Biological Psychology

BACKGROUND: An individual's adaptation to threatening environments under uncertainty is reflected in stress responses. Predictability (the ability to anticipate events) and controllability (the ability to control outcomes) are central to how one adapts, yet their joint influence on aversive learning remains unclear. METHODS: Thirty healthy adults completed a probabilistic aversive learning task in which cue-outcome contingencies varied across levels of predictability and controllability, i.e. whether shock intensity depended on prediction accuracy. Prediction accuracy, reaction time, subjective stress ratings, and skin conductance responses were recorded throughout. Trial-wise learning dynamics were estimated using the Volatile Kalman Filter. RESULTS: Prediction accuracy reduced as environments became less predictable and negatively associated with higher learning rates across predictability levels, with the strongest relationship observed in highly predictable blocks. Skin conductance responses showed that moderately predictable environments elicited responses like those in highly predictable environments when accurate predictions reduced shock intensity, but resembled responses in unpredictable environments when shock intensity was uncontrollable. Model comparison revealed a double dissociation between subjective stress ratings and skin conductance responses. Subjective ratings were best explained by model-derived volatility when prediction accuracy determined shock intensity and by belief uncertainty when it was independent of prediction accuracy, whereas skin conductance responses showed the reverse pattern. Reaction times were best explained by belief uncertainty when predictions influenced shock intensity. Higher anxiety was associated with elevated learning rates in highly and moderately predictable blocks when predictions did not control shock intensity.

Creative performance fluctuates from moment to moment, suggesting that it depends partly on transient internal states present before creative thinking begins. Although such fluctuations have been identified in central neural activity, it remains unclear whether they are also reflected in autonomic physiology. We examined whether cardiac and electrodermal activity during a brief pre-trial resting period was associated with subsequent creative performance. Photoplethysmography, electrocardiography, and electrodermal activity were recorded while 28 participants completed the Alternative Uses Test and Fusion Innovation Test, assessing divergent and convergent creative thinking, respectively. Data from 27 participants was included in the analyses. Heart rate, heart rate variability, tonic skin conductance level, and phasic skin conductance activity were extracted from observation windows ranging from 11 to 30 s within a 30-s pre-trial rest period. Trial-level creativity was evaluated using GPT-based ratings of novelty, feasibility, and goal attainment. Linear mixed-effects models showed that higher pre-trial tonic skin conductance level was consistently associated with better subsequent creative performance across tasks, with overall model fit peaking at a 21-s observation window. Permutation-based feature-importance analysis provided convergent support for the contribution of tonic skin conductance, whereas the cardiac and phasic electrodermal indicators showed no reliable independent associations. However, the model explained only a small proportion of behavioral variance. These findings suggest that tonic sympathetic arousal reflects a momentary physiological state associated with creativity potential, while autonomic signals alone remain insufficient for accurate individual-level prediction.

BackgroundHigh-frequency heart-rate variability (HF-HRV) is a downstream marker of cortico-autonomic regulation linked to executive function. A recent proposal suggests it indexes regulatory processes because it is both a product of and contributor to neural organization within the prefrontal cortex. The oscillatory phase of HF-HRV has been reported to modulate fronto-central EEG amplitude at rest, and this coupling is attenuated in individuals with schizophrenia relative to healthy controls. We evaluated this brain-body correspondence in relation to anxiety symptomatology, which is likewise associated with autonomic dysregulation. MethodsConcurrent EEG and electrocardiography (ECG) were recorded in 22 nonclinical adults at rest and during a mental arithmetic task. Participants rated anxiety severity using the Generalized Anxiety Disorder 7-item scale (GAD-7). We evaluated between-person associations between anxiety severity and HF-HRV-EEG coupling, as well as within-person differences in coupling between rest and mental arithmetic. ResultsAnxiety symptomatology was associated with decreased resting-state phase-amplitude coupling between HF-HRV phase and fronto-central theta amplitude, independent of EEG and HRV covariates. Relative to rest, HF-HRV-theta coupling increased during a cognitive task, independent of condition-related changes in EEG, HR, or respiration. Anxiety severity moderated the task-evoked changes in heart-brain coupling such that more anxious individuals exhibited larger condition-related differences. Simple-slope analyses indicated anxietys effect on coupling at rest was absent when engaged in a task. ConclusionsHF-HRV-theta phase-amplitude coupling captured anxiety-related and state-dependent variance not evident in conventional cardiac or neural indices. This coupling may index a state-sensitive component of cortico-autonomic regulation, although its putative functional role requires direct testing.

