mathematics. Yet, as far as we know, no attempts have been made to evaluate the relationship between a certain structure of qualia and the structure of a candidate underlying physical substrate. Towards this ambitious goal of linking the structures of qualia and physical, we set out to make an empirical first step by focusing on experienced dissimilarity of visual motion in human participants and stimulus-evoked neural population response geometry recorded from mouse primary visual cortex. From human participants (N=171), we obtained dissimilarity ratings of visual motion experiences induced by 48 stimuli, spanning across 8 directions and 6 spatial frequencies. Analysis revealed a human dissimilarity structure that was not well captured by a simple monotonic function of physical motion-direction difference alone nor by a pure orientation-symmetry account. From nine individual mice, we recorded single-neuron activity (n=751) with optical imaging in both awake and lightly anaesthetised conditions (isoflurane 0.6-0.8%). From neuron population responses to a similar set of motion stimuli, we computed a distance matrix that is comparable to our human dissimilarity matrix. Quantitative analyses show structural commonalities between a human dissimilarity structure and mouse neural structure, where a categorical organisation of stimulus direction best explained both. These commonalities were similar in awake and anaesthetised recordings, suggesting that this coarse V1 geometry is relatively insensitive to this type of anaesthesia; future work combining behaviour with causal intervention is required to relate such neural structures to conscious experience. Finally, we list several empirical factors that can be improved to promote our qualia structure approach in the future. Graphical AbstractSimilarity structures derived from a pairwise similarity rating task in humans revealed a mismatch between physical stimuli and subjective experience of similarity of visual motion: participants robustly rated their experience of opposite direction stimuli as similar. In mice, we used 2-photon Ca2+ imaging to record the activity of c.80 neurons per mouse in the primary visual cortex (V1) of nine mice. By computing a correlation matrix that records the similarity of responses to the same pairwise comparisons as in humans across all neurons, we can then represent neuronal distance. This revealed that mice neurons also exhibited the same dissociation between physical stimuli and responses as in humans at lower spatial frequencies. By comparing these dissimilarity and distance matrices using linear mixed effects modelling, we showed that a categorical model, not direction or orientation, best explains both human and mice data. Ultimately, this study is a methodological proof-of-concept for comparing structures of experience to candidate structures of a possible neural substrate in order to constrain their candidacy. O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/679834v2_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@768e3forg.highwire.dtl.DTLVardef@174e501org.highwire.dtl.DTLVardef@166cf75org.highwire.dtl.DTLVardef@be1a6e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIVisual motion feels more similar for opposite than diagonal directions C_LIO_LIPairwise similarity ratings reveal structure of visual motion experience C_LIO_LIHuman motion experience and mouse visual cortex share a common geometry C_LIO_LIMouse visual cortex codes motion similarly when awake and lightly anaesthetised C_LI

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Anesthesia Induces Shifts in Spatial Frequency Preference in the Primary Visual Cortex

This study examined the impact of general anesthesia on visual processing in the mouse primary visual cortex (V1). By directly comparing visual responses in the same layer 2/3 neurons between the awake and anesthetized states, we found that isoflurane anesthesia significantly shifts the preferred spatial frequency (SF) of neurons to lower values while leaving orientation tuning intact. This downward SF shift impaired the ability to encode fine visual details (high-SF) but not coarse information (low-SF). The shift was observed across all recorded cell types--including excitatory, somatostatin-expressing (SOM), and parvalbumin-expressing (PV) inhibitory neurons--though their respective magnitudes and underlying patterns were distinct. Furthermore, modulation of response gain and tuning sharpness was observed exclusively in SOM neurons. These results highlight the specific effects of anesthesia, which vary with visual feature and neuron type. These findings suggest that reduced sensory perception during anesthesia is linked to a degradation in spatial resolution at the cellular level.

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Cortical connectivity, local dynamics and stability correlates of global conscious states

Consciousness emerges from complex neural processes, yet its precise neurobiological correlates remain uncertain. Here a space-time-resolved inference-based framework is applied to estimate the neurophysiological variables of a wholecortex model and analyze the neural mechanism correlates of global consciousness by way of a correlation analysis between behavioural and neural variable time-series. Using magnetoencephalography (MEG) data from 15 participants under Xenon-induced anesthesia, interconnected neural mass models (NMMs) were developed and time-evolving regional neurophysiological variables and inter-regional connectivity strengths were inferred from the data. Analyses revealed significant correlations between consciousness levels and inter-regional connectivity, particularly in posterior parietal, occipital, and prefrontal regions. Moreover, results support a parietal, rather than frontal, network backbone to facilitate global consciousness. Regional-level analyses further identified correlates of consciousness within the posterior parietal and occipital regions. Lastly, reductions in consciousness were linked to stabilized cortical dynamics, reflected by changes in the eigenmodes of the system. This framework provides a novel, inference-based approach to investigating consciousness, offering a time-resolved perspective on neural mechanism correlates during altered states.

