Communications Psychology

Fine-grained temporal structures emerge in smartphone behavioral recordings over multi-day periods. Complex systems research suggests that emergent temporal structures reflect underlying resource constraints of the system. Here we test whether cognitive abilities measured through speeded tasks (spanning fractions of a second) are reflected in emergent smartphone temporal structures spanning days, revealing how cognitive resource limitations shape naturalistic behavior. We analyzed smartphone tap interval patterns accumulated over several days and used decision tree regression models to predict performance in simple and choice reaction time tasks from these patterns. Simple reaction time was poorly predicted (R2 = 0.003), indicating that basic sensorimotor constraints play only a marginal role in shaping real-world behavioral timing. In contrast, choice reaction time was moderately predictable (R2 = 0.4), demonstrating that higher-order cognitive constraints prominently influence naturalistic temporal organization. Notably, while task performance operates at sub-second timescales, predictive temporal patterns in smartphone behavior spanned milliseconds to several seconds and was accumulated over days, revealing the broad, multi-scale influence of cognitive resource constraints on everyday behavior. Both predicted and measured choice reaction times showed age-related decline, but the decline was more pronounced in predicted values, suggesting that age-related cognitive changes may be amplified in naturalistic contexts. These findings demonstrate that emergent temporal structures in smartphone use can reveal how cognitive processes measured using speeded tasks manifest, or fail to manifest, in real-world behavior. Complex-systems approaches can bridge laboratory and naturalistic assessments of cognition, revealing which cognitive processes meaningfully constrain real-world behavior.

Human economic decisions are highly sensitive to contexts. Deciding between two competing alternatives can be notoriously biased by their overall value ( magnitude effect) or by a third decoy option ( distractor effect). Some prominent explanations appeal to diminishing value sensitivity and divisive normalization in value representations, i.e., representational bias, that feed into the choice stage. However, these explanations have recently come under scrutiny due to empirical inconsistencies and mounting alternative theories. Here, we posit that context-sensitive choices may not stem from representational biases but rather emerge as by-products of asymmetric sampling during value learning. In a reward-guided choice task, participants aimed to maximize cumulative rewards through trial and error. The task introduced alternating blocks with either a colored distractor or a neutral notional distractor. We observed decreased choice accuracy when higher-value distractors were present, a pattern that persisted even in the notional distractor blocks. Using computational modeling, we show that this phenomenon falls out naturally from a simple learning rule without relying on any additional mechanism such as divisive normalization or nonlinear utility. Furthermore, we found that, contrary to divisive normalization, choice accuracy was not influenced by distractor value but strongly depended on the magnitude of the targets values per se. This magnitude sensitivity was also found in the notional distractor conditions and could lawfully be reproduced by the learning model. Importantly, when counterfactual feedback eliminated sampling asymmetry, the observed decision bias vanished. Our results suggest that the genesis of context-sensitive choices may lie in the learning dynamics themselves, specifically sampling asymmetry, rather than in pre-decisional representational biases. This finding reframes the discourse on irrational decision-making, attributing it to acquired biases during the learning process, not necessarily computational intricacies at the choice stage.

Enhancing memory function is essential for daily living and cognitive health, particularly amid population aging and cognitive decline. However, current methods for memory enhancement often require specialized interventions or effortful practice. Here, we present evidence that the intrinsic memorability of images can serve as a simple, scalable, and involuntary strategy for improving associative memory. We studied its effects on associative memory in adults of distinct ages, memory retention in cognitively impaired older adults, and vocabulary learning in foreign language learners. Our results show that highly memorable images significantly enhance the recall of associated words, especially when the cue image and target word are semantically unrelated. Notably, these effects persist for at least one week and are robust across age groups and cognitive status. In a foreign language vocabulary task, pairing words with memorable images led to either improved recall accuracy or reduced learning time. These findings highlight that, in addition to being an intrinsic memory-enhancing property, image memorability also serves as a general facilitator of associative memory, via a method that is easy to adopt, requires minimal conscious effort, and can benefit the general public, particularly learners and cognitive impairment patients in educational and clinical settings.

