Age-related differences in auditory spatial attention depend on task switching complexity.
Bibliographic record
Abstract
We investigated the role of simple and complex switching of auditory attention in a multi-talker, multi-spatial listening situation with target location uncertainty. In all conditions, a target sentence from an edited version of the CRM corpus was presented from one spatial location and competing sentences from two different locations, with cues specifying the target’s callsign identity and the probability of its location. Four probability specifications indicated the likelihood of the target being presented at the left, center, and right locations (0-100-0, 10-80-10, 20-60-20, and 33-33-33). In conditions requiring simple switches of attention, the task was to report key words from the target sentence. In conditions requiring complex attention switching, when target callsigns were presented from one of the unlikely locations, the listener’s task was to report key words presented from the other unlikely location. A total of eight younger and eight older adults who had normal audiometric thresholds below 4 kHz participated. The key finding is that, whereas both age groups performed similarly in conditions requiring simple switches of attention, older performed worse than younger listeners in conditions requiring complex switching. Switching complexity may explain, in part, why older adults with relatively good audiograms report difficulty communicating in complex listening situations.
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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) | 0.000 | 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) | 0.002 | 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".