Eye movement measures of "invented idiom" processing reflect frequency, meaning dominance, and compositionality during training
Bibliographic record
Abstract
Fluently using and understanding figurative language is crucial for successful communication.For example, idioms are phrases whose meanings are not identifiable through analysis of their constituent words (e.g., kick the bucket, which means to die suddenly in English), and are a very common class of figurative language.While a great deal of research has investigated how adults process idioms, variability across idioms as well as people's experiences with idioms make it difficult to isolate the specific linguistic factors that promote their comprehension.In attempt to circumvent this limitation, this thesis makes use of an invented idiom paradigm where such factors can be precisely controlled.Specifically, 29 participants learned a series of 24 novel idioms in a semantic, forced-choice training phrase.During this training phase, the way the invented idioms were learned varied in two respects: repetition (the number of times each participant was exposed to a given idiom) and meaning dominance (whether they were exposed to an idiom more in its figurative sense or its literal sense).During a subsequent test phase, participants' eye movements were recorded as they read test sentences containing the newly learned idioms in different contexts.Eye movement comprehension measures consisted of participants' first pass gaze duration for the idiom and disambiguating regions of individual test sentences, and total reading time for the idiom region.The results showed that both training repetition and dominance impacted comprehension participants' reading times in a manner that was modulated by the idioms' varying levels of decomposability (i.e., the extent to which their figurative meanings relate to their non-idiomatic literal meaning).These results are discussed in the context of the existing hypotheses about idiom processing.
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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 | 0.002 |
| 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 | 0.001 |
| 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".