The Effect of a Concurrent Working Memory Task and Temporal Offsets on the Integration of Auditory and Visual Speech Information
Bibliographic record
Abstract
Audiovisual speech perception is an everyday occurrence of multisensory integration. Conflicting visual speech information can influence the perception of acoustic speech (namely the McGurk effect), and auditory and visual speech are integrated over a rather wide range of temporal offsets. This research examined whether the addition of a concurrent cognitive load task would affect the audiovisual integration in a McGurk speech task and whether the cognitive load task would cause more interference at increasing offsets. The amount of integration was measured by the proportion of responses in incongruent trials that did not correspond to the audio (McGurk response). An eye-tracker was also used to examine whether the amount of temporal offset and the presence of a concurrent cognitive load task would influence gaze behavior. Results from this experiment show a very modest but statistically significant decrease in the number of McGurk responses when subjects also perform a cognitive load task, and that this effect is relatively constant across the various temporal offsets. Participant's gaze behavior was also influenced by the addition of a cognitive load task. Gaze was less centralized on the face, less time was spent looking at the mouth and more time was spent looking at the eyes, when a concurrent cognitive load task was added to the speech task.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".