Biology subjects

Chambon, V.

Publications and source records attributed to Chambon, V..

12 recordsLinked to original sources

bioRxiv2026

Automation Disrupts, Explanations Restore: The Neural Signatures of Agency Loss and Recovery in Human-AI Interaction

Automation has been shown to weaken the sense of agency (SoA), the experience of controlling ones actions and their outcomes, by disrupting the predictive link between intention and effect. Explainable AI (XAI) has been proposed as a solution, yet the neurocognitive mechanisms through which explanations restore agency remain unclear. Across three EEG experiments using an autonomous-driving paradigm, we examined how automation and different forms of AI explanations modulate explicit agency judgments and early neural markers of agency-related predictive processing. In Experiment 1, automation reduced explicit feelings of control and was associated with reduced sensory attenuation, as reflected by increased P1-N1 amplitudes, decreased N1-P2 amplitudes, and delayed N1 latencies. In Experiment 2, distal (goal-level) explanations partially restored agency and selectively modulated early auditory responses, decreasing P1-N1 and increasing N1-P2 amplitudes. In Experiment 3, combining distal and proximal (trajectory-level) explanations produced the strongest behavioural and neural restoration of agency, yielding a graded attenuation of P1-N1 and enhanced N1-P2 responses along with accelerated N1 latencies. Across all experiments, mismatch negativity (MMN) remained unaffected, indicating that pre-attentive deviance detection is preserved regardless of agency or explainability. Together, these results identify component-specific EEG markers that track fluctuations in the sense of agency and demonstrate that multi-level intention sharing by AI systems enhances both predictive engagement and explicit control experience. This work provides a neurocognitive foundation for designing explainable autonomous systems capable of maintaining user agency.

neuroscience↗

bioRxiv2026

Decoding Agency-Related Neural States During Human-AI Interaction in Autonomous Driving Using EEG and Deep Learning

The sense of agency, the experience of controlling ones actions and their consequences, is a fundamental component of human interaction with autonomous systems. As artificial intelligence increasingly mediates decision-making in domains such as autonomous driving, understanding and monitoring agency-related processes becomes critical for maintaining user engagement, trust, and appropriate levels of control. However, existing approaches to measuring agency rely primarily on subjective reports or event-based neural markers, which are poorly suited for naturalistic and continuous human-AI interaction. In this study, we investigate whether agency-related neural states can be decoded from ongoing electroencephalographic (EEG) activity using deep learning. Across two experiments, we manipulated agency through (i) decision authority (human vs AI control) and (ii) system explainability (AI with vs without intention-based explanations). Behavioral results confirmed that both manipulations significantly modulated participants perceived control. At the neural level, we trained EEGNet-based models to decode agency-modulating experimental conditions from pre-feedback EEG activity. Decoding performance was robust at the intra-subject level and remained significantly above chance across participants using a leave-one-subject-out framework, demonstrating partial cross-subject generalization of agency-related neural representations. Spectral ablation analyses revealed that low-frequency activity, particularly in the delta and theta bands, made the dominant contribution to decoding performance. Complementary time-frequency analyses showed that these bands exhibited increased power under reduced-agency conditions, specifically during the post-keypress, pre-feedback interval. Together, these findings indicate that agency-related information is embedded in continuous, low-frequency neural dynamics associated with predictive monitoring processes. By demonstrating that such information can be decoded from single-trial EEG in an offline setting, this work provides a foundation for future real-time, non-intrusive monitoring of user states in human-AI interaction. These results open new avenues for the development of neuroadaptive systems capable of dynamically regulating automation and explainability to preserve human agency.

bioengineering↗

bioRxiv2023

Spatial N-glycan rearrangement on α5β1 integrin nucleates galectin-3 oligomers to determine endocytic fate α

Membrane glycoproteins frequently adopt different conformations when altering between active and inactive states. Here, we discover a molecular switch that exploits dynamic spatial rearrangements of N-glycans during such conformational transitions to control protein function. For the conformationally switchable cell adhesion glycoprotein 5{beta}1 integrin, we find that only the bent-closed state arranges N-glycans to nucleate the formation of up to tetrameric oligomers of the glycan-binding protein galectin-3. We propose a structural model of how these galectin-3 oligomers are assembled and how they clamp the bent-closed state to prime it for endocytic uptake and subsequent retrograde trafficking to the Golgi for polarized distribution in cells. Our findings highlight an unexpectedly dynamic regulation of the glycan landscape at the cell surface to achieve oligomerization of galectin-3. Galectin-3 oligomers are thereby identified as decoders of defined spatial patterns of N-glycans and as functional extracellular interactors of specifically the bent- closed conformational state of 5{beta}1 integrin and possibly other family members.

