Coupled neural systems underlie the production and comprehension of naturalistic narrative speech
Bibliographic record
Abstract
Neuroimaging studies of language have typically focused on either production or comprehension of single speech utterances such as syllables, words, or sentences. In this study we used a new approach to functional MRI acquisition and analysis to characterize the neural responses during production and comprehension of complex real-life speech. First, using a time-warp based intrasubject correlation method, we identified all areas that are reliably activated in the brains of speakers telling a 15-min-long narrative. Next, we identified areas that are reliably activated in the brains of listeners as they comprehended that same narrative. This allowed us to identify networks of brain regions specific to production and comprehension, as well as those that are shared between the two processes. The results indicate that production of a real-life narrative is not localized to the left hemisphere but recruits an extensive bilateral network, which overlaps extensively with the comprehension system. Moreover, by directly comparing the neural activity time courses during production and comprehension of the same narrative we were able to identify not only the spatial overlap of activity but also areas in which the neural activity is coupled across the speaker's and listener's brains during production and comprehension of the same narrative. We demonstrate widespread bilateral coupling between production- and comprehension-related processing within both linguistic and nonlinguistic areas, exposing the surprising extent of shared processes across the two systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".