Perceptual invariance of nonlinear Focus+Context transformations
Bibliographic record
Abstract
Focus+Context techniques are commonly used in visualization systems to simultaneously provide both the details and the context of a particular dataset. This paper proposes a new methodology to empirically investigate the effect of various Focus+Context transformations on human perception. This methodology is based on the shaker paradigm, which tests performance for a visual task on an image that is rapidly alternated with a transformed version of itself. An important aspect of this technique is that it can determine two different kinds of perceptual cost: (i) the effect on the perception of a static transformed image, and (ii) the effect of the dynamics of the transformation itself. This technique has been successfully applied to determine the extent to which human perception is invariant to scaling and rotation [Rensink 2004]. In this paper, we extend this approach to examine nonlinear fisheye transformations of the type typically used in a Focus+Context system. We show that there exists a no-cost zone where performance is unaffected by an abrupt, noticeable fisheye transformation, and that its extent can be determined. The lack of perceptual cost in regards to these sudden changes contradicts the belief that they are necessarily detrimental to performance, and suggests that smoothly animated transformations between visual states are not always necessary. We show that this technique also can map out low-cost zones where transformations result in only a slight degradation of performance. Finally, we show that rectangular grids have no positive effect on performance, acting only as a form of visual clutter. These results therefore demonstrate that the perceptual costs of nonlinear transformations can be successfully quantified. Interestingly, they show that some kinds of sudden transformation can be experienced with minimal or no perceptual cost. This contradicts the belief that sudden changes are necessarily detrimental to performance, and suggests that smoothly animated transformations between visual states are not always necessary.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".