Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders
Read the original on arXiv Machine Learning →The study examines whether personalized latent dynamics can reveal clinically relevant differences even when predictive accuracy is similar. Using a lightweight CNN–Transformer EEG foundation model pretrained on the Temple University EEG Corpus, the authors map EEG representations to a shared latent-state space and fit sparse multinomial logistic transition distributions for each of 198 subjects (99 epilepsy, 99 non‑epilepsy). Results show that epilepsy subjects exhibit denser learned dependency structures at k=4 and k=6, while held‑out log‑likelihood and next‑state prediction are nearly identical between groups, indicating that similar predictive fit does not imply similar learned dynamics.
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