Psychophysiology

The heartbeat-evoked potential (HEP) reflects the cortical processing of cardiac afferent signals. However, it remains unclear whether trial-level interoceptive prediction errors can be quantified directly from spontaneous resting cardiac fluctuations and whether these model-derived errors are associated with HEP amplitude. Here, we applied a Kalman filter, implemented as a sequential Bayesian estimation procedure, to resting-state EEG and ECG recordings from 21 healthy adults to estimate trial-by-trial signed prediction errors in RR-intervals. Positive prediction errors reflected unexpected cardiac deceleration, whereas negative prediction errors reflected unexpected cardiac acceleration. Cluster-based permutation tests showed that unexpected cardiac acceleration was associated with greater fronto-centro-parietal HEP amplitude than unexpected deceleration in an early post-R-peak window, spanning FC1, CP1, Pz, CP2, Cz, C4 and FC2 from 215 to 250 ms. A Bayesian linear mixed-effects model further indicated a credible negative association between signed prediction error and HEP amplitude after controlling for respiratory phase and preceding RR interval. In a secondary connectivity analysis, unexpected acceleration was associated with stronger Cz-to-frontal beta-band phase synchrony during a later post-R-peak window from 250 to 500 ms. Exploratory individual-difference analyses suggested that neuroticism was negatively correlated with late frontal HEP amplitude during unexpected acceleration, but not during unexpected deceleration or when trials were pooled across conditions. These findings demonstrate that spontaneous cardiac fluctuations can be used to derive trial-level computational estimates of interoceptive prediction error and that these estimates are reflected in early HEP amplitude. They further suggest that the cortical processing of unexpected cardiac acceleration may be related to individual differences in affective personality traits.

Emotions are dynamic experiences that unfold over time, yet most affective neuroscience studies have relied on static stimuli and laboratory-based EEG systems. This study examined whether emotional valence and arousal can be reliably decoded using a consumer-grade wearable EEG device in naturalistic contexts. Forty-three participants viewed video clips designed to elicit four core affect categories including high-arousal positive, low-arousal positive, high-arousal negative, and low-arousal negative, while EEG signals were continuously recorded. Multivariate analyses, including classification, multidimensional scaling (MDS), and intersubject correlation (ISC), were employed to assess affective representation and neural synchrony. Behavioral data demonstrated robust classification of both valence and arousal, whereas EEG data yielded consistent above-chance classification of valence but less stable decoding of arousal, particularly in within-participant analyses. MDS revealed that both behavioral and EEG responses were primarily organized along the valence dimension, with weaker separation along arousal. ISC analyses further indicated frequency- and region-specific neural synchrony, with stronger alignment in left and temporal electrodes, though overall ISC values were modest, likely reflecting the brief duration of stimuli. Taken together, these findings suggest that valence is more stably represented in both subjective and neural domains, whereas arousal may require time-resolved or longer-duration approaches for reliable decoding. This work demonstrates the feasibility and limitations of employing wearable EEG for theory-driven affective neuroscience, underscoring its potential for scalable and ecologically valid emotion research beyond laboratory settings.

Mental imagery is known to be accompanied by autonomic responses, traditionally viewed as merely downstream consequences of imagery. Recent theoretical work has challenged this view, proposing that mental imagery requires the integration of cortical sensory representations with ascending interoceptive signals supplied by the autonomic nervous system. These two views make opposite predictions: if autonomic activity is only a consequence of imagery, then the responsiveness of the autonomic nervous system should not predict imagery vividness. If instead autonomic input shapes the generation of mental images, individuals with greater autonomic responsiveness should experience more vivid imagery. The present study tested these competing predictions by examining whether individual differences in cardiac vagal reactivity (indexed by the magnitude of HRV change in response to a paced breathing manipulation) predict self-reported visual imagery vividness. Imagery vividness was assessed using the Vividness of Visual Imagery Questionnaire (VVIQ) at a separate time point from the paced breathing protocol, ensuring that any observed relationship between cardiac vagal capacity cannot reflect autonomic activation driven by imagery itself. The key result was that cardiac vagal reactivity (indexed by RMSSD change normalized by mean R-R interval), significantly predicted higher VVIQ scores (r = .30, p = .031). These findings demonstrate that vividness of mental imagery is not exclusively central in origin but also shaped by the capacity of the autonomic nervous system to enter a high-parasympathetic state. Imagery thus likely involves bidirectional autonomic-cortical interaction, with descending pathways triggering the intention to generate an image and ascending interoceptive signals contributing to its generation.

