Subjectively homogeneous noise over written text as a tool to investigate the perceptual mechanisms involved in reading
Bibliographic record
Abstract
In an effort to understand the factors influencing text legibility in natural reading, we adapted the visual spread method (Poirier, Gosselin, & Arguin, 2008) to natural text. Stimuli were sentences conforming to MNREAD standards (Legge, Ross, Luebker, & LaMay 1989) mixed with dynamic probabilistic noise-i.e., each pixel in the image is associated with a probability that its polarity is inverted on a given refresh cycle of the display screen. Noise level varied continuously over the image as initially determined by Gaussian-filtered noise. Participants adjusted noise levels in the text using the mouse until the text appeared homogenously noisy. We assume that participants increased (or decreased) noise at locations where stimulus features were easy (or difficult) to encode and thus that local noise settings correlate with legibility. Data from 11 participants and 30 sentences revealed interesting effects, demonstrating the validity of the method for assessing the impact of various factors on noise resistance in natural text. For example, participants increased noise over (a) spaces and adjacent letters, (b) the second half of words, (c) words with more orthographic neighbors but fewer phonological neighbors, (d) less useful word types, (e) less complex letters, and (f) diagnostic letters (a novel metric). Our observations also offer significant insights on constraints acting upon letter identification as well as on higher-level processes that are involved in reading.
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 | 0.009 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".