From Speech Semantics to Brain Activity—Timescales Are Key in Their Information Transfer
Bibliographic record
Abstract
Fluctuating timescales are present in nature and are commonly observed in music, movies, brain activity, and speech. In human speech, semantic timescales span from single words to complete sentences and vary throughout conversation. Similarly, the brain's intrinsic neuronal timescales (INT), reflected in temporally correlated activity, carry information across time. How are these semantic and neuronal timescales related? Our combined semantic input and functional magnetic resonance imaging (fMRI) study using the 7 Tesla Human Connectome Project movie-watching dataset reveals information transfer from speech's semantic timescales to the brain's INT. We extracted two semantic time-series, sentence similarity and word depth, using Sentence-BERT (SBERT) and WordNet, respectively. The timescales of both semantic signals and the brain's activity were quantified using the autocorrelation window (ACW), with a dynamic, time-varying analysis approach. This allows testing for information transfer from the simultaneously varying semantic timescales to the brain's varying timescales via Transfer Entropy (TE). We report three main findings: (1) Sentence similarity and word depth time-series exhibit high and systematic fluctuations over time. (2) Dynamic ACW analysis captures the dominant timescales in both semantic input (sentence similarity and word depth) and the brain's continuously varying INT. (3) Significant TE from the varying semantic timescales to the brain's simultaneously varying INT. We also demonstrate that the information transfer only emerges on the level of timescales, and is absent when comparing the two raw semantic input time-series with the BOLD signal, respectively. Conclusively, we demonstrate the key role of timescales in the information transfer from semantic inputs to the brain's neural activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".