Biology subjects

Varlet, M.

Publications and source records attributed to Varlet, M..

16 recordsLinked to original sources

bioRxiv2026

Neural categorisation of rhythm in human newborns

Music is ubiquitous across cultures, and humans engage in social interactions through music from early in life. A cornerstone of these interactions is rhythm categorisation, allowing the sheer diversity of rhythmic inputs to be mapped onto internal rhythm categories. The pervasiveness of certain rhythm categories across cultures suggests that rhythm categorisation may be partially rooted in neurobiological predispositions constraining the internal representations of rhythm. However, this view appears to conflict with the diversity of music practices across cultures, which points to a role of experience in rhythm categorisation. To assess the relative contributions of neurobiological predispositions and experience, we tested 0- to 3-day-old newborns, capitalising on their minimal postnatal experience with music. Using electroencephalography (EEG), we recorded, in newborns and adults, brain responses to drum sounds whose pattern of relative interval duration ranged from isochronous to long-short rhythms (1:1 - 2:1 interval ratio). Results indicate significant categorisation in newborns's neural responses, with a representational structure that correlate with those of adults, yet with a greater degree of differentiation across rhythms. These findings reveal neurobiological predispositions enabling rhythm categorisation to be present at birth and further shaped across the lifespan, highlighting the evolutionary roots and plasticity supporting this human cultural practice.

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bioRxiv2026

Enhanced neural encoding of individual exemplars in the Super-Recogniser brain is specific to faces

Super-Recognisers are known for their exceptional ability to encode and remember faces. Recent work has found robust neural codes for face identity in brain activity. Here we test whether the enhanced individuation of face identity in super-recogniser neural codes extends to exemplars from non-face categories. We recorded 64-channel electroencephalography (EEG) as 22 Super-Recognisers and 20 typical recognisers viewed rapid, randomised sequences of faces, dogs, houses, and cars (10 different images of 10 different exemplars per category). Using a neural decoding approach, we found that Super-Recognisers neural responses contained stronger and more temporally sustained information discriminating individual face identities. Moreover, Super-Recognisers displayed increased inter-individual consistency in the organisation of their face-identity representations, suggesting a more stable and/or canonical representational structure in individuals with superior face recognition abilities. Crucially, there was no evidence to suggest that these encoding advantages generalised to non-face categories: neural discrimination of individual dogs, cars, and houses was comparable across groups, and group-differences in inter-subject consistency were evident only for faces. Together, these findings suggest that enhanced perceptual encoding underlying exceptional face recognition is highly category-specific, supporting the view that expertise in this group reflects specialised mechanisms for processing facial identity rather than broadly enhanced visual discrimination capacities.

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bioRxiv2026

Robust neural face identity codes in the Super-Recogniser brain

Super-Recognisers show exceptional ability in face recognition, providing a natural model of how perceptual systems optimise for individuating visually similar stimuli in variable viewing conditions. However, the neural representations supporting this extreme perceptual expertise are unknown. Here, we tested whether Super-Recognisers (n = 23) differed from typical recognisers (n = 21) in the dimensional organisation of neural face identity coding. We recorded 64-channel electroencephalography while participants viewed random and rapidly-presented sequences containing 10 naturally varying images of 40 unfamiliar identities. Using time-resolved representational similarity analysis we measured the geometry of identity representations, their consistency across observers, and how clearly they specified face identity. Although neural expression of identity information was robust in both groups, we found three key differences between Super-Recognisers and typical recognisers. First, the geometry of face identity representations differed between groups. Second, Super-Recognisers showed greater inter-individual consistency in representational geometry. Third, Super-Recognisers neural signals discriminated between face identities more strongly than those of typical recognisers. Differences in the coding of broader face categories (sex, age, ethnicity) were notably weaker, suggesting that the observed group differences reflected fine-scale differences in identity coding rather than global reshaping of representational geometry. Strikingly, all three differences emerged within a common mid-latency interval ([~]300-500ms), implicating higher-stages of face processing associated with representations that are sensitive to face familiarity and link between perceptual and semantic domains. Together, these findings indicate that individual differences in face recognition ability reflect higher-level differences in neural identity coding, rather than enhanced early sensory processing. Significance statementSuper-Recognisers have an exceptional ability to recognise unfamiliar faces, yet the neural basis of this advantage has remained unclear. We find brain activity expresses the identity of unfamiliar faces more strongly and consistently in Super-Recognisers than typical recognisers. These advantages were specific to individual identity and could not be explained by enhanced encoding of broader facial categories such as sex, age, or ethnicity. Critically, group differences emerged during a mid-latency stage of processing ([~]300-500 ms) associated with higher-level representations that support stable identity coding across variable images. The findings suggest that exceptional face recognition arises from more robust neural representations of individual identity rather than enhanced early visual processing, providing new insight into the neural basis of perceptual expertise.

