Communications Psychology

Humans exhibit a pervasive drive toward self-consistency, often failing to revise previous decisions even when confronted with contradictory evidence. Here, we investigate the computational mechanisms underlying decision revision in perceptual tasks, examining the regulatory role of metacognition. To do so, we capitalize on a novel paradigm in which participants are repeatedly presented with identical sensory information and allowed to revise their choices after each exposure. Our results reveal that repeated exposure to the same stimulus systematically biases subsequent judgments toward prior responses. Using drift-diffusion modeling, we tested competing explanations incorporating different assumptions about how prior choices affect evidence accumulation. Our findings indicate that consistency biases emerge from asymmetric sensory weighting, selectively amplifying information consistent with previous choices--a phenomenon akin to confirmation bias. Crucially, individuals with higher metacognitive skills exhibited weaker confirmatory biases and more flexible integration of repeated sensory information, enabling greater adaptability in decision-making. These findings highlight the continuous nature of perceptual inference and underscore metacognitions pivotal role in mitigating bias and optimizing decision flexibility.

Self-supervised foundation models of aging are increasingly built from longitudinal data (biobanks, electronic health records, wearables) that is inherently incomplete: no individual is followed across a whole lifetime, and how much of each life is captured varies widely. This raises two linked questions: is it worth modeling an individual's whole life course rather than its current state, and can such a model be built from brief, fragmentary records? No human cohort can settle them, because none offers a complete life to compare against. We turn to the African turquoise killifish (Nothobranchius furzeri), tracked from youth to natural death in publicly released recordings, as a controlled testbed: its complete lifespans provide the full-life reference that human data lacks. On these data we build LifeMAE, a two-stage selfsupervised model: a day encoder that summarizes each day of behavior, then a life-course encoder over the trajectory of those daily summaries. We find that the day encoder alone is already strong: from a single day of behavior it predicts chronological age, separates long- from short-lived individuals (coarsely), and flags nearness to death. Adding the life-course encoder improves on none of the three; each is matched by trivially aggregating the day-level predictions (a smoother for age, an early-life average for lifespan). Near-term mortality seems the exception, where the whole-life model looks far better (AUROC 0.81 to 0.91), but the gain is not behavioral: it reflects where each day falls within the observation window (a cue supplied by the model's encoding of time), and a single-day model given that cue closes the gap at any observation length. For these traits, an individual's place in its life course is legible from a single day: the trajectory stage is unnecessary, and the record it needs is as short as one day, the finest grain our day-level setup resolves. For characterizing a cohort, this favors observing many individuals briefly over tracking a few for long. The result joins a growing body of work in which deep and foundation models, fairly benchmarked, fail to beat deliberately simple baselines. We add a concrete mechanism for the over-optimism: a model's encoding of time can leak the very quantity it predicts, which backwardlooking evaluation mistakes for learned biology, so only evaluation fixed to the moment of prediction is trustworthy.

Social media users often feel that time vanishes while scrolling, but real feeds confound novelty, rewards, social signals, and self-paced control, leaving the driver of this distortion unclear. We tested whether self-paced visual exploration is sufficient to compress subjective time by comparing active scrolling with passive, yoked viewing and a static baseline. Twenty-three adults viewed sequences of natural images under three within-subject conditions: Scrolling (self-paced mouse clicks), Watching (a passive, yoked replay of their own scrolling sequence), and a Baseline (a static image). Participants estimated the elapsed duration of each block. Subjective duration was most compressed under Scrolling (48% of elapsed time), followed by Watching (51%) and Baseline (65%). Two sources separated these effects. Adding back the empty inter-image fixations brought the image-rich conditions to within seconds of the Baseline, showing that observers barely counted the blank gaps; the Scrolling--Watching difference, by contrast, was independent of these shared gaps, isolating self-paced control as a second source of compression. Electrophysiology linked that control to anticipatory neural states and the timing of early visual responses, with no amplified encoding of individual images. The results favor an attention-weighted account of timing, on which subjective duration tracks how much attention reaches the clock, a resource that a self-paced stream and its uncounted gaps both draw away.

