Pupil-size sensitivity to listening demand depends on motivational state
Bibliographic record
Abstract
Abstract Motivation plays a role when a listener needs to understand speech under acoustically demanding conditions. Previous work has demonstrated pupil-linked arousal being sensitive to both listening demands and motivational state during listening. It is less clear how motivational state affects the temporal evolution of the pupil size and its relation to subsequent behavior. We used an auditory gap-detection task (N=33) to study the joint impact of listening demand and motivational state on the pupil-size response and examine its temporal evolution. Task difficulty and a listener’s motivational state were orthogonally manipulated through changes in gap duration and monetary-reward prospect. We show that participants’ performance decreased with task difficulty, but that reward prospect enhanced performance under hard listening conditions. Pupil size increased with both increased task difficulty and higher reward prospect, and this reward-prospect effect was largest under difficult listening conditions. Moreover, pupil-size time courses differed between detected and missed gaps, suggesting that the pupil response indicates upcoming behavior. Larger pre-gap pupil size was further associated with faster response times on a trial-by-trial within-participant level. Our results reiterate the utility of pupil size as an objective and temporally sensitive measure in audiology. However, such assessments of cognitive-resource recruitment need to consider the individual’s motivational state. Significance statement The literature is inconclusive to what degree pupil size may be representing the interaction of listening task demand and an individual’s motivational state. Using an auditory gap-detection task, we manipulated both the degree of listening demand and a person’s motivational state, and investigated the temporal evolution of the pupil-size response and its relation to behavior. We find that the pupil size represents the interaction of demand and motivational state. These results highlight the importance of considering a person’s motivational state when using pupil size as a clinical measure. Pupil size appears as a key tool when assessing the impact of motivational state on the recruiting of cognitive resources.
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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 | 0.004 |
| 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) | 0.003 | 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".