A distorted view is good for finding something in a crowd, not for reading its exact value

What it is

The mechanism conclusions of the previous cards land on tasks as one selection rule: the distortion view's home ground is "finding things" — locating a target in a long list, a large map, a dense menu, trading warp for target-plus-global on one screen; its forbidden zone is "reading numbers" — magnitude comparison, trend judgement, precise measurement, the very encodings that distortion corrupts.

Why it happens

The division follows from what each task's encodings need: navigation and location run on existence and order (where is the target, what is nearby), both preserved under monotone warp, so distortion performs; reading and comparison run on quantitative position and length (by how much, how long), both corrupted by warp, so distortion fails. The experimental literature matches the inference: focus-plus-context and distortion techniques often beat linear zooming on target search and list browsing, while losing or showing no gain on precise comparison. The engineering meaning is to treat the distortion view as a navigation tool, not an analysis tool — it answers "where," never "how much."

Studying it

Validating the boundary follows the task-typing paradigm: implement fisheye and linear versions of the same dataset, measure time and accuracy separately by task type (target search, order judgement, quantitative comparison, distance estimation); the literature pattern is fisheye winning search tasks and degrading quantitative ones. Studies must also control learning effects — fisheye has a perceptible learning curve, and novice-versus-practised differences confound conclusions, so within-subject training or balanced grouping is required. Results from different fisheye functions (Gaussian, hyperbolic) do not extrapolate to each other; reports must name the function used.

Where it stops holding

The division is a continuum, not a binary: the closer the task to the navigation end, the larger distortion's net gain; the closer to the quantitative end, the larger its net cost. Mixed tasks (browsing while roughly comparing) sit mid-spectrum, where mild distortion plus label compensation can work. Distortion's payoff also depends on content structure — linear lists (naturally one-dimensional) benefit most reliably, two-dimensional maps next, unstructured content not at all.

Applying it

• Qualify the task before selecting: navigation work takes the fisheye, quantitative work takes plain zoom or dual views.
• For mixed tasks, combine "fisheye for navigation + click to switch to a linear view for reading" and let each mode do its own job.
• Verification: compare distorted and linear versions on both sides of the target task (a location task and a comparison task); introduce the fisheye only when location wins and comparison does not suffer.

Related

• Same group: U6.07.1 Fisheye achieves focus plus context by local magnification and peripheral compression · U6.07.2 Geometric distortion breaks positional encoding; magnitudes stop being readable · U6.07.3 Distortion shifts as the focus moves; targets drift and become hard to hit · U6.07.5 A way to switch the distortion off and return to the plain view must exist
• Nearby: U6.06.4 The pattern saves gaze switches but adds spatial comprehension load · U6.06.2 The difference from overview-plus-detail is whether they occupy two views
• Search terms: fisheye task suitability · distortion evaluation · navigation vs analysis