Psychophysiology

Flow, as defined by Mihalyi Csikszentmihalyi (1975), is a holistic sensation experienced when individuals are fully immersed in an activity, resulting in a mental state characterized by a diminished sense of self and altered perception of time. To investigate the global neural dynamics underlying flow, we employed EEG microstate analysis to capture the spatial and temporal properties of dominant transient global brain states (Lehmann et al., 1998). In a study involving 43 participants playing the video game Thumper for 25 minutes, we extracted three four-minute EEG segments from each session corresponding to reported experiences of flow, boredom, and frustration, as determined by self-reports and performance metrics. Across conditions, six distinct microstate topographies (A-F) accounted for most of the global variance. Given that reduced self-referential processing is a key feature of flow, we hypothesized that flow would modulate the properties of microstates C and E, which have been associated with brain regions resembling the default mode network (DMN). Compared to boredom and frustration, the flow condition showed significantly decreased global explained variance, mean duration, time coverage, and occurrence frequency of microstate E, as well as reduced mean duration and time coverage of microstate C. These findings suggest that microstates associated with self-referential processing are shorter and less frequent during flow than during boredom and frustration. This supports the notion that the flow experience modulates global brain dynamics, particularly within the DMN. Furthermore, our results align with previous research reporting reduced DMN activity during meditative and psychedelic states, reinforcing the idea of diminished self-awareness in such conditions.

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.

Subthreshold depression is associated with significant functional impairment and elevated risk of major depressive disorder. A negative self-concept may disrupt the implicit positive association evoked by ones own face, impairing incidental encoding of self-relevant information. Whether subthreshold depression involves a selective deficit in encoding self-face identity remains unclear. The attribute amnesia paradigm is well suited to address this question because it can dissociate attentional selection from working memory encoding. Using this paradigm, we examined the issue across two experiments. Experiment 1 employed nonsocial stimuli (animal drawings) and confirmed an intact attribute amnesia effect in subthreshold depression (n = 30) comparable to healthy controls (n = 30), ruling out a generalized encoding deficit. Experiment 2 replaced targets with faces (self or other) and revealed a selective enhancement of the attribute amnesia effect for self-face identity in subthreshold depression. Specifically, on the surprise trial, accuracy for self-face identity dropped to near-chance levels in the subthreshold depression group, whereas no such deficit emerged for other-faces or in controls. Encoding recovered rapidly once explicit memory expectations were introduced, indicating intact basic encoding capacity. These findings suggest that subthreshold depression is associated with a specific impairment in incidentally encoding self-face identity. This impairment likely stems from a negative self-concept that weakens self-face salience under incidental encoding conditions. By capturing this selective encoding failure, the present study reveals that the self-processing deficit in subthreshold depression can arise at the gating stage between attention and working memory consolidation.

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 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.

When humans learn under conditions of uncertainty, they dynamically adjust how they prepare for and respond to feedback. In navigating uncertain environments, the brain minimizes error by continuously refining internal models via memory updating (MU). Feedback is critical for MU, and anticipatory neural mechanisms shape how feedback is processed, likely reflecting learned environmental certainty. However, the literature has largely focused on post-feedback activity, leaving pre-feedback certainty-related mechanisms less understood. The present study aims to address this gap by examining how certainty modulates anticipatory states, preceding feedback and subsequent MU. We examined oscillatory activity prior to performance feedback in a reanalysis of EEG data previously published by Hassall and colleagues (2023). Twenty-one participants (16 female, Mage = 25.81 years) predicted the strength of cartoon characters with varying predictability levels which were learned through exposure. Feedback on prediction accuracy was presented via an animated rising bar. Results revealed that theta power is modulated by accumulative feedback. Linear mixed-effects models revealed an interaction between predictability-related certainty and learning stage: in late learning, higher performance was associated with increased pre-feedback alpha and beta power for low-certainty trials, whereas in early learning, higher performance was associated with decreased beta power. These learning-related modulations in alpha and beta power suggest that initial learning is marked by adaptable exploratory processing. Subsequent learning exhibited increased alpha-mediated inhibition and beta-related anticipatory activity for lower certainty trials, indicative of dynamic strategy refinement and selective engagement of task-relevant information. These results demonstrate that certainty shapes preparatory oscillatory activity associated with MU.

