Nature Human Behaviour

Confirmation bias impacts judgments and decisions across a range of domains including finance, policy and science. Here we examine whether explicitly labelling information as true or false disrupts a core underlying computational mechanism that can generate this pervasive bias - asymmetric learning. Human participants (Study 1: N=47; Study 2: N=57) completed a 2 alternative forced choice (2AFC) task previously used to test for the presence of confirmation bias. Participants made choices between pairs of options that could win or lose money and received either factual or counterfactual feedback after each choice. We introduced a key novel feature into the task - providing explicit cues that signalled to participants whether feedback they had seen was true (verified) or false (debunked). Learning in response to feedback was attenuated under false compared to true labels but was present under both. Fitting participants choices to computational models enabled us to examine how sensitivity to the feedback varied as a function of both the label (true/false) and confirmation (confirmatory/disconfirmatory). This revealed a distinct pattern of learning rates typical of confirmation bias (enhanced learning from positive prediction errors for chosen options and from negative prediction errors for unchosen options) in response to both true and false labels. The findings highlight how confirmation bias plays an important role in the effectiveness of interventions designed to verify true and/or debunk false claims. Verification is less likely to succeed when information disconfirms prior beliefs. Conversely, debunking false claims is unlikely to succeed when the information confirms ones prior beliefs.

Generative artificial intelligence (genAI) tools are increasingly used by prospective higher education (HE) applicants seeking guidance on university and programme selection. Despite rapidly expanding use, little is known about how genAI systems may introduce or amplify bias in undergraduate admissions decision-making. Here, we systematically examined patterns of bias across three widely used genAI chatbots (ChatGPT, Copilot, Gemini) using neuroscience as a representative UK undergraduate programme. We constructed 216 prompts that varied by applicant characteristics (e.g. gender, study type, academic attainment). Each prompt was submitted to all three chatbots, generating 648 responses and 3240 individual programme recommendations. Output responses underwent text analysis (e.g. n-grams, gender-coded language), and national HE markers of esteem (REF21, TEF23, NSS24) were analysed. Applicant grades and priorities produced the strongest effects on genAI outputs. Higher-grade applicants and those prioritising research received significantly more masculine-coded language, independent of applicant gender. N-gram patterns also diverged: high-grade prompts more frequently elicited terms relating to excellence and research intensity, whereas lower-grade prompts produced greater emphasis on widening access. Recommendations were systematically skewed, with higher grades, private schooling, and research-focused priorities increasing the likelihood of recommending elite institutions and programmes with higher entry requirements. Critically, the gender-coded language of outputs predicted institutional characteristics: masculine-coded responses were associated with recommendations featuring higher entry thresholds and stronger research performance, while feminine-coded responses favoured institutions with higher student satisfaction. These findings reveal clear, systematic biases in how genAI guides prospective HE applicants. Such biases risk reinforcing existing educational and socioeconomic inequalities, underscoring the need for transparency, regulation, and oversight in the use of genAI within HE decision-making. HighlightsO_LIGenAI is widely used by HE applicants despite little study of its biases. C_LIO_LI216 prompts across 3 chatbots generated 3240 programme suggestions. C_LIO_LIGrades and priorities drove major shifts in language and recommendations. C_LIO_LIGender-coded wording mapped onto research strength and entry standards. C_LIO_LIGenAI biases may reinforce inequalities in HE admissions decision-making. C_LI

The Balloon Analogue Risk Task (BART) is widely used to assess risk-taking and impulsivity, yet existing computational models struggle to unify sequential and prior evaluation strategies or fully capture uncertainty-driven information-seeking behavior. To address this, we introduce a novel computational framework grounded in the Active Inference Framework (AIF), which conceptualizes behavior as the minimization of expected free energy. Model comparisons demonstrate that AIF-based models statistically outperform existing benchmarks. Furthermore, we applied this framework to investigate impulsivity in women with Premenstrual Syndrome (PMS). Our model revealed that the PMS group exhibited significantly higher values in inverse precision of policy ({beta}0) and the phase difference of this parameter was only observed in PMS group. This suggests that high {beta}0 serves as a robust computational marker, reflecting both the trait impulsivity inherent in PMS and its state-like exacerbation across the menstrual cycle. Lastly, our findings indicate that impulsivity in PMS manifests not as a learning deficit, but as heightened sensitivity to trial-by-trial sequential evaluation at the expense of stable, pre-planned prior policies. This framework provides a neurobiologically plausible and mechanistically granular understanding of risk-taking, offering new avenues for computational psychiatry.

