Cross-modal association between auditory and visual-spatial information in Mandarin tone perception
Bibliographic record
Abstract
Speech perception involves multiple input modalities. Research has indicated that perceivers may establish a cross-modal association between auditory and visual-spatial events to aid perception. Such intermodal relations can be particularly beneficial for non-native perceivers who need additional resources to process challenging new sounds. This study examines how co-speech hand gestures mimicking pitch contours in space affect non-native Mandarin tone perception. Native English as well as Mandarin perceivers identified tones with either congruent or incongruent auditory-facial and gestural (AF/G) input. Perceivers also identified congruent and incongruent auditory-facial (A/F) stimuli. Native Mandarin results showed the expected ceiling-level performance in the congruent A/F and AF/G conditions. In the incongruent conditions, while A/F identification was primarily auditory-based, AF/G identification was partially based on gestures, demonstrating the use of gestures as valid cues in tone identification. The English perceivers’ performance was poor in the congruent A/F condition, but improved significantly in AF/G. While the incongruent A/F identification showed some reliance on facial information, incongruent AF/G identification relied more on gestural than auditory-facial information. These results indicate positive effects of facial and especially gestural input on non-native tone perception, suggesting that cross-modal (visual-spatial) resources can be recruited to aid auditory perception when phonetic demands are high.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".