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Record W2313426939 · doi:10.1177/154193120504900342

Visual Sensitivity of Dynamic Graphical Objects

2005· article· en· W2313426939 on OpenAlex
Munira Jessa, Catherine M. Burns

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.

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2005
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGraphical user interfaceComputer scienceGraphical displayFeature (linguistics)Human–computer interactionGraphical modelVisualizationSensitivity (control systems)Computer graphics (images)Computer visionArtificial intelligenceEngineeringProgramming language

Abstract

fetched live from OpenAlex

Advanced display design, such as Ecological Interface Design (EID), makes extensive use of complex graphical objects. Research has shown that by following EID methodologies, operators have better performance with the EID displays (Pawlak and Vicente, 1996). However, past research does not consider visual aspects of the graphical objects used in EID. Of particular interest is how different design decisions of graphical objects affect the performance of the objects used within that design. We examined the visual sensitivity of dynamic graphical objects, examining which features make certain graphical objects visually superior for certain tasks. It was found that for simple dynamic objects, a line changing in angle was the most noticeable emergent feature. For complex graphical objects, those that mimic a “bull's eye” should be used for target-indicator displays, “solid objects” should be used for comparison meters, and changes in shape sizes should be used in trend meters. These findings provide guidance for designers of dynamic advanced graphical displays.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.257
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