REM negatively predicts statistical learning but not other forms of gist
Bibliographic record
Abstract
Abstract Human memory for recent events is believed to undergo reactivation during sleep. This process is thought to be relevant for the consolidation of both individual episodic memories and gist extraction, the formation of generalized memory representations from multiple, related memories. Which kinds of gist are actually enhanced, however, is the subject of less consensus. To address this question, we focused our design on four types of gist: inferential gist (relations extracted across non-contiguous events), statistical learning (regularities extracted from a series), summary gist (a theme abstracted from a temporally contiguous series of items), and category gist (characterization of a stimulus at a higher level in the semantic hierarchy). Sixty-nine participants (30 men, 38 women, and 1 other) completed memory encoding tasks addressing these types of gist and corresponding retrieval tasks the same evening, the morning after, and one week later. Inferential gist was retained over a week, whereas memory for category gist, summary gist, and statistical learning decayed. Higher proportions of REM were associated with worse performance in a statistical learning task controlling for time. Our results support that REM sleep is involved in schema disintegration, which works against participants’ ability to identify regularities within temporal series. SIGNIFICANCE STATEMENT To gain the most from our experiences, we extract from them the most important elements, or “gist”, with sleep believed to facilitate this process. However, what is referred to as gist varies considerably across studies. We report categorically different mnemonic trajectories of two classes of gist. In particular, we show that gist involving synthesis across relational memories is retained over time, whereas other gists were subject to substantial decay. Moreover, our evidence supports the idea that REM works to discretize, rather than synthesize experiences. Future research should test similar constructs in different tasks to determine whether these findings are generalizable. Our research suggests that patients with reduced REM sleep may experience more interference between similar memories.
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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 | 0.008 |
| 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".