Psychological Review

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.

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.

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.

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.

How cognitive control influences the correction of motor errors is an important question. In this study, we examined how the slow and fast processes that constitute motor adaptation are modulated by cognitive load. To address this question, we used a novel approach that differed from traditional dual-task experiments that have assessed the role of fast processes by decreasing attention to the motor adaptation task. Here, cognitive load was manipulated using a decision-making task with easy and hard difficulty levels, while simultaneously adapting to a perturbation and maintaining attention on the task. Our results showed that the hard decision-making task increased cognitive load relative to the easy task, resulting in attenuated steady-state learning. The relative contributions of the fast and slow components were assessed using a dual state-space model, which showed that the former was larger under the high-load condition, whereas the latter was larger under the easy-load condition, consistent with a competition model in which a shared error signal is partitioned between the two processes. We also showed that the effect of cognitive load on fast processes was not attributable to increased reaction times or task performance. Taken together, these results support the notion that faster explicit motor adaptation processes derive from cognitive processes involved in action selection.

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.

Consensus is rarely a one-off achievement. In a changing world, groups must repeatedly update judgments they have already agreed upon, making consensus updating a common feature of real-world collective decisions. However, previous research on collective decision-making has focused mainly on how groups reach a single consensus, leaving the updating of established consensus largely unexamined. This raises two unanswered questions: does consensus updating involve biases analogous to sunk cost, and are such biases related to the cost of the previous consensus? Building a Fake Consensus paradigm, we manipulated consensus-formation cost and examined how dyads updated a subjectively established consensus after new information arrived. An independent individual experiment provided information-matched updating baselines, allowing us to determine whether dyads updated less or more than individuals responding to the same evidence. Contrary to sunk-cost predictions, low-cost consensus led dyads to update less than the individual baseline, whereas medium- and high-cost consensus led them to update more. The pathway through perceived consensus sharedness was significant, and the association between sharedness and updating was stronger at higher left temporoparietal junction inter-brain synchrony. These findings indicate that the history of consensus formation shapes subsequent updating by altering the psychological meaning of shared endorsement, while interpersonal alignment determines how this shared experience translates into coordinated behavior. To our knowledge, this is the first experimentally controlled study of consensus updating in teams to isolate formation history from actual disagreement, providing a new paradigm for studying how collective judgments change after agreement.

Humans efficiently learn the temporal structure of speech, yet the underlying cognitive mechanisms remain unclear. Recent research in visuospatial sequential memory has proposed the successor representation (SR), important in reinforcement learning, which encodes multi-step transitional relationships. To test whether SR also supports the structural learning of rapidly changing speech syllable sequences, we adapted a pyramid-structured sequential learning paradigm using syllable streams presented at a natural speech rate (~ 4 Hz). Participants exhibited robust structural learning, evidenced by asymmetric memory performance modulated by adjacent transitional probabilities. Notably, the SR model captured recognition performance for non-adjacent syllable pairs. Moreover, SR-based learning effects generalized across all syllable sequences, regardless of adjacency. Furthermore, the effectiveness of SR learning was associated with cognitive flexibility and working memory capacity. Together, these findings suggest that humans rely on effortful SR-like mechanisms for the structural learning of syllable sequences, highlighting a modality-general computational principle for sequential learning across various temporal modulation scales.

Mistakes make up our daily lives. Once a mistake has been committed, it is common to pause before you continue. This slowing is called post-error slowing (PES). Although several theories have been proposed to account for PES in humans, findings across cognitive tasks show variation in how PES occurs, and it is unclear how this phenomenon may translate across modalities to a key model organism of human cognition, macaque monkeys. To determine whether monkeys exhibit PES, we tested three rhesus macaque monkeys in components of cognitive sequential decision-making tasks that used either eye tracking or touch screens to report responses. Here we show that monkeys exhibit non-adaptive, error type-dependent PES across modalities. Task structure and context also modulated PES in the eye tracking task, and no modality improved task performance following PES. These results suggest that monkey PES is a combination of cognitive and non-cognitive related responses to error that are modulated by task structures such as error type and task context. Overall, the flexibility in monkey PES is comparable to that of humans, further supporting the importance of monkeys as a translational model in investigating the mechanisms of PES.

Flexible decision making depends on internal representations that capture the latent structure of a task, allowing prior knowledge to be applied in novel circumstances. Spatial navigation provides a useful model for studying flexible choice behavior, because spatial location is itself a latent variable that must be inferred rather than directly perceived. Here, we trained rats on a navigation task that required them to reach the same goal via two distinct paths from different starting locations, and then return to one of the two starting locations before beginning the next trial. Some rats were trained to rely on distal visual landmarks, allowing task states to be identified from current sensory information, whereas others were trained to rely on path integration, requiring task states to be inferred from self-motion information accumulated over the preceding trajectory. Rats trained to rely on path integration acquired the task similarly to rats trained to rely on distal visual landmarks. However, reliance on path integration enhanced rats' ability to generalize their learning to novel situations, including finding the goal from an untrained starting location and adapting to a relocated goal that reversed learned choice sequences. These findings suggest that reliance on path integration promotes the acquisition and use of mental representations that capture the latent spatial structure of navigational tasks, thereby favoring flexible navigational decisions over rigid retrieval of reinforced responses. More broadly, our findings demonstrate how information available during training can shape representations learned from experience and thereby constrain the flexibility of decision making during inference.

