Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders

Key points

• As AI models move toward clinical decision-making and personalized treatment, understanding what a model learns is important beyond predictive accuracy alone.1
• A lightweight CNN--Transformer EEG foundation model pretrained on the Temple University EEG Corpus (TUEG) extracts segment-level representations.1
• 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 $Wn$.1
• 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; $p{=}5.2\times10^{-5}$).1

Sources (1)

• [1]Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 07:54 PM

  We investigate whether personalized latent dynamics reveal clinically associated differences even when predictive fit is similar.

  As AI models move toward clinical decision-making and personalized treatment, understanding what a model learns is important beyond predictive accuracy alone.

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