Cognitive loads and time courses related to word order preference in Kaqchikel sentence production: an NIRS and eye-tracking study
Bibliographic record
Abstract
The word order that is easiest to understand in a language generally coincides with the word order most frequently used in that language. In Kaqchikel, however, there is a discrepancy between the two: the syntactically basic VOS incurs the least cognitive load, whereas SVO is most frequently employed. This suggests that processing load is primarily determined by grammatical processes, whereas word order selection is affected by additional conceptual factors. Thus, the agent could be conceptually more salient than other elements even for Kaqchikel speakers. This hypothesis leads us to the following expectations: (1) utterance latency should be shorter for SVO sentences than for VOS sentences; (2) Kaqchikel speakers should pay more attention to agents than to other elements during sentence production; and (3) despite these, the cognitive load during sentence production should be higher for SVO than for VOS. A Kaqchikel sentence production experiment confirmed all three expectations.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".