Pupillometry is sensitive to speech masking during story listening: a commentary on the critical role of modeling temporal trends
Bibliographic record
Abstract
An increase in pupil size is an important index of listening effort, for example, when listening to speech masked by noise. Specifically, the pupil dilates as the signal-to-noise ratio decreases. A growing body of work aims to assess listening effort under naturalistic conditions using continuous speech, such as spoken stories. However, a recent study found that pupil size was sensitive to speech masking only when listening to sentences but not under naturalistic conditions when listening to stories. The pupil typically constricts with increasing time on task during an experimental block or session, and it may be necessary to account for this temporal trend in experimental design and data analysis in paradigms using longer, continuous stimuli. In the current work, we re-analyze the previously published pupil data, taking into account a problematic constraint of randomization and time-on-task, and use the data to outline methodological solutions for accounting for temporal trends in physiological data using linear mixed models. The results show that, in contrast to the previous work, pupil size is indeed sensitive to speech masking even during continuous story listening. Furthermore, accounting for the temporal trend further allowed modeling the dynamic changes in the speech masking effect on pupil size over time as the continuous story unfolded. After demonstrating the importance of accounting for temporal trends in the analysis of empirical data, we provide simulations, methodological considerations, and user recommendations for the analysis of temporal trends in experimental data using linear mixed models.
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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 | 0.040 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".