Most studies of perceptual illusions rely on explicit reports alone, offering a limited view into the computations that underlie ambiguous sensory experiences. Here, we introduce a multivariate computational approach to model paradoxical heat sensation (PHS), a thermosensory illusion in which skin cooling evokes sensations of warmth, as an individual-specific process. We tested 75 healthy adults (aged 21-80) using a perceptual decision-making task with stimuli designed to elicit PHS. Binary perceptual choices, response times and confidence ratings were jointly analysed using hierarchical multivariate Bayesian modelling to characterise PHS at both the population and individual levels across the lifespan. We tested two distinct perceptual profiles: "true perceivers", who experience PHS similarly to a veridical warm percept; and "unsure perceivers", who perceive PHS as an ambiguous experience. At the group level, behaviour was best explained by the unsure perceiver model, suggesting that PHS is often an uncertain experience, rather than a categorical misperception. At the individual level, however, both profiles were represented, with considerable inter-individual variability in model fit. Older participants were more likely to report PHS and did so at lower levels of thermal contrast, but we observed no correlation between age and perceptual profile. This suggests that while ageing increases the likelihood of experiencing PHS, it does not alter the qualitative nature of its perception. These findings also show how multivariate modelling of perceptual, decisional and metacognitive responses can reveal distinct, subjective profiles and their variation across individuals and age.

The brain continuously integrates rapidly changing visual input across eye movements to maintain stable perception, yet the precise mechanisms underpinning dynamic working memory and how these break down in brain diseases remain unclear. We developed a novel eye-tracking paradigm and computational models to investigate how spatial and colour information are updated across saccades. Our findings reveal that saccades selectively impair spatial but not colour memory. Computational modelling identified that spatial representations are maintained in a dual eye-centred frame of reference which is actively updated by a noisy memory of saccades but is vulnerable to interference. Using this model, we found that specific mechanistic failures in initial encoding and memory decay, rather than the saccadic updating process itself, account for spatial working memory deficits in Alzheimers and Parkinsons disease. These results provide a mechanistic understanding of how dynamic spatial memory operates in health and its disruption in neurodegenerative disorders.

Reinforcement learning models can be combined with sequential sampling models to fit choice-RT data. The combined models, known as RL-SSMs, explain a wide range of choice-RT patterns in repeated decision tasks. The present study shows how constraining an RL-SSM with eye gaze data can further enhance its predictive ability. Our model assumes that learned option values and relative gaze independently influence the accumulation of evidence prior to choice. We evaluated the model on data from two eye-tracking experiments (total N = 133) and find that it makes better out-of-sample predictions than other models with different ways of integrating values and gaze at the decision stage. Further, we show that it captures a variety of empirical effects, including the finding that choices become more accurate as the higher-value option receives a greater proportion of the total fixation time. The model can be used to understand how learned option values interact with visual attention to influence choice, joining together two major--but mostly separate--research traditions in the cognitive science of decision making.

A long-standing debate in neuropsychology concerns whether perception and memory function as independent systems or interact to support cognition. To investigate this, we developed the Face Memory and Perception (FMP) task, a novel paradigm designed to systematically disentangle whether and how these processes interact under different conditions. Across five independent datasets with over 900 participants in total, we observed consistent evidence that face perception and working memory operate independently when task demands are low, but in more complex conditions, these processes appear to interact. Notably, this interaction emerged only when the interference directly involved face-processing mechanisms, and did not arise from a general increase in cognitive load. Rather than the use of shared resources by overlapping cognitive processes, this interaction was driven by a shift in behavioural strategy from holistic to feature-based face processing as a result of maintenance-disrupting interference. These results underscore the fundamental independence of perception and working memory while also explaining some of the conditions under which interactions might be observed.

