Niloufar Razmi

This study develops a Bayesian model of temporal structure learning and shows it aligns with human behavior in predicting outcomes across different temporal environments.

The paper investigates how fluctuations in arousal signals reflect latent state transitions that enhance behavioral optimization through rapid mental context adjustments.

This paper introduces a neural network model that explains adaptive learning by dynamically adjusting internal state representations based on environmental statistics and feedback.