Language-specific Tonal Features Drive Speaker-Listener Neural Synchronization (2503.05211v1)

Abstract: Verbal communication transmits information across diverse linguistic levels, with neural synchronization (NS) between speakers and listeners emerging as a putative mechanism underlying successful exchange. However, the specific speech features driving this synchronization and how language-specific versus universal characteristics facilitate information transfer remain poorly understood. We developed a novel content-based interbrain encoding model to disentangle the contributions of acoustic and linguistic features to speaker-listener NS during Mandarin storytelling and listening, as measured via magnetoencephalography (MEG). Results revealed robust NS throughout frontotemporal-parietal networks with systematic time lags between speech production and perception. Crucially, suprasegmental lexical tone features (tone categories, pitch height, and pitch contour), essential for lexical meaning in Mandarin, contributed more significantly to NS than either acoustic elements or universal segmental units (consonants and vowels). These tonal features generated distinctive spatiotemporal NS patterns, creating language-specific neural "communication channels" that facilitated efficient representation sharing between interlocutors. Furthermore, the strength and patterns of NS driven by these language-specific features predicted communication success. These findings demonstrate the neural mechanisms underlying shared representations during verbal exchange and highlight how language-specific features can shape neural coupling to optimize information transfer during human communication.