Sleep Contributions to Hippocampal Gist Extraction
Bibliographic record
Abstract
To better understand the world, humans scan the information contents of our experiences for patterns, corresponding to the extraction of gist, or essential meaning (Brainerd & Reyna, 1990). Research suggests that some forms of gist extraction require several hours, and even sleep for further processing (Ellenbogen et al., 2007; Payne et al., 2009), perhaps becoming subsequently reactivated in the hippocampus (Marshall & Born, 2007). Interestingly, the hippocampus may itself be functionally specialized for gist extraction in its anterior segment (Poppenk et al., 2013). The current study investigated the role of the anterior/posterior hippocampus and sleep stages in predicting patterns of change in gist memory over the course of a week. To assess this link, I identified four types of gist (inferential, statistical, multi-item, and single-item) that were described in recent reviews (Landmann et al., 2014; Stickgold & Walker, 2013). 104 participants were recruited, 67 of whom passed eligibility criteria and completed three behavioural sessions (evening before sleep, 12 hours later in the morning after sleep, and one week after the first session) and an MRI several weeks later as part of a broader battery of tasks. I found evidence that inferential gist in a transitive inference task increased over time, suggesting that new information is being formed. I also found that REM, rather than slow-wave sleep, predicted gist extraction in a number of different tasks. Lastly, hippocampal volumes predicted immediate rather than delayed gist extraction.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".