Brain activity supporting alternating speech for semantic words: simultaneous magnetoencephalographic recording
Bibliographic record
Abstract
Communication, especially conversation, is essential for human social life. Many previous studies have examined the neuroscientific underpinnings of conversation, i.e. language comprehension and speech production. However, conversation inherently involves two or more people, and unless two people actually interact with one another, the nature of the conversation cannot be truly revealed. Therefore, in this study, we used two magnetoencephalographs that were connected together, and simultaneously recorded brain activity while two people took turns speaking in a word association/alphabet completion task. We compared the amplitude modulation of the alpha- and beta-band rhythms within each of the 62 brain regions under semantic (word association; less predictable) and non-semantic (alphabet completion; more predictable) conditions. We found that the amplitudes of the rhythms were significantly different between conditions in a wide range of brain regions. Additionally, significant differences were observed in nearly the same group of brain regions after versus before each utterance, indicating that a wide range of brain areas is involved in predicting a conversation partner's next utterance. This result supports the idea that mentalizing, e.g. predicting another person's speech, plays an important role in conversation, and suggests that the neural network implicated in mentalizing extends over a wide range of brain regions.
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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 | 0.000 | 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) | 0.001 | 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".