Communications Psychology

Cohorts and health systems give people batteries of questionnaires on symptoms and life circumstances, and a person's difficulties can show up in several at once. Finding who is faring badly means scoring the whole battery. Methods that span a battery estimate its structure from responses rather than from what the questions mean, and work on question wording does not score the people who answered. Here we build a semantic frame of reference from published question text alone, and score respondents on it. We use language models to group 577 questions from 50 questionnaires into components fixed before any cohort answers, each interpretable because it rests on a few questions. We place any battery on these components from its question text and score each respondent by how unusual their position is. On an All of Us battery answered by 22,332 participants, we find a group burdened across many questionnaires. In health records and access surveys that never entered the scoring, this group reaches care less easily than the rest of the cohort, and being more distressed does not explain the difference. Built once, the frame scores any battery, and each score shows which questions produced it.

Confidence evaluates the likely accuracy of a current decision. However, to be maximally informative about accuracy, confidence judgments should incorporate information about ones broader decision tendencies, such as their propensity to favor specific alternatives. We distinguish bias-aware confidence, which considers such tendencies, from bias-blind confidence, which relies only on evidence available on the current trial. To adjudicate between bias-aware and bias-blind confidence, we identified a signature of bias-aware confidence: the down-weighting of confidence for alternatives that a participant is biased toward. We then used a large dataset (N = 200) spanning 4- and 8-choice digit-classification tasks to show that humans reliably exhibit this signature of bias-aware confidence. This effect was reduced under speed pressure and could not be explained by guessing. In contrast to the human results, artificial neural networks (ANNs) trained for object recognition lacked this signature of bias- aware confidence. Importantly, augmenting ANNs with a metacognitive module that allows confidence to take the networks biases into account led to the emergence of human-like bias- aware confidence. These findings show that human confidence incorporates not only information from the current trial but also longer-term decision tendencies, and that this capacity - absent in standard ANNs - can be conferred through specialized metacognitive mechanisms.

Healthy aging has long been associated with impairments in learning from rewards. However, typical paradigms do not adequately distinguish between being motivated by information to learn about the world and being motivated by rewarding outcomes from our decisions. The present study describes how these distinct motivations guide learning about the environment across the lifespan. Participants (N=169; ages 18-71 [M=39, SD=15.4]) completed a task based on the board game Battleship, making sequential choices about which tile to uncover on a grid with the goal of finding and learning hidden shapes. While older adults performed worse on the task in terms of overall score, their search behavior stabilized earlier. Older adults also preferred to seek information over reward, spreading their choices more widely, making more choices per trial, and acquiring more information per trial. Additionally, older adults showed a persistent reliance on information gained from the outcomes of their choices. These findings suggest that, as individuals age, they prefer to seek information, which earns them fewer points and slows learning but incurs no cost to long-run projected performance. Despite the conventional view linking age with diminished learning, in a complex environment that disentangled information and reward, older adults remained engaged with no projected learning deficits. Our findings challenge assumptions about cognitive decline in aging and reframe information seeking in older adults as a persistent motivational preference that need not serve learning, raising the question of why the drive to seek information intensifies with age even when it confers no advantage.

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.

Task demand has a long-standing history in experimental psychology, with numerous investigations of the capacity of different systems and processes under varying cognitive load. Whilst demand is a broadly used term in the literature, it is limited by a lack of cross-domain operationalisation, and thus can be a confound in many inter-domain investigations. To address this, we generated and validated two large-scale, open-source, widely applicable stimuli sets that: (i) can probe a wide, continuous range of objective behaviourally-measured processing demand, and (ii) can be directly matched across two highly contrastive cognitive domains (semantic and visuospatial processing). We normed these tasks in a large, online cohort of healthy adults, enabling future studies to utilise and benefit from reliable baseline measures of demand. Using a matching algorithm that takes into account both speed and accuracy, we then closely paired each semantic judgement trial with a trial of comparable difficulty from the corresponding visuospatial task to produce a cross domain matched set of over 400 items. Finally, we analysed the psycholinguistic and perceptual properties of these stimuli to explore some of the underlying factors driving demand.

