Biology subjects

van Wassenhove, V.

Publications and source records attributed to van Wassenhove, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Aging effects on retrospective and naturalistic encoding of time and their relationship with memory.

In everyday life, attention is not explicitly directed toward time. Yet, we can reconstruct how long an experience lasted from the memory traces it left behind, a process intertwined with episodic memory and subjective experience. As both memory and temporal processing change with advancing age, investigating aging effects could provide critical insights on these reconstructive processes. In this study, 31 younger (aged 24{+/-}3) and 36 older (aged 69{+/-}6) healthy adults completed two EEG sessions: 1) a resting-state recording followed by an unexpected retrospective time estimation, and 2) a video-based prospective duration estimation task where participants encoded and judged the duration of 50 naturalistic videos (2-32s) varying in eventfulness. Using a cycle-by-cycle burst analysis of alpha (: 8-12 Hz), we examined oscillatory correlates of both retrospective temporal judgement and prospective temporal processing. Behaviorally, both groups reconstructed short intervals similarly, but older adults produced longer prospective duration estimates than younger adults for longer video clips (16-32s), particularly under low-eventfulness. Regression analyses also showed that individual differences in older adults' prospective duration judgments were influenced by both their retrospective time estimation and episodic memory abilities, especially for longer videos. At the neural level, resting-state -burst dynamics were higher in older adults and predicted, with -power, longer retrospective estimates in older adults. Additionally, resting-state -burst activity predicted -burst during video encoding in both groups. Together, these findings reveal that individual -power and -burst dynamics could constitute memory-based time-tracking mechanism that aging reshapes in ways that manifest behaviorally under naturalistic conditions.

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bioRxiv2026

Uncovering the representational geometry of durations

Is there a geometry of time in the human mind? A canonical measure of time in psychology is duration, a time interval quantifiable as a magnitude. Durations have been proposed to be arranged along a mental timeline: a unidimensional, linear, and spatialised representation of time. Here, we asked whether such a mental timeline is sufficient to account for the experience of duration. To address this, we tested the same participants in two experiments: a behavioural similarity judgment task, in which participants rated the similarity of duration pairs, and an electroencephalography (EEG) experiment in which they detected oddball durations in a sequence. Behavioural and EEG data were used to construct representational dissimilarity matrices, whose geometry was compared against theoretical models of duration organisation. Our results reveal that most variance in behavioural similarity judgements is explained by three latent dimensions, interpretable as: magnitude (monotonic ordering of durations), contextual encoding (distance to the geometric mean of the duration set), and a periodic component. These three dimensions are jointly consistent with a latent generalised helical model, which provided excellent fit to the behavioural data. Individual helical model parameters further correlated with endogenous neural oscillations measured during rest, suggesting that an individual's duration space is partially constrained by intrinsic dynamics. The neural geometry was also found to be dynamic, unfolding in two successive stages: a strong logarithmic encoding of durations peaking around 150 ms after duration offset, followed by a spring-like geometry starting around 300 ms after offset. Together, these findings describe multidimensional psychological and neural geometries of duration space, and characterise their relationship.

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bioRxiv2026

Nested Contextual change and the temporal compression of episodic memory

How does the structure of events influence the when and the where of life experiences in comparison to the what? We developed a novel virtual reality (VR) environment to understand how the quantity of information within nested structures influence participants memory for events. Participants moved through a series of virtual rooms (events) where images (items) appeared in randomised locations on a 3 by 3 grid located on a wall. Participants were asked to remember the what (old/new), when (timeline location), and where (grid location), of the images they experienced. Two types of nested events were tested (6 rooms, each containing 4 images; 3 rooms, each containing 8 images), all lasting an equivalent number of seconds. We found a strong temporal compression effect at nested levels in which participants remembered early items and events happening later, and later items and events happening earlier, than the original experience. Crucially, presenting four-item events resulted in a greater compression rate than eight-item events. We also found greater temporal distances between pairs of items occurring within eight-item events than pairs of items which occurred on either side of a boundary. Memory for when depends on the nested compression of information within events.

