Biology subjects

Jerbi, K.

Publications and source records attributed to Jerbi, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Task-optimized neural networks reveal distinct contributions of specialized and broader visual learning to neural representations of face familiarity

How neural activity across the ventral visual hierarchy supports face recognition is an open question. A long-standing debate asks whether face processing, particularly in fusiform cortex, relies on face-specific computations or representations shared with broader visual recognition. Here we combine source-resolved magnetoencephalography (MEG) with task-optimized neural networks as controlled computational models of visual experience. Rather than manipulating long-term expertise in human observers, we systematically vary learning objective on the model side-what the networks are trained to recognize-while holding architecture and loss function constant within model comparisons. We then ask which learned representational geometries best align with neural responses, where and when. We measured millisecond-resolved brain-model alignment across V1, lateral occipital cortex (LOC) and fusiform cortex while participants viewed familiar, unfamiliar and scrambled faces. The same stimuli were presented to seven CNN architectures trained for face-identity recognition (FR), object-category recognition (OR) or object categorization including a face category (Dual), alongside untrained controls. Familiarity produced a stage-dependent dissociation: in LOC, familiar faces showed earlier brain-model alignment than unfamiliar faces in the M170 range, an effect most consistent in models trained for face-recognition, whereas broader objectives produced more variable, architecture-dependent peak alignment latencies. In fusiform cortex they showed stronger alignment around the M200 range. This fusiform advantage was not uniquely associated with face-recognition training: dual- and object-trained models showed greater fusiform correspondence than face-trained models. Together, these findings support stage-dependent specialization, with training for face-identity recognition constraining intermediate-stage timing while later fusiform representations remain compatible with representational structure acquired through broader visual computations.

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bioRxiv2026

Critical dynamics in spontaneous EEG predict perturbational complexity in disorders of consciousness with measurable evoked responses

Identifying which severely brain-injured patients retain the capacity for consciousness remains a major challenge in neurocritical care. The perturbational complexity index (PCI) provides a reliable assessment of consciousness capacity, but its reliance on transcranial magnetic stimulation and EEG (TMS-EEG) limits bedside scalability. PCI and brain criticality capture complementary dimensions of brain dynamics: PCI quantifies the complexity of the brains evoked response to perturbation, whereas criticality characterizes the intrinsic organization of spontaneous activity. Here, we tested whether resting-state EEG signatures of criticality predict PCImax in disorders of consciousness, extending prior findings from anesthesia to severe brain injury. In 26 patients with vascular, traumatic, or anoxic brain injury, multivariate criticality related features did not generalize PCImax prediction across the full heterogeneous cohort. However, criticality features predicted PCImax when analyses were restricted to non-anoxic patients and when restricting analyses to patients with non-zero PCImax values. These findings suggest that spontaneous criticality measures index the brains intrinsic dynamical regime that supports complex perturbational responses, while their correspondence with PCImax depends on whether the injured brain retains sufficient capacity to sustain large-scale evoked responses. Together, our results extend the relationship between resting-state criticality and evoked perturbational complexity to disorders of consciousness and support the development of stratified EEG measures in severe brain injury.

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bioRxiv2026

Distinct SMA beta bursts support the development of anticipatory postural control in children

To produce smooth and coordinated movements, the brain generates anticipatory postural adjustments (APA) - feedforward motor commands that counteract predicted postural disturbances before they occur. Although APA emerge early in life, their maturation extends into early adulthood, yet little is known about the underlying neural mechanisms. Using magnetoencephalography during a naturalistic bimanual load-lifting task, we investigated anticipatory postural control in children aged 7-12 years. In this task, lifting a load from the contralateral postural forearm induces upward elbow rotation, which is anticipatorily counteracted by inhibition of Biceps brachii postural activity, stabilizing forearm posture. Surprisingly, despite greater variability in inhibition timing and less efficient forearm stabilization in children, the mechanism of anticipatory inhibition was consistent with that recently described in adults: it was mediated by transient oscillatory beta bursts (19-24 Hz), associated with reduced supplementary motor area (SMA) excitability indexed by high-gamma (90-130 Hz) power suppression, and preceded by directed influences from lateral prefrontal and ventral premotor cortex. However, burst frequency was lower in children, and an additional APA mechanism was recruited: while 19-24 Hz bursts were linked to immediate SMA suppression and stronger muscle inhibition, higher-frequency bursts (24-29 Hz) were associated with delayed ([~]100 ms) SMA suppression mediated by alpha activity (8 Hz), which attenuated post-inhibition forearm instability. These results reveal that children in middle childhood already engage adult-like neural mechanisms for anticipatory inhibition, while additionally recruiting a compensatory mechanism to counteract postural imprecision, providing new insights into the neural basis of APA maturation and its alteration in neurodevelopmental conditions.