Healthy older adults are more susceptible to false memories than young adults. Traditional false memory paradigms leverage semantic overlap, shared meaning, to induce false memories, but experiences can also overlap temporally whereby they occur close together in time. Prior work shows that older adults have impaired episodic memory, memory for events within a spatiotemporal context, corresponding to an overall shift toward semantic memory and away from episodic memory across the lifespan. We hypothesize that compared to young adults, older adults rely more heavily on semantic versus episodic information, which promotes false memory. We collected behavioral data in young and older adults performing an old/new recognition memory task in which we manipulated the degree of semantic and temporal overlap between study words and included critical lures, unstudied words that semantically overlap with study words. We find that whereas young and older adults are similarly reliant on semantic relative to episodic information to support false memory, the two age groups differ in their reliance on semantic relative to episodic information to support true memory. These results suggest that differences in the orientation of attention -- toward semantic vs. episodic information -- may underlie age-related memory changes.

Background: Normative Z-scores are essential for interpreting individual cognitive performance relative to healthy reference populations. Traditional norms often compare individuals within predefined groups, such as age bands. This is useful and easy to interpret, but it can lose information because variables such as age change continuously and because age, sex, and education may contribute jointly to performance. Regression-based norms estimate the expected score for each individual from their specific covariates and then express the difference between the observed and expected score as a normative Z-score. Therefore, the key methodological question is not simply whether a more complex model can be fitted, but whether that added complexity changes the Z-score in a meaningful way. Methods: We implemented a comparative framework and fitted ordinary least squares (OLS), generalized additive models (GAM), and generalized additive models for location, scale, and shape (GAMLSS) to the seven public reference datasets distributed in the NormData R package. The datasets span processing speed, verbal memory, verbal fluency, academic achievement, personality, and anxiety. For each dataset we compared each regression model with the classic subgroup norm, and then compared the regression models with one another. Metrics included Pearson r, residual RMSE, and the percentage of participants whose model-based Z-score agreed with the classic Z-score. Results: All three models reproduced the classic Z-scores closely (r = 0.964-1.000). OLS and GAM produced nearly identical Z-scores throughout (r = 0.996-1.000), indicating that the observed age trajectories were smooth enough for a quadratic term to capture. GAMLSS diverged from OLS mainly in datasets with non-constant residual variance (Fluency, sigma-ratio = 1.79; TMAS, sigma-ratio = 1.50), where modeling the scale parameter recovered dispersion differences that the classic subgroup method encodes implicitly. Conclusions: Across seven heterogeneous reference datasets, OLS provides a sufficient normative Z-score computation when the sample size is large enough and residual variance is approximately constant. GAM is useful when the mean trajectory is strongly nonlinear; GAMLSS is useful when residual variance changes across covariate profiles. The practical message is deliberately simple: add model complexity only when the data show what that complexity contributes to the individual Z-score.

You remember what you attend to and you attend to what you remember. Memory is supported through engagement of mnemonic brain states, multivariate whole brain activity patterns that modulate down- stream processing and behavior. Attention can be focused externally to the environment or internally to thoughts and mental representations. To understand and promote successful cognition, it is critical to establish the extent to which the same brain states support both memory and attention processes. We recorded scalp EEG during three tasks in which we manipulated external and internal attention demands. We applied an independently-validated mnemonic state classifier to these data and find evidence in sup- port of our hypothesis that memory encoding and retrieval states map onto the external/internal axis of attention. Furthermore, our findings reveal sub-second fluctuations in mnemonic states. These results demonstrate that domain general mnemonic states support attentional orienting and can be used to detect moment-to-moment shifts in attention.

Where and how we move our eyes through a natural scene depends jointly on the scene and on the viewer. How the spatial and temporal organization of viewing changes across the lifespan, and whether those changes relate to memory, remains unclear. We recorded eye movements from a lifespan cohort (N = 179, ages 5-79) during free viewing of naturalistic scenes, then tested recognition across graded levels of image degradation. Characterizing each observer by how closely their viewing corresponded to that of age peers, young adults, and a stimulus-driven salience model, we found a developmental dissociation: consistency in where the eyes were directed increased monotonically with age, whereas consistency in how they moved -- saccade direction, length, and fixation duration -- followed an inverted-U peaking in young adulthood. Recognition sensitivity followed an inverted-U of the same form. Across all three reference frames, typicality in how the eyes moved, but not in their spatial targeting, predicted recognition.

