Neural Tracking of Language Emerges from Distributed Synchronization, Sensitivity to Syntax, and Statistics

Abstract Neural tracking, how brain activity synchronizes with speech, remains contentious regarding whether it's driven by lexical statistical information or linguistic structures. Previous research provided evidence for both accounts, but methodological limitations made direct comparisons difficult. We used isochronous Turkish stimuli with head-final phrase structure to eliminate confounds of word category repetition at the phrase level. By controlling acoustic features, word repetition, meaning-relatedness, transitional probabilities, and syntactic structure, we isolated distinct contributions of lexical statistics and syntactic structure to neural tracking. Results showed both factors independently shape neural responses, with complex linguistic structures engaging broader brain networks. Computational modeling revealed that hierarchically-coupled oscillations explained neural data better than statistical models alone, supporting structured linguistic representation building alongside word-level statistics. Our model proposes meaning emerges through flexible coupling among neural populations encoding lexical categories. These findings demonstrate that hierarchical processing crucially contributes to neural speech tracking beyond statistical repetitions.