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bioRxiv2025

Spontaneous oscillatory activity in episodic timing: an EEG replication study and its limitations

Episodic timing refers to the one-shot, automatic encoding of temporal information in the brain, in the absence of attention to time. A previous magnetoencephalography (MEG) study showed that the relative burst time of spontaneous alpha oscillations () during quiet wakefulness was a selective predictor of retrospective duration estimation. This observation was interpreted as embodying the "ticks" of an internal contextual clock. Herein, we replicate and extend these findings using electroencephalography (EEG), assess robustness to time-on-task effects, and test the generalizability in virtual reality (VR) environments. In three EEG experiments, 147 participants underwent 4-minute eyes-open resting-state recordings followed by an unexpected retrospective duration estimation task. Experiment 1 tested participants before any tasks, Experiment 2 after 90 minutes of timing tasks, and Experiment 3 in VR environments of different sizes. We successfully replicated the original MEG findings in Experiment 1 but did not in Experiment 2. We explain the lack of replication through time-on-task effects (changes in power and topography) and contextual changes yielding a cognitive strategy based on temporal expectation (supported by a fast passage-of-time). In Experiment 3, we did not find the expected duration underestimation in VR, and did not replicate the correlation between bursts and retrospective time estimates. Overall, EEG captures the burst marker of episodic timing, its reliability depends critically on experimental context. Our findings highlight the importance of controlling experimental context when using bursts as a neural marker of episodic timing. Significance StatementHow does the brain automatically keep track of time during everyday experiences? This study investigates alpha brain activity as a marker of contextual changes during quiet wakefulness. We successfully replicated the original findings using EEG, which is more widespread than MEG, but found some limitations. This neural marker is sensitive to mental fatigue and experimental context, with participants adopting temporal expectation strategies that alter the relation between alpha and temporal estimation. Virtual reality environments also affected behavior in a way that suggested prospective timing which the marker is known not to capture. As alterations in timing affect numerous neurological and psychiatric conditions, establishing a robust neural marker of experiential time has important implications for both basic neuroscience and clinical applications.

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bioRxiv2025

Alpha power indexes working memory load for durations

Timing, that is estimating, comparing, or remembering how long events last, requires the temporary storage of durations. How durations are stored in working memory is unknown, despite the widely held view of memory systems central role in timing. Here, we investigated the neural signatures of a sequence of durations (n-item sequence) held in working memory. We recorded human participants using magnetoencephalography (MEG) while they performed an n-item delayed reproduction task, which required to encode a sequence of durations, maintain it, and then reproduce it. The number of items in a sequence (one or three) and the duration of the sequence were orthogonalized. Our results show that during working memory maintenance, the number of durations, not the duration of the sequence, affected recall precision and could be decoded from alpha and beta oscillatory activity. Parieto-occipital alpha power showed a direct link with the precision of temporal reproduction. Our results extend earlier behavioral findings suggesting that durations are itemized in working memory and that their number, not their duration, modulates recall precision (Herbst et al., 2025). Crucially, we establish that alpha power reflects a universal signature of working memory load and mediates recall precision, even for abstract information such as duration.

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bioRxiv2024

Separating sensory from timing processes: a cognitive encoding and neural decoding approach

The internal clock is a psychological model for timing behavior. According to information theory, psychological time might be a manifestation of information flow during sensory processing. Herein, we tested three hypotheses: (1) whether sensory adaptation reduces (or novelty increases) the rate of the internal clock (2) whether the speed of the clock reflects the amount of cortical sensory processing? (3) whether motion tunes clock speed. The current study used an oddball paradigm in which participants detected duration changes while being recorded with electroencephalography (EEG). For data analysis, we combined cognitive modeling with neural decoding techniques. Specifically, we designed Adaptive-Thought-of-Control (ACT-R) models to explain human data and linked them to the sensory EEG features discovered through machine learning. Our results indicate that timing performance is influenced by both timing and non-timing factors. The internal clock may reflect the amount of sensory processing, thereby clarifying a long-standing sensory timing mystery.