Infant environments are rich in rhythms, many of which are social in nature. These rhythms are proposed to play an important role in early communication and interpersonal synchrony. In this cross-sectional electroencephalography (EEG) study with 3- and 6-month-olds (n=31 and n=30, respectively), we examined whether the infant brain tracks the rhythmicity of locomotion-related biological motion in the visual domain and which experiential factors relate to this ability. We found robust neural tracking of biological motion rhythms at both ages, with no effects of age or orientation (upright or inverted). Additionally, we found that caregiver-reported practice of infant carrying/babywearing and caregiver attitudes toward social touch were linked to infant neural tracking of biological motion rhythms, particularly in the inverted condition. Finally, exploratory analyses revealed a lateralisation effect, whereby the left hemisphere processed rightward (vs. leftward) biological motion rhythms more strongly. Our findings suggest that from early on, the infant brain tracks the rhythmicity of whole-body biological motion. Furthermore, caregiver touch-related practices, particularly infant carrying/babywearing, may play a role in infant neural tracking of socially-relevant rhythms.

Past work has indicated that expectation can modulate neural responses to visual stimuli, but it is unclear whether these effects remain consistent across different types of unexpected stimuli. Here, we measured and compared neural prediction effects associated with semantic category and presentation frequency-based expectations in real-world object stimuli. Participants (n = 35) viewed real-world object images in rapid serial visual presentation (RSVP) streams. Semantically unexpected stimuli occurred when a stimulus was presented in a semantically incongruent stream. Low-frequency violations occurred when a rarely presented stimulus was displayed in a semantically congruent stream. Multivariate pattern analysis of electroencephalography (EEG) was used to quantify and compare the degree of information represented in neural activity for stimuli in different prediction conditions. Semantically expected stimuli yielded lower decoding accuracy relative to random (unpredictable) stimuli (125-313 ms post-onset) while semantically unexpected stimuli exhibited increased decoding accuracy (199-238 ms & 523-559 ms). Low-frequency violations yielded decoding accuracy which was not significantly different from semantically expected stimuli. Exploratory analyses indicated that dissimilarity between expected and presented stimuli quantified in terms of higher-level stimulus features, but not low-level visual features, modulated the observed neural prediction effects. These results demonstrate that different types of prediction violations have distinct modulatory effects on neural responses, providing novel insight into the neural implementation of predictive processing.

Singing is an innate human behaviour present across cultures and the lifespan. Despite lacking direct biological advantages, its ubiquity suggests that it is intrinsically rewarding. This research aimed to investigate the underlying factors that explain variability in sensitivity to deriving reward and enjoyment from natural singing in the general population. In Study 1 (n = 606), an initial pool of items describing daily, non-professional singing behaviours were administered to an international adult sample. Exploratory factor analysis revealed a unidimensional structure of 20 items with acceptable model fit, organized into five facets representing distinct domains of singing-related rewards: 1) pleasure and emotional evocation, 2) social singing reward, 3) singing frequency, 4) mood regulation through singing, and 5) inattentional singing during routine tasks. In Study 2 (n = 430), confirmatory factor analysis in a new sample supported this structure. When both samples were combined (n = 1036), the unidimensional model defined by these five facets showed acceptable to excellent goodness-of-fit indices, supporting the conceptualization of singing reward as a multidimensional construct with differentiated facets. This led to the Barcelona-Aarhus Natural Singing Engagement Questionnaire (BANSEQ), which demonstrated excellent reliability ( = .94) and population-level stability. Study 3 (n = 1036) tested the convergent validity of BANSEQ with measures of music reward and engagement and identified sociodemographic and psychological correlates across the five facets of singing reward. Overall, these findings characterize the sources of individual differences in the hedonic experience of natural singing and propose BANSEQ as a robust psychometric tool for its assessment in the general population.

