A psychophysical approach for investigating format readability online
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it