Supplementary material from "Cultural influence of social information use in pedestrian road-crossing behaviours"

;Table S1 : Statistical values (Z-value and P-value) for each GzLM. * : the light color condition is not anymore significant since the variable was tested in interaction with all other variables. Figure S1: Picture of the site “Train Station”, Strasbourg, France Figure S2: picture of the site “Pont des Corbeaux”, Strasbourg, France Figure S3: picture of the site “Place Broglie”, Strasbourg, France Figure S4: picture of the site “Train Station”, Nagoya, Japan Figure S5: picture of the site “Maruei”, Nagoya, Japan Figure S6: picture of the site “Excelco”, Nagoya, JapanFigure S7: Figure of the site “Osu-Kannon”, Nagoya, JapanFigure S8: Graphs showing the clustering results of the hierarchal clustering analysis following a Principal component analysis. a.) Factor map showing the distribution of data according to two PCA dimensions and the clusters in different colours. b.) Variables factor map showing the contributions and correlations of the different variables to the dimensions. c.) Dendogram showing how the analysis clustered data. Table S2: Variance for each dimension given by the Principal component analysisTable S3: Correlations of the variables to the dimensions given by the Principal Component AnalysisTable S4: Contributions of the variables to the dimensions given by the Principal Component AnalysisTable S5: Correlations (for quantitative variables) or regression coefficients (for qualitative ones) between varaibles and dimensions as well as ones p-values indicating how variables participate to the variance of dimensions.