A psychophysical approach for investigating format readability online
Bibliographic record
Abstract
We introduce a scientific tool designed for online reading performance studies. Tool assesses optimum reading format for individuals by allowing experimenters to manipulate various text parameters. Developed using psychophysical research, the tool utilizes online testing via Pavlovia and Psychopy, enabling large-scale participant testing with reduced environmental noise and increased external validity. Our tool’s primary function is to assess reading performance across various typefaces, font parameters (e.g., weight, width, etc.), letter spacings by ranking comprehension scores and reading speed. The tool focuses on paragraph reading (approximately 150-word paragraphs), though it can also evaluate other forms of reading such as single word recognition and sentence reading. Stimuli are presented as .jpg images of texts with `modified fonts or spacings. Using images of texts instead of directly rendering using the browser, prevents potential incompatibility problems across different monitors while manipulating letter spacing and axes of variable fonts. We outline the methodology, emphasizing the tool's reliance on automatic randomization and counterbalancing, and the creation of stimulus sets. We provide a pilot study as an example to explain the configuration of tool’s settings and how counterbalancing functions. Example also outlines how behavioral performance measures such as comprehension scores, reading speed calculations (as words per minutes), and experimental conditions are registered in the data file. Overall, we provide an overview of the tool's design, functionality, and potential to expand the capabilities of online readability studies.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".