Biology subjects

Meyniel, F.

Publications and source records attributed to Meyniel, F..

17 recordsLinked to original sources

bioRxiv2026

Functional Templates in fMRI: Building Accurate and Interpretable Group-Level Decoders

Inter-individual variability poses a significant challenge in decoding brain activity across subjects. While standard anatomical registration procedures reduce morphological differences, they fail to capture functional variability between subjects. Functional alignment methods address this issue by establishing voxel-to-voxel correspondences between pairs of individuals, thereby constructing a shared functional space. This shared space can be extended at the group level by generating a functional template. However, despite the availability of toolboxes, functional templates remain underused in fMRI analysis. Adopting this approach is currently difficult due to the diversity of existing methods and the lack of clear guidelines. Comprehensive evaluations of functional templates are limited to movie-watching paradigms. Here, we extensively compare functional alignment methods (Optimal Transport, Procrustes, Ridge regression, and Shared Response Model) and template construction strategies (in-sample, out-of-sample, pairwise) within the more general framework of task decoding. In this framework, decoding accuracy measures how well individual activation patterns align. Across multiple tasks and datasets, we demonstrate that population templates built using Optimal Transport (a) yield the highest decoding accuracy, (b) are not significantly biased by individual subjects, which facilitates generalization to new subjects, and (c) preserve the cortical signal topography.

neuroscience↗

bioRxiv2026

Pupil Dynamics Reflect Uncertainty-Driven Adjustments of Probability Learning

Adaptive learning in dynamic environments depends on the brains ability to represent and respond to uncertainty. Changes in arousal-mediated pupil diameter provide insight into the neural basis of this process. Prior work has focused on magnitude-based inference tasks, and revealed that learning is primarily influenced by the detection of change points and the corresponding modulation of arousal. In comparison, the role of arousal systems in learning contexts where changes are harder to detect, such as in probability learning, remains poorly understood. Here, we combined pupillometry with a probability learning task in which human participants estimated a hidden generative probability of stimuli that changed abruptly and unpredictably. We adopted a Bayesian ideal observer model to estimate, trial-by-trial, the optimal apparent learning rate and the dynamics of two uncertainty-associated factors - namely, change point probability (the probability that the generative process has abruptly changed) and prior uncertainty (about the current beliefs on this generative process). In line with normative theory, participants heavily relied on prior uncertainty to adjust apparent learning rates. Pupil analyses revealed temporally distinct profiles that separate uncertainty factors: phasic dilations tracked change point probability while tonic pupil dilations tracked prior uncertainty. Mediation analyses further indicated that both phasic and tonic pupil signals at least partially carried the effects of change point probability and prior uncertainty on apparent learning rates, respectively. Together, our results demonstrate that probability learning in a dynamic environment is underpinned by computationally rational integration of latent uncertainty factors, implemented through arousal-associated responses.

neuroscience↗

bioRxiv2025

Neuromodulatory systems partially account for the topography of cortical networks of learning under uncertainty

Learning in dynamic and stochastic environments is notoriously difficult. Neuromodulatory systems may shape this process, thereby constraining where learning-related neural activity emerges according to the spatial distribution of receptors and transporters across the brain. However, the extent of this constraint, and which neuromodulatory systems contribute the most, remains unclear. Here, we focus on fMRI data from four probabilistic learning studies that was combined with a Bayesian ideal observer model. This model formalizes latent computational variables such as confidence and surprise that drive human learning. We show that the functional correlates of confidence, and to a lesser extent surprise, exhibit strong spatial invariance across tasks. This invariance suggests that these functional correlates of learning reflect a stable cortical organization largely independent of sensory modality and task structure. We found that this invariance aligns with the cortical chemoarchitecture using 20 PET-derived receptor and transporter density maps. Distinct receptor and transporter patterns mapped onto confidence- and surprise-related activity, including both known catecholamine pathways and novel opioid associations. Together, our findings provide evidence for a neuromodulatory account of adaptive learning and offer receptor-level hypotheses. More broadly, our approach provides a general framework to understand how neuromodulatory systems shape diverse cognitive processes.

neuroscience↗

bioRxiv2025

Multiple Routes to Metacognitive Judgments of Working Memory in the Macaque Prefrontal Cortex

