Short Article: Tracking the Transition from Sublexical to Lexical Processing: On the Creation of Orthographic and Phonological Lexical Representations
Bibliographic record
Abstract
Participants read aloud nonword letter strings, one at a time, which varied in the number of letters. The standard result is observed in two experiments; the time to begin reading aloud increases as letter length increases. This result is standardly understood as reflecting the operation of a serial, left-to-right translation of graphemes into phonemes. The novel result is that the effect of letter length is statistically eliminated by a small number of repetitions. This elimination suggests that these nonwords are no longer always being read aloud via a serial left-to-right sublexical process. Instead, the data are taken as evidence that new orthographic and phonological lexical entries have been created for these nonwords and are now read at least sometimes by recourse to the lexical route. Experiment 2 replicates the interaction between nonword letter length and repetition observed in Experiment 1 and also demonstrates that this interaction is not seen when participants merely classify the string as appearing in upper or lower case. Implications for existing dual-route models of reading aloud and Share's self-teaching hypothesis 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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".