Testing for lexical competition during reading: Fast priming with orthographic neighbors.
Bibliographic record
Abstract
Recent studies have found that masked word primes that are orthographic neighbors of the target inhibit lexical decision latencies (Davis & Lupker, 2006; Nakayama, Sears, & Lupker, 2008), consistent with the predictions of lexical competition models of visual word identification (e.g., Grainger & Jacobs, 1996). In contrast, using the fast priming paradigm (Sereno & Rayner, 1992), orthographically similar primes produced facilitation in a reading task (H. Lee, Rayner, & Pollatsek, 1999; Y. Lee, Binder, Kim, Pollatsek, & Rayner, 1999). Experiment 1 replicated this facilitation effect using orthographic neighbor primes. In Experiment 2, neighbor primes and targets were presented in different cases (e.g., SIDE-tide); in this situation, the facilitation effect disappeared. However, nonword neighbor primes (e.g., KIDE-tide) still significantly facilitated reading of targets (Experiment 3). Taken together, these results suggest that it is possible to explain the priming effects from word neighbor primes in fast priming experiments in terms of the interactions between the inhibitory and facilitory processes embodied in lexical competition models.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".