Integrating words that refer to typical sequences of events.
Bibliographic record
Abstract
The organisation and processing of event concepts in semantic memory is an important issue in language processing and memory research. The present study tested whether pairs of words denoting events that can plausibly occur in sequence (marinate-grill) generate expectancies for a target that denotes a subsequently occurring event (chew). In Experiment 1, two events that tend to occur sequentially primed the third. In Experiment 2, the individual primes (i.e., marinate and grill separately) did not prime their related event targets. Experiments 1 and 2 used a lexical-decision task on the target. Therefore, information from both primes must be integrated to sufficiently activate knowledge of the subsequently occurring target. This is the first study to demonstrate priming among words denoting sequentially occurring events. In Experiment 3, a relatedness decision task, processing of these event triplets was facilitated when the first two event words were presented in a temporally correct order compared with when their order was reversed. These findings are not predicted by spreading activation theory and cannot be simulated by corpus-based models that do not include order-sensitive measures. We interpret the results as evidence for the role of situation models and the use of world knowledge during online language comprehension, even in the absence of sentential contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".