Unit charts are a common type of chart for visualizing scientific data. A unit chart is a chart used to communicate quantities of things by making the number of symbols on the chart proportional to the number of items represented. An accurate perception of the order of magnitude is essential to evaluating whether a unit chart can effectively convey information. Previous studies have primarily focused on perceptual properties at small order-of-magnitude scales or the efficacy of pictographs in unit charts. However, few researchers have explored the perceptual effectiveness of unit charts when representing large orders of magnitude. In this study, we performed a series of sampling measurements to investigate the visual–perceptual characteristics of unit charts when representing asymmetric interactions such as large-scale numbers. The results showed that under the restriction of the current conventional display medium, unit charts still offer a significant advantage over bar charts in a single-scale visual overview. However, this comes at the cost of a longer response time. Although this study constitutes basic research, accumulating evidence about how people reason about magnitudes beyond human perception is critical to the field of information science. This study may contribute to understanding how viewers perceive unit charts and the factors that influence graphical perception. This article provides some specific guidelines for designing unit charts that may be useful to visualization designers.

There were two errors in the original publication

The Symmetry Editorial Office retracts the article titled “A Perception Study for Unit Charts in the Context of Large-Magnitude Data Representation”

Introduction: Color preference research has mainly focused on isolated color samples, whereas architectural color is experienced within spatial and functional contexts. This study examined whether abstract hue preference remains stable when comparable chromatic conditions are evaluated in a makerspace-like interior. Methods: A three-stage design was used. Experiment 1 established baseline preferences for ten Munsell hues under D65 illumination. Experiment 2 embedded hue and saturation manipulations into a simulated makerspace and collected Preference and Comfort ratings together with eye-tracking and baseline-normalized pupillometric data under luminance control. Experiment 3 tested the stability of the findings through an online replication and a multi-viewpoint validation. Results: Preference orderings observed for isolated samples did not remain stable in the spatial context. Cooler or moderately chromatic conditions were generally rated more positively than vivid warm treatments. Saturation effects were non-monotonic, with moderate conditions outperforming the most intense treatment. Visually intense conditions attracted attention without producing more favorable evaluations, and pupillometric differences varied systematically across conditions. Discussion: These findings suggest that environmental color evaluation in cognitively demanding settings is better understood as context-sensitive spatial appraisal rather than as a direct extension of abstract hue liking.

This study presents an Eco-Efficient Deep Learning (EEDL) framework for cultural digitization that integrates visual data and contextual cultural metadata within a lightweight multimodal architecture. The framework is designed to balance classification performance and computational efficiency in resource-constrained cultural heritage environments. A multimodal dataset comprising 3,000 cultural heritage records was used to evaluate the proposed approach across six artifact classes. Experimental results demonstrated that the EEDL framework achieved the highest classification performance among all evaluated models, obtaining an accuracy of 0.918 and an F1-score of 0.912 while reducing parameter count, computational complexity, inference latency, and energy consumption relative to the uncompressed baseline. Comparative experiments with MobileNetV3, EfficientNet-Lite, MobileViT, TinyViT, and reproduced state-of-the-art methods confirmed the effectiveness of the proposed framework. Ablation and multimodal fusion analyses further demonstrated that contextual metadata contributes substantially to classification performance and reduces ambiguity among visually similar artifact categories. Statistical validation across repeated runs confirmed the robustness and reproducibility of the reported results. The findings suggest that combining multimodal metadata integration with eco-efficient model design can improve both predictive performance and deployment efficiency for cultural digitization applications operating under computational and energy constraints.

Background: Research on appearance anxiety and social media use has predominantly treated social media exposure as the cause and appearance anxiety as the outcome. This study examined the reverse and bidirectional pathways: whether appearance anxiety is prospectively associated with three functionally distinct appearance-oriented social media behaviors, and whether these behaviors are associated with subsequent anxiety, in young Chinese women. Method: A two-wave panel design with a 6-month interval was employed with 339 young Chinese women (aged 18–35; 87.1% Wave-2 retention). Cross-lagged panel models (CLPM) estimated bidirectional prospective between-persons associations, with an assumption-dependent phantom-variable sensitivity analysis used only to assess robustness under fixed stable-variance assumptions. Fear of negative evaluation (FNE) was examined as a candidate dispositional link using a partially longitudinal indirect-association model (T1 anxiety -> T1 FNE -> T2 behavior, controlling for baseline behavior), since FNE was assessed only at baseline. Whether cognitive-level media literacy attenuated this indirect association was addressed as a research question. Results: Appearance anxiety was prospectively associated with all three T2 behaviors at the between-persons level (βs = 0.28–0.39, p s < 0.001), and reverse-direction paths were of comparable or larger magnitude (βs = 0.38–0.50); both directions held in sensitivity analyses. Findings were consistent with a modest indirect association through FNE (indirect effects = 0.057–0.095), smaller than the direct effects of anxiety (0.235–0.339). An exploratory extension testing media literacy as a moderator of the FNE-to-behavior link found a significant, counter-theoretical effect for beauty-filter use only. Conclusions: Appearance anxiety and appearance-oriented social media behaviors show prospective bidirectional between-persons associations in a Chinese platform ecology, robust to sensitivity checks. FNE may function as one candidate link between habitual appearance anxiety and downstream appearance-management behavior, though a reverse-order robustness check could not rule out FNE preceding anxiety. The present study did not find statistically reliable evidence that cognitive-level media literacy buffers this pathway, tentatively suggesting that effective intervention may need to target appearance-specific anxiety directly rather than relying solely on cognitive media literacy education. An exploratory finding that media literacy may strengthen, rather than weaken, the FNE-behavior link for beauty-filter use is counter-theoretical and warrants confirmatory replication before informing intervention design.