United we fall: All-or-none forgetting of complex episodic events.
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
Do complex event representations fragment over time, or are they instead forgotten in an all-or-none manner? For example, if we met a friend in a café and they gave us a present, do we forget the constituent elements of this event (location, person, and object) independently, or would the whole event be forgotten? Research suggests that item-based memories are forgotten in a fragmented manner. However, we do not know how more complex episodic, event-based memories are forgotten. We assessed both retrieval accuracy and dependency-the statistical association between the retrieval successes of different elements from the same event-for complex events. Across 4 experiments, we show that retrieval dependency is found both immediately after learning and following a 12-hr and 1-week delay. Further, the amount of retrieval dependency after a delay is greater than that predicted by a model of independent forgetting. This dependency was only seen for coherent "closed-loops," where all pairwise associations between locations, people, and objects were encoded. When "open-loops" were learned, where only 2 out of the 3 possible associations were encoded, no dependency was seen immediately after learning or after a delay. Finally, we also provide evidence for higher retention rates for closed-loops than for open-loops. Therefore, closed-loops do not fragment as a function of forgetting and are retained for longer than are open-loops. Our findings suggest that coherent episodic events are not only retrieved, but also forgotten, in an all-or-none manner. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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