Facing a Trend of Icon Simplicity: Evidence from Event-Related Potentials
Bibliographic record
Abstract
Application icons are a pivotal part of graphical user interface of mobile devices. Despite a trend away from complexity to simplicity in user interface design, there is lack of evidence supporting the superiority of icon simplicity from a psychophysiological perspective. This study investigates the effects of icon complexity and simplicity on user cognition using an event-related potentials (ERPs) technique. Eighteen participants completed an icon cognition and evaluation experiment in an electrophysiological laboratory. Their subjective evaluations, behavioral data, and ERP data were recorded and analyzed. The results of subjective evaluation showed that the simplest icons were regarded as more useful in helping subjects extract icon information than more complex ones. For the ERP measures, P1 amplitudes induced by complex icons were larger than those elicited by simple icons. In the parietal area, P2 amplitudes and latency were larger and later for complex icons than for simple ones. Simple icons are subjectively more helpful than complex ones, partially because they demand fewer attention resources in early stimulus-driven perceptual detection of icon features (P1 during 120–190 ms) and induce more positive emotional arousal (P2 during 190–200 ms). Simple icon designs minimize cognitive demands and are deemed more helpful than complex ones. Our study highlights that the ERP technique represents a tool to explore how users process icon and interface design.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".