Sensory Integration from an Impossible Source: Perceiving Simulated Faces
Bibliographic record
Abstract
Recent research has shown that aero-tactile cues influence speech perception without the presence of an acoustic signal (Bicevskis, Derrick & Gick, 2016); when participants viewed a bilabial articulation that co-occurred with a puff of air felt on the skin, they were significantly more likely to perceive it as aspirated. These results and others (Gick & Derrick, 2009, etc.) suggest that this integration is relatively automatic, enough so that it does not require the physical presence of the source to arise. However, it may be that perceivers are willing to extend physical capabilities to these non-present sources because they are human and therefore possible sources of the aero-tactile cue. The current study examines whether aero-tactile information from an impossible source—a computer-animated face on a computer monitor—can affect perception of aspirated consonants. Sixteen native English speakers are shown an animated video of a computer-animated head performing a bilabial plosive but hear only babble noise through headphones. Some of the presentations are accompanied by a light, synchronous puff of air on the neck. They are asked to identify the syllable as either /ba/ or /pa/. Analysis of this two-alternative forced choice response task will be presented. Evidence of integration from an impossible source would support the idea that visual-tactile integration is an automatic process that occurs even in the absence of an interlocutor capable of producing the stimuli.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".