On Coding Non-Contiguous Letter Combinations
Bibliographic record
Abstract
Starting from the hypothesis that printed word identification initially involves the parallel mapping of visual features onto location-specific letter identities, we analyze the type of information that would be involved in optimally mapping this location-specific orthographic code onto a location-invariant lexical code. We assume that some intermediate level of coding exists between individual letters and whole words, and that this involves the representation of letter combinations. We then investigate the nature of this intermediate level of coding given the constraints of optimality. This intermediate level of coding is expected to compress data while retaining as much information as possible about word identity. Information conveyed by letters is a function of how much they constrain word identity and how visible they are. Optimization of this coding is a combination of minimizing resources (using the most compact representations) and maximizing information. We show that in a large proportion of cases, non-contiguous letter sequences contain more information than contiguous sequences, while at the same time requiring less precise coding. Moreover, we found that the best predictor of human performance in orthographic priming experiments was within-word ranking of conditional probabilities, rather than average conditional probabilities. We conclude that from an optimality perspective, readers learn to select certain contiguous and non-contiguous letter combinations as information that provides the best cue to word identity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".