The effect of hearing loss on auditory spatial attention
Bibliographic record
Abstract
To understand better how hearing loss affects spatial listening abilities in complex multi-talker situations, we investigated auditory spatial attention in 8 older adults with normal audiometric thresholds below 4 kHz and 8 older adults with bilateral sloping sensorineural hearing losses ranging from moderate to severe in the higher frequencies. In a condition with real spatial separation, a target sentence from the Coordinate Response Measure (CRM) corpus was presented from one spatial location and competing sentences from two different locations, with pre-trial cues specifying the target’s identity and location. In a condition with simulated spatial separation, corresponding perceived spatial locations of the target and competitors were achieved through exploitation of the precedence effect. Seven different probability speci cations indicated the likelihood of the target being presented at the three locations (100-0-0, 80-0- 20, 60-0-40, 0-0-100, 20-0-80, 40-0-60, and 50-0-50, respectively for 0°, 45°, and 90° azimuth). As expected, hearing-impaired listeners performed worse than normal-hearing listeners across all listening conditions, but especially in conditions where the location of the target was less certain. For both groups, performance was superior when the target was presented from the more probable location than when it was presented from the less probable location. Implications for clinical practice and future research will be 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".