Co-speech gestures can interfere with learning foreign language words*
Bibliographic record
Abstract
Abstract Co-speech gestures can help the learning, processing, and memory of words and concepts, particularly motoric and spatial concepts such as verbs. The purpose of the present studies was to test whether co-speech gestures support the learning of words through gist traces of movement. We asked English monolinguals to learn 40 Cantonese words (20 verbs and 20 nouns). In two studies, we found support for the gist traces of congruent gestures being movement: participants who saw congruent gestures while hearing Cantonese words thought they had seen more verbs than participants in any other condition. However, gist traces were unrelated to the accurate recall of either nouns or verbs. In both studies, learning Cantonese words accompanied by congruent gestures tended to interfere with the learning of nouns (but not verbs). In Study 2, we ruled out the possibility that this interference was due either to gestures conveying representational information in another medium or to distraction from moving hands. We argue that gestures can interfere with learning foreign language words when they represent the referents (e.g., show shape or size) because learners must interpret the hands as something other than hands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 | 0.000 |
| 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".