Neuroscience of Consciousness

Hallucinatory experiences, defined as perception in the absence of external stimuli, can occur in both pathological and non-pathological states and can be broadly phenomenologically divided into those of a simple and a complex nature. Non-pathological visual hallucinations can be induced experimentally using a variety of stimulation conditions. To assess whether these techniques drive a shared underlying hallucinatory mechanism, despite these differences, we compared two methods: flicker and perceptual deprivation (Ganzfeld). Specifically, we measured the frequency and complexity of the hallucinations produced by these techniques. We utilised button press, retrospective drawing, interviews, and questionnaires to quantify hallucinatory experience in 20 participants. With both experimental techniques, we found that simple hallucinations were more common than complex hallucinations. We also found that on average, flicker was more effective than Ganzfeld at eliciting a higher number of hallucinations, though Ganzfeld hallucinations were longer than flicker hallucinations. There was no interaction between experimental condition and hallucination complexity, suggesting that the increased bottom-up visual input in flicker increased both simple and complex hallucinations similarly. A correlation was observed between the total proportional time spent hallucinating in flicker and Ganzfeld, which was replicated in a retrospective questionnaire measure of experienced intensity, suggesting a shared hallucinatory mechanism between the two methodologies. We attribute these findings to a shared low-level core hallucinatory mechanism, such as excitability of visual cortex, which is amplified in flicker compared to Ganzfeld due to heightened bottom-up input.

Lucid dreaming (LD), during which the dreamer is aware that they are dreaming, is frequently induced in laboratory settings by delivering sensory cues during rapid eye movement (REM) sleep. These cues should be incorporated into ongoing dreams and can trigger reflective awareness. This approach relies on the continuity between waking experiences and dream content. In sleep laboratories, participants often dream of the experimental setting itself (lab dreaming), providing a predictable context in which lucidity may emerge. The present studies leveraged this phenomenon by explicitly training participants to associate the sleep laboratory with reflective awareness prior to sleep. Across three studies (total N = 101), participants completed a morning nap following verbal LD instructions and presleep audio designed to prime recognition of the laboratory context in dreams. In addition, conditions included immersive virtual reality (VR) rehearsal of the laboratory environment, VR combined with haptic stimulation (HS) during REM sleep, or VR containing subtle fake system errors intended to prompt reflective checking. LD frequency was assessed through external ratings of signal-verified LD (SVLD) dream reports. Lucidity rates were high across all conditions, with approximately 40-45% of dreams externally rated as lucid and 11%-32% SVLDs occurring in every group. However, neither VR rehearsal, haptic stimulation, nor implicit VR errors increased lucidity relative to the baseline laboratory induction procedure. Exploratory analyses investigated the overlap between laboratory dreaming, false awakenings (FAs), and lucidity. These findings suggest that explicit training focused on the predictable context of the sleep laboratory may already provide a powerful pathway to lucidity, with additional technological manipulations offering limited benefit under a single-nap protocol. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/711049v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@191373corg.highwire.dtl.DTLVardef@c1490corg.highwire.dtl.DTLVardef@1a2c193org.highwire.dtl.DTLVardef@52c5d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

Clarifying the mechanisms underlying the emergence of consciousness remains a fundamental challenge in modern neuroscience. Integrated Information Theory (IIT) provides a mathematical framework derived from the phenomenological properties of consciousness as its axioms. IIT proposes that consciousness is identical to a systems intrinsic cause-effect information structure, quantified by integrated information {Phi}. While IIT predicts that the {Phi} of a neuronal system should decrease during the loss of consciousness, this hypothesis has remained untested at the neural circuit level. The present study provides empirical support for this IIT prediction. It was found that {Phi} within local circuits decreases during non-rapid-eye-movement (NREM) sleep compared to wakefulness and REM sleep, independent of cortical laminar structure or firing rates or regions. The reduction in {Phi} was particularly pronounced during off-periods, when neural activity is collectively suppressed. These results imply that consciousness is an information structure that cannot be reduced to the properties of individual system elements (such as firing rates), and that its collapse is fundamentally linked to the loss of consciousness. The findings provide critical empirical support for IIT as a mathematical theory aiming to explain conscious experiences. Significance StatementThis study bridges the gap between abstract mathematical theories of consciousness and high-resolution neurophysiology. According to Integrated Information Theory (IIT), conscious existence depends on a systems intrinsic cause-effect structure. By analyzing neural population activity, this study demonstrates that the transition from wakefulness to NREM sleep is characterized by a reduction in integrated information ({Phi}) within local circuits. This reduction is most pronounced during NREM off-periods, where causal integration is effectively severed, leading to a breakdown of the systems intrinsic information structure. These findings provide a neural foundation for IIT and suggest that consciousness is underpinned by specific, irreducible cause-effect structures within the brain.