The gastric-brain axis is a burgeoning field of neuroscience; however, inferences from neuroimaging research are often constrained by the high dimensionality of methodological choices potentially leading to disparate outcomes. This study addresses such concerns by performing a multiverse analysis of gastric-brain coupling in humans. We systematically evaluated 1,728 unique analytic pipelines using electroencephalography (EEG) and electrogastrography (EGG) data to quantify the robustness of observed gastric-brain coupling. Our results reveal that whilst analytic decisions influence the magnitude of observed coupling, at the group level the phenomenon remains relatively robust across the parameter space. High inter-individual variance can, however, be observed. Coupling was observed in the alpha, theta, and beta bands, with the latter two bands showing robust coupling across the largest number of electrodes. Robust coupling across frequency bands was primarily seen in medial electrodes, with some left lateral coupling also observed. Overall, these findings suggest that gastric-brain coupling is likely to be a robust physiological feature in healthy participants, providing a stable foundation for future studies.

In contemporary Cognitive Neuroscience, increasing attention is devoted to brain-body interactions, as an expanding body of literature suggests that processing the external world may not emerge from the brain in isolation but rather from the coordinated contribution of multiple bodily systems. These interactions have been extensively studied in the context of interoception. However, evidence linking them to visual perception remains scarce. To address this gap, the present study examines heart-brain interactions during a visual feature integration task. The task of the participants required shape and color integration of features to identify a target while inhibiting distractor-related information. Cardiac and neural activity were simultaneously recorded, enabling the assessment of the heart rate (HR), heart-evoked potentials (HEP), and, albeit seldom reported previously, heart-evoked oscillations (HEO). Pre-stimulus cardiac-related neural activity differed between correctly and incorrectly integrated features. HEO analysis revealed alpha and low beta band modulations before target onset, which vanished when cardiac time-locking was removed, indicating that they were specifically driven by brain-heart coupling rather than by ongoing brain activity alone. These findings provide the first evidence that HEO dynamics contribute to successful perceptual integration and extend previous work on HEPs from stimulus detection to higher-level perceptual processes. More broadly, they suggest that cardiac signals shape early brain states that bias perception, supporting theoretical frameworks proposing an active role for bodily signals in perceptual processing. HighlightsO_LIPre-stimulus heart-evoked potentials differ between correct and incorrect feature integration. C_LIO_LIHeartbeat-locked alpha and low beta activity increase before correct feature integration C_LIO_LIPre-stimulus oscillatory effects vanish without cardiac activity, revealing HEO contribution. C_LIO_LIBrain-heart coupling biases perceptual outcomes. C_LI

Motivation fluctuates across the menstrual cycle, yet the computational mechanisms underlying these changes remain unclear. We tested whether hormonally defined cycle phases selectively alter distinct components of effort-based motivation in naturally cycling women (n=51), who completed an effort-reward decision-making task and an effort psychophysics task in both the late-follicular and mid-luteal phases, alongside electrocardiography and ecological momentary assessment. Hierarchical Bayesian modelling revealed that the anticipated cost of physical effort (effort sensitivity) was selectively elevated in the mid-luteal phase, with no corresponding change in reward sensitivity. The luteal increase in effort sensitivity was attenuated in women who entered that phase after days of higher affective valence and arousal, indicating that positive affective state buffers cyclical motivational vulnerability. Complementing these findings, phase-related individual differences in effort the mapping between objective and perceived effort (effort differentiation) were not different by phase, but were moderated by heart rate variability and momentary affective states, revealing stable person-level variation in how the cycle shapes effortful experience. Together, these results identify effort sensitivity as a specific computational mechanism of cyclical motivational change, with implications for understanding the elevated burden of cycle-related psychiatric conditions across the female reproductive lifespan.

