Observers use gesture to disambiguate contrastive expressions of preference
Bibliographic record
Abstract
Abstract We present two studies exploring how participants respond when a speaker contrasts two ideas, then expresses an ambiguous preference towards one of them. Study 1 showed that, when reading a speaker’s preference as text, participants tended to choose whatever was said last as matching the speaker’s preference, reflecting the recent-mention bias of anaphora resolution. In Study 2, we asked whether this pattern changed for audio versions of our stimuli. We found that it did not. We then asked whether observers used gesture to disambiguate the speaker’s preference. Participants watched videos in which two statements were spoken. Co-speech gestures were produced during each statement, in two different locations. Next, an ambiguous preference for one option was spoken. In ‘gesture disambiguating’ trials, this statement was accompanied by a gesture in the same spatial location as the gesture accompanying the first statement. In ‘gesture non-disambiguating’ trials, no third gesture occurred. Participants chose the first statement as matching the speaker’s preference more often for gesture disambiguating compared to non-disambiguating trials. Our findings add to the literature on resolution of ambiguous anaphoric reference involving concrete entities and discourse deixis, and we extend this literature to show that gestures indexing abstract ideas are also used during discourse comprehension.
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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 | 0.002 | 0.019 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".