Is there phonologically based priming in the same−different task? Evidence from Japanese−English bilinguals.
Bibliographic record
Abstract
Norris and colleagues (Kinoshita & Norris, 2009; Norris & Kinoshita, 2008; Norris, Kinoshita, & van Casteren, 2010) have suggested that priming effects in the masked prime same-different task are based solely on prelexical orthographic codes. This suggestion was evaluated by examining phonological priming in that task using Japanese-English bilinguals. Targets and reference words were English words with the primes written in Katakana script, a syllabic script that is orthographically quite different from the Roman letter script used in writing English. Phonological priming was observed both when the primes were Japanese cognate translation equivalents of the English target/reference words (Experiment 1) and when the primes were phonologically similar Katakana nonwords (Experiment 2), with the former effects being substantially larger than the noncognate translation priming effects reported by Lupker, Perea, and Nakayama (2015). These results indicate that the same-different task is influenced by phonological information. One implication is that, due to the fact that phonology and orthography are inevitably confounded in Roman letter languages, previously reported priming effects in those languages may have been at least partly due to phonological, rather than orthographic, similarity. The potential extent of this problem, the nature of the matching process in the same-different task, and the implications for using this task as a means of investigating the orthographic code in reading are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".