Human Perception of Structure in Shaded Space-Filling Visualizations
Bibliographic record
Abstract
Very early in the object recognition process the human visual system extracts shading information. While shading can enhance the visibility of structures, it can have a negative impact on the judgment of sizes of elements in a structure. In certain visualization systems the underlying hierarchical structure is not noticeably explicit, such as in space-filling techniques. We hypothesize that in such cases, shading can make the structure more explicit. In this paper, we report the results of two experiments designed to investigate the effects of shading information on extracting the structure in space-filling visualizations. In the first experiment subjects performed better with the visualization tool with shading on structure-based tasks. Our results do not show that shading impairs users' judgment on size-based tasks. A subjective evaluation shows that users preferred interacting with the system when shading was available. The second experiment was designed to investigate further users' capacity to identify structural elements within the space-filling visualization. A substructure recognition task was employed in this experiment and results show that users are capable of identifying sub-structures quicker and with fewer errors when the visualization tool was equipped with shading information than without. The results of both experiments provide evidence that shading information can be used to effectively obtain structural information from spacefilling visualizations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".