Psychophysics of variable fonts: Speed and comprehension measures
Bibliographic record
Abstract
While many previous studies have investigated how text appearance affects reading performance, variable fonts allow different parameters, like slant and character weight, to be customized along a set of continuous axes. This gives us the unique ability to use psychophysical techniques with them in a new way, and assess how these manipulations impact reading speed and comprehension. To test this, we had participants read passages where the appearance of text had been customized, with each complete passage consisting of four screens of customized text. We measured the speed with which they completed this reading task, and assessed comprehension with multiple-choice probe questions after they had read each passage. We varied five parameters with the Roboto Flex font: slant, width, weight, thick stroke, and thin stroke, with five different levels per parameter, spanning their full range. We show that slant produced the largest changes in reading speed, with words per minute (WPM) increasing as slant decreased, with a 38.4% change over the range of slants tested. In contrast, thick stroke produced the smallest change in reading speed (22.7% of maximum), with WPM increasing as thick stroke width decreased. Thin stroke produced the fastest reading speeds at both extremes, with WMP being highest when thin stroke was set to its minimum. Critically, comprehension was not affected by these manipulations, and was stable for all participants in all conditions (mean comprehension accuracy, 81.5%). Our results suggest that the customizability of variable fonts has significant potential to increase reading speed without impeding comprehension, and the nature of variable fonts lends itself to new psychophysical investigation of the impact of font appearance in reading.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".