Functional connectivity (FC) is a statistical measure that reflects the degree of phase consistency between two signals and provides insights about potential interactions between two brain regions. Previous studies have reported conflicting results on the effect of meditation on FC, with some showing enhancement while others reporting suppression of FC. However, even though meditation increases power over a broad frequency range between 15-200 Hz and beyond, most FC studies have reported changes over fixed and narrow frequency bands below 50 Hz. Further, meditation-induced changes in power spectral density (PSD) and FC have never been compared with changes with other factors such as age, gender and stimulus. We recorded electroencephalogram (EEG) from open-eyed meditators (N=35) and their gender-and age-matched controls (N=36) and found that meditation was associated with a state decrease in FC across a broad frequency range (15-200 Hz), while PSD showed both trait and state enhancement. Furthermore, visual gratings, which are known to enhance narrow-band gamma power, led to reduced gamma FC in both meditators and controls. We also compared the effect of aging and gender on a different dataset of healthy middle-aged (N=78) and elderly (N=89) participants and found differences in distinct frequency bands that were limited to a narrow range. We also found that often-used average referencing heavily distorted the FC and gave uninterpretable results. Overall, our results suggest distinct neural mechanisms underlying healthy aging, vision, and meditation and further recommend caution while using average referencing to study phase-based metrics. Significance statementMeditation research has reported inconsistent effects on functional connectivity (FC), partly because most studies examined only narrow low-frequency bands despite meditation altering brain activity across a much broader frequency band. This study demonstrates that meditation produces a broadband state reduction in FC across 15-200 Hz, while simultaneously enhancing power. In contrast, healthy aging, gender, and visual stimulation showed frequency-specific effects confined to alpha (8-12 Hz) and high-beta (20-36 Hz) bands, highlighting meditations unique large-scale neural signature. The study also shows that average referencing can severely distort phase-based FC estimates, leading to misleading interpretations. These findings clarify conflicting literature, distinguish meditation from other neural modulators, and provide important methodological guidance for EEG connectivity research.

The precise coordination of slow oscillations (SO) and sleep spindles during non-rapid eye movement (NREM) sleep supports memory consolidation and may serve as a sensitive marker of cognitive aging. However, longitudinal changes in their oscillatory dynamics in midlife and older age remain poorly understood. Using polysomnography with high-density EEG at two timepoints over [~]2.5 years, we examined changes in local NREM slow wave (SW), sleep spindle (occurring in the 11-16 Hz sigma range), and SO-sigma coupling strength in cognitively unimpaired middle-aged to older adults at risk for Alzheimers disease. Fronto-central SO-sigma power coupling strength significantly declined over time, independent of changes in multiple measures of SW and sleep spindle expression. Local declines in multiple sleep spindle measures were also observed. Greater baseline levels of cerebrospinal fluid (CSF) neurogranin, a postsynaptic protein abundantly expressed in the dendritic spines of the hippocampus and cerebral cortex and implicated in calcium-dependent synaptic plasticity, predicted the magnitude of longitudinal decline in SO-fast sigma coupling strength, which in turn predicted episodic memory performance changes. These findings suggest that longitudinal changes in local sleep oscillatory dynamics are related to decreased synaptic integrity and may serve as an early indicator of memory decline in older adults at risk for Alzheimers disease.

The present study examined whether thematic reversal anomalies are processed similarly across subject and object experiencer constructions in Malayalam. Event-related brain potentials (ERPs) were recorded as 30 first-language speakers of Malayalam read transitive sentences with the two types of experiencer verbs, in which the thematic role assignment for the preceding arguments was either correct or reverse. The reversal anomaly became apparent only at the position of the experiencer verb. A linear mixed-models analysis confirmed a biphasic N400-P600 effect at the verb for both verb types when the argument roles were reverse. Thus, our results suggest a uniform processing strategy for TRAs irrespective of the type of experiencer verb involved. However, the N400 amplitude was larger for the object experiencer verb compared to subject experiencer verbs. We suggest that the quantitative difference observed for object experiencer verbs is due to the inverse linking of grammatical function and thematic roles associated with these verbs. In other words, verb-specific linking properties modulate the processing of TRAs involving object experiencer verbs. We argue that this modulation occurs because the parser recalibrates cue weighting when the expected form-to-meaning mappings are overridden by the inverse linking properties of object experiencer verbs.

Pupil diameter is widely used as an index of arousal and brain state, yet it remains unclear whether slow pupil-linked state fluctuations systematically modulate the effective capacity of temporal attention. Here, we tested this question using an auditory attentional blink paradigm, in which participants were required to detect the first (T1) and second (T2) targets. In Experiment 1, trial-by-trial analyses revealed that successful T2 detection, given correct T1 detection (T2|T1), was associated with smaller baseline pupil size. Furthermore, analyses focusing on slow pupil fluctuations (< 0.2 Hz) revealed that the T2|T1 detection accuracy increased during the pupil dilation phase occurring 0-2 seconds after pupil constriction. In Experiment 2, we used real-time pupillometry to trigger stimulus presentation during predefined phases of ongoing slow pupil dynamics. This closed-loop manipulation produced reliable phase-dependent differences in T2|T1 detection accuracy. Critically, the effect of pupil phase remained significant in a linear mixed-effects model that included baseline pupil size as a covariate, indicating that it cannot be explained by baseline pupil size alone. Together, these findings demonstrate that temporal attentional capacity is shaped not only by arousal level but also by the phase of slow pupil-linked brain-state fluctuations. Our results suggest that the attentional blink reflects a dynamically regulated, state-dependent limitation rather than a fixed processing bottleneck.

