# Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence

arXiv: https://arxiv.org/abs/2512.20929 · HTML: https://mlsanity.com/paper/2512.20929/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 1 of 1)

- [Neural and phenotypic representation under the free-energy principle](https://mlsanity.com/paper/2008.03238/) (2020)

## Abstract (arXiv)

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.