The impact of font on typo detection: a novel visual search paradigm
Bibliographic record
Abstract
In visual search tasks, we scan our environment to identify a target (e.g., looking for your suitcase at the Tampa airport baggage claim). A better understanding of the factors that lead to success and failure in visual search requires a task that mimics our lived experiences but permits a high degree of experimental control. Here, we developed a novel typo detection paradigm to uncover how attentional processes interact with visual properties of stimuli in a word-based visual search task. Participants (N=9) scanned pseudo-paragraphs comprised of random 5-, 6-, and 7-letter words to find typos (i.e., incorrectly spelled words), which were present on 50% of trials. Five types of typos were included: transpositions (two letters swapped), insertions (a letter added to a word), deletions (a letter removed from a word), repetitions (a letter repeated), and substitutions (a letter replaced with another letter). In addition, half of the trials were presented in an “easy-to-read” font (Arial) and half were presented in a “hard-to-read” font (a version of Roboto Flex with narrow width and a high stroke contrast). Font had a main effect on reaction time: participants responded more slowly when the stimuli were presented in the hard-to-read font. Font had no overall impact on accuracy. However, participants were worst at identifying transposition errors, and for these trials, font did have a significant effect, such that performance was lower for the hard- versus easy-to-read font. These findings also highlight substantial individual differences in performance and sensitivity to font manipulation. Taken together, these results indicate that the appearance of text does impact visual search for typos, but only for specific types of errors. This paradigm can elucidate how constraints in peripheral vision (e.g., crowding) impact visual search performance for text-based stimuli.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".