Neighbourhood Density, Word Frequency, and Spelling-Sound Regularity Effects in Naming: Similarities and Differences Between Skilled Readers and the Dual Route Cascaded Computational Model.
Bibliographic record
Abstract
An experiment with skilled readers and a series of simulations with the Dual Route Cascaded model (Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001) investigated the joint effects of stimulus quality and Neighbourhood Density (N) in nonword naming. Neighbourhood Density and stimulus quality yielded additive effects on RT for skilled readers whereas the model produced an interaction between these factors. A further set of simulations show that DRC also produces an interaction between stimulus quality and (1) word frequency, (2) spelling-sound regularity, and, (3) nonword letter length. None of these three factors interact with stimulus quality in performance by skilled readers. It is suggested that DRC's assumption of cascaded processing throughout represents a central problem. A proposal as to how the model can be modified to accommodate these and other problematic data is 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".