Can co-speech hand gestures facilitate learning of non-native tones?
Bibliographic record
Abstract
Speech perception research has indicated that information from multiple input modalities (e.g., auditory, visual) facilitates second language (L2) speech learning. However, co-speech gestural information has shown mixed results. While L2 learners may benefit from this additional channel of information, it may also be inhibitory as learners may experience excessive cognitive load. This study examines the role of metaphoric hand gestures in L2 lexical tone learning using previously established laboratory training procedures. Training stimuli include Mandarin tones produced by native Mandarin speakers, with concurrent hand gestures mimicking pitch contours in space. Native Canadian English speakers are trained to perceive tones presented in one of three modalities: audio-visual (AV, speaker voice and face), audio-gesture (AG, speaker voice and hand gestures) and audio-visual-gesture (AVG). The effects of training are assessed by comparing the pre-training and post-training tone identification results. Greater improvements for the AVG compared to AV group would indicate the facilitative role of gestures. However, greater improvements for the AG or AV compared to AVG group would support the cognitive overload account. Findings are discussed in terms of how sensory-motor and cognitive domains cooperate functionally in speech perception and learning. [Equal contributions by KE, BH, and YW; Work supported by SSHRC]
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".