Many cognitive processes depend on integrating information as it becomes available to construct meaningful interpretations. Prior work has shown graded and incremental context effects, especially in language, but it remains less clear whether contextual integration exhibits a comparable temporal profile across symbolic domains when structured input is examined within congruent sequences. Twenty-seven participants processed congruent four-element sequences designed to be structurally comparable across lexical, algebraic, and graphical domains while event-related potentials were recorded. In the 250-500 ms interval, mean amplitudes increased systematically with sequence position within a predefined centro-parietal region of interest (p < .001). The Domain x Position interaction did not reach significance (p = .056), although modest domain-related differences in the buildup profile cannot be ruled out. A follow-up analysis showed that the increase to the response-relevant final position was larger than earlier increases (p < .001). Additional analyses indicated maximal amplitudes over parietal sites and the clearest graded increase over central sites. These findings indicate that context-sensitive activity was progressive but not uniform across sequence positions, with the strongest increase occurring when the sequence reached its final, response-relevant completion point. The presence of position-related increases across lexical, algebraic, and graphical domains is consistent with the view that centro-parietal ERP activity in the 250-500 ms window tracks the progressive buildup of contextual integration during structured sequence processing. HighlightsO_LIContext-sensitive ERP activity increased across sequence position. C_LIO_LIThe strongest increase occurred at the final completion point. C_LIO_LIMaximal amplitudes were observed over parietal electrodes. C_LIO_LICentral sites best captured graded position-related modulation. C_LIO_LIPosition-related buildup was observed across symbolic domains. C_LI

Recognizing familiar faces is essential in our everyday life. ERP studies have identified three components sensitive to face familiarity (N170, N250, and SFE), but whether these signals arise from visual experience, identity information, or semantic knowledge remains to be directly tested. Using a sequential familiarization paradigm, we progressively trained the same initially unfamiliar faces with visual exposure, identity associations, and biographical knowledge, recording EEG after each phase. The N250 emerged immediately after visual familiarization and remained stable thereafter; the N170 appeared only after identity familiarization; and the SFE exhibited a graded, enhanced pattern: absent after visual exposure, emerging after identity training, and reaching maximum effect after semantic familiarization. These findings provide the first direct evidence that these three ERP markers are differentially driven by distinct types of information, revealing the temporal dynamics through which person-related knowledge transforms a face percept into the recognition of a known person.

The sensory content and temporal structure of stimuli have been shown to consistently bias duration perception. Temporal intervals filled with continuous sensory input ("filled intervals"), are often perceived as lasting longer than intervals marked only by their onset and offset ("empty intervals"). Despite this robust behavioral finding, it remains unclear whether filled and empty intervals rely on similar or distinct neural mechanisms and, more generally, how sensory format shapes the neural processing of millisecond time. To address this question, we asked twenty-one healthy participants to reproduce visual durations across different stimulus configurations while high-density scalp EEG was recorded. Behavioral results revealed differences in performance across stimulus configurations. Event-related potentials (ERPs) recorded at occipito-parietal and fronto-central electrodes between 0.1 and 0.4 s after duration offset were modulated in amplitude by both stimulus duration and format. These modulations scaled with the sensory load of the stimulus and its duration, suggesting a common underlying mechanism. A Representational Similarity Analysis (RSA) of the ERP data showed that perceived time was represented more strongly than physical time particularly at occipito-parietal electrodes, but only within the 0.2-0.3 s post-offset window, where stimulus format exerted a pronounced effect on the ERP signal. These findings highlight the role of sensory processing in shaping duration perception and its neural coding, and reveal an early neural signature of perceived time in occipito-parietal electrodes. 1 Significance statementOur perception of subsecond durations is distorted by the sensory content of stimuli. Here, we investigated how stimulus configuration shapes the neural correlates of visual duration perception. Specifically, we asked whether temporal intervals filled with continuous sensory input are processed differently from those lacking such content. We found that, between 0.2 and 0.3 s after interval offset, ERP amplitudes were modulated by stimulus content, and in this same temporal window the EEG signal reflected the perceptual bias. These findings support the view that duration processing and perception are deeply rooted in sensory processing.

In non-verbal communication, observers infer emotions from visible facial movements, yet emotional experiences are described in internal bodily terms (e.g., "my heart skipped a beat"). This contrast highlights a tension between external sensory cues and internal signals. In this context, we examined an overlooked gap in affective science: what makes an emotional portrayal believable, and do believability judgments reflect only what observers can see or also the portraying person's internal cardiac dynamics? To test this, we created 311 scenario-driven acting clips designed to avoid prototypical posed displays. For each clip, we quantified facial movement magnitude from the video, recorded ECG during preparation and enactment, and collected actors' self-reports. Online participants (N = 371) viewed these clips and provided emotion recognition responses and continuous ratings of believability, valence, or arousal. The results show that believability decreased as movement magnitude increased, with a non-linear relationship indicating a stronger penalty as motion increased. Valence further shaped this pattern, with increasing movement reducing believability more strongly for portrayals with negative valence. This effect persisted after accounting for intended emotion, perceived arousal, and emotion recognizability. Cardiac dynamics varied during performance, and actors' higher heart rate variability was associated with higher believability for positively valenced portrayals. Together, these findings show that believability is driven by visible movement cues interpreted in relation to valence, with actors' cardiac dynamics showing selective alignment with believability. These results identify core components of believable emotional expressions and provide a basis for studying such judgments in everyday social interaction.

