Question 1

How do you summarize where a whole group looked over time, when a scarf plot only shows one person?

Accepted Answer

You move from individual traces to a cohort summary: divide the recording into equal time bins and record what share of the group fixated each area of interest in each bin. Plotting that share over time shows how collective attention shifts—shared phases, hand-offs between elements. The price is the loss of each person’s exact scanpath; order and transitions stay in the individual scarf plots, so the two are meant to be read together.

Question 2

Why use several different charts for the same aggregated eye-tracking data instead of one?

Accepted Answer

Because the questions differ and, by graphical-perception research, no single encoding answers them all well. A stacked silhouette captures overall engagement but reads one AOI’s values poorly; separated curves read precisely but hide the part-to-whole picture; rank, dominance, and sparsity need yet other layouts. Several views of the same numbers let the analyst match the encoding to the question instead of forcing one chart to do everything.

Question 3

Stream, Ridgeline, Distribution, Heatmap—which question does each one answer best?

Accepted Answer

Stream (a centered streamgraph) is the overview: overall engagement and big shifts in how attention is split. Ridgeline (separate curves on a shared scale) follows one AOI and shows when it peaks. Distribution (bars re-sorted by size each bin) shows which AOI leads and when the lead changes. Heatmap (a color grid of AOI × time) handles many AOIs at once—empty stretches, moments when several light up together—where height- or area-based charts get too crowded.

Question 4

What does aggregate AOI visualization throw away, and when does that matter?

Accepted Answer

Summing across people discards scanpath order and transitions, and—most importantly—averages away individual differences: a cohort split into two strategies (half fixate an element intensely, half ignore it) can look like moderate, uniform interest. Results also depend on choices made beforehand—bin width and how the AOIs were drawn. So treat these views as a way to generate hypotheses, then check them against individual data and alternative settings.

Question 5

Why can a cohort’s attention add up to less than 100% at a given moment?

Accepted Answer

Only fixations inside a defined AOI count toward the total. Saccades, blinks, lost tracking, looks at unmapped regions, and participants who already finished contribute nothing, so a bin can sum to well under 100%. The views keep this visible rather than hiding it—in the Stream the silhouette thins and tapers toward the end—because it carries real information about saccadic phases, data quality, and who is still on task.

Question 6

If streamgraphs and heatmaps already exist, what is actually new here?

Accepted Answer

Not the charts, and not the underlying number—the share of a group fixating each AOI per bin is deliberately simple. What the paper adds is the reasoning around them: a clear map from question to encoding, an honest account of where each encoding misleads, and the integration of all four with individual scarf plots and exportable, reproducible state in one open-source tool. The value is the design knowledge and the workflow, not the novelty of the visual forms.

Vizualizační pohledy na časově členěnou obsazenost oblastí zájmu (AOI) v analýze eye-trackingu

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