Communications Psychology

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.

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.

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.

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.

When objects are embedded in a complex lighting environment, the illumination they receive depends on their geometric relationship to the environment. We measured performance in an odd-one-out color constancy task, with objects rendered in complex lighting environments. Observers identified the odd object among four objects, with two rendered under each of two lighting environments, sampled from a wide range of object orientations and camera positions. Constancy was high. The presence of a surrounding context increased accuracy. Gloss provided no measurable benefit. We also tested a condition in which the global color impression provided misleading information about the lighting environment, but this impaired performance only slightly. These findings suggest that human color constancy remains robust even under complex lighting environments, contrary to predictions of many simple models of illuminant discounting.

Multisensory synchrony judgments depend on recent history: the point of subjective simultaneity (PSS) shifts toward recently experienced timing relationships. This shift, known as rapid temporal recalibration, is almost always interpreted as perceptual, a change in when events appear simultaneous. Yet serial dependence in such judgments can arise from two mechanistically distinct sources that a raw PSS shift cannot separate: a genuine perceptual realignment, which slides the psychometric function sideways along the multisensory asynchrony axis (an odd effect), and a decisional carryover, a bias to repeat the previous verdict, which lifts the function vertically without moving the PSS (an even effect). We re-analysed eight published datasets investigating rapid recalibration spanning audiovisual, audiotactile, and visuotactile pairings and audiovisual speech, decomposing serial dependence into these two components within a single mixed-effects model. The components dissociated by modality: perceptual recalibration was robust for audiovisual pairs but weak for audiotactile and absent for visuotactile pairings, whereas the decisional carryover was largest for the tactile pairings and weak for audiovisual pairings, so that the two traded off across the senses. In audiovisual speech, the decisional carryover was gated by the spoken syllable but not by the talkers identity and did not persist beyond one trial, identifying it as a content-specific, one-step decision memory rather than a motor habit or a multi-trial store. Separating the perceptual and decisional contributions to a timing judgment thus re-draws the map of temporal recalibration and revises several conclusions drawn from the undecomposed PSS.

Everyday problems often require choosing among actions with different future consequences, even though fully evaluating those consequences is often computationally infeasible. The human brain is remarkable in its ability to solve such complex problems efficiently. In this paper, we hypothesize that this capacity arises from a principle inspired by Bayesian accounts of perception and sensorimotor control: evidence derived from effortful forward planning is integrated with task-specific preferences akin to priors, weighted by the uncertainty of these preferences. To test this hypothesis, we used a sequential decision-making task with a relatively large state space. Model comparison showed that participants' deviations from optimal forward planning are best captured by task-specific preferences, rather than by unsystematic errors, recent action-history effects, or offer-context biases. Moreover, the influence of forward planning on participants' responses depended on preference uncertainty, as evidenced by both choice behavior and reaction times, and preferences were not arbitrary but instead supported above-stochastic performance even in the absence of forward planning. Together, these results are consistent with a Bayesian-inspired interpretation of decision making, in which costly forward planning is integrated with state-dependent priors or preferences. These preferences may provide an inexpensive, quickly available first-pass solution, which is complemented by forward planning when preference-based guidance is uncertain.

Spontaneous eye blinks have long been linked to cognitive processing, yet how task demands shape blink timing and its relationship to behavioral performance remains unclear. We examined spontaneous blink behavior in 576 adults performing two variants of the Continuous Performance Task (CPT). Blink occurrence and timing were most strongly modulated by the experimental condition in the more demanding CPT-AX task, whereas their association with response time was stronger in the CPT-X task, where more consistent blink timing predicted faster responses. This dissociation suggests that task structure changes not only blink behavior but also the behavioral relevance of blink timing. These findings are consistent with an adaptive scheduling account of spontaneous blinking and provide a conceptual framework for understanding when and why blink timing contains chronometric information about ongoing cognition.