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bioRxiv2026

A multibrain advantage for cooperative human behaviour

Collaboration is a cornerstone of human behaviour, enabling individuals to achieve complex goals that transcend individual capabilities. While previous research has largely focused on the alignment of neural representations across brains during social interactions, real-world cooperation frequently involves complementary roles, where success depends on integrating distinct yet interdependent representations. Here, we combined electroencephalography (EEG) hyperscanning and multivariate pattern analysis (MVPA) methods to investigate neural representations in pairs of participants as they jointly performed a speeded two-dimensional visual matching task with asymmetric controls to encourage task division (i.e., attending to different stimulus features). Our results provide direct neural evidence of the emergence of selective information encoding within individual brains and complementary information encoding across brains during successful cooperation. Specifically, optimally divided task dimensions give rise to a late (> 485 ms) multibrain advantage, wherein the combined neural signals of the two participants carry more target-related information than that of either brain individually. Notably, the magnitude of these information-processing modulations within and across brains robustly predicted pairs collective task performance. These findings have broader implications for future research on collaboration, highlighting the need to move beyond representational alignment to harness diverse human as well as machine capabilities across our societies to unlock collective potential.

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bioRxiv2025

A comprehensive EEG dataset for investigating visual touch perception

Visual touch perception is fundamental to body awareness and social bonding, helping us perceive ourselves and connect with others. However, understanding its neural basis has been limited by a lack of comprehensive public datasets. To address this, we present an electroencephalography (EEG) and self-report dataset from 80 participants. It includes neural responses from 2,880 trials per participant, during which participants viewed touch interactions between two hands involving either direct skin contact or object-mediated touch. Complementing the EEG data are self-report measures on empathy, perspective-taking, vicarious touch, and mirror-touch synaesthesia, providing a robust resource for exploring individual differences in visual touch perception. Ninety unique videos were presented in four orientations, with trials evenly split between self- and other-perspectives, and showing touch to either the left or right hand. This design supports investigation into how viewing perspective and hand laterality influence touch processing. Technical validation reveals distinct neural patterns associated with visual, sensory, and emotional touch dimensions, highlighting the datasets potential to advance research into diverse aspects of visual touch perception.

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bioRxiv2025

Robust Neural Decoding with low density EEG

High-density Electroencephalography (EEG) recording enhances spatial resolution for neural signal decoding, yet the relationship between electrode density and decoding performance remains unclear. To address this, we systematically investigated decoding accuracy across electrode configurations of varying densities (16, 32, 64, 96, and 128 electrodes) using visual grating stimuli characterized by orientation, contrast, spatial frequency, and color. As expected, decoding accuracy increased with electrode density. Remarkably, however, reliable above-chance decoding was still achieved with as few as 16 electrodes, highlighting the robustness of decodable neural signals. To test the generalization of these results to more complex natural stimuli, we conducted a similar analysis with a diverse set of naturalistic images categorizable into living/non-living and moving/non-moving. The results consistently showed that effective decoding persists even with a 16-electrode configuration, showing robust decoding efficacy even for complex naturalistic stimuli. These findings demonstrate both the benefits of higher-density EEG and the robustness of neural decoding under sparse spatial sampling, providing new insights into how efficiently and broadly neural signals can be decoded.