One proposed function of imagery is to make thoughts more emotionally evocative through sensory simulations. Here we report a novel test of this theory utilizing a special population with no visual imagery: Aphantasia. After using multi-method verification of aphantasia, we show that this condition, but not the general population, is associated with a flat-line physiological response to frightening written, but not perceptual scenarios, supporting imagerys critical role in emotion.

Neural correlates of consciousness are often evaluated along single dimensions, such as large-scale integration or metabolic activity, yet it remains unclear whether either is sufficient, or whether conscious states require the joint satisfaction of multiple biological constraints. Here, we evaluate the prediction that conscious states occupy a regime defined by the co-occurrence of sufficient metabolic support and preserved perturbational complexity. We performed a targeted cross-study synthesis of published benchmarks from [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) and transcranial magnetic stimulation combined with electroencephalography (TMS-EEG). Metabolic values and perturbational complexity index (PCI) values were mapped across disorders of consciousness, sleep, and anaesthesia into a shared two-dimensional state space. Across independent cohorts, a lower bound near ~42-46% of normal cortical metabolism and a complexity threshold near PCI* {approx} 0.31 consistently separate unconscious from conscious conditions. Of 16 conditions with complete data, all 9 conscious states occupied the joint regime (above both thresholds), while all 7 unconscious states fell below at least one threshold (Fishers exact test, p = 8.74 x 10-5). The sole off-diagonal placement -- NREM sleep, with preserved metabolism but reduced complexity -- was unconscious, supporting the prediction that metabolic support alone is insufficient without preserved integrative dynamics. These findings support a joint metabolic-integrative constraint on consciousness and motivate a testable prediction: reportable experience should not occur outside this regime. Direct evaluation will require prospective within-subject multimodal studies combining FDG-PET and TMS-EEG.

Research on mental visual imagery typically relies on vividness ratings. However, vividness is ill-defined as it lacks an objective reference. Here, we present survey results that suggest vividness is nevertheless a robust measure. It explains individual differences of a broad range of subjective experiences, from the detail of mental imagery, the propensity to report having other internally generated visual experiences, and the vividness of visual dreams. Critically, simple vividness ratings can replace the protracted questionnaires commonly used for this purpose and reduce methodological issues with these instruments. We further find that vividness is closely linked with the experience of "seeing" mental images or projecting them into the external world. People who report seeing mental images with their eyes shut are also more likely to experience externally projected imagery. Nevertheless, many people report having mental depictions but without seeing. Overall, our results indicate we should redefine visual aphantasia to distinguish individuals with faint or unseen visual images from those completely lacking a pictorial representation.

Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimulation that would normally support such perceptions. Although all VHs share this core characteristic, there are substantial phenomenological differences between VHs that have different aetiologies, such as those arising from neurological conditions, visual loss, or psychedelic compounds. Here, we examine the potential mechanistic basis of these differences by leveraging recent advances in visualising the learned representations of a coupled classifier and generative deep neural network - an approach we call computational (neuro)phenomenology. Examining three aetiologically distinct populations in which VHs occur - neurological conditions (Parkinsons Disease and Lewy Body Dementia), visual loss (Charles Bonnet Syndrome, CBS), and psychedelics - we identify three dimensions relevant to distinguishing these classes of VHs: realism (veridicality), dependence on sensory input (spontaneity), and complexity. By selectively tuning the parameters of the visualisation algorithm to reflect influence along each of these phenomenological dimensions we were able to generate synthetic VHs that were characteristic of the VHs experienced by each aetiology. We verified the validity of this approach experimentally in two studies that examined the phenomenology of VHs in neurological and CBS patients, and in people with recent psychedelic experience. These studies confirmed the existence of phenomenological differences across these three dimensions between groups, and crucially, found that the appropriate synthetic VHs were representative of each groups hallucinatory phenomenology. Together, our findings highlight the phenomenological diversity of VHs associated with distinct causal factors and demonstrate how a neural network model of visual phenomenology can successfully capture the distinctive visual characteristics of hallucinatory experience.