Older adults typically exhibit reduced episodic memory precision, while maintaining relatively preserved retrieval success. However, little is known about whether these age-related differences are influenced by internal biological factors (e.g., chronotype) or external testing conditions (e.g., experiment format, time of day). To address this question, younger and older adults completed an object-location memory task online, either in the morning or evening, and either with or without video-based supervision. Memory performance was assessed using mean absolute error and Bayesian hierarchical mixture modelling, which dissociated memory precision ({kappa}) from the probability of memory-based responding ({theta}). Immediate memory performance was high across age groups, irrespective of time of day. Greater encoding error predicted greater retrieval error, and participants tested in the video condition exhibited lower recall error and higher recognition performance than those tested without supervision. Consistent with prior studies, older adults exhibited reduced memory precision relative to younger adults, whereas retrieval success remained largely intact. However, Bayesian Hierarchical Modelling provided no evidence that time of day, experiment format, or their interactions with age influenced either memory precision or retrieval probability. Older adults reported stronger morning preferences than younger adults, and participants generally selected testing times that aligned with their chronotype. Nevertheless, alignment between chronotype and testing time did not modulate memory performance. The results indicate that age-related differences in episodic memory precision are robust across variations in testing context, including time of day, experiment format, and individual differences in chronotype. More broadly, the results support the feasibility of remote and unsupervised testing for memory precision research and suggest that chronotype and testing time are unlikely to be major sources of variability in immediate object-location memory performance.

Despite the ubiquitous nature of visual search in our daily lives, our knowledge of the underlying mechanisms is almost entirely derived from studies using simple, synthetic stimuli briefly flashed on a computer monitor. In real life, however, visual search occurs in three-dimensional environments over much longer timescales and involves the dynamic coordination of attention, memory, and action planning, generating novel predictions that cannot be tested with traditional approaches. Here, we examined how self-guided visual search unfolds under naturalistic conditions that extend over space and time. We asked participants (N=80) to find specific objects (e.g., a green lamp, an orange shelf) in an immersive, visually rich virtual reality environment using range-limited teleportation (2 m steps). Consistent with our hypotheses, we found that self-guided search engaged a hierarchical information-seeking policy characterized at each stage by recurring plan-and-execute cycles paired with subgoal-specific attentional priorities. Subgoal priorities progressed from wayfinding to identification of clusters of target-similar objects, and then to specific target features. These search priorities progressively narrowed the search space through coordinated eye, head, and body movements that recurred throughout the task. We conclude that naturalistic search is hierarchical, built from sequential subgoals with shifting attentional priorities that single-level tasks cannot capture. Moreover, immersive VR with eye-head-body tracking dissociates motor planning from execution moments and reveals how looking content shifts with each task subgoal while the person moves through space, something desktop tasks cannot capture.

Recognising people precedes and informs most social interactions, yet it is not clear why individuals differ so markedly in face recognition ability. Exposure-based accounts propose that it is shaped by visual experience with faces, known as 'face diet', but most prior work treats exposure as passively acquired, thereby masking the active and dynamic nature of visual attention in natural settings. Here we examined whether spontaneous attention to faces in everyday environments predicts face recognition ability. Eighty participants wore mobile eye-tracking glasses while walking through a busy university campus and during a face-to-face interaction, before completing standard measures of face recognition ability. Fixations directed to faces showed stable individual differences in both settings. People who looked more at faces scored higher on our composite measure of face recognition ability, and gaze in the two settings predicted ability independently, together accounting for 22% of its variance. These findings suggest that differences in how people actively sample visual information from their environments may be an overlooked source of variance in perceptual expertise.

In visuomotor sequential tasks, chaining actions smoothly requires tight coordination between sensory and motor systems to deal with competing demands, such as reducing uncertainty about future opportunities for action while controlling ongoing ones. In a football-based decision-making task, experts and novices used different strategies to overcome these competing demands, demonstrating a proactive and a sequential visuomotor strategy, respectively. We propose a computational explanation of expertise-related difference in policy selection, grounded in the active inference framework. We simulated the selection of sequence of actions using a partially observable Markov decision process (POMDP) model, and we manipulated three parameters: the reward structure, the prior beliefs about state transition, and the sensory precision. Simulations showed that selection of the proactive strategy relied on strong prior beliefs, high sensory precision and high valuation of fast passing. Conversely, the sequential policy was selected when rapid performance was not highly valued, or under noisy prior beliefs, or lower level of sensory precision. Decreasing sensory precision and adding noise in prior beliefs also decreased decision accuracy. These results suggest that policy selection leading to novice or expert visuomotor strategy reflects differences in the underlying generative model, particularly in how agents value different aspects of the task.

