The influence of event similarity on the detailed recall of autobiographical memories
Bibliographic record
Abstract
Memories for life events are thought to be organized based on their relationships with one another, affecting the order in which events are recalled, such that similar events tend to be recalled together. However, less is known about how detailed recall for a given event is affected by its associations to other events. Here, we used a cued autobiographical memory recall task where participants verbally recalled events corresponding to personal photographs. Importantly, we characterized the temporal, spatial, and semantic associations between each event to assess how similarity between adjacently cued events affected detailed recall. We found that participants provided more non-episodic details for cued events when the preceding event was both semantically similar and either temporally or spatially dissimilar. However, similarity along time, space, or semantics between adjacent events did not affect the episodic details recalled. We interpret this by considering organization at the level of a life narrative, rather than individual events. When recalling a stream of personal events, we may feel obligated to justify any seeming discrepancies between adjacent events that are semantically similar, yet simultaneously temporally or spatially dissimilar—to do so, we provide additional supplementary detail to help maintain global coherence across the events in our lives.
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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 | 0.001 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".