Cognitive coupling during reading.
Bibliographic record
Abstract
We hypothesize that cognitively engaged readers dynamically adjust their reading times with respect to text complexity (i.e., reading times should increase for difficult sections and decrease for easier ones) and failure to do so should impair comprehension. This hypothesis is consistent with theories of text comprehension but has surprisingly been untested. We tested this hypothesis by analyzing 4 datasets in which participants (N = 484) read expository texts using a self-paced reading paradigm. Participants self-reported mind wandering in response to pseudorandom thought-probes during reading and completed comprehension assessments after reading. We computed two measures of cognitive coupling by regressing each participant's paragraph-level reading times on two measures of text complexity: Flesch-Kincaid Grade Level and Word Concreteness scores. The two coupling measures yielded convergent findings: coupling was a negative predictor of mind wandering and a positive predictor of both text- and inference-level comprehension. Goodness-of-fit, measured with Akaike information criterion, also improved after adding coupling to the reading-time only models. Furthermore, cognitive coupling mediated the relationship between mind wandering and comprehension, supporting the hypothesis that mind wandering engenders a decoupling of attention from external stimuli. (PsycINFO Database Record
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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 | 0.015 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".