Tracking the Ironical Eye: Eye Tracking Studies on Irony and Sarcasm
Bibliographic record
Abstract
This chapter describes how people read and interpret ironical language. Tracking people’s rapid eye movements as they read can be an informative measure of the underling cognitive and linguistic processes operating during online written language comprehension. Attardo introduces some of the technologies employed in measuring eye movements during reading and suggests why these assessments can provide critical insights into how irony interpretation rapidly unfolds word-by-word as one reads. He reviews various experimental studies on irony and sarcasm understanding that provide explicit empirical tests of different theories of irony (e.g., multistate models, graded salience, parallel-constraint models predictive processing models). He also explores what the study of eye tracking reveals about the influence of contextual factors and individual differences in irony interpretation, as well as the phenomenon known as “gaze aversion” when listeners momentarily look away from speakers’ faces when hearing ironic language. Attardo closes his chapter with an important discussion of the sometimes contentious relations between psycholinguistic experiments and philosophical arguments on the ways people use and interpret irony in discourse.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".