Quantitatively modelling the phenomenology of Alzheimer's-type dementia using frame-theoretic semantics and psycholinguistic norms

BackgroundThe phenomenology of Alzheimer's disease-the "what is it like?" of the condition-has previously been approached via either quantitative analyses of granular linguistic features or qualitative analyses of lived experience.ObjectiveWhat is lacking is an empirically credible framework that captures high-level qualitative insight by way of cognitive models rather than surface linguistic features.MethodsFrame semantics and a 1.19 million-token psycholinguistic word norm lookup were applied to two topic-matched corpora: the DementiaBank Pitt Cookie Theft recordings and a COVID-era diary corpus combining CoSoWELL controls with Dementia Diaries entries. Gradient-boosted classifiers under participant-level cross-validation were compared against Fraser et al.'s 52-feature benchmark and statistically evaluated.ResultsA combined classifier contributed information statistically independent of the Fraser set on both corpora. On Pitt, four variance-based features defined a variance-collapse signature, with imageability being most significant; the within-document standard deviation of imageability replicated on COVID. Twelve semantic-frame comparisons survived Holm correction; Kinship was under-used on both corpora, giving a second cross-corpus anchor alongside imageability. A four-feature minimal classifier drawing equally from both feature families achieved AUC 0.748 on Pitt and 0.781 on COVID.ConclusionsAlzheimer's-type dementia is characterized not by a shift in the mean of any linguistic dimension but by a flattening of the phenomenological range across which a speaker moves when describing and experiencing the world. Because the diagnostic labels available in both corpora are coarse, the extent to which this flattening reflects a unitary phenomenology of Alzheimer's disease rather than heterogeneity within the diagnostic category remains to be established.