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

Niger has the worlds highest adolescent fertility rate. Social network (SN) approaches to family planning may improve intervention impact through diffusion beyond direct beneficiaries. We tested a social network modification of a community-based family planning intervention to increase contraceptive use compared to standard implementation and control.Three-arm cluster-randomized trial in 56 rural villages in Maradi, Niger. Eligible participants were adolescent wives (AW) aged 15-19 with 0-1 children and their husbands. Villages were randomized using covariate-constrained randomization (Minirand): standard Kulawa (100% coverage), SN modification (50% coverage pairing AW-mother-in-law dyads with adopt-a-friend diffusion), or control. Interventions were delivered over 12 months. Blinding of participants and implementers was infeasible; analysts were blinded. Primary outcome was current contraceptive use assessed at endline and analyzed using intention-to-treat difference-in-differences logistic regression adjusting for clustering; no missing data were imputed. ClinicalTrials.gov NCT05777473; trial closed to enrollment.From May 1 to September 30, 2022, 1,538 female AW were enrolled (517 control, 532 Kulawa, 489 Kulawa SN); 1,396 (90.8%) completed endline (May-August 2024). Compared to control, the SN arm significantly increased contraceptive use (AOR 2.36, 95% CI 1.27-4.44); the standard arm showed no significant effect (AOR 1.36, 95% CI 0.76-2.41). Within SN villages, both non-participants (AOR 2.66, 95% CI 1.25-5.70) and direct participants (AOR 2.10, 95% CI 0.99-4.44) showed increased use versus control, demonstrating behavioral diffusion. No intervention-related adverse events were observed in any arm. An SN approach targeting AWs, husbands, mothers-in-law, and adopted friends achieved greater effects than standard implementation despite 50% lower coverage, with evidence of diffusion to non-participants. Leveraging social networks may improve impact of family planning programs in high-fertility settings.

Large language models are increasingly used for clinical guidance while their parent companies introduce advertising. We tested whether pharmaceutical ads embedded in the prompts of 12 models from OpenAI, Anthropic, and Google shift drug recommendations across 258,660 API calls and four experiments probing distinct epistemic conditions. When two drugs were both guideline-appropriate, advertising shifted selection of the advertised drug by +12.7 percentage points (P < 0.001), with some model-scenario pairs shifting from 0% to 100%. Google models were the most susceptible (+29.8 pp), followed by OpenAI (+10.9 pp), while Anthropic models showed minimal change (+2.0 pp). When the advertised product lacked evidence or was clinically suboptimal, models resisted. This reveals a structured vulnerability: advertising does not override medical knowledge but fills the space where clinical evidence is underdetermined. An open-response sub-analysis (2,340 calls across three representative models) confirmed that advertising restructures free-text clinical reasoning: models echoed ad claims at 2.7 times the baseline rate while maintaining high stated confidence and rarely disclosing the ad. Susceptibility was provider-dependent (Google: +29.8 pp; OpenAI: +10.9 pp; Anthropic: +2.0 pp). Because this bias operates within clinically correct answers, it is invisible to accuracy-based evaluation, identifying a class of AI safety vulnerability that standard testing cannot detect.

We reconstructed the neoselachian diversity over the past 145 million years using new occurrence dataset and DeepDive1-3. We recovered a small decline through the K/Pg following a steady increase during the Cretaceous, and a prolonged, substantial decline towards the present following a mid-Eocene peak2. Guinot et al. argue that our conclusions are compromised by problems in the underlying data and by the way extinction magnitude across the K/Pg was quantified. They cast doubt particularly on the pattern across the K/Pg, which they consider to be at odds with all previous analyses. They raise no issue with the Cretaceous trend, even though it was recovered with the same dataset and methods. We audited the alleged data issues reported in Guinot et al. and found that they mostly reflect operational choices (see Supplementary Information). However, we applied their data treatment and ran sensitivity tests to evaluate how this approach affects our results, specifically around the K/Pg. None of our tests recovered a diversity collapse for neoselachians during this interval. As such, we demonstrate that our findings are robust and consistent across different data treatments.

