The spatio-temporal dynamics of visual letter recognition
Why this work is in the frame
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Bibliographic record
Abstract
We applied the Bubbles technique to reveal directly the spatio-temporal features of uppercase Arial letter identification. We asked four normal readers to each identify 26,000 letters that were randomly sampled in space and time; afterwards, we performed multiple linear regressions on the participant's response accuracy and the space-time samples. We contend that each cluster of connected significant regression coefficients is a letter feature. To bridge the gap between the letter identification literature and this experiment, we also determined the relative importance of the features proposed in the letter identification literature. Results show clear modulations of the relative importance of the letter features of some letters across time, demonstrating that letter features are not always extracted simultaneously at constant speeds. Furthermore, of all the feature classes proposed in the literature, line terminations and horizontals appear to be the two most important for letter identification.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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