Nature Human Behaviour

Deciding when to stop gathering information and commit to a choice is a fundamental challenge in decision-making under uncertainty. Normative characterizations such as Partially Observable Markov Decision Processes (POMDPs) prescribe mathematically optimal stopping rules; however, human evidence gathering systematically departs from optimality. Pathological departures -- such as the excessive indecisiveness characteristic of obsessive-compulsive disorder (OCD) -- offer an important opportunity to investigate the cognitive mechanisms involved in stopping. We extend a POMDP framework to incorporate key candidate suboptimalities: a biased prior belief, transient evidence exaggeration, progressive forgetting, boosted costs of error, temporal regulation (patience and urgency), and misperception of a deadline. We evaluate this model in a pre-existing dataset comprising 105 participants spanning healthy controls, generalised anxiety disorder, and the OCD spectrum performing an information gathering task with controlled, stochastic, deadlines. Model comparison reveals that human sequential choices are broadly governed by subjective risk penalties and time-dependent urgency, with a smaller and less certain contribution from an over-weighting of recent evidence, which a random-effects comparison does not support at the population level. Individuals differ in how that over-weighting is implemented: in one deadline condition, subjects divide almost evenly between models carrying a transient exaggeration of the newest sample, models carrying progressive forgetting of older evidence, and models carrying no recency mechanism at all. Crucially, while risk sensitivity and choice stochasticity act as shared mechanisms across conditions, mechanisms such as belief bias and patience are more variable. Finally, using OCD as a clinical case study, we demonstrate that simulating choices from the fitted exaggeration model reproduces model-agnostic regression signatures of clinical indecision, which the forgetting and no-recency accounts do not. These findings offer a generative foundation for dissecting clinical departures in information gathering across the obsessive-compulsive spectrum.

The UK Biobank offers an unprecedented opportunity to examine associations between brain imaging and mental health at scale, but their magnitude and replicability remain unclear. We correlated 17,580 imaging-derived phenotypes across seven MRI modalities with 309 mental health phenotypes in 8,415 participants, split into discovery and validation subsets. Associations were generally small (median |r| = 0.018; top 1% of 0.108), and they replicated poorly: only 9.0% of significant discovery associations were significant in validation. Eating disorders showed the largest effects of any mental disorders but replicated in only 6.0% of cases, whereas substance use showed the highest replication (14.5%). Susceptibility-weighted imaging produced the most replicable imaging associations. Longitudinal changes in brain and mental health measures were weakly related. We provide an interactive dashboard that returns the effect-size and replicability benchmarks for any combination of mental health category and imaging modality, supporting variable selection and study design in UK Biobank research.

Appetite is shaped not only by physiological need but by the sensory experience of eating and its social context. Can watching food being eaten itself induce satiety, and does it matter who is seen eating? In a functional MRI study (N = 41), participants watched videos of a wanted snack being eaten from their own perspective (self) or, using identical footage shown vertically inverted, as another's (other), holding food identity and visual content constant so that only the attributed agent varied. Observed eating reduced wanting, but not liking, for the eaten foods, whether one's own or another's; crucially, reported fullness increased only when the eating was seen as one's own. In the brain, food-value regions responded to watching eating in both conditions, a shared signal that strengthened over time. Yet only self-attributed eating engaged a self-specific, value-related response in the ventral striatum and medial prefrontal cortex, and orbitofrontal activity during self-eating scaled with each person's reported satiety, whereas watching another eat instead engaged the temporoparietal mentalizing network without a comparable rise in fullness. A large online survey (N = 1,000; ages 15-97) reproduced the behavioral effect across the adult lifespan, and a real-eating experiment reproduced its sensory-specific pattern. Watching eating, and whose eating it is, can thus recalibrate appetite through separate food-value and social-cognitive routes. This "observational satiety" offers a non-invasive route to study food wanting, of potential relevance to social eating and to today's food-media environments.

While creativity is widely recognized as multifaceted, genome-wide association studies have largely focused on individual measures. Here, we used genomic structural equation modeling of six creativity-related GWASs and identified two latent genetic factors: a general factor and an artistic factor, with 22 and 13 genome-wide significant loci, respectively, nine of which are novel. The general factor was almost perfectly genetically correlated with educational attainment (rg = 0.96), suggesting that it primarily captured genetic influences shared with cognitive and educational processes rather than creativity-specific variation. In contrast, the artistic factor showed only a weak genetic correlation with educational attainment (rg = 0.11) and consistent positive genetic correlations with psychiatric disorders, whereas associations for the general factor were largely null or negative. Biological annotation analyses further identified distinct enrichment patterns unique to the artistic factor, providing initial evidence that different dimensions of creativity may be characterized by partially distinct neurobiological pathways.

