Neuroscience of Consciousness

Experiences in which the sense of the body becomes distorted offer a unique window into how the brain constructs the bodily self. Despite their clinical relevance in conditions such as depersonalization and body-image disorders, large-scale brain dynamics underlying unusual bodily experiences (UBEs) remain poorly understood, and no prior study has examined them systematically across wakefulness and sleep. Here, we analyzed a high-density EEG dataset including 36 UBE episodes, such as floating sensations, distorted body boundaries, and out-of-body experiences, recorded across rapid eye-movement (REM) sleep, light sleep, sleep arousals, and wakefulness during meditation (N=20). We quantified the temporal irreversibility of neural dynamics, a marker of cortical hierarchical organization, and complemented these analyses with whole-brain signal complexity measures following a within-subject design. UBEs were consistently associated with a global reduction in neural irreversibility and cortical hierarchical differentiation relative to non-UBEs and wakefulness, along with a concurrent increase in whole-brain complexity to levels comparable to wakefulness. Together, these findings suggest that UBEs reflect a neural state combining high informational richness with a transient flattening of cortical hierarchy, a profile that resembles that observed in psychedelic states. Key pointsO_LIUBEs are associated with a global flattening of brain hierarchy measured by EEG. C_LIO_LIUBEs are associated with increased global EEG complexity, resembling the psychedelic state. C_LI

Mental images are challenging to study, given that our conscious experience is notoriously hard to access. The currently prevalent introspective methods are inherently subjective and can thus only provide limited access to their qualities. Here, we developed a neuro-computational approach that approximates and assesses the properties of mental images without the need for introspection. To enable this approach, we collected a large-scale EEG dataset (10 participants, 10 sessions each, 43,200 trials total) of participants imagining 16 scenes based on text prompts. We employed AI image generation to create candidate image sets that approximate the content of mental images (based on the imagined text prompts), computationally simulated visual cortex responses to these images and then assessed their representational alignment with rhythmic EEG responses during imagery. In line with previous reports, mid- to high-level features of the AI-generated candidate images yielded reliable alignment with human alpha activity. By manipulating the qualities of the candidate images, we then tested which qualities predisposed higher representational alignment with cortical imagery representations. We found an increased representational alignment for spatially blurred and low contrast images, providing evidence for the prevalent notion of a reduced sensory quality of mental images. We further found that mental imagery may be characterized by a psychedelic image style, which envelops the images in visual flows that distort the image proportions. These results show that our approach can objectively capture qualities of mental images without the need of introspection, providing a hypothesis-based alternative to emerging reconstruction approaches.

Effortful cognition is typically associated with controlled, task-constrained neural processing, whereas effortless awareness may require a more flexible, internally driven mode of brain organization. Critical brain dynamics provide a principled framework for characterizing this shift, as systems near criticality are thought to balance stability and flexibility, allowing efficient information processing without excessive control. Transcendental Meditation (TM), characterized by a shift from effortful mental engagement to effortless awareness, offers a natural model for testing this possibility. Here, we investigated whether critical brain dynamics track TM as a global meditative state, or instead reflect moment-to-moment fluctuations in subjective effortlessness. We combined high-density electroencephalography (EEG) with time-resolved phenomenological reports using Temporal Experience Tracing (TET). Experienced TM practitioners (N = 33) and matched controls (N = 33) completed resting-state recordings before and after a 30-minute TM or silent counting control task. Long-range temporal correlations (LRTCs) were quantified using detrended fluctuation analysis, while functional excitation/inhibition (fEI) balance was used to estimate directional deviations from criticality. State-based analyses showed that TM increased alpha and beta LRTCs relative to pre- and post-resting state within meditators, but revealed no robust between-group differences in either LRTCs or fEI balance. In contrast, neurophenomenological analyses showed that subjective effortlessness was robustly associated with increased theta, alpha, beta, and broadband LRTCs, with significantly stronger relationships in meditators than controls. Restricting analyses to low-effort periods further revealed higher beta LRTCs in meditators, a difference missed by conventional state comparisons. These findings identify scale-free neural dynamics as a candidate marker of "letting go" during meditation.