Mindfulness-based interventions effectively reduce stress and anxiety, yet the neural mechanisms underlying contemplative practices and their optimal implementation parameters remain poorly understood. A critical barrier to real-world application is the absence of validated minimally invasive neural recording technologies. Here, we simultaneously recorded full-coverage scalp and around-ear EEG during a 2x2x2 factorial design manipulating (1) cognitive state (mental arithmetic stress vs. passive viewing), (2) recovery strategy (mindful breathing meditation vs. mind-wandering), and (3) sensory context (eyes open vs. eyes closed). Mental arithmetic robustly elevated subjective stress and modulated canonical oscillatory patterns: increased midline frontal theta power (3-7 Hz), suppressed posterior alpha power (10-12 Hz), and enhanced posterior beta and gamma power (25-48 Hz). All rest conditions reduced subjective stress following stress induction, with eyes-closed mindful breathing producing maximal reduction. Critically, mindful breathing differentially modulated temporal beta and gamma power in a context-dependent manner, with effects determined by prior cognitive state and eye position. Eyes-closed meditation maximally suppressed gamma power within 20 seconds following arithmetic stress, whereas eyes-open meditation alone was sufficient for gamma suppression following passive viewing. Around-ear electrodes detected these stress and meditation signatures with comparable fidelity to scalp recordings. These findings reveal that mindful breathing engages rapid, context-dependent neural regulation mechanisms and establish that wearable EEG can reliably capture these dynamics, enabling real-world stress monitoring and mindfulness guidance. Impact StatementMindfulness-based interventions reduce stress, yet their neural mechanisms and optimal implementation remain unclear, partly due to limited real-world neural measurement tools. Using simultaneous scalp and around-ear EEG, we show that mindful breathing rapidly and context-dependently regulates stress-related brain activity via changes in high-frequency EEG oscillations (i.e., gamma band activity), with effects shaped by prior cognitive state and eye condition. Importantly, around-ear EEG captured these neural signatures with fidelity comparable to scalp recordings, enabling wearable neurotechnology for real-world stress monitoring and personalized mindfulness guidance.

Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) frequently co-occur in Veterans, producing overlapping symptoms and shared autonomic dysregulation. Heart rate variability (HRV) offers a noninvasive measure of autonomic function. Univariate HRV analyses often fail to capture complex, multivariate patterns associated with comorbidity. This study applied machine learning to HRV features extracted from MEG-derived electrocardiogram (M-ECG) signals to differentiate Veterans with TBI alone (TBI-alone; n = 42) from those with comorbid PTSD (TBI+PTSD; n = 40). Time-domain, frequency-domain, geometric, and nonlinear HRV metrics were analyzed using nested cross-validated Random Forest and XGBoost classifiers, with Boruta-based feature selection and SHapley Additive exPlanations for model interpretability. Both classifiers achieved above-chance discrimination (Random Forest AUC = 0.663; XGBoost AUC = 0.635). Multivariate models identified distributed autonomic signatures in TBI+PTSD, including altered sympathovagal balance, increased low-frequency proportion, and greater heart rate complexity. In contrast, univariate HRV differences were subtle and did not survive correction for multiple comparisons. These findings demonstrate how using multivariate machine learning HRV analysis could help with detecting comorbidity-specific autonomic patterns, suggesting that HRV-derived signatures may serve as exploratory biomarkers for risk assessment and targeted interventions in Veterans with TBI and PTSD.