Focusing on an organisms task at hand is instrumental for intelligent and goal-driven behavior. However, humans and other animals often fail to pay sustained attention across long time intervals. Failing to stay on-task may cause one to miss crucial task-relevant signals, leading to impaired performance, which can have serious consequences. Therefore, it is important to understand the neural basis of attentional lapses. One promising neural marker of attentional lapses is the frontal P3 (fP3) EEG component, which has been suggested to reflect the susceptibility to incoming sensory input. Following this, we hypothesized that the fP3 1) predicts imminent lapses of attention, and 2) that it should predict upcoming lapses of attention across modalities. In two experiments, we found that the fP3 reliably tracked lapses of attention of sustained attention already seconds preceding the crucial visual signal. We further extended this to the auditory domain: Already 1.5s ahead of the incoming auditory target, the fP3 revealed whether that target was detected or not. Detailed topographic analyses did, however, reveal a slight dissociation between modalities in underlying intracranial source configurations. In sum, this work revealed a supramodal neural signature of susceptibility, which tracks lapses of sustained attention seconds ahead of the critical incoming sensory input.

Summary paragraphA hallmark of learning is the need for sensory stimuli (Ginns, 2015; McGraw et al., 2009; Reinwein, 2012; Spence, 1950) so that learning is fundamentally based on sensory input signals affecting behaviour, physiology, and neurology. If behavioural measures of learning can be causally linked to physiological and neurological variables, a broader understanding of the mechanisms related to learning in schools, learning disabilities, and learning and health issues may emerge (McGraw et al., 2009). Despite decades of research on the physiological/neurological variable of sympathetic activation, learning, and achievement (Horvers et al., 2021), any causal relation remains unclear (Cowley et al., 2014; Mason et al., 2020; Pijeira-Diaz et al., 2016; Sung et al., 2023; Yu et al., 2024) and issues with instrument validation remain (Costantini et al., 2023; Hu et al., 2024; Milstein & Gordon, 2020; Van Der Mee et al., 2021). Here we investigate the effect of sensory input on sympathetic activation by using validated instruments for skin conductance measurement (Batista et al., 2019) and whether sympathetic activation is connected to learning in a cognitive laboratory context and an ecologically valid classroom context. In both contexts, we found a physiological variable which correlated with learning and that sensory input affected this variable while student movement did not. These sensory inputs varied depending on the different instructional activities the students participated in. Together, these findings bring us one step closer to a model linking sensory input to behavioural, physiological, and neurological variables.

Meditation has been associated with improvements in attention, emotional regulation, and mental well-being, motivating increasing interest in objective methods for assessing meditative states. In this study, we investigate whether EEG-based machine learning can reliably distinguish between multiple meditation styles and mind-wandering states. EEG data were recorded from experienced meditators performing three meditation styles, Shamatha, Vipassana, and Metta, together with an eyes-closed mind-wandering condition. EEG signals were preprocessed to remove artifacts, and features were extracted from frequency, time-frequency, and time domains. Classification was evaluated using both intra-subject and inter-subject strategies with multiple machine learning classifiers. Results demonstrate high intra-subject classification accuracy across meditation-versus-mind-wandering and meditation-style comparisons, indicating strongly discriminative subject-specific neural signatures. In contrast, inter-subject performance decreased substantially, particularly for distinguishing meditation styles, suggesting considerable inter-individual variability in meditation-related EEG patterns. Furthermore, temporal analysis revealed that classification performance increase over time, indicating that the neural distinctions between meditation states become increasingly pronounced over time. Additionally, t-SNE visualization showed clear within-subject clustering but increased overlap across subjects, explaining the reduced inter-subject generalization. Overall, these findings highlight the potential of EEG-based machine learning for personalized assessment and monitoring of meditative states while emphasizing the challenges of developing subject-independent meditation classification systems.