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bioRxiv2026

LSD Relaxes Structural Constraints on Brain Dynamics and Default Mode Decoupling Tracks Ego Dissolution

Psychedelics profoundly alter conscious experience, yet how they reshape the relationship between brain anatomy and electrophysiological dynamics remains unclear. Here we use source-localized magnetoencephalography mapped onto connectome harmonics to quantify structure-function coupling in humans under lysergic acid diethylamide (LSD) and placebo. LSD induces a robust decoupling of low-frequency (theta, alpha and beta) activity from anatomical constraints, indicating a global loosening of structure-aligned large-scale dynamics. High-frequency gamma activity shows selective reorganization rather than uniform disruption. Decoupling within core default-mode network regions predicts ego dissolution intensity across individuals, linking frequency-selective DMN reorganization to subjective loss of self. Functional decoding further reveals system-specific rebalancing: visual and attentional systems preferentially decouple while auditory networks exhibit strengthened coupling. Together, these findings provide electrophysiological evidence that psychedelic states emerge from a frequency-dependent relaxation of structural constraints on brain activity and identify default-mode reorganization as a neural correlate of ego dissolution. These results offer a mechanistic framework for understanding how LSD may exert therapeutic effects by transiently relaxing rigid structural constraints and enhancing dynamical flexibility within networks involved in self-related processing.

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bioRxiv2026

LSD Reconfigures Cortical Dynamics Through Faster Brain Rhythms and Increased Fractal Dimension

Lysergic acid diethylamide (LSD) profoundly alters conscious experience, yet the electrophysiological mechanisms by which it reshapes neural dynamics remain incompletely understood. A hallmark of psychedelic states is widespread cortical desynchronization, typically inferred from reductions in spectral power, but whether such effects reflect genuine weakening of neural oscillations or are confounded by shifts in oscillatory peak frequencies remains unresolved. Here, we address this gap by combining source-resolved magnetoencephalography (MEG), spectral parameterization, temporal complexity metrics, and interpretable machine learning in an LSD versus placebo design, with and without music. We show that LSD induces robust, spatially structured increases in alpha and beta peak frequencies alongside genuine attenuation of oscillatory power, with these effects displaying partly dissociable cortical patterns. Beyond rhythmic activity, LSD is associated with flattening of the aperiodic 1/f spectral slope and increased neural signal fractality and complexity, preferentially affecting sensory, language, emotion, and imagery-related networks while sparing motor cortex. Machine-learning analyses further identify peak-frequency shifts, aperiodic parameters, and complexity measures as key discriminators of the psychedelic state. Music does not robustly amplify these neural signatures and instead shows a trend toward attenuation. Together, these findings provide a comprehensive electrophysiological account of how LSD reorganizes large-scale human brain dynamics and highlight features that may differentiate its neural signature from that of other psychedelics.

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bioRxiv2026

Shaped by meaning, weighted by reliability: New insights into multisensory integration

In everyday perception, sensory information is rarely interpreted in isolation. Instead, observers generate expectations about what is likely to occur next, using contextual information to guide decisions, particularly when sensory input is weak, ambiguous, or uncertain. These expectations can facilitate behaviour when they align with incoming evidence, but impose costs when they conflict, shaping how information from different senses contributes to behaviour. However, it remains unclear what kind of multisensory process is engaged when meaning guides decisions, and whether multisensory facilitation and non-independence reflect a single integrative mechanism or distinct routes shaped by context. Here, we examined how semantic context, visual clarity, and the relationship between auditory and visual signals jointly shape behaviour in a semantic correspondence task using dynamic, naturalistic events. Participants judged whether a target matched a preceding concept prime, establishing an expectation at the level of meaning. Targets were auditory-only, visual-only (clear or blurry), or audiovisual (AV), with AV components conveying either the same or different meanings across modalities. AV facilitation was not a general consequence of stimulus redundancy. When the prime accurately predicted the target and auditory and visual signals conveyed the same meaning, degrading visual input increased the contribution of auditory information, leading to faster responses and violations of the race-model inequality. By contrast, when the prime indicated the target but auditory and visual signals conveyed competing meanings, AV stimulation produced robust behavioural costs relative to vision. When the prime did not match the target, congruent and incongruent audiovisual events produced comparable effects, and race-model violations were observed even when auditory and visual signals encoded different concepts, indicating convergence at the level of the response rather than shared perceptual content. Finally, exploratory clustering revealed two participant subgroups with comparable overall response speed but systematically different multisensory profiles when semantic expectations did not allow reliable anticipation of the upcoming event. Together, these findings show that multisensory effects in semantic decisions are shaped by meaning and arise through two functionally distinct routes: one in which reduced sensory reliability promotes joint use of congruent audiovisual information, and another in which perceptually different signals converge on the same response when semantic expectations are not met. HighlightsO_LISemantic expectations shape how multisensory information is used to guide responses. C_LIO_LIReduced sensory reliability enables multisensory facilitation when signals convey the same meaning. C_LIO_LIMultisensory effects arise when sensory signals support the same response. C_LIO_LIIndividuals adopt distinct strategies for resolving uncertainty in multisensory decisions. C_LI

