Neighborhood frequency effects in simple and complex span: Do high-frequency neighbors help or hurt?
Bibliographic record
Abstract
A word's orthographic neighborhood is the set of words that differ from the target word by one letter. Both Roodenrys (2009) and Robert et al. (Journal of Psycholinguistic Research, 44, 119-125, 2015) posit that orthographic neighbors are activated when the target word is encountered in tasks such as simple and complex span. The two accounts differ in that the former predicts a beneficial effect of this activation, because it produces feedback activation that helps redintegrate the target word, whereas the latter predicts a detrimental effect, because the need to overcome the greater interference from the larger number of higher-frequency items reduces the processing resources available. Four experiments assess the predictions of these two accounts. Experiments 1 and 2 found a beneficial effect of having a higher- compared with a lower-frequency neighborhood in both a simple and a complex span task. Experiments 3 and 4 found no detrimental effect of having one or more neighbors with higher frequency than the target in both a simple and complex span task. Implications for the two theories are 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".