What Makes A Great Toggle Button? (Case Study, Part 2) — Smashing Magazine

Beyond Color: The Visual Language of Toggle Buttons

Designing a toggle button that unambiguously communicates its active state remains one of UI design's most persistent challenges. Color alone is rarely sufficient. A range of other visual cues—typography, shape, depth, and iconography—can reinforce the selected option, but their effectiveness varies significantly. To identify which cues actually work, we tested 27 distinct visual treatments in a case study involving over 100 real users. The findings reveal which signals are reliable, which are dangerously subtle, and what you should adopt in your designs.

A Wide Field of Candidate Cues

A typical toggle button can carry far more state information than a simple color swap. Text weight and case, the inclusion of checkmarks, background insets that mimic a pressed button, drop shadows suggesting elevation, and even the physical separation of segments all contribute to the interface's message. While designers frequently combine multiple cues, users may still misread the controls that modern systems deploy daily—especially on mobile and in accessibility-focused settings.

The issue is compounded by the psychological weight of convention. Interfaces often override the user's expectation of a control. For example, the primary action button on a website commonly appears as the most color-filled element in the form, but in toolbars, flat design has made the concept of "digging into the page" ambiguous.

Testing Methodology and Findings

We divided the candidate interfaces into groups and observed how consistently participants picked the correct state. The starkest findings follow.

Fill Reversal and Stylization Did Not Help

A complete reversal of background—switching from a filled segment to a fully vacant one—performed surprisingly poorly. Users frequently perceived the uncolored segment as the one that had been pressed, treating a light area as tactile contrast. Stylized details, such as gradient edges, offered no corrective power over plain flat colors.

Presence of an Explicit Check Icon Helped, But Comes With Caveats

The use of a checkmark inside the selected program was a powerful signifier. However, notice the interpretation issue: in many toggle groups, users treat a list of options as a list of commands rather than a statement of current state. If the toggle controls a setting that switches its operation mode, a checkmark reads naturally. If it governs data filtering characteristics such as sort order of something, the checkmark can read as an activation request instead.

Absolute Best Performer—Corner Reconfiguration

Far less common approach was modifying the standard borders. A simple combination—sharp 90-degree corners for the segment and slightly rounded corners on the remaining, non-active side—was decoded by users with near-universal clarity. The effect imitates a printed break in a rail structure, implying that the selected option no longer continues as a button, but rather resides somewhere on top.

Driving Design Predictivity

This study suggests that humans do not rely on a single visual cue when asking which part of a collapsed button is active. They decode a range of physical metaphors—like pressing, filling, enclosing, and elevating—and act on the combination. While marketing conventions determine what an interface looks like at first glance, the cues that truly persuade users into accuracy come from the margins: not just hue change and extra shadow, but from geometry and semiotic constraints.

Chosen one key takeaway: the border layout of a disabled element can carry as much weight as the flat selection color. This mirrors a finding of a second study on font contrast and readability in setting panels. Designers may benefit by incorporating the experience of analogous hardware—e.g., including an underpressing background drop—as it corresponded with the heightened accuracies seen in web results.

Actionable Guidelines For Your Controls

1. Do not equate the most perceived on element feature with improved comprehension; employ multiple related signifiers.
2. Avoid complete reversal of the filled state if users might define the action as start-command. Persistent rail lines can disambiguate.
3. Make round corners function as a guide if working with borderless segments.
4. Occasionally, real-world indentation more strongly indicates the currently tied feature than any color or font case applied inward.
5. Stay suspicious of icons when a row status is to be inferred rather than changed.

The broader lesson is one of conceptual continuity—rather than picking colors from a board to achieve contrast, map each potential way users imagine state—switching hardware sliders, toggles, latches—back to the details that those objects rely on; an outline system then follows naturally. The physical anchors turn abstract pixels into decisions users can trust.

Measuring Toggle Clarity: Success-Confidence Scores

Before diving into the results, we need to explain how we quantified the performance of each visual cue. After disqualifying participants who appeared to answer randomly, we were left with the following sample sizes per test:

To objectively compare toggles across tests with different time limits and participant counts, we developed a weighted metric called the Success-Confidence score. The metric combines the percentage of correct answers with the average confidence of participants, as measured by a Likert scale following each question.

