Functional Near-Infrared Spectroscopy as a Measure of Listening Effort in Older Adults Who Use Hearing Aids
Bibliographic record
Abstract
Listening effort may be reduced when hearing aids improve access to the acoustic signal. However, this possibility is difficult to evaluate because many neuroimaging methods used to measure listening effort are incompatible with hearing aid use. Functional near-infrared spectroscopy (fNIRS), which can be used to measure the concentration of oxygen in the prefrontal cortex (PFC), appears to be well-suited to this application. The first aim of this study was to establish whether fNIRS could measure cognitive effort during listening in older adults who use hearing aids. The second aim was to use fNIRS to determine if listening effort, a form of cognitive effort, differed depending on whether or not hearing aids were used when listening to sound presented at 35 dB SL (flat gain). Sixteen older adults who were experienced hearing aid users completed an auditory n-back task and a visual n-back task; both tasks were completed with and without hearing aids. We found that PFC oxygenation increased with n-back working memory demand in both modalities, supporting the use of fNIRS to measure cognitive effort during listening in this population. PFC oxygenation was weakly and nonsignificantly correlated with self-reported listening effort and reaction time, respectively, suggesting that PFC oxygenation assesses a dimension of listening effort that differs from these other measures. Furthermore, the extent to which hearing aids reduced PFC oxygenation in the left lateral PFC was positively correlated with age and pure-tone average thresholds. The implications of these findings as well as future directions are discussed.
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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) | 0.001 | 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".