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.

Cardiac interoception is commonly assessed by comparing subjective estimates of heart rate with physiological measurements. But beliefs can easily obscure these measures, such that they cannot readily distinguish sensitivity to afferent signals from the influence of prior expectations; this ultimately challenges the notion that behaviours under these tasks could reflect interoceptive processes at all. Here, we develop a computational framework that uses naturally occurring heart rate variability to quantify how strongly perceptual beliefs are updated by physiological evidence. We formalise interoception as weighted Bayesian updating under the joint influence of expectations and afferents, and derive a new measure, cardiac interoceptive sensitivity, that quantifies the extent to which beliefs move with incoming signals. Applying this to the largest Heart Rate Discrimination dataset to date (n=549), a task providing robust estimates of cardiac beliefs, we find that sensitivity is weak in the healthy population, but shows large interindividual differences, with 43% of the participants exhibiting behaviours at least minimally compatible with interoceptive processing. These findings challenge the interpretation of conventional measures of cardiac interoception. They also introduce a new framework to relate bodily signals that cannot be controlled experimentally to external signals that can, laying a computational foundation for embodied psychophysics.

Recalling scenes and events depends on how information is relationally organised in memory, but it is unclear whether the organisation of eye movements during encoding, rather than simply how much is explored, shapes what is remembered. Using a scene-viewing paradigm and drawing-based free recall task to capture the content and structure of remembered scenes, we examined whether scene memory was better explained by 'lower-order' measures reflecting the magnitude and spatial extent of sampling, or 'higher-order' measures capturing gaze organisation and temporal dynamics. Relative to lower-order measures (number of fixations and scanpath length), memory precision was better predicted by repeated sampling of viewed regions (refixations) and gaze distributions (stationary gaze entropy) during encoding. Thus, the organisation of visual attention, rather than sampling quantity, contributed to scene representations that preserved relationships between elements. These findings highlight a role for spatial gaze dynamics in optimally encoding organised, relational scene representations for episodic memory.

Bayesian decision theory proposes that people make statistically rational decisions by combining prior knowledge with sensory information (likelihoods). This framework successfully explains many aspects of human behaviour. However, debate persists over whether people perform precise Bayesian computations (i.e., explicit Bayesian strategy) or rely on less demanding strategies - such as approximations or heuristics - that produce Bayesian-like behaviour (i.e., implicit Bayesian strategy). To address this, we examined people's sensitivity to metamers: different prior-likelihood combinations yielding identical optimal policies. An explicit Bayesian observer would show a temporary performance drop immediately after a switch of prior-likelihood combination, followed by recovery, reflecting prior updating. In two studies, we trained participants to estimate hidden target locations drawn from a Gaussian prior. On each trial, scattered dots provided likelihood information. Over time, participants learned the prior and combined it with likelihood information to infer target locations. We then covertly introduced an untrained prior-likelihood metamer. Unlike explicit Bayesian observers, participants' performance declined after the switch and persisted throughout the untrained pair presentation. This finding challenges strict Bayesian interpretations of task performance and suggests that participants rely instead on likelihood-sensitive strategy that is neither explicit Bayesian nor does it not fully integrate prior information. Our study demonstrates how metamer manipulations can distinguish behaviour that merely appears Bayesian, from behaviour genuinely produced by Bayesian computations, and calls for the use of metamers for ruling out alternative explanations of Bayesian-like behaviours.