Visual hallucinations (VHs) occur across psychedelic states and diverse psychiatric and neurological conditions, yet their phenomenology remains difficult to characterise. Empirical research on VHs is hindered by the lack of large-scale phenomenological datasets, which limits both mechanistic accounts and the systematic characterisation of when and how they arise. Stroboscopic light stimulation (SLS) viewed with closed eyes provides a reliable, non-pharmacological method of inducing VHs in healthy populations. These hallucinations typically consist of vivid colours and dynamic geometric patterns that resemble simple VHs described in both psychedelic and clinical contexts, suggesting partially overlapping neural mechanisms. We developed and applied an unsupervised computer-vision pipeline to analyse a large dataset of 10,598 drawings made following exposure to hallucination-inducing SLS. These drawings were produced by attendees of Dreamachine, a large-scale public installation designed to elicit stroboscopically induced visual hallucinations (SIVHs). We extracted feature embeddings with a self-supervised deep vision transformer, then applied dimensionality reduction and density-based clustering to identify recurrent visual motifs in a data-driven manner. The majority of drawings contained geometric forms, consistent with prior observations of simple VHs under SLS. However, we also identified novel and underreported geometric formations, such as concentric squares, crosses, hyperbolic patterns, and other geometries. Our results show how an unsupervised computer-vision pipeline can organise large, openly shared phenomenological datasets into interpretable classes. By mapping the diversity of simple geometric VHs at scale, this work places new constraints on existing theoretical accounts and motivates targeted experimental work linking SLS parameters, neural dynamics, and geometric visual hallucinations.

Dreams are universal yet deeply personal experiences. While memory and personal concerns influence dream content, the impact of other individual, generalizable traits remains poorly understood. To address this gap, we built a multimodal dataset including dream and wakefulness reports, alongside demographic, psychometric, cognitive, and sleep-related measures in a large adult cohort. Natural language processing characterized the semantic features that quantitatively distinguish dream from wakefulness reports, with this distinction significantly modulated by individual-specific factors. Longitudinal and cross-sample analyses further demonstrated that major external events, such as the COVID-19 pandemic, affect dream content, leaving lasting traces. Overall, the findings highlight a dynamic interplay between stable individual traits and external events in shaping dream experiences, offering novel insights into the cognitive and emotional architecture of dreaming.

Sleep deprivation amplifies emotional reactivity to brief, overt threats in the external environment. However, its effects on emotional responses to prolonged, ambiguous threat remain unclear. In this pre-registered study, we combined virtual reality, psychophysiology and multidimensional experience sampling to test the hypothesis that sleep deprivation disrupts emotional adaptation to sustained or resolving threat. Following a night of restful sleep or total sleep deprivation, healthy young adults navigated an immersive virtual world that alternated between ambiguously threatening and non-threatening contexts. Despite initial increases in emotional arousal, sleep-rested individuals quickly downregulated affective responses to ambiguous threat, reflecting efficient adaptation to the aversive virtual environment. Sleep-deprived individuals, by contrast, were unable to overturn elevated arousal responses, and exhibited a breakdown of goal-orientated, emotional control. Interestingly, resting heart rate variability, an index of affective regulatory capacity, mitigated arousal responses to ambiguous threat after sleep deprivation. These findings suggest that insufficient sleep prevents an adaptive renormalisation of emotional arousal during prolonged and ambiguous threat, giving rise to a maladaptive state of anxiety.