Objectives: The rising prevalence of financial exploitation among older adults has prompted research on the cognitive mechanisms underlying financial decision making in later life. Prior work has demonstrated a link between cognitive impairment and financial vulnerability. The present study examined the unique contributions of composite measures of memory and executive functioning to scam susceptibility in middle-aged to older adults without dementia. Methods: One hundred and fifty-one participants (age= 68.2 {+/-} 8.0 years, 72.2% female, years of education= 16.4 {+/-} 2.2) completed standard neuropsychological measures from the Uniform Data Set (UDS), Version 3, and the California Verbal Learning Test, Second Edition, and a self-report scam susceptibility measure. Psychometrically robust composite scores, the UDS-M+ and UDS3-EF, were computed to represent memory and executive functioning domains. Linear regression models examined independent associations between UDS-M+ and UDS3-EF performance and scam susceptibility. Nested model comparisons evaluated unique contributions of the UDS-M+ and UDS3-EF when included in a joint model. Results: Lower UDS-M+ (b= -0.25, 95% CI [-0.45, -0.04], p= .018) and lower UDS3-EF (b= -0.33, 95% CI [-0.54, -0.12], p= .002) scores were independently associated with greater scam susceptibility after adjusting for age, sex, years of education, and income. However, only UDS3-EF remained significant in a joint model. Post hoc analyses revealed that only memory was associated with scam susceptibility in middle-aged adults whereas only executive functioning was significant in older adults. Conclusions: These findings highlight the importance of monitoring memory and executive functioning across middle to older adulthood when considering susceptibility to scams.

Free recall of naturalistic stimuli, such as films and stories, reveals what people remember and how they organize it. Scoring such recall requires matching each recalled utterance to the encoded stimulus, and doing this manually often makes large studies impractical. Existing automated methods mostly return similarity or aggregate scores rather than explicit matches between recalled and stimulus content. Here we introduce an open-source, model-agnostic pipeline that uses a large language model (LLM) to divide the stimulus and the recall into information units and to match units between the two texts. The output is a table of unit-to-unit matches with accuracy labels, from which standard analysis scripts compute recall measures. We validated the pipeline with four LLMs (Claude Sonnet 4.5, GPT 5.4, Gemini 2.5 Pro, and Gemini 2.5 Flash) on three datasets. On typed recall of four short stories, agreement between each model and two trained raters ({phi} = .73-.79) was close to the agreement between the raters themselves ({phi} = .77). A second run reproduced each model's matches ({phi} = .91-.97) more closely than the raters agreed with each other. On spoken recall of 10 films, every model attributed recall to the correct film with high agreement. On uncorrected speech-to-text recall of a television episode, agreement varied more across models. The median cost was under $1 per participant for every model. These results suggest that the pipeline can serve as a reliable, low-cost alternative to manual scoring of free recall.

Age-related cognitive decline in humans is well established, yet how neurobiological variations influence brain dynamics, and subsequently behavioral performance, remains insufficiently explored. Microscale accounts of cognitive aging revolve around attenuations in dopamine (DA) receptors and transporters (DAT). In contrast, macroscale accounts associate individual declines in cognitive performance with functional brain networks' reorganization and white-matter tracts' deterioration. We endeavor to bridge this gap with an amortized dopaminergic aging model (ADAM) which assembles aging brain twins by coupling dopaminergic modulation with structural connectomes to constrain emergent whole-brain dynamics. ADAM thereby augments established DAT decline with personalized estimates, through a reusable and generalizable estimator capable of disentangling neuromodulatory from structural contributions to age-related decline within each individual. To that end, we embed structural connectomes into a low-dimensional space exhibiting high reconstruction fidelity and -pseudonymized- individual identifiability. The spectrum of structural embeddings is subsequently updated with fMRI observations to estimate DA clearance timescales in 3,650 adults aged 36 to 95 years across five independent aging cohorts. Our analysis reveals an age-related elevation of DA clearance timescales. Critically, low performers exhibit elevated clearance in adults with marked structural degradation, and only within sliding age windows centred between 56 and 76 years. This supports a contribution of dopamine to aging, and reveals a structure- and age-conditional association between its clearance and cognitive performance. Our findings suggest that structural connectivity gates the macroscale reach of dopaminergic modulation, thus rendering the gain-control framework of neuromodulation mechanistically explicit across individuals and cohorts. ADAM therefore provides a biologically plausible framework that anchors interindividual variability in cognitive aging to perturbations in the dopaminergic system by learning cohort-scale mappings which can be shared across sites, establishing a transferable testbed for precision neuromodulation and future in-silico pharmacological trials.

