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Record W1989209178 · doi:10.1177/154193120204600304

Mental Workload and the Display of Abstraction Hierarchy Information

2002· article· en· W1989209178 on OpenAlexaff
Catherine M. Burns, Laura Thompson, Antonio Miguel Bernal Rodríguez

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2002
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWorkloadPupil diameterComputer scienceEye trackingPupilAbstractionHierarchyHuman–computer interactionContrast (vision)Pupil sizeComputer visionInformation retrievalPsychology

Abstract

fetched live from OpenAlex

In designing large ecological displays, designers are faced with the question of how to display multiple levels of abstract information. Previous research has shown that people may perform better, in terms of diagnosis speed and accuracy, if multiple levels of information are presented in an integrated format (Burns, 2000). We repeated the study of Burns (2000) which looks at providing abstract information in three formats - one level at a time, windowed and integrated. We collected eye tracking data at intervals throughout the experiment. Our eye-tracker was able to collect pupil diameter measures and changes. Results showed no notable difference in pupil diameter measures between the integrated condition and the one level at a time condition, but notably higher increases in pupil diameter when abstract information was in separate windows. Furthermore, pupil diameters increased over time in the windowed condition, suggesting that workload with this display may have been increasing. These preliminary data suggest that separating levels of abstract information may increase the mental workload of operators.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.335

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 communication0.0000.001
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 head0.015
GPT teacher head0.262
Teacher spread0.246 · 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 designQualitative
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

Citations4
Published2002
Admission routes1
Has abstractyes

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