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Record W2122054562 · doi:10.1080/15248370802247939

Eye Gaze Provides a Window on Children's Understanding of Verbal Irony

2008· article· en· W2122054562 on OpenAlex
Emma A. Climie, Penny M. Pexman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Cognition and Development · 2008
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychologyIronyGazeSarcasmComprehensionLiteral (mathematical logic)Eye trackingLiteral and figurative languageContext (archaeology)Cognitive psychologyEye movementNonverbal communicationLinguisticsCommunicationComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We investigated how children solve the interpretive problem of verbal irony. Children 5 to 8 years of age and a group of adults were presented with ironic and literal remarks in the context of short puppet shows. The speaker puppet's personality was manipulated as a cue to intent; that is, speakers were described as funny or serious. We measured all participants' interpretations of the remarks and also children's eye gaze and response latencies as they made their interpretations. As expected, children were less accurate than adults in their judgments of speaker intent. Although children took longer to judge speaker intent for ironic remarks than literal remarks, eye gaze data showed no evidence that children had a literal-first bias in their processing of ironic language. Instead, children's eye gaze behavior suggested that they considered an ironic interpretation even in the earliest moments of processing. We argue that these results are most consistent with a parallel constraint satisfaction framework for irony comprehension.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.284
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it