Social Cognitive and Affective Neuroscience

How children come to evaluate social-affective cues is shaped within the family, yet the neural expression of this process and its dependence on family relationships remain unclear. We tested whether parent-child similarity in the neural coding of affective judgment varies with family environment, and whether it relates to youth affective distress. Twenty-five parent-child dyads (youth, mean age 11.8; parents mean age 42 years) judged faces morphed along an angry-to-happy continuum as positive or negative during fMRI. For each participant, we defined an evaluative choice axis distinguishing faces judged positive from negative, independent of expression intensity. Using searchlight-based parent-child cross-decoding, we tested whether one dyad member's evaluative coding predicted the other member's judgments, indexing shared evaluative coding rather than shared sensitivity to expression intensity. Inference focused a priori on medial prefrontal cortex. There was no reliable average parent-child neural similarity across the sample. Instead, higher family conflict was associated with lower parent-child neural similarity in ventromedial prefrontal cortex (vmPFC). Demonstrating specificity to negative relational strain, this effect was not observed for complementary dimensions of family cohesion or identity. Moreover, the effect was specific to true dyads rather than random pairings and to vmPFC rather than a face-selective network or other medial prefrontal regions. Lower vmPFC similarity showed a preliminary association with higher youth affective distress. These findings indicate that affective valuation, rather than sensory encoding, may be a representational level at which perceived family conflict is reflected in parent-child neural similarity.

Nonduality is a foundational but conceptually elusive notion across several contemplative traditions. Nondual traditions challenge the assumption that a subject-object structure characteristic of ordinary experience is an intrinsic feature of conscious awareness. However, little remains known about the neurocognitive mechanisms associated with such experiential state. Here, we investigated how Open Presence (OP) meditation, a form of non-dual mindfulness practice, modulates bodily self-representation and large-scale brain functional organization. We combined the Full-Body Illusion Experience (FBIE), a virtual reality paradigm manipulating bodily self-processing, with resting-state functional connectivity gradient analyses in expert meditators (>10,000 hours of practice) and meditation novices. We hypothesized that OP would attenuate bodily self susceptibility as measured by FBIE and increase large-scale integration of functional brain networks, consistent with prior findings linking reduced self-boundaries and ego-dissolution to increased connectome integration. Seventy-five participants (28 experts, 47 novices) underwent fMRI scanning during OP meditation. Brain network organization was assessed using connectivity gradients and network dispersion/ eccentricity metrics. Group differences were evaluated using bootstrap statistics and support vector classification. Compared with novices, expert practitioners showed reduced global network eccentricity during OP, particularly within dorsal attention, ventral attention, and frontoparietal networks, suggesting greater large-scale integration of functional networks. These neural patterns were positively correlated with FBIE self-report measures and negatively with cognitive defusion scores, a construct thought to reflect reduced self-grasping toward thoughts and mental contents. Together, these findings suggest that nondual meditation is associated with alterations in self-representation and increased large-scale functional integration, providing candidate neural markers of nondual awareness.

Hedonic valence, the intrinsic pleasantness or unpleasantness of an experience, is fundamental to human psychological functioning. Yet, how valence is represented in the brain remains an open question. Functional MRI studies have demonstrated that the brain encodes both positive and negative valence, but this evidence largely stems from experiments using simplified, controlled stimuli, such as images, sounds, or words. As a result, it remains unclear how valence is processed during rich, naturalistic experiences that more closely reflect real life. In addition, most studies adopt a single statistical model, raising concerns about the robustness of their findings. This study used a formal voxel-wise Bayesian model selection approach to test alternative statistical models supporting Bipo-larity, Valence-General, and Bivalence hypotheses to identify the most optimal model of valence representation during narrative listening. Our results provide evidence for the Bipolar model. We identified distributed brain re-gions that selectively encode valence as a bipolar continuum (negative to positive) during narrative comprehen-sion, including classical emotion-related hubs such as ventromedial prefrontal cortex, as well as regions not tradi-tionally associated with emotion processing, such as inferior occipital cortex, supramarginal cortex, inferior frontal cortex, and middle cingulate. Regions selectively encoding arousal and those broadly responsive to both valence and arousal were also identified. These findings highlight the importance of using formal model comparison and naturalistic paradigms in affective neuroscience, advancing our understanding of how valence is represented in the brain during real-world experiences.

