[no title]
Bibliographic record
Abstract
A word is unreadable unless its letters are separately identifiable. Taken from the recent article of Pelli, Farell and Moore (2003), this sentence underlines the crucial importance of letter identification for visual word recognition. Congruently, a fundamental purpose in the study of visual letter recognition is the discovery of the features responsible for accurate letter identification. In the last three decades, researchers have proposed sets of individual features, which predict relatively well letter identification performance and letter confusions. However, these descriptions are usually based more on intuitions regarding the information underlying letter similarity or on confusion matrices than on empirical data directly assessing the information used to recognize letters accurately. In the present study, we aim to reveal the potent features (Gosselin & Schyns, 2002) mediating uppercase and lowercase letter identification in Arial font letters. For this purpose, we explicitly determined the portions of each individual letter which drives its accurate identification. Six participants each identified 26,000 uppercase and lowercase Arial font letters (for a total of 312,000 trials) sampled in image location and spatial frequency by Bubbles (Gosselin & Schyns, 2001). Separate analyses for each individual letter revealed the potent features for uppercase and lowercase Arial font letter identification. The results show that high spatial frequencies support the identification of features that discriminate among visually similar letters (e.g., ‘O’ and ‘Q’ in uppercase). In contrast, low spatial frequencies carry information about the features that are shared among subsets of visually similar letters. These observations are discussed in relation to a letter identification model in which low spatial frequencies are processed initially to determine the subset of the alphabet the target belongs to. Then, high spatial frequencies are processed for information allowing unique letter identification.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.130 | 0.090 |
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".