Design and Evaluation of a Perceptual-Based Object Group Selection Technique
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.
Bibliographic record
Abstract
Selecting groups of objects is a frequent task in graphical user interfaces since it precedes all manipulation operations. Current selection techniques such as lasso become time-consuming and error-prone in dense configurations or when the area covered by targets is large or hard to reach. Perceptual-based selection techniques can considerably improve the selection task when the targets have a perceptual structure, driven by Gestalt principles of proximity and good continuity. However, current techniques use ad hoc grouping algorithms that often lack evidence from perception science. Moreover, they do not allow selecting arbitrary groups (i.e. without a perceptual structure) or modifying a selection. This paper presents a domain-independent perceptual-based selection technique that addresses these issues. It is built upon an established group detection model from perception research and provides intuitive interaction techniques for selecting (whole or partial) groups with curvilinear or random structures. Our user study shows that this technique not only outperforms rectangle selection and lasso techniques when targets have perceptual structure, but also it is competitive when targets have arbitrary arrangements.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it