Extended data figure 3. Two-tiered hierarchical organization of syllables and phonemes.

a. Temporal decoding models demonstrate a similar syllable-phoneme hierarchy for each ROI (except rMFG). Dotted lines indicate chance decoding. Horizontal bars indicate durations of significant decoding. The temporal evolution of syllables and phoneme activations were obtained using time-resolved neural decoding (200 ms window; 10 ms step size). Solid lines indicate average across 20 instances of decoding iterations, and shaded bars indicate standard errors across instances. Horizontal bars indicate durations of significant decoding (one-sided temporal permutation cluster test, cluster forming threshold p < 0.05; 250 shuffled decoding models). b. Colored histograms indicate distributions of peak decoding times for both syllable and phoneme for each ROI network. Significant differences in decoding distributions indicate a temporal progression across the ROI networks for both syllables and phonemes (one-way ANOVA with post-hoc Tukey HSD test, p < 0.05, two-sided, for exact p-values see Main text). Syllable coding was earlier in time than phoneme coding (one-sided Mann-Whitney U test, p < 0.05 – for exact p-values see Main text; except peak decoding at monitoring ROIs, p = 0.079), demonstrating a network-level organization in accordance with the proposed hierarchical representation of syllables and phonemes. The difference in activation timing of the hierarchical representation varied for each ROI group, with maximum difference between syllable and phoneme activation during planning (250–350 ms), followed by articulation (110 – 130 ms), and then minimally (17 – 25 ms) during monitoring (n = 400 pairwise differences between syllables and phoneme times per group, pairwise one-sided t-test, planning vs. articulation: t(798) = 71, p < 0.001; articulation vs. monitoring, t(798) = 101, p < 0.001; planning vs. monitoring: t(798) = 117.8, p < 0.001). Error bars indicate mean differences (± SEM) between syllable and phoneme times for each speech network.