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.

Human decision-making behavior varies widely across individuals and task conditions. This variability is often interpreted in terms of different suboptimal decision strategies, but the principles that govern these suboptimalities remain poorly understood. We propose that some of these suboptimalities can be understood in terms of limited-capacity, but information-efficient, inference processes that inform decision-making. We developed and used new theoretical and empirical approaches to compare the amount of information used (capacity) to the effectiveness with which it was used (accuracy) by individual participants performing simple inference tasks. Variable, suboptimal performance was explained largely by inference that had variable, limited information capacity. Across these capacity limits, and regardless of whether the inference strategy was based on optimal or heuristic principles, the information was used effectively to maximize accuracy for a given capacity. This form of flexible and efficient information bottleneck reflects fundamental capacity-accuracy tradeoffs that structure individual variability.

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.

Confirmation bias impacts judgments and decisions across a range of domains including finance, policy and science. Here we examine whether explicitly labelling information as true or false disrupts a core underlying computational mechanism that can generate this pervasive bias - asymmetric learning. Human participants (Study 1: N=47; Study 2: N=57) completed a 2 alternative forced choice (2AFC) task previously used to test for the presence of confirmation bias. Participants made choices between pairs of options that could win or lose money and received either factual or counterfactual feedback after each choice. We introduced a key novel feature into the task - providing explicit cues that signalled to participants whether feedback they had seen was true (verified) or false (debunked). Learning in response to feedback was attenuated under false compared to true labels but was present under both. Fitting participants choices to computational models enabled us to examine how sensitivity to the feedback varied as a function of both the label (true/false) and confirmation (confirmatory/disconfirmatory). This revealed a distinct pattern of learning rates typical of confirmation bias (enhanced learning from positive prediction errors for chosen options and from negative prediction errors for unchosen options) in response to both true and false labels. The findings highlight how confirmation bias plays an important role in the effectiveness of interventions designed to verify true and/or debunk false claims. Verification is less likely to succeed when information disconfirms prior beliefs. Conversely, debunking false claims is unlikely to succeed when the information confirms ones prior beliefs.

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.

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

Goal-directed behavior often requires sustained effort across a sequence of interdependent decisions, yet the determinants of persistence in such contexts remain poorly understood. Here, we investigated how individuals regulate persistence in a novel sequential effort-based task in which they controlled an avatar through successive checkpoints to reach a final goal and could make repeated attempts following failure. At each attempt, participants could choose either to persist in the same task or to disengage toward an easier but less rewarding alternative. We found that decisions to persist or disengage were jointly shaped by multiple interacting factors. Disengagement increased with task difficulty and lower skill level. It also increased with repeated attempts and time-on-task, indexing fatigue, and with accumulated errors, indexing lack of progress. Conversely, proximity to the goal promoted persistence and shaped decision dynamics by reducing choice conflict during persistence decisions and increasing hesitation during disengagement near the goal. Notably, clearing the first checkpoint produced a sharp increase in persistence, suggesting that early success plays a pivotal role. Furthermore, persistence reflected both retrospective and prospective evaluations of effort, with prior investment promoting commitment and anticipated effort reducing it. Finally, disengagement was preceded by short-term performance decline but not by gradual increases in decision conflict, suggesting relatively abrupt strategy shifts following repeated failures. Together, these findings provide a comprehensive account of persistence in sequential effortful tasks, showing that decisions to persist or disengage are jointly shaped by multiple factors related to fatigue, (lack of) progress, goal proximity, and early success.

The willingness to exert cognitive effort is essential but is constrained by the subjective cost of effort. Although effortful tasks are often avoided, positive bias about ones own performance may help sustain engagement with cognitive demands. Here, participants completed an effort-based decision-making task and reported trial-by-trial predictions of their own performance, allowing us to quantify performance prediction error (PPE) as the discrepancy between subjective and objective accuracy. The results showed that PPE was predominantly positive and increased with effort level, indicating greater overestimation under higher cognitive demands. Using a computational model, we show that choices were best explained by a learning model in which rewarded trials accompanied by positive PPE decreased subsequent sensitivity to effort. A confidence-based control model did not provide a better account of choices, suggesting that this effect was better captured by positive performance bias than by confidence alone. Our findings provide a computational account of how biased self-evaluation may attenuate the subjective cost of cognitive effort and extend the positive bias literature to the task need for cognitive effort.

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.

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

Suicidal risk may be encoded in everyday communication patterns but diluted in routine digital interactions. We introduce a method for surfacing this latent signal: training per-person language model agents on individuals' authored text (the on-screen text each participant typed, captured whenever a keyboard was visible in screenshots) and placing those agents in simulated social interactionsa communicative stress test. Using data from 79 adults with recent suicidal ideation, we ne-tuned individual LoRA adapters on Qwen3-8B using each participant's authored text, then placed agents in standardized conversations with probe personas. Agent-generated risk language was associated with EMA-measured suicidal ideation (r= .576, p < .001), with a single neutral small-talk probe performing nearly as well (r= 551). A shue control conrmed the signal is person-specic (r= .071 when adapters were mismatched), and automated descriptions of participants' general smartphone activity produced no signal, conrming specicity to interpersonal communication. A prompt ablation demonstrated partial robustness to removal of disclosure-encouraging language (r = .430). This proof-of-concept demonstrates that simulated social interaction can amplify latent vulnerability signals, bridging digital phenotyping, generative AI, andsuicide theory.

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.

