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Record W2024082361 · doi:10.1145/1140491.1140515

Effects of 2D geometric transformations on visual memory

2006· article· en· W2024082361 on OpenAlexafffund
Heidi Lam, Ronald A. Rensink, Tamara Munzner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScalingRotation (mathematics)Transformation (genetics)PolarComputer scienceNonlinear systemPolar coordinate systemReduction (mathematics)VisualizationAlgorithmGeometryComputer visionArtificial intelligenceMathematicsPhysics

Abstract

fetched live from OpenAlex

In order to provide well-grounded guidelines for interface design, we systematically examined the effects of 2D geometric transformations and background grids on visual memory. We studied scaling, rotation, rectangular fisheye, and polar fisheye transformations. Based on response time and accuracy results, we found a no-cost zone for each transformation type within which performance is unaffected. Results indicated that scaling had no effect down to at least 20% reduction. Rotation had a no-cost zone of up to 45 degrees, after which the response time increased to 5.4 s from the 3.4 s baseline without significant drop in accuracy. Interestingly, polar fisheye transformations had less effect on accuracy than their rectangular counterparts. The presence of grids extended these zones and significantly improved accuracy in all but the fisheye polar transformations. Our results therefore provided guidance on the types and levels of nonlinear transformations that could be used without affecting performance, and provided insights into the roles of transformations and grids on visual memory.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.788
Threshold uncertainty score0.308

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 studies0.0000.000
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 head0.004
GPT teacher head0.237
Teacher spread0.232 · 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 designBench or experimental
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

Citations18
Published2006
Admission routes2
Has abstractyes

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