Biology subjects

Grabot, L.

Publications and source records attributed to Grabot, L..

8 recordsLinked to original sources

bioRxiv2026

Modeling how contextual and structural biases shape duration perception

Human time perception is flexible and shaped by both structural constraints and contextual influences. Disentangling these sources of bias is essential for understanding the predictive mechanisms underlying temporal perception, yet no unified model currently integrates them. Here, we quantified precisely the structural and contextual biases in a duration discrimination task to constrain models of duration perception. Using a two-interval duration discrimination task, participants judged which of the two stimuli lasted longer. Stimuli were both visual, both auditory, or one of each modality. Consistent with previous findings, auditory stimuli were perceived longer than visual stimuli of equal duration, reflecting intrinsic properties of audiovisual neural processing. Contextual biases were manipulated through different duration distributions from which stimuli were drawn, a procedure known to influence temporal judgments. Our results show that duration discrimination relies on distinct representations of each stimulus distribution and is best explained by a combination of Bayesian inference and rescaling. While Bayesian inference accounts for contextual effects that bring perceived durations closer to the mean of each distribution, rescaling mechanisms have the opposite effects. These findings challenge existing accounts of contextual effects in time perception and suggest that the brain normalizes temporal representations to adapt to environmental statistics. FundingsThis work was supported by the FRC and the UNAFAM association, and scientific expertise is provided by the FRCs Scientific Advisory Board, and the Anneliese Maier Award to Pascal Mamassian from the Alexander von Humboldt Foundation. Data availabilityBehavioral data and modeling codes will be open-access upon publication.

neuroscience↗

bioRxiv2025

Locally induced traveling waves generate globally observable traveling waves

Cortical traveling waves have been proposed as a fundamental mechanism for neural communication and computation. Methodological uncertainties currently limit the interpretability of non-invasive, extracranial traveling wave data, sparking debates about their cortical origin. Studies using EEG or MEG typically report waves that cover large portions of the sensor array which are often interpreted as reflecting long range cortical waves. Meanwhile, invasive, intracranial recordings in humans and animals routinely find both local, mesoscopic waves and large scale, macroscopic waves in cortex. Whether the global sensor-array waves found with EEG/MEG necessarily correspond to macroscopic cortical waves or whether they are merely projections of local dynamics remains unclear. In this study, we made use of the well-established retinotopic organization of early visual cortex to generate traveling waves with known properties in human participants (N=19, m/f) via targeted visual stimulation, while simultaneously recording MEG and EEG. The inducer stimuli were designed to elicit waves whose traveling direction in mesoscopic retinotopic visual areas depends on stimulus direction, while leaving macroscopic activation patterns along the visual hierarchy largely unchanged. We observed that the preferred direction of traveling waves across the sensor array was influenced by that of the visual stimulus, but only at the stimulation frequency. Comparison between single-trial and trial-averaged responses further showed considerable temporal variation in traveling wave patterns across trials. Our results highlight that under tight experimental control, non-invasive, extracranial recordings can recover mesoscopic traveling wave activity, thus making them viable tools for the investigation of spatially constrained wave dynamics. Significant statementNon-invasively obtained time-resolved neuroimaging data is often thought to primarily reflect neural dynamics on the largest spatial scales. In the context of cortical traveling waves, this assumption can lead to a misinterpretation of spatio-temporal patterns observed in the sensor array. We here show that it is in principle possible that the global sensor array data is dominated by spatially constrained, local cortical traveling wave activity. Our findings crucially inform the ongoing discussion about the origin of traveling waves observed in surface recordings.