Understanding the neural dynamics underlying expressive musical performance remains a major challenge at the intersection of neuroscience, music cognition, and computational modeling. While EEG studies of emotion have largely focused on passive exposure to affective stimuli, comparatively little research has examined oscillatory brain activity during active musical expression. The present single-subject study investigated whether band-limited EEG activity recorded during expressive piano performance by a professional concert pianist contains sufficient discriminative structure to support supervised multi-class classification of musically defined emotional categories. MethodsEEG was recorded from 128 scalp sites while a professional concert pianist performed emotionally characterized excerpts from Bach, Beethoven, and Chopin in a continuous naturalistic session. Musical excerpts had been previously categorized and perceptually validated according to emotional valence, tempo, energy/arousal, and tonal structure. From the continuous EEG recording, 180 non-overlapping 2-second artifact-free segments were extracted, yielding 30 segments for each emotional category. Mean spectral power was computed within theta (3.5-7.5 Hz), alpha (7.5-12.5 Hz), and high-beta (24-30 Hz) frequency bands across selected centro-parietal and posterior electrodes, resulting in 24 EEG-derived features per segment. Linear Support Vector Machine, Random Forest, and Gradient Boosting classifiers were evaluated using an 80/20 train-test split combined with 5-fold cross-validation. ResultsEEG-only classification achieved above-chance performance across models, with Random Forest yielding the highest accuracy (0.42), macro F1-score (0.414), and Cohens {kappa} (0.30), exceeding the theoretical chance level of 0.167. Feature importance analysis revealed distributed contributions across theta, alpha, and high-beta oscillatory activity, particularly over parietal and occipital regions, without evidence for a single dominant neural marker. Inclusion of an additional binary arousal-related feature substantially improved Random Forest performance (accuracy = 0.58; macro F1 = 0.579; {kappa} = 0.50), indicating that arousal organization contributed strongly to category separability within the classification framework. ConclusionsThese findings suggest that oscillatory EEG activity accompanying expressive musical action contains measurable statistical structure associated with emotionally differentiated performance states. Rather than identifying discrete neural correlates of emotion, the present results provide a computational characterization of distributed oscillatory dynamics emerging during expressive motor-acoustic interaction, extending affective EEG research beyond passive perception paradigms toward ecologically grounded musical performance contexts.

The P300 event-related potential is a core index of attention and context updating, but the trial-by-trial factors that shape its amplitude remain incompletely characterized. Within-trial root mean square (RMS) amplitude is often used as a summary of "early activity," yet RMS is algebraically a sum of mean and variance components (RMS{superscript 2} = mean{superscript 2} + variance) and so cannot, on its own, distinguish amplitude-driven from variability-driven coupling. Using single-trial EEG from the ERP CORE auditory oddball dataset (N = 27 retained from 40 after a {+/-}100 {micro}V peak-to-peak rejection criterion; 1,084 trials, 52.2% targets), we decomposed early-window (0-150 ms) activity at Fz and Pz into mean and standard-deviation components and modelled their associations with P300 amplitude (300-600 ms at Pz) using linear mixed-effects regression. Three findings emerge. First, early-window RMS at Fz showed only a small negative association with P300 amplitude ({beta} = -0.074, p = 0.006, marginal R{superscript 2} {approx} 0.01), three times smaller than the originally reported effect and accounting for [~]1% of P300 variance. Second, when RMS was decomposed, the early-window mean amplitude at Fz competed against the within-trial standard deviation; only the mean carried predictive weight, and its sign was positive ({beta} = +0.107, p = 2x10-{square}), the opposite sign of the RMS effect. Third, a per-electrode mixed-effects model identified Pz as the site where early activity was most strongly coupled to the P300, and at Pz the early-window mean was a powerful positive predictor of P300 amplitude ({beta} = +0.568, p < 10-{superscript 1}{square}, marginal R{superscript 2} {approx} 0.31), with a slope similar across target and standard trials and robust to baseline-window subtraction ({beta} = +0.538, p < 10-{superscript 1}{square}). Exploratory information-theoretic complexity measures (permutation entropy, sample entropy, Lempel-Ziv) showed no Bonferroni-significant association. The same-electrode parietal coupling is interpreted as evidence for a continuous parietal generator whose pre-300 ms leading edge is captured by the early window; we therefore frame this as a substantive observation about parietal cortical dynamics rather than a methodological artifact, while acknowledging that it constrains causal inference.