The ability to evaluate ones own memory is known as metamemory. Whether metamemory is inherent to memory strength or requires additional computation in the brain remains largely unknown. We investigated the metacognitive mechanism of working memory (WM) using two-photon calcium imaging in the prefrontal cortex of macaque monkeys, who were trained to memorize spatial sequences of varying difficulties. In some trials, after viewing the sequence, monkeys could opt out of retrieval for a smaller reward, reflecting their confidence in WM (meta-WM). We discovered that PFC neurons encoded WM strength by jointly representing the remembered locations through population coding and their associated uncertainties. This WM strength faithfully predicted the monkeys recall performance and opt-out decisions. In addition to memory strength, other factors-- trial history and arousal--encoded in baseline activity predicted opt-out decisions, serving as cues for meta-WM. We identified a code of meta-WM itself that integrated WM strength and these cues. Importantly, WM strength, cues, and meta-WM were represented in different subspaces within the same PFC population. The dynamics and geometry of PFC activity implement metacognitive computations, integrating WM strength with cues into a meta-WM signal that guides behavior.

neuroscience↗

bioRxiv2024

Disentangling the effects of Anxious, Autistic and Psychotic Traits on Perceptual Inference

The brain combines sensory information and prior information, taking into account uncertainty, to perceive the world. This inference process approaches optimality in humans but with inter-individual differences associated with psychological traits. Previous results on these differences are in fact highly heterogeneous and even contradictory. We highlight experimental, modeling, and analysis choices that may contribute to this heterogeneity. We propose a set of tasks utilizing explicit and implicit priors, combined with computational modeling, to isolate the decision and learning stages of perceptual inference. Using a multidimensional approach, we characterized differences in perceptual inference associated with anxious, autistic and psychotic traits in two large samples from the general population. Our findings reveal that anxious, autistic, and psychotic traits form three distinct, yet correlated, dimensions. More anxious traits were associated with enhanced performance and greater reliance on sensory information at the decision stage. Autistic traits were not associated with any difference in perceptual inference; results for psychotic traits were inconsistent across the two samples. Results are partly different when using unidimensional analyses. Together, these results stress the importance of a multidimensional approach that takes anxious traits into account to characterize inter-individual differences in perceptual inference.

neuroscience↗

bioRxiv2024

Multiple and subject-specific roles of uncertainty in reward-guided decision-making

Decision-making in noisy, changing, and partially observable environments entails a basic tradeoff between immediate reward and longer-term information gain, known as the exploration-exploitation dilemma. Computationally, an effective way to balance this tradeoff is by leveraging uncertainty to guide exploration. Yet, in humans, empirical findings are mixed, from suggesting uncertainty-seeking to indifference and avoidance. In a novel bandit task that better captures uncertainty-driven behavior, we find multiple roles for uncertainty in human choices. First, stable and psychologically meaningful individual differences in uncertainty preferences actually range from seeking to avoidance, which can manifest as null group-level effects. Second, uncertainty modulates the use of basic decision heuristics that imperfectly exploit immediate rewards: a repetition bias and win-stay-lose-shift heuristic. These heuristics interact with uncertainty, favoring heuristic choices under higher uncertainty. These results, highlighting the rich and varied structure of reward-based choice, are a step to understanding its functional basis and dysfunction in psychopathology.

neuroscience↗

bioRxiv2023

Two determinants of dynamic adaptive learning for magnitudes and probabilities

Humans face a dynamic world that requires them to constantly update their knowledge. Each observation should influence their knowledge to a varying degree depending on whether it arises from a stochastic fluctuation or an environmental change. Thus, humans should dynamically adapt their learning rate based on each observation. Although crucial for characterizing the learning process, these dynamic adjustments have only been investigated empirically in magnitude learning. Another important type of learning is probability learning. The latter differs from the former in that individual observations are much less informative and a single one is insufficient to distinguish environmental changes from stochasticity. Do humans dynamically adapt their learning rate for probabilities? What determinants drive their dynamic adjustments in magnitude and probability learning? To answer these questions, we measured the subjects learning rate dynamics directly through real-time continuous reports during magnitude and probability learning. We found that subjects dynamically adapt their learning rate in both types of learning. After a change point, they increase their learning rate suddenly for magnitudes and prolongedly for probabilities. Their dynamics are driven differentially by two determinants: change-point probability, the main determinant for magnitudes, and prior uncertainty, the main determinant for probabilities. These results are fully in line with normative theory, both qualitatively and quantitatively. Overall, our findings demonstrate a remarkable human ability for dynamic adaptive learning under uncertainty, and guide studies of the neural mechanisms of learning, highlighting different determinants for magnitudes and probabilities. Significance statementIn a dynamic world, we must constantly update our knowledge based on the observations we make. However, how much should we update our knowledge after each observation? Here, we have demonstrated two principles in humans that govern their updating and by which they are capable of dynamic adaptive learning. The first principle is that when they observe a highly surprising event indicating a likely change in the environment, humans reset their knowledge and perform one-shot learning. The second principle is that when their knowledge is more uncertain, humans update it more quickly. We further found that these two principles are differentially called upon in two key learning contexts that could be associated with different brain mechanisms: magnitude learning (which primarily requires adaptation to surprise, under the first principle) and probability learning (which primarily requires adaptation to uncertainty, under the second principle). Our findings advance understanding of the mechanisms of human learning, with implications for the brain and the development of adaptive machines.