cell biology↗

bioRxiv2023

Growth factor-induced desialylation for the fast control of endocytosis

It is commonly assumed that the glycan makeup of glycoproteins that reach the cell surface is final and static. Here, we challenge this notion by the discovery of a molecular switch that induces acute and reversible changes of glycans on the plasma membrane. We demonstrate that within minutes, the epidermal growth factor triggers the galectin-driven endocytosis of cell surface glycoproteins, such as integrins, that are key regulators of cell adhesion and migration. The onset of this process, mediated by the Na+/H+ antiporter NHE-1 and the neuraminidases Neu1/3, requires the pH-triggered enzymatic removal of sialic acids whose presence otherwise prevents galectin binding. Desialylated glycoproteins are then retrogradely transported to the Golgi apparatus where their glycan makeup is reset, and their function is repurposed to regulate EGF-dependent invasive cell migration. Glycosylation at the cell surface thereby emerges as a dynamic and reversible regulatory post-translational modification that controls a highly adaptable trafficking pathway.

cell biology↗

bioRxiv2022

Choice seeking is motivated by the intrinsic need for personal control

When deciding between options that do or do not lead to future choices, humans often choose to choose. We studied choice seeking by asking subjects to decide between a choice opportunity or performing a computer-selected action. Subjects preferred choice when these options were equally rewarded, even deterministically, and were willing to trade extrinsic rewards for the opportunity to choose. We explained individual variability in choice seeking using reinforcement learning models incorporating risk sensitivity and overvaluation of rewards obtained through choice. Degrading perceived controllability diminished choice preference, although willingness to repeat selection of choice opportunities remained unchanged. In choices following these repeats, subjects were sensitive to rewards following freely chosen actions, but ignored environmental information in a manner consistent with a desire to maintain personal control. Choice seeking appears to reflect the intrinsic need for personal control, which competes with extrinsic reward properties and external information to motivate behavior. Author summaryHuman decisions can often be explained by the balancing of potential rewards and punishments. However, some research suggests that humans also prefer opportunities to choose, even when these have no impact on future rewards or punishments. Thus, opportunities to choose may be intrinsically motivating, although this has never been experimentally tested against alternative explanations such as cognitive dissonance or exploration. We conducted behavioral experiments and used computational modelling to provide compelling evidence that choice opportunities are indeed intrinsically rewarding. Moreover, we found that human choice preference varied according to individual risk attitudes, and expressed a need for personal control that competes with maximizing reward intake.

neuroscience↗

bioRxiv2021

Specific cognitive signatures of information seeking in controllable environments

In uncertain environments, seeking information about alternative choice options is essential for adaptive learning and decision-making. However, information seeking is usually confounded with changes-of-mind about the reliability of the preferred option. Here, we exploited the fact that information seeking requires control over which option to sample to isolate its behavioral and neurophysiological signatures. We found that changes-of-mind occurring with control require more evidence against the current option, are associated with reduced confidence, but are nevertheless more likely to be confirmed on the next decision. Multimodal neurophysiological recordings showed that these changes-of-mind are preceded by stronger activation of the dorsal attention network in magnetoencephalography (MEG), and followed by increased pupil-linked arousal during the presentation of decision outcomes. Together, these findings indicate that information seeking increases the saliency of evidence perceived as the direct consequence of ones own actions. HighlightsO_LIExploration-exploitation dilemmas confound information seeking with changes-of-mind. C_LIO_LIUnlike changes-of-mind, information seeking requires control over information sampling. C_LIO_LIInformation seeking is associated with reduced confidence and active hypothesis testing. C_LIO_LIStronger neurophysiological correlates of attention and arousal during information seeking. C_LI

neuroscience↗

bioRxiv2019

Interacting with volatile environments stabilizes hidden-state inference and its brain signatures

Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalo-graphic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty.

neuroscience↗

bioRxiv2019

Choosing and learning: outcome valence differentially affects learning from free versus forced choices

Positivity bias refers to learning more from positive than negative events. This learning asymmetry could either reflect a preference for positive events in general, or be the upshot of a more general, and perhaps, ubiquitous, \"choice-confirmation\" bias, whereby agents preferentially integrate information that confirms their previous decision. We systematically compared these two theories with 3 experiments mixing free- and forced-choice conditions, featuring factual and counterfactual learning and varying action requirements across \"go\" and \"no-go\" trials. Computational analyses of learning rates showed clear and robust evidence in favour of the \"choice-confirmation\" theory: participants amplified positive prediction errors in free-choice conditions while being valence-neutral on forced-choice conditions. We suggest that a choice-confirmation bias is adaptive to the extent that it reinforces actions that are most likely to meet an individuals needs, i.e. freely chosen actions. In contrast, outcomes from unchosen actions are more likely to be treated impartially, i.e. to be assigned no special value in self-determined decisions.

