The word-length effect in reading: A review
Bibliographic record
Abstract
The finding that visual processing of a word correlates with the number of its letters has an extensive history. In healthy subjects, a variety of methods, including perceptual thresholds, naming and lexical decision times, and ocular motor parameters, show modest effects that interact with high-order effects like frequency. Whether this indicates serial processing of letters under some conditions or indexes low-level visual factors related to word length is unclear. Word-length effects are larger in pure alexia, where they probably reflect a serial letter-by-letter strategy, due to failure of lexical whole-word processing and variable dysfunction in letter encoding. In pure alexia, the word-length effect is systematically related to mean naming latency, with the word-length effect becoming proportionally greater as naming latency becomes more delayed in severe cases. Other conditions may also generate enhanced word-length effects. This occurs in right hemianopia: Computer simulations suggest a criterion of 160 ms/letter to distinguish hemianopic dyslexia from pure alexia. Normal reading development is accompanied by a decrease in word-length effects, whereas persistently elevated word-length effects are characteristic of developmental dyslexia. Little is known about word-length effects in other reading disorders. We conclude that the word-length effect captures the efficiency of the perceptual reading process in development, normal reading, and a number of reading disorders, even if its mechanistic implications are not always clear.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".