Pupil dilation is sensitive to semantic ambiguity and acoustic degradation
Bibliographic record
Abstract
Abstract Speech comprehension is often challenged by background noise or other acoustic interference. It can also be challenged by linguistic factors, such as complex syntax, or the presence of words with more than one meaning. Pupillometry is increasingly recognized as a technique that provides a window onto acoustic challenges, but this work has not been well integrated with an older literature linking pupil dilation to “mental effort”, which would include linguistic challenges. Here, we measured pupil dilation while listeners heard spoken sentences with clear sentence-level meaning that contained words with more than one meaning (“The shell was fired towards the tank”) or matched sentences without ambiguous words (“Her secrets were written in her diary”). This semantic-ambiguity manipulation was crossed with an acoustic manipulation: two levels of a 30-talker babble masker in Experiment 1; and presence or absence of a pink noise masker in Experiment 2. Speech comprehension, indexed by a semantic relatedness task, was high (above 82% correct) in all conditions. Pupils dilated when sentences included semantically ambiguous words compared to matched sentences and when maskers were present compared to absent (Experiment 2) or were more compared to less intense (Experiment 1). The current results reinforce the idea that many different challenges to speech comprehension, that afford different cognitive processes and are met by the brain in different ways, manifest as an increase in pupil dilation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".