neuroscience↗

bioRxiv2024

Traveling Waves in the Human Visual Cortex: a MEG-EEG Model-Based Approach

Brain oscillations might be traveling waves propagating in cortex. Studying their propagation within single cortical areas has mostly been restricted to invasive measurements. Their investigation in healthy humans, however, requires non-invasive recordings, such as MEG or EEG. Identifying traveling waves with these techniques is challenging because source summation, volume conduction, and low signal-to-noise ratios make it difficult to localize cortical activity from sensor responses. The difficulty is compounded by the lack of a known ground truth in traveling wave experiments. Rather than source-localizing cortical responses from sensor activity, we developed a two-part model-based neuroimaging approach: (1) The putative neural sources of a propagating oscillation were modeled within primary visual cortex (V1) via retinotopic mapping from functional MRI recordings (encoding model); and (2) the modeled sources were projected onto MEG and EEG sensors to predict the resulting signal using a biophysical head model. We tested our model by comparing its predictions against the MEG-EEG signal obtained when participants viewed visual stimuli designed to elicit either fovea-to-periphery or periphery-to-fovea traveling waves or standing waves in V1, in which ground truth cortical waves could be reasonably assumed. Correlations on within-sensor phase and amplitude relations between predicted and measured data revealed good model performance. Crucially, the model predicted sensor data more accurately when the input to the model was a traveling wave going in the stimulus direction compared to when the input was a standing wave, or a traveling wave in a different direction. Furthermore, model accuracy peaked at the spatial and temporal frequency parameters of the visual stimulation. Together, our model successfully recovers traveling wave properties in cortex when they are induced by traveling waves in stimuli. This provides a sound basis for using MEG-EEG to study endogenous traveling waves in cortex and test hypotheses related with their role in cognition. Author SummaryBrain oscillations, thought to be crucial for many cognitive processes, might actually be waves that travel across the brains surface. Understanding these traveling waves is notoriously difficult because current non-invasive methods like magneto- and electro-encephalography (MEG-EEG) face significant technical limitations. To address this challenge, we developed a new approach that combines brain imaging techniques and computational modeling. We focused on the primary visual cortical area (V1) of the brain and created a model that simulates traveling activity across the cortex and predicts how these traveling waves should appear in EEG and MEG recordings. We tested our model by comparing its predictions with brain data collected when participants view visual patterns specifically designed to induce traveling waves in the visual system. The results show that our model accurately captures the direction and pattern of the traveling waves, as well as the specific parameters of the visual stimuli. This novel modeling tool offers a promising method for studying endogenous traveling waves and will enable neuroscientists to explore hypotheses about the spatiotemporal organization of brain activity and its role in cognition.

neuroscience↗

bioRxiv2021

Periodic attention operates faster during more complex visual search

Attention has been found to sample visual information periodically, in a wide range of frequencies below 20 Hz. This periodicity may be supported by brain oscillations at corresponding frequencies. We propose that part of the discrepancy in periodic frequencies observed in the literature is due to differences in attentional demands, resulting from heterogeneity in tasks performed. To test this hypothesis, we used visual search and manipulated task complexity, i.e., target discriminability (high, medium, low) and number of distractors (set size), while electro-encephalography was simultaneously recorded. We replicated previous results showing that the phase of pre-stimulus low-frequency oscillations predicts search performance. Crucially, such effects were observed at increasing frequencies within the theta-alpha range (6-18 Hz) for decreasing target discriminability. In medium and low discriminability conditions, correct responses were further associated with higher post-stimulus phase-locking than incorrect ones, in increasing frequency and latency. Finally, the larger the set size, the later the post-stimulus effect peaked. Together, these results suggest that increased complexity (lower discriminability or larger set size) requires more attentional cycles to perform the task, partially explaining discrepancies between reports of attentional sampling. Low-frequency oscillations structure the temporal dynamics of neural activity and aid top-down, attentional control for efficient visual processing.

neuroscience↗

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.

neuroscience↗

bioRxiv2019

Alpha Activity Reflects the Effort to Compensate an Individual Bias in Human Perception

