Rapid Serial Visual Presentation of transposed-word sequences in the grammatical decision task: an examination of the roles of temporal and spatial cues to word order
Bibliographic record
Abstract
Transposing two words in a sentence (e.g. “cat” and “was” in “the white cat was big”) creates a sequence that is harder to classify as ungrammatical than control sequences (e.g. “the white was cat slowly”), suggesting that word position coding is noisy and can be affected by syntactic expectations. In the present research, this transposed-word effect was examined more closely using Rapid Serial Visual Presentation (RSVP) formats which provided either clear temporal cues to word order but no spatial cues, or both types of cues. Compared to when all words were presented simultaneously, the two RSVP formats reduced the transposed-word effect to the same degree while having no parallel impact on another ungrammatical comparison condition involving no transposition. These results are discussed in the context of serial and parallel models of reading as well as models that propose a later processing stage for the locus of the transposed-word effect.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".