[deleting a post by <@U06SS0DHZD1> and reposting w...
# thinking-together
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[deleting a post by @Marek Rogalski and reposting with added a description — I want to discourage posting naked URLs without context and relying on the generated preview] On Mastodon, @Marek Rogalski shared the following discussion prompt: [at <https://www.youtube.com/watch?v=ViP31ILeRuo&t=1915s |32m in the talk >*<https://www.youtube.com/watch?v=ViP31ILeRuo&t=1915s |Hackover 2025 - Patterns in Chaos: How Data Visualisation Helps To See the Invisible>*, the speaker yote]…
brings up an interesting thought re readable visual design - that visualizations should encode the important variables using features that we've evolved to recognize instantly - on the most primitive level. The example given is rather simple: large vs small... But if we pull this a little further, it might lead us to some interesting, totally not explored visualizations:
- using familiar vs unfamiliar faces as data points
- using movement (especially movement of complex foreground against complex background) to highlight key area
- proximity (stereoscopic images) - this could be combined with movement, so that important points move back & forth
- we're really good at predicting trajectories of thrown objects - can this be utilized somehow?
I don't have much to add here, but I love the idea of using qualitative, detailed images (like faces) in place of more abstract quantitative forms (colored shapes) in datavis.
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Schooloscope (c. 2010) used Chernoff faces to convey high-level data. It's a super underexplored area, especially when you move into 3D. Most visualizations in 3D you see are just extruded 2D ones, instead of digging into e.g. visual perception research to come up with methods that use more of our capabilities, like Understanding how people group information over time: A new technique – Llewyn Paine, Ph.D. | Strategic customer research which studying visual grouping in the time domain, and discovered that shadows offered stronger perceptual grouping clues than overlaps alone.
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I've heard visualization researchers adopt 'categorical perception' from psychology, for what I think of as Gestalt principles. I think there's an inherent stumbling block wherein every possible term for 'perceptual grouping' has been used in HCI by different groups at different time.
I envy the ecology graduate students because they're so dang good at drawing. I mean, at encoding qualitative data through bespoke images, rather than over-relying on charts and similar abstractions.