Revisiting Channel Effectiveness: A Multi-Dimensional Evaluation with Primitive Visual Stimuli
Soohyun Lee (Seoul National University), Seokhyeon Park (Seoul National University), Minsuk Chang (Georgia Institute of Technology), Jinwook Seo (Seoul National University)
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Presentation
- Session
- Did you see that? Are you sure?
- Time
- Wednesday, Nov 11, 13:12 – 13:24 (US/Eastern) · session 13:00 – 14:30
- Room
- Hall Essex center
- Presenting from
- Boston
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Abstract
Established channel effectiveness rankings primarily assess magnitude estimation accuracy in complete chart contexts, often neglecting other perceptual tasks such as discriminability, separability, and pop-out. To address this gap, we conducted crowdsourced experiments on seven core visual channels (position, length, tilt, area, curvature, luminance, and saturation) using primitive visual stimuli, a set of visual marks without chart-specific scaffolding to isolate channel-level variation. We evaluated these channels across four perceptual tasks (accuracy, discriminability, separability, and pop-out) and found that channel effectiveness is fundamentally multi-dimensional, with rankings shifting substantially across tasks. For instance, while spatial channels maintain an overall advantage, accuracy depends strongly on whether a fixed spatial anchor is available. Discriminability varies dramatically across channels and value ranges, a pattern we formalized with a novel Anchored Harmonic Weber model. Pairwise channel interactions are often strongly asymmetric. Finally, we identify a dissociation between estimation accuracy and preattentive detection: length shows only moderate detection effectiveness despite top-tier accuracy, while area achieves the highest detection rates despite poor quantitative accuracy, though the latter advantage may partly reflect stimulus-level cues. We synthesize these findings into a scenario-driven perspective for context-sensitive channel selection.
For Practitioners
Anyone who creates charts - visualization designers, dashboard and BI tool builders, data journalists, and scientists or analysts preparing figures. Our per-task channel rankings and scenario dimensions (task stakes, granularity, interference, response time) help them choose encodings that match how their charts are actually read.