The Effects of Reading Comprehension and Launch Site on Frequency–Predictability Interactions during Paragraph Reading
Bibliographic record
Abstract
We used eye movement measures of paragraph reading to examine whether word frequency and predictability interact during the earliest stages of lexical processing, with a specific focus on whether these effects are modulated by individual differences in reading comprehension or launch site (i.e., saccade length between the prior and currently fixated word--a proxy for the amount of parafoveal word processing). The joint impact of frequency and predictability on reading will elucidate whether these variables additively or multiplicatively affect the earliest stages of lexical access, which, in turn, has implications for computational models of eye movements during reading. Linear mixed effects models revealed additive effects during both early- and late-stage reading, where predictability effects were comparable for low- and high-frequency words. Moreover, less cautious readers (e.g., readers who engaged in skimming, scanning, mindless reading) demonstrated smaller frequency effects than more cautious readers. Taken together, our findings suggest that during extended reading, frequency and predictability exert additive influences on lexical and postlexical processing, and that individual differences in reading comprehension modulate sensitivity to the effects of word frequency.
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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 | 0.000 |
| 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) | 0.003 | 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".