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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".