The Impact of Event Similarity on Recalls of Repeated Events
Bibliographic record
Abstract
Psychologists are becoming increasingly interested in studying memories for repeated events as a bridge between 'episodic' memory (i.e., memory for specific events localized in time and place) and 'semantic' memory (i.e., memory for general facts and information). However, the relative contribution of episodic and semantic memory in recalls of repeated events has yet to be determined: do repeated events rely more on episodic memory or semantic memory? Moreover, do different repeated events utilize these forms of memory to different degrees? Prior experimental research has shown that children are more accurate in their recall of specific episodes of repeated events when the repeated events are low in similarity. Conversely, when episodes of repeated events are high in similarity, children tend to recall more details about the 'gist' of the event or, in other words, the details that are fixed across episodes (Danby et al., 2019). Do recalls of repeated events in adults follow a similar pattern, such that repeated events lower in similarity utilize more episodic memory and repeated events high in similarity utilize more semantic memory? Here, the “similarity” of an event refers to a continuum from low-similarity (where each episode of a repeated event is very different) to high-similarity (where each episode of a repeated event is very similar). Danby, M. C., Sharman, S. J., Brubacher, S. P., & Powell, M. B. (2019). The effects of episode similarity on children’s reports of a repeated event. Memory, 27(4), 561–567. https://doi.org/10.1080/09658211.2018.1529798.
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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 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".