Psychological Review

The ability to evaluate ones own knowledge states is often studied using paradigms in which participants make a decision and subsequently report their confidence. This structure has motivated hierarchical models in which confidence arises from a metacognitive process, distinct from the decision process itself, that estimates the probability that the choice is correct (Meyniel et al., 2015; Pouget et al., 2016; Fleming and Daw, 2017). Here, we contrast this framework with an alternative based on an intentional architecture (Shadlen et al., 2008). In this account, choice and confidence are determined simultaneously through a multidimensional drift-diffusion process, where each dimension represents one choice-confidence combination (Ratcliff and Starns, 2009, 2013). Choice, response time, and confidence jointly emerge when one of these accumulators reaches a decision bound. To adjudicate between these accounts, we fit both models to behavioral data from two perceptual tasks: a random-dots motion discrimination task with incentivized confidence reports, and a luminance discrimination task without feedback or incentives. The integrated model provided a superior fit for the incentivized motion task, whereas the hierarchical model more accurately captured behavior in the un-incentivized luminance task. These results suggest that confidence does not rely on a single computational mechanism, but rather its implementation may adapt to the specific demands and structure of the task.

A widely used framework for studying the computational mechanisms of decision making is the Drift Diffusion Model (DDM). To account for the presence of both fast and slow errors in empirical data, the DDM incorporates across-trial variability in parameters such as the drift rate and the starting point. Although these variability parameters enable the model to reproduce both fast and slow errors, they rely on the assumption that over trials each parameter is independently sampled. As a result, the DDM effectively predicts that errors-- whether fast or slow--occur randomly over time. However, in empirical data this assumption is violated, as error responses are often temporally clustered. To address this limitation, we introduce the autocorrelated DDM, in which trial-to-trial fluctuations in drift rate, starting point, and boundary evolve according to first-order autoregressive (AR1) processes. Using simulations, we demonstrate that, unlike the across-trial variability DDM, the autocorrelated DDM naturally accounts for temporal clustering of errors. We further show that model parameters can be reliably recovered using Amortized Bayesian Inference, even with as few as 500 trials. Finally, fits to empirical data indicate that the autocorrelated DDM provides the best account of error clustering, highlighting that computational parameters fluctuate over time, despite typically being estimated as fixed across trials.

Decisions based on evidence accumulated over time require rules governing when to end the accumulation process and commit to a choice. These rules control inherent trade-offs between decision speed and accuracy, which require careful balance to maximize quantities that depend on both like reward rate. We previously showed that, to maximize reward rate, normative decision rules adapt to changing task conditions (Barendregt et al., 2022). Here we used a novel task to examine whether and how people use adaptive rules for individual decisions under a variety of conditions, including changes in decision outcomes across trials and changes in evidence quality both across and within trials. We found that the participants tended to use rules that adjusted, at least partially, to predictable changes in task conditions to improve reward rate, consistent with a rationally bounded implementation of normative principles. These findings help inform our understanding of the extent and limits of flexible decision formation in the brain.

The progress of learning is usually quantified by averaging responses across participants and/or multiple trials within a block. However, such approaches obscure the trial-by-trial progress of learning, which has been shown recently to express a rich variety of dynamics. An alternative approach which does not suffer from this problem is the detection and analysis of points of behavioral change, i.e., change-point analysis. Using change-point analysis, we reanalyzed data from human participants in different predictive learning tasks in which learned contingencies underwent reversal. We find that responses of individual participants were more accurately characterized by behavioral change points than the average learning curve. Importantly, change points significantly shifted to later trials during reversal learning indicating that reversal learning is more difficult than the initial learning. In a computational model based on deep reinforcement learning, we show that the change point shift required the replay of previous experiences, which in turn depends on the hippocampus. This finding is consistent with studies showing that lesions of the hippocampus yield faster reversal learning. In summary, we reaffirm the importance of the analysis of single participant responses, show that phenomenological learning rates are slower during reversal learning, and provide a theoretical account for this difference.

The benefits of routines for daily functioning are widely acknowledged, yet, despite their apparent importance, methods for quantifying routine maintenance and the causes of their disruption remain lacking. Here, we propose a novel means of defining and quantifying routines (transition entropy). Using the transition entropy, we show that routines can be robustly elicited on tasks that require searching through a grid of squares for a hidden target. Over two experiments (N=100 each), we show that use of routines--as quantified by transition entropy--is robustly perturbed by frequent switches between search grids, as locations specific to the currently irrelevant grid become competitive for selection. Using a normative model that tracks task dynamics, we show that disruption to routines can be attributed to reduced sensitivity to the odds of success for completing a task. This suggests that routine maintenance may be disrupted by over-sensitivity to a lack of reward early in routine performance, or increased expectations regarding the utility of pursuing other tasks.

