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Temporal expectation in turn-taking: prosodic cues modulate behavioral and neural signatures of turn-end prediction in conversation
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DOI：10.1016/j.bandl.2026.105723.png)
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Abstract
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The rapid turn-taking in conversation requires accurately predicting and planning the timing of turns (Levinson & Torreira, 2015). However, most previous studies have focused on predicting others' turn-ends, neglecting the self-initiated processes of turn-taking. Additionally, there is limited neural evidence on how prosodic context influences turn-end prediction. This study examined neural responses concerning both turn-end prediction and turn-planning in an EEG experiment. We used cross-splicing to generate sentences with varying prosodic contexts and lengths so that participants could not anticipate turn-ends using sentence length. Participants listened to a requesting statement and verbally responded with the requested item upon hearing a beep, which either matched or mismatched the turn-end indicated by the prosodic context, rendering a 2 (prosodic context) by 2 (beep-context congruence) design. Results showed that the beep sound elicited a more negative N2 amplitude and less positive P3 amplitude under incongruent relative to congruent conditions and reduced late positivity (LP) under long versus short prosodic contexts. Single-trial analyses revealed that a smaller amplitude of LP was associated with longer reaction time (RT) regardless of prosodic context. Prosodic context moderated the relationship between P3 amplitude and RT, showing a positive correlation under short prosodic contexts but no correlation under long prosodic contexts. These findings suggest that participants use prosodic context to predict upcoming turn-ends, aiding in planning their responses. Listeners likely combine event-based cues (e.g., beep) and interval-based cues (e.g., prosody) to build temporal speech structures, which are processed by a dualpathway neurocognitive system underlying temporal prediction.
Keywords:
Turn-end Prediction
Cross-splicing
Prosody
EEG
Temporal Processing
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