The aperiodic, 1/f-like component of electrophysiological activity is increasingly recognized as a meaningful feature of neural function, rather than background noise. In parallel, many EEG studies report transient changes in oscillatory power following stimulus onset and interpret these effects as signatures of attention, salience, or cognitive control. However, such conclusions usually rely on baseline normalization procedures that assume aperiodic activity remains stable from pre-to post-stimulus periods. Using high-density EEG recordings from typically developing children, we tested this assumption in two paradigms: an audiovisual simple reaction-time task (n = 36) and a visual oddball task (n = 38). For each task, conventional spectral analyses were compared with analyses that explicitly modeled and removed the aperiodic component in both pre- and post-stimulus windows. Across tasks, stimulus onset was associated with robust increases in aperiodic exponent and offset, indicating systematic changes in the 1/f component of the spectrum. In the audiovisual task, these changes were modality-specific, with central, parieto-occipital, or combined topographies depending on stimulus type. These effects were reduced but remained significant after ERP removal, indicating that they were not fully explained by phase-locked activity. Critically, once aperiodic activity was accounted for, the apparent post-stimulus increase in theta power was largely abolished in both tasks, including the canonical fronto-central theta enhancement to infrequent targets in the oddball paradigm. The conventional method also overestimated the magnitude of beta desynchronization, particularly in the induced (ERP-removed) signal. The apparent gamma desynchronization detected by conventional analyses was reversed after aperiodic correction, revealing either synchronization or no change, indicating that it reflects a spurious consequence of spectral slope steepening rather than a true suppression of gamma oscillatory activity. In contrast, alpha desynchronization remained robust after aperiodic correction and was in fact enhanced, suggesting it reflects genuine oscillatory suppression. Together, these findings indicate that a substantial portion of conventional time-frequency effects, particularly apparent theta synchronization, may reflect changes in aperiodic activity in response to stimulation rather than genuine periodic oscillations, challenging core assumptions of conventional time-frequency analyses. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/732609v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1d25f43org.highwire.dtl.DTLVardef@6c476aorg.highwire.dtl.DTLVardef@c4a02aorg.highwire.dtl.DTLVardef@ef417d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

To fully profile how ovarian hormones interact with cerebrovascular function, it is vital to consider, not just resting physiology, but also dynamic aspects of cerebrovasculature that support neural activity. This study uses hypercapnic cerebrovascular reactivity (CVR) and the visually-evoked haemodynamic response function (HRF) to investigate the influence of menstrual-related changes in oestradiol and progesterone on dynamic aspects of the cerebrovascular system. 20 menstruating females (age mean[SD]=23.01[4.01]years) completed a 3T MRI scanning session during the early follicular, late follicular, and mid-luteal phases of their menstrual cycle. Circulating hormones were measured via blood samples. Simultaneous blood oxygen level dependant (BOLD)-CVR and cerebral blood flow (CBF)-CVR data were collected using a pseudocontinuous arterial spin labelling (pCASL) acquisition using a dual-excitation (DEXI) readout during periods of hypercapnia (5% CO2). The HRF was estimated using a whole brain EPI scan during high-contrast radial checkerboard presentation. Both oestradiol and additional progesterone variance were associated with increased CVR (both BOLD-CVR and CBF-CVR; p<0.001) and altered HRF shape (p<0.005). No statistically significant regional effects were found. A secondary experiment investigated the impact of using either canonical or individually mapped HRF in a standard fMRI processing pipeline; namely, population receptive field (pRF) mapping. Results across phases suggest that neither hormone was associated with pRF size when modelled using a canonical HRF (both p>0.05). However, a significant neuroendocrine influence on pRF sizes was discovered when using individually measured HRFs (p<0.001). This study found evidence that dynamic cerebrovascular functions are sensitive to menstrual-related ovarian hormones, which may be a potential mechanism underlying menstrual symptomatology and has implications for fMRI studies that assume intact neurovascular coupling processes in women, regardless of menstrual staging, to make inferences about neural activity.

Humans make emotional facial expressions and have a cardiac response when they catch themselves in a mistake or receive feedback about task performance. We tested whether rats exhibit similar visceral responses in the context of metacognition. We assessed heart rate variability (HRV) and machine learning-detected facial expressions as female and male rats detected and stopped in-progress mistakes and received post-choice rewards or error cues. HRV increased during internally detected mistakes, as well as in response to external error cues for both sexes. Errors were associated with an HRV response when parasympathetic tone was higher, while rewards were associated with an HRV response when sympathetic tone was higher. We observed sex-specific effects of cardiac interoception on cognitive control over real-time action correction, in that low parasympathetic tone was associated with reduced ability to stop in-progress mistakes exclusively in females. Rats made facial expressions during mistake detection and in response to task feedback. Outcome-related facial expressions were valence-specific, in that the facial expression after error feedback was delayed relative to the post-reward facial expression. Our results suggest that rats have a visceral experience during metacognitive monitoring.