Confidence judgments play a critical role in guiding behavior by shaping information-seeking, learning, and decision strategies. These functions are most effective when confidence is well calibrated, that is, when subjective uncertainty aligns with the objective uncertainty in the presented evidence. Motivated by this, we investigated how people form confidence judgments from noisy samples of information, and whether they use statistically grounded strategies or rely on heuristics. Participants performed two categorization tasks, one with visual orientation stimuli and one with number stimuli. In each task, participants saw sequentially presented observations and made a decision about the generating category and simultaneously reported their confidence in that decision. We independently manipulated the number of observations and standard deviation of the sample to assess whether confidence reflected an integrated estimate of both sources of statistical uncertainty. Behaviorally, confidence and accuracy both increased with larger sample sizes and lower variability. Furthermore, confidence and accuracy were equivalent in samples matched for standard error, suggesting that participants relied on a statistically grounded strategy. Computational modeling further supported this interpretation: a model that scaled confidence according to the standard error of the sample mean provided the best fit to the data, outperforming more heuristic and Bayesian alternatives. This pattern generalized across the orientation and number tasks, suggesting a domain-general strategy for uncertainty estimation. Together, these findings demonstrate that people use structured, statistically grounded strategies to compute their confidence, supporting well-calibrated decision-making even in the absence of full Bayesian inference.

Health visiting is England's universal home visiting programme for families with children under five and a key pillar of early intervention policy. Since the 2015 devolution of commissioning to Local Authorities (LAs), the service has faced sustained financial and workforce pressures, yet there is limited systematic evidence on whether resources and delivery have evolved differentially across areas and along the deprivation gradient. Using new Freedom of Information (FOI) data, we estimate how health visiting inputs (spending and workforce) and mandated contact delivery vary in levels and trajectories by baseline deprivation. FOI requests covered 147 English LAs (four pairs submitted joint returns), providing annual 2016-2021 Full-Time Equivalent (FTE) data on Health Visitors (HVs) and Clinical Skill Mix Staff (CSMS), which we link to DHSC Health Visitor Service Delivery Metrics reporting completion of the five mandated 0-5 reviews (New Birth Visits, 6-8 week reviews, 12-month reviews, 2-2.5 year reviews, and 2-2.5 year reviews completed with ASQ-3) and to LA revenue outturn expenditure on mandated and non-mandated 0-5 public health services (real-terms total and per child under five). Between 2016 and 2021, HV FTE fell by around one-fifth while CSMS expanded by roughly one-third, consistent with an overall contraction and a shift toward lower-band staff. To test whether these changes map onto underlying disadvantage, we stratify LAs into tertiles of baseline deprivation using the 2015 Income Deprivation Affecting Children Index (IDACI) and implement a three-part empirical strategy: (i) plotting tertile means over time, (ii) testing within-year cross-sectional differences using parametric and non-parametric methods with pairwise comparisons, and (iii) estimating LA fixed-effects regressions with Year x IDACI interactions under both a flexible year-by-year specification and a parsimonious linear-trend specification to assess differential trajectories. We find persistent cross-sectional gradients in per-child spending that are broadly progressive (more deprived LAs spend more per child on both mandated and non-mandated 0-5 services), while fixed-effects models show little evidence that spending trajectories differ systematically by deprivation. Workforce trends are more uneven: HV FTE declines more slowly and CSMS FTE grows more slowly in more deprived LAs in the linear-trend specification, while per-child HV trajectories show no differential trends. Despite these input differences, completion of mandated contacts is relatively stable across the deprivation gradient; the only consistent differential trend is faster improvement in the 6-8 week review in more deprived areas. Meanwhile, caseload pressure rises, increasing most sharply in the most deprived LAs in the pre-pandemic years, suggesting that completion-based performance measures may mask heterogeneities in service capacity and intensity. Finally, we quantify the resources required to restore recommended caseloads, implying the need for approximately 3,100 additional FTE staff and around 120 million GBP annually (plus training costs).

Functional neuroimaging aims to uncover brain processes underlying behavior and disease, yet studies are often underpowered to detect these effects. How this literature has shaped our understanding of brain function remains unknown, and little guidance exists for planning better powered studies. An underappreciated barrier is that commonly reported effect sizes across the brain are inflated, biasing study planning. Here, we introduce a correction for this inflation bias and show how more accurate studies can be planned using corrected effect size benchmarks from a mega-analysis of 63 typical studies across seven large datasets (52,979 participants). We find that common methods of planning studies based on uncorrected effects lead to roughly half the expected detections at typical sample sizes, with limited spatial overlap with original findings. These missed effects collectively explain meaningful additional variance in the desired outcome. We show how to recover missed effects by planning not only for power but also for a target number of detections via corrected benchmarks, or by taking a whole-brain approach with multivariate effects that individual research groups can detect (n < 50 compared to n > 1,000 for a typical univariate effect). These findings lay the groundwork for more informed study planning and a richer understanding of the widespread nature of brain effects, with implications for shared challenges (and solutions) across biomedicine.

