Adaptation of Gap Predictions in Filler-Gap Dependency Processing during Reading
Bibliographic record
Abstract
Syntactic adaptation effects have been demonstrated for an expanding list of structure types, but the mechanism underlying this effect is still being explored. In the current work on filler-gap dependency processing, we examined whether exposing participants to a less common gap location—prepositional object (PO) gaps—altered their gap predictions, and whether these effects would transfer across tasks when this input was presented in a quasi-naturalistic way (i.e., by reading stories). In Experiment 1, we demonstrated that comprehenders dampened their direct object (DO) gap predictions following exposure to PO gaps. However, Experiments 2A and 2B suggest that these adaptation effects did not transfer when the quasi-naturalistic exposure phase was presented as a separate task (Experiment 2A) and when they also needed to generalize from a syntactic to a semantic measure of direct object gap predictions (i.e., filled gap vs. plausibility mismatch sentences; Experiment 2B). Overall, these experiments add filler-gap dependency processing, as well as the gap predictions associated with it, to the growing list of structures demonstrating adaptation effects, while also suggesting that this effect may be specific to a singular experimental task environment.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".