Transposed letter priming effects and allographic variation in Arabic: Insights from lexical decision and the same–different task.
Bibliographic record
Abstract
Reading is resilient to distortion of letter order within a word. This is evidenced in the "transposed-letter (TL) priming effect," the finding that a prime generated by transposing adjacent letters in a word (e.g., jugde) facilitates recognition of the base word (e.g., JUDGE), more than a "substituted-letter" control prime in which the transposed letters are replaced by unrelated letters (e.g., junpe -JUDGE). The TL priming effect is well documented for European languages that are written using the Roman alphabet. Unlike these languages, Arabic has a unique position-dependent allography whereby some letters change shape according to their position within a word. We investigate the TL priming effect using a lexical decision (Experiment 1) and a same-different match task with Arabic words (Experiment 2) and nonwords (Experiment 3). No TL priming effects were found in Experiment 1, suggesting that the lexical-decision task engages lexical access processes that are sensitive to the Semitic nonlinear morphological structure. Experiments 2 and 3 revealed a robust TL priming effect overall. Nonallographic TL primes produced significantly larger facilitation than allographic TL primes, indicating that Arabic readers use allographic variation to resolve the uncertainty in letter order during the early stages of orthographic processing. The implication of these results for current letter position coding models is discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".