Why does an angry face feel uncomfortable? The answer is that it signals a threat. However, a face is only part of an encounter, and distance, facial stimulus type, and gaze may shape discomfort regardless of perceived anger. To separate these cues, we conducted three within-subjects virtual reality experiments. In each experiment, 24 adults viewed avatars at intimate, personal, and social distances (30, 100, and 300 cm, respectively) and rated the faces perceived anger and their own discomfort; head movement was recorded in Experiments 2 and 3. In Experiment 1, the expression (angry, neutral) and facial color (natural, red) were crossed with distance; in Experiment 2, a featureless mannequin served as a nonface comparison; and in Experiment 3, the gaze direction (direct, averted) was manipulated. Expression primarily determined perceived anger, whereas distance predominantly determined discomfort: A nearby neutral face was uncomfortable despite low perceived anger (Experiment 1). A neutral human face was more uncomfortable than a mannequin, although both received similarly low perceived-anger ratings (Experiment 2). Direct gaze increased the discomfort without changing perceived anger (Experiment 3). Backward head movement exhibited a similar pattern, with participants leaning back more from human faces than from the mannequin. These results indicate that the discomfort associated with an angry face is not merely explained by perceived anger. Instead, social discomfort was differentially associated with interpersonal distance and gaze direction and differed between the human-face and mannequin conditions.

During wakefulness, we are used to perceive the environment through our senses, act on it and take these inputs to update our experiences and build the perceptions. When the inputs are not longer accurate or structured, people would sometimes have hallucinatory experiences. Whether such experiences are associated with distinct patterns of thought, and how they relate to large scale brain dynamics, remains unclear. To address these questions, we combined experience sampling protocol with EEG recording during multimodal Ganzfeld, where participants were exposed to unstructured, uniform visual and auditory stimulation. Participants repeatedly reported the complexity of their visual experiences together with ongoing thoughts related to perceptual belief, prediction perception mismatch, active updating, and prior mentation. EEG microstates were extracted to characterize the temporal dynamics of large-scale brain networks. We found that visual complexity was related to all four dimensions, but partly distinct in simple and complex visual experiences. These phenomenological changes were accompanied by distinct, and often nonlinear, dynamics of large-scale brain networks involved in visual processing, salience detection, and internally directed cognition. It also indicates that this paradigm might be a valuable model for investigating the mechanisms underlying hallucinatory experiences in psychosis.

Museum visits typically follow curator-defined routes that constrain how visitors shape their own experience, yet choice is widely held to heighten engagement, autonomy, and enjoyment. Virtual reality (VR) offers a setting in which to study these processes because it combines ecological immersion with precise, continuous behavioral measurement. We investigated (i) whether VR- derived behavioral signals are associated with self-reported enjoyment during a virtual museum tour, and (ii) whether the level of agency afforded to visitors influences enjoyment. Forty-eight adults completed a room-scale, life-size VR tour (8 * 4 m) of seven paintings from the Tel Aviv Museum of Art, each accompanied by a synchronized audio guide. Synchronized gaze and head- position streams were logged continuously (50 Hz) and segmented into painting-level viewing episodes using a trial-and-tile pipeline that intersects each painting's trial interval with an empirically defined spatial window in front of the canvas. Participants were randomly assigned to one of three agency conditions, Active (choice before every artwork), Semi-Active (choice for the first three), or Passive (fixed route),while the artwork sequence was held identical. Self- reported enjoyment at the tour and painting levels did not differ reliably across agency conditions. Among VR-derived measures, gaze engagement during the audio guide showed the clearest (though modest) association with painting-level liking, whereas locomotion and pacing measures were weak and inconsistent predictors. Agency nonetheless reliably modulated several gaze- and time-based viewing measures. The findings reveal a dissociation between subjective enjoyment and the micro-structure of viewing, and establish a reusable framework for full-tour, painting-level behavioral analysis in immersive settings.