Sleepiness is a leading proximate cause of drowsy-driving fatalities, medical errors and industrial accidents, yet it has resisted mechanistic prediction; although it arises from well-characterized sleep-wake physiology, it is experienced as a subjective state and has lacked a quantitative link to the underlying dynamics. We previously showed that subjective sleepiness maps linearly, with a protocol-invariant form, onto the signed distance H - H+ between the homeostatic pressure H and the circadian-modulated sleep-onset threshold H+. This single quantity predicts sleepiness accurately but is mechanistically ambiguous: the same value can arise either because H sits far from the boundary or because the threshold H+(t) has shifted with circadian phase, and these two origins call for entirely different interpretations and interventions. Here we resolve this ambiguity by decomposing H - H+ into two mechanistically separable axes-intensity and phase. The intensity axis is the time-averaged margin [<] H - H+[>], set by how far, on average, H sits from the sleep boundary: slowed homeostatic accumulation accounts for the paradoxically blunted sleepiness of older adults, and pharmacological suppression of H accounts for the dose-dependent alerting effect of caffeine. The phase axis is set by the circadian modulation of H+(t): under a forced-desynchrony protocol, in which the pacemaker free-runs and the homeostatic and circadian processes are experimentally decoupled, sleepiness tracks the circadian profile of H+(t) across all phases while the intensity mapping itself remains unchanged-a clean dissociation of the two axes. By resolving felt sleepiness into these two physiological degrees of freedom, this framework renders previously isolated phenomena-aging, caffeine and circadian misalignment-commensurable within a single theory and provides a physiologically interpretable basis for prospective fatigue-risk prediction. Author summaryWhy people feel sleepy after sleep loss, or at particular times of day, remains difficult to predict from physiology alone. Sleep and wake are shaped by two interacting processes: a daily circadian rhythm and a homeostatic pressure that builds during wakefulness. In earlier work, we linked subjective sleepiness ratings to a simple geometric quantity-how close sleep pressure sits to a circadian sleep-onset boundary. That link is useful, but ambiguous: the same distance can arise either because pressure itself has changed, or because the boundary has moved with circadian phase. Here we use a computational model of the sleep-wake switch, extended to include the wake-stabilizing orexin system, to separate these contributions into an intensity axis and a phase axis. We find that aging and caffeine mainly alter how large the average distance to the boundary becomes, whereas forced desynchrony mainly alters how that distance varies across circadian phase. This dissociation offers a compact way to interpret several otherwise separate observations within one quantitative picture, and a step toward more physiologically grounded fatigue-risk assessment.

Memory faces a fundamental challenge. It has to become flexible enough to be accessed by novel cues without losing the specific information that defines each individual episode. Whether memory transformation solves this problem by sacrificing details or by adaptively expanding retrieval while preserving them is unclear. We tested this by manipulating the match between a memory and its retrieval cue. Participants retrieved object associations after 6, 24 and 48 hours using either the original encoded image or novel exemplars that were more typical or more distinctive of the same object category. Retrieval initially benefited from the originally encoded image, but this advantage was reduced across delay. By 48 hours, a never-seen, more typical exemplar triggered retrieval as effectively as the original one, while the same convergence was not evident for less-typical exemplars. Crucially, such cue expansion was not accompanied by a loss of perceptual specificity. Participants retained access to the originally encoded exemplar across delays, while errors increasingly favoured more typical alternatives. At the item level, stronger episodic memories predicted better access to perceptual detail, but did not predict the tendency of errors for category-like information, which instead increased with delay. This dissociation suggests that memory generalisation and the continued accessibility of visual detail reflect partly separable transformations rather than opposite ends of a single continuum.

Longitudinal animal experiments generate behavior that is individual, history-dependent, and sometimes affected by ordinary procedural irregularities, yet analyses commonly reduce such records to pooled averages or synchronous trial-level explanations. We introduce history-structured forecasting as an auditable framework for determining whether an animals own preceding behavior carries predictive information beyond current-trial context. We applied the framework to 213,990 events from rats performing an auditory duration-discrimination task, using leakage-safe chronological forward-chaining, explicit trivial baselines, and controls that reset, exchange, or disrupt behavioral history. A transparent gradient-boosted model achieved 51.7% four-class accuracy, exceeding last-action persistence (40.9%) and prefix-derived subject-modal prediction (37.2%); decline-class AUPRC was 0.525 against a prevalence baseline of 0.303. Validation showed that the predictive advantage depended predominantly on each animals short-range sequential action history rather than group-level history, subject identity alone, or reward/correctness features, and strengthened on genuine choice trials. Forecasting remained informative across all 23 labeled animals, including six with recoverable records affected by incorrect training programming. These results revise the interpretation of rewarded give-up behavior while demonstrating how recoverable irregular records can be retained in transparent robustness analyses. History-structured forecasting offers a reusable open-science strategy for extracting reproducible evidence from imperfect longitudinal animal records without creating an artificially clean cohort.