The most highly predictive polygenic scores in the behavioural sciences are for cognitive traits, especially general cognitive ability (g) and educational attainment. We combined polygenic scores derived from genome-wide association studies of adult g and educational attainment to create adult 'polygenic g scores' which we used to chart the course of cognitive development of 10,000 white British children from toddlerhood through early adulthood. We integrated cross-sectional regression, latent growth curve, and confirmatory factor analysis to systematically characterise cognitive development. Polygenic g score showed minimal prediction in toddlerhood, modest prediction in childhood, and substantial prediction by early adulthood accounting for 12% of the variance. Higher polygenic g scores were associated with faster cognitive growth in latent growth models. Prediction was strongest for a cross-time latent cognitive factor (15%) capturing cognitive ability across development. By integrating polygenic prediction directly into a structural equation model framework, we provided a theoretical upper bound of genetic influences on g under minimal measurement error. We also examined the polygenic g score's prediction of educational achievement, behaviour problems, and anthropometric outcomes and found similar developmental increases in prediction for educational achievement. Together, our findings demonstrate that adult polygenic g scores can be a useful tool for charting the development of cognitive traits.

Despite well-established benefits to mothers and children, breastfeeding rates fall short of WHO recommendations world-wide. To inform effective support strategies, we investigated how maternal factors influence breastfeeding success. We estimated the causal effects of sociodemographic, cardiometabolic, psychiatric, and perinatal factors on breastfeeding initiation, duration, and exclusivity, by triangulating Mendelian randomization and multivariable regression analyses using data from 72,653 mothers and 317,651 offspring across four European cohorts. Triangulated results robustly demonstrated that higher education, lower BMI, and lower propensities for smoking, insomnia, and depression improved breastfeeding success. Each additional 3.4years in education increased initiation odds by 2.32 folds (95% CI:1.94,2.77) and prolonged exclusive breastfeeding ({beta}=0.21standard deviations, 95% CI:0.17,0.24). Smoking, depression and BMI mediated 26%, 14%, and 12% of education effect on exclusivity, respectively. We found little evidence for effects of blood pressure, cholesterol and perinatal factors. We provide new robust evidence that maternal cardiometabolic and psychiatric factors partially mediate the causal effect of maternal education on breastfeeding. Interventions targeting maternal health could support breastfeeding, reducing maternal and infant health disparities.

Significance StatementAdaptive behavior depends on knowing when to persist and when to let go; even when letting go appears as avoidance. While classical accounts of avoidance emphasize reward-effort trade-offs, we show that these decisions are critically guided by meta-control and inferences about outcome controllability and agency. Using a novel paradigm, we dissociate drivers of avoidance and demonstrate that threat does not uniformly promote disengagement. When outcome control is preserved, threat instead increases persistence, particularly following experiences that build agency in failure-safe contexts. We formalize these dynamics in the Meta-Arbitration of Control and Agency Q-learning (MACA-Q) model, which captures how experience-dependent beliefs about agency guide learning and choice across contexts. Our results show that similar avoidance behaviors can arise from distinct computational pathways. This shifts the focus from global avoidance biases to the dynamic regulation of agency as a core principle of adaptive behavior, with implications for neuroscience, psychiatry, and adaptive artificial intelligence. Adaptive behavior requires deciding when to persist and when to disengage under uncertainty and partial outcome control. Avoidance has often been studied as a response to threat or cost, yet existing paradigms cannot disentangle whether disengagement reflects threat sensitivity, expected failure, or reduced perceived control. We introduce a persistence-escape paradigm that independently manipulates incentive structures, effort demands, and outcome controllability. In a large online sample (N = 457), we show that avoidance is context-dependent rather than a stable, global trait. When outcome control was preserved under threat, the typical avoidance response reversed, promoting persistence rather than withdrawal. At the individual level, high-performing individuals were not uniformly more persistent, but more selective, disengaging when control was low. Moreover, higher anxiety symptoms were linked to cost-dominant evaluation and reduced use of accumulated competence. Conversely, higher depressive symptoms were linked to diminished sensitivity to effort and higher expected failure. To explain these behavioral patterns, we developed the Meta-Arbitration of Control and Agency Q-learning (MACA-Q) model, which embeds value learning and affective evaluation within a meta-control architecture. Critically, we formalize agency as a dynamically inferred learning gate, distinct from self-efficacy, that determines whether outcomes are treated as informative based on controllability and feedback reliability. The model explains context-specific avoidance and reveals that similar behaviors can arise from distinct computational pathways. It further shows how experience in failure-safe contexts guides subsequent behavior in adverse contexts. Our findings show that avoidance is guided by the dynamic regulation of engagement based on inferred controllability and competence. By combining a novel paradigm with a computational model, we provide a formal account of agency and a unifying framework in which meta-control regulates adaptive and maladaptive engagement across contexts, with implications for neuroscience, psychiatry, and adaptive artificial intelligence.