Identifying neural signatures of consciousness remains a central challenge in neuroscience. Sleep offers a tractable model for comparing brain activity in the presence or absence of subjective experience while minimizing behavioral responsiveness confounds. Using overnight 256 electrode high-density EEG in 140 participants and a serial-awakening paradigm, we analyzed 699 non-rapid eye movement (NREM) sleep stage 2 and 3 awakenings (351 dreaming experience, 348 no experience). Features from the 60s preceding awakening included regional spectral power, lagged-coherence connectivity, graph-theoretic metrics and gamma-to-alpha power ratios. Dreaming experiences were associated with shifts in posterior spectral balance, particularly reduced alpha and delta power and increased gamma-related measures, together with altered large scale network organization. In participant-level cross-validated machine-learning analyses, all classifiers performed above chance, with the best ensemble model reaching an ROC-AUC of 0.80 and average precision of 0.80. These findings identify reproducible posterior electrophysiological and network-level signatures of conscious states during NREM sleep.

Alexithymia is the inability to identify and describe emotions, associated with an increased risk of somatic and psychiatric disease. Alexithymia is also characterized by reduced subjective sleep quality. However, it is not well-known if this reduction in sleep quality reflects physiological changes in sleep, a specific alteration in perceived sleep quality not captured by objective metrics, or a non-specific spillover of the negative affective style which characterizes alexithymia. In BSETS, a large (N=228) EEG study of naturalistic sleep, we found that alexithymia symptoms are associated with worse habitual (AIS) and current (GSQS) subjective sleep quality ({beta}AIS=0.207, pAIS=0.002, {beta}GSQS=0.203, pGSQS=0.002). However, we observed no significant association between alexithymia and EEG-based sleep efficiency ({beta}=- 0.017, p=0.791). The association with subjective sleep quality was almost completely attenuated when controlling for either depressive symptoms (PHQ-9, {beta}AIS=0.001, pAIS=0.982, {beta}GSQS=0.044, pGSQS=0.486) or trait neuroticism (ZKPQ, BFI-44, {beta}AIS=0.057, pAIS=0.442, {beta}GSQS=0.048, pGSQS=0.514). A possible exception is the Difficulty Identifying Feelings alexithymia dimension, reflecting complaints about the labeling of interoceptive experiences, which was associated with lower subjective sleep quality even after covariate control. This pattern of findings suggests that alexithymia is characterized only by alterations of subjective sleep quality, and even these reflect a non-specific increase in negative affect which influences self-reports of sleep quality, rather than a specific reduction in perceived sleep quality itself.

Sleep microarchitecture is defined by brief electrophysiological events such as K-complexes(KCs), which are traditionally interpreted as discrete oscillatory phenomena involved in sleep maintenance, sensory gating, and cortical down-states. However, electroencephalography(EEG) also contains aperiodic, broadband activity that reflects non-oscillatory population dynamics and varies systematically across sleep stages. Because KCs are large, low-frequency transient waveforms, they also manifest across a broader frequency range, which creates a bidirectional measurement problem in which discrete events and aperiodic activity are difficult to disentangle. This is because aperiodic dynamics alone can generate fluctuations that resemble KCs, while conversely, the KC waveform itself can inflate the low-frequency end of the spectrum, steepening the fitted exponent even when the underlying aperiodic state is unchanged. These confounds blur what constitutes a real KC and a real aperiodic change in the presence of a KC. A KC-locked change in the aperiodic exponent could therefore reflect a genuine cortical state shift, a measurement artifact from the waveform biasing the spectral fit, or both. Here, we use time-resolved spectral parameterization of overnight sleep EEG to characterize aperiodic dynamics around expert-annotated KCs, with a series of controls designed to separate genuine aperiodic state changes from waveform artifacts. A component of the KC-locked aperiodic change survives these controls, suggesting that KCs are accompanied by a genuine shift toward a higher-exponent, inhibition-dominated cortical state. These findings establish aperiodic activity as a meaningful index of state dynamics during KCs and highlight measurement confounds that any event-locked spectral analysis of large transients must consider.