Current debates regarding the relative contribution of saliency versus semantics to gaze control often rely on comparing the predictive power of saliency and meaning maps. We argue that such indirect, global approaches are fundamentally limited because fixations arise from heterogeneous, local causes that are conflated in whole-scene comparisons. To substantiate this claim, we used a direct method where participants explicitly identified the reasons for fixation at specific clusters of high fixation density, distinguishing between low-level saliency and various semantic categories, as well as the most important one. The obtained judgments revealed that multiple factors contribute simultaneously to gaze control. Although their influence varied across fixation clusters, semantics generally dominated saliency. Notably, abstract semantic categories, particularly "unknown/unusual," proved important, highlighting the role of prior knowledge and novelty besides personal relevance in guiding attention. To interpret these findings in the context of existing models, we propose a framework distinguishing between processes highlighting interesting locations in the image from a sampling strategy translating this information into scanpaths. Within this framework, classic saliency and meaning maps are viewed as restricted inputs to the strategy, whereas deep learning-based models (e.g., DeepGaze IIE) are more general and may also implicitly encode aspects of the strategy itself. Consistent with this, we found that the predictive performance of DeepGaze IIE varied less significantly with the specific reasons for fixation than that of classic saliency and meaning map approaches.

Multiple object tracking (MOT) is a core function of dynamic visual attention that relies on the ability to simultaneously monitor several moving objects. Although MOT performance is known to decline with age, and to depend on efficient oculomotor strategies, how these processes interact across the adult lifespan and under degraded visual input remains poorly understood. Here, we examined the effects of aging on MOT under normal and gaze-contingent viewing conditions simulating central and peripheral visual field loss. Sixty participants aged 20-80 years completed a MOT task while eye movements were recorded, enabling characterization of performance and oculomotor behavior across five viewing conditions. Behavioral results revealed a continuous decline in tracking performance across adulthood, indicating a graded rather than categorical effect of age. Performance was strongly reduced by visual-field restrictions, with the largest impairments under central vision occlusion. Eye-tracking analyses showed that better performance was associated with greater reliance on centroid-based gaze strategies, consistent with distributed monitoring of target configurations. Critically, older adults relied more on focal, target-based tracking under conditions simulating peripheral vision loss, and less on centroid-based strategies; this shift was associated with poorer performance. In contrast, oculomotor behavior during full-field viewing was largely preserved across age. Together, these findings suggest that aging affects multiple object tracking through combined sensory, attentional, and oculomotor mechanisms. Beyond a reduction in capacity, age-related decline also reflects systematic changes in visual sampling strategies during dynamic tracking.

Transitive inference (TI) is the ability to reason about transitive relationships in an ordered set of items (e.g., if A>B and B>C, then A>C). TI is widely held to depend on a linear representation of the serial (rank) order of those items. By what computational mechanism is such an ordering constructed during learning, and how is it used to make choices that obey transitivity? Here we take a minimalist approach, applying least-squares estimation (LSE) to a serial learning task commonly used to test TI in humans and animals. In this formulation, LSE computes a linear classifier that maps task conditions onto behavioral outcomes. This algorithm makes no explicit assumptions about transitivity or serial order, yet it reproduces key empirical features of TI; namely, the ability to generalize beyond the training set, and a symbolic distance effect (SDE) in performance accuracy. Applying the classifier to individual items produces an internally ordered representation of rank from which both generalization and the SDE naturally emerge. The approach also yields a decision mechanism, in the form of a differencing operation, for selecting the correct item from any pair. These findings reframe TI as a linear classification problem, challenging conventional assumptions about the cognitive mechanisms required for transitive reasoning.

Past sensory experience shapes our perceptual decision-making in the now. Popular models frame perceptual decisions as either attracted towards or repelled away from recent sensory information, but it is unclear when and why these distinct effects emerge. We here ask whether effects turn from attractive to repulsive depending on the level of surprise elicited by the precision-weighted discrepancy between past and present sensory states. This model is based upon the idea that attraction is adaptive for optimizing efficiency and accuracy when discrepancies are small, because they likely reflect sensory noise rather than real change in the environment. In contrast, repulsion may reflect the upweighting of counterfactual evidence when discrepancies are large because they more likely signal the need for model updating. We test this model on a large amount of recently-collated trial-by-trial serial dependence data and consistently find support for it across the dataset, participant, and trial-by-trial level. Specifically, serial dependence effects are attractive at low discrepancies between past and current sensory states but turn repulsive when discrepancies are larger. Higher sensory precision is found to accelerate this flip by reducing the modal discrepancy threshold required to trigger repulsion effects. We discuss how these findings necessitate extending existing theories of serial dependence, and how they may resolve conflicts in the broader predictive processing, learning and perception literatures.

