Understanding the Relative Contributions of Lower‐Level Word Processes, Higher‐Level Processes, and Working Memory to Reading Comprehension Performance in Proficient Adult Readers
Bibliographic record
Abstract
Abstract Although a considerable amount of evidence has been amassed regarding the contributions of lower‐level word processes, higher‐level processes, and working memory to reading comprehension, little is known about the relationships among these sources of individual differences or their relative contributions to reading comprehension performance. This study addresses these shortcomings by using structural equation modeling. The principal structural equation model tested in this study—called the cognitive components‐resource model of reading comprehension—proposes a set of specific relationships among lower‐level word processing, higher‐level processes, and working memory. This model is then systematically compared with a series of other models that propose alternative relationships among these three sources of individual differences. The results show that, although working memory influences higher‐level processes, speed of lower‐level word processing exerts little to no influence on higher‐level processes or working memory. The results also show that a variant of the cognitive components‐resource model of reading comprehension accounts for 62% of the variance in reading comprehension performance. Taken as a whole, the present study informs theories of reading comprehension by proposing relationships among important sources of individual differences. It also provides a foundation for future research seeking to test and compare theories of reading comprehension and other sources of individual differences.
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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 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".