Sleep spindles, transient 11-16 Hz oscillatory bursts, are a defining electrographic feature of non-rem (NREM) sleep and a key biomarker of sleep physiology. A need for efficient and reliable identification of spindles motivates a large literature on automated detection algorithms. In this literature, annotation by trained sleep specialists remains the gold standard against which automated methods are trained, tuned and evaluated. However, inter-scorer agreement among experts is modest, which leaves a significant role for subjective judgment in the definition of a spindle. Finding objective, scorer-independent, criteria for identifying spindles remains an unresolved challenge. We report here a robust, highly specific, and previously unrecognized signature of spindle activity in the fourth-order spectrum (trispectrum), from which we identify the presence of spindles, characterize their waveforms, and obtain an optimal detection filter through a decomposition of the trispectrum (HOSD). Although it is a strictly blind, data-driven method, HOSD-based spindle identification and detection agrees well with expert annotation (median AUROC ~0.9), yet identifies many more events at the native threshold than both human scorers and comparison detectors. Many of these additional detections are confirmed as meeting AASM spindle criteria by four blinded specialists, demonstrating that spindle-like oscillatory bursting is prevalent below conventional human and automated detection thresholds. We observe that N2 sleep is distinguished principally by high-amplitude bursts, while low-amplitude bursting persists throughout NREM sleep, being globally suppressed only in REM sleep. We also describe robust identification of recording-specific spindle waveform properties such as frequency deceleration.

Humans rapidly assess the beauty of natural scene images. Previous EEG work suggests neural representations of beauty emerge early and are temporally sustained. Complementary fMRI work pinpoints the neural correlates of beauty to visual, frontal, and default-mode network areas. An integrated view of the spatiotemporal dynamics that give rise to the perception of beauty, however, is lacking. Beyond the beauty of the depicted scene, the quality of the image itself influences its perceived beauty, and it is unknown how the brain separates these two factors. To address these questions, we recorded EEG (N = 52) and fMRI (N = 29) data while participants rated the beauty of 100 natural scene photographs. Another group of participants (N = 46) rated the image quality of the same photographs. Separate representational similarity analyses on the EEG and fMRI data revealed early and sustained beauty-related representations across widespread cortical areas. In contrast, representations of image quality emerged earlier, had markedly different representational dynamics, and were predominantly localized to visual cortex. In a model-based EEG-fMRI fusion analysis, we investigated how the correspondence between temporally resolved EEG signals and spatially resolved fMRI signals is explained by beauty ratings. Our results suggest that beauty-related representations emerge early (from around 165ms and peaking at 275ms post-onset), are long-lasting, and primarily originate from high-level visual cortex. This spatiotemporal signature persisted when controlling for image-quality ratings. Our findings emphasize the importance of perceptual processing for perceived beauty and suggest that the brain represents aesthetic appeal independently of image quality.

Communication between the brain and cardiovascular system is increasingly recognized as a fundamental determinant of health, yet the mechanisms coordinating neural and cardiac rhythms remain poorly understood. Sleep provides a unique physiological state for investigating neurovisceral integration because cortical slow oscillations (SOs), thalamocortical sleep spindles, and autonomic activity undergo profound reorganization during non-rapid eye movement (NREM) sleep. We analyzed overnight high-density electroencephalography and electrocardiography recordings from 30 healthy adults and quantified interactions among SOs, sleep spindles, cardiac R-peaks, heart rate variability (HRV), spectral slope, and Lempel-Ziv complexity. To assess integrated brain-heart coordination, we developed a Joint Phase-Amplitude Coupling (JPAC) metric that quantifies the corelation between SO phase-dependent modulation of spindle amplitude and cardiac timing. SO-spindle coupling showed the expected fronto-central distribution but was not significantly related to autonomic or cortical state markers. In contrast, SO-R-peak coupling and JPAC systematically tracked physiological state. Stronger JPAC was associated with higher high-frequency HRV, steeper spectral slopes, and lower neural complexity, indicating enhanced coupling during synchronized, parasympathetically dominated NREM sleep. These findings demonstrate coordinated organization of cortical, thalamocortical, and cardiac rhythms within a common oscillatory framework and identify NREM sleep as a state and potential treatment target of large-scale neurovisceral integration.

