Alexandre Zénon

This study shows that transcutaneous vagus nerve stimulation (tVNS) improves perceptual decision accuracy by enhancing evidence accumulation, particularly when applied early in a task.

The paper shows that pupil size changes reflect Bayesian inference in arithmetic problem solving, with greater information gain from operands linked to faster responses.

This study compares behavioral and neural differences between physical and observational motor learning under disruption, finding comparable outcomes but distinct neural strategies.

This study examines fronto-motor circuits related to effort-based decision-making and apathy in healthy individuals, finding associations between apathy scores and structural and effective connectivity in specific circuits.

This study uses transfer entropy to quantify cognitive demand in a tracking task and finds it correlates with subjective effort and pupillary response.

The paper investigates how task goals influence the relationship between decision and movement speed, finding that coregulation occurs as a default but is replaced by a tradeoff when detrimental to reward rate.

This study uses tVNS to investigate the role of the LC-NE system in perceptual decision-making, finding it improves accuracy without affecting reaction times, supporting the gain hypothesis.

Passive visual stimulation affects task performance based on cognitive load and arousal, suggesting neural network activity influences mental fatigue.

The paper measures information transfer in human visuomotor tracking, distinguishing feedback and feedforward components, showing feedback scales with task difficulty.

The paper proposes an information theoretic framework to explain pupillary responses to cognitive processes by linking pupil dilation to information processing metrics like KL divergence.

This paper introduces a Bayesian learning model with adaptive forgetting to improve reinforcement learning by dynamically adjusting to environmental changes and balancing exploration-exploitation.

This paper introduces a data-driven method using a Recurrent Auto-Encoder to fit the Full Drift Diffusion Model, improving parameter inference and performance over traditional approaches.

The paper frames cognitive costs as information-theoretic costs, explaining why certain tasks are costly and linking these costs to brain implementation and metabolic changes.

The paper investigates how objective fatigue affects task avoidance in cognitive experiments, finding a correlation between performance decline and avoidance, while subjective fatigue and effort are unrelated.