Cognitive processes in comprehension of science texts: the role of co‐activation in confronting misconceptions
Bibliographic record
Abstract
Abstract In this paper, we investigate the effects of readers' incorrect knowledge on the on‐line comprehension processes during reading of science texts, with an eye towards examining the conditions that encourage revision of such knowledge. We employed computational (Landscape Model) and empirical (think‐aloud and reading times) methods to compare comprehension processes by readers with correct and incorrect background knowledge, respectively. Science texts were presented in either regular or refutation versions; Prior research using off‐line methods suggests that refutation versions promote revision in readers with incorrect knowledge. The results of the current study indicate that incorrect knowledge systematically influences both type and content of processing. Moreover, simultaneous activation of correct and incorrect conceptions during reading plays an essential role in knowledge revision: The computational simulations show that refutation texts create optimal circumstances for co‐activation of the incorrect and correct conceptions and the empirical data show that such a co‐activation is associated with inconsistency detection and revision activities by the readers with incorrect knowledge. These findings provide insights in the effects of misconceptions on the on‐line text processing and have important implications for the development of methods for achieving revision during reading. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.004 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".