Low-Frequency Entrainment to Visual Motion Underlies Sign Language Comprehension
| Autor: | Julia Krebs, S. C. Borneman, Ronnie B. Wilbur, Evie Malaia |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
vision
Computer science Speech recognition 0206 medical engineering Biomedical Engineering Context (language use) 02 engineering and technology Electroencephalography Sign language computer.software_genre Signal Article Sign Language Internal Medicine medicine Humans Speech EEG Language perceptual sampling Parsing medicine.diagnostic_test General Neuroscience Rehabilitation 020601 biomedical engineering Entrainment (biomusicology) Comprehension Gesture recognition Speech Perception computer language comprehension |
| Zdroj: | IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society |
| ISSN: | 1558-0210 1534-4320 |
| DOI: | 10.1109/tnsre.2021.3127724 |
| Popis: | When people listen to speech, neural activity tracks the entropy fluctuation in the acoustic envelope of the signal. This signal-based entrainment has been shown to be the basis of speech parsing and comprehension. In this electroencephalography (EEG) study, we compute sign language users’ cortical tracking of changes in visual dynamics of the communicative signal in the time-direct videos of sign language, and their time-reversed counterparts, and assess the relative contribution of response frequencies between.2 and 12.4 Hz to comprehension using a machine learning approach to brain state classification. Lower frequencies of EEG response (.2–4 Hz) yield 100% classification accuracy, while information about cortical tracking of the visual envelope in higher frequencies is less informative. This suggests that signers rely on lower visual frequency data, such as envelope of visual signal, for sign language comprehension. In the context of real-time language processing, given the speed of comprehension responses, this suggests that fluent signers employ a predictive processing heuristic based on sign language knowledge. |
| Databáze: | OpenAIRE |
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