Repetition deficits, list context, and word-class interactions in the RSVP of words in sentences.
Bibliographic record
Abstract
We report a failure to find a repetition deficit in recall following the rapid serial visual presentation (RSVP) of words within sentences, using adjectives rather than nouns as the critical items. In a series of experiments that ruled out participant and procedural differences as the source of the failure, both word class and list context were found to moderate the repetition deficit, but grammatical necessity did not. The presence in the list of sentences in which the repeated adjectives were separated by more than three words (i.e., more than 400 ms in RSVP) not only eliminated the repetition deficit for the recall of those sentences but also for the recall of sentences in which the repeated adjectives were separated by three or fewer words (i.e., less than 400 ms in RSVP). However, although substantially reduced, a repetition deficit with noun-based materials was still found in this list context. Matching the adjective-based sentences with the noun-based sentences in sentence length and position of the critical items revealed that the moderating effect of word-class on the repetition deficit was mediated by the biases in sentence structure that using different word classes tend to induce.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".