Neural dynamics of phoneme sequences reveal position-invariant code for content and order

Laura Gwilliams (), Jean-Remi King, Alec Marantz and David Poeppel
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Laura Gwilliams: University of California
Jean-Remi King: New York University
Alec Marantz: New York University
David Poeppel: New York University

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: Abstract Speech consists of a continuously-varying acoustic signal. Yet human listeners experience it as sequences of discrete speech sounds, which are used to recognise discrete words. To examine how the human brain appropriately sequences the speech signal, we recorded two-hour magnetoencephalograms from 21 participants listening to short narratives. Our analyses show that the brain continuously encodes the three most recently heard speech sounds in parallel, and maintains this information long past its dissipation from the sensory input. Each speech sound representation evolves over time, jointly encoding both its phonetic features and the amount of time elapsed since onset. As a result, this dynamic neural pattern encodes both the relative order and phonetic content of the speech sequence. These representations are active earlier when phonemes are more predictable, and are sustained longer when lexical identity is uncertain. Our results show how phonetic sequences in natural speech are represented at the level of populations of neurons, providing insight into what intermediary representations exist between the sensory input and sub-lexical units. The flexibility in the dynamics of these representations paves the way for further understanding of how such sequences may be used to interface with higher order structure such as lexical identity.

Date: 2022
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DOI: 10.1038/s41467-022-34326-1

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