Tongue shape variations in laughter and speech: Exploring movement patterns
Bibliographic record
Abstract
Laughter is a common non-verbal communicative function of the vocal tract [Krepsz et al., 2024, CognitiveProcessing, 25(1)]. Previous studies using real-time magnetic resonance imaging (rtMRI) of the vocal tract found spontaneous (natural) laughter to be less speech-like compared to volitional (induced) laughter [Belyk & McGettigan, 2022, Phil. Trans. Royal Soc. B, 377(1863)]. Expanding on prior research on laughter production [Belyk & McGettigan, 2022] and ultrasound imaging comparing the production of spoken vowels to trombone notes [Heyne & Derrick, 2019, Frontiers in Psych., 10(2597)], the present study employs ultrasound imaging to analyze tongue shape variations between spontaneous and induced laughter, to ascertain whether laughter draws on the speech movement inventory. Video stimuli were presented to elicit spontaneous laughter, and vowels [i, ɪ, e, ɛ, æ, ʌ, ə, ʊ, u, o, ɔ, ɑ] were produced to compare articulatory postures. Acoustic analyses using Praat [Boersma & Weenink, 2024] and ultrasound data will be presented comparing spontaneous vs. natural laughter and their similarity to vowels in the speech inventory. Induced laughter is predicted to draw on speech-like behaviour, exhibiting similar tongue postures to speech compared to natural laughter. Implications will be discussed regarding similarities and differences in tongue positioning between spontaneous, volitional laughter, and speech.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".