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.

Positive overgeneralization - the tendency to generalize from specific successes to broad expectations of future reward - has been linked to vulnerability to mania. Because positive overgeneralization has primarily been assessed using self-report measures, we have limited insight into the underlying cognitive process. Here, we introduce a behavioral paradigm designed to quantify how learned value generalizes to novel stimuli. We quantify individual generalization profiles by fitting psychometric functions to choice data. In an online transdiagnostic study (N=163), we show that task-based breadth of reward generalization is associated with both higher self-reported positive overgeneralization and subclinical bipolar symptoms. To provide a computational account of positive overgeneralization, we implement a reinforcement-learning model in which self-efficacy modulates the influence of anticipated future value during learning. We show that increasing this modulation reproduces the broader value propagation observed empirically. Together, these findings provide a behavioral and computational framework for studying positive overgeneralization, and suggest a mechanistic pathway by which success-related shifts in value representations may bias learning in ways relevant to bipolar risk.

Flexible decision making depends on retrieving and recombining memories. Yet because this process unfolds covertly, its governing principles remain unknown. Here we use gaze reinstatement to uncover the hidden dynamics and computational logic of memory retrieval during flexible behavior. As people deliberated on a blank screen, they directed their gaze toward the encoding locations of decision-relevant experiences, and these fixations shaped their evolving choice. A task-optimized recurrent neural network captured both their behavior and gaze patterns by learning to balance retrieval costs against expected gains in decision quality. These results demonstrate that flexible decisions emerge from a resource-rational process in which memories are sampled to construct decision variables on the fly.

Compartmental models of infectious disease transmission make assumptions about human behaviors. Specifically, they parameterize interactions across population groups, assumed to have distinct epidemiologically-relevant behavioral patterns, primarily through contact matrices stratified by demographic variables such as age, gender, or socioeconomic status. Although such demographic characteristics are readily measurable, they may inadequately capture the social and psychological forces that govern protective behaviors. Drawing on 20 waves of a national survey conducted throughout the COVID-19 pandemic in the United States, we show that institutional trust - particularly trust in public health agencies, physicians, and hospitals - is a dominant predictor of protective behavior adoption. For mask wearing during periods of strongest pandemic activity, for example, institutional trust explains more behavioral variance across population groups than age, income, education, and partisan affiliation combined. In unadjusted analyses, the difference in protective behavior adoption between individuals with the highest and lowest trust in the CDC was four- to six-fold larger than the corresponding differences by age, income, or educational attainment, and exceeded the difference between Democratic and Republican respondents. This association was institutionally specific (e.g., the relationship attenuates for trust in banks), and behaviorally specific (e.g., trust in the CDC is associated with protective behaviors but not visiting a doctor). The latter suggests that trust modifies voluntary compliance with public health recommendations rather than access to or use of healthcare. We conclude that compartmental models of disease transmission would be substantially improved by incorporating institutional trust as a stratifying variable. We additionally offer a trust-integrated mathematical modeling framework and recommendations for the data infrastructure needed for its implementation.

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.

Why do multi-attribute choices so often depart from classical weighted-additive decision rules? Rather than attributing such deviations solely to biases or heuristics, we propose a resource-rational account in which value differences are encoded via capacity-limited information channels. Under resource-rational compression, these difference representations are systematically distorted, such that behavior deviates from weighted-additive predictions because value differences are not represented veridically. This theoretical account makes testable predictions about power-law relationships between true and internally represented differences. The amount of power-law-like compression is determined by information-processing capacity, emergent long-tailed prior distributions over attribute differences, and, in choice contexts, goal-dependent subjective weights that govern the allocation of limited capacity across attribute channels. We test and find support for these predictions in an attribute difference-estimation task and by reanalyzing existing food- and social-choice datasets. These results provide converging evidence for a normative, information-theoretic account of systematic nonlinearities in multi-attribute decision making. Together they show how goals can interact with cognitive capacity and priors to shape representational precision in ways that may facilitate or impair decision making.

Anxiety and depression are globally prevalent conditions associated with maladaptive decision making. However, whether affective symptoms primarily amplify threat avoidance or dampen motivational drive remains debated, and behavioural studies yield inconsistent findings. Here we show that both anxiety and depression impair the fundamental cognitive process of translating objective value into decision evidence. Across independent cohorts from the US and India, participants evaluated risky gambles while we assessed choice behaviour and the centroparietal positivity, an EEG marker of accumulating decision evidence. Prospect theory parameters, like risk and loss aversion, showed little association with symptom severity. Conversely, hierarchical drift-diffusion modelling revealed that higher symptom scores predicted attenuated value sensitivity during evidence accumulation, whereas decision caution remained intact. This reduction in value sensitivity suggests internalizing symptoms disrupt choice at the value-to-evidence interface, offering a unified mechanism underlying biased decision making in affective disorders.

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.

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.

The ability to mentally rotate objects is a fundamental feature of human cognition, and humans can use this ability to make choices about objects based on their geometry. However, remarkably little is known about how such choices are reached, and what sort of visual information might facilitate them. We devised an experiment where participants had to mentally simulate an object's rotation to choose which of two objects was better for a subsequent task based on its shape alone. We also tracked their gaze while they made their choice, to see which visual information they were using to facilitate this mental simulation. We found that participants were consistently able to choose the most suitable object for the task, and, remarkably, the visual information they sampled was directly linked to their choices. Put simply, participants made better choices when they looked at more informative regions of the objects, and participants who sampled regions that were better for facilitating mental simulation made better choices overall. These findings reveal a direct link between fixations, simulation, and decision-making, suggesting that to perform any fine-grained mental simulation people need to direct their gaze at specific, informative points of an object to simulate its two-dimensional proximal image displacement.

