Studying attention to IPCC climate change maps with mobile eye-tracking

S8 Fig. Frequency plot of survey results, item-wise. (PDF) S9 Fig. Frequency plots of survey results, by four subcategories. (PDF) S10 Fig. Correlation plots between four subscales of the CCAS. (PDF) S1 Table. Gaze metrics for maps. (PDF) S2 Table. Nonparametric ANOVA (Kruskal-Wallis test) for maps, comparing single and paired viewing conditions. (PDF) S3 Table. Gaze metrics for maps, broken down by projection timeframe and type. (PDF) S4 Table. Gaze metrics for maps, fully broken down. (PDF) S5 Table. Gaze metrics for maps divided into two AOIs. (PDF) S6 Table. Gaze metrics for paintings. (PDF) S7 Table. Nonparametric ANOVA (Kruskal-Wallis test) for paintings, comparing single and paired viewing conditions. (PDF) S8 Table. Gaze metrics for paintings, fully broken down. (PDF) S9 Table. Survey descriptive statistics by CCAS subscales. (PDF) S10 Table. Survey correlation statistics for CCAS subscales. (PDF) S11 Table. Correlations between gaze metrics and total CCAS score. (PDF) S1 File. The Climate Change Anxiety Scale (CCAS). (PDF) Acknowledgments The authors extend their gratitude to Prof Szonya Durant for providing access to the RHUL VR Lab (Virtual Reality Laboratory, Department of Psychology, Royal Holloway, University of London) as the data collection site; and to Dr E´tienne Serbe-Kamp for the valuable comments on the original draft. Author Contributions Conceptualization: Doga Gulhan. PLOS ONE Studying attention to IPCC climate change maps with mobile eye-tracking PLOS ONE | https://doi.org/10.1371/journal.pone.0316909 January 10, 2025 14 / 17