Associations between speech understanding and auditory and visual tests of verbal working memory: effects of linguistic complexity, task, age, and hearing loss
Bibliographic record
Abstract
Listeners with hearing loss commonly report having difficulty understanding speech, particularly in noisy environments. Their difficulties could be due to auditory and cognitive processing problems. Performance on speech-in-noise tests has been correlated with reading working memory span (RWMS), a measure often chosen to avoid the effects of hearing loss. If the goal is to assess the cognitive consequences of listeners' auditory processing abilities, however, then listening working memory span (LWMS) could be a more informative measure. Some studies have examined the effects of different degrees and types of masking on working memory, but less is known about the demands placed on working memory depending on the linguistic complexity of the target speech or the task used to measure speech understanding in listeners with hearing loss. Compared to RWMS, LWMS measures using different speech targets and maskers may provide a more ecologically valid approach. To examine the contributions of RWMS and LWMS to speech understanding, we administered two working memory measures (a traditional RWMS measure and a new LWMS measure), and a battery of tests varying in the linguistic complexity of the speech materials, the presence of babble masking, and the task. Participants were a group of younger listeners with normal hearing and two groups of older listeners with hearing loss (n = 24 per group). There was a significant group difference and a wider range in performance on LWMS than on RWMS. There was a significant correlation between both working memory measures only for the oldest listeners with hearing loss. Notably, there were only few significant correlations among the working memory and speech understanding measures. These findings suggest that working memory measures reflect individual differences that are distinct from those tapped by these measures of speech understanding.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".