Neural state changes during movie watching relate to episodic memory in younger and older adults
Bibliographic record
Abstract
Event segmentation is a key feature underlying the ability to remember real-life occurrences. Onthe neural level, event boundaries have been shown to align with boundaries between neuralstates – stable patterns of brain activity maintained over time. These neural states provide avaluable window into the neural underpinnings of event perception. To investigate how neuralstate boundaries relate to memory across the lifespan, we used the data-driven Greedy StateBoundary Search (GSBS) method to implicitly identify neural state changes in younger and olderadults’ electroencephalography (EEG) data during movie-watching. Memory for the movie wastested and related to 1) neural state correspondence across individuals and 2) the degree towhich the pattern of activity changes at boundaries. Neural state boundaries significantlyaligned across people, but did not differ with age nor relate to memory. The degree of change atneural state boundaries also did not differ with age, but was positively related to memory forthe movie. These findings suggest that age differences in the perception of naturalistic eventsmay be less pronounced than previously thought, at least when measured implicitly, and thatgreater distinction between successive neural states relates to better memory for one’sexperiences regardless of age.
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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 | 0.002 |
| 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".