{"ID":2825356,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.20929","arxiv_id":"2512.20929","title":"Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence","abstract":"Human language processing relies on the brain's capacity for predictive inference. We present a machine learning framework for decoding neural (EEG) responses to dynamic visual language stimuli in Deaf signers. Using coherence between neural signals and optical flow-derived motion features, we construct spatiotemporal representations of predictive neural dynamics. Through entropy-based feature selection, we identify frequency-specific neural signatures that differentiate interpretable linguistic input from linguistically disrupted (time-reversed) stimuli. Our results reveal distributed left-hemispheric and frontal low-frequency coherence as key features in language comprehension, with experience-dependent neural signatures correlating with age. This work demonstrates a novel multimodal approach for probing experience-driven generative models of perception in the brain.","short_abstract":"Human language processing relies on the brain's capacity for predictive inference. We present a machine learning framework for decoding neural (EEG) responses to dynamic visual language stimuli in Deaf signers. Using coherence between neural signals and optical flow-derived motion features, we construct spatiotemporal...","url_abs":"https://arxiv.org/abs/2512.20929","url_pdf":"https://arxiv.org/pdf/2512.20929v1","authors":"[\"Sean C. Borneman\",\"Julia Krebs\",\"Ronnie B. Wilbur\",\"Evie A. Malaia\"]","published":"2025-12-24T04:19:20Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.CL\"]","methods":"[]","has_code":false}