The statistical reader: The role of orthographic regularities in reading.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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