Review: moderate weight loss reduces functional disability but does not reduce pain in obese patients with knee osteoarthritisCommentary
Bibliographic record
Abstract
Narratives may provide a general context, unrestricted by space and time, which can be used to organize episodic memories into networks of related events. However, it is not clear how narrative contexts are represented in the brain. Here we test the novel hypothesis that the formation of narrative-based contextual representations in humans relies on the same hippocampal mechanisms that enable formation of spatiotemporal contexts in rodents. Participants watched a movie consisting of two interleaved narratives while we monitored their brain activity using fMRI. We used representational similarity analysis, a type of multivariate pattern analysis, which uses across-voxel correlations as a proxy for neural-pattern similarity, to examine whether the patterns of neural activity can be used to differentiate between narratives and recurring narrative elements, such as people and locations. We demonstrate that the neural activity patterns in the hippocampus differentiate between event nodes (people and locations) and narratives (different stories) and that these narrative-context representations diverge gradually over time akin to remapping-induced spatial maps represented by rodent place cells. SIGNIFICANCE STATEMENT Narratives, especially in movie format, are very engaging and can be used to investigate neural mechanisms underlying cognitive functions in more naturalistic settings than that of traditional paradigms. Narratives also provide a more general context, unrestricted by space and time, that can be used to organize memories into networks of related events. For this reason, narratives are ideally suited to engage neural mechanisms underlying episodic memory formation. In this study, participants watched a movie with two interleaved narratives while their brain activity was monitored using fMRI. We show that the hippocampus, which is involved in formation of spatiotemporal contexts in episodic memory, also represents gradually diverging narrative contexts as well as narrative elements, such as people and locations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".