Abstract prediction of morphosyntactic features: Evidence from processing cataphors in Dutch
Bibliographic record
Abstract
When comprehenders predict a specific lexical noun in a highly constraining context, they also activate the grammatical features, such as gender, of that noun. Evidence for such lexically mediated prediction comes from ERP studies that show that comprehenders are surprised by adjectives and determiners that mismatch the features of a highly predictable noun. In this study, we investigated whether comprehenders can (i) predict an abstract noun phrase in an upcoming argument position (without pre-activating a specific lexical item) and (ii) assign morphosyntactic features to the head noun of that phrase. To do so we used the processing of Dutch cataphors as a test case. We tested whether seeing a cataphor in a preposed clause triggered a prediction of a feature-matching antecedent NP in main subject position. If comprehenders predicted a feature-matching subject, we reasoned that they should also expect an agreeing main verb, which comes before the subject because Dutch is a V2 language. A single-word prediction experiment showed that comprehenders expect a main verb matching the number of the cataphor. In a follow-up self-paced reading experiment, we found a number-mismatch effect if the V2 main verb did not agree with the cataphor. We take the results as evidence that comprehenders predicted a matching antecedent in subject position. We argue that the results are better explained as involving prediction of an abstract noun phrase marked for morphological features, rather than a specific lexical item.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".