neuroscience↗

bioRxiv2023

A dissociation between the use of implicit and explicit priors in perceptual inference

The brain constantly uses prior knowledge of the statistics of its environment to shape perception. These statistics are often implicit (not directly observable) and gradually learned from observation; but they can also be explicitly communicated to the observer, especially in humans. In value-based decision-making, these priors are treated differently depending on their implicit or explicit origin creating the "experience-description gap". Here, we show that the same distinction also applies to perception. We created a pair of categorization tasks with implicit and explicit priors respectively, and manipulated the strength of priors and sensory likelihood within the same human subjects. Perceptual decisions were influenced by priors in both tasks, and subjects updated their priors in the implicit task as the true statistics changed. Using Bayesian models of learning and perception, we found that the weight of the sensory likelihood in perceptual decisions was highly correlated across subjects between tasks, and slightly stronger in the implicit task. By contrast, the weight of priors was much less correlated across tasks, and it increased markedly from the explicit task to the implicit task. The same conclusion holds when using the subjects reported priors. Model comparison also showed that different computations underpinned perceptual decisions depending on the origin of the priors. Taken together, those results support a dissociation in perceptual inference between the use of implicit and explicit priors. This conclusion could resolve conflicting results generated by the indiscriminate use of implicit and explicit priors when studying perception in healthy subjects and patients. Significance StatementThe use of prior knowledge is ubiquitous in brain processes. However, this use is not always appropriate, and the weights assigned to priors in perceptual decisions are quite heterogeneous across studies. Here, we tested whether the origin of priors can partially explain this heterogeneity. Priors that are explicitly communicated (e.g., a road sign indicating the likely presence of wildlife) appear to be used differently from priors that remain implicit and are learned from experience (e.g., previous encounters with wildlife). We show that the use of implicit and explicit priors is unrelated across subjects. This dissociation may explain why previous findings on the use of explicit and implicit priors (e.g. abnormally strong or weak priors) are often inconsistent.

neuroscience↗

bioRxiv2022

A characterization of the neural representation of confidence during probabilistic learning

Learning in a stochastic and changing environment is a difficult task. Models of learning typically postulate that observations that deviate from the learned predictions are surprising and used to update those predictions. Bayesian accounts further posit the existence of a confidence-weighting mechanism: learning should be modulated by the confidence level that accompanies those predictions. However, the neural bases of this confidence are much less known than the ones of surprise. Here, we used a dynamic probability learning task and high-field MRI to identify putative cortical regions involved in the representation of confidence about predictions during human learning. We devised a stringent test based on the conjunction of four criteria. We localized several regions in parietal and frontal cortices whose activity is sensitive to the confidence of an ideal observer, specifically so with respect to potential confounds (surprise and predictability), and in a way that is invariant to which item is predicted. We also tested for functionality in two ways. First, we localized regions whose activity patterns at the subject level showed an effect of both confidence and surprise in qualitative agreement with the confidence-weighting principle. Second, we found neural representations of ideal confidence that also accounted for subjective confidence. Taken together, those results identify a set of cortical regions potentially implicated in the confidence-weighting of learning.

neuroscience↗

bioRxiv2022

Brainstem fMRI signaling of surprise across different types of deviant stimuli

The detection of deviant stimuli is crucial to orient and adapt our behavior. Previous work showed that infrequent (hence deviant) stimuli elicit phasic activation of the brainstem locus coeruleus (LC), which releases noradrenaline and controls central arousal. However, it is unclear whether the detection of behaviorally-relevant deviant events selectively trigger LC responses, or also other neuromodulatory systems related to dopamine, acetylcholine, and serotonin. Here, we combined human fMRI recordings optimized for brainstem imaging with pupillometry (a peripheral marker of central arousal) to perform a mapping of deviant-related responses in subcortical structures. Participants had to detect deviant items in a "local-global" paradigm that distinguishes between deviance based on the stimulus probability and the sequence structure. fMRI responses to deviant stimuli were quite distributed, detected in the LC but also other subcortical nuclei and many cortical areas. Both types of deviance elicited responses in the pupil, LC and other neuromodulatory systems. Our results reveal that the detection of task-relevant deviant items recruits the same multiple subcortical systems across computationally different types of deviance.