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A neuroimaging dataset during sequential color qualia similarity judgments with and without reports

Recent neuroscientific research has advanced our understanding of consciousness, yet the connection between specific qualitative aspects of consciousness, known as "qualia," and particular brain regions or networks remains elusive. Traditional methods that rely on verbal descriptions from participants pose challenges in neuroimaging studies. To address this, our group has introduced a novel "qualia structure" paradigm that leverages exhaustive, structural, and relational comparisons among qualia instead of verbal reports. In this study, we present the first fMRI dataset that captures relational similarity judgments among two out of nine color qualia per trial from 35 participants. This dataset also includes a "no-report" condition in half of the trials to assess the impact of overt reporting. Additionally, each participants color discriminability was evaluated with a hue test conducted outside the scanner. Our data offer valuable insights into the brain functions associated with color qualia and contribute to a deeper understanding of the neural foundations of consciousness.

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Emergence of integrated information at macro timescales in real neural recordings

How a system generates conscious experience remains an elusive question. One approach towards answering this is to consider the information available in the system from the perspective of the system itself. Integrated information theory (IIT) proposes a measure to capture this, integrated information ({Phi}). While {Phi} can be computed at any spatiotemporal scale, IIT posits that it be applied at the scale at which the measure is maximised. Importantly, {Phi} should emerge to be maximal not at the smallest spatiotemporal scale, but at some macro scale where system elements or timesteps are grouped into larger elements or timesteps. Emergence in this sense has been demonstrated in artificial binary systems, but it remains unclear whether it occurs in real neural recordings which are generally continuous and noisy. Here we first utilise a computational model to confirm that {Phi} becomes maximal at the temporal scales underlying its generative mechanisms. Second, we search for emergence in local field potentials from the fly brain recorded during wakefulness and anaesthesia, finding that normalised {Phi} (wake/anaesthesia), but not raw {Phi} values, peaks at 5 ms. Lastly, we extend our model to investigate why raw {Phi} values themselves did not peak. This work extends the application of {Phi} to simple artificial systems consisting of logic gates towards searching for emergence of a macro spatiotemporal scale in real neural systems.

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Beyond traditional visual sleep scoring: massive feature extraction and unsupervised clustering of sleep time series

Sleep is classically measured with electrophysiological recordings, which are then scored based on guidelines tailored for the visual inspection of these recordings. As such, these rules reflect a limited range of features easily captured by the human eye and do not always reflect the physiological changes associated with sleep. Here we present a novel analysis framework that characterizes sleep using over 7700 time-series features from the hctsa software. We used clustering to categorize sleep epochs based on the similarity of their features, without relying on established scoring conventions. The resulting structure overlapped substantially with that defined by visual scoring and we report novel features that are highly discriminative of sleep stages. However, we also observed discrepancies as hctsa features unraveled distinctive properties within traditional sleep stages. Our framework lays the groundwork for a data-driven exploration of sleep and the identification of new signatures of sleep disorders and conscious sleep states.

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Neural and computational processes of accelerated perceptual awareness and decisions: A 7T fMRI Study

Rapidly detecting salient information in our environments is critical for survival. Visual processing in subcortical areas like the pulvinar and amygdala have been shown to facilitate unconscious processing of salient stimuli. It is unknown, however, if and how these areas might interact with cortical networks to facilitate faster conscious perception of salient stimuli. Here we investigated these neural processes using 7T functional Magnetic Resonance Imaging (fMRI) in concert with computational modelling while participants (n = 32) engaged in a breaking continuous flash suppression paradigm (bCFS) in which fearful and neutral faces are initially suppressed from conscious perception but then eventually "breakthrough" into awareness. We found that participants reported faster breakthrough times for fearful faces compared to neutral faces. Drift-diffusion modelling suggested that perceptual evidence was accumulated at a faster rate for fearful faces compared to neutral faces. For both neutral and fearful faces, faster response times coincided with greater activity in the amygdala (specifically within its subregions, including superficial, basolateral and amygdalo-striatal transition area) and the insula. Faster rates of evidence accumulation coincided with greater activity in frontoparietal regions and the occipital lobe, as well as the amygdala. Overall, our findings suggest that hastened perceptual awareness of salient stimuli recruits the amygdala and, more specifically, is driven by accelerated evidence accumulation in fronto-parietal and visual areas. In sum, we have uncovered and mapped distinct neural computations that accelerate perceptual awareness of visually suppressed faces.

