Pupillometry reveals effects of pitch manipulation within and across words on listening effort and short-term memory
Bibliographic record
Abstract
For individuals with hearing loss, even successful speech communication comes at a cost. Cochlear implants transmit degraded information, specifically for voice pitch, which demands extra and sustained listening effort. The current study hypothesized that abnormal pitch patterns contribute to the additional listening effort, even in non-tonal language native speaking normally hearing listeners. We manipulated the fundamental frequency (F0) within and across words, while participants listen and repeat (simple intelligibility task), or listen, repeat, and later recall (concurrent encoding task) the words. In both experiments, the F0 manipulations resulted in small changes in intelligibility but no difference in free recall or subjective effort ratings. Pupillary metrics were yet sensitive to these manipulations: pupil dilations were larger when words were monotonized (flat contour) or inverted (the natural contour flipped upside-down), and larger when successive words were organized into a melodic pattern. The most likely interpretation is that the natural or expected F0 contour of a word contributes to its identity and facilitate its matching and retrieval from the phonological representation stored in long-term memory. Consequently, degrading words' F0 contour can result in extra listening effort. Our results call for solutions to improve pitch saliency and naturalness in future development of cochlear implants' signal processing strategies, even for non-tonal languages.
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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".