People navigate dozens of social interactions daily. Through these, they construct and maintain diverse social networks. While personal dispositions drive how people interact and connect others, the resulting networks feed back into daily social experiences by creating interaction opportunities and modulating cognitive processes. Here, we show that people's moment-to-moment interaction behaviors are informative of the structure of their personal social network, even if the interaction occurs between strangers. We paired 104 strangers to perform an interactive motor task and investigated whether emergent leader-follower roles and spontaneous movement synchronization reflected similarities or differences in their personal networks. Participants with tighter networks (smaller, denser, more constrained, and with fewer communities) relative to the interaction partner adapted their movements to the partner more. Using multivariate temporal response functions (mTRF) to measure neural encoding of self and other's movements, we found that, conversely, participants with less tight networks exhibited higher neural encoding of their partner's movements. This suggests that the network effect on behavioral adaptation was unlikely to be driven by heightened monitoring of the partner. Higher spontaneous movement synchronization was associated with higher personality agreeableness and personality similarity between interacting partners. Our results demonstrate that signatures of personal social environment manifest dynamically in real-time behaviors and that these effects are independent of personality effects. Thus, social networks set an additional context for interpersonal interaction even for partners without a prior relationship.

Empathy enables individuals to attune to others' experiences through shared affective, sensorimotor, and neural representations, but its influence on higher-level decision-making and social alignment remains unknown. We examined whether empathy promotes social conformity and whether this effect depends on physiological arousal, visual attention, and socio-emotional traits. Seventy-three participants completed an empathy-for-pain task followed by a modified random-dot task in which they interacted with previously seen empathy targets, while physiological signals and eye movements were recorded. Drift-diffusion modeling showed that feedback from empathy targets increased conformity, reflected in stronger decision bias toward targets, faster evidence accumulation, and reduced decision conservatism. Visual attention to the eyes or mouth was linked to greater conformity depending on the context. Heart rate variability was unrelated to conformity, whereas phasic skin conductance was associated with smaller bias changes and greater conservatism. Alexithymia and autistic traits further shaped the empathy-conformity relationships. These findings suggest that empathic shared representation extends beyond affective resonance to influence social alignment in decision-making.

Gaming addiction may be contagious through social interaction. In team competitive video games, individuals of varying addiction levels are often paired. This raises the question of whether, and through what mechanisms, one individuals addiction level is affected by other players. Methodologically, addressing this question requires understanding both what happens between brains and within individual brains during a gaming session. To this end, we used HYPER-NESS (Hyper Brain Network Estimation via Source Separation), a novel analytical framework to decompose and weight the distinct contributions of inter-brain and intra-brain processes to social brain networks. We applied this framework to a hyperscanning fNIRS dataset where dyads were scanned while playing a video game together against experimenters. We found that inter-brain and intra-brain contributions to the social brain networks were differentially associated with changes in game reward and social reward sensitivity, depending on the addiction level of ones partner. Granger causality analysis of both social and individual brain networks revealed influences from the high-addiction partner to low-addiction partner. Furthermore, significant cross-frequency coupling was found selectively between low-addiction players, supporting the idea that this form of inter-brain interactions underpins the joint processing of task-relevant rewards. To our knowledge, these findings provide the first neural-level account for the hypothesis that gaming addiction propagates though dyadic social interaction.

