Electrophysiological indices of hierarchical speech processing differentially reflect the comprehension of speech in noise
Shyanthony R. Synigal; Andrew J. Anderson; Edmund C. Lalor · 2026 · eNeuro
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
ABSTRACT The past few years have seen an increase in the use of encoding models to explain neural responses to natural speech. The goal of these models is to characterize how the human brain converts acoustic energy into distinct linguistic representations that enable everyday speech comprehension. For example, researchers have shown that electroencephalography (EEG) data can be modeled in terms of acoustic features of speech, such as its amplitude envelope or spectrogram, linguistic features such as phonemes and phoneme probability, and higher-level linguistic features like context-based word
Abstract by Shyanthony R. Synigal; Andrew J. Anderson; Edmund C. Lalor, eNeuro (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1523/eneuro.0069-26.2026