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Record W1968012046 · doi:10.1089/cpb.2007.0028

Neural Correlates of “Absence” in Interactive Simulator Protocols

2008· article· en· W1968012046 on OpenAlexaff
Henry J. Moller

Bibliographic record

VenueCyberPsychology & Behavior · 2008
Typearticle
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsPsychomotor learningConsciousnessConstruct (python library)PerceptionTask (project management)Cognitive psychologyPsychologyDriving simulatorSimulator sicknessProtocol (science)Computer scienceHuman–computer interactionSimulationVirtual realityCognitionNeuroscienceMedicine

Abstract

fetched live from OpenAlex

While much has been written about the importance of the experience of presence in simulation protocols, the role of "absence" deserves closer attention. The role of subjective experience and neurophysiologic processes involved in fluctuating states of consciousness is a key issue in developing assessment and treatment tools using interactive immersive simulator tasks. This paper proposes that when engaging in an interactive simulator task, there are fluctuations of consciousness that determine both motivational engagement and measured performance. Rather than expecting a continuous experience, both in terms of perceptual and motor output flow, factors such as circadian fluctuations, fatigue, and actual intrusion of sleep into waking consciousness are relevant in assessments and treatments using virtual environment-based tasks. These factors are particularly relevant in treatment populations with neurological and psychiatric disorders, where such disturbances are more common. To illustrate this construct, a series of experiments examining these phenomena in relation to a standardized driving-simulation protocol to detect psychomotor impairment developed for clinical and diagnostic testing in a sleep laboratory setting are reviewed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.048
GPT teacher head0.378
Teacher spread0.330 · 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.

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

Citations2
Published2008
Admission routes1
Has abstractyes

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