Florent Meyniel

This study shows that pupil dynamics reflect uncertainty-driven adjustments in probability learning by tracking change point probability and prior uncertainty through phasic and tonic pupil responses.

This paper compares functional alignment methods and template construction strategies in fMRI to improve group-level decoding accuracy and interpretability.

The paper identifies a nonlinear neural code for event probability in the human brain using fMRI, showing distinct tuning curves in prefrontal and parietal cortices.

The paper shows that confidence and surprise in learning tasks have stable cortical patterns linked to neuromodulatory systems and receptor distributions.

This study identifies how macaque prefrontal cortex encodes metacognitive judgments of working memory by integrating memory strength and contextual cues.

This paper shows that implicit and explicit priors are used differently in perceptual inference, with implicit priors having a stronger influence and less consistency across subjects.

This study examines how anxious, autistic, and schizotypal traits influence perceptual inference in two population samples, finding distinct but correlated effects, particularly for trait anxiety.

This study identifies multiple individual and heuristic-based roles of uncertainty in human reward-guided decision-making using a novel bandit task.

This study investigates how humans dynamically adjust their learning rates for magnitudes and probabilities, identifying two distinct determinants for each type of learning.

This study uses fMRI and pupillometry to show that both types of deviant stimuli activate multiple subcortical systems, including the locus coeruleus, in a 'local-global' paradigm.

This study identifies cortical regions involved in representing confidence during probabilistic learning using a dynamic task and MRI.

The paper investigates how humans process sequences by distinguishing between statistical and rule-based systems, finding that a hierarchical Bayesian model best explains behavior.

The paper introduces a recurrent neural network architecture that enables accurate sequence prediction in stochastic and structured environments through gating, lateral connections, and recurrent weight training.

The paper maps the dynamics of perceptual decision signals across the human cortex using magnetoencephalography and identifies a low-frequency endogenous component in primary visual cortex.

This study investigates how brain dynamics reflect confidence-weighted learning by analyzing magneto-encephalography data during a probability learning task.

This study shows that confidence reports can distinguish hierarchical from non-hierarchical learning models under uncertainty.

The paper shows that confidence biases in decision-making are explained by differences in participants' prior expectations, which can predict confidence reports without assumptions about the task.

The paper shows that people update beliefs more strongly with positive outcomes than negative ones in a learning task, linking this to enhanced prediction error signaling in reward circuits.