Human memory is typically studied by direct questioning, and the recollection of events is investigated through verbal reports. Thus, current research confounds memory per-se with its report. Critically, the ability to investigate memory retrieval in populations with deficient verbal ability is limited. Here, using the MEGA (Memory Episode Gaze Anticipation) paradigm, we show that monitoring anticipatory gaze using eye tracking can quantify memory retrieval without verbal report. Upon repeated viewing of movie clips, eye gaze patterns anticipating salient events can quantify their memory traces seconds before these events appear on the screen. A series of five experiments with a total of 145 participants using either tailor-made animations or naturalistic movies consistently reveal that accumulated gaze proximity to the event can index memory. Machine learning-based classification can identify whether a given viewing is associated with memory for the event based on single-trial data of gaze features. Detailed comparison to verbal reports establishes that anticipatory gaze marks recollection of associative memory about the event, whereas pupil dilation captures familiarity. Finally, anticipatory gaze reveals beneficial effects of sleep on memory retrieval without verbal report, illustrating its broad applicability across cognitive research and clinical domains.

As humans we communicate important information through fine nuances in our facial expressions, but because conscious motor representations are noisy, we might not be able to report these fine but meaningful movements. Here we measured how much explicit metacognitive information young adults have about their own facial expressions. Participants imitated pictures of themselves making facial expressions and triggered a camera to take a picture of them while doing so. They then rated confidence (how well they thought they imitated each expression). We defined metacognitive access to facial expressions as the relationship between objective performance (how well the two pictures matched) and subjective confidence ratings. Metacognitive access to facial expressions was very poor when we considered all face features indiscriminately. Instead, machine learning analyses revealed that participants rated confidence based on idiosyncratic subsets of features. We conclude that metacognitive access to own facial expressions is partial, and surprisingly limited.

Cognitive operations require recently encountered information to remain available beyond the moment of sensory input. However, how such transient representations are accessed, and how they differ from sensory processing and long-term memory, remains unclear. Here, we combine hierarchical drift-diffusion modelling with behavioural manipulations to dissociate the decision processes underlying feature prioritisation in visual working memory. We first reanalysed a previously collected working memory dataset to characterise semantic and perceptual judgements at the level of latent decision processes. Semantic judgements were associated with reduced non-decision time across conditions, indicating faster access to task-relevant information, while advantages in evidence accumulation emerged selectively under higher cognitive demands. Two further experiments manipulated attentional prioritisation using retro-cues and dissociated the effects of interference from mere maintenance. Across manipulations, semantic prioritisation was selectively expressed in pre-accumulation processes and was amplified when representations fell outside the focus of attention or had to be maintained under interference. Together, these results suggest that semantic representations remain more readily accessible than perceptual details when working memory representations fall outside the focus of attention, consistent with a shift towards more abstract, long-term memory-like formats under conditions of limited attentional support.

Attention control is a basic behavioral process that has been studied for decades. The currently best models of attention control are deep networks trained on free-viewing behavior to predict bottom-up attention control--saliency. We introduce COCO-Search18, the first dataset of laboratory-quality goal-directed behavior large enough to train deep-network models. We collected eye-movement behavior from 10 people searching for each of 18 target-object categories in 6202 natural-scene images, yielding[~] 300,000 search fixations. We thoroughly characterize COCO-Search18, and benchmark it using three machine-learning methods: a ResNet50 object detector, a ResNet50 trained on fixation-density maps, and an inverse-reinforcement-learning model trained on behavioral search scanpaths. Models were also trained/tested on images transformed to approximate a foveated retina, a fundamental biological constraint. These models, each having a different reliance on behavioral training, collectively comprise the new state-of-the-art in predicting goal-directed search fixations. Our expectation is that future work using COCO-Search18 will far surpass these initial efforts, finding applications in domains ranging from human-computer interactive systems that can anticipate a persons intent and render assistance to the potentially early identification of attention-related clinical disorders (ADHD, PTSD, phobia) based on deviation from neurotypical fixation behavior.

