Santiago Herce Castañón

The paper introduces a Bayesian classifier framework to predict behaviour by operationalizing it as a Conductome, demonstrating its effectiveness on a dataset predicting sedentariness.

The paper analyzes longitudinal eye-tracking data to show divergent gaze patterns in children with autism compared to typically developing peers during social interaction viewing, with differences increasing over time.

The paper investigates how generative models aid human generalization across tasks by analyzing sequence learning with multiple tasks from a common generative family.

This study shows humans adapt near-optimally to sensory noise but exhibit suboptimal cognitive judgments under integration noise, captured by a Bayesian model blind to this noise.

The paper shows that the medial prefrontal cortex network computes value homeostasis by tracking imbalance and redress in goal-related assets during decision-making.

This paper presents a unified model of distraction that explains perceptual, cognitive, and economic decision-making phenomena and predicts new behavioral outcomes.

The paper shows robust averaging improves decision-making performance in the presence of late noise by reducing the impact of noisy neural computations during decision-making.