Inhibitory control matures progressively from childhood to early adulthood, yet the neural mechanisms driving this development and their relevance to psychiatric risk remain poorly understood. Guided by the Dual Cognitive Control model, we leveraged longitudinal fMRI from two independent cohorts in the US (ABCD, ages 9-12) and Europe (IMAGEN, ages 14-22) to map the spatiotemporal dynamics of reactive and proactive control using novel single-trial modeling and representational similarity analysis. We found both reactive and proactive stopping networks stabilize after mid-adolescence, tracking the developmental patterns of inhibitory control and behavioral stability. By decoding trial-by-trial fluctuations along a speed-caution continuum, we demonstrate that brain-behavior coupling to a proactive "Safe state" tightens progressively with age. Furthermore, network-level representational coherence of this Safe state emerged as a scanner-invariant, trait-like biomarker that robustly predicted inhibitory control, behavioral stability, and transdiagnostic psychopathology across multiple developmental windows, providing a validated neural phenotype for precision psychiatry.

Functional asymmetry between the cerebral hemispheres is a defining feature of the sensorimotor system, with the dominant hemisphere playing a central role in motor control. Whether motor learning is similarly lateralized, however, remains unresolved. To tackle this question, we combined a comprehensive meta-analysis (114 datasets) with a series of well-powered, preregistered experiments (N = 526) to test two core behavioral predictions of hemispheric lateralization in sensorimotor adaptation, a canonical form of motor learning: (1) adaptation is preferentially expressed in the dominant hand and (2) transfers asymmetrically between limbs. Across both approaches, we found that adaptation and interlimb transfer were strikingly symmetric. Together, these findings support a fundamental dissociation in the neural organization of skilled behavior: whereas motor control is lateralized to the dominant hemisphere, motor learning is supported by a neural architecture that functions symmetrically.

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.

Natural audiovisual perception may not be fully captured by decomposing movies into auditory and visual streams. I introduce a computational-counterfactual framework that keeps movie viewing intact while varying only AI-derived descriptions of the same clips. Using 7 Tesla movie fMRI imaging data from 176 participants, I tested whether cortical responses were better predicted by native audiovisual semantics than by a dimension-matched additive reconstruction from audio-only and video-only descriptions. The native model outperformed the matched additive baseline under content-aware purged cross-validation, with strongest gains in auditory, visual, and dorsal attention systems. Representational-similarity, feature-replacement, and content-gating analyses showed that the advantage reflected feature- and network-specific routing linked to coherent audiovisual semantic emergence rather than raw auditory-visual discrepancy. The effect survived stronger temporal purging and repeat-content exclusion, suggesting that intact movie viewing evokes cortical structure aligned with native audiovisual meaning beyond additive unimodal semantics.

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 seamlessly process multi-voice music into a coherent perceptual whole. Yet the neural strategies supporting this experience remain unclear. One fundamental component of this process is the formation of melody, a core structural element of music. Previous work on monophonic listening has provided strong evidence for the neurophysiological basis of melody processing, for example indicating predictive processing as a foundational mechanism underlying melody encoding. However, considerable uncertainty remains about how melodies are formed during polyphonic music listening, as existing theories (e.g., divided attention, figure-ground model, stream integration) fail to unify the full range of empirical findings. Here, we combined behavioural measures with non-invasive electroencephalography (EEG) to probe spontaneous attentional bias and melodic expectation while participants listened to two-voice classical excerpts. Our uninstructed listening paradigm eliminated a major experimental constraint, creating a more ecologically valid setting. We found that attention bias was significantly influenced by both the high-voice superiority effect and intrinsic melodic statistics. We then employed transformer-based models to generate next-note expectation profiles and test competing theories of polyphonic perception. Drawing on our findings, we propose a weighted-integration framework in which attentional bias calibrates the overall degree of integration of the competing streams. In doing so, the proposed framework reconciles previous divergent accounts by showing that, even under free-listening conditions, melodies emerge through an attention-guided statistical integration mechanism. HighlightsO_LIEEG can be used to decode spontaneous attention during the uninstructed listening of polyphonic music. C_LIO_LIBehavioural and neural data indicate that spontaneous attention is influenced by both high-voice superiority and melodic contour. C_LIO_LIAttention bias impacts the neural encoding of the polyphonic streams, with strongest effects within 200 ms after note onset. C_LIO_LIStimuli that produced a stronger attention bias aligned with monophonic-model expectations, whereas stimuli with a weaker bias aligned with the Stream-Integration model. C_LIO_LIWe propose a bi-directional influence between attention and prediction mechanisms, with horizontal statistics impacting attention (i.e., salience), and attention impacting melody extraction. C_LI

The ability to derive neural-level language coding models holds great scientific and clinical potential. Current approaches are limited by the scale and ethological validity of input data; applications requiring large, rare, or naturalistic samples in particular would benefit from the ability to infer neural coding from incidental everyday speech. Here we present a novel pipeline designed to leverage spontaneous and incidental naturalistic speech. This pipeline performs transcription, segmentation, and video-assisted diarization, as well as alignment and spike detection of neural data. We apply this pipeline to a dataset derived from 21 patients (6+ days each, over 800 hours and 5 million words total). We benchmark both encoding and decoding models against extensive and rare ground-truth control datasets consisting of human-curated word-level temporal alignment and manually sorted spikes. We further validate our approach by quantifying representational drift, effect of dataset size, and differences between six brain areas. Together, these findings demonstrate that incidental natural speech is sufficiently processed in the brain to enable the estimation neural-level embeddings.