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Record W1981702747 · doi:10.1145/1385569.1385642

The effect of animated transitions in zooming interfaces

2008· article· en· W1981702747 on OpenAlex
Maruthappan Shanmugasundaram, Pourang Irani

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsZoomAnimationComputer scienceWorkspaceTask (project management)Human–computer interactionComputer graphics (images)Transition (genetics)Artificial intelligenceEngineeringRobot

Abstract

fetched live from OpenAlex

Zooming interfaces use animated transitions to smoothly shift the users view between different scales of the workspace. Animated transitions assist in preserving the spatial relationships between views. However, they also increase the overall interaction time. To identify whether zooming interfaces should take advantage of animations, we carried out one experiment that explores the effects of smooth transitions on a spatial task. With metro maps, users were asked to identify the number of metro stops between different subway lines with and without animated zoom-in/out transitions. The results of the experiment show that animated transitions can have significant benefits on user performance - participants in the animation conditions were twice as fast and overall made fewer errors than in the non-animated conditions. In addition, short animations were found to be as effective as long ones, suggesting that some of the costs of animations can be avoided. Users also preferred interacting with animated transitions than without. Our study gives empirical evidence on the benefits of animated transitions in zooming interfaces.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.076

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 head0.006
GPT teacher head0.208
Teacher spread0.201 · 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

Quick stats

Citations24
Published2008
Admission routes1
Has abstractyes

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