Animals exposed to pairings of a neutral stimulus with reward acquire a conditioned response to the neutral stimulus. A prominent hypothesis, formalized in the Temporal Difference (TD) learning algorithm, is that animals learn to predict the future reward associated with the neutral stimulus ("value"). Though the TD algorithm does not explicitly specify what drives conditioned responding, a typical assumption is that it reflects the animals estimate of value. In TD learning, value estimates are updated using reward prediction error (RPE, the discrepancy between observed and predicted reward), and are thought to be signaled by the phasic activity of midbrain dopamine neurons. This hypothesis posits that dopamines effects on conditioned responding are mediated entirely by its effects on learning. However, recent experimental and theoretical evidence suggests that dopamine may play a more direct role in modulating conditioned responding. We use a combination of data analysis and computational modeling to probe the relationship between dopamine and conditioned responding. Our results suggest that dopamine directly modulates conditioned responding, in addition to its role in learning. These findings can be captured by a model in which dopamine RPE acts both indirectly (via learning) and directly on conditioned responding.

Working memory (WM) provides a flexible but capacity-limited workspace for maintaining information over short intervals, whereas long-term memory (LTM) serves as a vast and enduring repository for preserving information over extended periods. Decades of research suggest that they are two distinct yet connected systems that together enable adaptive behavior. The link between WM and LTM may not be straightforward, however, as recent evidence has shown that activation-dependent competition among items in WM can weaken their representations in LTM. In the current study, we examined how dynamic competition among items for limited WM resources affects their retention in LTM. We induced competition between items by manipulating temporal expectations in a WM task with either a short (1 s) or a long (4 s) memory delay. Human participants (N = 20) initially prioritized items expected to be tested early, but shifted their priority to items expected to be tested later when the early test did not occur. Using electroencephalography (EEG) and multivariate pattern analysis (MVPA), we tracked the dynamic fluctuations in WM contents based on expected task relevance across the delay window. We linked these temporal profiles during WM with the long-term recognition performance of each item and found that forgetting was associated with a marked decrease in neural evidence for items deemed no longer relevant during the later delay period. These results demonstrate that WM representations fluctuate with temporal expectations and that the de-prioritization of items during WM maintenance is what drives their long-term forgetting.

Aesthetic preference is a primary driver of social behavior in the digital era, yet the extent to which these preferences remain consistent across disparate domains remains poorly understood. We hypothesize that aesthetic judgment is governed by a domain-invariant latent structure, such that individuals who exhibit similar preferences in one category will demonstrate comparable alignment in seemingly unrelated domains. To test this, we recruited 37 participants to evaluate stimuli across three distinct aesthetic domains: art, faces (male and female), and scenes. We developed a novel computational framework that reformulates cross-domain preference as a user-based collaborative filtering problem, encoding individual profiles through inter-subject similarity matrices. Our model successfully predicted participant responses in a target domain based on their similarity to the cohort in a separate source domain. These results demonstrate robust cross-domain consistency, suggesting that aesthetic evaluation is mediated by an abstract, domain-general mechanism rather than being purely stimulus-dependent. We propose that this consistency is rooted in a shared neurophysiological pathway, likely involving the orbitofrontal cortex (OFC) and the Default Mode Network (DMN), and discuss how these findings provide a foundation for more sophisticated, cross-modal recommendation systems and the study of individual social identity.

