Prosodic temporal alignment of co-speech gestures to speech facilitates referent resolution.
Bibliographic record
Abstract
Using a referent detection paradigm, we examined whether listeners can determine the object speakers are referring to by using the temporal alignment between the motion speakers impose on objects and their labeling utterances. Stimuli were created by videotaping speakers labeling a novel creature. Without being explicitly instructed to do so, speakers moved the creature during labeling. Trajectories of these motions were used to animate photographs of the creature. Participants in subsequent perception studies heard these labeling utterances while seeing side-by-side animations of two identical creatures in which only the target creature moved as originally intended by the speaker. Using the cross-modal temporal relationship between speech and referent motion, participants identified which creature the speaker was labeling, even when the labeling utterances were low-pass filtered to remove their semantic content or replaced by tone analogues. However, when the prosodic structure was eliminated by reversing the speech signal, participants no longer detected the referent as readily. These results provide strong support for a prosodic cross-modal alignment hypothesis. Speakers produce a perceptible link between the motion they impose upon a referent and the prosodic structure of their speech, and listeners readily use this prosodic cross-modal relationship to resolve referential ambiguity in word-learning situations.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".