neuroscience↗

bioRxiv2018

Believing in one's power: a counterfactual heuristic for goal-directed control

Most people envision themselves as operant agents endowed with the capacity to bring about changes in the outside world. This ability to monitor one's own causal power has long been suggested to rest upon a specific model of causal inference, i.e., a model of how our actions causally relate to their consequences. What this model is and how it may explain departures from optimal inference, e.g., illusory control and self-attribution biases, are still conjecture. To address this question, we designed a series of novel experiments requiring participants to continuously monitor their causal influence over the task environment by discriminating changes that were caused by their own actions from changes that were not. Comparing different models of choice, we found that participants' behaviour was best explained by a model deriving the consequences of the forgone action from the current action that was taken and assuming relative divergence between both. Importantly, this model agrees with the intuitive way of construing causal power as \"difference-making\" in which causally efficacious actions are actions that make a difference to the world. We suggest that our model outperformed all competitors because it closely mirrors people's belief in their causal power - a belief that is well-suited to learning action-outcome associations in controllable environments. We speculate that this belief may be part of the reason why reflecting upon one's own causal power fundamentally differs from reasoning about external causes.

neuroscience↗

bioRxiv2018

Trading off the cost of conflict against expected rewards

Value-based decision-making involves trading off the cost associated with an action against its expected reward. Research has shown that both physical and mental effort constitute such subjective costs, biasing choices away from effortful actions, and discounting the value of obtained rewards. Facing conflicts between competing action alternatives is considered aversive, as recruiting cognitive control to overcome conflict is effortful. Yet, it remains unclear whether conflict is also perceived as a cost in value-based decisions. The present study investigated this question by embedding irrelevant distractors (flanker arrows) within a reversal-learning task, with intermixed free and instructed trials. Results showed that participants learned to adapt their choices to maximize rewards, but were nevertheless biased to follow the suggestions of irrelevant distractors. Thus, the perceived cost of being in conflict with an external suggestion could sometimes trump internal value representations. By adapting computational models of reinforcement learning, we assessed the influence of conflict at both the decision and learning stages. Modelling the decision showed that conflict was avoided when evidence for either action alternative was weak, demonstrating that the cost of conflict was traded off against expected rewards. During the learning phase, we found that learning rates were reduced in instructed, relative to free, choices. Learning rates were further reduced by conflict between an instruction and subjective action values, whereas learning was not robustly influenced by conflict between ones actions and external distractors. Our results show that the subjective cost of conflict factors into value-based decision-making, and highlights that different types of conflict may have different effects on learning about action outcomes.

neuroscience↗

bioRxiv2018

Lack of functional caveolae in Cav3 mutated human dystrophic myotubes results in deficient mechanoprotection and IL6/STAT3 mechanosignaling.

Caveolin-3 is the major structural protein of caveolae in muscle cells. Mutations in the CAV3 gene cause different type of muscle disorders mostly characterized by defects in membrane integrity and repair, deregulation in the expression of various muscle proteins and deregulation of several muscle associated signaling pathways. We show here that myotubes derived from patients bearing the CAV3 P28L and R26Q mutations present a lack of functional caveolae at the plasma membrane which results in an abnormal mechanoresponse. Mutant myotubes can no longer buffer the increase of membrane tension induced by mechanical stress and present an hyperactivation of the IL6/STAT3 signaling pathway at rest and under mechanical stress. The impaired mechanical regulation of the IL6/STAT3 signaling pathway by caveolae leads to chronic activation and a higher expression of muscle specific genes. These defects could be reversed by reassembling a pool of functional caveolae through expression of wild type Cav3. Our findings bring more mechanistic insight into human Cav3 associated muscle disorders and show a general defect in the mechanoresponse of CAV3 P28L and R26Q myotubes.

cell biology↗

bioRxiv2017

Focal Versus Distributed Temporal Cortex Activity For Speech Sound Category Assignment

Percepts and words can be decoded from largely distributed neural activity measures. The existence of widespread representations might, however, conflict with the fundamental notions of hierarchical processing and efficient coding. Using fMRI and MEG during syllable identification, we first show that sensory and decisional activity co-localize to a restricted part of the posterior superior temporal cortex. Next, using intracortical recordings we demonstrate that early and focal neural activity in this region distinguishes correct from incorrect decisions and can be machine-decoded to classify syllables. Crucially, significant machine-decoding was possible from neuronal activity sampled across widespread regions, despite weak or absent sensory or decision-related responses. These findings show that a complex behavior like speech sound categorization relies on an efficient readout of focal neural activity, while distributed activity, although decodable by machine-learning, reflects collateral processes of sensory perception and decision.

neuroscience↗