Perceptual decision-making refers to the class of decisions in which sensory evidence is used to categorize percepts and guide actions. Conventionally, categorical decisions are thought to precede motor actions. However, recent studies in nonhuman primates challenge this assumption - when perceptual decisions were uncoupled from the actions they bear upon, animals postponed the decisions until relevant response options were revealed. To determine whether this postponement stems from cognitive limitations unique to nonhuman primates, we conducted a similar experiment with human subjects. Naive subjects viewed a random-dot motion (RDM) stimulus that was difficult to categorize. After a delay period following the RDM, two choice targets were presented and subjects decided which target lay closer to the perceived motion direction. Decision accuracy varied across subjects, reflecting individual differences in ability to integrate motion evidence. Notably, subjects with higher decision accuracy showed prolonged deliberation after choice-target presentation. Furthermore, the time they took to report their decisions depended on the strength of the motion evidence. This pattern of accuracy and decision reporting time could be accounted for by a bounded diffusion model in which subjects sequentially sample stored sensory information from memory during the target selection phase. When the RDM was challenging to categorize, the subsequent appearance of the targets provided a framework to interrogate stored evidence and render a decision. Our results reveal a strategic feature of working memory of retaining information based on its future utility. This observation opens new avenues for investigating how memory and decision-making interact.

Learning from experience is central to human decision making, yet research on experience-based choice remains fragmented across paradigms and disciplines. We present the Decision-from-Experience Database (DfE-DB), a standardized, openly accessible resource comprising 3.8 million trial-level decisions from 11,921 participants across 168 studies and 13 paradigms. By harmonizing raw behavioral data and classifying studies along 13 key design features, the database enables quantitative comparisons previously obscured by heterogeneous task and data structures. Using this resource, we show that choice tendencies--toward higher risk, expected value, or experienced mean--vary substantially across paradigms and are strongly shaped by core design features such as feedback type, outcome structure, stationarity, and sampling. These features explain substantial cross-study variability and reveal underexplored paradigm variants. DfE-DB provides the empirical infrastructure necessary to test the generality of behavioral phenomena and computational models, fostering a more integrated science of decisions from experience.

Distinguishing between reliable and unreliable internal sensory representations is crucial for a successful interaction with the environment. Precise estimates of the uncertainty of sensory representations are critical to optimally integrate multiple sensory modalities and produce a coherent interpretation of the world. However, once a multisensory percept is produced, it is not yet known whether humans still have access to the uncertainty of each sensory modality. Here, we asked human participants to perform a series of temporal bisection tasks, in either unimodal visual or auditory conditions, or in bimodal audiovisual conditions where vision and audition could be congruent or not. The validity of their temporal bisection was assessed by asking participants to choose which of two consecutive decisions they felt more confident of being correct, focussing only on the visual modality. We found that once multisensory information is integrated, participants could no longer access the unisensory information to evaluate the validity of their decisions. Comparing three generative models of confidence, we show that confidence judgments are fooled by the fused bimodal percept. These results highlight that some critical information is lost between perceptual and metaperceptual stages of processing in the human brain.

Working Memory (WM) keeps information temporarily available for upcoming tasks. How the contents of WM are distinguished from perceptual representations on the one hand, and from long-term memories on the other, is still debated. Here, we leveraged recent evidence for a reversal of processing dynamics when retrieving episodic long-term memories as opposed to perceiving visual input. In two experiments (n=75 and n=103), we asked participants to hold one or more items in WM and to report their low-level perceptual and high-level semantic qualities. In both experiments, we found faster responses to the items semantic qualities, indicating prioritization over visuo-perceptual aspects, when two or more items were held concurrently in WM. These dynamics of accessing information in multi-item WM were akin to those in retrieving episodic long-term memories and opposite to those in processing visual inputs. Little to no semantic prioritization was evident during single-item maintenance, consistent with a strictly capacity-limited focus of attention within which WM information can be transformed into a prospective action plan.