Seven EEG recordings over thirteen months revealed highly stereotyped transitions between distinct brain dynamics in an advanced meditator, providing a rare experimental window onto large-scale brain-state dynamics. We report an intensive longitudinal EEG case study of an experienced Tibetan tantric practitioner recorded across seven different measurement sessions, including concentrative and analytical meditation, three Dissolution of Elements sessions, nap, and reading sessions. Across conditions, the EEG repeatedly entered abrupt and reversible "ON" periods lasting tens of seconds. These periods were characterized by high-amplitude delta-theta activity, a structured 7-8 Hz component, fronto-central predominance, and a recurrent transient complex preceding state onset. Compared with matched pre- and post-event intervals, ON periods showed increased spectral power and directed connectivity, reduced relative variability, and high cross-session similarity, consistent with a recurrent and stereotyped macroscopic neurophysiological regime. Additional analyses did not support a straightforward explanation in terms of respiratory-rate changes, sleep-related variations, or overt movement artifacts. During Dissolution of Elements meditation event counts were consistent with the reported structure of the practice. However, given the current state of knowledge about the phenomenon, there is insufficient evidence to establish a close association with either the type of meditation session or the specific practices undertaken during the practitioners many years of retreat. We therefore distinguish the robust observation of a recurrent EEG regime from the more tentative hypothesis that it is related to advanced tantric meditative practice. What does appear to be well documented, however, is an unusual, abrupt, and reversible large-scale EEG reconfiguration in a deeply phenotyped expert volunteer, highlighting the value of intensive longitudinal single-participant designs for identifying and characterizing rare neurophysiological phenomena.

Analysis does not always enhance aesthetic experience. Philosophical accounts have long suggested that decomposing an aesthetic experience into determinate components may weaken it, yet this possibility has rarely been tested experimentally. To examine whether, when, and how analysis produces divergent effects on aesthetic experience, we conducted two experiments manipulating analysis depth. Experiment 1 showed that, during affective analysis of visual art, deep analysis produced a significantly weaker increase in aesthetic ratings than shallow analysis. In Experiment 2, we selected this condition to investigate the underlying mechanism. The behavioral effect was replicated: deep analysis removed the increase produced by shallow analysis without reducing ratings below the image baseline. Frequency-resolved brain network analysis further revealed a stronger task-related component and higher spatial entropy within the default mode network under deep analysis. Network-behavior correlations observed under shallow analysis were absent under deep analysis, suggesting reduced correspondence between the default-mode network (DMN) organization and aesthetic experience. Exploratory analyses further showed that spatial weights in the lateral temporal cortex and inferior parietal lobule were associated with smaller increases in aesthetic ratings. Together, these findings indicate that deeper analysis can selectively weaken improvements in aesthetic experience by altering how affective information is organized within the DMN.

Sleep is critical for memory consolidation and emotional regulation, yet the respective roles of non-rapid eye movement (NREM) and rapid eye movement (REM) sleep remain unclear. Here, using a within-subject crossover design, we recorded high-density electroencephalography (EEG) across two experimental nights while participants viewed neutral or aversive film clips in a counterbalanced order. Combining with daytime functional localizers establishing neural patterns of aversive vs. neutral emotional processing, multivariate pattern analysis revealed that the reactivation of aversive vs. neutral memory during nocturnal sleep was both stage-dependent and event-specific. In NREM sleep, valence-specific reactivation was time-locked to slow oscillation (SO)-spindle complexes but not to either event alone; in REM sleep, reactivation occurred selectively during phasic REM periods marked by rapid eye movements. Critically, NREM SO-spindle coupling percentage was associated with consolidation of temporal memories; whereas phasic REM reactivation strength was linked to overnight dissipation of negative affect. Our findings provide direct evidence that sleep reprocesses emotional experiences through dissociable stage- and event-specific mechanisms, laying out a framework for future targeted sleep-based interventions.

