Calling All Codes: Interactive Effects of Semantics, Phonology, and Orthography Produce Dissociations in a Repetition Blindness Paradigm
Bibliographic record
Abstract
We used the repetition blindness (RB) paradigm to examine the roles of semantics, phonology, and orthography on report from rapidly presented 5-item word lists. Semantic primes in positions 1 and 3 in the list preceded homographic homophones, homographic heterophones, and heterographic homophones as critical targets in positions 2 and 4. All codes (i.e., semantic, phonologic, and orthographic) were repeated, or meaning changed while only phonology, only orthography, or both were repeated for the critical targets. Using a scoring procedure that considered order of report, we assessed facilitation for report of the first instance of the repeated target (a novel aspect of our procedure) and RB for report of the second instance of the repeated target. Except when accompanied by a change in orthography, a change in meaning reduced RB relative to when all codes were repeated. Facilitation in the report of the first instance of the repeated item occurred only if meaning changed and phonology was repeated (independent of whether orthography changed). Finally, recall was worse for the meaning change nonrepeated control lists (which instantiated 3 unrelated meanings in the list, including the last unrelated item) than for the no-meaning change control lists (which instantiated 2 unrelated meanings). We discuss the relevance of these findings for extant accounts of repetition blindness.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".