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bioRxiv2025

Intrinsic Neural Oscillations Predict Verbal Learning Performance and Encoding Strategy Use

Individuals adopt different encoding strategies to facilitate learning. However, few studies have investigated the neurophysiological basis that support these different encoding strategies across individuals. The present work addresses this gap by extending our previous findings on the direct relationship between cortical spectral power, measured via resting-state magnetoencephalography, and performance on standard cognitive test results. Our results highlight the complex interactions between endogenous brain oscillations, learning and verbal encoding strategies assessed by the California Verbal Learning Test (CVLT-2). First, we found that resting-state theta oscillations were significantly associated with verbal learning and subjective clustering strategies. Second, we observed that semantic clustering is facilitated by oscillatory patterns in left sensory-motor brain regions. Finally, our analyses revealed that serial and semantic clustering strategies are related to opposite regression patterns, indicating a competitive interaction. Together, these findings provide novel insights into the neural oscillatory dynamics that support diverse encoding strategies in verbal learning.

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bioRxiv2025

Uncovering executive function profiles within interindividual variability: A data driven clustering exploration of design fluency in school-aged children

This study applied unsupervised machine learning to performances on a design fluency task, to identify distinct executive function (EF) profiles among 113 neurotypical Canadian children (62% female, 74% White, aged 7-13). By tracking design, repetition, and strategy production every minute for five minutes, the results revealed two profiles: Profile A generated fewer designs but exhibited a more stable production pattern across time. Profile B, initially more productive, showed a steeper increase in repetition errors and a decline in design production. These findings demonstrate how EF interindividual variability in neurotypical children can be captured through naturally emerging performance patterns, highlighting the value of temporal analysis in differentiating executive functioning profiles.

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bioRxiv2025

MEEGNet: An open source python library for the application of convolutional neural networks to MEG

Artificial Neural Networks (ANNs) are rapidly gaining traction in neuroscience, proving invaluable for decoding and modeling brain signals from techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Although these networks are beginning to find applications in magnetoencephalography (MEG), their use in this domain is still in the early stages. Here, we introduces MEEGNet, a novel Python library paired with an intuitive convolutional neural network (CNN) architecture designed primarily for MEG data, yet adaptable to EEG signals. The MEEGNet model was trained and cross-validated using MEG data from 643 participants across four classification tasks, including auditory and visual stimulus classification and age prediction. Our model achieves competitive performance across all tasks, with a notable balance of accuracy and efficiency--for instance, reaching 92.70% test accuracy in an auditory vs. visual classification task while maintaining shorter training times than other architectures. The MEEGNet pipeline also integrates latent space visualization tools, adapted for MEG and EEG data. These include saliency maps and Grad-CAM methods, which enhance the interpretability of ANN-based classification and help address the black-box critique of such models. Importantly, the MEEGNet library is designed for extensibility, allowing the neuroscience and machine learning communities to add functionalities and ANN models. By prioritizing usability, transparency, and interpretability, MEEGNet empowers MEG- and EEG-based research with a user-friendly and modular framework.