Affective flexibility-the capacity to flexibly process emotional information-has been assessed with affective task switching, in which response times consistently reveal larger behavioral costs when switching to the affective task. These traditional metrics of task behavior, however, discard the sub-trial dynamics that produce them and are vulnerable to trial-sampling noise. Subject-specific generative models were independently fitted (22 adults) with response-time sequences. The learned dynamics were visualized as continuous trajectories in a labeled embedding that was shared across person-specific models, yielding a suite of interpretable geometric metrics that captured how sub-trial behavior is jointly shaped by experimental conditions and between-subject variability. Switch-repeat onset distance tracked individual differences in switch costs; early- and late-epoch tortuosity provided convergence with classical sub-process models; and angular alignment between trajectories supported a task set flexibility account of asymmetric switch costs. Overall, our approach demonstrates the utility of latent geometry for deriving mechanistically informative measures of individual task behavior.

Learning in uncertain environments requires identifying the relevant associations between stimuli, actions, and outcomes and determining how strongly to update these associations. Although often treated separately, these components likely interact in the brain. We hypothesized that this interaction shapes individual learning rates according to cue-choice alignment and reward outcome, thereby improving discrimination between competing cues. We tested this hypothesis using a probabilistic learning task in which human participants predicted outcomes based on multiple cues and reward feedback. We measured gaze and manipulated cue saliency to assess and influence which cues were preferentially processed during choice and feedback. Computational modeling revealed that learning rates were selectively enhanced for cues supporting the chosen option after reward and for cues opposing it after no reward. This learning-rate asymmetry based on cue-choice alignment sharpened discrimination among predictive cues, increased robustness to noise, and improved performance. Moreover, differential gaze toward supporting and opposing cues predicted this asymmetry, which was causally altered by manipulating cue saliency. Together, our results suggest that attention provides a unifying mechanism for coordinating what we learn from with how much we learn, helping preserve distinctions among competing cues and bringing several learning asymmetries within a common framework.

A neural decoder can predict a mental-state label without using information specific to that state. We made auditory-attention attribution falsifiable by requiring candidate evidence to persist in disjoint data, respond to capacity-matched substitutions of physical organization or listener/population template, and remain testable after target-event exclusion or command-identity residualization; two event-related datasets also permitted electrooculography (EOG)-only comparisons. The design drew on Wang and Zahls three-dimensional Kakeya proof strategy: examine the organized family and its concentration, not only the strongest member. Across six EEG datasets, averaging four neural-speech margins improved 5-s decoding relative to the leading margin in three evaluation sets whose rules were fixed before their results were computed (41 participants; study-equal gain, 0.0201; 95% interval, 0.0125-0.0279). A 16-cell scalp-direction-delay representation replicated in a participant-disjoint cohort and exceeded the mean of 15 capacity-matched remappings. Across three continuous-speech datasets (43 participants; 86 directed transfers), listener-matched weights outranked other-listener weights by 0.0969 and wrong mappings by 0.1371, although accuracy did not improve universally. In two hierarchical interfaces, a parent-stream error score retained AUCs of 0.968 and 0.965 after oracle-label exclusion of all target-command events. It depended on the physical command-stream mapping, exceeded an EOG-only comparator, and generalized within listeners after training-only removal of command identity. Eight electrodes retained 59-77% of binding specificity, but one listener-consistency criterion failed. The main contribution is a transferable standard for testing what information supports a decoded psychological construct. Significance StatementInspired by the proof strategy of the three-dimensional Kakeya theorem, we turn "a neural decoder reads auditory attention" from an interpretation of accuracy into a falsifiable test of evidence attribution. Engineering can exploit any stable predictor; science of latent mental constructs must ask whether the proposed construct remains necessary after plausible alternatives are removed or substituted. Across six electroencephalography (EEG) datasets, task-organized scores survived disjoint data and were challenged by matched substitutions of physical mapping or listener template, target-event exclusion, command-identity residualization, and EOG-only comparison. This framework does not prove that attention is the only cause. It offers neuroscience and brain-computer interfaces (BCIs) a standard: evidence should transport, its proposed organization should matter, and credible shortcuts should fail.

