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Record W2316009442 · doi:10.1177/154193120304700147

Calibration of Confidence in Situation Awareness Queries

2003· article· en· W2316009442 on OpenAlexaff
Frederick M. J. Lichacz, Brad Cain, Sejal Patel

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2003
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of WaterlooDefence Research and Development CanadaCanadian Armed Forces
Fundersnot available
KeywordsOverconfidence effectAir traffic controlConfidence intervalPerceptionWorkloadAviationTask (project management)Computer sciencePsychologyCalibrationSimulationSocial psychologyStatisticsEngineeringMathematics

Abstract

fetched live from OpenAlex

Two experiments using a simulated Air Traffic Control task were conducted to examine the relationship between situation awareness and confidence under conditions of high and low temporal and perceptual demand. In the first experiment, participants were required to actively manipulate the aircraft on the radar screen. In the second experiment, the participants were required to passively observe the simulated air traffic. In both experiments, Endsley's SAGAT was used to query the subjects' SA and confidence in their responses. The results of this study revealed that whereas SA was affected primarily by perceptual demand, confidence was affected by both time pressure and workload. Moreover, the participants' under/overconfidence was affected primarily by perceptual demand. However, the participants were clearly overconfident in response to difficult SA queries and predominantly underconfident to easy SA queries. These results have implications for our understanding of the relationship between SA and performance in aviation research.

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 score0.232
Threshold uncertainty score0.343

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 head0.028
GPT teacher head0.304
Teacher spread0.277 · 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

Citations8
Published2003
Admission routes1
Has abstractyes

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