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bioRxiv2025

Meditation induces shifts in neural oscillations, braincomplexity and critical dynamics: Novel insights fromMEG

While the beneficial impacts of meditation are increasingly acknowledged, its underlying neural mechanisms remain poorly understood. We examined the electrophysiological brain signals of expert Buddhist monks during two established meditation methods known as Samatha and Vipassana, which employ focused attention and open monitoring technique. By combining source-space magnetoencephalography (MEG) with advanced signal processing and machine learning tools, we provide an unprecedented assessment of the role of brain oscillations, complexity and criticality in meditation. In addition to power spectral density (PSD), we computed long-range temporal correlations (LRTC), deviation from criticality coefficient (DCC), Lempel-Ziv complexity (LZC), 1/f slope, Higuchi fractal dimension (HFD), and spectral entropy. Our findings indicate increased levels of neural signal complexity during both meditation practices compared to the resting state, along-side widespread reductions in gamma-band LRTC and 1/f slope. Importantly, the DCC analysis revealed a separation between Samatha and Vipassana, suggesting that their distinct phenomenological properties are mediated by specific computational characteristics of their dynamic states. Furthermore, in contrast to most previous reports, we observed a decrease in oscillatory gamma power during meditation, a divergence we attribute to the correction of the power spectrum by the 1/f slope. We discuss how these results advance our comprehension of the neural processes associated with focused attention and open monitoring meditation practices.

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bioRxiv2025

Hitting the right pitch: Cortical tracking of fundamental frequency changes across speech rates in auditory and sensorimotor regions

Low-frequency neural oscillations contribute to the parsing of continuous speech into linguistic units. Little is known however on the coupling of brain rhythms to higher-frequencies in speech such as fundamental frequency (F0) or pitch. Using magnetoencephalography, we investigated whole-brain cortical tracking of F0 while participants listened to sentences produced at normal rate or fast rate, where pitch naturally increases, and to artificially accelerated sentences, where F0 remains unchanged. Our results revealed significant brain-to-F0 coupling across all speech rates not only in right auditory but also in right parietal, insular, and pre- and postcentral regions, likely including the ventral larynx area. Importantly, the cortico-acoustic coupling peak frequency was higher for natural fast speech to reflect the corresponding F0 increase compared to normal rate and time-compressed speech. These findings demonstrate the engagement of an auditory-somato-motor network in F0 tracking, supporting its role in facilitating phonemic processing during the perception of naturally-produced speech.

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bioRxiv2024

Caffeine induces age-dependent increases in brain complexity and criticality during sleep

Caffeine is the most widely consumed psychoactive stimulant worldwide. Yet important gaps persist in understanding its effects on the brain, especially during sleep. We analyzed sleep electroen-cephalography (EEG) in 40 subjects, contrasting 200mg of caffeine against a placebo condition, utilizing inferential statistics and machine learning. We found that caffeine ingestion led to an increase in brain complexity, a widespread flattening of the power spectrums 1/f-like slope, and a reduction in long-range temporal correlations. Being most prominent during non-rapid eye movement (NREM) sleep, these results suggest that caffeine shifts the brain towards a critical regime and more diverse neural dynamics. Interestingly, this was more pronounced in younger adults (20-27 years) compared to middle-aged participants (41-58 years) during rapid eye movement (REM) sleep, while no significant age effects were observed during NREM. Interpreting these data in the light of modeling and empirical work on EEG-derived measures of excitation-inhibition balance suggests that caffeine promotes a shift in brain dynamics towards increased neural excitation and closer proximity to a critical regime, particularly during NREM sleep.

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bioRxiv2024

Structure-function coupling and decoupling during movie-watching and resting-state: Novel insights bridging EEG and structural imaging

The intricate structural and functional architecture of the brain enables a wide range of cognitive processes ranging from perception and action to higher-order abstract thinking. Despite important progress, the relationship between the brains structural and functional properties is not yet fully established. In particular, the way the brains anatomy shapes its electrophysiological dynamics remains elusive. The electroencephalography (EEG) activity recorded during naturalistic tasks is thought to exhibit patterns of coupling with the underlying brain structure that vary as a function of behavior. Yet these patterns have not yet been sufficiently quantified. We address this gap by jointly examining individual Diffusion-Weighted Imaging (DWI) scans and continuous EEG recorded during video-watching and resting state, using a Graph Signal Processing (GSP) framework. By decomposing the structural graph into Eigenmodes and expressing the EEG activity as an extension of anatomy, GSP provides a way to quantify the structure-function coupling. We elucidate how the structure shapes function during naturalistic tasks such as movie-watching and how this association is modulated by tasks. We quantify the coupling relationship in a region-, time-, frequency-resolved manner. First of all, our findings indicate that the EEG activity in the sensorimotor cortex is strongly coupled with brain structure, while the activity in higher-order systems is less constrained by anatomy, i.e., shows more flexibility. In addition, we found that watching videos was associated with stronger structure-function coupling in the sensorimotor cortex, as compared to resting-state data. Second, time-resolved analysis revealed that the unimodal systems undergo minimal temporal fluctuation in structure-function association, and the transmodal system displays highest temporal fluctuations, with the exception of PCC seeing low fluctuations. Lastly, our frequency-resolved analysis revealed a consistent topography across different EEG rhythms, suggesting a similar relationship with the anatomical structure across frequency bands. Together, this unprecedented characterization of the link between structure and function using continuous EEG during naturalistic behavior underscores the role of anatomy in shaping ongoing cognitive processes. Taken together, by combining the temporal and spectral resolution of EEG and the methodological advantages of GSP, our work sheds new light onto the anatomo-functional organization of the brain.