Consciousness is often studied by asking whether particular brain regions or systems are privileged. We tested a distributed, decentred and context-sensitive alternative using 579 consciousness-focused neuroimaging studies. Semantic clustering of titles and abstracts identified seven domains whose corrected meta-analytic maps showed limited overlap and no invariant cross-domain core. These maps occupied 18.0% of grey matter while extending across 91.0% of brain regions. After controlling for reporting topography, distributed cognitive, sensorimotor and organismic-affective maps reconstructed the residual consciousness landscape better than spatial surrogates. Organismic-affective maps contributed most, but functional contributions varied contextually across domains. Theory-associated regional loci added no additional meaningful prediction. Information carried by the functional families was both redundant and synergistic. Coactivation-network and hypergraph analyses revealed a shared network repertoire but context-dependent higher-order coalitions. These results argue against an invariant core, supporting a distributed neurobiology of consciousness in which specialised systems are weighted and combined differently across contexts.

When we appreciate visual art, we often take our time: We visit museums or galleries and deliberately engage with artworks and their meaning. Yet, our liking might at least partly be shaped by incidental encoding of the artworks' visual features, a process initially independent from sustained engagement. Here, we show that liking for visual art can be predicted from rapid and automatic responses in the visual brain. We recorded EEG responses while participants viewed 999 artworks from the Vienna Art Picture System (VAPS), which features complex artworks that span different categories and art styles. Artworks were shown with a 300ms stimulus-onset-asynchrony (150ms on, 150 off), and participants responded to occasional target images. Using representational similarity analysis, we predicted the geometry of neural representations from the artworks' similarity in liking (based on ratings from a different group of observers), while simultaneously controlling for similarities in other properties (category, art style, valence, arousal, and familiarity). Crucially, neural responses emerging within the first 150ms were robustly connected to liking, even though the task did not afford any liking judgments. Strikingly, this early neural correlate of liking was also observed in a second experiment, in which we presented the same 999 artworks with a 50ms stimulus-onset-asynchrony. Our results show that even under ultra-fast presentation conditions, automatic neural responses rapidly indicate liking. While fully appreciating an artwork may still need deliberate and prolonged engagement, the perceptual basis of whether we like or dislike an artwork seems to be laid in a split second.

Sleepiness is a leading proximate cause of drowsy-driving fatalities, medical errors and industrial accidents, yet it has resisted mechanistic prediction; although it arises from well-characterized sleep-wake physiology, it is experienced as a subjective state and has lacked a quantitative link to the underlying dynamics. We previously showed that subjective sleepiness maps linearly, with a protocol-invariant form, onto the signed distance H - H+ between the homeostatic pressure H and the circadian-modulated sleep-onset threshold H+. This single quantity predicts sleepiness accurately but is mechanistically ambiguous: the same value can arise either because H sits far from the boundary or because the threshold H+(t) has shifted with circadian phase, and these two origins call for entirely different interpretations and interventions. Here we resolve this ambiguity by decomposing H - H+ into two mechanistically separable axes-intensity and phase. The intensity axis is the time-averaged margin [<] H - H+[>], set by how far, on average, H sits from the sleep boundary: slowed homeostatic accumulation accounts for the paradoxically blunted sleepiness of older adults, and pharmacological suppression of H accounts for the dose-dependent alerting effect of caffeine. The phase axis is set by the circadian modulation of H+(t): under a forced-desynchrony protocol, in which the pacemaker free-runs and the homeostatic and circadian processes are experimentally decoupled, sleepiness tracks the circadian profile of H+(t) across all phases while the intensity mapping itself remains unchanged-a clean dissociation of the two axes. By resolving felt sleepiness into these two physiological degrees of freedom, this framework renders previously isolated phenomena-aging, caffeine and circadian misalignment-commensurable within a single theory and provides a physiologically interpretable basis for prospective fatigue-risk prediction. Author summaryWhy people feel sleepy after sleep loss, or at particular times of day, remains difficult to predict from physiology alone. Sleep and wake are shaped by two interacting processes: a daily circadian rhythm and a homeostatic pressure that builds during wakefulness. In earlier work, we linked subjective sleepiness ratings to a simple geometric quantity-how close sleep pressure sits to a circadian sleep-onset boundary. That link is useful, but ambiguous: the same distance can arise either because pressure itself has changed, or because the boundary has moved with circadian phase. Here we use a computational model of the sleep-wake switch, extended to include the wake-stabilizing orexin system, to separate these contributions into an intensity axis and a phase axis. We find that aging and caffeine mainly alter how large the average distance to the boundary becomes, whereas forced desynchrony mainly alters how that distance varies across circadian phase. This dissociation offers a compact way to interpret several otherwise separate observations within one quantitative picture, and a step toward more physiologically grounded fatigue-risk assessment.