BackgroundIntegrated Information Theory (IIT) predicts gradual consciousness changes, while Global Workspace Theory (GWT) emphasizes discrete threshold events. We tested whether sleep stage transitions exhibit these distinct patterns using quantitative EEG analysis. MethodsWe analyzed 622 sleep stage transitions from 12 healthy adults (Wake[-&gt;]N1, N1[-&gt;]N2, N2[-&gt;]N3, REM[-&gt;]Wake) using the Consciousness Gradient Index (CGI = {surd}({varphi} x {rho}) x 10). We employed two approaches: (1) an adaptive method selecting transition-specific metrics (alpha power, spindle density, spectral entropy), and (2) control analyses applying uniform metrics (spectral entropy, Lempel-Ziv complexity) across all transitions. ResultsThe adaptive approach showed N1[-&gt;]N2 as the steepest transition (slope = -2.161, d = 2.814, p < 0.001). However, control analyses revealed this pattern to be metric-dependent. With spectral entropy applied uniformly, all transitions showed near-zero slopes (0.001-0.004), with N1[-&gt;]N2 ranking 3rd in magnitude. With Lempel-Ziv complexity, N1[-&gt;]N2 showed the smallest magnitude (0.001). Neither control supported N1[-&gt;]N2 as uniquely steep. ConclusionsThe apparent "dual architecture" resulted from selecting metrics based on known neurophysiological changes at each transition, creating circular reasoning. Control analyses using unbiased metrics showed no meaningful consciousness changes during any transition. This study demonstrates the importance of validation with uniformly-applied metrics and serves as a methodological warning about metric selection effects in consciousness research. The adaptive CGI framework successfully detected known neurophysiological changes but did not reveal fundamental differences in consciousness transition mechanisms.

The use of meditation as a tool to improve human wellbeing is receiving considerable scientific interest. However, most existing research has focused on concentration-based practices. One powerful alternative is jhana meditation, which leads to states characterised by self-reinforcing bliss, potentially useful for a variety of clinical and scientific domains. However, our understanding of these states is limited by small amounts of data and poor access to experts. To enable new insights, here we release the largest to date and first open-access dataset of electroencephalographic and physiological recordings in expert Jhana meditators. This includes 100+ hours of data in N=26 subjects across three retreats, alongside a detailed description and example code illustrating analysis of the data. This open dataset release can enable wider collaboration and has the potential to move us closer to an understanding of endogenously generated altered states of consciousness.

Previous studies suggest that visual mental imagery (VMI) acts as a weaker form of top-down visual perception (VP), with the two becoming more similar as VMI vividness increases. However, this relationship remains ill-defined, and it is unclear precisely how much weaker VMI is relative to VP. Here, we introduce an original probabilistic deep learning approach to quantify vividness at the neural level. Thirty-four participants either imagined or perceived stimuli presented at varying levels of vividness and provided trial-by-trial, picture-based vividness ratings. EEG activity recorded during VP was used to train a convolutional neural network (EEGNet) to predict perceived vividness from eight posterior electrodes located around early visual areas. A leave-one-subject-out cross-validation procedure showed that the model generalised across participants with above-chance accuracy during VP. On VP trials, predictions tracked vividness labels, with reliable interpolation to new vivid labels not included during training. Applied to VMI trials, mean expected VMI vividness remained substantially lower than expected vividness for seen stimuli but slightly higher than baseline, supporting a barely rather than quasi depictive imagery. For 91% of participants, mean expected VMI vividness was also lower than, yet scaled with, mean reported VMI vividness. This framework provides a principled way to quantify and compare VMI and VP on a shared neural-behavioural scale, with implications for studying individual differences and aphantasia.

AO_SCPLOWBSTRACTC_SCPLOWSpontaneous thoughts constitute most of everyday inner experience, yet long-standing methodological challenges obscure a thorough exploration of their content and neurophysiological underpinnings. Traditional approaches relying on thought probes impose strict constraints on phenomenological reports, whereas online verbal reports disrupt the natural flow of experience while interfering neural signals with motor artifacts. Here, we designed and tested an alternative approach to assess the neural basis of spontaneous thoughts combining delayed verbal retrospective free reports (RFR) with automated phenomenological ratings generated by large language models (LLMs). Twenty-two participants performed an eyes-closed free-thinking task, providing reports that were evaluated along ten phenomenological dimensions by four state-of-the-art LLMs and a panel of human raters. Machine-learning models (ML) were then trained to decode LLM-derived ratings from EEG spectral, complexity, and connectivity features. Our analyses showed that inter-rater agreement among LLMs exceeded that of human raters whereas ML models achieved above-chance accuracy for the prediction of emotional valence. These findings provide support for the use of LLMs for a scalable phenomenological annotation of spontaneous thoughts and suggest that their affective dimensions can be decoded from concurrent EEG activity.