Biases in sensory perception can arise from both experimental manipulations and personal trait-like features. These idiosyncratic biases and their neural underpinnings are often overlooked in studies on the physiology underlying perception. A potential candidate mechanism reflecting such idiosyncratic biases could be spontaneous alpha band activity, a prominent brain rhythm known to influence perceptual reports in general. Using a temporal order judgement task, we here tested the hypothesis that alpha power reflects the overcoming of an idiosyncratic bias. Importantly, to understand the interplay between idiosyncratic biases and contextual (temporary) biases induced by experimental manipulations, we quantified this relation before and after temporal recalibration. Using EEG recordings in human participants (male and female), we find that pre-stimulus frontal alpha power correlates with the tendency to respond relative to an own idiosyncratic bias, with stronger alpha leading to responses matching the bias. In contrast, alpha power does not predict response correctness. These results also hold after temporal recalibration and are specific to the alpha band, suggesting that alpha band activity reflects, directly or indirectly, processes that help to overcome an individuals momentary bias in perception. We propose that combined with established roles of parietal alpha in the encoding of sensory information frontal alpha reflects complementary mechanisms influencing perceptual decisions. Significance statementThe brain is a biased organ, frequently generating systematically distorted percepts of the world, leading each of us to evolve in our own subjective reality. However, such biases are often overlooked or considered noise when studying the neural mechanisms underlying perception. We show that spontaneous alpha band activity predicts the degree of biasedness of human choices in a time perception task, suggesting that alpha activity indexes processes needed to overcome an individuals idiosyncratic bias. This result provides a window onto the neural underpinnings of subjective perception, and offers the possibility to quantify or manipulate such priors in future studies.

neuroscience↗

bioRxiv2017

Oscillatory multiplexing indexes precision

Oscillatory coupling has been implicated in the representation and in the processing of information in the brain. Specific hypotheses suggest that oscillatory coupling may be relevant for the temporal coding of information but to which extent this may translate to conscious timing is unknown. Here, we tested the hypothesis that the temporal precision of self-generated timed actions may be controlled by phase-amplitude coupling (PAC). Using a timing task, we show the existence of significant alpha-beta (-{beta}) PAC, robust at the individual level, and specific to temporal production. Second, an increase in the strength of -{beta} PAC was associated with a smaller variance in time production, i.e. an increased precision in timing, but there was no correlation with the duration of the produced interval. Our results suggest an active role for -{beta} coupling in maintaining the precision of the endogenous temporal goal during time production: specifically, oscillations may maintain the content of current cognitive states, thus securing the endogenous temporal code for duration estimation instantiated in {beta} band. Oscillatory multiplexing may thus index the variance of neuronal computations, which translates into the precision of behavioral performance.

neuroscience↗

bioRxiv2017

Non-linear Auto-Regressive Models for Cross-Frequency Coupling in Neural Time Series

We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model \"goodness of fit\" via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling.\n\nAuthor SummaryNeural oscillations synchronize information across brain areas at various anatomical and temporal scales. Of particular relevance, slow fluctuations of brain activity have been shown to affect high frequency neural activity, by regulating the excitability level of neural populations. Such cross-frequency-coupling can take several forms. In the most frequently observed type, the power of high frequency activity is time-locked to a specific phase of slow frequency oscillations, yielding phase-amplitude-coupling (PAC). Even when readily observed in neural recordings, such non-linear coupling is particularly challenging to formally characterize. Typically, neuroscientists use band-pass filtering and Hilbert transforms with ad-hoc correlations. Here, we explicitly address current limitations and propose an alternative probabilistic signal modeling approach, for which statistical inference is fast and well-posed. To statistically model PAC, we propose to use non-linear auto-regressive models which estimate the spectral modulation of a signal conditionally to a driving signal. This conditional spectral analysis enables easy model selection and clear hypothesis-testing by using the likelihood of a given model. We demonstrate the advantage of the model-based approach on three datasets acquired in rats and in humans. We further provide novel neuroscientific insights on previously reported PAC phenomena, capturing two mechanisms in PAC: influence of amplitude and directionality estimation.

neuroscience↗