Interpretation: EEG correlates of speech-to-language transformation reflect a blend of contextualized lexical and sub-lexical representation.

<p><b>(a):</b> To examine the linguistic nature of Whisper’s EEG predictions we referenced them to a pure language model (GPT-2-medium). We focused on GPT-2 L16 based on independent fMRI research ([<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1...

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Bibliographic Details
Main Author:	Andrew J. Anderson (20161392) (author)
Other Authors:	Chris Davis (647632) (author), Edmund C. Lalor (11450457) (author)
Published:	2024
Subjects:	Neuroscience Physiology Sociology Science Policy Biological Sciences not elsewhere classified speaker &# 8220 layer depth advantage intermediary representational stage learning models reveal transform continuous speech selectively attended speech contained eeg model complete brain models accurate eeg models model eeg recordings models may complete picture eeg modeling brain responses brain mechanism model electroencephalogram unattended speech speech rate xlink "> whisper provides text transformation superficial level reflects elements providing end promising approach listening conditions linguistic processing lexical representation end accounts audiobook comprehension
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Interpretation: EEG correlates of speech-to-language transformation reflect a blend of contextualized lexical and sub-lexical representation.

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