Negative polarity illusions are robust with both ‘ever’ and ‘any’ (when linear position is held constant)
Bibliographic record
Abstract
Many studies have used linguistic illusions to probe the representations and mechanisms used during incremental language comprehension. A crucial component of this research program is mapping out when illusions occur and when they do not. To this end, we investigate the generality of a linguistic illusion observed with negative polarity items (NPIs). Most previous work has only investigated the illusion using a single NPI, ever (or its analogue in other languages), but all models of the illusion phenomenon implicitly predict that illusions should generalize across different NPIs. In apparent contradiction to this prediction Parker and Phillips (2016) found reliable illusions with ever, but not with the previously untested NPI any. In their original paper, the authors suggested that the asymmetry stemmed from differences in the linear position of the two NPIs in their test items. However, the authors did not establish the basic empirical generalization that any is, in fact, susceptible to the illusion when the confound of linear position is factored out. As such, their findings are equally compatible with the hypothesis that there is fine-grained lexical variation in inherent susceptibility to the illusion, which would have serious implications for all theories of the phenomenon. To settle the empirical record, we conducted a higher-power study comparing ever and any using items adapted from Parker and Phillips (2016) such that the two NPIs occupied the same ordinal position in their test sentences. We find comparable illusions for both NPIs, a welcome result for all candidate theories of the phenomenon and consistent with the distance-based explanation for its absence in Parker and Phillips (2016).
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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 | 0.002 | 0.025 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".