When people engage in spontaneous thought under low external constraint, thought content changes continuously and can shift across topics. However, the cognitive computational principles governing these transitions remain unclear, as does whether similar dynamics emerge in artificial language generation. Here, we formalized spontaneous narrative dynamics using a foraging-inspired framework, constructing an agent based on the marginal value theorem (MVT) that balances exploration and exploitation while selecting among informational resources represented in a participant-specific, high-order patch network. Patches were defined as partially overlapping composite structures containing second-order paths and third-order interactions, with their available resources quantified using multilevel structure values. In a multi-theme free spoken narrative task, the model predicted the temporal organization of human patch activation substantially better than random-shuffle baselines, although the strength of model conformity varied across participant-theme samples. Model-predicted patch transitions corresponded to systematic semantic segmentation and reorganization across multiple representational levels. Transition rates were positively associated with the perceived importance and likelihood of the narrated events. These findings were replicated when the modeled memory environment was enriched with additional hub-related narrative resources in a supplemental experiment. Control-model analyses further showed that patch-network topology, resource depletion history, or either order of structure value alone could explain only part of the observed dynamics; the most stable human prediction required foraging-related decisions guided jointly by multilevel structure values. Narratives generated by large language models (LLMs) also showed detectable conformity with the same framework, but the full model captured LLM content less consistently, while simpler control models accounted for a larger proportion of their patch-level dynamics. Together, these findings support a computational account in which patch-level narrative dynamics are organized by value-guided foraging over structured memory resources, while revealing graded differences in the strength and stability of these dynamics across human and artificial language generation.

Language is vastly ambiguous, yet in context our brains resolve ambiguity effortlessly. This is particularly evident at the lexical level: Although most words can convey a wide range of related meanings, humans rapidly and automatically select the one appropriate for the context. We investigated the neural mechanisms underlying this process by focusing on a case where, intuitively, one meaning appears more basic than another: nouns describing animate entities, such as tiger, which can also refer to representations of those entities when combined with a material modifier, as in plastic tiger. Using magnetoencephalography, we decoded animacy from neural signals and found that even in such cases, our approach revealed no evidence that the tiger in plastic tiger is represented as a sentient animal in any stage of processing. Instead, phrase-level animacy--plastic tiger as inanimate--was decodable as early as 210 ms post-stimulus onset and persisted for several hundred milliseconds. These findings challenge theories assuming a shift from a basic sense of a word to a more derived one, highlighting instead the role of compositional context in shaping word meanings.

Human immunodeficiency virus (HIV) and hepatitis B virus (HBV) coinfection is associated with accelerated liver disease, but whether coinfection is associated with newly documented social determinants of health (SDoH) is unclear. We conducted a retrospective cohort study using TriNetX across 110 U.S. healthcare organizations (2010-2026). We propensity score matched adults with HIV/HBV to adults with HIV or HBV monoinfection. We organized newly documented SDoH indicators using a dynamic individual-level framework with four clinically recognized domains of social disadvantage: material vulnerability, healthcare access and engagement, interpersonal adversity, and psychosocial vulnerability. Matched cohorts included 10,071 HIV/HBV-HIV pairs and 9,659 HIV/HBV-HBV pairs (mean age, 47 years; 79% male; 66% non-White; median follow-up, 3.3 years). Over 178,900 person-years, HIV/HBV was associated with higher risk of the primary SDoH composite compared with HIV (11.5% vs 9.7%; incidence rate, 2.50 vs 1.97 per 100 person-years; hazard ratio [HR], 1.25; 95% confidence interval [CI], 1.15-1.37) and HBV (11.0% vs 6.4%; incidence rate, 2.39 vs 1.67; HR, 1.50; 95% CI, 1.35-1.67). HIV/HBV was also associated with higher material vulnerability and healthcare access and engagement composites in both comparisons, including housing instability, food insecurity, financial insecurity, insurance instability, and care disengagement/nonadherence (HR range, 1.22-3.33 vs HIV; 1.31-1.94 vs HBV). In the HBV comparison, HIV/HBV was additionally associated with interpersonal adversity, primary support stressors, and violence or victimization (HR range, 1.36-2.16). Findings were robust across sensitivity analyses. HIV/HBV was associated with more newly documented SDoH than monoinfection, supporting dynamic SDoH assessment.