Lucid dream (LD) induction using external sensory stimulation has most commonly relied on distal cues such as lights or auditory signals, with mixed success rates. In this study, we investigated whether more direct bodily stimulation targeting the muscle and vestibular systems could influence LD induction. We compared electrical muscle stimulation (EMS) and galvanic vestibular stimulation (GVS), each combined with a two-week cognitive training protocol including dream journaling, reality checks, and association training. Twenty-eight participants (14 per group) completed two counterbalanced morning naps: one with stimulation (STIM) during REM sleep and with one sham-stimulation control (SHAM). EMS and GVS stimulation did not lead to increased incorporation of the stimulus. Lucidity rates were high in both EMS conditions, highlighting the substantial role of elevated baseline lucidity in induction studies, cognitive training, and expectation effects. In contrast, GVS stimulation significantly increased externally rated lucidity and DLQ questionnaire scores compared to control. Overall, the findings indicate that galvanic vestibular stimulation can increase dream lucidity. Future work should further examine the mechanisms by which vestibular stimulation influences dream awareness and its potential role in lucid dream induction. O_FIG O_LINKSMALLFIG WIDTH=191 HEIGHT=200 SRC="FIGDIR/small/711028v1_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@7de78forg.highwire.dtl.DTLVardef@1ed7628org.highwire.dtl.DTLVardef@e84964org.highwire.dtl.DTLVardef@2a5f5e_HPS_FORMAT_FIGEXP M_FIG C_FIG

The brain continuously integrates information from the external environment (exteroception) and the internal bodily milieu (interoception). How the balance between these two processing streams shifts across vigilance states with differing levels of environmental responsiveness, however, remains poorly understood. Here, we examined neural responses to external auditory and internal cardiac signals across wakefulness and REM sleep microstates - tonic and phasic REM - which are characterized by progressively reduced responsiveness to external stimulation. High-density EEG was recorded in healthy participants (n=25). Auditory evoked potentials (AEPs) and heartbeat evoked potentials (HEPs) served as indices of exteroception and interoception, respectively, and were compared across vigilance states. AEPs progressively decreased from wakefulness to tonic REM and were most attenuated during phasic REM. In contrast, HEPs were preserved across REM microstates and were enhanced relative to wakefulness, indicating sustained - and even amplified - processing of cardiac signals during REM sleep. To quantify the relative weighting of external and internal signals, we introduce an exteroceptive-interoceptive index, defined as the ratio of auditory to cardiac neural responses. This index decreased systematically across vigilance states, revealing a graded shift from externally oriented processing during wakefulness to internally oriented processing during phasic REM, with tonic REM occupying an intermediate position. Together, these findings demonstrate that while responsiveness to external stimuli diminishes during phasic REM, the brain continues to prioritize physiologically relevant internal signals. The exteroceptive-interoceptive balance may thus provide a novel, mechanistically grounded marker of altered consciousness, particularly informative in contexts where behavioural responsiveness cannot be assessed. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/712081v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@1e46a9borg.highwire.dtl.DTLVardef@112f050org.highwire.dtl.DTLVardef@5f5249org.highwire.dtl.DTLVardef@135cc4_HPS_FORMAT_FIGEXP M_FIG C_FIG

