Effects of cues to event segmentation on subsequent memory
Bibliographic record
Abstract
To remember everyday activity it is important to encode it effectively, and one important component of everyday activity is that it consists of events. People who segment activity into events more adaptively have better subsequent memory for that activity, and event boundaries are remembered better than event middles. The current study asked whether intervening to improve segmentation by cuing effective event boundaries would enhance subsequent memory for events. We selected a set of movies that had previously been segmented by a large sample of observers and edited them to provide visual and auditory cues to encourage segmentation. For each movie, cues were placed either at event boundaries or event middles, or the movie was left unedited. To further support the encoding of our everyday event movies, we also included post-viewing summaries of the movies. We hypothesized that cuing at event boundaries would improve memory, and that this might reduce age differences in memory. For both younger and older adults, we found that cuing event boundaries improved memory-particularly for the boundaries that were cued. Cuing event middles also improved memory, though to a lesser degree; this suggests that imposing a segmental structure on activity may facilitate memory encoding, even when segmentation is not optimal. These results provide evidence that structural cuing can improve memory for everyday events in younger and older adults.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| 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".