The effect of age on auditory spatial attention in conditions of real and simulated spatial separation
Bibliographic record
Abstract
The contributions of auditory and cognitive factors to age-dependent differences in auditory spatial attention were investigated. In conditions of real spatial separation, the target sentence was presented from a central location and competing sentences were presented from left and right locations. In conditions of simulated spatial separation, different apparent spatial locations of the target and competitors were induced using the precedence effect. The identity of the target was cued by a callsign presented either prior to or following each target sentence, and the probability that the target would be presented at the three locations was specified at the beginning of each block. Younger and older adults with normal hearing sensitivity below 4 kHz completed all 16 conditions (2-spatial separation method X 2-callsign conditions X 4-probability conditions). Overall, younger adults performed better than older adults. For both age groups, performance improved with target location certainty, with a priori target cueing, and when location differences were real rather than simulated. For both age groups, the contributions of natural spatial cues were most pronounced when the target occurred at "unlikely" spatial listening locations. This suggests that both age groups benefit similarly from richer acoustical cues and a priori information in difficult listening environments.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".