Text-based and memory-based metrics of cognitive coupling.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study was an investigation of the relation between cognitive coupling, a correlation between text difficulty and reading time, and other measures of mind wandering during reading. To measure cognitive coupling, we manipulated the text difficulty of individual sentences. Because mind wandering may shift attention away from the text, we predicted a cognitive coupling interaction, that is, that the effect of difficulty on processing time should be less when readers are off task. We also manipulated the consistency of a target sentence's content with a prior information. Analogous to the text-based cognitive coupling, we predicted an interaction of consistency with task focus: The impact of this consistency should be less noticeable when readers are off task. The results demonstrated the predicted text-based cognitive-coupling effect: There was less of an effect of text difficulty when readers reported being off task. However, there was no such interaction between consistency and task focus. We conclude that the consistency effect may depend on the relatively automatic activation of prior information rather than requiring consciously retrieving related information from memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it