Stress involves coordinated central and peripheral processes that unfold dynamically and can be assessed through brain, autonomic, endocrine, and subjective measures. Centrally, acute stress has been linked to altered functional connectivity, particularly in the salience (SN), frontoparietal networks (FPN), and default mode networks (DMN). Here, we used cortical gradients to characterize stress-related reconfiguration in macroscale functional space and assessed their relation to peripheral stress measures. We performed secondary analyses on data from 67 young males completing the Trier Social Stress Test or a control task with resting-state fMRI before and after, concurrent peripheral (autonomic, endocrine) and subjective measures. To assess region- and network-specific changes in functional organization, we derived eccentricity and within- and between-network dispersion for the first three cortical gradients. Acute stress was associated with selective gradient reconfigurations in the right ventral prefrontal cortex and left insula and with increased SN-DMN and SN-FPN dispersion, indicating DMN and FPN decoupling from the SN. Although no associations with peripheral or subjective stress measures survived multiple-comparison correction, nominal effects suggested partly distinct links of saliva cortisol with local gradient changes and HRV with network-level reconfiguration. Together, these findings show that acute stress selectively reconfigures macroscale cortical organization.

Advanced meditation offers a powerful lens for investigating consciousness and for understanding how sustained training may contribute to human flourishing. In the spirit of neurophenomenology, we combine first-person reports with model-free empirical analyses and formal whole-brain modeling to investigate the mechanisms underlying advanced meditative states and minimal phenomenal experience (MPE). Specifically, we focus on jh[a]na meditation, a type of advanced concentrative absorption meditation (ACAM-J). Advanced practitioners accessed the eight ACAM-J states during ultra-high-field 7T functional magnetic resonance imaging. For each state, we first characterize empirical functional connectivity and then build a mechanistic whole-brain model that reproduces brain activity by modeling the dynamical regimes of different brain networks. We found that the later ACAM-J states, taken here as candidates for MPE, show increased large-scale functional integration and a shift of functional network dynamics toward near-critical working points. The default mode network (DMN) exhibits the largest shift, from a distant noise-driven regime during the control condition to near-critical dynamics during ACAM-J. We also observed that the trajectory of ACAM-J states is non-linear, with prominent reconfigurations at key meditative milestones. Our results suggest that MPE, as instantiated in later ACAM-J states, corresponds to a globally susceptible state where near-critical dynamics dominate. We interpret this near-critical regime as a form of "openness", in which constrained and differentiated patterns of brain activity give way to greater flexibility. In particular, increased DMN susceptibility is correlated with broader attention and reduced narrative thought, consistent with a more flexible mode of self-related processing. In this context, advanced meditation provides a powerful model for studying how sustained contemplative practice can profoundly shape brain dynamics and provide a window into core aspects of consciousness.

Across contemplative traditions, deeper states of meditation are described as states of heightened clarity, vividness, and stillness of mind, yet what this clarity corresponds to in the brain has remained difficult to specify. The functional signal-to-noise ratio (f-SNR) framework frames mental clarity as a measurable property of neural signals: the degree to which brain activity tracks the causes of sensory signals rather than endogenous, irrelevant fluctuations. It predicts that deepening meditation should raise f-SNR, expressing sensory events more faithfully in neural signals against ongoing background activity. We tested this prediction across different levels of meditative depth. Twenty-nine experienced Vipassana practitioners meditated while auditory tones were presented, periodically reporting their depth of meditation. f-SNR was quantified from event-related potentials (ERPs) in a fronto-central P3 window and from single-trial decodability of auditory tone-evoked activity against no-tone background EEG. High-depth states were associated with greater ERP signal-to-noise ratio, stronger single-trial signal consistency, and improved decodability of auditory tones. These results suggest that meditative depth is expressed in the reproducibility and stimulus-background separability of sensory responses, consistent with deep meditation enhancing the brain's functional signal-to-noise ratio by improving the clarity of sensory signals and reducing endogenous noise.