Spontaneous thought changes over time, yet the moment-to-moment factors shaping these changes remain poorly understood. We examined whether heartbeat-related bodily processing, operating largely outside explicit awareness, is associated with the organization of ongoing thought. Forty adults performed an auditory attention task with intermittent thought probes in which auditory events were scheduled either 200 ms after each detected R peak (synch condition) or independently of ongoing cardiac timing (asynch condition), with occasional omissions in both conditions. Heartbeat-synchronous omissions in this paradigm have been shown to induce cardiac deceleration and modulate heartbeat-evoked potentials (HEPs). The score of the heartbeat counting task (HCT) served as a behavioral index related to cardiac interoceptive accuracy. The results showed that higher HCT score was associated with a stronger synch-asynch shift toward more self-related and less task/self-unrelated thought. HEPs showed a synch-related negative shift across several thought groups, although the magnitude of this condition effect did not reliably differ among thought groups. Overall thought-group distributions were similar across conditions, while transition analyses suggested a tendency in the synch condition toward more frequent transitions linking predominantly interoceptive stimulus-dependent thought with both on-task and self-related thought. Together, these findings suggest that heartbeat-related bodily processing may influence the organization of spontaneous thought, particularly in individuals with greater cardiac interoceptive accuracy. Bodily signals may therefore act as an automatic constraint on the ongoing stream of thought, biasing which thought contents and transitions become more likely over time.

Laboratory stress tasks are widely used to assess individual differences in acute stress reactivity, yet it remains unclear how these responses correspond to stress experienced in everyday life. Here, we combined the Montreal imaging stress task (MIST) with ecological momentary assessment (EMA) over three months to assess acute and everyday stress in 67 healthy women. Greater within-person variability in everyday stress, but not average stress levels, were associated with stronger overall stress-related brain responses (b = 0.73, p = .039), with a whole-brain association particularly evident in the bilateral caudate (rROI = .32, pcluster.FWE < .001). Greater everyday stress variability was also associated with stronger stress-related functional connectivity between the ventromedial prefrontal cortex (vmPFC) and parietal and posterior medial regions (pcluster.FWE < .001). We conclude that acute neural stress responses relate more closely to fluctuations in perceived stress than to how stressed an individual feels on average. This suggests that laboratory stress tasks capture acute stress responsivity that is distinct from average stress exposure, highlighting the importance of considering what these tasks measure when interpreting individual differences in acute stress responses.

Gut-brain communication has emerged as a rapidly expanding field of research, with recent electrophysiological studies revealing rhythmic gut-brain coupling between gastric activity and brain oscillations in humans. Gut motility is a key determinant of gastrointestinal function, but it remains unclear whether individual differences in gut motility reflected by weekly bowel movements (e.g., defecation frequency) are associated with differences in gut-brain coupling. Here, we address this question by examining women with self-reported daily bowel movements (N = 38) and women with less frequent bowel movements (N = 38). We recorded simultaneous electroencephalography (EEG) and electrogastrography (EGG) at fasting state, performed cognitive assessments, and analysed faecal short-chain fatty acids (SCFAs) as markers of colonic fermentation. In a subset of participants, EEG-EGG coupling was assessed twice over an interval of at least eight weeks to assess test-retest reliability. In this group, EEG-EGG coupling showed moderate test-retest reliability (Intraclass Correlation Coefficient (ICC) = 0.50). When comparing the two groups of women, the phase-amplitude coupling (PAC) analysis between EEG and EGG signals revealed a significantly stronger gut-brain coupling in women with daily bowel movements compared to women with less frequent bowel movements (p = 0.03). We additionally found that women with daily bowel movements made less errors in the cognitive tasks and had higher levels of faecal SCFAs. A path analysis suggested that bowel movements significantly affect gut-brain phase-amplitude coupling through faecal SCFAs. However, neither faecal SCFAs nor phase-amplitude coupling significantly predicted cognitive performance, suggesting the existence of alternative pathways for the association between bowel movements and cognitive performance. Together, our findings suggest that the strength of gut-brain coupling is associated with bowel movements and cognitive performance, making EEG-EGG coupling a promising marker of human gut-brain interactions.

