Learning to read upside-down: a study of perceptual expertise and acquisition
Bibliographic record
Abstract
Introduction: Reading is an expert visual and ocular motor function, learned almost exclusively in a single orientation. Characterizing this expertise can be accomplished by contrasts between reading of normal and inverted text, in which perceptual but not linguistic factors are altered. Objective: Our goal was to examine this inversion effect in healthy subjects reading text, to derive behavioural and ocular motor markers of perceptual reading expertise, and to study these parameters before and after training with inverted reading. Methods: Seven subjects underwent a 10-week program of 30 half-hour sessions of reading novels with pages displayed inverted on computer monitors. Before and after training we assessed reading of upright and inverted single words for response time and word-length effects, and reading of paragraphs for time required, accuracy, and ocular motor parameters. Results: Subjects gained about 1.17 words/minute with each session, or a substantial 35 words/minute over the entire training period. Before training, inverted reading was characterized by long reading times and large word-length effects, with eye movements showing more and longer fixations, more and smaller forward saccades, and more regressive saccades. Training partially reversed many of these effects in single word and text reading, with the best gains occurring in reading aloud time and proportion of regressive saccades, and the least change in forward saccade amplitude. Conclusions: Reading speed and ocular motor parameters can serve as markers of perceptual expertise during reading, and that training with inverted text over 10 weeks results in gains of about 30% in reading expertise. This approach may be useful in the rehabilitation of patients with hemianopic dyslexia, as inverted reading has the potential of restoring parafoveal preview and visual span in front of the currently fixated letter. Meeting abstract presented at VSS 2014
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".