The ability to seek reward and avoid punishment is a fundamental survival instinct. In natural environments, however, the statistics of rewards and punishments can change independently of one another. In addition, individuals experiencing anxiety and depression may be selectively biased to process rewards and punishments differently. Here, we examine how humans adapt their behavior in such dynamic environments, using a task where cues are associated with independently changing probabilities of rewards (wins) and punishments (losses). We demonstrate that participants dynamically adjust their learning rates based on the relative volatility of each valence. Neuroimaging reveals that this behavioral flexibility is supported by valence-specific segregation of volatility signals within distinct subregions of the anterior cingulate cortex (ACC: perigenual and dorsal). Furthermore, individuals with higher levels of anxiety and depression exhibit a relative reduction in loss-volatility learning adaptation, accompanied by less distinct neural encoding of win and loss volatility in both ACC subregions. Together, our findings indicate that flexible adaptation to separate reward and punishment contingencies relies on distinct, valence-specific tracking of volatility within the prefrontal cortex. These findings suggest a potential computational and neuroanatomical framework for understanding maladaptive learning in affective disorders.

Frontolimbic circuits connecting the frontal cortex with the amygdala and the hippocampus are critical for emotion and learning. These connections undergo major changes throughout childhood and adolescence, yet the principles guiding their maturation remain unclear. Analyzing large developmental cohorts, we show that fronto-hippocampus connectivity develops rapidly in early childhood and stabilizes thereafter. In contrast, fronto-amygdala connectivity becomes progressively differentiated from fronto-hippocampus connectivity, with development most pronounced during adolescence. This entailed hippocampus and amygdala respectively becoming preferentially tethered to association-related regions and sensorimotor-related regions. Greater adversity exposure was associated with more differentiated fronto-amygdala connectivity for a given age. Higher cognitive ability was associated with less differentiated fronto-hippocampus connectivity. These findings suggest that frontolimbic connectivity development follows a principle of differentiation, with circuit-specific timing and sensitivity to stress and learning. This work provides a foundation for understanding typical and atypical frontolimbic circuitry development from birth to emerging adulthood. Significance StatementThe frontolimbic circuit, especially the fronto-hippocampus and fronto-amygdala circuitry, is integral for cognitive and affective processes. Yet its maturation has been challenging to characterize due to variable connectivity growth across the frontal cortex. Here, we reveal that this variability follows a principle of differentiation, where the hippocampus and the amygdala acquire respectively unique connectivity profiles with the frontal cortex at disparate developmental stages. Deviations from these normative trajectories were linked to cognitive ability and cumulative adversity, respectively. This approach provides a framework to unify inconsistent findings while serving as a foundation for identifying circuit-specific windows of plasticity and vulnerability.

Social decision-making depends on inferring others hidden preferences from observable behavior. Yet it remains unclear how humans combine choices with process cues such as response times and gaze when learning about others in real-time interaction. Here we combine a novel multi-attribute bargaining task with eye-tracking and show that multiple decision-process cues support preference inference. Across 75 buyer-seller dyads, buyers acceptance rates tracked offer utility, rejection speed reflected decision confidence, and first fixations preferentially targeted the highest-weighted attribute. Sellers adapted subsequent offers using choices, response times, and, when available, gaze cues. A hierarchical inference and choice model suggested that sellers balanced expected utility with expected information gain and updated their beliefs in a Bayesian manner. Although gaze access did not improve overall performance, it changed how sellers used attentional information. These findings shed light on how humans infer others hidden preferences from decision dynamics in real-time social interaction.

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.

Intense aesthetic experiences are among the most complex responses arising from the interaction of mind, brain, and context. Observations from fMRI suggest that when viewers feel highly moved by artworks, the underlying neural states differ from those accompanying less intense responses, particularly through recruitment of the DMN. Using electroencephalography and Bayesian category-specific cumulative link mixed models, we investigated whether such putative peak aesthetic responses exhibit threshold-specific neurodynamics rather than linear scaling with intensity. Twenty-two Chilean participants viewed 113 diverse local artworks whilst rating how moved they felt on a four-point scale. We analysed both canonical oscillatory power ({theta}-{gamma}) and aperiodic components (offset and exponent) during the contemplation window and the post-elicitor window. Threshold-specific effects were found: spectral features differentiated the highest rating category from moderate responses, rather than scaling uniformly across all intensity levels. During artwork visualisation, power in the {beta}1 and {beta}2 bands, as well as the interaction of {beta}1 with the 1/f exponent, predicted the transition to the most intense response; during the post-elicitor window, the aperiodic 1/f exponent predicted the transition from very low to higher-intensity responses. Modelling individual differences in spectral signatures (in the and {gamma} bands) credibly improved predictive performance (approximate leave-one-out cross-validation; elpd_loo), suggesting that neural variability reflects meaningful mechanistic heterogeneity in aesthetic processing rather than mere noise. These findings speak to a broader question, how the brain marks the intensity of conscious experience, and, more specifically, support the hypothesis that being intensely moved constitutes a qualitatively distinct neural state, characterised by specific configurations of oscillatory dynamics and cortical excitability that modulate the transition from low and moderate to peak engagement.

