Event-based plausibility immediately influences on-line language comprehension.
Bibliographic record
Abstract
In some theories of sentence comprehension, linguistically relevant lexical knowledge, such as selectional restrictions, is privileged in terms of the time-course of its access and influence. We examined whether event knowledge computed by combining multiple concepts can rapidly influence language understanding even in the absence of selectional restriction violations. Specifically, we investigated whether instruments can combine with actions to influence comprehension of ensuing patients of (as in Rayner, Warren, Juhuasz, & Liversedge, 2004; Warren & McConnell, 2007). Instrument-verb-patient triplets were created in a norming study designed to tap directly into event knowledge. In self-paced reading (Experiment 1), participants were faster to read patient nouns, such as hair, when they were typical of the instrument-action pair (Donna used the shampoo to wash vs. the hose to wash). Experiment 2 showed that these results were not due to direct instrument-patient relations. Experiment 3 replicated Experiment 1 using eyetracking, with effects of event typicality observed in first fixation and gaze durations on the patient noun. This research demonstrates that conceptual event-based expectations are computed and used rapidly and dynamically during on-line language comprehension. We discuss relationships among plausibility and predictability, as well as their implications. We conclude that selectional restrictions may be best considered as event-based conceptual knowledge rather than lexical-grammatical knowledge.
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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 | 0.001 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".