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bioRxiv2023

Unravelling the neural dynamics of hypnotic susceptibility: Aperiodic neural activity as a central feature of hypnosis

How well a person responds to hypnosis is a stable trait, which exhibits considerable inter-individual diversity across the general population. Yet, its neural underpinning remains elusive. Here, we address this gap by combining EEG data, multivariate statistics, and machine learning in order to identify brain patterns that differentiate between individuals high and low in susceptibility to hypnosis. In particular, we computed the periodic and aperiodic components of the EEG power spectrum, as well as graph theoretical measures derived from functional connectivity, from data acquired at rest (pre-induction) and under hypnosis (post-induction). We found that the 1/f slope of the EEG spectrum at rest was the best predictor of hypnotic susceptibility. Our findings support the idea that hypnotic susceptibility is a trait linked to the balance of cortical excitation and inhibition at baseline and offers novel perspectives on the neural foundations of hypnotic susceptibility. Future work can explore the contribution of background 1/f activity as a novel target to distinguish the responsiveness of individuals to hypnosis at baseline in the clinic. Significance StatementHypnotic phenomena reflect the ability to alter ones subjective experiences based on targeted verbal suggestions. This ability varies greatly in the population. The brain correlates to explain this variability remain elusive. Addressing this gap, our study employs machine learning to predict hypnotic susceptibility. By recording electroencephalography (EEG) before and after a hypnotic induction and analyzing diverse neurophysiological features, we were able to determine that several features differentiate between high and low hypnotic susceptible individuals both at baseline and during hypnosis. Our analysis revealed that the paramount discriminative feature is non-oscillatory EEG activity before the induction--a new finding in the field. This outcome aligns with the idea that hypnotic susceptibility represents a latent trait observable through a plain five-minutes resting-state EEG.

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bioRxiv2023

Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia

1Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigated dynamical properties of the resting-state electroencephalogram of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. We then studied the relation of these dynamic properties with the perturbational complexity index (PCI), which has shown remarkably high sensitivity in detecting consciousness independent of behavior. All participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams)., enabling an experimental dissociation between unresponsiveness and unconsciousness. We estimated (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related measures, and found that states of unconsciousness were characterized by a distancing from both the edge of activity propagation and the edge of chaos. We were then able to predict individual subjects PCI (i.e., PCImax) with a mean absolute error below 7%. Our results establish a firm link between the PCI and criticality and provide further evidence for the role of criticality in the emergence of consciousness. 2 Significance StatementComplexity has long been of interest in consciousness science and had a fundamental impact on many of todays theories of consciousness. The perturbational complexity index (PCI) uses the complexity of the brains response to cortical perturbations to quantify the presence of consciousness. We propose criticality as a unifying framework underlying maximal complexity and sensitivity to perturbation in the conscious brain. We demonstrate that criticality measures derived from resting-state electroencephalography can distinguish conscious from unconscious states, using propofol, xenon and ketamine anesthesia, and from these measures we were able to predict the PCI with a mean error below 7%. Our results support the hypothesis that critical brain dynamics are implicated in the emergence of consciousness and may provide new directions for the assessment of consciousness.

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bioRxiv2023

Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study

Limb movement direction can be inferred from motor cortex activity. In humans, such decoding has been predominantly demonstrated using the spectral power of electrophysiological signals recorded in sensorimotor areas during movement execution. Yet, it remains unclear to what extent intended hand movement direction can be predicted from brain signals recorded during movement planning. Furthermore, whether other oscillatory features beyond power are also involved in direction encoding is not fully understood. Here, we set out to probe the directional-tuning of oscillatory phase, amplitude and Phase-Amplitude Coupling (PAC) during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. To this end, we recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. We found that LFP power significantly predicted hand-movement direction at execution but also during planning. While successful classification during planning primarily involved low-frequency power in a fronto-parietal circuit, decoding during execution was largely mediated by higher frequency activity in motor and premotor areas. Interestingly, LFP phase at very low frequencies (<1.5 Hz) led to significant decoding in premotor brain regions during execution. The machine learning framework also showed PAC to be uniformly modulated across directions through the task. Cross-temporal generalization analyses revealed that several stable brain patterns in prefrontal and premotor brain regions encode directions across both planning and execution. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%) during both planning and execution. The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans.

