The stability of visual–aerotactile effects across multiple presentations of a single token
Bibliographic record
Abstract
Previous research has shown that the sensation of airflow causes bilabial stop closures to be perceived as aspirated even when paired with silent articulations rather than an acoustic signal [Bicevskis et al. 2016, JASA 140(5): 3531–3539]. However, some evidence suggests that perceivers integrate this cue differently if the silent articulations come from an animated face [Keough et al. 2017, Canadian Acoustics 45(3):176–177] rather than a human one. Participants shifted from a strong initial /ba/ bias to a strong /pa/ bias by the second half of the experiment, suggesting the participants learned to associate the video with the aspirated articulation through experience with the airflow. One explanation for the above findings is methodological: participants saw a single video clip while previous work exposed participants to multiple videos. The current study reports two experiments using a single clip with a human face (originally from Bicevskis et al. 2016). We found no evidence of a bias shift, indicating that the findings reported by Keough et al. are not attributable to the use of a single video. Instead, our findings suggest that aero-tactile cues shift consonant perception regardless of the number of recordings presented as long as the speaking face is human.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".