Arthur Prat-Carrabin

This study shows that Webers law arises from efficient coding principles and can be broken by altering stimulus frequency distributions.

This study introduces a gain-adaptive recurrent network model that explains adaptive tuning curves and reconciles adapter repulsion and prior attraction in sensory systems.

The paper shows that perceptual imprecision in number sense depends on task context and prior distribution, suggesting endogenous determination of perceptual noise.

This study shows that human parietal cortex dynamically recalibrates number representations based on context, improving behavioral accuracy through distributed range adaptation.

The paper examines Bayesian observer variability in a numerosity-estimation task and finds anti-Weber behavior, similar to human subjects, suggesting variability is influenced by prior distribution skewness.

The paper shows that imprecise counting of observations in averaging tasks leads to primacy and recency effects, modeled by a Bayesian central tendency mechanism.

This study investigates how humans adapt their inference processes to temporal structure in stimuli, showing suboptimal behavior that can be modeled by sampling-based inference.