The Idiom Processing Advantage is Explained By Surprisal
Bibliographic record
Abstract
It has been repeatedly found that idioms are processed faster than syntactically matched literal phrases, in both comprehension and production. This has led to debate about whether idioms are accessed as chunks or built compositionally, with different studies attempting to measure the effect of compositionality on processing, with differing conclusions. This paper looks at idiom processing through the lens of information update, in particular surprisal theory, which is a standard theory of sentence processing. Compositionality is just one aspect of a word's predictability; we argue that surprisal, as an expectation-based theory, provides a more general unifying framework for understanding the idiom processing advantage. In this paper, comprehension and production experiments on verb-object idioms reveal that the idiom processing advantage can be largely explained by the fact that idioms have lower surprisal than matched literal phrases. The results indicate that the idiom advantage manifests primarily on the noun in verb-object idioms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".