Psychophysics of variable fonts: Do multiple font features interact to impact readability?
Bibliographic record
Abstract
When choosing a font, we have some intuitive understanding of why a particular font may feel easier to read, but what elements of a font actually affect readability? To answer this question, we used variable fonts, in which every element, such as the width or stroke contrast of each letter, can be adjusted on a continuous axis. Previously, we have shown that changes within a single axis can change saccade amplitude and reading duration thresholds (Guidi et al. VSS2024). In a new study, we examined how these axes impact readability in combination by manipulating text appearance on two axes, thin stroke and width, at three levels per axis across the full range, for a total of 9 conditions. Participants read a series of sentences in each font condition while gaze position was tracked, classifying each sentence as true or false. Sentence presentation duration was staircased and we calculated duration thresholds needed for 80% classification accuracy for each condition. Thicker thin strokes decreased duration thresholds across all width settings, while the thinnest thin strokes resulted in the highest duration thresholds (i.e., the slowest reading performance). These extreme thin strokes impacted reading speed regardless of the width of the text. Eye tracking data revealed that participants partially compensated for increased text width by increasing their saccade amplitudes. By understanding how different font elements interact with each other, we may be able to understand what parts of text presentation affect readability the most, which can then be used to help maximize reading efficiency.
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 | 0.008 |
| 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 | 0.001 | 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".