Pupil responses indicate selection of task-relevant background sounds during a dual continuous listening task
Bibliographic record
Abstract
Abstract Auditory attention can be voluntarily directed towards a sound source or automatically captured by background sounds, which may be either relevant, such that the listener shifts their attention to them, or irrelevant such that the listener tries to ignore or inhibit them. The ability to switch focus to a relevant sound source while inhibiting an irrelevant one requires attentional control and is crucial for navigating busy auditory scenes. Objective measures of attentional control could be beneficial in clinical contexts, such as fitting hearing aids. In a dual-task paradigm, we investigated whether pupil responses reflect relevance-dependent attentional selectivity. Participants with self-reported normal hearing (N = 21, Age: 27 to 66 years, pure tone average: −4 to +26 dB HL) listened to continuous speech from the front (primary task) while background sounds, consisting of cue names followed immediately by two-digit numbers, were presented from the left and right. The participant was told that one side, either right or left, was relevant and the other, irrelevant. The secondary task involved memorizing and later recognizing numbers from the relevant side. We observed increased pupil responses to sounds from the relevant side compared to the irrelevant side, indicating selectivity. Exploratory analysis showed that participants who exhibited stronger selectivity recognized more numbers correctly. Interestingly, pupil responses did not differ between hits and misses, but a stronger response to stream confusions versus correct rejections was found, suggesting that participants were more challenged by inhibiting irrelevant sounds than shifting attention to relevant sounds. In sum, our findings demonstrate that pupillometry provides valuable insights into attentional control abilities.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".