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Record W3092751690 · doi:10.1093/scan/nsaa140

The protective effect of daytime sleep on planning and risk-related decision-making in emerging adults

2020· article· en· W3092751690 on OpenAlexaff
Lau Esther Yuet Ying, Lam Yeuk Ching, Benjamin Rusak, Tseng Chia-huei, Lee Tatia Mei Chun

Bibliographic record

VenueSocial Cognitive and Affective Neuroscience · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychologyDaytimeSleep (system call)Social psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

We assessed the effect of a daytime sleep opportunity on planning and risk-related decision-making in emerging adults using multiple neurobehavioral assessments. A total of 136 healthy emerging adults (20.0 ± 1.5 years), 65% female, performed the Risky-Gains Task and the Tower of London test twice. Between these assessments, they were randomized to either have a sleep opportunity monitored by polysomnography (Sleep group, n = 101) or to stay awake (Wake group, n = 35). During Test 2, in comparison to the Sleep group, the Wake group showed increased sleepiness, worse planning ability and more decrease in reaction times when selecting risky choices. Changes in Tower of London test steps used and Risky-Gains Task response time correlated with the number of central and frontal fast sleep spindles, respectively. These results indicate that among emerging adults who commonly have poor sleep patterns, a daytime sleep opportunity was related to better planning ability, better psychomotor vigilance and stable response speeds in risk-related decision-making. Changes in planning and risk-related decision-making correlated with the number of sleep spindles during the nap, supporting a specific role for sleep in modulating planning and potentially other higher-order cognitive functions.

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)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.822
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.315
Teacher spread0.304 · 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 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

Citations7
Published2020
Admission routes1
Has abstractyes

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