We begin by calculating the average confidence for correct and incorrect answers separately, for each toggle. This value ranges from 0 to 1. If, for example, every participant who answered correctly reported being "Absolutely sure," the average confidence for correct answers would be 1. The raw Success-Confidence score is then derived from those two averages:

Success-Confidence score = (correct_num * correct_conf) - (incorrect_num * incorrect_conf)

correct_num -> number of correct answers

incorrect_num -> number of incorrect answers for toggle

correct_conf -> average confidence with correct answers

incorrect_conf -> average confidence with incorrect answers 

Finally, because each test had a different number of participants, we normalized the score by dividing by the total number of participants for that test. The resulting normalized score falls on a scale of -1 to 1. A score of 1 indicates that all participants answered correctly with high confidence, while a score of -1 means all answers were incorrect with high confidence.

Findings by Visual Cue

Text Weight and Size

Our expectation that bold text effectively communicates the active state was confirmed. The toggle with emboldened text achieved a low error rate of 1.7% and a Success-Confidence score of 0.86, earning the third-best average rank among all tested toggles. For designs where all fonts share the same weight, bolding the active label is a simple and functional solution.

The relationship between font size and clarity was also confirmed, though with a caveat. The toggle with the smallest difference in font size performed worst, with an error rate of nearly 15% and a confidence score of 0.63. The toggle with the greatest size difference performed significantly better, with an error rate of 4.4% and a score of 0.81. However, bold text remains the preferable approach: it is easier to implement than a noticeable font size increase, and it performs better.

Color in Text Labels

Several color-based approaches failed to communicate the active state. Inverted color combinations, such as black/white and orange/blue, produced error rates of 19.3% and 23.7%, with confidence scores of only 0.56 and 0.41. Participants who answered correctly were rarely confident, suggesting that colors of equal visual importance are a poor cue.

The red/green combination was even less effective, with a 32.5% error rate, a 0.32 confidence score, and an average rank of 24.67. Green may have been interpreted as an indicator rather than a state, and red-green colorblindness, the most common form, likely contributed to the poor results. This cue should be avoided entirely.

Pairing a color with white text worked well, achieving an average rank of 9.33. Pairing that same color with black was problematic, showing a 14% error rate and 0.59 confidence. Black text appears to compete with color for visual dominance, and simply changing the inactive state from black to a color is not a reliable cue.

Using gray for the inactive label sits between black and white in effectiveness, with an average rank of 12. Using a lower saturation of the same hue proved stronger than gray. The saturation-difference toggle achieved an 8.7% error rate and a 0.77 confidence score, inspiring more certainty in users than the primary-vs-gray alternative.

Color in Backgrounds

Filling toggle halves with inverted colors followed the same pattern as text-based coloring, with black/white performing best, followed by orange/blue, then red/green. However, background fills outperformed their text counterparts by 5 to 8 percentage points in error rate. The black/white background combo still performed best, despite the potential confusion of dark and light mode settings.

Different saturations of the same color were a viable, if average, cue. Orange shades achieved a 9.7% error rate and a 0.72 confidence score, outperforming the gray variant, which had a 15% error rate and a 0.63 score. Combining saturation changes with another cue is advisable for better clarity.

The pairing of a saturated color with a lighter gray performed especially well. This toggle had only a 6.1% error rate, a 0.79 confidence score, and a sixth-best average rank. A saturated color with a darker gray was less effective (12.3% error rate). When using saturated fills, they should be contrasted against a light, low-saturation gray.

Shapes, Icons, and Raison d'Être

The fully inverted button, where the active state receives a colored fill and the inactive state is solid, presents a tricky trade-off. It achieved strong results overall (6.1% error rate, 0.78 confidence, seventh place) but showed a dramatic decline when participants had more time. With 20 seconds to reflect, the error rate more than doubled, from 3.5% to 8.8%. Users' instincts were correct at first, but time led them to second-guess the inverted styling.

A highlighted outline around the active state was a reliable cue, with an 8.8% error rate and 0.76 confidence. It places in the top half of toggles but is outperformed by other visual signals, so combining it with another cue is recommended.

The standout result came from a toggle where the inactive button matched the surrounding background color. This achieved a 0.9% error rate, a confidence score above 0.89, and the best overall average rank of 1.33. Making the inactive option blend into its surroundings is the most effective way to communicate which selection is active.

