Assessing the sensitivity of the pupil dilation response to different sources of difficulty in interactive conversation
Bibliographic record
Abstract
Recent studies have investigated how effort and difficulty during conversational interactions can be inferred from pupillary responses. Yet, the primary intervention in these studies is background noise, the presence of which inherently modulates the size of the pupil. This study investigates this concern by comparing pupillary responses between conditions where acoustic (background noise) and non-acoustic (second language) factors were varied to alter expected conversational difficulty. Data was collected from 20 pairs of university-aged, normal-hearing, native-Ukrainian talkers who held conversations in their first language (Ukrainian) and a second language (English) both in the presence and absence of background noise. Pupil responses were measured around the starts and ends of conversational turns and assessed for differences in size and temporal dynamics based on language proficiency and background noise. Results will compare how these aspects of the pupil response vary based on the source of conversational difficulty. The implications of these findings will be discussed, including the potential for extending the pupil response from a general marker of effort/attention to one that can differentiate between distinct higher-level processes. The impact of these findings for understanding the effects of other sources of difficulty, such as hearing loss, will also be considered.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".