Genetics research frequently intersects with ethnicity, nationality, and race, making it uniquely vulnerable to misrepresentation. Yet, 25 years after the initial sequencing of the human genome, there is little understanding of how human genetics research exists in the public-facing information ecosystem. We analyze 3,050,422 historical revisions from 6,738 Wikipedia pages about ethnicity, nationality, and race spanning 25 years. We find genetics terminology is present in 14.8% of these pages (55.5% in the top 1,000 pages) and in 67.8% of pages about nationalities, suggesting research is synthesized to present a biological element to ethnicity and nationality. We also find that 10.1% of 56,908 discussions from these pages contain genetics terminology. We further analyze responses from three popular chatbots queried about nationalities and find that they commonly reference both genetics and Wikipedia. Lastly, we analyze 133 pages from Grokipedia, an AI-generated encyclopedia, and find it mentions genetics more frequently than Wikipedia and hallucinates or misrepresents human genetics research.

Depressive disorders are one of the most common psychiatric conditions worldwide. We systematically screened a prespecified exposure panel for associations with depressive symptoms and evaluated cross-wave replication among Brazilian adults. This preregistered exposure-wide association study used independent, nationally representative cross-sectional samples from the 2013 (n=60,202) and 2019 (n=88,531) Brazilian National Health Surveys. 31 general exposures were assessed with survey-weighted regression; four occupational exposures were analysed separately. The primary outcome was a positive Patient Health Questionnaire-9 screen (PHQ-9 >=10); continuous PHQ-9 score was secondary. Discoveries required a Benjamini-Yekutieli-adjusted p<0.05 in 2013; replication required the same coefficient direction and raw p<0.05 in 2019. 21 general exposures were primary discoveries, and all replicated. Associations spanned health status/health care (n=11), behaviour/participation (n=5), and social/material context (n=5). Poor or very poor vs very good self-rated health showed the largest association (adjusted prevalence ratio 10.97, 95% CI 8.79-13.69 in 2013; 12.33, 10.19-14.92 in 2019). Replicated correlates also included morbidity, smoking, prolonged television viewing, diet, group activities, education, income, sanitation, and nearby public space. All 25 continuous-outcome discoveries replicated. All four occupational associations retained the same direction and raw p<0.05 in 2019. This recurrent profile provides a reproducible map for prioritizing longitudinal research but, because both waves were cross-sectional and exposures were modelled separately, does not establish temporality, causality, or independent effects.

Abrupt social and political transformations in twentieth-century Germany may have shifted how polygenic indices relate to educational outcomes across birth cohorts. Following a prespecified analysis plan, we harmonized genomic, educational, and regional data from four German studies, comprising 13,049 individuals born between 1918 and 1995. Polygenic index associations with educational attainment were robust and strengthened modestly across more recent birth cohorts, with no detectable difference between East and West Germany. Associations with intergenerational educational mobility showed no evidence of such strengthening but were stronger in the East than in the West. Polygenic indices were less predictive of educational attainment and educational mobility for women than for men, with no evidence of regional variation. We discuss the methodological limitations of polygenic indices for examining gene-environment interactions, and emphasize that evaluating whether these differences represent progress towards equal educational opportunity requires a normative framework that social science genetics alone cannot supply.

Cognitive neuroscience often seeks brain activity that is consistent across people. Individual differences in brain organization have received less attention. Bilinguals, in particular, remain understudied at the individual level, as neuroimaging has mostly approached them as a group. We analyzed functional magnetic resonance imaging (fMRI) data from 77 Chinese-English bilinguals. We decoded each person's current language state, Chinese or English, from cortex-wide activity and assessed cross-person transfer across participant pairs. We identified a low-dimensional population representation, derived outside each tested pair, that was sufficient to support transfer, with performance nearly matching that of the full cortical pattern. Variation in this transfer tracked how early participants had learned English. Removing these dimensions barely changed within person accuracy, but transfer progressively declined until it collapsed. Our findings reveal two levels of organization: a common component that supports transfer between brains and an individual component that remains reliably stable within a person across sessions.

How do psychedelic drugs alter human brain function? Answering this question relies on our ability to understand how information is stored, transferred, and transformed across the cortex. Here, we leverage a precise mathematical framework for multivariate information decomposition to construct the first comprehensive picture of these processes across five compounds: LSD, psilocybin, ketamine, DMT and 5-MeO-DMT (N=80). We provide a formal and empirical explanation of major prior results, revealing sets of competing underlying phenomena masked by extant approaches. We extend this analysis to novel higher-order phenomena, and we demonstrate how all of these observations can emerge from a minimal generative model in which neural populations operate with reduced self-coupling. In sum, our results suggest that the psychedelic brain is one in which cortical populations become alike, but not necessarily together. This has important implications for models of how psychedelics reorganize brain dynamics to alter subjective experience and alleviate mental health conditions.

