Playing “guess who?”: when an episodic specificity induction increases trace distinctiveness and reduces memory errors during event reconstruction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it