Masked priming with orthographic neighbors: A test of the lexical competition assumption.
Bibliographic record
Abstract
In models of visual word identification that incorporate inhibitory competition among activated lexical units, a word's higher frequency neighbors will be the word's strongest competitors. Preactivation of these neighbors by a prime is predicted to delay the word's identification. Using the masked priming paradigm (K. I. Forster & C. Davis, 1984, J. Segui and J. Grainger (1990) reported that, consistent with this prediction, a higher frequency neighbor prime delayed the responses to a lower frequency target, whereas a lower frequency neighbor prime did not delay the responses to a higher frequency target. In the present experiments, using English stimuli, it was found that this pattern held only when the primes and targets had few neighbors; when the primes and targets had many neighbors, lower frequency primes delayed responses to higher frequency targets essentially as much as higher frequency primes delayed responses to lower frequency targets. Several possible explanations for these findings are discussed along with their theoretical implications. Considered together, the results are most consistent with activation-based accounts of the masked priming effect.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".