Construction, integration, and mind wandering in reading.
Bibliographic record
Abstract
In two experiments, we investigated how text recall was related to moment-to-moment variations in mental state while reading, and how both recall and mental state were related to the interest value of the text. In both experiments, subjects read either an interesting text (a segment of Rice's Interview with the Vampire [A. Rice, 1997, Interview with the vampire, New York. NY: Ballantine Books] or a less interesting text (a segment of Thackery's The History of Pendennis [W. M. Thackery, 2009/1914, The history of Pendennis, Project Gutenberg, Retrieved from http://www.gutenberg.org/ebooks/7265]). The texts were read sentence-by-sentence on a computer screen, and subjects were periodically interrupted to answer a probe question. In Experiment 1, the probe asked whether subjects were attending to the text; in Experiment 2, the probe asked whether subjects were engaged with the story world. After reading the text, subjects were asked to recall as much of the story as possible. Recall of the material just prior to the probe was examined as a function of the whether the ratings were high, medium, or low. As expected, both on-task ratings and engagement ratings were higher for Interview than for Pendennis, but there were a substantial number of medium ratings given to both stories. In Experiment 1, there was a clear effect of story on recall over and above the effect of on-task rating. However, in Experiment 2, recall was purely a function of engagement rating. The results were interpreted in terms of a model in which recall is largely determined by the situation model representation of the narrative and in which engagement ratings (but not on-task ratings) provide a relatively pure index of the allocation of resources to processing of the situation model.
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.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".