A clinician guiding a stroke patient through a 45-minute rehabilitation session, a coach planning a training day, a teacher choosing the order of practice problems, they all face the same question: "given everything practiced so far, what should the next trial be?" The motor-learning literature offers two coarse answers, blocked and interleaved ("random") practice, with a well-known dissociation, blocked practice gives faster acquisition but worse retention, while interleaved practice gives the opposite. We argue that this dissociation is not a fixed property of practice schedules but a shadow of a richer structure. In particular, for a learner whose memory has a fast shared component and slower context-specific components, the best schedule should be a function of the learners current internal state and the time remaining before the retention probe. We make this precise in a minimal two-context fast-slow learner model whose optimal schedules can be computed exactly for short sessions and approximated by a structured beam-search upper bound for longer ones. The optimal schedule is not blocked, not interleaved, and not a single rule; it is a family of schedules determined by how much retention is weighted relative to acquisition. The family has three regimes (alternating, mixed, blocked-with-late-correction) and for long sessions, the optimal schedule has an interpretable structure -- exploit one context, repair the neglected one, then interleave to lock in retention. We then investigate whether a reinforcement-learning teacher, observing only the learners actions and errors without access to their internal memory states, can learn these optimal policies from interaction alone. Comparing these learned policies against the exact optima, we show that a model-free agent (PPO) recovers the short-horizon schedules and the long-horizon block-repair-interleave motif in the intermediate regime, but the benchmark also exposes a sharp failure in the acquisition-dominated regime, where PPO collapses to pure blocking and misses a sparse terminal correction. A warm-start diagnostic shows this failure is a genuine metastability of policy gradients rather than a tuning artifact, with blocked-plus-switch and pure-blocked acting as competing attractors that PPO cannot stabilize between. A hyperparameter sweep over observation history reveals that the agent requires very little behavioral context to plan optimally, demonstrating that partial observability is not a major barrier to finding optimal practice schedules. Finally, we discuss the implications of our framework for motor adaptation and contextual interference, offering practical insights on how instructors can design finite practice sessions to favor long-term retention.

INTRODUCTIONDo continuous cognitive totals capture patient-relevant transitions, or does decline have discrete structure? METHODSWe formalize a discrete cognitive resolution (DCR) model in which decline is loss of binary discriminative coordinates, with deterministic emissions tying items to a shared low-dimensional mask. Using natively discrete item-level data (ADAS-Cog, 688 ADNI participants; MoCA sub-items, 13,323 NACC participants), we tested pre-specified signatures by out-of-sample log-loss against continuous-drift, per-item, and mixed-effects IRT competitors, with a coordinate-label permutation null (S2). RESULTSDCR beat both pre-specified baselines (ADNI 0.436 vs 0.965; NACC 0.566 vs 0.602). S2 was decisive in ADNI (AUC 0.782; null 0.529, P < .001); in NACC the signal concentrated in orientation (AUC 0.718). Mixed-effects IRT achieved lower log-loss than DCR. DISCUSSIONCognitive decline shows discrete coordinate structure when items are single-coordinate probes. The claim is structural, not predictive; encoding is decisive.

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.

Cognitive flexibility is the ability to change the way of responding when the demands of the environment change. This study tested how cognitive flexibility develops across the lifespan. We used a new computerized task that gives a continuous score instead of just right or wrong answers. 221 healthy adults aged 18 to 71 completed the Continuous-score Probabilistic Reversal Learning Test (CPRLT). We calculated mean absolute error and adjusted error for rule-based learning, and fitted a Rescorla-Wagner model to estimate each persons learning rate (alpha) for reward-based learning. All three scores have one breakpoint, performance improved rapidly from childhood to young adulthood, then declined slowly. Rule-based learning peaked around age 20. Reward-based learning peaked earlier, around age 18. This suggests that reward-based learning matures before rule-based learning. The pattern fits with brain development: reward circuits mature earlier, while prefrontal regions for rule-based learning develop later. Our continuous measure captured this difference, which binary tasks would miss.

Sleep gets worse with age and is correlated with risk for disease and mortality. The possibility that poor sleep causes aging to accelerate has prompted interest in improving sleep to slow aging and prevent disease. However, the existing evidence on the link between poor sleep and accelerated aging is unclear. Here, we tested for correlation and causation between poor sleep and accelerated aging using five independent datasets of adults (total N > 64,000). We found strong evidence for a correlation between poor sleep and fast aging that is consistent across young, middle, and late adulthood and across aging biomarkers derived from different tissues and modalities. We found that this correlation is robust to the influence of chronic disease burden, but not to the influence of shared genetic and early environmental factors among twins. Finally, we found mixed evidence for a causal influence of poor sleep on accelerated aging using Mendelian randomization. Our findings indicate that the correlation between poor sleep and accelerated aging is highly robust; however, the claim that poor sleep causes aging to accelerate is not consistently supported.