Confidence guides our decisions: it determines when we commit to a choice, gather more information, or change our minds. Prominent models treat confidence as a readout of the evidence in support of a decision: given the same evidence, a similar confidence level should follow, regardless of what the decision-maker is trying to achieve. Here we show instead that confidence formation is relative to the goals of the decision-maker. Across value-based and perceptual decisions in different sensory modalities, we manipulated the behavioural goal, requiring participants to select the option with either the most or the least evidence. Reversing the goal reversed the relationship between evidence and confidence: biases previously considered fixed features of a confidence computation, such as the positive evidence bias (PEB), followed the goal rather than the stimulus. We developed a new computational model, GOAL (Goal-Oriented Asymmetric Likelihood), in which evidence is weighted according to its relevance for the current goal, and validated it in behavioural and physiological (pupillometry) experiments. Our findings support a new account of metacognition in which confidence is constructed relative to the goals of the decision-maker, prioritising goal-supportive evidence.

The development of large language models (LLMs) offers promising opportunities for predicting human behavior across diverse contexts. However, most prior work has emphasized behavioral imitation, with limited attention to transparent or interpretable models of the cognitive mechanisms underlying human decisions. In this study, we introduce MindEvolve, an autonomous workflow designed to predict behavior in social interactions by generating interpretable symbolic models of cognition. We systematically evaluate the modeling capabilities of multiple LLMs across a battery of socioeconomic games covering four core domains of social cognition: economic preferences, social preferences, social reasoning (theory of mind), and recursive planning. The symbolic models proposed by the LLMs are assessed by expert human evaluators for interpretability and theoretical coherence. Our results show that while most LLMs robustly capture economic and social preferences in relatively simple strategic settings, and in some cases generate novel models that integrate broader knowledge than those proposed by human experts, their capacity to model more complex psychological processes remains limited. However, a subset of state-of-the-art models demonstrates promising performance in capturing higher-order reasoning processes such as theory of mind and recursive planning. Together, these findings highlight the emerging potential of LLMs not only to imitate behavior, but to generate interpretable, mechanistic accounts of human social decision-making. Our results provide a roadmap for advancing LLM-based cognitive modeling toward human-expert-level theory construction.

Causality appears to play a central role in narratives, but how it influences later memory and how the brain creates causal structure is unclear. Here, participants watched and recalled a TV show featuring five temporally interleaved storylines during fMRI, and different participants determined cause-effect relationships between each pair of events. Behaviorally, causality significantly influenced recall organization, with the top cause-effect relationship predicting recall transitions best among several predictors. Neurally, across-event patterns in regions of the default mode network (DMN) reflected causal structure, and DMN patterns at event boundaries specifically reactivated prior, causally related events, suggesting causality predicts our ability to stitch together related events across temporal gaps. Additionally, causal network distance between successive events predicted the strength of DMN pattern changes across event boundaries, suggesting causality also predicts stronger representational switching between unrelated events. Together, these findings suggest that DMN regions perform the mechanisms required to build the complex network of causal associations underlying the comprehension and recall of real-world memories.

Intelligence is often considered to be fundamentally different from other personality traits. Twin studies show that intelligence and the Big Five personality traits are partly influenced by the same genetic factors, but it remains unclear to what degree shared genes, SNPs, and biological pathways influence them. To address these questions, we analyzed the genetic associations of all six traits. First, we calculated their genetic correlations using LD score regression. We then quantified polygenic overlap using a bivariate causal mixture model, finding that approximately 90% of the genetic variants influencing the Big Five personality traits also impact intelligence. Finally, we performed gene prioritization and gene set enrichment analysis on each trait, once again finding substantial overlap. At each level, intelligence was as similar to the Big Five traits as they were to one another. Genetically and biologically, intelligence fits in with the rest of personality.

In language, not only do sentence meanings depend on how words are structured together, but word meanings also depend on their roles in the structure. While we have some understanding of how the brain grasps complex meanings built from simpler parts, we know far less about how linguistic structure reshapes the meanings of the words themselves. Here we used magnetoencephalography (29 human participants: 16 women, 9 men, 4 nonbinary) to track the neural dynamics of animacy, a fundamental semantic feature that guides conceptual organization. Regardless of how animacy was operationalized (as discrete semantic categories or more abstractly), the same pattern emerged: When word-level animacy conflicted with the structural context, it disappeared from the word's neural representation. Our findings show how compositional processes rapidly reconfigure neural traces of word meanings, allowing phrase-level meanings to even override word-level features.