Contributions of reader- and text-level characteristics to eye-movement patterns during passage reading.
Bibliographic record
Abstract
The present research presents a novel method for investigating how characteristics of texts (words, sentences, and passages) and individuals (verbal and general cognitive skills) jointly influence eye-movement patterns over the time-course of reading, as well as comprehension accuracy. Fifty-one proficient readers read passages of varying complexity from the Gray Oral Reading Test, while their eye-movements were recorded. Participants also completed a large battery of tests assessing various components of reading comprehension ability (vocabulary size, decoding, phonological awareness, and experience with print), as well as general cognitive and executive skills. We used the Random Forests nonparametric regression technique to simultaneously estimate relative importance of all predictors. This method enabled us to trace the temporal engagement of individual predictors and entire predictor groups on eye-movements during reading, while avoiding the problems of model overfitting and collinearity, typical of parametric regression methods. Our findings both confirmed well-established results of prior research and pointed to a space of hypotheses that is as yet unexplored. (PsycINFO Database Record (c) 2018 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 | 0.001 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".