Salience-dependent disruption of sustained auditory attention can be inferred from evoked pupil responses and neural tracking of task-irrelevant sounds
Bibliographic record
Abstract
Abstract Stimulus-driven attention allows us to react to relevant stimuli (and imminent danger!) outside our current focus of attention. But irrelevant stimuli can also disrupt attention; for example, during listening to speech. The degree to which sound captures attention is called salience, which can be estimated by existing, behaviorally validated, computational models (Huang & Elhilali, 2017). Here we examined whether neurophysiological responses to task-irrelevant sounds indicate the degree of distraction during a sustained-listening task and how much this depends on individual hearing thresholds. N = 47 Danish-speaking adults (28/19 female/male; mean age: 60.1, SD: 15.9 years) with heterogenous hearing thresholds (PTA; mean: 25.5, SD: 18.0 dbHL) listened to continuous speech while one-second-long, task-irrelevant natural sounds (distractors) of varying computed salience were presented at unpredictable times and locations. Eye tracking and electroencephalography were used to estimate pupil response and neural tracking, respectively. The task-irrelevant sounds evoked a consistent pupil response (PR), distractor-tracking (DT) and a drop of target-tracking (ΔTT), and statistical modelling of these three measures within subjects showed that all three are enhanced for sounds with higher computed salience. Participants with larger PR showed a stronger drop in target tracking (ΔTT) and performed worse in target speech comprehension. We conclude that distraction can be inferred from neurophysiological responses to task-irrelevant stimuli. These results are a first step towards neurophysiological assessment of attention dynamics during continuous listening, with potential applications in hearing-care diagnostics. Significance statement Successful speech-in-noise comprehension in daily life does not only depend on the acuity of the auditory input, but also cognitive factors like attentional control. Being able to measure distraction-dependent neurophysiological responses to peripheral, task-irrelevant stimuli would enable monitoring the extent to which the attentional focus is instantaneously captured away from a target under sustained attention. Here we show that especially pupil response and neural tracking of distractor sounds reflect the degree to which people with both normal and elevated hearing thresholds are distracted. Such a measure could be used to non-invasively track the focus of attention and thus could find application in hearing care diagnostics, where cognitive factors like attentional control are being increasingly recognized as important.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".