Learning speech-internal cues to pronoun interpretation from co-speech gesture: a training study
Bibliographic record
Abstract
This study explores whether children can learn a structural processing bias relevant to pronoun interpretation from brief training. Over three days, 42 five-year-olds were exposed to narratives exhibiting a first-mentioned tendency. Two characters were introduced, and the first-mentioned was later described engaging in a solo activity. In our primary condition of interest, the Gesture Training condition, the solo-activity sentence contained an ambiguous pronoun, but co-speech gesture clarified the referent. There were two comparison conditions. In the Gender Training condition the characters were different genders, thereby avoiding ambiguity. In the Name Training condition, the first-mentioned name was simply repeated. Ambiguous pronoun interpretation was tested pre- and post-training. Children in the Gesture condition were significantly more likely to interpret ambiguous pronouns as the first-mentioned character after training. Results from the comparison conditions were ambiguous: there was a small but non-significant effect of training, but also no significant differences between conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".