Smooth Rotation of 2-D and 3-D Representations of Terrain: An Investigation Into the Utility of Visual Momentum
Bibliographic record
Abstract
OBJECTIVE: The potential advantage of visual momentum in the form of smooth rotation between two-dimensional (2-D) and three-dimensional (3-D) displays of geographic terrain was examined. BACKGROUND: The relative effectiveness of 2-D and 3-D displays is task dependent, leading to the need for multiple frames of reference as users switch tasks. The use of smooth rotation to provide visual momentum has received little scrutiny in the task-switching context. A cognitive model of the processes involved in switching viewpoints on a set of spatial elements is proposed. METHODS: In three experiments, participants judged the properties of two points placed on terrain depicted as 2-D or 3-D displays. Participants indicated whether Point A was higher than Point B, or whether Point B could be seen from Point A. Participants performed the two tasks in pairs of trials, switching tasks and displays within the pair. In the continuous transition condition the display dynamically rotated in depth from one display format to the other. In the discrete condition there was an instantaneous viewpoint shift that varied across experiments (Experiment 1: immediate; Experiment 2: delay; Experiment 3: preview). RESULTS: Performance after continuous transition was superior to that after discrete transition. CONCLUSION: The visual momentum provided by smooth rotation helped users switch tasks. APPLICATION: The use of dynamic transition is recommended when observers examine multiple views of terrain over time. The model may serve as a useful heuristic for designers. The results are pertinent to command and control, geological engineering, urban planning, and imagery analysis domains.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".