From high-energy consumption to efficient prediction: Developmental changes in neural dynamics of syllable processing

Poster Session E - Monday, March 9, 2026, 2:30 – 4:30 pm PDT, Fairview/Kitsilano Ballrooms

Xiaoqi Yang¹ (mi77.new@gmail.com), Yuting Meng¹, Hua Fan¹, Ling Liu¹; ¹Beijing Language and Culture University, Beijing, China

Syllables are fundamental units of speech perception, yet the neural dynamics governing their processing in the developing brain remain to be fully elucidated. This study utilized Magnetoencephalography (MEG) to investigate syllable processing in 27 Mandarin-speaking children (4–10 years; mean=7.5, SD= 1.5) and 27 adults（19-25 years; mean=22.6，SD2.7） using a paired-stimulus discrimination task. Stimuli comprised eight Mandarin monosyllables varying by consonant, vowel, or tone, along with their time-reversed counterparts to examine linguistic experience effects. Behavioral results confirmed that children effectively distinguished syllables (accuracy >80%). Event-related field (ERF) analysis revealed distinct neural strategies. While early sensory components (M50, M100) were similar between groups, children demonstrated significantly broader cortical activation and stronger amplitudes in the late P3 component (p < 0.05). Crucially, language experience modulated responses differently. During the first stimulus (S1) processing, reversed speech induced higher P3 amplitudes in children than forward speech (t=1.8901, p=0.0323), a difference absent in adults, suggesting compensatory neural recruitment for unfamiliar signals. Furthermore, distinct context-utilization strategies emerged: adults displayed an efficiency-driven pattern (stable S1, enhanced S2), whereas children exhibited a "high-energy" mode with robust P3 responses to both S1 and S2 (p< 0.001). These findings indicate that pediatric speech processing is resource-intensive, relying on sustained neural engagement to parse acoustic-phonetic features. We conclude that language experience shapes the transition from exhaustive sensory analysis to efficient, context-driven neural processing.

Topic Area: PERCEPTION & ACTION: Development & aging