Event Syntax and Event Semantics as Constraints on Availability of Discourse
Bibliographic record
Abstract
The understanding of how temporal information constrains situation models is far from complete. In tehse two experiments participants read short stories, where the antecedent sentence was manipulated with respect to varying tense, grammatical and lexical verb aspect (Experiment 1), and then by varying grammatical and lexical verb aspect in conjunction with long and short duration events (Experiment 2). We used electrophysiological measures time-locked to the anaphoric referent to investigate how the brain responds to these temporal constraints. The purpose was to investigate teh possibility that these variables have an influence on the availability of discourse concepts in situation models. In Experiment 1, the anaphoric referent elicited a larger N400 when it was presented previously in a perfective antecedent sentence than an imperfective sentence. This N400 difference for grammatical aspect was limited to antecdent sentences with accomplishments as there was no statistical evidence for activities. Tense did not influence availability regardless of lexical aspect. In Experiment 2, N400 amplitudes were again modulated by grammatical aspect and this effect was also found to be limited to when antecedent sentences contained accomplishments. Furthermore, the results demonstrated that the imperfective advantage observed for accomplishments is not present after an intervening event with a long time shift. The implications of these findings are discussed in terms of theories of situation models.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".