Anticipatory neurophysiological activity before unpredictable emotional events has been reported for decades, but the effects are small, difficult to replicate, and obtained almost exclusively in stripped-down laboratory paradigms. Whether they reflect genuine predictive processes or methodological artifacts remains debated; one rarely tested possibility is that such settings mute it. We examined anticipation in an immersive virtual reality (VR) driving simulation whose collision outcomes were assigned by a quantum random number generator, while EEG, photoplethysmography (PPG), EMG, EOG and inertial (IMU) signals were recorded from a single wireless dry-electrode headset built into the VR display. Sixteen participants passively observed 120 driving sequences ending in a collision or no collision. Length-matched pre- and post-stimulus windows were analysed identically with mass-univariate hierarchical general linear models under permutation cluster correction, plus machine-learning classification across held-out participants. Two post-stimulus clusters differentiated the conditions, the largest peaking at 451.6 ms (d = -1.35), with broadband power modulation robust to baseline modelling, decoded from held-out participants with up to 93.8% accuracy. No time-domain effect or decodable information preceded stimulus onset, but a broadband spectral difference did, surviving preregistered controls for expectation bias (run length), time-on-task build-up and ocular or facial-muscle artifact: directionally consistent in 13 of 15 participants, with pre- and post-stimulus power coupled in all 16. Heart rate differentiated the conditions neither alone nor when added to the classifier. The study was preregistered; the BIDS-format data, analysis code and an open-source EEGLAB plugin are publicly available.

Certain conscious contents--such as the covert name of a viewed object--arise involuntarily and resist suppression, a phenomenon captured by the Reflexive Imagery Task (RIT). Whether a subliminal prime can bias which of two simultaneously present objects captures such an involuntary naming response has not been established. We adapted a two-object RIT into a self-contained, browser-based instrument. Thirty-eight adults (M age = 21.6 years, SD = 2.3; 26 female) viewed 24 object pairs and were instructed to fixate a central cross, to refrain from thinking of the objects names, and to click an object whenever its name intruded into awareness. On each trial a masked prime (17 ms, flanked by pattern masks) was either the exact name of one object (Exact Word Prime), a semantic associate of one object (Semantic Prime), or a neutral string (No Prime), defining a primed side per trial. A post-experiment debriefing confirmed that participants noticed the masks but none consciously perceived or could identify the prime words, indicating that the primes were subliminal. Analyses excluded 91 of 912 trials (10.0%) with more than five clicks. The priming effect was robust across measures: an omnibus comparison of clicks across conditions was significant (Friedman {chi}{superscript 2}(2) = 6.59, p = .037); within both prime conditions participants clicked the primed side more than the not-primed side (Exact Word, p = .039; Semantic, p = .005); and the primed-to-not-primed ratio exceeded parity by roughly 57-59% (Laplace-smoothed ratio {approx} 1.58; one-sample Wilcoxon p < .001 for each). A Bayesian Poisson generalized linear mixed model with random participant intercepts confirmed a credible primed-side advantage (incidence rate ratio = 1.72, 95% credible interval [1.57, 1.90]) that did not differ between prime types. No general left/right response bias emerged. An exploratory ight-side x Exact Word Prime interaction was inconsistent across model classes and is reported as hypothesis-generating. The findings indicate that subliminal lexical and semantic primes can steer the spatial locus of involuntary object naming.