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Record W4404444549 · doi:10.1093/sleepadvances/zpae083

Sleep and retrieval practice both strengthen and distort story recollection

2024· article· en· W4404444549 on OpenAlexaff
Dan Denis, Carissa DiPietro, R. Nathan Spreng, Daniel L. Schacter, Robert Stickgold, Jessica D. Payne

Bibliographic record

VenueSLEEP Advances · 2024
Typearticle
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Mental HealthNational Institute on AgingNational Institutes of HealthNational Science Foundation
KeywordsRecallSleep (system call)PsychologyCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

Over time, memories lose episodic detail and become distorted, a process with serious ramifications for eyewitness identification. What are the processes contributing to such transformations over time? We investigated the roles of post-learning sleep and retrieval practice in memory accuracy and distortion, using a naturalistic story recollection task. Undergraduate students listened to a recording of the "War of the Ghosts," a Native American folktale, and were assigned to either a sleep or wake delay group, and either a retrieval practice or listen-only study condition. We found higher accuracy after sleep compared to wake in the listen-only condition, but not in the retrieval practice condition. This effect was driven by participants in the wake, retrieval practice condition showing superior memory compared to the wake, listen-only condition. A similar pattern was found for memory distortion, with both sleep and retrieval practice being associated with more inferences of nonpresented, but story-related information, compared to the wake, listen-only condition. These findings suggest both sleep and retrieval practice contribute to narrative memory stabilization and distortion.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.315
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations5
Published2024
Admission routes1
Has abstractyes

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