Biology subjects

Wyart, V.

Publications and source records attributed to Wyart, V..

At least 19 recordsLinked to original sources

bioRxiv2026

Flexible compression of sensory evidence makes human decisions more resilient to noisy neural computations

Categorizing ambiguous stimuli requires accumulating sensory evidence over time through noisy neural computations. Theories disagree on whether this evidence is accumulated in its native sensory space or in an abstract and compressed category space that retains only decision-relevant information. Here we designed a visual categorization task in which revealing decision categories before or after a stimulus sequence determines which space can be used to accumulate evidence, without changing the optimal decision. Humans were more accurate, and their inferences less noisy, when they could accumulate evidence in category space. Concurrent magnetoencephalographic (MEG) recordings revealed flexible and accuracy-predictive representations of evidence compressed onto decision categories in these conditions. Recurrent neural networks (RNNs) trained on the same task developed the same strategy, which made their decisions more resilient to noisy computations. Together, these findings suggest that the human brain mitigates the cost of noisy inferences by focusing limited computational resources on decision-relevant information.

neuroscience↗

bioRxiv2026

Neural abstraction of evidence accumulation enables flexible multidimensional perceptual decisions

Human perceptual decisions adapt to rich and changing contexts but are typically tested under impoverished conditions. To probe this flexibility, we examined decisions requiring both the selection and the combination of multiple sensory features across sequences of objects. Here we show that human decision flexibility extends beyond feature selection to combination, even across sensory modalities, supported by the abstraction of underlying neural representations. Rapid magnetoencephalographic (MEG) responses reveal a hierarchy of object representations that become increasingly abstract and gated according to their decision relevance. A shared abstract representation of updates reflects evidence accumulation collapsed onto the relevant axis across unidimensional (selection-based) and multidimensional (combination-based) contexts. Recurrent neural networks tested under the same conditions acquire and benefit from abstract representations only when operating under human levels of computational noise. Together, these findings provide a flexibility through abstraction account of human decisions across a broader range of everyday perceptual conditions.

neuroscience↗

bioRxiv2026

Early life adversity is associated with mental health and life histories through short-term mindsets

Early life adversity is associated with increased risk for psychopathology and shifts in life history (LH) strategies, but the psychological mechanisms underlying these associations remain unclear. Drawing on evolutionary-developmental theory, we examined whether short-term mindsets mediate associations between dimensions of childhood adversity and mental health and LH-related outcomes. In a UK-representative sample of 877 adults, we assessed threat, deprivation, and unpredictability, alongside internalizing and externalizing symptoms, borderline features, and a latent factor capturing reproductive versus somatic maintenance effort. Structural equation models showed that adversity predicted poorer mental health and faster LH strategies. Short-term mindsets, indexed by lower future orientation and higher affective impulsivity, mediated effects of adversity, particularly unpredictability. Further decomposition of unpredictability into short-timescale and long-timescale forms revealed dissociable effects, with short-timescale unpredictability primarily linked to psychopathology and long-timescale unpredictability to reproductive-oriented LH strategies.

neuroscience↗

bioRxiv2026

Augmenting the Bayesian Brain with learned and reusable world-model components for flexible cognition

The Bayesian Brain hypothesis assumes that cognition relies on internal generative models of the world, yet existing implementations remain constrained by pre-specified, task-specific generative structures and computationally heavy iterative inference schemes. Here, we introduce modular neural state-space models as a scalable realization of the Bayesian Brain, replacing fixed generative structures and pre-specified inference rules with learned world-model components and amortized neural updates. This framework preserves the core commitment to explaining observations through hidden causes while making inference learned and reusable rather than pre-specified and task-specific. Our modular implementation of these models affords learned components to be seamlessly recombined and stacked across superficially different tasks that share similar latent dynamics. Such computational reuse supports zero-shot generalization and predicts selective correlations of inference parameters between tasks. We confirm these key predictions in human behavior, identifying learned and reusable world-model components as a candidate computational principle for flexible cognition.

