Examining Older Adults' Visual Speech Benefit: Effects of Two Talking Faces With and Without Cueing
Bibliographic record
Abstract
PURPOSE: The aim of this study was to investigate whether older adults experience a reduced visual speech benefit when viewing multiple talking faces, potentially due to increased cognitive processing demands. The current experiment investigated this by presenting a talker's auditory and visual speech (target talker) and an extra talking face. METHOD: = 70 years) completed a speech-perception-in-noise task across four conditions: valid cue two-talking-face, ambiguous cue two-talking-face, one-talking-face, and static-face (auditory speech only) conditions. In the two-talking-face conditions, the faces had the same identity and swapped locations randomly across trials, with either valid or ambiguous cues indicating the target face location. RESULTS: Speech recognition was superior in the valid cue condition compared to the ambiguous cue condition, with this cueing effect being significantly smaller in older adults. Younger adults' performance in the valid cue condition generally matched their one-talking-face condition performance, whereas older adults performed considerably worse in the valid cue condition. CONCLUSIONS: We suggest that this age effect was due to older adults being distracted by the irrelevant talking face. This distraction account may have implications for the extent that older adults get a visual speech benefit in group conversations. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29318336.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".