Finally, the embossed/debossed variants were a clear failure. Both embossed toggles had an 83.3% error rate and a Success-Confidence score of -0.58. In digital interfaces, embossing is typically used to add visual weight to an element, and users likely perceived the raised button as the active one.

Top Performers and Time-Based Effects

Ranking the Visual Cues

Rankings were calculated separately for the 5-second tests, the 20-second tests, and the combined set. The three worst toggles were the red/green text label (average rank 24.67), an embossed button without a shadow (26.33), and an embossed button with a shadow (26.67).

The three best toggles were bold text (average rank 2.67), the check sign (2.33), and the inactive-button-fades-into-background design (1.33).

The check sign performed nearly as well as the background approach, with a 5% error rate and a 0.86 confidence score. Its only drawbacks are potential design clutter and unintended associations with checkboxes. A radio button icon performed respectably (9% error rate, 0.80 confidence) but carries a semantic mismatch: radio buttons imply a form selection, while toggles denote an immediate state change.

The 5-Second vs. 20-Second Difference

Our secondary goal was to measure how additional observation time changes perception. Overall, the 20-second tests yielded lower error rates and higher Success-Confidence scores, as expected, though the difference was not significant across the board.

Certain toggles, however, showed marked improvements with more time. Toggles #4, #11, and #18 each gained six ranks in the 20-second test. For toggle #4, which relies on a subtle font size difference, the added time allowed some participants to notice the cue. Toggles #11 and #18 both use saturation to distinguish active from inactive. With more time, fewer users incorrectly assumed the lighter color was the highlighted one.

Other toggles performed worse with additional time. Toggle #15 lost the most ranks, and toggle #17 saw the largest negative shifts in both error rate and confidence. Both designs feature a dark half and a light half. Under time pressure, users tended to pick the darker side as active. Given time to reflect, they began to doubt that instinct, questioning whether the brighter side might actually be the highlighted one. A well-designed toggle should not invite this kind of hesitation.

Where The Research Goes Next

Every cue tested in the study was simple and singular. That leaves obvious room for follow-up work that probes combinations and variations: does a bolder inactive label still read correctly when the active state is bold but less heavy? Do paired cues outperform a single strong one? Intuition suggests yes, but the study’s own results show that seemingly obvious answers need empirical verification.

Color is another open thread. Would desaturated or differently-hued greens behave like the saturated orange that performed well? And since all toggles were tested in isolation, a natural extension is to place them inside real navigation mockups with varied color schemes, where surrounding context may shift how cues are interpreted.

Design Guidance From The Data

The study produced enough user data to support concrete recommendations for toggle design. The central takeaways:

• Prefer color fill over color text. A saturated, lively color paired with a light neutral gray works best when applied to the background of the active and inactive states. Match the inactive fill to the surrounding page background when possible to sharpen the distinction.
• Avoid high-contrast color pairs of similar visual weight. Red/green is especially risky given cultural associations that muddy meaning. Black and white also underperform — users frequently confuse those toggles with dark/light mode controls.
• Typography alone can carry the state if used boldly. A thick-versus-thin weight contrast is the strongest font-based signal. Size differences work too, but they must be decisive. Font weight and size are flexible enough to reinforce other cues as well.
• Skip embossment as a primary cue. It fails to communicate active state reliably, and even a plain border performs better. If visual depth is desired, layer embossment on top of a stronger cue like bold text or a color fill.
• Established patterns still win. Ticks and radio-button icons both tested well. The trade-off is integration: radio buttons are semantically different controls, and ticks can feel dated, evoking paper forms more than digital interfaces. If a radio icon is the answer, a radio button itself may be the better component.

Following these patterns should reduce the hesitation users feel when toggling between states — the core problem this study set out to solve.

Further Reading

• “Why Toggle Buttons Are Confusing”, UX Movement
• “History Of Toggle + UI/UX Case Study + UI Samples”, Ramulu Nelli
• “Stop Misusing Toggle Switches”, UX Movement
• 1991 Video Of The HCIL Touchscreen Toggle Switches (University of Maryland)
• “What’s The Most Effective Way To Present Two-State Toggle Buttons?”, Thomas Tullis, Azilah Iskandar, Lori Ledoux
• Color Theory In Web UI Design — UXPin
• UX Research Tools — UXtweak

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