Ocular measures are increasingly used as non-invasive proxies of cognitive processes such as attention and listening effort. However, their interpretation in aging populations is complicated by concurrent changes in ocular physiology and oculomotor control, raising a critical question: to what extent do age-related differences in these measures reflect cognitive rather than other physiological factors? Here, we dissociate these contributions by characterizing ocular dynamics (resting and event-evoked) during passive fixation in younger (N = 98, 18-35 years) and older adults (N = 71, 60+ years). Aging is associated with pronounced alterations in pupil dynamics, including reduced baseline variability and slower, attenuated responses to both auditory and visual events. In contrast, microsaccade dynamics did not correlate with aging. Across measures, ocular responses showed moderate-to-high within-subject stability across blocks, and factor analysis in the older cohort revealed separable components reflecting instantaneous pupil responsivity, sustained pupil responsivity, and microsaccade dynamics, with additional variance associated with sensory decline and age-related changes in pupil dynamics. Together, these findings demonstrate a clear dissociation: pupil-based metrics are strongly influenced by aging, whereas microsaccades remain comparatively stable across age groups. This dissociation provides a principled basis for interpreting ocular indices in aging research and highlights the need to account for baseline physiological differences when inferring cognitive processes from eye-based measures.

The types of faces that infants see impact their developing ability to engage with and individuate people from familiar and unfamiliar social groups, a phenomenon known as perceptual narrowing. However, the neural mechanisms that underlie infants processing of different faces as a function of experience remain poorly understood. To address this gap, the present study analyzes electroencephalography data collected while 3-month-olds (N=24), 6-month-olds (N=26), and 9-month-olds (N=18) viewed female and male faces of a familiar or unfamiliar social group. Infants neural responses to faces differed by group familiarity from 3 months of age, with increased responses to the more familiar face types in early components (P1, N290), and to the more unfamiliar face types in later components (P400, Nc). Face sex and group familiarity interacted to shape N290 and P400 amplitudes at 3- and 9-months. Specifically, N290 amplitudes were greater in response to female faces of a familiar group at 3 months, and to male faces of a familiar group at 9 months. In contrast, P400 amplitudes were greater in response to male faces of an unfamiliar group at 3 months old, and greatest in response to both female faces of a familiar group and to male faces of an unfamiliar group at 9 months. Source reconstruction of the Nc revealed greater reconstructed current density in response to faces of an unfamiliar social group across all ages. These findings contribute to a growing body of knowledge examining how perceptual experiences shape infants understanding of their social world.

Learning in adulthood is embedded in everyday social life, in which periods of psychosocial stress alternate with recovery. The autonomic nervous system regulates how the body responds to environmental demands, yet individuals differ markedly in this regulation. It remains unknown whether such individual differences in bodily regulation modulate the ability to learn probabilistic patterns from sensory input. Here, we investigated statistical learning of probabilistic patterns in speech streams in a six-hour experiment incorporating psychosocial stress and recovery to approximate everyday conditions. Sixty-five adults were exposed to novel speech streams in high- and low-stress contexts, with learning assessed immediately after exposure and following a rest period. Heart rate variability was recorded throughout the experiment to capture individual differences in autonomic reactivity to stress and recovery. From these measures, we constructed composite proxies of sympathetic (SNS) and parasympathetic (PNS) nervous system reactivity. Individuals with congruent SNS-PNS reactivity--either jointly high or jointly low--showed superior statistical learning outcomes across stress contexts. SNS reactivity preferentially supported encoding, whereas PNS reactivity supported consolidation. Moreover, the effect of SNS activation during speech exposure on statistical learning depended on individuals SNS reactivity profiles. These findings demonstrate that individual differences in bodily regulation are tightly linked to the ability to learn statistical dependencies in stressful environments. Overall, the findings highlight the essential role of brain-body-environment interactions in statistical learning.

Early-life stress elevates the risk of developing neuropsychiatric disorders. However, the mechanism underlying this vulnerability, and how they contribute to sex differences in these disorders, remain to be understood. Here, we use multivariate brain-based predictive models to examine how the number, positive or negative appraisal, and impact of adolescent stressful life events reported either by the youth or their caregivers are reflected in neuroanatomy (cortical thickness, surface area, cortical and subcortical gray matter volume, and T1 intensity measures). We used data from the Adolescent Brain Cognitive Development (ABCD) study at 2-year (N = 6,301, age 11-12), 4-year (N = 5,000, age 13-14) and 6-year (N = 3,226, age 15-16) follow-up time points to examine the sex-independent and sex-specific neural correlates of stressful life events. Our analyses showed mostly non-significant associations between stressful life events and neuroanatomy. However, we did find that the number of positively appraised stressful life events reported by the caregivers at the 4-year follow-up was significantly associated with cortical thickness, independent of sex, and with surface area in females only. Across three developmental timepoints, seven neuroanatomical measures, two reporting perspectives, and both sex-independent and sex-specific analyses, we show that the number, appraisal, and impact of stressful life events are largely not reflected in adolescent neuroanatomy.