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Record W7020847684

The NASA MATB-II Predicts Prospective Memory Performance During Complex Simulated Flight

2019· article· en· W7020847684 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCORE Scholar (Wright State University) · 2019
Typearticle
Languageen
FieldPsychology
TopicCognitive Functions and Memory
Canadian institutionsCarleton University
FundersOntario Innovation Trust
KeywordsProspective memoryProspective cohort studyHuman multitaskingCognitionAssociation (psychology)Workload
DOInot available

Abstract

fetched live from OpenAlex

Prospective memory is essential for flight, where failures can result in incorrect flight control settings, leading to loss of life and equipment. Furthermore, prospective memory is highly-sensitive to pilot age, cognition, and experience. This research reports on the relation of the NASA Multi-Attribute Test Battery-II (MATB-II) to prospective memory during simulated VFR flight (N=51). Prospective memory was indexed with specialized radio calls that were associated with non-focal visual cues. Linear regression models examined the relative association of MATB-II variables to prospective memory in low and high workloads. System monitoring, psychomotor tracking, and resource management, generally at higher difficulty levels, were the variables most predictive of prospective memory, r2 =0.41. Pilot experience improved the model in the high workload condition. Estimating risk for prospective memory failures via multitasking ability, with a focus on monitoring tasks, may inform cognitive assessment approaches to enhance aviation safety.

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.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.017
GPT teacher head0.226
Teacher spread0.210 · 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