Anxiety-related traits (ARTs) have been linked to altered fear learning, but previous studies have typically examined different experimental phases and response systems, limiting the comparability of findings and the accumulation of consistent evidence. Here, we comprehensively examined associations between ARTs and fear conditioning across acquisition, extinction and renewal and across subjective, physiological and neural response systems in a well-powered sample (N = 267) using a two-day differential conditioning paradigm. ARTs were operationalized as a composite of trait anxiety, neuroticism, and intolerance of uncertainty and conditioned responding was assessed using skin conductance responses, fear-potentiated startle, US expectancy ratings, fear ratings, and functional magnetic resonance imaging. Higher ARTs were consistently associated with elevated subjective fear and US expectancy to both threat and safety cues during extinction and renewal, without corresponding elevations in physiological responding. At the same time, ARTs were not associated with threat-safety discrimination in subjective or physiological measures across phases, while neural associations were limited to reduced dorsal anterior cingulate cortex discrimination during early renewal. These findings suggest that ARTs are characterized by a CS unspecific cognitive bias toward heightened threat expectancy and evaluation rather than altered associative fear learning, highlighting the importance of distinguishing conditioned discrimination from general levels of responding across response systems.

Aversion to inequity is innate in humans and constitutes a major contributor to social tension. Such aversion can be triggered either when one receives more (advantageous inequity-AI) or less (disadvantageous inequity-DI) than others. However, it remains unclear whether oxytocin can modulate how humans respond to inequity. The present pre-registered study combined behavioral measures, computational modelling, and fMRI in a novel monetary allocation evaluation paradigm to examine oxytocins modulation of behavioral and neural responses to inequity. Results showed that oxytocin increased perceived fairness and AI allocation choices via enhancing AI preference independent of gain vs. loss contexts, which was further confirmed by model-based results of oxytocin decreasing AI aversion. In parallel at the neural level, oxytocin increased fairness rating-related activity in regions within fairness-related norm enforcement systems involved in norm violation detection, salience processing, and cognitive control. Multivariate-based representational similarity analyses provided further evidence that oxytocin induced distinct representational patterns of inequity in these core regions. These results suggest that oxytocin promoted AI preference possibly by strengthening regulatory control to support the pursuit of economic self-interest, while simultaneously enhancing the salience of AI allocations. Our study provides new insights into behavioral and neural mechanisms through which oxytocin modulates fairness-related behavior.

Metastability is a fundamental dynamical property of large-scale brain networks and reflects the capacity of the brain to flexibly reorganize transient coordination patterns. In this study, we investigated whether metastable properties of resting-state electroencephalographic (EEG) phase synchronization networks are associated with individual differences in autistic traits. Resting-state EEG data from 88 neurotypical adults were analyzed using two complementary metrics: synchrony coalition entropy (SCE), which quantifies the diversity of transient phase synchronization patterns, and the metastability index (MSI), which quantifies temporal variance in global phase synchronization. SCE showed frequency-specific associations with the Autism-Spectrum Quotient (AQ) attention-switching subscore at 18-24 Hz and the communication subscore at 4-8 Hz, suggesting that frequency- and network-specific patterns of metastable synchronization are associated with distinct aspects of autistic traits. In contrast, MSI showed a modest association with the social-skill subscore in the lower-beta range, but this effect did not survive a cluster-based permutation test. This exploratory observation suggests that global synchronization variability may capture a weaker, complementary aspect of trait-related metastable dynamics. These findings suggest that, within a neurotypical population, individual differences in autistic traits may be more sensitively captured by the repertoire of transient phase synchronization patterns, as indexed by SCE, than by global phase synchronization variability, as indexed by MSI. Moreover, the associations of distinct AQ subscores with SCE in different frequency ranges suggest that different dimensions of autistic traits may be related to metastable network dynamics operating at different temporal scales. Author SummaryThe brain constantly coordinates activity across many regions, and this coordination changes over time rather than remaining constant. Understanding these dynamic patterns is important for explaining individual differences in cognition and behavior. In this study, we focused on a dynamical property called "metastability," which describes how brain activity flexibly shifts between different patterns of coordination. Instead of remaining in a stable state, the brain repeatedly forms and dissolves coordinated activity across regions. We analyzed brain signals recorded with resting-state electroencephalography (EEG) and examined whether these dynamic patterns were related to individual differences in autistic traits. We found that different aspects of time-varying coordination were linked to different dimensions of autistic traits in a neurotypical population. These findings suggest that examining how brain activity changes over time, rather than relying only on time-averaged measures, can reveal neural features associated with individual differences in autistic traits. Our study highlights metastability as a useful concept for understanding the flexible and dynamic nature of human brain function.

