Dark Mode or Light Mode? Exploring the Impact of Contrast Polarity on Visualization Performance Between Age Groups

TL;DR

The paper investigates whether contrast polarity (light vs dark) affects data-visualization performance across age groups (Young Adults and People in Late Adulthood) using Bar, Line, and Scatter visualizations. It employs a crowdsourced experiment and analyzes accuracy and time at both aggregate and individual levels, using bootstrapped CIs and a per-participant percent-difference metric to capture heterogeneous effects. Findings indicate no consistent age-related polarity advantage; polarity benefits are distributed across individuals, and polarity impacts on time are comparable in magnitude to visualization type. The work underscores the practical value of offering visualizations in both polarities to support a broad audience and informs design guidelines for accessibility and personalization.

Abstract

This study examines the impact of positive and negative contrast polarities (i.e., light and dark modes) on the performance of younger adults and people in their late adulthood (PLA). In a crowdsourced study with 134 participants (69 below age 60, 66 aged 60 and above), we assessed their accuracy and time performing analysis tasks across three common visualization types (Bar, Line, Scatterplot) and two contrast polarities (positive and negative). We observed that, across both age groups, the polarity that led to better performance and the resulting amount of improvement varied on an individual basis, with each polarity benefiting comparable proportions of participants. However, the contrast polarity that led to better performance did not always match their preferred polarity. Additionally, we observed that the choice of contrast polarity can have an impact on time similar to that of the choice of visualization type, resulting in an average percent difference of around 36%. These findings indicate that, overall, the effects of contrast polarity on visual analysis performance do not noticeably change with age. Furthermore, they underscore the importance of making visualizations available in both contrast polarities to better-support a broad audience with differing needs. Supplementary materials for this work can be found at https://osf.io/539a4/.

Figures (5)

• Figure 1: Example study stimuli, combinations of visualization (Bar Chart, Line Chart, Scatterplot) and contrast polarity (positive, negative).
• Figure 2: Bootstrapped average () percent differences for YA and PLA resulting from each design factor (contrast polarity, visualization) and for each metric (accuracy, time). Confidence intervals are Bonferroni-corrected for 4 comparisons.
• Figure 3: Bootstrapped average () percent differences for YA and PLA , with participants grouped by their better-performing contrast polarity for each metric (accuracy, time). Confidence intervals are Bonferroni-corrected for 4 comparisons.
• Figure 4: Proportions of participants who performed best with positive (YA , PLA ) and negative contrast polarity (YA , PLA ), and those who performed equally well with both . Participants are filtered across increasing maximum threshold values of percent difference ($y$-axis), with data grouped by metric (accuracy, time) and age group. A dashed line at $y=50\%$ is provided for reference.
• Figure 5: The number of YA who preferred each contrast polarity (positive , negative , or no preference ) as well as PLA who preferred each contrast polarity (positive , negative , or no preference ), grouped by the contrast polarity (negative = NC, positive = PC, equal performance) with which they achieved their best performance (accuracy, time). Bars where the preferred polarity matches the best-performing polarity are marked with an ⁎.