The influence of concurrent working memory tasks on the visual contributions to speech.
Bibliographic record
Abstract
Visual speech information can influence the perception of acoustic speech. In acoustically noisy environments, people can often understand more words correctly if they can see a talker as well as hear them. Additionally, conflicting visual speech information can influence the perception of acoustic speech (namely, the McGurk effect), causing a percept of a sound that was not present in actual acoustic speech. This auditory and visual speech information does not need to be perfectly synchronous in order to be integrated. Rather, there is a “synchrony window” over which this information can be integrated. The extent to which a distracting cognitive load task affects the influence of the visual speech is still not well understood. It is also unknown whether a distracting cognitive task has an influence on the integration of temporally asynchronous speech. A series of experiments using both speech-in-noise and McGurk tasks with concurrent working memory tasks was used to address this question. The temporal offset in some of the McGurk tasks was also manipulated. Overall, results suggest that while some interference of the cognitive task can be observed, this influence is quite small and does not have a substantial influence on the integration of asynchronous speech.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.022 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".