Objective and Subjective Hearing Difficulties Are Associated With Lower Inhibitory Control
Bibliographic record
Abstract
OBJECTIVE: Evidence suggests that hearing loss increases the risk of cognitive impairment. However, the relationship between hearing loss and cognition can vary considerably across studies, which may be partially explained by demographic and health factors that are not systematically accounted for in statistical models. DESIGN: Middle-aged to older adult participants (N = 149) completed a web-based assessment that included speech-in-noise (SiN) and self-report measures of hearing, as well as auditory and visual cognitive interference (Stroop) tasks. Correlations between hearing and cognitive interference measures were performed with and without controlling for age, sex, education, depression, anxiety, and self-rated health. RESULTS: The risk of having objective SiN difficulties differed between males and females. All demographic and health variables, except education, influenced the likelihood of reporting hearing difficulties. Small but significant relationships between objective and reported hearing difficulties and the measures of cognitive interference were observed when analyses were controlled for demographic and health factors. Furthermore, when stratifying analyses for males and females, different relationships between hearing and cognitive interference measures were found. Self-reported difficulty with spatial hearing and objective SiN performance were better predictors of inhibitory control in females, whereas self-reported difficulty with speech was a better predictor of inhibitory control in males. This suggests that inhibitory control is associated with different listening abilities in males and females. CONCLUSIONS: The results highlight the importance of controlling for participant characteristics when assessing the relationship between hearing and cognitive interference, which may also be the case for other cognitive functions, but this requires further investigations. Furthermore, this study is the first to show that the relationship between hearing and cognitive interference can be captured using web-based tasks that are simple to implement and administer at home without any assistance, paving the way for future online screening tests assessing the effects of hearing loss on cognition.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".