Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912787727384576 |
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| author | Borneman, Sean C. Krebs, Julia Wilbur, Ronnie B. Malaia, Evie A. |
| author_facet | Borneman, Sean C. Krebs, Julia Wilbur, Ronnie B. Malaia, Evie A. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_20929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence Borneman, Sean C. Krebs, Julia Wilbur, Ronnie B. Malaia, Evie A. Neurons and Cognition Computation and Language 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. |
| title | Decoding Predictive Inference in Visual Language Processing via Spatiotemporal Neural Coherence |
| topic | Neurons and Cognition Computation and Language |
| url | https://arxiv.org/abs/2512.20929 |