An Extension of a Parallel‐Distributed Processing Framework of Reading Aloud in Japanese: Human Nonword Reading Accuracy Does Not Require a Sequential Mechanism
Bibliographic record
Abstract
Humans can pronounce a nonword (e.g., rint). Some researchers have interpreted this behavior as requiring a sequential mechanism by which a grapheme-phoneme correspondence rule is applied to each grapheme in turn. However, several parallel-distributed processing (PDP) models in English have simulated human nonword reading accuracy without a sequential mechanism. Interestingly, the Japanese psycholinguistic literature went partly in the same direction, but it has since concluded that a sequential parsing mechanism is required to reproduce human nonword reading accuracy. In this study, by manipulating the list composition (i.e., pure word/nonword list vs. mixed list), we demonstrated that past psycholinguistic studies in Japanese have overestimated human nonword reading accuracy. When the more fairly reevaluated human performance was targeted, a newly implemented Japanese PDP model simulated the target accuracy as well as the error patterns. These findings suggest that PDP models are a more parsimonious way of explaining reading across various languages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".