Bibliographic record
Abstract
Hearing impairment affects many older adults but is often diagnosed decades after speech comprehension in noisy situations has become effortful. Accurate assessment of listening effort may thus help diagnose hearing impairment earlier. However, pupillometry—the most used approach to assess listening effort—has limitations that hinder its use in practice. The current study explores a novel way to assess listening effort through eye movements. Building on cognitive and neurophysiological work, we examine the hypothesis that eye movements decrease when speech listening becomes challenging. In three experiments with human participants from both sexes, we demonstrate, consistent with this hypothesis, that fixation duration increases and spatial gaze dispersion decreases with increasing speech masking. Eye movements decreased during effortful speech listening for different visual scenes (free viewing, object tracking) and speech materials (simple sentences, naturalistic stories). In contrast, pupillometry was less sensitive to speech masking during story listening, suggesting pupillometric measures may not be as effective for the assessments of listening effort in naturalistic speech-listening paradigms. Our results reveal a critical link between eye movements and cognitive load, suggesting that neural activity in the brain regions that support the regulation of eye movements, such as frontal eye field and superior colliculus, are modulated when listening is effortful. SIGNIFICANCE STATEMENT Assessment of listening effort is critical for early diagnosis of age-related hearing loss. Pupillometry is most used but has several disadvantages. The current study explores a novel way to assess listening effort through eye movements. We examine the hypothesis that eye movements decrease when speech listening becomes effortful. We demonstrate, consistent with this hypothesis, that fixation duration increases and gaze dispersion decreases with increasing speech masking. Eye movements decreased during effortful speech listening for different visual scenes (free viewing, object tracking) and speech materials (sentences, naturalistic stories). Our results reveal a critical link between eye movements and cognitive load, suggesting that neural activity in brain regions that support the regulation of eye movements are modulated when listening is effortful.
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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 | 0.002 |
| 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".