Frontal and temporal contributions to understanding the iconic co‐speech gestures that accompany speech
Bibliographic record
Abstract
In everyday conversation, listeners often rely on a speaker's gestures to clarify any ambiguities in the verbal message. Using fMRI during naturalistic story comprehension, we examined which brain regions in the listener are sensitive to speakers' iconic gestures. We focused on iconic gestures that contribute information not found in the speaker's talk, compared with those that convey information redundant with the speaker's talk. We found that three regions-left inferior frontal gyrus triangular (IFGTr) and opercular (IFGOp) portions, and left posterior middle temporal gyrus (MTGp)--responded more strongly when gestures added information to nonspecific language, compared with when they conveyed the same information in more specific language; in other words, when gesture disambiguated speech as opposed to reinforced it. An increased BOLD response was not found in these regions when the nonspecific language was produced without gesture, suggesting that IFGTr, IFGOp, and MTGp are involved in integrating semantic information across gesture and speech. In addition, we found that activity in the posterior superior temporal sulcus (STSp), previously thought to be involved in gesture-speech integration, was not sensitive to the gesture-speech relation. Together, these findings clarify the neurobiology of gesture-speech integration and contribute to an emerging picture of how listeners glean meaning from gestures that accompany speech.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".