Background: Acute cortisol responses to psychosocial stress vary substantially across individuals, yet how this variability is reflected in post-stress resting-state functional connectivity (rsFC) remains unclear. Although prior work has linked stress-related endocrine responses to brain connectivity, studies have been limited by small samples, region-of-interest approaches, or a sole focus on group-level analyses. Here, we investigated whether acute cortisol increase is associated with, and can be predicted from, whole-brain post-stress rsFC. Methods: We analyzed 339 healthy participants from two ScanSTRESS datasets using complementary inferential and predictive approaches. First, we used the Network-Based Statistic (NBS) to identify connected rsFC networks associated with acute cortisol increase, controlling for age, site, and sex/hormonal status. Second, we predicted participants' acute cortisol increase from their connectivity patterns using NBS-Predict and Connectome-Based Predictive Modeling (CPM). Together, we examined the cortisol-rsFC relationship at the population and individual levels. Results: Greater cortisol responses were associated with lower post-stress rsFC within a significant distributed network comprising 258 connections among 78 regions, centered on thalamic nuclei and pallidal regions and extending to default-mode, limbic, orbitofrontal, and cerebellar regions. Sex-stratified analyses revealed a significant negative association only in females, but formal sex-difference contrasts were not significant. NBS-Predict and CPM yielded modest but significant out-of-sample prediction, with predictive networks converging on subcortical and posterior cingulate regions. Conclusions: Post-stress rsFC carries convergent inferential and predictive information about individual HPA-axis reactivity. Stronger cortisol responses were characterized by reduced connectivity within a distributed subcortical-cingulate network, supporting a network-level perspective on neural-endocrine coupling following acute stress.

Metacognition is the ability to reflect on and evaluate our own cognitive processes. It is often altered in psychopathology. Yet, the computational mechanisms underlying these alterations remain unclear. In this work, we extend Hierarchical Gaussian Filter (HGF) models to jointly fit trial-by-trial predictions and confidence ratings in a predictive inference task, providing an individualised characterisation on metacognitive processing. Applying our cognitive computational model to a large subclinical open dataset (N=430), we are able to achieve, on average, excellent fit of prediction responses [Formula] and a moderate to good fit of confidence ratings [Formula]. Analysis of experimental change-points revealed that our model accurately captures confidence self-reports dynamics around these change-points. Posterior parameter estimates reveal a negative effect of sensory input prediction errors and a positive effect of sensory input prediction precision on confidence ratings, respectively. In addition, we replicate state-of-the-art findings related to compulsivity as measured by a transdiagnostic factor score, such as inflated confidence and a decoupling of action updates (here, prediction errors) and confidence in compulsivity. These results demonstrate the robustness of our methodology and the potential of joint prediction-confidence modelling to uncover latent metacognitive alterations in psychopathology.

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.

Although a substantial body of evidence has demonstrated that emotional interference affects cognitive performance, relatively little is known about its temporal evolution and the respective brain regions underlying its regulation. The present study investigated the temporal dynamics of emotional interference and the contribution of prefrontal regions involved in its regulation. Forty-eight participants completed a 2-back task and a Set-switching task under neutral and negative emotional conditions while receiving sham, dorsolateral prefrontal cortex (dlPFC), or ventromedial prefrontal cortex (vmPFC) stimulation. Stimulation was administered either online during task performance or after a 5 min pre-task period. Consistent with previous findings, negative emotions impaired executive performance, particularly during high-demand updating conditions. Critically, time-resolved analyses revealed that emotional interference evolved dynamically throughout task performance and was differentially modulated by prefrontal stimulation. The most consistent stimulation effects emerged after approximately 10 minutes of cumulative stimulation exposure and varied as a function of the stimulation site, executive-control demands, and stimulation timing. Notably, online stimulation produced more consistent modulation than pre-task stimulation. Together, these findings indicate that both emotional interference and its neuromodulation are dynamic processes. More broadly, they suggest that the contribution of prefrontal control systems to emotion-cognition interactions may be better understood through their temporal evolution rather than through static measures of performance alone.