neuroscience↗

bioRxiv2025

Dopamine drives a positive reward bias on human reinforcement learning

Formal theories of reinforcement learning (RL) prescribe a clearly defined function for dopamine, namely modulating learning via reward prediction errors (RPEs). Yet, empirical evidence in humans remains scarce, and recent advances introducing noisy RL cast doubt on a simple one-to-one mapping between neurotransmitters and computational mechanisms. Here, we detail a double-blind, placebo-controlled, randomised pharmacological study using the dopamine precursor L-DOPA, while healthy volunteers performed a volatile two-armed bandit task. Behaviourally, L-DOPA decreased switching behaviour following below-average rewards. Algorithmic RL modelling of human behaviour supported a dual effect of L-DOPA on the rate and precision of learning. By leveraging recurrent neural networks (RNNs) as implementational models of RL, we explain this dual effect through a single inference-time modulation, whereby L-DOPA triggers a positive reward bias at the input of the recurrent layer that implements RL. Our findings highlight a unifying mechanism at the implementation level that explain seemingly disparate algorithmic effects of dopamine.

neuroscience↗

bioRxiv2025

A dual information seeking strategy improves imprecise human inferences outside the explore-exploit tradeoff

Everyday decisions aim not only to earn rewards but also to learn about the world. Across three studies (total N = 702), we examined how people gather epistemic information stripped of rewarding value, and compared their strategy with reward seeking in otherwise matched conditions. Computational modeling of human behavior revealed a two-stage information-seeking policy, where participants first repeatedly sample each novel option in turn to test provisional hypotheses, a process we call streaking, before transitioning to uncertainty-guided exploration. While artificial neural networks trained to optimize inference accuracy acquired uncertainty-guided exploration but not early streaking, this two-stage policy improves human inference accuracy under noisy belief updating. Streaking and uncertainty-guided exploration tend to be co-expressed in the same individuals but map onto distinct psychological traits. Together, these results offer a novel account of human information seeking, clarifying its motives and benefits in epistemic contexts beyond the reward-centric explore-exploit tradeoff.

neuroscience↗

bioRxiv2025

Deep bidirectional interplay between sensory integration and latent rule discovery

Deciphering the structure of variable sensory input is key to building an accurate model of ones environment. Humans can integrate stimulus sequences to estimate their sensory statistics, predict the timing of upcoming stimuli, but also discover rules governing sequence generation. However, whether these three forms of inference operate independently or synergistically remains untested. Here we report selective interactions between sensory integration, temporal prediction, and rule discovery in humans. Participants were exposed to rhythmic sequences of 10 stimuli governed or not by a latent rule - a predictable change in stimulus statistics after 5 stimuli - and then asked to predict the 10th stimulus from incomplete sequences. Individual differences in sensory integration timescale for rule-free sequences predicted efficient rule discovery. Conversely, discovering the latent rule shaped the timescale and format of sensory integration for rule-based sequences. Tampering with the rhythmicity of stimulus presentation impaired rule discovery without affecting sensory integration accuracy. Selective perturbations of recurrent neural networks (RNNs) trained in the same conditions confirmed these specific interactions. Together, these findings provide novel insights into the flexibility of human inferences based on variable yet predictable sensory input.

neuroscience↗

bioRxiv2023

Compulsivity is linked to maladaptive choice variability but unaltered reinforcement learning under uncertainty

Compulsivity has been associated with variable behavior under uncertainty. However, previous work has not distinguished between two main sources of behavioral variability: the stochastic selection of choice options that do not maximize expected reward (choice variability), and random noise in the reinforcement learning process that updates option values from choice outcomes (learning variability). Here we studied the relation between dimensional compulsivity and behavioral variability, using a computational model which dissociates its two sources. We found that compulsivity is associated with more frequent switches between options, triggered by increased choice variability but no change in learning variability. This effect of compulsivity on the trait component of choice variability is observed even in conditions where this source of behavioral variability yields no cognitive benefits. These findings indicate that compulsive individuals make variable and maladaptive choices under uncertainty, but do not hold degraded representations of option values.