Binocular vision depends on the integration of matching visual features across the two eyes, while conflicting interocular signals can engage active inhibitory processes in the visual system. To investigate the temporal dynamics of these putative inhibitory processes, we examined how transitions between different binocular correlation states influence perceptual detectability and response speed. Using dynamic random-dot correlograms - free of monocular cues and allowing precise interocular manipulation - we presented brief target intervals embedded in longer background sequences. Stimuli varied in binocular correlation: correlated (C) patterns contained identical luminance profiles in both eyes, anticorrelated (A) patterns had inverted luminance dots, and uncorrelated (U) patterns had independent dot arrangements. Across three experiments, we measured (1) the presentation duration threshold required to detect a change in correlation, (2) simple reaction times (RTs) to the same transitions at suprathreshold levels, and (3) psychometric functions across durations for selected transitions. In Experiment 1, A[->]C transitions yielded significantly higher duration thresholds than C[->]A, indicating a suppressive influence associated with prior anticorrelation. In contrast, Experiment 2 showed that A[->]C transitions produced the shortest RTs, while C[->]U transitions were slowest, suggesting a rebound-like facilitation following prior suppression. Experiment 3 confirmed these temporal and contrast dependences, with opposite changes in contrast threshold and reaction times between transitions toward and away from the correlated fusional states. This divergence between perceptual onset and reaction time is consistent with a two-phase account in which binocular anticorrelation is associated with an initial suppressive phase followed by rebound-like facilitation that accelerates responses once the target becomes detectable. These findings are consistent with current models of binocular rivalry and fusion, and provide a temporally resolved behavioral perspective on how inhibitory control in sensory systems may dynamically influence subsequent responsiveness under conditions of perceptual ambiguity.

Each individual person looks at natural scenes in their own unique way, resulting in a distinct perceptual experience of the world. However, little is known about why such differences in gaze emerge. Here, we test the hypothesis that idiosyncrasies in gaze behavior are predicted by inter-subject variations in internal models--expectations about how scenes typically look. In two experiments, we first characterized participants personal internal models by asking them to draw typical bathroom and kitchen scenes. Individual differences in these drawings were quantified using an objective deep learning pipeline and, in turn, related to individual differences in gaze behavior. In Experiment 1, where participants freely viewed a set of kitchen and bathroom photographs, inter-subject similarities in internal models did not predict inter-subject similarities in gaze. In Experiment 2, we encouraged strategic exploration through gaze-contingent viewing and a memory task. Here, inter-subject similarities in internal models predicted similarities in fixation frequency and the sequence in which different object categories were inspected. These findings suggest that the influence of internal models on visual exploration is stronger under increased sensory uncertainty and when expectation-guided sampling of the environment is encouraged. Together, our results provide new insights into how individual expectations shape gaze behavior and help explain why people differ in how they explore the visual world.

Mental fatigue is known to impair cognitive and motor performance, but its impact on motor learning remains unclear. This study examined how mental fatigue affects skill acquisition in a sequential finger-tapping task. Twenty-eight participants were assigned to either a mental fatigue group, which completed a thirty-minute Stroop task, or a control group, which watched a documentary of equivalent duration. Both groups then trained on the finger-tapping task across multiple practice blocks with brief rest periods. Overall motor skill improved similarly in both groups. However, mental fatigue altered the pattern of acquisition: participants in the fatigue group showed decreased performance during practice blocks, which was compensated by larger gains during inter-block rest periods. A strong negative correlation was observed between online decrements and offline improvements, indicating that greater declines during practice were associated with larger gains during rest. This study highlights the critical role of rest periods in maintaining learning under cognitively demanding conditions and provides insight into how internal states, such as mental fatigue, can selectively influence the expression of performance without compromising overall learning.

A critical aspect of human cognition is the ability to use our knowledge about the laws of physics to make predictions about physical events. Whether this ability is based on abstract processes or is grounded in our body-environment interactions remains an open debate. We used physical reasoning under altered gravity as a model system to show that humans real-time embodied experience modifies their high-level physical reasoning. Specifically, we tested participants in computerised reasoning games, while disrupting their gravitational signalling using Galvanic Vestibular Stimulation (GVS). Participants failed more and had suboptimal strategies under the GVS condition compared to no-GVS in games requiring reasoning about terrestrial gravity. However, the effects of GVS were reduced when the games included reasoning about altered gravity. Our findings demonstrate how the physical experience of the body shifts high-level cognitive skill as reasoning, suggesting that humans mental representation of the world is grounded in adaptable physical mechanisms.

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. These findings demonstrate that complex-systems approaches can bridge laboratory and naturalistic assessments of cognition, revealing which cognitive processes meaningfully constrain real-world behavior.

Resource sharing is a fundamental form of social exchange underlying the formation and maintenance of social bonds in humans and other species. While reciprocity has long been proposed as a key mechanism in group interactions, the dynamic processes underlying resource allocation remain poorly understood. In this study, we employed computational modeling to investigate the temporal dynamics of resource sharing in a novel group decision-making task across three experiments. We found that, beyond the well-documented reciprocity, participants exhibited consistent alternating behavior, characterized by the switching between potential recipients. This alternation was not driven by fairness concerns but reflected a strategic balance between maintaining stable partnerships and exploring alternatives. Crucially, a reinforcement learning model incorporating Theory of Mind (ToM) consistently outperformed all alternative models. These findings highlight the critical role of ToM in social decision-making and suggest that mentalizing others intentions may be essential for effective resource sharing and social bond formation.

