Strategic effects in word naming: Examining the route-emphasis versus time-criterion accounts.
Bibliographic record
Abstract
K. Rastle and M. Coltheart (1999) demonstrated that both nonwords and low-frequency regular words are named more slowly when mixed with first-phoneme irregular word fillers (e.g., CHEF) than when mixed with third-phoneme irregular word fillers (e.g., GLOW). Those authors suggested that their effects were due to a strategic de-emphasis of the nonlexical route when first-phoneme irregular fillers were used. An alternative explanation is that these results simply reflect a more lax position of a time criterion (S. J. Lupker, P. Brown, & L. Colombo, 1997) in the first-phoneme irregular filler condition. We contrasted these 2 accounts in 4 experiments. In all experiments, target naming latencies were longer when the fillers were harder to name, regardless of whether the fillers were nonwords or exception words. These results strongly favor a time-criterion account of K. Rastle and M. Coltheart's effects.
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 | 0.001 | 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) | 0.003 | 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".