Blinks are a ubiquitous yet largely unnoticed aspect of human vision, despite causing frequent interruptions of visual input that can amount to up to 10% of waking time. By leveraging a large dataset of unconstrained gaze behavior during two natural tasks, we found a novel behavioral strategy to limit the impact of blinks: blinks were strategically coupled with head movements, which minimizes information loss due to unreliable visual input during head movements. Specifically, blink probability increased with higher head velocities and showed strong temporal modulation relative to head movement onset. Blink probability was reduced before head movement initiation and then peaked during the head movement. The strength of this coupling was tailored to the individual needs of participants, with participants with higher baseline blink showing a stronger synchronization. This indicates that blinks are a part of an individually coordinated strategy when orchestrating eye and head movements during unconstrained natural behavior.

Anxiety has been linked to difficulty sustaining engagement with ongoing tasks, even when continued engagement would yield greater rewards, yet the underlying mechanisms remain unclear. Here we examined how trait anxiety shapes sequential foraging decisions using a patch-leaving task grounded in the Marginal Value Theorem (MVT), a normative framework for explore-exploit decisions previously used to reveal altered computations in conditions such as problem gambling and attention-deficit hyperactivity disorder. Across two independent cohorts, participants adjusted patch residence times according to environmental opportunity costs, indicating preserved sensitivity to task structure irrespective of anxiety levels. Despite this, individuals with higher trait anxiety consistently left patches earlier and accrued fewer rewards. Drift diffusion modeling revealed that these deviations arose from reduced reward-driven evidence accumulation, rather than impaired environmental sensitivity or altered learning: trait anxiety reliably reduced the drift rate governing continued exploitation, providing a computational account of premature disengagement. This effect remained robust after accounting for pupil-linked arousal and baseline stress biomarkers, including salivary -amylase and cortisol, which independently constrained decision dynamics. Together, these findings identify reduced reward-evidence accumulation as a core mechanism through which anxiety promotes premature disengagement from rewarding environments.

In animal learning research, learning is often represented by plotting a behavioral measure as a function of training trials. A particularly clear case is habituation, a basic form of learning in which repeated presentation of a stimulus produces a decrement in responding. Although retention tests provide the strongest basis for evaluating durable habituation once short-lived performance effects have dissipated, the pattern of response change across stimulus repetitions, or habituation curve, remains theoretically and empirically relevant because it is used to characterize determinants of habituation, individual and clinical profiles, and functional forms, including linear, curvilinear, asymptotic, and mixed incremental-decremental patterns of responding. However, group averaged curves may conceal substantial individual heterogeneity. Here, we analyzed archived human eyeblink habituation data from 157 participants to ask whether the curve shape selected for the group average reflects the curve shapes observed at the individual level. Five candidate functions were fitted separately to each participant and to the corresponding group average. No single function characterized most individuals. More importantly, the model selected for the group average differed from the most frequent individual model in all four groups. When data were pooled across groups, the average favored a dual-process form, a shape that matched the individual plurality in none of them. Simulation analyses showed that averaging heterogeneous individual trajectories can itself produce a group curve that favors a more complex model. Our findings show that group averaged habituation curves should not be treated as direct descriptions of the typical individual trajectory.

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.

The ability to orient or attend to sensory events is generally greater in response to visual and auditory cues occurring together than in response to single-modality cues occurring alone. In such cases the perceptual fusion of cross-modal stimuli (multisensory integration) depends on low-level features (e.g., location, intensity) and follows well established principles. However, less is known about multisensory integration mechanisms when behavioral responses are less direct and require top-down control. Here we investigate this in human participants using an urgent multisensory choice task that effectively dissociates stimulus-driven and goal-driven contributions to performance based on their distinct temporal signatures. Task conditions varied the modality of the cues (auditory, visual, or both), their location (left or right), and the rule defining the correct choice (look toward or away from a given cue). When spatially coincident cues were associated with the same response rule ("look away"), we observed multisensory enhancement and performance remained close to a statistical expectation as the choice process unfolded. However, when spatially disparate cues were associated with different rules but the same target, one cue dominated performance and the other produced crossmodal capture, i.e., low-level competition. The results indicate that the efficacy of multisensory integration is dictated by the stimulus-and goal-driven signals produced by each cue, with all four factors rapidly interacting in accordance to the dynamics of spatial attention. Significance StatementAuditory and visual stimuli located near each other in space and time are typically bound into a single sensory percept that draws attention most effectively. However, it is unclear whether such "multisensory integration" occurs during behaviors that go beyond directly attending or orienting to cue stimuli and require top-down control. We investigated this using a novel task design with which stimulus-driven and goal-driven contributions to performance can be accurately identified. We found that multisensory enhancement depends not so much on the complexity of the requested cue-response associations, but rather on the timing and alignment of the stimulus-and goal-driven signals derived from each cue (auditory and visual) -- similar to the way that such signals dictate the allocation of spatial attention.