Pretend play is a hallmark behavior in childhood where children create nonliteral meanings. Empirical data supporting the role of social cognition and the decoupling from literality are still scarce during early development. We explored here how the comprehension of pretense affects the visual exploratory behavior of toddlers (n = 44) and adults (n = 65) when they were exposed to short video clips in which an actress performed either real actions (e.g., eating jelly) or pretend actions (e.g., pretending to eat with imaginary food), while varying the complexity of those actions. We analyzed participants exploration of the face in the videos as exploitation of social information. We showed that all observers paid more attention to the face in pretend scenarios than in real ones, measured as longer total looking time in adults and more fixations and revisits to the face in both age groups. We also found more gaze shifts (a measure of information sampling) between the face and the moving hand in the pretend videos in both age groups, mainly at the initial stages of the actions. Additionally, analyses of the scanpaths structure using gaze entropy showed less order in the exploration of pretend videos in both age groups, suggesting that pretense involved greater uncertainty and increased information seeking. The less structured trajectories were observed again mainly in complex pretend scenarios. Taken together, our gaze results indicate that from its developmental origins, the comprehension of pretense relies on social processes linked with information seeking and exploration. Significance StatementDevelopmental theories have long debated whether pretend games are born in conjunction with social capacities in the second year or become integrated later in life. Our study shows that, much like adults, toddlers visually explore pretend scenes gathering more social information and in a less structured manner compared to real-world scenarios, suggesting that the emerging capacity to play with the meaning of things is linked with that of thinking of other minds early in life.

Neuronal activity alters extracellular ion concentrations and electric potentials. Ephaptic effects refer to the feedback influence that these extracellular changes can have on neuronal activity. While electric ephaptic effects occur on a fast timescale due to extracellular potential perturbations, ionic ephaptic effects are driven by slower, accumulative changes in ion concentrations. Among the previous computational studies of ephaptic effects, the vast majority have focused exclusively on electric effects, while ionic ephaptic effects have largely been neglected. In this work, we present an electrodiffusive computational framework consisting of two-compartment neurons that interact via a shared extracellular space. By accounting for both electric potentials and ion-concentration dynamics in a self-consistent manner, our framework enables us to explore the relative roles of electric and ionic ephaptic effects. Through numerical experiments, we demonstrate that ionic and electric ephaptic interactions play very different roles. While ionic ephaptic interactions increase population firing rates, electric ephaptic interactions primarily drive subtle shifts in spike timing. Furthermore, we show that these spike shifts cause the phase difference (the distance in spike times between a small collection of neurons) to converge to a stable, unique phase difference, which we coin the ephaptic intrinsic phase preference. Author summaryNeurons predominantly communicate through synapses: specialized contact points where a brief electrical signal, known as a spike or action potential, in one neuron influences another. Neurons generate these spikes by exchanging ions with the surrounding extracellular space. This way, spiking neurons alter extracellular ion concentrations and electric potentials. Since neurons are sensitive to such changes in their environment, they can also influence one another indirectly through the shared extracellular medium. This form of non-synaptic interaction is known as ephaptic coupling. Most computational models of neuronal activity neglect ephaptic interactions, and those that include them typically consider only electric effects while ignoring ionic contributions. As a result, the relative roles of electric and ionic ephaptic effects remain poorly understood. Here, we introduce a computational framework that accounts for both mechanisms in a self-consistent way. Our results show a functional distinction: ionic ephaptic effects act slowly, regulating population firing rates, whereas electric ephaptic effects act on millisecond timescales and subtly shift spike timing. These shifts cause spike-time differences between neurons to converge to a stable value, a phenomenon we call ephaptic intrinsic phase preference.