Speech comprehension draws on both temporal structure and contextual prediction, yet how these mechanisms coordinate is poorly understood. Time-compressed speech provides a controlled probe: by degrading temporal structure, it reveals the architecture of ordinary speech comprehension. Using 3x compression with silence insertion, we varied delivery rate, temporal regularity, and boundary alignment (syllabic vs. time-defined) across two behavioural experiments. Comprehension peaked near the upper theta boundary and declined at slower and faster rates. Temporal regularity helped only when boundaries coincided with syllabic onsets, while periodic pacing alone was insufficient. Contextual predictability (word-level entropy) facilitated comprehension when temporal cues were least effective, but only under syllabic segmentation. Computational modeling confirmed that {beta}-mediated contextual prediction selectively benefited syllabic-aligned conditions, was detrimental under time-based segmentation, and better reproduced human pattern overall. Together, these results suggest that contextual prediction is continuously active but behaviorally visible only when temporal scaffolding is insufficient and syllabic structure is preserved.

Stimulant use disorder (StimUD) is a significant public health problem, but genetic studies have been limited by small sample sizes. We conducted genome-wide association studies (GWAS) of StimUD in the Million Veteran Program (MVP) and All of Us (AOU), followed by meta-analysis with FinnGen and 10 additional datasets, for a total of 709,369 individuals (Ncases=33,977, Ncontrols=675,392) in four broad ancestry groups: European (EUR) (Ncases=22,564, Ncontrols=624,672), African (AFR) (Ncases=7,574, Ncontrols=34,189), Admixed American (AMR) (Ncases=3,657, Ncontrols=15,698), and East Asian (EAS) (Ncases=182, Ncontrols=833). Population-specific SNP heritability was 6.1% in EUR and 2.4% in AFR. We discovered a total of 19 genome-wide-significant loci, six in EUR, including DRD2*rs5794864, P=7.32E-10, one in AFR, five in a multi-ancestry meta-analysis, including CHRNA5*rs55781567, P=3.27E-9, two in a male-only meta-analysis, including FTO*rs8057044, P=9.50E10-9, and five in a meta-analysis of sex-stratified results. In a hold-out AOU subsample (NEUR=18,841, NAFR=12,263, NAMR=9,739), ancestry-specific polygenic risk scores were significantly associated with StimUD in EUR (OR=3.28, 95% confidence interval (CI)=2.89-3.71) and AMR (OR=2.01, 95% CI=1.71-2.37). Transcriptome-wide association studies, fine-mapping, and colocalization analyses prioritized additional genes (e.g., GPX1, BSN). Genetic correlation, Mendelian randomization, and causal mixture analyses revealed relationships with other substance use and use disorder phenotypes, including cannabis use disorder (rg=0.94, P=5.43E-237) and opioid use disorder (rg=1.01, P=4.40E-107), and other psychiatric traits, including anxiety, depression, neuroticism, and attention-deficit/hyperactivity disorder. This is the first well-powered GWAS of StimUD, and it offers significant insights into disease biology.

Adaptive learning requires distinguishing volatility, changes in the latent state of the environment, from moment-to-moment stochasticity of observations. The two demand opposite adjustments to the learning rate: volatility calls for faster updating, stochasticity for slower. Disentangling them is computationally difficult because both inflate experienced variance, leaving the inference prone to systematic individual differences with potential consequences for psychopathology. Three computational phenotypes capture this variation: intact learners; stochasticity-blind learners, who over-update by treating noise as change; and volatility-blind learners, who under-update by treating change as noise. In two large online samples and across three tasks, we found a double dissociation between these phenotypes and transdiagnostic psychiatric dimensions: stochasticity-blind learners scored higher on Internalizing (anxiety, depression), volatility-blind learners on Externalizing (behavioral addiction, compulsivity). Distinct symptom dimensions thus correspond to distinct failures of inference about uncertainty, supporting a selective rather than generalized account of learning-under-uncertainty deficits in psychopathology.

