Phonologically-based priming in the same-different task with L1 readers.
Bibliographic record
Abstract
The present experiment provides an investigation of a promising new tool, the masked priming same-different task, for investigating the orthographic coding process. Orthographic coding is the process of establishing a mental representation of the letters and letter order in the word being read which is then used by readers to access higher-level (e.g., semantic) information about that word. Prior research (e.g., Norris & Kinoshita, 2008) had suggested that performance in this task may be based entirely on orthographic codes. As reported by Lupker, Nakayama, and Perea (2015a), however, in at least some circumstances, phonological codes also play a role. Specifically, even though their 2 languages are completely different orthographically, Lupker et al.'s Japanese-English bilinguals showed priming in this task when masked L1 primes were phonologically similar to L2 targets. An obvious follow-up question is whether Lupker et al.'s effect might have resulted from a strategy that was adopted by their bilinguals to aid in processing of, and memory for, the somewhat unfamiliar L2 targets. In the present experiment, Japanese readers responded to (Japanese) Kanji targets with phonologically identical primes (on "related" trials) being presented in a completely different but highly familiar Japanese script, Hiragana. Once again, significant priming effects were observed, indicating that, although performance in the masked priming same-different task may be mainly based on orthographic codes, phonological codes can play a role even when the stimuli being matched are familiar words from a reader's L1. (PsycINFO Database Record
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".