The Influence of Line Length: A Pilot Study
Bibliographic record
Abstract
The aim of this work is to understand the impact of typography on humans in terms of reading.Several tests were carried out to achieve the proposed objective of understanding the influence of different visual variables to ensure a more precise reading process.Four variables were studied using various sensors, such as the Brain Computer Interaction (BCI) device, to measure brain activity (active, neutral, and calm) and heart rate activity (HRA).The reading time and the number of errors were also considered.The results show that the visual variables have a different impact considering the type of text (scientific and children's text) as well as the reading medium (paper or screen).In addition, the results show that preferences vary according to the type of visual variables as predicted and as confirmed by the measurements taken during the reading process.However, oddly enough, the participants when questioned during the survey, their answers were not coincident by the measurement results.For this reason, this empirical study in interaction design is important as a future reference approach to the perceptibility and readability of text.On the other hand, the use of BCI and HRA parameters is not widely described in the literature.So, this paper allowed to perceive and identify the most adaptable typographic parameters both in the type of text and in the reading medium.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".