Can we rescue a percept that would otherwise be processed non-consciously? While pre-stimulus alerting is known to facilitate conscious access, the effects of retro-cues remain ambiguous due to methodological confounds in existing literature. Specifically, most studies finding retro-cue benefits have relied on spatial features (such as lateralized targets or cues) which confound alerting with spatial selection. Our design addresses this gap by employing central visual targets and non-lateralized auditory cues, thereby isolating the temporal boost of phasic alerting from spatial orienting. Across four experiments, participants reported the presence and orientation of a central Gabor patch presented at near-threshold ([~]50% detection) or higher visibility ([~]75% detection) levels. An auditory alerting tone was presented prior, simultaneously or after the Gabor, at various short and long stimulus onset asynchronies, with both short and long temporal ranges. Results consistently showed that pre-stimulus and simultaneous cues significantly enhanced conscious perception, increasing both seen rates and (in some experiments) perceptual sensitivity. Crucially, the effectiveness of retro-cues strictly depended on stimulus visibility. While retro-cues provided no benefit under near-threshold conditions, an alerting cue presented 200 ms after target offset significantly increased the proportion of seen targets when target visibility was higher. This suggests that a sufficiently robust sensory trace can be retrospectively rescued or promoted into awareness by a late alerting boost, and that pure alerting retro-cues are able to modulate conscious perception even when no spatial features are involved. These findings demonstrate a decoupling of stimulus onset from the timing of conscious access, providing a behavioural platform to arbitrate between competing models of consciousness such as the Global Neuronal Workspace Theory and the phenomenal/access distinction of consciousness.

Subjective experiences are hard to capture quantitatively without losing depth and nuance, and subjective report analyses are time-consuming, their interpretation contested. We describe Temporal Experience Tracing, a method that captures relevant aspects of the unified conscious experience over a continuous period of time. The continuous multidimensional description of an experience allows us to computationally reconstruct common experience states. Applied to data from 852 meditations - from novice (n=20) and an experienced (n=12) meditators practising Breathing, Loving-Kindness and Open-Monitoring meditation - we reconstructed four recurring experience states with an average duration of 6:46 min (SD = 5:50 min) and their transition dynamics. Three of the experience states assimilated the three meditation styles practiced, and a fourth experience state represented a common low-motivational, off-task state for both groups. We found that participants in both groups spent more time in the task-related experience state during Loving Kindness meditation than other meditation styles and were less likely to transition into an off-task experience state during Loving Kindness meditation than during Breathing meditation. We demonstrate that drawing the dynamics of experience enables the quantitative analysis of subjective experiences, transforming the time dimension of the stream of consciousness from narrative to measurable.

Lucid dreaming (LD), during which the dreamer becomes aware of the dream state, offers a unique opportunity for a variety of applications, including motor practice, personal well-being, and nightmare therapy. However, these applications largely depend on a dreamers ability to control their dreams. While LD research has traditionally focused on induction techniques to increase dream frequency, the equally crucial skill of dream control remains underexplored. This study provides an initial investigation into the mechanisms of dream control and its potential influencing factors. We specifically examined whether a complex motor skill--juggling--could be performed within a lucid dream, creating a particularly challenging lucid dream task, which calls for a high level of dream control. Eight healthy participants (aged 24-50) underwent overnight polysomnography (PSG) at the University of Berns Institute for Sports Science, provided detailed dream reports, and completed questionnaires assessing dream control, self-efficacy, personality traits, mindfulness, motivation, and intention setting. Of these, four participants experienced lucid dreams, and of these, two demonstrated high dream control with successful LD juggling attempts. Trait differences between non-lucid and lucid dreamers in the lab were examined, with a focus on low-to-no dream control versus high dream control among the lucid dreamers. The two lucid dream juggling attempts are described in detail, providing insight into the challenges of executing complex tasks within a lucid dream. While this study lacks in sample size, it highlights the potential roles of many psychological traits, such as belief, motivation, and self-efficacy, in shaping dream control abilities. This study helps to lay the groundwork for future research aimed at investigating lucid dream control and therefore optimizing LD applications in therapy, sports training, and cognitive science.

The content of consciousness (cC) constitutes an essential part of human life and is at the very heart of the hard problem of consciousness. The cC of a person (e.g., study participant) has been examined indirectly by evaluating the persons behavioral reports, bodily signs, or neural signals. However, the measures do not reflect the full spectrum of the persons cC. In this paper, we define a method, called "CHANging Consciousness Epistemically" (CHANCE), to consciously experience a cC that would be identical to that experienced by another person, and thus directly know the entire spectrum of the others cC. In addition, the ontologically subjective knowledge about a persons cC may be considered epistemically objective and scientific data. The CHANCE method comprises two empirical steps: (1) identifying the minimally sufficient, content-specific neural correlates of consciousness (mscNCC) and (2) reproducing a specific mscNCC in different brains.