Visual Word Expertise: A Study of Inversion and the Word-Length Effect, with Perceptual Transforms
Bibliographic record
Abstract
The word-length effect may indicate whether reading is proceeding in an efficient whole-word fashion or by serial letter processing. If it is an index of an orientation-dependent expert reading mechanism, then it should show an inversion effect, with a large difference between upright and upside-down text that is specific for normally configured text. We measured response time of healthy subjects reading 3- to 9-letter words presented in normal configuration, in mirror reflection or spelt backward, in either upright or inverted orientation. The word-length effect showed an inversion effect specific for normal text, as it was not seen for either backward or mirrored text, a result that differed from that for simple mean response times. Also, the word-length effect was smaller for backward than for mirrored text, suggesting that reading of transformed text uses primarily local letters rather than global word forms. We conclude that the word-length effect is a suitable index of expert reading, and reveals that reading under perceptually difficult conditions relies on a sublexical letter-based strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 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".