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bioRxiv2024

Encoding of task regularities links grid-like signals to human timing behavior

Grid cells have been proposed to encode task regularities that allow predicting future states. Entorhinal grid-like signals might therefore mirror behavioral biases associated with relying on task regularities, like regression-to-the-mean biases in time estimation. Here, we tested this proposal using functional magnetic resonance imaging and a rapid timing task in humans. Indeed, trial-wise entorhinal activity reflected task accuracy and the degree to which interval estimates regressed towards the mean of all tested intervals. Grid-like signals were observed exclusively for the interval closest to the mean, which was explained by differences in temporal grid stability across intervals. Finally, both behavioral and entorhinal results were explained by a Bayesian observer model that assumes the integration of current-trial sensory evidence with prior expectations. Together, we find that entorhinal activity and grid-like signals reflect behavioral performance in a timing task, supporting the proposed role of grid cells in encoding task structure for predictive processing.

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bioRxiv2022

Characterizing endogenous delta oscillations non-invasively in humans

Rhythmic activity in the delta frequency range (0.5 - 3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings pewwrformed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.

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bioRxiv2022

Implicit versus explicit timing - separate or shared mechanisms?

AO_SCPLOWBSTRACTC_SCPLOWTime implicitly shapes cognition, but time is also explicitly represented, for instance in the form of durations. Parsimoniously, the brain could use the same mechanisms for implicit and explicit timing. Yet, the evidence has been equivocal, revealing both joint versus separate signatures of timing. Here, we directly compared implicit and explicit timing using magnetoencephalography, whose temporal resolution allows investigating the different stages of the timing processes. Implicit temporal predictability was induced in an auditory paradigm by a manipulation of the foreperiod. Participants received two consecutive task instructions: discriminate pitch (indirect measure of implicit timing) or duration (direct measure of explicit timing). The results show that the human brain efficiently extracts implicit temporal statistics of sensory environments, to enhance the behavioral and neural responses to auditory stimuli, but that those temporal predictions did not improve explicit timing. In both tasks, attentional orienting in time during predictive foreperiods was indexed by an increase in alpha power over visual and parietal areas. Furthermore, pre-target induced beta power in sensorimotor and parietal areas increased during implicit compared to explicit timing, in line with the suggested role for beta oscillations in temporal prediction. Interestingly, no distinct neural dynamics emerged when participants explicitly paid attention to time, compared to implicit timing. Our work thus indicates that implicit timing shapes the behavioral and sensory response in an automatic way, and is reflected in oscillatory neural dynamics, while the translation of implicit temporal statistics to explicit durations remains somewhat inconclusive, possibly due to the more abstract nature of this task.

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bioRxiv2021

Episodic timing: how spontaneous alpha clocks, retrospectively

We seldom time life events intently yet recalling the duration of events is lifelike. Is episodic time the outcome of a rational after-thought or of physiological clocks keeping track of time without our conscious awareness of it? To answer this, we recorded human brain activity with magnetoencephalography (MEG) during quiet wakefulness. Unbeknownst to participants, we asked them after the MEG recording to guess its duration. In the absence of overt attention to time, the relative amount of time participants alpha brain rhythms ( ~10 Hz) were in bursting mode predicted participants retrospective duration estimate. This relation was absent when participants prospectively measured elapsed time during the MEG recording. We conclude that bursts embody discrete states of awareness for episodic timing. One-Sentence SummaryIn the human brain, the relative number of alpha oscillatory bursts at ~10 Hz can tell time when the observer does not attend to it.

