All together now: Random Forests analysis reveals the joint impact of multiple statistical regularities on eye-movements during reading
Bibliographic record
Abstract
A large and growing number of recent studies has embraced a statistical learning view of reading, revealing that readers utilize an array of regularities that are available in writing systems as they process printed words and texts. However, previous studies have focused on the impact of one regularity (or an otherwise small number of cues). Therefore, we currently have a limited understanding of (1) whether different regularities each carry unique explanatory power, beyond other (collinear) cues; (2) how do regularities at different levels of the input contribute to reading behavior; and (3) whether regularities vary in their contributions across processing stages. To answer these questions, we employ Random Forests analyses on a large-scale, eye-movement, passage-reading database from English first- and second-language readers, evaluating the relative importance of a large number of regularities on multiple eye-movement dependent variables. First, our findings demonstrate that, each regularity uniquely contributes to the model's performance. Second, we show that both text-level regularities (e.g., predictability) and word-level regularities (including print-speech and print-meaning regularities), contribute to continuous text reading. Third, we document varying contributions of some regularities over time, with later reading measures being more impacted by text-level regularities. These results support and extend statistical learning theories of reading, showing that readers are attuned to a range of regularities in their writing system, which jointly guide naturalistic reading behavior.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".