Cross-Modal Prediction in Speech Perception
Bibliographic record
Abstract
Speech perception often benefits from vision of the speaker's lip movements when they are available. One potential mechanism underlying this reported gain in perception arising from audio-visual integration is on-line prediction. In this study we address whether the preceding speech context in a single modality can improve audiovisual processing and whether this improvement is based on on-line information-transfer across sensory modalities. In the experiments presented here, during each trial, a speech fragment (context) presented in a single sensory modality (voice or lips) was immediately continued by an audiovisual target fragment. Participants made speeded judgments about whether voice and lips were in agreement in the target fragment. The leading single sensory context and the subsequent audiovisual target fragment could be continuous in either one modality only, both (context in one modality continues into both modalities in the target fragment) or neither modalities (i.e., discontinuous). The results showed quicker audiovisual matching responses when context was continuous with the target within either the visual or auditory channel (Experiment 1). Critically, prior visual context also provided an advantage when it was cross-modally continuous (with the auditory channel in the target), but auditory to visual cross-modal continuity resulted in no advantage (Experiment 2). This suggests that visual speech information can provide an on-line benefit for processing the upcoming auditory input through the use of predictive mechanisms. We hypothesize that this benefit is expressed at an early level of speech analysis.
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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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".