Playing “guess who?”: when an episodic specificity induction increases trace distinctiveness and reduces memory errors during event reconstruction
Bibliographic record
Abstract
The constructive nature of memory implies a possible confusion between details of similar events. Memory interventions should thus target the reduction of memory errors. We postulate that a brief intervention called Episodic Specificity Induction (ESI) facilitates the sensorimotor simulation of event-related details by improving the distinctiveness of the event memory trace. As such, ESI should reduce memory errors only when event memory traces are strongly overlapping based on their sensorimotor features. Participants memorised videos showing characters performing an action on a given object. The characters were either visually very similar to each other or very distinct (low vs. high distinctiveness condition). Next, participants performed either an imagination version of the ESI or a control induction. Finally, a voice announced one of the actions seen and a character was then briefly displayed. The participants had to indicate whether the association was correct. For incorrect associations, in the low distinctiveness condition, false alarms were more likely than in the high distinctiveness condition and were reduced after the ESI. It suggests that facilitating the simulation of specific details through the ESI increased trace distinctiveness and reduced memory errors at the critical time of event reconstruction. Future clinical applications might be possible.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".