Effects of aging and noncanonical form presentation on idiom processing: Evidence from eye tracking
Bibliographic record
Abstract
Abstract Comprehending idioms (e.g., bite the bullet) requires that people appreciate their figurative meanings while suppressing literal interpretations of the phrase. While much is known about idioms, an open question is how healthy aging and noncanonical form presentation affect idiom comprehension when the task is to read sentences silently for comprehension. Here, younger and older adults read sentences containing idioms or literal phrases, while we monitored their eye movements. Idioms were presented in a canonical or a noncanonical form (e.g., bite the iron bullet). To assess whether people integrate figurative or literal interpretations of idioms, a disambiguating region that was figuratively or literally biased followed the idiom in each sentence. During early stages of reading, older adults showed facilitation for canonical idioms, suggesting a greater sensitivity to stored idiomatic forms. During later stages of reading, older adults showed slower reading times when canonical idioms were biased toward their literal interpretation, suggesting they were more likely to interpret idioms figuratively on the first pass. In contrast, noncanonical form presentation slowed comprehension of figurative meanings comparably in younger and older participants. We conclude that idioms may be more strongly entrenched in older adults, and that noncanonical form presentation slows comprehension of figurative meanings.
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 | 0.000 | 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 | 0.000 |
| 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".