The effect of interaural intensity cues and expectations of target location on word identification in multi-talker scenes for younger and older adults
Bibliographic record
Abstract
Research on word identification in binaural conditions usually examines auditory abilities in simple, static environments. Research on attention usually examines cognitive abilities to divide and switch attention between multiple stimuli in more complex and dynamic scenes. To investigate cognitive-auditory interactions in uencing age-related differences in listening in complex situations, we tested younger and older listeners’ abilities to identify target words in conditions where we manipulated the availability of interaural cues and expectations concerning the likelihood of the target being heard at a primary location. Interaural cues were manipulated by presenting the target and two competing sentences from different loudspeakers (real spatial separation) or from three perceived locations induced using the precedence effect (simulated spatial separation). Prior to the presentation of a target, the listener was cued for the probability (1.0, 0.8, 0.6, 0.33) of it being presented at the primary location. Younger adults outperformed older adults and performance was better when the target was presented at the expected location. Eliminating interaural intensity cues had no effect when targets occurred at the expected location, but performance was reduced when the targets were presented at less expected locations. For both age groups, rich interaural cues enhance attention in dynamic listening environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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".