MétaCan
Menu
Back to cohort
Record W2314562367 · doi:10.1177/154193120404801603

Advantage for Visual Momentum Not Based on Preview

2004· article· en· W2314562367 on OpenAlexaff
Justin G. Hollands, Nada Pavlovic, Yukari Enomoto, Haiying Jiang

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2004
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsDefence Research and Development Canada
FundersVedecká Grantová Agentúra MŠVVaŠ SR a SAV
KeywordsTerrainRotation (mathematics)AzimuthTransition (genetics)Orientation (vector space)Task (project management)Position (finance)Momentum (technical analysis)Computer scienceComputer visionMotion (physics)GeometryArtificial intelligencePsychologySimulationMathematicsEngineeringGeography

Abstract

fetched live from OpenAlex

Previous research has indicated that smooth rotation of geographic terrain between two- and three-dimensional (2D and 3D) views aids task switching. However, the time taken to show the smooth rotation may also provide a terrain preview for a post-rotation judgment. To test this possibility, we examined a situation where preview was provided but smooth transition violated. Twenty-four participants made judgments about the properties of two points placed on 2D or 3D displays of terrain. Participants performed the tasks in pairs of trials, switching tasks and displays between trials. In the continuous transition condition, the display rotated in depth and in azimuth from one display format to the other. In the discrete transition condition, the azimuth rotation was in the opposite direction, and then the terrain ”snapped” to the final orientation. The results showed that response time after transition was less for the continuous condition. We argue that smooth transition to the correct position provided improved visual momentum between 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.

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: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.451

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 head0.014
GPT teacher head0.244
Teacher spread0.231 · 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

Citations3
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Human Factors and Ergonomics Society Annual MeetingSame topicSpatial Cognition and NavigationFrench-language works237,207