Theft, including shoplifting, extorts a pervasive societal and economic burden. However, the neurobehavioral mechanisms underlying recurrent theft remain sparsely understood. In this study, we investigated social cognition deficits in theft recidivists with kleptomania (TR+K) and instrumental theft recidivists without kleptomania (TR-K) compared to control subjects without criminal records (CT), for which the Social Norms Questionnaire (SNQ-22) to assess explicit moral knowledge, alongside the Dictator Game (DG) and Hawk-Dove Game (HDG) to evaluate discretionary and competitive resource allocation with others, respectively, were administered. Bayesian statistical analyses revealed that all groups demonstrated comparable social norm recognition in SNQ-22 and prosociality in the DG. However, distinct behavioral profiles emerged in specific contexts, such that TR+K exhibited more unfairness than CT and TR-K at discretionary resource allocations in the DG, whereas in the HDG, TR-K demonstrated more aggressive, resource-monopolizing responses, particularly when against an aggressive opponent, than CT and TR+K. These results suggest that theft recidivism may stem from contextual failures rather than general deficits in moral knowledge, which are distinct between TR+K rooted in the internal factor, such as heightened loss aversion, and TR-K characterized by impulsivity over the external factor, such as social conflicts with others.

Recognizing others emotions is central to social interaction. Traditional biological psychology infers emotional responding via laboratory measures, whereas contemporary computer vision algorithms claim to identify emotions unobtrusively from facial video. However, the validity of such algorithms for classifying spontaneous emotional responses occurring without explicit communicative intent remains debated. We compared established psychophysiological measures (EEG, facial EMG, EDA activity) with the open-source facial behavior toolkit OpenFace for classifying participants spontaneous responses during free viewing of happiness-inducing, disgust-inducing, and neutral pictures. Participants provided valence and arousal ratings and later selected the basic emotion that best matched their reaction which served as the classification criterion. Using within-participants single-trial support vector machine (SVM) classification, EEG achieved the highest accuracy (40%), followed by facial EMG (37%); OpenFace reached 36%. All methods except EDA exceeded chance performance (33.3%) and were lower compared to human raters (48%). Predictions declined slightly for across-participants SVMs, being at chance for OpenFace and EDA. The results indicate that in principle both, psychophysiological measures and video-derived facial action units, can capture diagnostically relevant aspects of emotional responding during picture viewing, but that their performance is limited when expressions are spontaneous and not produced for communicative purposes. Inter-individual variability in expressivity and physiological responding likely contributes to these limitations and should be considered when deploying automatic emotion recognition in research or applied settings.

In this study, we investigated the neural and behavioral basis of motivationally objective versus subjective value-based decisions. Using a within-subject fMRI design, healthy participants performed a risky decision-making task that elicited different levels of subjectivity in decision-making in two task conditions. In the Best or objective condition, choices were rewarded only when they were objectively best on a given trial, incentivizing decisions based on externally specified per-trial point maximization. In the Choice or subjective condition, participants received the reward associated with the chosen option, irrespective of how it compared to the unchosen option, allowing greater freedom to exercise subjective preferences in decision policy. Behaviorally, participants relied more on objectively optimal policy in the Best than the Choice condition. There was also a greater consensus across participants in behaviorally displayed and self-reported policies in the Best condition as well as a greater commitment to a single policy by individual participants in this condition, further confirming more objective behavior in the Best condition, compared to Choice. Moreover, behavioral inferences showed a greater agreement with self-report in the Best condition. Our fMRI results showed that the decision-making in Choice, relative to the Best condition, was associated with greater BOLD response in mid-cingulum/posterior cingulate cortex and dorsal anterior cingulate cortex, suggesting their involvement in less externally constrained, or motivationally subjective, decision-making.

PurposeSpeech perception is a fundamental part of everyday communication that relies on more than simple identification of words and sentences. Attention and listening engagement both contribute to speech perception, while representing distinct aspects of the listening experience. Attention is typically associated with cognitive focus, whereas listening engagement additionally involves cognitive and affective immersion in sound. Despite their importance, these states remain difficult to disentangle, behaviorally and physiologically. Both have been linked to interpersonal synchronization (the synchronization of biobehavioral signals across individuals), raising questions about what this synchronization actually reflects. MethodIn this study, we disentangled attention and listening engagement by independently manipulating both factors within a single experiment. Thirty participants listened to two simultaneously presented streams of meaningful speech and were instructed to focus on only one. Both attended and unattended stimuli were designed to be either engaging or non-engaging. Neural activity was recorded using EEG, while physiological responses were measured using heart rate and electrodermal activity. ResultsInterpersonal synchronization was computed from neural and bodily signals, alongside a self- report measure of listening engagement and auditory attention decoding (AAD), a neural measure of selective attention. Interpersonal synchronization of all three modalities significantly predicted listening engagement, whereas neural interpersonal synchronization was the only measure that significantly predicted attention. These findings suggest that attention is primarily driven by cognitive processes represented in the brain, while listening engagement additionally involves affective processes that are more strongly reflected in bodily responses. ConclusionsOverall, this study demonstrates that different forms of interpersonal synchronization reflect distinct dimensions of the listening experience and supports interpersonal synchronization as a potential objective marker of listening engagement.

