Putting the testing effect to the test in the wild: Retrieval enhances real-world memories and promotes their semantic integration while preserving episodic integrity
Bibliographic record
Abstract
Retrieval practice—actively recalling information—is an established memory-strengthening technique. However, understanding how retrieval transforms memory requires examining its effects on memories that evolve across multiple episodic and semantic dimensions, as is typical of real-world events. Thus, we investigated how repeatedly retrieving event details without feedback versus restudying the same details influenced memory for an episodically rich and meaningful staged event after 14 days (n = 26 per group). Retrieval enhanced retention of successfully-reviewed content, providing the first testing effect demonstration for real-world events. Retrieval also increased the incorporation of pre-existing semantic information into recall narratives, suggesting enhanced event integration with pre-existing knowledge, perhaps via co-activation of semantically-related content during retrieval. However, this semantic integration did not enhance—or impair—broader episodic memory beyond successfully-reviewed content. These findings suggest that retrieval reshapes memories by integrating recalled content into semantic knowledge networks—a mechanism that may underlie the testing effect—while preserving the overall integrity of episodic representations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".