Learning is typically understood as a process driven by prediction errors, when outcomes differ from expectations. Yet it remains unclear whether outcomes that perfectly match expectations are psychologically and computationally meaningful. Here, we tested whether zero prediction errors shape affect, belief updating, and neural feedback processing in human reinforcement learning. Participants repeatedly predicted rewards in environments varying in uncertainty, with a subset of trial outcomes manipulated to exactly match their predictions. Zero prediction errors produced the highest momentary happiness, and computational modeling showed that behavior was best explained by a model in which zero prediction errors induce a distinct latent belief state that guides subsequent updating, particularly under higher uncertainty and in individuals with greater intolerance of uncertainty. Outcome-locked EEG analyses further showed that zero prediction errors elicited distinct P3-like responses, with residual neural activity predicting attenuated updating after zero prediction errors but enhanced updating after standard prediction errors. These findings suggest that perfect predictions are not neutral, but informative events that actively shape affect, behavior, and neural feedback processing.

Alcohol drinking motives vary among individuals and shape experiences and beliefs about alcohol, influencing the processing of alcohol-related cues. In real-life settings, these cues are contextually rich, amplifying the role of such individualized drinking motives on cue processing. However, previous literature has primarily relied on images of alcohol, which lack contexts and differ significantly from real-life. Here, aiming to investigate real-life craving, we examined the role of alcohol drinking motives in craving in response to naturalistic alcohol-drinking videos. We asked fifty-three problematic alcohol users to speak about their reasons for drinking alcohol to capture unique alcohol drinking motives of each individual. Participants also underwent functional MRI while watching fifteen alcohol-drinking videos, and reported their subjective level of craving and self-relatedness for each video. Behavioral data analysis revealed that individuals with greater alcohol use severity tended to report greater cue-induced craving, but only when they reported that a video was related to themselves. Inter-subject representational similarity analysis showed that participants with similar alcohol drinking motives, reflected in shared drinking reasons and similar self-relatedness to the videos, exhibited synchronized craving-related neural responses during video-watching. Notably, these shared neural processes mediated the link between similar drinking motives and similar self-reported craving levels across participants. Together, our findings highlight the crucial role of alcohol drinking motives in shaping cue-induced alcohol craving, and provide deeper insights into craving in real-world contexts.

Super-Recognisers show exceptional ability in face recognition, providing a natural model of how perceptual systems optimise for individuating visually similar stimuli in variable viewing conditions. However, the neural representations supporting this extreme perceptual expertise are unknown. Here, we tested whether Super-Recognisers (n = 23) differed from typical recognisers (n = 21) in the dimensional organisation of neural face identity coding. We recorded 64-channel electroencephalography while participants viewed random and rapidly-presented sequences containing 10 naturally varying images of 40 unfamiliar identities. Using time-resolved representational similarity analysis we measured the geometry of identity representations, their consistency across observers, and how clearly they specified face identity. Although neural expression of identity information was robust in both groups, we found three key differences between Super-Recognisers and typical recognisers. First, the geometry of face identity representations differed between groups. Second, Super-Recognisers showed greater inter-individual consistency in representational geometry. Third, Super-Recognisers' neural signals discriminated between face identities more strongly than those of typical recognisers. Differences in the coding of broader face categories (sex, age, ethnicity) were notably weaker, suggesting that the observed group differences reflected fine-scale differences in identity coding rather than global reshaping of representational geometry. Strikingly, all three differences emerged within a common mid-latency interval (~300-500ms), implicating higher-stages of face processing associated with representations that are sensitive to face familiarity and link between perceptual and semantic domains. Together, these findings indicate that individual differences in face recognition ability reflect higher-level differences in neural identity coding, rather than enhanced early sensory processing.