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bioRxiv2021

Hippocampus and striatum encode distinct task regularities that guide human timing behavior

The brain encodes the statistical regularities of the environment in a task-specific yet flexible and generalizable format. Here, we seek to understand this process by converging two parallel lines of research, one centered on sensorimotor timing, and the other on cognitive mapping in the hippocampal system. By combining functional magnetic resonance imaging (fMRI) with a fastpaced time-to-contact (TTC) estimation task, we found that the hippocampus signaled behavioral feedback received in each trial as well as performance improvements across trials along with reward-processing regions. Critically, it signaled performance improvements independent from the tested intervals, and its activity accounted for the trial-wise regression-to-the-mean biases in TTC estimation. This suggests that the hippocampus supports the rapid encoding of temporal context even on short time scales in a behavior-dependent manner. Our results emphasize the central role of the hippocampus in statistical learning and position it at the core of a brain-wide network updating sensorimotor representations in real time for flexible behavior.

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bioRxiv2021

Multisensory correlation computations in the human brain uncovered by a time-resolved encoding model

Neural mechanisms that arbitrate between integrating and segregating multisensory information are essential for complex scene analysis and for the resolution of the multisensory correspondence problem. However, these mechanisms and their dynamics remain largely unknown, partly because classical models of multisensory integration are static. Here, we used the Multisensory Correlation Detector, a model that provides a good explanatory power for human behavior while incorporating dynamic computations. Participants judged whether sequences of auditory and visual signals originated from the same source (causal inference) or whether one modality was leading the other (temporal order), while being recorded with magnetoencephalography. To test the match between the Multisensory Correlation Detector dynamics and the magnetoencephalographic recordings, we developed a novel dynamic encoding-model approach of electrophysiological activity, which relied on temporal response functions. First, we confirm that the Multisensory Correlation Detector explains causal inference and temporal order patterns well. Second, we found strong fits of brain activity to the two outputs of the Multisensory Correlation Detector in temporo-parietal cortices, a region with known multisensory integrative properties. Finally, we report an asymmetry in the goodness of the fits, which were more reliable during the causal inference than during the temporal order judgment task. Overall, our results suggest the plausible existence of multisensory correlation detectors in the human brain, which explain why and how causal inference is strongly driven by the temporal correlation of multisensory signals.

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bioRxiv2021

Postdiction: when temporal regularity drives space perception through pre-stimulus alpha oscillations

During postdiction, the last stimulus of a sequence changes the perception of the preceding ones: in the rabbit illusion, a three-flash series presented regularly in time, but not in space, is - illusory - perceived as spatially regular. Such a reorganization of (spatial) perception could be driven by internal priors, e.g. favoring slow motion for the rabbit illusion. Although postdiction is a ubiquitous phenomenon, its neural underpinnings remain poorly understood. Here, we focused on the role of priors during postdiction and hypothesized that these could be reflected by alpha oscillations (8-12Hz), previously observed to correlate with idiosyncratic biases. We presented human participants with ambiguous visual stimuli that elicited the rabbit illusion on about half the trials, allowing us to contrast MEG-EEG brain responses to the same physical events causing distinct percepts. Given that a strong prior will increase the overall probability of perceiving the illusion, we used the percentage of perceived illusion as a proxy for an individuals prior. We found that high fronto-parietal alpha power was associated with perceiving the sequence according to individual biases: participants with high susceptibility to the illusion would report the illusion, while participants with low susceptibility would report the veridical sequence. Additionally, we found that pre-stimulus alpha phase in occipital and frontal areas dissociated illusory from non-illusory trials. These results point to a dissociated relation of the power and timing of alpha band activity to illusory perception, with power reflecting prior expectations and phase influencing behavioral performance, potentially due to the modulation of sensory uncertainty. Significance StatementLate events may sometimes influence how earlier events are perceived, as if the arrow of time was reversed in the brain. This surprising phenomenon, called postdiction, is observed in the rabbit illusion, and highlights a predominant mechanism for perceptual processes. Perception builds up from the combination of prior expectations with incoming sensory evidence, which takes time. We showed that pre-stimulus neural activity, and more specifically alpha oscillations (8-12Hz), play a double role in postdiction. Fronto-parietal alpha power reflects individual prior expectation, while occipital and frontal alpha phase predicts illusory perception. Postdiction might actually be a means of compensating for the neural delays inherent in perceptual processes, so that the arrow of perceptual time matches the arrow of physical time.