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Steady state evoked potential (SSEP) responses in the primary and secondary somatosensory cortices of anesthetized cats: nonlinearity characterized by harmonic and intermodulation frequencies

When presented with an oscillatory sensory input at a particular frequency, F [Hz], neural systems respond with the corresponding frequency, f [Hz], and its multiples. When the input includes two frequencies (F1 and F2) and they are nonlinearly integrated in the system, responses at intermodulation frequencies (i.e., n1*f1+n2*f2 [Hz], where n1 and n2 are non-zero integers) emerge. Utilizing these properties, the steady state evoked potential (SSEP) paradigm allows us to characterize linear and nonlinear neural computation performed in cortical neurocircuitry. Here, we analyzed the steady state evoked local field potentials (LFPs) recorded from the primary (S1) and secondary (S2) somatosensory cortex of anesthetized (ketamine-xylazine) cats while we presented slow (F1=23Hz) and fast (F2=200Hz) somatosensory vibration to the contralateral paw pads and digits. Over 9 experimental sessions, we recorded LFPs from N=1620 and N=1008 bipolar-referenced sites in S1 and S2 using electrode arrays. Power spectral analyses revealed strong responses at 1) the fundamental (f1, f2), 2) its harmonic, 3) the intermodulation frequencies, and 4) broadband frequencies (50-150Hz). To compare the computational architecture in S1 and S2, we employed simple computational modeling. Our modeling results necessitate nonlinear computation to explain SSEP in S2 more than S1. Combined with our current analysis of LFPs, our paradigm offers a rare opportunity to constrain the computational architecture of hierarchical organization of S1 and S2 and to reveal how a large-scale SSEP can emerge from local neural population activities.

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Wandering minds, sleepy brains: lapses of attention and local sleep in wakefulness.

Attentional lapses are ubiquitous and can negatively impact performance. They correlate with mind wandering, or thoughts that are unrelated to ongoing tasks and environmental demands. In other cases, the stream of consciousness itself comes to a halt and the mind goes blank. What is happening in the brain that leads to these mental states? To understand the neural mechanisms underlying attentional lapses, we cross-analysed the behaviour, subjective experience and neural activity of healthy participants performing a task. Random interruptions prompted participants to indicate their mental states as task-focused, mind-wandering or mind-blanking. High-density electroencephalography revealed the occurrence of spatially and temporally localized slow waves, a pattern of neural activity characteristic of the transition toward sleep. These slow waves accompanied behavioural markers of lapses and preceded reports of mind wandering and mind blanking. Furthermore, the location of slow waves distinguished sluggish versus impulsive behaviours, mind wandering versus mind blanking. Our results suggest attentional lapses share a common physiological origin: the emergence of local sleep-like activity within the awake brain.

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Team flow is a unique brain state associated with enhanced information integration and neural synchrony

Team flow occurs when a group of people reaches high task engagement while sharing a common goal as in sports teams and music bands. While team flow is a superior enjoyable experience to individuals experiencing flow or regular socialization, the neural basis for such superiority is still unclear. Here, we addressed this question utilizing a music rhythm task and electroencephalogram hyper-scanning. Experimental manipulations held the motor task constant while disrupted the hedonic musical correspondence to blocking flow or occluded the partners body and task feedback to block social interaction. The manipulations effectiveness was confirmed using psychometric ratings and an objective measure for the depth of flow experience through the inhibition of the auditory-evoked potential to a task-irrelevant stimulus. Spectral power analysis revealed higher beta/gamma power specific to team flow at the left temporal cortex. Causal interaction analysis revealed that the left temporal cortex receives information from areas encoding individual flow or socialization. The left temporal cortex was also significantly involved in integrated information at both the intra- and inter-brains levels. Moreover, team flow resulted in enhanced global inter-brain integrated information and neural synchrony. Thus, our report presents neural evidence that team flow results in a distinct brain state and suggests a neurocognitive mechanism by which the brain creates this unique experience. Data AvailabilityAll data and analysis codes used in the preparation of this article are available at https://osf.io/3b4hp.

