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Record W4390796998 · doi:10.1101/2024.01.10.575038

Sleep selectively and durably enhances real-world sequence memory

2024· preprint· en· W4390796998 on OpenAlexafffund
Nicholas B. Diamond, Sharon Simpson, Daniel Pérez, Brian J. Murray, Stuart Fogel, Brian Levine

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreRoyal Ottawa Mental Health CentreUniversity of OttawaBaycrest HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSleep (system call)Sequence (biology)PsychologyComputer scienceBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Sleep is thought to play a critical role in the retention of episodic memories. Yet it remains unclear whether and how sleep actively transforms memory for specific experiences. More generally, little is known about sleep’s effects on memory for multidimensional real-world experiences, both overnight and in the days to months that follow. In an exception to the law of forgetting, we showed that sleep actively and selectively improves retrieval of a one-time real-world experience (a controlled but immersive art tour) – specifically boosting memory for the order of tour items (sequential associations), but not perceptual details from the tour (featural associations). This above-baseline increase in sequence memory was not evident after a matched period of wakefulness. Moreover, the sleep-induced advantage of sequence over featural memory grew over time up to one-year post-encoding. Finally, overnight polysomnography showed that sleep-related memory enhancement was associated with the duration and neurophysiological hallmarks of slow-wave sleep previously linked to neural replay, particularly spindle-slow wave coupling. These results suggest that sleep serves a crucial and selective role in enhancing sequential organization in episodic memory at the expense of specific details, linking sleep-related neural mechanisms to the transformation and enhancement of memory for complex real-life experiences. Significance Statement Sleep affects the retention of episodic memories. Yet, it remains unclear whether sleep active transforms how we remember past experiences, overnight and beyond. We investigated memory for different dimensions underlying a dynamic real-world event – sequential associations versus atemporal featural associations – before and after sleep or wakefulness, and serially up to a year later. Sleep actively and selectively enhanced sequence memory, with this preferential sequence retention growing with time. Overnight memory enhancement is associated with the duration and neurophysiological hallmarks of slow-wave sleep previously linked to sequential neural replay, particularly spindle-slow wave coupling. Our findings support an active role for sleep in transforming different aspects of real-world memory, with sequence structure coming to dominate long-term memory for dynamic real-world experiences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.037
GPT teacher head0.285
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes2
Has abstractyes

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