Capturing learning on the fly: an eye-tracking method to quantify prediction errors and updating the prior
This study introduces a novel eye-tracking framework to reveal that statistical learning in noisy environments relies less on error-driven updates and more on conservative, repetition-based strategies that prioritize model stability.