Abstract
As AI models move toward clinical decision-making and personalized treatment, understanding \emph{what} a model learns is important beyond predictive accuracy alone. We investigate whether personalized latent dynamics reveal clinically associated differences even when predictive fit is similar. A lightweight CNN--Transformer EEG foundation model pretrained on the Temple University EEG Corpus (TUEG) extracts segment-level representations. Using the Temple University Epilepsy Corpus (TUEP), representations are mapped to a shared latent-state space, and sparse multinomial logistic transition distributions (mLTD) are fit independently to each subject to obtain personalized transition-dependency graphs $W_n$. Analyses include $n{=}198$ subjects (99 epilepsy / 99 non-epilepsy). At $k{=}4$, epilepsy subjects exhibit substantially denser learned dependency structure ($p{=}1.1\times10^{-7}$), with the same pattern at $k{=}6$ (19.90 vs. 13.46; $p{=}5.2\times10^{-5}$). Graph-derived features provide moderate group discrimination under 5-fold subject-wise cross-validation (AUROC 0.68 at $k{=}4$; 0.65 at $k{=}6$). In contrast, held-out log-likelihood is nearly identical between groups at $k{=}4$ ($-0.992$ vs. $-0.991$; $p{=}0.95$), with similarly matched next-state prediction (AUROC 0.855 vs. 0.861; $p{=}0.54$). Thus, similar predictive fit does not imply similar learned dynamics: groups can be comparably predictable while differing substantially in the internal dynamical structure learned by personalized models. This distinction motivates evaluating learned structure alongside predictive performance in personalized clinical models.
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Cite this article
APA 7
Peng, R. H. T., & Bui, N. (2026). Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders. https://omanscience.com/en/articles/similar-predictive-fit-but-different-latent-dynamics-characterizing-learned-dynamical-structure-in-personalized-models-of-brain-disorders
MLA 9
Peng, Rita Huan-Ting, and Nhat Bui. "Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders." https://omanscience.com/en/articles/similar-predictive-fit-but-different-latent-dynamics-characterizing-learned-dynamical-structure-in-personalized-models-of-brain-disorders.
Chicago (author–date)
Peng, Rita Huan-Ting, and Nhat Bui. 2026. "Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders." https://omanscience.com/en/articles/similar-predictive-fit-but-different-latent-dynamics-characterizing-learned-dynamical-structure-in-personalized-models-of-brain-disorders.
Harvard
Peng, R. H. T. and Bui, N. (2026) 'Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders', Available at: https://omanscience.com/en/articles/similar-predictive-fit-but-different-latent-dynamics-characterizing-learned-dynamical-structure-in-personalized-models-of-brain-disorders.
Vancouver
Peng RHT, Bui N. Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders. https://omanscience.com/en/articles/similar-predictive-fit-but-different-latent-dynamics-characterizing-learned-dynamical-structure-in-personalized-models-of-brain-disorders
IEEE
R. H. T. Peng, and N. Bui, "Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders," https://omanscience.com/en/articles/similar-predictive-fit-but-different-latent-dynamics-characterizing-learned-dynamical-structure-in-personalized-models-of-brain-disorders.