Inverse reinforcement learning can recover reward functions from observed behavior, but interpreting those rewards remains a fundamental challenge for understanding intelligent behavior and decision-making. To address this challenge, we introduce a novel framework for reward interpretation that combines reward-function analysis, latent mode assignments, and short-history behavioral analysis to infer latent motivations and behavioral dynamics. As a proof-of-concept, we instantiated the framework using switching inverse reinforcement learning on a large-scale dataset of multi-agent social interactions. Our framework interpreted the learned latent modes as cautious and volatile motivational profiles, demonstrating that recovered reward functions can reveal distinct patterns of behavioral dynamics. More broadly, these findings suggest that the proposed framework provides a promising approach for reverse-engineering and interpreting latent rewards underlying intelligent behavior and decision-making.

Humans rely on multiple sensory modalities, such as vision, audition, and touch, to perceive materials in everyday life. Previous research shows that multisensory perception leads to facilitation, yet the mechanisms responsible for this facilitation remain poorly understood. One potential mechanism is crossmodal prediction, whereby input from one modality generates predictions about another. While substantial research on multisensory facilitation has focused on bottom-up processes, such as spatial, temporal, and semantic congruency, the role of crossmodal predictions, particularly in material perception, has received little attention. To address this gap, we conducted two experiments, a reaction time task and a material rating task, in which participants viewed computer-generated animations of familiar objects being dropped to the ground. The paradigm exploited the natural temporal structure of impact events: pre-impact visual appearance provides information about an objects material and therefore can generate expectations about the forthcoming impact sound. Critically, participants saw the event only until before the impact, after which the video was masked. Thus, vision and audition were temporally aligned but not presented concurrently, allowing us to isolate the influence of visually driven expectations on the incoming auditory information without a bottom-up conflict. In some trials, the sound matched the expected material, but in a subset, it was incongruent, violating expectations elicited by the preceding visual information. Across both experiments, participants took longer to respond on incongruent than congruent trials, suggesting increased processing demands. In the rating task, incongruent trials also shifted material judgments, such that ratings reflected a weighted combination of incoming auditory information and visually driven predictions, with large individual differences in relative cue weighting. These findings suggest that priors on material properties from one modality, specifically vision, not only establish high-level expectations within the modality about an objects future state, but also extend across modalities.

Attentional control balances proactive suppression of predictable distractors with reactive suppression of unexpected ones. Yet, how internal states such as alertness shape this balance is unclear. Using pupillometry and eye tracking across two probability-cueing experiments (conducted in 2024) with varying distractor prevalence, we distinguished tonic (baseline pupil size across blocks) from trial-level pupil size fluctuations (trial-by-trial residual variability in pre-stimulus pupil size). With moderate prevalence, suppression of frequent-region distractors developed gradually, whereas high prevalence induced near-immediate suppression. Behavioral measures (e.g., reaction times) were closely linked to tonic and trial-level pupil size fluctuations. Critically, both alertness components jointly influenced control: during early learning, heightened trial-level pupil size increased distractor capture and reduced target fixations, whereas later on, suppression shifted to a proactive mode resilient to trial-level fluctuations. Under high prevalence, this shift occurred faster. Notably, higher trial-level pupil size generally accelerated first target selection. These findings show that tonic alertness and trial-level alertness fluctuations dynamically regulate reactive and proactive control during statistical learning. Impact StatementThis study shows that people become better at ignoring predictable distractions over time, but that this improvement depends not only on what they have learned about the task environment, but also on their current level of alertness. By combining eye tracking and pupil measures, we found that temporary increases in alertness can sometimes help people orient more quickly to relevant information, yet during earlier stages of learning they can also make attention more vulnerable to distracting events. These findings suggest that successful focus in complex environments depends on a dynamic interplay between learned expectations and moment-to-moment fluctuations in mental state, with implications for understanding sustained attention in settings such as monitoring, driving, and other tasks that require people to stay engaged while resisting distraction.