On the joint effects of stimulus quality, regularity, and lexicality when reading aloud: New challenges.
Bibliographic record
Abstract
A number of computational models have been developed over the last 2 decades that are remarkably successful at explaining the process of translating print into sound. Nevertheless, 2 of the most successful computational accounts on the table fail to simulate the results from factorial experiments reported in this article in which university students read aloud letter strings that varied in terms of spelling-sound regularity and lexicality (regular words vs. exception words vs. nonwords) and stimulus quality (bright vs. dim). Skilled readers yielded additive effects of regularity and stimulus quality and additive effects of lexicality and stimulus quality on both RT and errors when nonwords were mixed with words. When only words appeared in the list, there was an interaction in which exception words were less affected by low stimulus quality than regular words were; no existing account anticipates or explains these results. We advance a hypothesis that assumes a novel module that accommodates these data and provide an existence proof in the form of a simulation.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".