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.

Cognitive control processes enable us to use recent contextual information to flexibly adapt to current task demands. This ability to adapt control processes is exhibited in response conflict tasks. In these tasks, participants must respond to a relevant piece of information while overcoming the tendency to respond to task-irrelevant information that cues an automatic or habitual response. Control demands can be updated in these tasks via two separate routes. Both errors and conflict between task-irrelevant and task-relevant information affect performance on subsequent trials. A body of work has implicated dorsolateral prefrontal cortex (DLPFC) in implementing these control adjustments. However, this work has primarily relied on correlational techniques or focused on a single index of control adaptation. Here, we combine inhibitory transcranial magnetic stimulation (TMS) over DLPFC with computational modeling of behavior to test the causal role of DLPFC in control adaptation. We show that inhibitory TMS to DLPFC reduces the effect of previous trial conflict, and committing an error on the previous trial further reduces adaptive effects. This result provides causal evidence for DLPFC playing a general role in implementing trial-to-trial control adjustments.

Perception has been proposed to involve an inferential process that combines noisy sensory evidence with prior expectations to estimate the latent states of the environment. Exact Bayesian inference is intractable in the continuous, high-dimensional state spaces of the natural world. Variational schemes address this by restricting the form of the posterior, whereas sampling schemes represent the posterior with a finite set of samples. In both cases the approximation concerns the posterior rather than the state space over which it is defined. Here we place the approximation elsewhere. Rather than restricting the form of the posterior, we restrict the state space, modeling environmental states as discrete and thereby making Bayesian filtering exact. Beliefs are then unconstrained in shape and what has to be learned can be reduced to a transition matrix updated online by a local rule. The cost is that the states, rather than the distribution, must be specified in advance. The Simultaneous Learning and Inference Model (SLIM) combines this exact filtering with a gated Hebbian rule that learns transitions from inferred rather than observed states. In simulations SLIM recovers environmental dynamics under sensory noise and adapts when those dynamics change, with error growing only once the sensor becomes uninformative. A hierarchical instantiation reproduces local and global prediction error effects. Applied to two auditory decision-making experiments under noise, SLIM reproduces behavioral signatures of expectation and supplies trial-level measures of expectation and surprise. Exact inference over a small discrete state space with a single local Hebbian rule is therefore sufficient to account for these phenomena, without an explicit optimization objective and without a parametric approximation to the posterior.

Hippocampal sharp-wave ripple (SWR) events occur during post-experience rest and sleep and play a significant role in memory consolidation. Recent ex vivo studies have found orexin-driven neuromodulatory effects on SWR parameters and proposed that they can influence memory consolidation. Additionally, behavioral studies demonstrate contradictory results regarding orexinergic effects on memory acquisition depending on task structure and intervention time. This inconsistency, along with the diffuse spread of orexin receptors (OXR) in the CA1 and CA3 hippocampus, challenges theoretical interpretation of orexinergic effects on memory. To reconcile this problem, we propose a rate-based model linking orexinergic tone-dependent SWR statistics to memory performance. The model combines an encoding module whose gain scales with the global orexinergic tone with a consolidation module driven by OX2R-specific replay dynamics. The model is based on a plasticity rule that considers SWR events as the unit of induction. Therefore, weight change requires sufficient replay content and depolarizing drive. We observed that, depending on the task structure, encoding-dominant learning benefits discrimination as the orexin drive increases. Consolidation-dominant learning shows a non-monotonic relationship with an internal optimum level of orexinergic tone. This interaction highlights that stronger truncation at higher receptor engagement shortens memory replay and reduces the efficacy of the consolidation process. The model predicts that the optimal regime depends on task complexity reflected in sequence length. Together, these results demonstrate how task demands determine whether increased orexinergic drive enhances or impairs effective consolidation and SWR-dependent replay in the hippocampus.

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.

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.

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.

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.

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.

Revising a decision while acting on it is a fundamental aspect of natural behavior, yet the underlying computations are difficult to track in real time. To explore how such belief updates unfold, we tested whether eye movements reveal a subject's change of mind during a naturalistic navigation task with a binary target choice. Humans and monkeys were required to navigate to one of two goal locations presented either simultaneously or sequentially in a virtual environment where explicit position cues were absent. Our findings reveal that gaze continuously tracks the latent variable, including changes of mind. We found that the target tracking accuracy before the second target is even presented during sequential target trials predicts whether the subject's navigational goal persists (first target choice) or changes (second target choice). These results underscore the importance of gaze in action planning and changes of mind, supporting the hypothesis that eye movements embody not only the current latent variable but also its uncertainty.