RT Journal Article
SR Electronic
T1 Cortical responses to natural speech reflect probabilistic phonotactics
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 359828
DO 10.1101/359828
A1 Giovanni M. Di Liberto
A1 Daniel Wong
A1 Gerda Ana Melnik
A1 Alain de Cheveigné
YR 2018
UL http://biorxiv.org/content/early/2018/06/30/359828.abstract
AB Humans comprehend speech despite the various challenges of real-world environments, such as loud noise and mispronunciation. Our auditory system is robust to these thanks to the integration of the upcoming sensory input with prior knowledge and expectations built on language-specific regularities. One such regularity regards the permissible phoneme sequences, which determine the likelihood that a word belongs to a given language (phonotactic probability; “blick” is more likely to be an English word than “bnick”). Previous research suggested that violations of these rules modulate brain evoked responses such as the N400 and the late positive complex. Yet several fundamental questions remain unresolved, especially regarding the neural encoding and integration strategy of phonotactic information. Here, we used linear modelling approaches to assess the influence of phonotactic probabilities on the brain responses to narrative speech measured with non-invasive EEG. We found that the relationship between continuous speech and EEG responses is best described when the speech descriptor includes phonotactic probabilities. This provides us with a methodology to isolate and measure the brain responses to phonotactics using natural speech at the individual subject-level. Furthermore, such low-frequency signals showed the strongest speech-EEG interactions at latencies of 100-400 ms, supporting a pre-lexical role of phonotactic information.Significance Statement Speech is composed of basic units, called phonemes, whose combinations comply with language-specific regularities determining whether a sequence “sounds” as a plausible word. Our ability to detect irregular combinations requires matching incoming sequences with our internal expectations, a process that supports speech segmentation and learning. However, the neural mechanisms underlying this phenomenon have not yet been established. Here, we examine this in the human brain using narrative speech. We identified a brain signal reflecting the likelihood that a word belongs to the language, which may offer new opportunities to investigate speech perception, learning, development, and impairment. Our data also suggest a pre-lexical role of this phenomenon, thus supporting and extending current mechanistic perspectives.