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bioRxiv2019

Precision timing with alpha-beta oscillatory coupling: stopwatch or motor control?

Precise timing is crucial for many behaviors ranging from street crossing, conversational speech, to athletic performance. The precision of motor timing has been suggested to result from the strength of phase-amplitude coupling (PAC) between the phase of alpha oscillations (, 8-12 Hz) and the power of beta activity ({beta}, 14-30 Hz), herein referred to as -{beta} PAC. The amplitude of {beta} oscillations has been proposed to code for temporally relevant information, and the locking of {beta} power to the phase of oscillations to maintain timing precision. Motor timing precision has at least two sources of variability: variability of timekeeping mechanism and variability of motor control. There is ambiguity to with of these two factors -{beta} PAC could be ascribed to. Whether -{beta} PAC indexes precision of internal timekeeping mechanisms like a stopwatch, or -{beta} PAC indexes motor control precision is unclear. To disentangle these two hypotheses, we tested how oscillatory coupling at different stages of time reproduction related to temporal precision. Human participants perceived, and subsequently reproduced, a time interval while magnetoencephalography was recorded. The data show a robust -{beta} PAC during both the encoding and the reproduction of a temporal interval, a pattern which could not be predicted for by the motor control account. Specifically, we found that timing precision resulted from the tradeoff between the strength of -{beta} PAC during the encoding and during the reproduction of intervals. We interpret these results as supporting evidence for the hypothesis that -{beta} PAC codes for precision of temporal representations in the human brain.\n\nHighlights- Encoding and reproducing temporal intervals implicate -{beta} PAC.\n- -{beta} PAC does not represent solely motor control.\n- -{beta} PAC maintains the precision of temporal representations.

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bioRxiv2019

Emergence of β and γ networks following multisensory training

Our perceptual reality relies on inferences about the causal structure of the world given by multiple sensory inputs. In ecological settings, multisensory events that cohere in time and space benefit inferential processes: hearing and seeing a speaker enhances speech comprehension, and the acoustic changes of flapping wings naturally pace the motion of a flock of birds. Here, we asked how a few minutes of (multi)sensory training could shape cortical interactions in a subsequent perceptual task, and investigated oscillatory activity and functional connectivity as a function of sensory history in training. Human participants performed a visual motion coherence discrimination task while being recorded with magnetoencephalography (MEG). Three groups of participants performed the same task with visual stimuli only, while listening to acoustic textures temporally comodulated with the strength of visual motion coherence, or with auditory noise uncorrelated with visual motion. The functional connectivity patterns before and after training were contrasted to resting-state networks to assess the variability of common task-relevant networks, and the emergence of new functional inter-actions following training. One main finding is the emergence of a large-scale synchronization in the high{gamma} (gamma: 60-120Hz) and {beta} (beta:15-30Hz) bands for individuals who underwent comodulated multisensory training. The post-training network involved prefrontal, parietal, and visual cortices. Our results suggest that the integration of evidence and decision-making strategies become more efficient following congruent multisensory training through plasticity in network routing and oscillatory regimes.

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bioRxiv2019

Evaluation of self-generated behavior: untangling metacognitive read-out and error-detection.