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Integrated information structure collapses with anesthetic loss of conscious arousal in Drosophila melanogaster

The physical basis of consciousness remains one of the most elusive concepts in current science. One influential conjecture is that consciousness is to do with some form of causality, measurable through information. The integrated information theory of consciousness (IIT) proposes that conscious experience, filled with rich and specific content, corresponds directly to a hierarchically organised, irreducible pattern of causal interactions; i.e. an integrated informational structure among elements of a system. Here, we tested this conjecture in a simple biological system (fruit flies), estimating the information structure of the system during wakefulness and general anesthesia. Consistent with this conjecture, we found that integrated interactions among populations of neurons during wakefulness collapsed to isolated clusters of interactions during anesthesia. We used classification analysis to quantify the accuracy of discrimination between wakeful and anesthetised states, and found that informational structures inferred conscious states with greater accuracy than a scalar summary of the structure, a measure which is generally championed as the main measure of IIT. In stark contrast to a view which assumes feedforward architecture for insect brains, especially fly visual systems, we found rich information structures, which cannot arise from purely feedforward systems, occurred across the fly brain. Further, these information structures collapsed uniformly across the brain during anesthesia. Our results speak to the potential utility of the novel concept of an "informational structure" as a measure for level of consciousness, above and beyond simple scalar values. Author summaryThe physical basis of consciousness remains elusive. Efforts to measure consciousness have generally been restricted to simple, scalar quantities which summarise the complexity of a system, inspired by integrated information theory, which links a multi-dimensional, informational structure to the contents of experience in a system. Due to the complexity of the definition of the structure, assessment of its utility as a measure of conscious arousal in a system has largely been ignored. In this manuscript we evaluate the utility of such an information structure in measuring the level of arousal in the fruit fly. Our results indicate that this structure can be more informative about the level of arousal in a system than even the single-value summary proposed by the theory itself. These results may push consciousness research towards the notion of multi-dimensional informational structures, instead of traditional scalar summaries.

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A neural representation of invisibility: when stimulus-specific neural activity negatively correlates with conscious experience

Although visual awareness of an object typically increases neural responses, we identify a neural response that increases prior to perceptual disappearances, and that scales with the amount of invisibility reported during perceptual filling-in. These findings challenge long-held assumptions regarding the neural correlates of consciousness and entrained visually evoked potentials, by showing that the strength of stimulus-specific neural activity can encode the conscious absence of a stimulus. Significance StatementThe focus of attention and the contents of consciousness frequently overlap. Yet what happens if this common correlation is broken? To test this, we asked human participants to attend and report on the invisibility of four visual objects which seemed to disappear, yet actually remained on screen. We found that neural activity increased, rather than decreased, when targets became invisible. This coincided with measures of attention that also increased when stimuli disappeared. Together, our data support recent suggestions that attention and conscious perception are distinct and separable. In our experiment, neural measures more strongly follow attention.

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Detecting (un)seen change: The neural underpinnings of (un)conscious prediction errors

Detecting changes in the environment is fundamental for our survival. According to predictive coding theory, detecting these irregularities relies both on incoming sensory information and our top-down prior expectations (or internal generative models) about the world. Prediction errors (PEs), detectable in event-related potentials (ERPs), occur when there is a mismatch between the sensory input and our internal model (i.e., a surprise event). Many changes occurring in our environment are irrelevant for survival and may remain unseen. Such changes, even if subtle, can nevertheless be detected by the brain without emerging into consciousness. What remains unclear is how these changes are processed in the brain at the network level. Here, we used a visual oddball paradigm, in which participants engaged in a central letter task during electroencephalographic (EEG) recordings while presented with task-irrelevant high- or low-coherence background, random-dot motion. Critically, once in a while, the direction of the dots changed. After the EEG session, we confirmed that changes in motion direction at high- and low-coherence were visible and invisible, respectively, using psychophysical measurements. ERP analyses revealed that changes in motion direction elicited PE regardless of the visibility, but with distinct spatiotemporal patterns. To understand these responses, we applied Dynamic Causal Modelling (DCM) to the EEG data. Bayesian Model Averaging showed visible PE relied on a release from adaptation (repetition suppression) within bilateral MT+, whereas invisible PE relied on adaptation at bilateral V1 (and left MT+). Furthermore, while feedforward upregulation was present for invisible PE, the visible change PE also included downregulation of feedback between right MT+ to V1. Our findings reveal a complex interplay of modulation in the generative network models underlying visible and invisible motion changes.