Social media users often feel that time vanishes while scrolling, but real feeds confound novelty, rewards, social signals, and self-paced control, leaving the driver of this distortion unclear. We tested whether self-paced visual exploration is sufficient to compress subjective time by comparing active scrolling with passive, yoked viewing and a static baseline. Twenty-three adults viewed sequences of natural images under three within-subject conditions: Scrolling (self-paced mouse clicks), Watching (a passive, yoked replay of their own scrolling sequence), and a Baseline (a static image). Participants estimated the elapsed duration of each block. Subjective duration was most compressed under Scrolling (48% of elapsed time), followed by Watching (51%) and Baseline (65%). Two sources separated these effects. Adding back the empty inter-image fixations brought the image-rich conditions to within seconds of the Baseline, showing that observers barely counted the blank gaps; the Scrolling--Watching difference, by contrast, was independent of these shared gaps, isolating self-paced control as a second source of compression. Electrophysiology linked that control to anticipatory neural states and the timing of early visual responses, with no amplified encoding of individual images. The results favor an attention-weighted account of timing, on which subjective duration tracks how much attention reaches the clock, a resource that a self-paced stream and its uncounted gaps both draw away.

How the brain constructs meaning across extended contexts remains poorly understood. While neural responses to words and sentences are well characterized, much less is known about the brain mechanisms supporting narrative comprehension. Sentence-level studies suggest that neural activation increases as word meanings are integrated into sentence meaning. At the discourse level, theories propose that narratives depend on situation models, possibly engaging networks beyond core language regions, including the default mode network. Because narrative comprehension unfolds over longer timescales, processing time may be a bottleneck. In this MEG study, we tested how representation size and presentation rate shape neural responses by varying linguistic structure (words, sentences, stories) and the speed of visual text in 1-4-word chunks. We found an early bilateral story effect in visual cortex, followed by a spatiotemporal progression of activity along the temporal lobes that culminated in a three-way contrast among word lists, sentence lists, and stories. Faster presentation altered this pattern: the left-lateralized story effect disappeared, and the right-lateralized effect became more spatially restricted. Under Fast presentation, significant effects were limited to left lateral language cortex distinguishing coherent inputs from word lists, and to two right-hemisphere story effects in extended language regions. We also observed a context effect in the Slow Story condition, with neural responses remaining constant as the narrative unfolded while they increased in the SentenceList and WordList conditions. This effect was absent under Fast presentation, suggesting story-specific comprehension that is temporally constrained. Together, the findings identify temporal constraints as a key determinant of the neural signatures of narrative processing.

Complex reasoning depends on flexible coordination among frontal, parietal, and thalamic systems, but the circuit mechanisms that support increasing relational demands remain unclear. We combined EEG with biologically grounded corticothalamic neural field modelling while participants solved relational problems of graded complexity. Successful reasoning was associated with dissociable frontoparietal dynamics. Frontal regions showed increased theta-band power, whereas parietal regions showed reduced alpha- and beta-band power. Theta-band phase synchronisation across frontoparietal-network nodes increased with problem complexity but was not associated with performance. By contrast, stronger beta-band synchronisation across the same network was associated with slower and less accurate responses as demands approached the highest complexity, suggesting that stronger coordination is not uniformly beneficial. Neural field modelling indicated that these regional spectral dynamics reflected specific complexity-dependent circuit adaptations. Parietal regions showed modulation of intracortical and corticothalamic gains, intrathalamic inhibition, prolonged loop delays, and faster synaptic filtering, whereas frontal regions primarily adjusted intracortical gains to maintain local excitatory-inhibitory balance and supported longer temporal integration windows. Together, these empirical and model-derived findings reveal complementary frontoparietal and corticothalamic mechanisms for relational reasoning.