Eye movements of younger and older adults decrease during story listening in background noise
Bibliographic record
Abstract
Abstract Assessments of listening effort are increasingly relevant to understanding the speech-comprehension difficulties experienced by older adults. Pupillometry is the most common tool to assess listening effort but has limitations. Recent research has shown that eye movements decrease when listening is effortful and proposed indicators of eye movements as alternative measures. However, much of the work was conducted in younger adults in trial-based sentence-listening paradigm, during concurrent visual stimulation. The extent to which eye movements index listening effort during con tinuous speech listening, independently of visual stimuli, and in older adults, is unknown. In the current study, younger and older adults listened to continuous stories with varying degrees of background noise under free and moving-dot viewing conditions. Eye movements decreased (as indexed by fixation duration, gaze dispersion, and saccade rate) with increasing speech masking. The reduction in eye movements did not depend on age group or viewing conditions, indicating that eye movements can be used to assess effects of speech masking in different visual situations and in people of different ages. The pupil area was only sensitive to speech masking early in the experiment. In sum, the current study suggests that eye movements are a potential tool to assess listening effort during continuous speech listening.
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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 | 0.001 |
| 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 | 0.000 |
| 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".