2Time-of-day severely impacts human decision-making, with real-world consequences. Studying shifts in decision-making strategy requires controlled, long timescale behavioral measurement and analyses that can extract insight from time-varying behavior. We introduce two complementary advances to address this gap: an autonomous 24-hour training facility for continuous behavioral measurement during decision-making and an interpretable modeling framework that captures non-stationary decision dynamics from reaction times and choices. Rats were trained on a visual evidence accumulation task across months, generating over a half million trials spanning the circadian period. Our model revealed latent behavioral states characterized by distinct evidence accumulation parameters, including differences in drift rate, bias, and decision-commitment time. These states recur across days and align with feeding schedules and the light-dark cycle, producing periodic fluctuations in performance over 24 hours. Together, these results demonstrate how continuous behavioral sampling combined with generative modeling uncovers long-timescale structure in decision-making obscured by stationary analyses. 1 HIGHLIGHTSO_LI24-hour live-in operant system allows autonomous training in cognitive tasks across months C_LIO_LI24-hour measurements reveal that rat performance fluctuates with time of day C_LIO_LINovel DDM-HMM framework identifies reaction time and accuracy shifts across multiple timescales C_LIO_LIDDM-HMM captures serial dependence in decisions that classic models ignore C_LI

Humans frequently make decisions in complex, high-dimensional environments, where identifying task-relevant information is critical for rapid behavior optimization. Humans outperform standard reinforcement learning agents in navigating such complexity, yet the cognitive strategies of humans remain unclear. To address this, we developed a novel multi-dimensional learning task in which only a subset of dimensions is reward-related. Crucially, unlike prior studies, subjects are uninformed of the true task dimensionality and have to identify them through exploration. This design closely mimics the ambiguity in real-world tasks. Our results have identified two stereotyped choice patterns that reveal "dimension-guided" strategies in exploration and exploitation. Cross-subject analyses suggest that dimension-guided exploration may promote the efficiency of reward-based learning. These findings indicate that humans leverage task dimensionality to guide exploration, and provide inspiration for improving exploration efficiency in AI agents.

The geometry of representations learned by deep neural networks is shaped jointly by architecture and pretraining objective, yet disentangling these two factors remains difficult. Here we isolate the contribution of pretraining objective by comparing two Vision Transformers from the same backbone family but trained under different objectives: language-image contrastive learning (CLIP) and ImageNet-21k classification. Using continuous Valence-Arousal prediction on the OASIS dataset as a probe of representational quality, we evaluated frozen features under Leave-One-Theme-Out and Leave-One-Category-Out cross-validation, the latter requiring extrapolation to entirely unseen semantic categories. The contrastively pretrained encoder generalized substantially better than the classification-pretrained encoder under both protocols, with the gap widening sharply when held-out categories required cross-category generalization. To characterize why the two representations differ, we developed a geometric analysis of prediction errors, treating per-image errors as vectors in the affective plane and quantifying their spatial structure via weighted phase-locking, trajectory-based occupancy entropy, and effective dimensionality. The classification-pretrained representation collapsed errors into a small number of attractor regions with a strong center-ward pull, whereas the language-aligned representation distributed errors broadly across the affective space. Layer-wise linear probing further revealed that affective information was distributed across depth in the contrastive encoder but increasingly concentrated in deeper layers of the classification encoder, mirroring the texture-bias and category-anchored statistics characteristic of ImageNet-trained representations. These results provide a representation-geometric account of how the choice of pretraining objective, holding architecture constant, determines whether learned features generalize across semantic boundaries or remain confined to category-bound visual regularities. HighlightsO_LIIsolate the effect of pretraining objective by holding the Vision Transformer backbone constant. C_LIO_LIContrastively pretrained features generalize across unseen semantic categories where classification-pretrained features fail. C_LIO_LIIntroduce a geometric analysis of prediction errors based on phase-locking and occupancy entropy. C_LIO_LIClassification pretraining produces concentrated error attractors and a rigid centerward bias. C_LIO_LIAffective information is distributed across depth in CLIP but localized in late layers of the classification ViT. C_LI

The perception of time is elastic, often deviating from physical intervals depending on how experience is structured. Yet, what determines how subjective time is constructed remains debated. Here, we tested whether perceived ongoing time is actively constructed from learned event representations rather than a dedicated internal clock. Using a novel pause-adjustment task across three statistical learning experiments, we measured temporal distortions during continuous listening to structured versus unstructured syllable streams. The presence of event structure systematically warped time: pauses were perceived as longer between pseudowords and shorter within pseudowords. This bidirectional temporal warping emerged online and remained stable across pause durations. Enriching these events with semantic meaning eliminated boundary-related dilation while preserving within-event compression. Moreover, physiological tracking of event structure, indexed by pupil dynamics, dissociated from the magnitude of temporal warping. These findings show that subjective time is constructed from hierarchical event representations and depends not only on where events are segmented, but also on how they are represented.

Why do we want to know the future? Humans and many animals pay for information to predict uncertain rewards, even when they cannot control them. The reason for this conserved yet seemingly paradoxical preference - "subjective value without instrumental value" - remains unknown. Here we develop a normative framework to explain the origins of such preferences, their persistence across species, and their contributions to survival. We formalize and evaluate theories that explain subjective value as originating from an adaptive estimate of advantage for solving core computational problems in naturalistic environments. We show that human and animal subjective values are remarkably well suited to solve these problems, accomplishing the goals of multiple theories simultaneously. We derive novel forms of information to enable existing theories to be dissociated, and show that pooling their subjective values improves performance across diverse environments. Thus, organisms may value information because it pays diverse dividends in nature.