When producing a duration, for instance by pressing a key for one second, the brain relies on self-generated neuronal dynamics to monitor the \"flow of time\". Converging evidence has suggested that the brain can also monitor itself monitoring time. Here, we investigated which brain mechanisms support metacognitive inferences when self-generating timing behavior. Although studies have shown that participants can reliably detect temporal errors when generating a duration (Akdogan & Balci, 2017; Kononowicz et al., 2017), the neural bases underlying the evaluation and the monitoring of this self-generated temporal behavior are unknown. Theories of psychological time have also remained silent about such self-evaluation abilities. How are temporal errors inferred on the basis of purely internally driven brain dynamics without external reference for time? We contrasted the error-detection hypothesis, in which error-detection would result from the comparison of competing motor plans with the read-out hypothesis, in which errors would result from inferring the state of an internal code for motor timing. Human participants generated a time interval, and evaluated the magnitude of their timing (first and second order behavioral judgments, respectively) while being recorded with time-resolved neuroimaging. Focusing on the neural signatures following the termination of self-generated duration, we found several regions involved in performance monitoring, which displayed a linear association between the power of (8-14 Hz) oscillations, and the duration of the produced interval. Altogether, our results support the read-out hypothesis and indicate that first-order signals may be integrated for the evaluation of self-generated behavior.\n\nSIGNIFICANCE STATEMENTWhen typing on a keyboard, the brain estimates where the finger should land, but also when. The endogenous generation of the when in time is naturally accompanied by timing errors which, quite remarkably, participants can accurately rate as being too short or too long, and also by how much. Here, we explored the brain mechanisms supporting such temporal metacognitive inferences. For this, we contrasted two working hypotheses (error-detection vs. read-out), and showed that the pattern of evoked and oscillatory brain activity parsimoniously accounted best for a read-out mechanism. Our results suggest the existence of meta-representations of time estimates.

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bioRxiv2018

Self-similarity and multifractality in human brain activity: a wavelet-based analysis of scale-free brain dynamics

BackgroundThe temporal structure of macroscopic brain activity displays both oscillatory and scale-free dynamics. While the functional relevance of neural oscillations has been largely investigated, both the nature and the role of scale-free dynamics in brain processing have been disputed.\n\nNew MethodHere, we offer a novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality. For this, we analyzed human brain activity recorded with magnetoencephalography (MEG) while participants were at rest or performing a task.\n\nResultsFirst, we report consistent infraslow (from 0.1 to 1.5 Hz) scalefree dynamics (i.e., self-similarity and multifractality) in resting-state and task data. Second, we observed a fronto-occipital gradient of self-similarity reminiscent of the known hierarchy of temporal scales from sensory to higherorder cortices; the anatomical gradient was more pronounced in task than in rest. Third, we observed a significant increase of multifractality during task as compared to rest. Additionally, the decrease in self-similarity and the increase in multifractality from rest to task were negatively correlated in regions involved in the task, suggesting a shift from structured global temporal dynamics in resting-state to locally bursty and non Gaussian scalefree structures during task.\n\nComparison with Existing Method(s)We showed that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale-free brain dynamics.\n\nConclusionsAltogether, our approach provides novel fine-grained characterizations of scale-free dynamics in human brain activity.\n\nHighlightsO_LIWe estimated scale-free human brain dynamics using wavelet-leader formalism.\nC_LIO_LIHigh-to-low self-similarity defined a fronto-occipital gradient.\nC_LIO_LIThe gradient was enhanced in task compared to resting-state.\nC_LIO_LIScale-free brain dynamics showed multifractal properties.\nC_LIO_LISelf-similarity decreased whereas multifractality increased from rest to task.\nC_LI

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bioRxiv2017

Temporal metacognition as the decoding of self-generated brain dynamics

Metacognition, the ability to know about ones thought process, is self-referential. Here, we combined psychophysics and time-resolved neuroimaging to explore metacognitive inference on the accuracy of a self-generated behavior. Human participants generated a time interval and evaluated the signed magnitude of their temporal production. We show that both self-generation and self-evaluation relied on the power of beta oscillations ({beta}; 15-40 Hz) with increases in early {beta} power predictive of increases in duration. We characterized the dynamics of {beta} power in a low dimensional space ({beta} state-space trajectories) as a function of timing and found that the more distinct trajectories, the more accurate metacognitive inferences were. These results suggest that {beta} states instantiates an internal variable determining the fate of the timing networks trajectory, possibly as release from inhibition. Altogether, our study describes oscillatory mechanisms for timing, suggesting that temporal metacognition relies on inferential processes of self-generated dynamics.

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