Extracting stable individual traits from behavior observed across diverse contexts is a central challenge in behavioral modeling. We propose a framework for inferring domain-invariant individual latent representations by jointly encoding behaviors across multiple domains. Using large-scale telemetry data from professional Counter-Strike 2 gameplay, we demonstrate that these representations are stable across distinct environments and roles, improving behavior prediction in novel domains. Our analysis reveals that complex idiosyncratic movement policies can be effectively compressed into low-dimensional embeddings, with as few as two dimensions capturing the majority of individual strategic variation. Crucially, the learned latent space forms a structured metric space where Euclidean distances predict the degradation of transfer performance. Furthermore, we show that the latent axes align with interpretable behavioral phenotypes, such as risk-taking and social cohesion. These findings suggest that multi-domain integration is a robust method for uncovering the functional structure of latent individuality in complex decision-making tasks, bridging the gap between high-dimensional telemetry data and meaningful psychological constructs.

Metacognition, the ability to evaluate whether ones decisions are correct, is crucial for adaptive behavior. However, confidence judgments are not bias-free: they can be systematically modulated by predictive cues and beliefs, favoring expectation-consistent judgments, while leaving metacognitive precision unchanged. Here, we identify parietal alpha activity as the causal mechanism linking expectations to metacognitive bias. In Study 1, EEG was recorded while 75 participants performed a visual detection task with symbolic cues signaling target probability. Cues induced a metacognitive bias in confidence without altering metacognitive sensitivity, and cue-driven alpha modulation over the right parietal cortex predicted the magnitude of this bias. In Study 2 (N = 88), continuous theta-burst stimulation (cTBS) over the right parietal cortex abolished cue-induced alpha modulation, thereby selectively reducing metacognitive bias, while sham stimulation had no effect. Together, these findings demonstrate that parietal alpha-mediated gain control causally shapes metacognitive judgments, revealing an oscillatory code for predictive metacognition.

Genetic variation contributes to individual differences in food liking and dietary behaviour. Genome-wide association studies (GWAS) have identified genetic variants associated with these traits, but most evidence comes from middle-aged and older populations. Young adulthood is a key life stage during which long-term dietary habits develop, yet the genetic basis of food liking during this period remains largely unexplored. We conducted GWAS of 97 food liking traits and two derived principal components (PCs) in 2,784 young adults (age 25) from the Avon Longitudinal Study of Parents and Children. The PCs captured broader food preference patterns reflecting preferences for diverse plant-based and seafood foods (PC1) and meat-based foods (PC2). GWAS identified 32 genome-wide significant associations across 24 traits. Cross-trait analyses indicated that several variants influenced liking across groups of related foods. For example, the lentil-associated variant rs76659918 showed associations with multiple foods, including honey, plain yogurt, chilli peppers, aubergines, avocado, and black olives, as well as PC1, whereas variants associated with bacon, burgers, and steak were linked to multiple meat-based foods and PC2. Exploratory analyses showed that TAS2R38 bitter-sensitive alleles were associated with lower liking for Brussels sprouts, with limited evidence for associations with other traits. Comparison with GWAS of food liking in the UK Biobank cohort (age 37-73) showed limited replication, with robust evidence only for the grapefruit-associated locus. This study identifies genetic variants associated with food liking in young adulthood and suggests that genetic influences operate at both the level of individual foods and broader food preference patterns.

During the COVID-19 pandemic, European mortality exhibited a marked East-West divide in both timing and magnitude, echoing longstanding longevity disparities in this region. Russia sits on the Eastern side: early restrictions were short-lived, and vaccine uptake remained low amid historically limited trust in government and science. Using weekly national and monthly regional mortality data disaggregated by age, sex, and cause of death, we estimated excess mortality from March 2020 to December 2021 using generalised additive models. We identify two major mortality peaks (late 2020-early 2021 and late 2021) and estimate 1,044,914 excess deaths, well above the 595,815 officially registered COVID-19 deaths. Non-COVID-19 excess was larger during the first peak, especially at ages 15-44. Cardiovascular diseases accounted for roughly 60% of the non-COVID-19 excess and we find no evidence of excess mortality from cancer or external causes. Among women, excess deaths were concentrated at older ages, whereas among men they clustered at working and older working ages, only partly reflecting differences in age structure. The highest excess mortality was found in the most populous regions, particularly the Central European and Volga parts. Temporal and spatial inconsistencies in cause-of-death coding may obscure indirect mortality burden and hinder the associated policy response.