Is Passive Priming Really Impervious to Verb Semantics? A High-Powered Replication of Messenger Et al. (2012)
Bibliographic record
Abstract
The aim of the present study was to conduct a particularly stringent pre-registered in-vestigation of the claim that there exists a level of linguistic representation that “includes syntactic category information but not semantic information” (Branigan & Pickering, 2017: 8). As a test case, we focussed on the English passive; a construction for which previous findings have been somewhat contradictory. On the one hand, several studies using different methodologies have found an advantage for theme-experiencer passives (e.g., The girl was shocked by the tiger; and also agent-patient passives; e.g., The girl was hit by the tiger) over experiencer-theme passives (e.g., The girl was ignored by the tiger). On the other hand, Messenger et al. (2012) found no evidence that theme-experiencer and experiencer-theme passives vary in their propensity to prime production of agent-patient passives. We therefore conducted an online replication of Messen-ger et al (2012) with a pre-registered appropriately powered sample (N=240). Although a large and significant priming effect (i.e., an effect of prime sentence type) was ob-served, a Bayesian analysis yielded only weak/anecdotal evidence (BF=2.11) for the crucial interaction of verb type by prime type; a finding that was robust to different coding and exclusion decisions, operationalizations of verb semantics (dichoto-mous/continuous), analysis frameworks (Bayesian/frequentist) and – as per a mixed-effects-multiverse analyses – random effects structures. Nevertheless, these findings do no not provide evidence for the absence of semantic effects (as has been argued for the findings of Messenger et al, 2012). We conclude that these and related findings are best explained by a model that includes both lexical, exemplar-level representations and rep-resentations at multiple higher levels of abstraction.
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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 | 0.022 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".