animal behavior and cognition↗

bioRxiv2022

Adaptive tuning of human learning and choice variability to unexpected uncertainty

Human value-based decisions are strikingly variable under uncertainty. This variability is known to arise from two distinct sources: variable choices aimed at exploring available options, and imprecise learning of option values due to limited cognitive resources. However, whether these two sources of decision variability are tuned to their specific costs and benefits remains unclear. To address this question, we compared the effects of expected and unexpected uncertainty on decision-making in the same reinforcement learning task. Across two large behavioral datasets, we found that humans choose more variably between options but simultaneously learn less imprecisely their values in response to unexpected uncertainty. Using simulations of learning agents, we demonstrate that these opposite adjustments reflect adaptive tuning of exploration and learning precision to the structure of uncertainty. Together, these findings indicate that humans regulate not only how much they explore uncertain options, but also how precisely they learn the values of these options. TeaserHumans regulate not only how much they explore uncertain options, but also how precisely they learn their values.

animal behavior and cognition↗

bioRxiv2022

Efficient metacognitive control of imprecise statistical inference

Statistical inference is the optimal process for forming and maintaining accurate beliefs about uncertain environments. However, human inference comes with costs due to its associated biases and limited precision. Indeed, biased or imprecise inference can trigger variable beliefs and unwarranted changes in behavior. Here, by studying decisions in a sequential categorization task based on noisy visual stimuli, we obtained converging evidence that humans reduce the variability of their beliefs by updating them only when the reliability of incoming sensory information is judged as sufficiently strong. Instead of integrating the evidence provided by all stimuli, participants actively discarded as much as a third of stimuli. This conditional belief updating strategy shows good test-retest reliability, correlates with perceptual confidence, and explains human behavior better than previously described strategies. This seemingly suboptimal strategy not only reduces the costs of imprecise computations, but counter-intuitively increases the accuracy of resulting decisions.

animal behavior and cognition↗

bioRxiv2021

Separable neural signatures of confidence during perceptual decisions

Perceptual confidence is an evaluation of the validity of perceptual decisions. While there is behavioural evidence that confidence evaluation differs from perceptual decision-making, disentangling these two processes remains a challenge at the neural level. Here we examined the electrical brain activity of human participants in a protracted perceptual decision-making task where observers tend to commit to perceptual decisions early whilst continuing to monitor sensory evidence for evaluating confidence. Premature decision commitments were revealed by patterns of spectral power overlying motor cortex, followed by an attenuation of the neural representation of perceptual decision evidence. A distinct neural representation was associated with the computation of confidence, with sources localised in the superior parietal and orbitofrontal cortices. In agreement with a dissociation between perception and confidence, these neural resources were recruited even after observers committed to their perceptual decisions, and thus delineate an integral neural circuit for evaluating perceptual decision confidence.

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↗

bioRxiv2020

Premature commitment to uncertain beliefs during human NMDA receptor hypofunction

In uncertain environments, accurate decision-making requires integrating ambiguous or conflicting signals - a cognitive inference process thought to require n-methyl-d-aspartate (NMDA) synaptic receptors. Here we characterized the causal impact of human NMDA receptor hypofunction on cognitive inference using placebo-controlled infusions of ketamine in a visual cue combination task. Participants tested under ketamine showed elevated uncertainty, together with impaired cognitive inference despite intact visual processing. This behavioral effect of ketamine was associated in patterns of electrical brain activity with degraded and unbalanced coding of presented cues in associative cortex, followed by premature response preparation in motor cortex. Through quantitative simulations, we propose that these cognitive alterations reflect an urge to explain away the elevated uncertainty triggered by ketamine. This compensatory mechanism may cause the emergence of psychotic symptoms observed under chronic NMDA receptor dysfunction, but also forge unusually strong beliefs when confronted with uncertainty in everyday life.

neuroscience↗

bioRxiv2020

Computation noise promotes cognitive resilience to adverse conditions during decision-making

