Long-term, multi-event surprise enhances autobiographical memory
Bibliographic record
Abstract
Abstract Neurobiological and psychological models of learning emphasize the importance of prediction errors (surprises) for memory formation. These effects have focused on memory for information surrounding a momentary surprising event; however, it is less clear whether surprise that unfolds across multiple events and timescales impacts memory. We asked basketball fans about their most positive and negative autobiographical memories of individual plays, games, and seasons, allowing surprise measurements spanning seconds, hours, and months. We used advanced analytics on National Basketball Association play-by-play data and betting odds spanning 17 seasons, >22K games, >5.6M plays to compute and align the estimated surprise values of each memory. We found that surprising events biased positive memories on the scale of seconds and months and negative memories across all three timescales. Critically, game and season memories could not be explained by surprise at shorter timescales, suggesting that long-term, multi-event surprise influences memory. These results expand the role of surprise in models of learning and reinforce its relevance in real-world domains.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".