Visual Signifiers and User Error: Quantifying Miscalibration and Ambiguity in Modern Minimalist UI Toggle Designs

Objective This study quantifies the perceptual reliability of four prevalent UI toggle types—Thumb-and-Track, Text, Icon, and Icon-with-Text—and examines the risk of user error and miscalibration associated with reduced visual redundancy. Background Minimalist single-signifier controls may compromise state visibility, while empirical evidence regarding the reliability of specific visual metaphors across display modes remains limited. Method In a controlled experiment, 40 participants judged the state (“ON” or “OFF”) and rated their confidence for 394 real-world toggle buttons presented in both Light and Dark modes. Data were analyzed using a framework integrating accuracy, confidence, and inter-user agreement to capture subtle ambiguities. Results Icon toggles showed substantially lower accuracy ( M = 0.63 , S D = 0.48 ) than Thumb-and-Track ( M = 0.98 , S D = 0.15 ) and Text-based designs ( M = 0.93 , S D = 0.25 ). They also yielded the lowest agreement ( M = 0.73 , S D = 0.05 ) and confidence ( M = 4.63 , S D = 1.13 ), accounting for 59% of confident misinterpretation cases ( 10 / 17 ), where users were highly confident yet incorrect. In contrast, designs incorporating redundant visual cues (e.g., Thumb-and-Track and Icon-with-Text) produced higher agreement and accuracy across conditions. Conclusion We suggest that (a) Icon-only toggles are high-risk patterns associated with perceptual miscalibration in both display modes; (b) visual redundancy supports state clarity; and (c) joint confidence–agreement criteria can help identify designs at risk of confident user misinterpretation. Application Potential applications include design reviews for state-sensitive systems using the proposed Confidence High–Agreement Low (C H A L ) criterion—high mean confidence (≥6.0) combined with low agreement (disagreement ratio >.33)—to identify controls at risk of confident perceptual disagreement. Luminance-rank invariance may additionally support evaluation across display modes.