Does native language temporal experience transfer to audio-visual synchrony perception?
Bibliographic record
Abstract
The temporal alignment of what we hear and see is fundamental for the cognitive organization of information from our environment. Research indicates that a perceiver's experience influences sensitivity to audio-visual (AV) synchrony. We theorize that experience that enhances sensitivity to speech sound distinctions in the temporal domain would enhance sensitivity in AV synchrony perception. With this basis, a perceiver whose native language (L1) involves duration-based phonemic distinctions would be expected to be more sensitive to AV synchrony in speech than for an L1 which has less use of temporal cues. In the current study, simultaneity judgment data for the syllable /ba/ were collected with 23 steps of AV alignments: from audio preceding the video (audio-lead) to the audio and video being physically aligned (synchronous) to video preceding the audio (video-lead). Two groups of participants differing in L1 experience with phonemic duration were included: native speakers of Norwegian (binary phonemic quantity distinction) and English (no phonemic quantity distinction). Preliminary results based on measures the audio-lead threshold (ALT) support the hypothesis that native language experience may influence broad mechanisms of timing, such as those moderating AV synchrony perception. Findings contribute to understanding the underpinnings of experience and AV synchrony perception.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".