Valentin Wyart

The paper shows humans make more accurate decisions by compressing sensory evidence into decision categories, and RNNs trained on the same task exhibit similar resilience to noisy computations.

This study shows human perceptual decisions use abstract neural representations to flexibly combine sensory features across contexts, supported by MEG data and recurrent neural networks.

The paper examines how short-term mindsets mediate the effects of childhood adversity on mental health and life history strategies in a UK sample.

This paper introduces modular neural state-space models to implement the Bayesian Brain hypothesis, enabling learned and reusable world-model components for flexible cognition.

The paper investigates how sensory integration, temporal prediction, and rule discovery interact in humans using rhythmic stimulus sequences and finds they operate interdependently.

This study investigates how dopamine affects reinforcement learning in humans using L-DOPA and finds it reduces switching after low rewards and influences learning rate and precision.

The paper shows humans use a two-stage strategy for epistemic information seeking, combining streaking and uncertainty-guided exploration to improve inference accuracy.

The paper shows that perceptual decisions about threat in facial displays are prioritized in neural coding, with earlier motor-related predictions for emotional stimuli than non-emotional ones.

The paper isolates the neural and cognitive signatures of information seeking by distinguishing it from changes-of-mind, finding increased attention and arousal during information seeking.

The paper identifies distinct neural signatures for perceptual decision-making and confidence evaluation in humans using electroencephalography.

The paper shows that interacting with volatile environments leads to more stable hidden-state inference and distinct brain activity patterns compared to observing cues.

This study investigates how NMDA receptor hypofunction affects cognitive inference under uncertainty, finding impaired decision-making and altered brain activity patterns.

This paper shows that adding computation noise to recurrent neural networks improves their resilience to adverse conditions during decision-making, mimicking human cognitive adaptability.

This study decodes and tracks multiple rapidly-changing visual images from human brain activity, showing the brain simultaneously represents multiple images at each time instant.

The paper shows that computational noise in reward-guided learning causes behavioral variability in volatile environments rather than reflecting exploration.

This study tracks canonical computations like neural adaptation and prediction-error signals in continuous visual stimulation using EEG and neuronal simulations, showing they can encode multiple stimuli sequentially.

The paper argues that model simulation is essential to support claims about behavioral function in computational cognitive neuroscience and proposes guidelines combining model comparison and simulation.