The statistical reader: The role of orthographic regularities in reading.
Bibliographic record
Abstract
Recent statistical learning views of reading posit that writing systems present to their readers a wide range of statistical regularities which are leveraged to process printed texts. While substantial research has focused on the "vertical" correlations between orthographic, phonological, and semantic units in a given writing system, here we employ information-theoretic measures to further consider "horizontal" regularities-the extent to which printed units predict and are predicted by other printed units, in one writing system compared to another. As a first step, we present a novel information-theoretic measure that captures how horizontal regularities constrain lexical access given the distribution of orthographic information in a writing system and considering realistic retinal and cognitive constraints. We then present a series of empirical studies serving as proof of concept, from both single-word reading experiments and analyses of eye movements during naturalistic reading, which examine how a reader who has internalized these regularities could leverage them for efficient uncertainty reduction regarding printed information while reading on-the-fly. Our findings converge on high-order general principles fleshed out in terms of explicit computational mechanisms that simultaneously apply to a wide range of writing systems and that can potentially explain behavioral outcomes across the trajectory of reading development and reading skill. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.004 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".