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bioRxiv2025

The neural processes of illusory occlusion in object recognition

Fast and accurate object recognition is crucial for effective behaviour in dynamic visual environments. In some cases, the visual system must overcome ambiguity in visual input during object recognition, such as when an object is partially hidden behind another. Recurrent processing between higher- and lower-order areas is thought to play a role in resolving such ambiguity, enabling the filling in of missing visual information. Here we examined this claim using a novel paradigm in which partial object images appear occluded by an illusory Kanizsa figure, perception of which also depends on recurrent processing by the grouping of Pacmen inducers. If both recognising the partial object and perceiving the illusory shape depend on recurrent processing, object recognition should vary as a function of the presence of illusory shape. Across two behavioural experiments and a separate EEG decoding study, we found no evidence for an interaction between illusion perception and object recognition, and different neural time courses for the illusory figures alone compared to those of the partial objects, which were decoded earlier. Ultimately, our study highlights the robustness of the visual system to solve the identity of the ambiguous object, independently from the processing of different ambiguities occurring at the same time, providing new insights into the mechanisms of recurrence in the early and late stages of information processing and its application to ambiguous object recognition.

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bioRxiv2025

Multimodal Human Perception of Object Dimensions: Evidence from Deep Neural Networks And Large Language Models

Object recognition in the human visual system is implemented within a hierarchy characterised by increasing feature complexity. Here, we investigated whether human-derived dimensions of object knowledge show a similar progressive emergence across layers in deep neural networks (DNNs), and how this emergence is shaped by architecture, learning objective, and stimulus statistics. To test this, we predicted human-derived dimensions from layer-wise activations of multiple DNNs and transformer models trained on large-scale datasets. Results showed that trained DNNs exhibit emergence profiles resembling theoretical expectations from human vision, with behaviourally relevant object dimensions largely absent in early layers, strengthening across layers, and peaking in later layers. Architectural mechanisms such as recurrence and skip connections amplified this encoding, learning objectives redistributed information across layers, and changes in stimulus statistics confirm that hierarchical emergence is a general principle extending to material perception. These findings demonstrate that the hierarchical emergence of human-derived dimensions is a fundamental property of trained networks and highlight design and input factors that shape layer-wise representational organisation, providing hypotheses for the structure of visual representations in the brain.

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bioRxiv2025

Multiple Neural Modules Orchestrate Conflict Processing

Cognitive conflict is a ubiquitous aspect of our daily life, yet its underlying neural mechanisms remain debated. Competing theories propose that conflict processing is governed by either a domain-general system, multiple conflict-specific modules, or an architecture involving partially overlapping mechanisms. The aim of the current study was to clarify how these mechanisms operate at the neural level using a data-driven decoding approach. We analyzed electroencephalogram (EEG) data from 507 healthy participants (ages 20-70) from the Dortmund Vital Study (ClinicalTrials.gov NCT05155397) who completed three conflict tasks: a Change Detection task, a Simon task, and a Stroop task. Using multivariate decoding to distinguish conflict from non-conflict trials, we observed robust within-task decoding across all tasks. However, cross-task decoding revealed shared conflict representations only in specific task pairs. These findings support an account, in which conflict processing relies on partially overlapping neural mechanisms rather than being fully domain-general or entirely task specific. We argue that conflict-related neural processes might be best conceptualized as a continuum of overlap and differentiation, with domain-generality and domain-specificity representing the endpoints of this spectrum. Research Transparency StatementThe authors declare no conflicts of interest. Funding: This work was supported by the International Visiting Scholar Program at The MARCS Institute for Brain, Behaviour, and Development of Western Sydney University (MS), and the Australian Research Council grants DE230100380 (TG) and DP220103047 (MV). Artificial intelligence: Artificial intelligence assisted technologies were utilized solely for language refinement and grammar corrections, ensuring clarity and readability without altering the original content or ideas. Ethics: The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Leibniz Research Centre for Working Environment and Human Factors, Ardeystra{beta}e 67 44139 Dortmund, Germany (IfADo; code A93-3) as detailed in the published study protocol (Gajewski et al., 2022). Preregistration: The current study was not preregistered. However, the dataset used in this manuscript originates from the Dortmund Vital Study, which is registered on ClinicalTrials.gov (NCT05155397). Data: A sample dataset is publicly available at https://doi.org/10.17605/OSF.IO/EDTNM Analysis Scripts: All analysis scripts are publicly available at https://doi.org/10.17605/OSF.IO/EDTNM.