Speech comprehension involves the inference of abstract information from continuous acoustic signals. Prior work suggests that electrophysiological activity is synchronized with abstract linguistic structures (phrases and sentences) during the processing of isochronous syllable sequences. It is yet unclear whether this prior evidence generalizes to natural speech comprehension, which requires the flexible processing of continuous speech, where syllables and other types of linguistic units are anisochronous. Our magnetoencephalography experiment investigated neural synchronization to acoustic (syllables) and abstract units (phrases and sentences) using continuous speech ranging from artificial isochronous to more natural anisochronous. We find that neural synchronization to phrases and sentences, but not syllables, is resilient to naturalistic anisochrony. This suggests that linguistic structure processing reflects endogenous inferences that are fundamentally distinct from the exogenous processing of syllables driven by speech acoustics. Lateralization and linear regression results extend this functional dissociation as hemispheric asymmetry: stimulus-independent leftward lateralization for linguistic structure processing but stimulus-driven rightward lateralization (or bilaterality) for both syllable and acoustic processing. Our findings provide a more realistic characterization of the flexible neural mechanisms supporting the efficient comprehension of natural speech.

A key component of intraneuronal communication is the modulation of postsynaptic firing frequencies by stochastic transmitter release from presynaptic neurons. The time interval between successive postsynaptic firings is called the inter-spike interval (ISI), and understanding its statistics is integral to neural information processing. We start with a model of an excitatory chemical synapse with postsynaptic neuron firing governed as per a classical integrate-and-fire model. Using a first-passage time framework, we derive exact analytical results for the ISI statistical moments, revealing parameter regimes driving precision in postsynaptic action potential timing. Next, we extended this analysis to include both an excitatory and an inhibitory presynaptic connection onto the same postsynaptic neuron. We consider both a fixed postsynaptic-firing threshold and a threshold that adapts based on the postsynaptic membrane potential history. Our analysis shows that the latter adaptive threshold can result in scenarios where increasing the inhibitory input frequency increases the postsynaptic firing frequency. Moreover, we characterize parameter regimes where ISI noise is hypo-exponential or hyperexponential based on its coefficient of variation being less than or higher than one, respectively.

Cognitive processes such as decision-making, working memory, and motor planning operate across a hierarchy of timescales, manifesting as rapid neural transients alongside slower physiological mechanisms like short-term plasticity. Conventional Dynamic Causal Modelling (DCM) limits our ability to study these dynamics by assuming stationary parameters, whilst recent time-varying approaches often rely on segmenting data into epochs. This segmentation artificially resets neural states between windows, fundamentally obscuring the continuous hysteresis essential to sequential processing. To address this limitation, we introduce DCM for Sequential Responses (DCM-SR), a generative framework that embeds parameter evolution directly within the first-level model whilst employing a continuous state-space formulation that removes the requirement for epoching. This approach generalises non-stationarity to all neural mass parameters, including synaptic gains and time constants, modelling them as piecewise smooth trajectories that evolve alongside continuous neural states. Consequently, the model explicitly captures two distinct forms of temporal memory: transient history dependence, where responses are shaped by the carryover effect of recent perturbations, and path dependence, where the systems trajectory through parameter space determines its responsiveness. The framework accommodates both exogenous, stimulus-locked transitions and endogenous, autonomous state changes, permitting inference on both external perturbations and internal drivers of network evolution. Simulations establish the models face validity, demonstrating robust parameter recovery and conservative model selection that accurately discriminates between genuine parameter evolution and spurious complexity. We applied the framework to empirical data from an auditory go/no-go task, modelling a full sequence of cognitive phases from initial cue processing and anticipation through to motor preparation and execution. This analysis established construct validity by resolving the biophysical generators of the contingent negative variation, attributing this slow potential to sustained thalamocortical drive and deep-layer hyperpolarisation rather than superficial-layer activity. Furthermore, the model captured trial-specific modulations of the hyperdirect pathway during motor inhibition, tracking the dynamic interplay between prefrontal executive control and basal ganglia gating. DCM-SR offers the first principled approach to decomposing compound signals such as slow cortical potentials into evolving synaptic mechanisms and continuous state trajectories, and provides a necessary bridge for investigating the biophysical implementation of extended cognitive phenomena including evidence accumulation and physiological hysteresis.

Receptive fields provide a concise description of the stimulus selectivity of visual neurons. But this stimulus selectivity is neither static nor linear, and these nonlinear effects are not well captured by standard linear or pseudo-linear receptive field models. At the same time, receptive field models incorporating nonlinear effects are largely empirical, and are not easily interpreted in terms of underlying cellular and synaptic mechanisms. Here we show that two nonlinear mechanisms in the primate outer retina shape neural responses and that these contribute significantly to responses to natural stimuli and to the retinal output signals. Incorporating these outer retinal nonlinearities into models for visual function will improve our ability to identify the mechanistic origin of specific features of downstream visual responses.