Effect of attentional load on audiovisual speech perception: evidence from ERPs
Bibliographic record
Abstract
Seeing articulatory movements influences perception of auditory speech. This is often reflected in a shortened latency of auditory event-related potentials (ERPs) generated in the auditory cortex. The present study addressed whether this early neural correlate of audiovisual interaction is modulated by attention. We recorded ERPs in 15 subjects while they were presented with auditory, visual, and audiovisual spoken syllables. Audiovisual stimuli consisted of incongruent auditory and visual components known to elicit a McGurk effect, i.e., a visually driven alteration in the auditory speech percept. In a Dual task condition, participants were asked to identify spoken syllables whilst monitoring a rapid visual stream of pictures for targets, i.e., they had to divide their attention. In a Single task condition, participants identified the syllables without any other tasks, i.e., they were asked to ignore the pictures and focus their attention fully on the spoken syllables. The McGurk effect was weaker in the Dual task than in the Single task condition, indicating an effect of attentional load on audiovisual speech perception. Early auditory ERP components, N1 and P2, peaked earlier to audiovisual stimuli than to auditory stimuli when attention was fully focused on syllables, indicating neurophysiological audiovisual interaction. This latency decrement was reduced when attention was loaded, suggesting that attention influences early neural processing of audiovisual speech. We conclude that reduced attention weakens the interaction between vision and audition in speech.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".