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bioRxiv2023

Tuning Minimum-Norm regularization parameters for optimal MEG connectivity estimation

The accurate characterization of cortical functional connectivity from Magnetoencephalography (MEG) data remains a challenging problem due to the subjective nature of the analysis, which requires several decisions at each step of the analysis pipeline, such as the choice of a source estimation algorithm, a connectivity metric and a cortical parcellation, to name but a few. Recent studies have emphasized the importance of selecting the regularization parameter in minimum norm estimates with caution, as variations in its value can result in significant differences in connectivity estimates. In particular, the amount of regularization that is optimal for MEG source estimation can actually be suboptimal for coherence-based MEG connectivity analysis. In this study, we expand upon previous work by examining a broader range of commonly used connectivity metrics, including the imaginary part of coherence, corrected imaginary part of Phase Locking Value, and weighted Phase Lag Index, within a larger and more realistic simulation scenario. Our results show that the best estimate of connectivity is achieved using a regularization parameter that is 1 or 2 orders of magnitude smaller than the one that yields the best source estimation. This remarkable difference may imply that previous work assessing source-space connectivity using minimum-norm may have benefited from using less regularization, as this may have helped reduce false positives. Importantly, we provide the code for MEG data simulation and analysis, offering the research community a valuable open source tool for informed selections of the regularization parameter when using minimum-norm for source space connectivity analyses. HighlightsO_LIThe regularization parameter of the Minimum Norm Estimate of neural activity impacts connectivity estimation C_LIO_LIWe study empirically the optimal parameter for connectivity estimation using realistic synthetic datasets C_LIO_LIWe find the optimal parameter for connectivity estimation is systematically smaller than the optimal parameter for source imaging; different connectivity metrics yield the same result C_LIO_LICode and data are available open source. C_LI

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bioRxiv2022

Differential and overlapping effects between exogenous and endogenous attention shape perceptual facilitation during visual processing

Visuospatial attention is not a monolithic process and can be divided into different functional systems. In this framework, exogenous attention reflects the involuntary orienting of attention resources following a salient event, whereas endogenous attention corresponds to voluntary orienting based on the goals and intentions of individuals. Previous work shows that these attention processes map onto distinct functional systems, yet evidence suggests that they are not fully independent. In the current work, we investigated the differential and overlapping effects of exogenous and endogenous attention on visual processing. We combined spatial cueing of visuospatial attention, electroencephalography (EEG), and multivariate pattern analysis (MVPA) to examine where and when the effects of exogenous and endogenous attention were maximally different and maximally similar. Critically, MVPA provided new insights by examining whether classifiers trained to decode the cueing effect for one attention process (e.g., exogenous attention) can successfully decode the cueing effect for the other attention process (e.g., endogenous attention). These analyses uncovered differential and overlapping effects between exogenous and endogenous attention. Next, we combined principal component analyses, single-trial event-related potentials, and mediation analysis to determine whether these effects facilitate perception, as indexed by the behavioral spatial cueing effects of exogenous and endogenous attention. This approach revealed that three EEG components shape the cueing effects of exogenous and endogenous attention at various times after target onset. Altogether, our study provides a comprehensive account about how overlapping and differential processes of endogenous and exogenous relate to perceptual facilitation in the context of visuospatial attention. Significance StatementTop-down and bottom-up attention represent separate functional systems in the brain. Previous research suggests, however, that they are not fully independent, and can interfere with each other. In the present study, the authors use machine learning techniques and recordings of brain activity to investigate differences and similarities between top-down and bottom-up attention during the visual processing of stimuli. This approach allowed them to explore how top-down and bottom-up attention processes facilitate perception. Their results show that top-down and bottom-up attention operate differently as early as 100ms after the onset of a target. In contrast, they operate similarly 200ms after the target onset. Most importantly, these effects are directly related to the participants perceptual behavior. In sum, our study shows that top-down and bottom-up attention support the perception of stimuli through overlapping and distinct spatio-temporal brain patterns.

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