Listening effort: The impact of simulated hearing loss on cognitive functions in young \nadults
Bibliographic record
Abstract
Previous research has suggested that hearing loss significantly impacts cognition in both school-age children and the elderly (e.g. Hicks & Tharpe, 2002; Lin, 2011). This study is the first to report the potential cognitive impacts of simulated, mild hearing loss for a young adult population. The current study investigated the effect of simulated mild, highfrequency hearing loss on performance and effort in a sample of 43 young adults (aged 18- \n23) who claimed to have normal hearing. On a standardized Speech-in-Nosie (SIN) Task, a significant effect of simulated hearing loss was found for both the sentence repetition accuracy, as well as the listening effort necessary to complete the task. Further, to test the \ninteraction of cognitive load and hearing loss on accuracy and effort, participants completed a Memory Task under single- and dual-task conditions. While question response \naccuracy significantly decreased under dual-task conditions, accuracy was not significantly affected across hearing loss conditions. However, task effort was significantly increased in the simulated hearing loss condition. Analysis of order effects also suggested the employment of cognitive strategy, particularly in the SIN Task where the hearing conditions did not facilitate the recruitment of additional cognitive resources. All results are discussed from the standpoint of the Resource Allocation Hypothesis (Rabbitt, 1968). \nOverall, results suggested that mild hearing loss does negatively impact cognition and listening effort in young adults. However, young adults may be largely unable to detect this loss by themselves (Le Prell, Hensley, Campbell, Hall, and Guire., 2011; Widen, Holmes, Johnson, Bohlin and Erlandson., 2009). The Canadian healthcare system is also \nill-equipped to detect such an impairment if it is not diagnosed in early childhood. Future research should place the spotlight on young adults to help remedy these problems.
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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".