Resolving the inner conflict between improving eating habits (change talk) and sticking to unhealthy ones (sustain talk) is a key target in communication-based behavioral change interventions such as motivational interviewing (MI). Recent work has shown that this inner conflict affects how tastiness and healthiness are traded off in dietary decision-making. The effect varied with body mass index (BMI). Here we aimed to identify why participants with higher BMI shifted toward healthier food choices after listening to change talk. An evidence accumulation model found that BMI affected health evidence sampling when listening to change talk, and taste evidence sampling when listening to sustain talk. A serial mediation analysis showed that the effect of BMI on change-talk-induced health evidence sampling was explained by stronger resting-state connectivity in the ventromedial prefrontal cortex within the default mode network (DMN), which in turn predicted greater motivation to change eating habits. These cross-sectional findings indicate that the intrinsic functional organization of valuation-related regions within the DMN is associated with motivation to change. They provide evidence that these neural and behavioral factors need to align with contextual cues, such as weight status (as reflected by BMI), and with reasons for behavioral change to promote healthier decision-making.

BackgroundSelf-compassion has shown to be an effective emotion regulation strategy, but its neural mechanisms remain understudied in adolescents with depression who suffer from social exclusion. This study employed event-related potentials to investigate the psychophysiological mechanisms underlying impaired self-compassion in adolescents with depression during social exclusion. MethodsThirty-seven adolescents with major depressive disorder (age = 16.46 {+/-} 2.02) and thirty-two demographically matched healthy controls (age = 16.54 {+/-} 2.29) completed a social exclusion scenario imagination task. This task presented participants with social exclusion scenarios, asking them to imagine themselves as the excluded individual and subsequently rate their negative emotions. The regulation session required participants to use self-compassionate statements to regulate their emotional responses while imagining and rate how much self-compassion they have engaged in; the non-regulation required them not to use those statements and rate their negative emotions immediately following imagination. EEG signals were recorded during the task, and the late positive potential (LPP) was analyzed to examine the neural responses associated with self-compassion regulation following social exclusion. ResultsCompared with the non-regulation condition, both groups showed less negative emotion during the self-compassion regulation condition. Adolescents with depression exhibited significantly lower self-compassion ratings than healthy controls, and showed a significantly smaller decrease in negative emotions. Higher self-compassion ratings were correlated with greater emotion regulation effect, particularly in adolescents with depression. Furthermore, LPP amplitudes were significantly higher in adolescents with depression than in healthy controls during both regulation and non-regulation conditions. ConclusionsAdolescents with depression were characterized by impairment in using self-compassion to downregulate negative emotions when confronted with social exclusion. LPP amplitudes were consistently elevated in adolescents with depression, underscoring their potential as a critical focus for psychophysiologically-informed therapies.

Depression is marked by blunted affective responses to context, which interoceptive accounts trace to altered neural representations of bodily states. Yet this evidence mainly concerns response magnitude, not how quickly affect is updated when contexts change. Here we tested whether depressive symptom severity is related to delayed affective updating, and whether cortical dynamics tracking cardiac states account for this delay. To this end, we applied a movie-watching paradigm with independently defined contextual shifts, continuous affect ratings, electroencephalography, and electrocardiography in individuals spanning a continuum of depressive symptoms. Combining deep representation learning and a dynamical systems framework, we quantified how quickly (speed) and how sharply (angle) cardiac-coupled cortical representations reorganized at each shift. Greater symptom severity predicted longer latency to enter the context-congruent affective state across contextual shifts, regardless of valence. In a cross-sectional mediation analysis, slower speed, but not angle, accounted for this association. This mediation was specific to depressive symptoms, contextual shifts, and cardiac-coupled neural dynamics. These findings extend the embodied account of depression from blunted affective intensity toward its inflexible updating at moments of contextual shift, and offer a broadly applicable framework for quantifying brain-body dynamics across affective dysfunctions.