Social isolation and loneliness are major determinants of well-being in older adults. Shrinking social networks and age-related shifts in social behavior and decision-making may increase vulnerability to loneliness. These changes in social decision-making strategies can be conceptualized within an exploration-exploitation tradeoff framework, in which social options with known outcomes are prioritized over uncertain ones. Yet, no study has characterized how complementary decision-making strategies, undirected novelty seeking and goal-directed exploration, relate to social network constructs or are supported by distinct neural systems. These strategies capture distinct forms of decision-making: undirected novelty seeking is driven by the intrinsic salience of novel options independent of expected informational gain, whereas goal-directed exploration involves deliberately sampling uncertain options to reduce uncertainty and optimize future choices. Here, we used fMRI and a novel social bandit reinforcement task to identify dissociable social constructs and brain systems linked to undirected novelty seeking and goal-directed exploration in older adults. Undirected novelty seeking was associated with social network size and with activity of temporoparietal and mid-cingulate brain areas. Goal-directed exploration predicted subjective feelings of social connectedness and was associated with activity of anterior cingulate and prefrontal brain areas. Crucially, choice probabilities from a non-social version of the three-arm bandit task were not associated with social decision-making nor with social constructs. Our findings suggest that, in later life, dissociable social decision-making strategies, when faced with unknown options, rely on distinct neural systems supporting social network size as well as subjective feelings of social connectedness.

Expectations--prior knowledge of environmental statistics--support adaptive behavior by reducing uncertainty during inference. However, the questions of where expectations come from and how that ought to impact their integration has remained largely unexplored. Here, we present a novel model formalizing the role of memory retrieval in the process of integrating expectations with uncertain sensory information. To do this, we first articulate how uncertainty about expectations--omitted from existing normative models--ought to induce memory retrieval processes. We then model expectations as a dynamic source of internal evidence that observers sample from in parallel to vision, and that are weighed according to a time-evolving estimate of their reliability relative to sensory evidence. Finally, we show that this single, principled model can reproduce several previously disparate observations of time-varying effects of expectations in neural and behavioral data, each of which was previously explained using different neural and computational mechanisms. These findings suggest that our model identifies a basic mechanism governing the integration of expectations with sensory evidence on assumptions that more closely approximate the dynamic, heterogeneous, and uncertain nature of real-world decision settings.

We introduce Dementia Language Models (DLMs)--a generalizable and controllable representation of cognitive impairment through language--alongside an evaluation framework for establishing their validity and clinical grounding. DLMs created by large language models fine-tuned on a small clinical corpus successfully generated patient-like narratives across unseen tasks, received predicted MMSE scores in the impaired range, and produced narratives that neurologists identified with accuracy comparable to real transcripts. The models' internal representations themselves, as well as their non-linguistic decision-making, supported cognitive-state detection in unseen cohorts. The effect was controllable: moving from Healthy toward Dementia in weight space progressively worsened language and predicted MMSE scores while increasing dementia probability. DLMs could support clinician training, hypothesis generation, and scalable experimentation, reserving patient involvement for where it is truly needed.

Exploration is fundamental to how children learn about the world. To explore a new environment comprehensively, a child must navigate without getting lost while simultaneously attending to salient information along the way. Both abilities improve across childhood, yet most studies examine them in isolation, often in constrained spaces with limited sensory complexity. Here, we asked 7- to 14-year-old children (N=42) and young adults (18-22; N=42) to freely explore a large furniture store in immersive virtual reality, which contained many objects and attention-capturing multisensory events. Surprisingly, despite traveling comparable distances, children explored less - they covered less spatial area and examined fewer unique objects than adults. Fine-grained analyses of their behaviors showed that the reduction in exploration was due to children spending more time backtracking and children taking longer to adapt to salient multisensory objects, which were uncorrelated across individuals after controlling for age and independently predicted exploration. However, only backtracking duration predicted success in a subsequent search task, demonstrating a link between navigational success during exploration and the quality of learned spatial representations for later use. Together, the results demonstrate that children explore less comprehensively than adults in large-scale spaces because they are susceptible to getting lost and perseverating on salient events nested within a large environment, but that these behaviors change with age. This suggests that while children are widely considered to be curious explorers within local environments, their ability to do so within larger environments is shaped by the maturation of cognitive systems that integrate experiences over complex contexts.