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bioRxiv2025

Neural decoding of competitive decision-making in Rock-Paper-Scissors using EEG hyperscanning

Social interactions are fundamental to daily life, yet social neuroscience research has often studied individuals brains in isolation. Hyperscanning, the simultaneous recording of neural data from multiple participants, enables real-time investigation of social processes by examining multiple brains while they interact. Previous hyperscanning research has largely focused on cooperative tasks, with fewer studies examining competitive contexts. Here, we obtained electroencephalography (EEG) hyperscanning data from 62 participants (31 pairs) who played a computerised version of the Rock-Paper-Scissors game, a classic paradigm for studying competitive decision-making. Although the optimal strategy is to be unpredictable and thus act randomly, participants exhibited behavioural biases, deviating from this ideal. Using multivariate decoding methods to measure neural representations within the two players brains in interaction, we found information about decisions made by participants during gameplay, revealing certain strategies. Notably, losers uniquely represented information about prior trials, suggesting this may impair optimal performance. These results reveal how competitive decision-making is shaped by cognitive biases and previous outcomes, highlighting the difficulty of achieving randomness in strategic contexts. This work advances our understanding of decision-making and cognitive dynamics in competitive interactions.

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bioRxiv2025

Unlocking information alignment between interacting brains with EEG hyperscanning

Social interactions shape our perception of the world, influencing how we interpret incoming information. Alignment between interacting individuals sensory and cognitive processes is key to successful cooperation and communication, but the neural processes underlying this alignment remain unknown. Here, we leveraged Representational Similarity Analysis (RSA) on electroencephalography (EEG) hyperscanning techniques to investigate information alignment in 24 pairs of participants who performed a categorisation task together based on agreed upon rules. Significant interbrain information alignment emerged within 45 ms of stimulus presentation and persisted for hundreds of milliseconds. Early alignment (45-180 ms) occurred in both real and randomly matched pseudo-pairs, reflecting shared sensory responses. Importantly, alignment after 200 ms strengthened with practice and was unique to real pairs, driven by shared representations associated with, and extending beyond, the categorisation rules they formed. Together, these findings highlight distinct processes underpinning interbrain information alignment during social interactions, that can be effectively captured and disentangled with Interbrain RSA.

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bioRxiv2024

Revealing rhythm categorization in human brain activity

Humans across cultures show an outstanding capacity to perceive, learn, and produce musical rhythms. These skills rely on mapping the infinite space of possible rhythmic sensory inputs onto a finite set of internal rhythm categories. What are the brain processes underlying rhythm categorization? We used electroencephalography (EEG) to measure brain activity as human participants listened to a continuum of rhythmic sequences characterized by repeating patterns of two inter-onset intervals. Using frequency and representational similarity analyses, we show that brain activity does not merely track the temporal structure of rhythmic inputs, but, instead, automatically produces categorical representation of rhythms. Surprisingly, despite this automaticity, these rhythm categories do not arise in the earliest stages of the ascending auditory pathway, but show strong similarity between implicit neural and overt behavioral responses. Together, these results and methodological advances constitute a critical step towards understanding the biological roots and diversity of musical behaviors across cultures.

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bioRxiv2024

Rapid visual engagement in neural processing of detailed touch interactions

Touch perception is an inherently multisensory process in which vision plays an essential role. However, our understanding of how vision encodes sensory and emotional-affective aspects of observed touch, and the timing of these processes, is still limited. Here we address this gap by investigating the neural dynamics of visual touch observation by analysing electroencephalographic (EEG) data from participants viewing detailed hand interactions from the Validated Touch-Video Database. We examined how the brain encodes basic body cues such as hand orientation and viewing perspective, in addition to sensory aspects, including the type of touch (e.g., stroking vs. pressing; hand vs. object touch) and the object involved (e.g., knife, brush), as well as emotional-affective dimensions. Using multivariate decoding, information about body cues was present within 60 ms, with sensory and emotional details, including valence, arousal, and pain present around 130 ms, demonstrating efficient early visual processing. Threat was most clearly identified by approximately 265 ms, similarly involving visual regions, suggesting that such evaluations require slightly extended neural engagement. Our findings reveal that bottom-up, automatic visual processing is integral to complex tactile assessments, important for rapidly extracting both the personal relevance and the sensory and emotional dimensions of touch.

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bioRxiv2023

Movement trajectories as a window into the dynamics of emerging neural representations.