Random noise in information processing systems is widely seen as detrimental to function. But despite the large trial-to-trial variability of neural activity and behavior, humans and other animals show a remarkable adaptability to unexpected adverse events occurring during task execution. This cognitive ability, described as constitutive of general intelligence, is missing from current artificial intelligence (AI) systems which feature exact (noise-free) computations. Here we show that implementing computation noise in recurrent neural networks boosts their cognitive resilience to a variety of adverse conditions entirely unseen during training, in a way that resembles human and animal cognition. In contrast to artificial agents with exact computations, noisy agents exhibit hallmarks of Bayesian inference acquired in a zero-shot fashion - without prior experience with conditions that require these computations for maximizing rewards. We further demonstrate that these cognitive benefits result from free-standing regularization of activity patterns in noisy neural networks. Together, these findings suggest that intelligence may ride on computation noise to promote near-optimal decision-making in adverse conditions without any engineered cognitive sophistication.

neuroscience↗

bioRxiv2019

Prioritized neural computations of socially-relevant signals during perceptual decision-making

Emotional signals, notably those signaling threat, benefit from prioritized processing in the human brain. Yet, it remains unclear whether perceptual decisions about the emotional, threat-related aspects of stimuli involve specific or similar neural computations compared to decisions about their non-threatening/non-emotional components. We developed a novel behavioral paradigm in which participants performed two different detection tasks (emotion vs. color) on the same, two-dimensional visual stimuli. Electroencephalographic (EEG) activity in a cluster of central electrodes reflected the amount of perceptual evidence around 100ms following stimulus onset, when the decision concerned emotion, not color. Second, participants choice could be predicted earlier for emotion (240ms) than for color (380ms) by the mu (10Hz) rhythm, which reflects motor preparation. Taken together, these findings indicate that perceptual decisions about threat-signaling dimensions of facial displays are associated with prioritized neural coding in action-related brain regions, supporting the motivational value of socially relevant signals.

neuroscience↗

bioRxiv2019

The Human Brain encodes a Chronicle of Visual Events at each Instant of Time

The human brain continuously processes streams of visual input. Yet, a single image typically triggers neural responses that extend beyond one second. To understand how such a slow computation copes with real-time processing, we recorded subjects electrical brain activity, while they watched ~5,000 rapidly-changing images. First, we show that each image can be decoded from brain activity for ~1 sec, and demonstrate that the brain simultaneously represents multiple images at each time instant. Second, dynamical system modeling reveals that these sustained representations can be explained by a specific chain of neural circuits, which consist of (i) a hidden maintenance mechanism, and (ii) an observable update mechanism. Third, this neural architecture is localized along the expected visual pathways. Finally, we show that the propagation of low-level representations across the visual hierarchy is a principle shared with deep convolutional networks. Together, these findings provide a general neural mechanism to simultaneously represent successive visual events. SignificanceOur retina are continuously bombarded with a rich flux of visual input. How our brain continuously processes such visual stream is a major challenge to neuroscience. Here, we developed techniques to decode and track, from human brain activity, multiple images flashed in rapid succession. Our results show that the brain simultaneously represents multiple successive images at each time instant. A hierarchy of neural assemblies which continuously propagate multiple visual contents explains our findings. Overall, this study sheds new light on the biological basis of our visual experience.

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↗

bioRxiv2018

Computational noise in reward-guided learning drives behavioral variability in volatile environments

When learning the value of actions in volatile environments, humans often make seemingly irrational decisions which fail to maximize expected value. We reasoned that these non-greedy decisions, instead of reflecting information seeking during choice, may be caused by computational noise in the learning of action values. Here, using reinforcement learning (RL) models of behavior and multimodal neurophysiological data, we show that the majority of non-greedy decisions stems from this learning noise. The trial-to-trial variability of sequential learning steps and their impact on behavior could be predicted both by BOLD responses to obtained rewards in the dorsal anterior cingulate cortex (dACC) and by phasic pupillary dilation - suggestive of neuromodulatory fluctuations driven by the locus coeruleus-norepinephrine (LC-NE) system. Together, these findings indicate that most of behavioral variability, rather than reflecting human exploration, is due to the limited computational precision of reward-guided learning.

neuroscience↗