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.

Mice are widely used to study the neural basis of perceptual decision-making, often with the ultimate goal of extrapolating insights to humans. Yet, whether humans and mice rely on similar computational strategies remains rarely tested, partly because the two species acquire knowledge differently. Animals learn from experience, while humans usually receive verbal task instructions. Here, we explicitly contrast these modes of learning in humans who perform a perceptual decision-making task adapted from a standardized rodent task. Specifically, we present an open behavioral dataset from 80 human participants who performed 600 trials of a visual contrast-discrimination task, matched to the standardized International Brain Laboratory task for mice (International Brain Laboratory et al., 2021, 2025). Participants either received verbal instructions ('Instructed') or were only told to discover the rules of the task by playing ('Uninstructed'). The dataset contains choices, response times, continuous cursor trajectories, eye-tracking and pupil recordings. Descriptive analyses show that Uninstructed participants were less accurate and had higher lapse rates than Instructed participants. They improved over the first 200 trials, approaching the performance of Instructed participants. These data provide a benchmark for testing whether computational strategies identified in mice also describe human behavior.

Motivational incentives are known to improve working memory (WM) when items differ in value, but whether they enhance performance when all items are equally valued, and if so, by what mechanism, remains unclear. Improvements could reflect enhanced memory-based representations or speed-accuracy trade-offs, whereby incentives promote slower, more cautious responding without improving memory itself. Here, we tested these alternatives across two experiments using an auditory WM task in which incentives varied across trials, but all items on a trial were equally incentivised. In Experiment 1,we allowed participants to resubmit their responses if desired. Reward-related improvements in WM performance were selectively observed when memory load was high, and only at serial positions associated with greater interference. These improvements were also dissociated from systematic response slowing and resubmission behaviour. This indicated that the benefits of incentives were unlikely to be attributable to response caution. Critically, in Experiment 2, motivational benefits persisted under strict response deadlines, where speed cannot be traded for accuracy. Computational modelling showed that reward selectively improved the precision of memory-based responses without reducing guessing. Together, these findings indicate that motivational incentives improve WM by enhancing the quality of memory-based representations, rather than by strategic slowing or altering response strategies.

Visual attention is shaped both by low-level perceptual features and by the semantic properties of scene regions. However, compared to the salience of low-level image features, the characterization of image properties that lead to scene understanding has been more difficult. The semantic properties of scene regions have been operationalized via meaning maps, proposed by Henderson and Hayes (2017), by having human observers rate the informativeness of scene patches. However, this rating procedure is labor-intensive and limits the method's versatility. An automated alternative, the DeepMeaning model, reduces rating costs but still requires human training data from a similar class of images, limiting generalizability. Here, we tested whether instruction-following vision-language models (VLMs) can generate human-like ratings in a zero-shot manner: using the original human-rating instructions as a single prompt without any task-specific training. We obtained meaningfulness ratings from both open-weight and proprietary VLMs for scene patches across three meaning-map datasets, and compared the resulting zero-shot AI meaning maps (AIMMs) to human meaning maps and maps obtained from the DeepMeaning model. Zero-shot AIMMs closely approximate human meaning maps, and maps from the task-specific DeepMeaning model, showing that VLMs can replicate human semantic judgments without any training. Zero-shot AIMMs demonstrate utility and validity, matching human meaning maps in their ability to predict human fixations and reproducing the same pattern of relative performance against other gaze-prediction models. We provide open-source code that allows researchers to use this zero-shot approach to quickly and flexibly explore meaning maps across spatial scales, prompts, and scene categories.