The Influence of Integration and Counterintuitiveness on Memory for Text
Bibliographic record
Abstract
The Influence of Integration and Counterintuitiveness on Memory for Text M. Afzal Upal Influence & Effects Research Group Adversarial Intent Section Defence Research & Development Canada Toronto Mary Harmon-Vukic Psychology Department Providence College Abstract: Recent studies suggest that counterintuitive ideas embedded in stories facilitate their subsequent recall, thus increasing the likelihood that such stories survive time and space. However, it could be that structure of coun- terintuitive stories affects memory rather than the distinctiveness of their contents. Indeed, Harmon-Vukic and Slone (2009) demonstrated that integration of story information eliminated the counterintuitiveness effect. The purpose of the present experiment was to further explore the influence of integration on memory for counterintuitive concepts. Participants were presented with a story containing elements that were either intuitive, minimally counterintuitive, or maximally counterintuitive. In addition, the stories were either integrated or not integrated. Participants were asked to recall the material either immediately, or one week later. Consistent with the results of Harmon-Vukic and Slone recall performance was best for integrated stories, regardless of level of intuitiveness. The same effect occurred on week later, although overall memory performance was lower.
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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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".