Transforming sensory inputs into meaningful neural representations is critical to adaptive behaviour in everyday environments. While non-invasive neuroimaging methods are the de-facto method for investigating neural representations, they remain expensive, not widely available, time-consuming, and restrictive in terms of the experimental conditions and participant populations they can be used with. Here we show that movement trajectories collected in online behavioural experiments can be used to measure the emergence and dynamics of neural representations with fine temporal resolution. By combining online computer mouse-tracking and publicly available neuroimaging (MEG and fMRI) data via Representational Similarity Analysis (RSA), we show that movement trajectories track the evolution of visual representations over time. We used a time constrained face/object categorization task on a previously published set of images containing human faces, illusory faces and objects to demonstrate that time-resolved representational structures derived from movement trajectories correlate with those derived from MEG, revealing the unfolding of category representations in comparable temporal detail (albeit delayed) to MEG. Furthermore, we show that movement-derived representational structures correlate with those derived from fMRI in most task-relevant brain areas, faces and objects selective areas in this proof of concept. Our results highlight the richness of movement trajectories and the power of the RSA framework to reveal and compare their information content, opening new avenues to better understand human perception.

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bioRxiv2020

Attention affects overall gain but not selective contrast at meter frequencies in the neural processing of rhythm

When listening to music, humans spontaneously perceive and synchronize movement to periodic pulses of meter. A growing body of evidence suggests that this widespread ability is related to neural processes that selectively enhance meter periodicities. However, to what extent these neural processes are affected by the attentional state of the listener remains largely unknown. Here, we recorded EEG while participants listened to auditory rhythms and detected small changes in tempo or pitch of the stimulus, or performed a visual task. The overall neural response to the auditory input decreased when participants attended the visual modality, indicating generally lower sensitivity to acoustic information. However, the selective contrast at meter periodicities did not differ across the three tasks. Moreover, this selective contrast could be trivially accounted for by biologically-plausible models of subcortical auditory processing, but only when meter periodicities were already prominent in the acoustic input. However, when meter periodicities were not prominent in the auditory input, the EEG responses could not be explained by low-level processing. This was also confirmed by early auditory responses that originate predominantly in early auditory areas and were recorded in the same EEG. The contrast at meter periodicities in these early responses was consistently smaller than in the EEG responses originating mainly from higher-level processing stages. Together, these results demonstrate that selective contrast at meter periodicities involves higher-level neural processes that may be engaged automatically, irrespective of behavioral context. This robust shaping of the neural representation of rhythm might thus contribute to spontaneous and effortless synchronization to musical meter in humans across cultures.

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bioRxiv2019

Hysteresis in the selective synchronization of brain activity to musical rhythm

When listening to musical rhythm, people tend to spontaneously perceive and move along with a periodic pulse-like meter. Moreover, perception and entrainment to the meter show remarkable stability in the face of dynamically changing rhythmic structure of music, even when acoustic cues to meter frequencies are degraded in the rhythmic input. Here we show that this perceptual phenomenon is supported by a selective synchronization of endogenous brain activity to the perceived meter, and that this neural synchronization is significantly shaped by recent context, especially when the incoming input becomes increasingly ambiguous. We recorded the EEG while non-musician and musician participants listened to nonrepeating rhythmic sequences where acoustic cues to meter frequencies either gradually decreased (from regular to ambiguous) or increased (from ambiguous to regular). We observed that neural activity selectively synchronized to the perceived meter persisted longer when the sequence gradually changed from regular to ambiguous compared to the opposite, thus demonstrating hysteresis in the neural processing of a dynamically changing rhythmic stimulus. This dependence on recent context was weaker in the neural responses of musicians, who also showed greater ability to tap along with a regular meter irrespective of stimulus ambiguity, thus reflecting greater stability relative to current and recent stimulus in musicians. Together, these asymmetric context effects demonstrate how the relative contribution of incoming and prior signals is continuously weighted to shape neural selection of functionally-relevant features and guide perceptual organization of dynamic input.\n\nSignificance statementWhen listening to musical rhythm, people tend to spontaneously perceive and move along with a periodic pulse-like meter. Moreover, perception and entrainment to the meter seem to show remarkable stability in the face of dynamically changing rhythmic structure of music. Here we show that this is supported by a selective synchronization of brain activity at meter frequencies. This selective neural synchronization persists longer when a nonrepeating sequence gradually transforms from a regular to an ambiguous rhythm compared to the opposite. This asymmetric context effect suggests that the brain processes rhythm based on a flexible combination of sensory and endogenous information. Such continuously updated neural emphasis on meter periodicities might therefore guide robust perceptual organization of a dynamic rhythmic input.

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