Our responses to environmental inputs depend on the variations of our own physiological activity. However, the mechanisms by which the integration of sensory information with interoceptive signals shape bodily responses to external events remain debated. In this pre-registered study, we hypothesized three possible mechanisms underlying such exteroceptive-interoceptive integration: cardiac surprise, active inference, and dynamic coupling. To test them, we implemented a closed-loop stimulation procedure to play auditory deviations from sequences either synchronized or not with heartbeats which varied in type (omissions or rare tones) and predictability (random or regular intervals). First, we replicated previous findings that cardiac activity slows down in response to sound omissions only when sounds are synchronized with heartbeats. Second, we showed that this effect extends to rare tones, excluding the dynamic coupling hypothesis. Third, we demonstrated that these responses do not depend on the predictability of auditory deviations, excluding both cardiac surprise and active inference hypotheses. In a control experiment, we further observed that behavioral responses depend on the type and predictability of auditory deviants: participants can discriminate subjectively which sounds were synchronized with their own heartbeats without evidence of a relationship to interoception nor cardiac responses. Overall, these results demonstrate that auditory deviations slow down cardiac responses when locked to heartbeats but independently from their type and regularity, calling for novel hypotheses to account for the interoceptive-exteroceptive integration of sensory signals into cardiac activity. Impact statementUsing a cardio-audio synchrony task, we show that cardiac responses slow down upon heartbeat-locked auditory deviations independently from their type or regularity, suggesting a simple, fundamental mechanism of integration of internal and external signals into bodily responses to the environment. The heart may provide a straightforward way to study basic self-related processes, without depending on behavior or self-report, which is especially valuable for individuals who are unable to respond or communicate.

Eye blinks are among the largest physiological artefacts in electroencephalography and are typically removed from neural recordings. Yet their timing may carry information about cognition. Here, we asked whether the temporal distribution of spontaneous blinks across trials provides a time-resolved behavioural signature of internal attentional focusing in working memory. In Experiment 1, blink-locked EEG analyses showed that blink timing was aligned with neural activity reflecting attentional focusing on a relevant internal representation. In Experiment 2, participants remembered the same visual information across conditions, but the relevant item was revealed either early, by a cue before report, or later, at report. Blink-frequency profiles shifted accordingly, increasing after the cue when selection was possible early and after the probe when selection was delayed. Post-cue blinks in the early-selection condition were also associated with better memory performance. Thus, more generally, spontaneous blinks provide an unobtrusive chronometric signal for tracking latent cognitive processing.

Wakeful rest after learning has been proposed to facilitate memory consolidation compared to engaging in a distraction task, with prior EEG studies linking slow oscillation power during rest to better memory performance. However, replication attempts have yielded mixed results. We investigated whether 10 minutes of wakeful rest would enhance associative memory performance relative to 10 minutes of a hippocampus-dependent auditory short-term memory distraction task, using a within-participant design with continuous EEG recording. We employed both a replication-inspired analytical approach, closely modeled on prior work, and a data-specific approach adapted to our dataset. Contrary to our hypotheses, we found no advantage of rest over distraction on associative memory performance. We did, however, observe an order effect: performance was better for the second learning than the first, and this improvement was more pronounced when rest was performed second compared to first. At the neurophysiological level, neither slow oscillation nor alpha power during the post-learning period correlated with memory performance, regardless of analytical pipeline, although cross-over analyses revealed that the choice of EEG reference influenced the direction of some correlations. At the phenomenological level, self-reported mental activity during rest and distraction, as well as trait daydreaming frequency, were not related to memory outcomes, despite the two conditions inducing distinct subjective cognitive states. Together, these findings do not support a robust benefit of post-learning wakeful rest over a hippocampus-dependent distraction task for associative memory, nor do they replicate prior EEG correlates of consolidation. We discuss methodological factors, including task-learning effects in within-participant designs, the coarseness of averaged spectral power measures, and variability in EEG preprocessing pipelines, that may contribute to inconsistencies across the literature, and we call for greater standardization and transparency in future studies.