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Conscious perception of flickering stimuli in binocular rivalry and continuous flash suppression is not affected by tACS-induced SSR modulation

The content of conscious perception is known to correlate with steady-state responses (SSRs), yet their causal relationship remains unclear. Can we manipulate conscious perception by directly interfering with SSRs through transcranial alternating current stimulation (tACS)? Here, we directly addressed this question in three experiments involving binocular rivalry and continuous flash suppression (CFS). Specifically, while participants (N=24) viewed either binocular rivalry or tried to detect stimuli masked by CFS, we applied sham or real tACS across parieto-occipital cortex at either the same or a different frequency and phase as an SSR eliciting flicker stimulus. We found that tACS did not differentially affect conscious perception in the forms of predominance, CFS detection accuracy, reaction time, or metacognitive sensitivity, confirmed by Bayesian statistics. We conclude that tACS application at frequencies of stimulus-induced SSRs does not have perceptual effects and that SSRs may be epiphenomenal to conscious perception.

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Mathematics and the brain - a category theoretic approach to go beyond the neural correlates of consciousness

Consciousness is a central issue in cognitive neuroscience. To explain the relationship between consciousness and its neural correlates, various theories have been proposed. We still lack a formal framework that can address the nature of the relationship between consciousness and its physical substrates though. Here, we provide a novel mathematical framework of Category Theory (CT), in which we can define and study the \"sameness\" between \"different\" domains of phenomena such as consciousness and its neural substrates. CT was designed and developed to deal with the \"relationships\" between various domains of phenomena. We introduce three concepts of CT including (i) category; (ii) inclusion functor and expansion functor; and (iii) natural transformation between the functors. Each of these mathematical concepts is related to specific features in the neural correlates of consciousness (NCC). In this novel framework, we will examine two of the major theories of consciousness: integrated information theory (IIT) of consciousness and temporo-spatial theory of consciousness (TTC). These theories concern the structural relationships among structures of physical substrates and subjective experiences. The three CT-based concepts, introduced in this paper, unravel some basic issues in our search for the NCC; while addressing the same questions, we show that IIT and TTC provide different albeit complementary answers. Importantly, our account suggests that we need to go beyond a traditional concept of NCC including both content-specific and full NCC. We need to shift our focus from the relationship between \"one\" neuronal and \"one\" phenomenal state to the relationship between a structure of neural states and a structure of phenomenal states. We conclude that CT unravels and highlights basic questions about the NCC in general which needs to be met and addressed by any future neuroscientific theory of consciousness.\n\nAuthor summaryNeuroscience has made considerable progress in uncovering the neural correlates of consciousness (NCC). At the same time, recent studies demonstrated the complexity of the neuronal mechanisms underlying consciousness. To make further progress in the neuroscience of consciousness, we need proper mathematical formalization of the neuronal mechanisms potentially underlying consciousness. Providing a first tentative attempt, our paper addresses both by (i) pointing out the specific problems of and proposing a new approach to go beyond the traditional approach of the neural correlates of consciousness, and (ii) by recruiting a recently popular mathematical formalization, category theory (CT). With CT, we provide mathematical formalization of the broader neural correlates of consciousness by its application to two of the major theories, integrated information theory (IIT) and temporo-spatial theory of consciousness (TTC). Together, our CT-based mathematical formalization of the neural correlates of consciousness including its specification in the terms of IIT and TTC allows to go beyond the current concept of NCC in both mathematical and neural terms.

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The Dream Catcher experiment: Blinded analyses disconfirm markers of dreaming consciousness in EEG spectral power

The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a graded and simplified form, capturing its core idea. The experiment involved a Data Team, who measured participants brain activity during sleep and collected dream reports, and a blinded Analysis Team, who was challenged to predict better than chance, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 one-minute polysomnograms of NREM sleep--27 of dreamful sleep (3 from each of the 9 participants) and 27 of dreamless sleep--redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of EEG spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power does not carry any signatures of phenomenal consciousness. Furthermore, we demonstrate an outright failure to replicate key findings of recently reported correlates of dreaming consciousness.\n\nHighlightsThe first reported attempt of the Dream Catcher test.\nThe correlates of conscious experience may not lie in EEG spectral power.\nReported markers of NREM dreaming consciousness misperformed in a blinded setting.\nThose markers also could not be confirmed in an unblinded setting.

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