Syntactic Complexity Effects in Sentence Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Syntactic complexity effects have been investigated extensively with respect to comprehension (e.g., Demberg & Keller, 2008; Gibson, 1998, 2000; Gordon et al., 2001, 2004; Grodner & Gibson, 2005; King & Just, 1991; Lewis & Vasishth, 2005; Lewis et al., 2006; McElree et al., 2003; Wanner & Maratsos, 1978). According to one prominent class of accounts (experience-based accounts; e.g., Hale, 2001; Levy, 2008; Gennari & MacDonald, 2008, 2009; Wells et al., 2009), certain structures cause comprehension difficulty due to their scarcity in the language. But why are some structures less frequent than others? In two elicited-production experiments we investigated syntactic complexity effects in relative clauses (Experiment 1) and wh-questions (Experiment 2) varying in whether or not they contained non-local dependencies. In both experiments, we found reliable durational differences between subject-extracted structures (which only contain local dependencies) and object-extracted structures (which contain nonlocal dependencies): Participants took longer to begin and produce object-extractions. Furthermore, participants were more likely to be disfluent in the object-extracted constructions. These results suggest that there is a cost associated with planning and uttering the more syntactically complex, object-extracted structures, and that this cost manifests in the form of longer durations and disfluencies. Although the precise nature of this cost remains to be determined, these effects provide one plausible explanation for the relative rarity of object-extractions: They are more costly to produce.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it