Is there a neighborhood frequency effect in English? Evidence from reading and lexical decision.
Bibliographic record
Abstract
What is the effect of a word's higher frequency neighbors on its identification time? According to activation-based models of word identification (J. Grainger & A. M. Jacobs, 1996; J. L. McClelland & D. E. Rumelhart, 1981), words with higher frequency neighbors will be processed more slowly than words without higher frequency neighbors because of the lexical competition mechanism embodied in these models. Although a critical prediction of these models, this inhibitory neighborhood frequency effect has been elusive in studies that have used English stimuli. In the present experiments, the effect of higher frequency neighbors was examined in the lexical decision task and when participants were reading sentences while their eye movements were monitored. Results suggest that higher frequency neighbors have little, if any, effect on the identification of English words. The implications for activation-based models of word identification are discussed.
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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 | 0.001 |
| 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".