Predictive and feedback signals differently shape the formation of language representations
The study compared neural representations from language models optimized for prediction versus those optimized for feedback during artificial language learning. Prediction-based models showed the largest group-level neural variance, suggesting they form a common architecture across learners, even though humans learn via feedback. Incorporating neural constraints improved the ability to predict individual differences in later generalization performance.