When we ask people to hold a color in working memory, what do they store? Do they remember colors as point estimates (e.g. a particular shade of red) or are memory representations richer, such as uncertainty distributions over feature space? We developed a novel paradigm (a betting game) to measure the nature of working memory representations. Participants were shown a set of colored circles and, after a brief memory delay, asked about one of the objects. Rather than reporting a single color, participants placed multiple bets to create distributions in color space. The dispersion of bets was correlated with performance, indicating that participants internal uncertainty guided bet placement. Furthermore, relative to the first response, memory performance improved when averaging across multiple bets, showing that memories contain more information than can be conveyed in a single response. Finally, information about the item in memory was present in subsequent responses even when the first response would generally be classified as a guess or report of an incorrect item, suggesting that such failures are not all-or-none. Thus, memory representations are more than noisy point estimates; they are surprisingly rich and probabilistic.

Prospective outcomes bias behavior in a "Pavlovian" manner: Reward prospect invigorates action, while punishment prospect suppresses it. Theories have posited Pavlovian biases as global action "priors" in unfamiliar or uncontrollable environments. However, this account fails to explain the strength of these biases--causing frequent action slips--even in well-known environments. We propose that Pavlovian control is additionally useful if flexibly recruited by instrumental control. Specifically, instrumental action plans might shape selective attention to reward/ punishment information and thus the input to Pavlovian control. In two eye-tracking samples (N = 35/ 64), we observed that Go/ NoGo action plans influenced when and for how long participants attended to reward/ punishment information, which in turn biased their responses in a Pavlovian manner. Participants with stronger attentional effects showed higher performance. Thus, humans appear to align Pavlovian control with their instrumental action plans, extending its role beyond action defaults to a powerful tool ensuring robust action execution.

Apparent facial age plays an important role in social interactions, serving a meaningful marker of biological aging. Although both humans and AIs achieve reasonable accuracy in estimating age from a persons face, performance remains imprecise, leaving substantial room for errors and biases. Drawing on principles from classical psychophysics, we demonstrate that the existing literature on age estimation suffers from a critical theoretical and methodological shortcoming, which casts doubt on established findings. We show that the conventional measure used to benchmark the accuracy of human and AI performance is fundamentally confounded by response bias. Consequently, we introduce a novel measure that eliminates this confound. A revised framework based on simulated data, reanalysis of existing data, and new experimental results, reveals fresh insights into how facial age is processed by humans and AIs. Our structure opens up new directions for future research and applications in the study of aging.

We developed a novel Proactive Reactive and Attentional Dynamics (PRAD) computational model designed to dissect the latent mechanisms of inhibitory control in human cognition. Leveraging data from over 7,500 participants in the NIH Adolescent Brain Cognitive Development study, we demonstrate that PRAD surpasses traditional models by integrating proactive, reactive, and attentional components of inhibitory control. Employing a hierarchical Bayesian framework, PRAD offers a granular view of the dynamics underpinning action execution and inhibition, provides debiased estimates of stop-signal reaction times, and elucidates individual and temporal variability in cognitive control processes. Our findings reveal significant intra-individual variability, challenging conventional assumptions of random variability across trials. By addressing nonergodicity and systematically accounting for the multicomponential nature of cognitive control, PRAD advances our understanding of the cognitive mechanisms driving individual differences in cognitive control and provides a sophisticated computational framework for dissecting dynamic cognitive processes across diverse populations. Our integrative approach significantly advances psychological theory about the multiple neurocognitive processes underlying cognitive control. We also demonstrate the relevance of our theoretical and modeling framework to understand the cognitive, neural, clinical, and exposomic factors related to cognitive control.

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.