Pupil Dilation Is Sensitive to Semantic Ambiguity and Acoustic Degradation
Bibliographic record
Abstract
Speech comprehension is challenged by background noise, acoustic interference, and linguistic factors, such as the presence of words with more than one meaning (homonyms and homophones). Previous work suggests that homophony in spoken language increases cognitive demand. Here, we measured pupil dilation—a physiological index of cognitive demand—while listeners heard high-ambiguity sentences, containing words with more than one meaning, or well-matched low-ambiguity sentences without ambiguous words. This semantic-ambiguity manipulation was crossed with an acoustic manipulation in two experiments. In Experiment 1, sentences were masked with 30-talker babble at 0 and +6 dB signal-to-noise ratio (SNR), and in Experiment 2, sentences were heard with or without a pink noise masker at –2 dB SNR. Speech comprehension was measured by asking listeners to judge the semantic relatedness of a visual probe word to the previous sentence. In both experiments, comprehension was lower for high- than for low-ambiguity sentences when SNRs were low. Pupils dilated more when sentences included ambiguous words, even when no noise was added (Experiment 2). Pupil also dilated more when SNRs were low. The effect of masking was larger than the effect of ambiguity for performance and pupil responses. This work demonstrates that the presence of homophones, a condition that is ubiquitous in natural language, increases cognitive demand and reduces intelligibility of speech heard with a noisy background.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".