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Record W2770654408 · doi:10.1097/wnr.0000000000000934

Task switching following 24 h of total sleep deprivation

2017· article· en· W2770654408 on OpenAlexafffund
Ann Nakashima, Fethi Bouak, Quan Lam, Ingrid Smith, Oshin Vartanian

Bibliographic record

VenueNeuroreport · 2017
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsUniversity of TorontoDefence Research and Development Canada
FundersMinistère de la Défense Nationale
KeywordsSleep deprivationNeuroscienceSleep (system call)Task (project management)PsychologyCognitionComputer science

Abstract

fetched live from OpenAlex

Task switching is a ubiquitous feature of many human activities that involve multitasking. In addition, owing to occupational demands, many individuals are required to engage in task switching under various levels of sleep deprivation, such as those who work in military and medical contexts. Nevertheless, little is known about the effects that sleep loss has on the neural bases of task switching. To address this shortcoming, we administered a cued switching task to participants following a night of normal sleep and also following a night of total sleep deprivation - in counterbalanced order. The behavioral results demonstrated a cost (i.e. longer reaction time) both as a function of sleep deprivation and task switching. Sleep deprivation resulted in greater activation in the frontoparietal network, whereas task switching was correlated with greater activation in the thalamus and superior temporal gyrus. However, despite increases in fatigue and sleepiness and a reduction in cognitive effectiveness (computed from actigraphic data), the reaction time cost associated with switching (i.e. switch cost) was not exacerbated by sleep deprivation. The results are discussed in terms of the involvement of executive functions in mitigating the effects of sleep deprivation on task switching.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.032
GPT teacher head0.318
Teacher spread0.287 · 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

Citations21
Published2017
Admission routes2
Has abstractyes

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