neuroscience↗

bioRxiv2022

Resource-Rational Account of Sequential Effects in Human Prediction

An abundant literature reports on sequential effects observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume nonstationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from .05 to .95. Although in this task the environment is static and the Bayesian belief converges, subjects predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this attractive effect, but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a repulsive effect, observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.

animal behavior and cognition↗

bioRxiv2021

Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

From decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environments latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments.

neuroscience↗

bioRxiv2020

Rational arbitration between statistics and rules in human sequence learning

Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence of a dissociation, in humans, between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.

neuroscience↗

bioRxiv2020

Disentangling Decision-related Feedforward and Feedback Signals in Human Cortex

Perceptual decisions entail the accumulation of sensory evidence for a particular choice towards an action plan. An influential framework holds that sensory cortical areas encode the instantaneous sensory evidence and downstream, action-related regions accumulate this evidence. The large-scale distribution of this computation across the cerebral cortex has remained largely elusive. We developed a regionally-specific magnetoencephalography decoding approach to exhaustively map the dynamics of stimulus- and choice-specific signals across the human cortical surface during a visual decision. Comparison with the evidence accumulation dynamics inferred from behavior enabled us to disentangle stimulus-dependent and endogenous components of choice-predictive activity across the visual cortical hierarchy. The endogenous component was present in primary visual cortex, expressed in a low (< 20 Hz) frequency-band, and its time course tracked, with delay, the build-up of choice-predictive activity in (pre-)motor regions. Our results are consistent with choice-specific cortical feedback signaling in a specific frequency channel during decision formation.

neuroscience↗

bioRxiv2019

Brain dynamics for confidence-weighted learning

Learning in a changing and uncertain environment is a difficult problem. A popular solution is to predict future observations and then use surprising outcomes to update those predictions. However, humans also have a sense of confidence that characterizes the precision of their predictions. Bayesian models use this confidence to regulate learning: for a given surprise, the update is smaller when confidence is higher. We explored the human brain dynamics sub-tending such a confidence-weighting using magneto-encephalography. During our volatile probability learning task, subjects confidence reports conformed with Bayesian inference. Several stimulus-evoked brain responses reflected surprise, and some of them were indeed further modulated by confidence. Confidence about predictions also modulated pupil-linked arousal and beta-range (15-30 Hz) oscillations, which in turn modulated specific stimulus-evoked surprise responses. Our results suggest thus that confidence about predictions modulates intrinsic properties of the brain state to amplify or dampen surprise responses evoked by discrepant observations.

neuroscience↗

bioRxiv2018

Subjective confidence reveals the hierarchical nature of learning under uncertainty

Hierarchical processing is pervasive in the brain, but its computational significance for learning in real-world conditions, with uncertainty and changes, is disputed. We show that previously proposed qualitative signatures which relied on reports of learned quantities or choices in simple experiments are insufficient to categorically distinguish hierarchical from non-hierarchical models of learning under uncertainty. Instead, we present a novel test which leverages a more complex task, whose hierarchical structure allows generalization between different statistics tracked in parallel. We use reports of confidence to quantitatively and qualitatively arbitrate between the two accounts of learning. Our results indicate that human subjects can track multiple, interdependent levels of uncertainty, and provide clear evidence for hierarchical processing, thereby challenging some influential neurocomputational accounts of learning.

neuroscience↗

bioRxiv2017

Variability in prior expectations explains biases in confidence reports

Confidence in a decision is defined statistically as the probability of that decision being correct. Humans, however, display systematic confidence biases, as has been exposed in various experiments. Here, we show that these biases vanish when taking into account participants' prior expectations, which we measure independently of the confidence report. We use a wagering experiment to show that modeling subjects' choices allows for classifying individuals according to their prior biases, which fully explain from first principles the differences in their later confidence reports. Our parameter-free confidence model predicts two counterintuitive patterns for individuals with different prior beliefs: pessimists should report higher confidence than optimists, and, for the same task difficulty, the confidence of pessimists should increase with the generosity of the task. These findings show how systematic confidence biases can be simply understood as differences in prior expectations.

animal behavior and cognition↗