The effect of speaker age on the perception of ironic insults.
Bibliographic record
Abstract
We investigated a cue that readers may use in determining whether a remark such as "You are so helpful!" is intended as a compliment or as an ironic insult. The cue was the age of the speaker. Remarks were preceded by a sentence that either invited a literal or ironic interpretation of the remark. Data were collected on the familiarity of the remark as an ironic statement, and the incongruity of the remark with the prior context. In Experiment 1, participants were asked to rate the intent of the speaker as to how ironic, mocking, polite, and funny they intended their remark to be. In Experiment 2, participants read the scenarios as their eye movements were tracked. The results showed that age of the speaker had an impact on first pass reading times when statements were not familiar as ironic statements. Our younger adult participants did not appear to immediately activate a nonliteral interpretation of an ambiguous remark made by an older adult unless they had evidence from past experience that the remark is often used as an insult. However, ratings of the ironic intent of the statements were unaffected by speaker age; the age of the speaker affects the ease of interpretation but not the final outcome. The results are consistent with constraint-based theories of sentence comprehension. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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 | 0.002 | 0.028 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".