Drawing as a window to emotion with insights from tech-transformed participant images

Abstract This study delves into expressing primary emotions anger, happiness, sadness, and fear through drawings. Moving beyond the well-researched color-emotion link, it explores under-examined aspects like spatial concepts and drawing styles. Employing Python and OpenCV for objective analysis, we make a breakthrough by converting subjective perceptions into measurable data through 728 digital images from 182 university students. For the prominent color chosen for each emotion, the majority of participants chose red for anger (73.11%), yellow for happiness (17.8%), blue for sadness (51.1%), and black for fear (40.7%). Happiness led with the highest saturation (68.52%) and brightness (75.44%) percentages, while fear recorded the lowest in both categories (47.33% saturation, 48.78% brightness). Fear, however, topped in color fill percentage (35.49%), with happiness at the lowest (25.14%). Tangible imagery prevailed (71.43–83.52%), with abstract styles peaking in fear representations (28.57%). Facial expressions were a common element (41.76–49.45%). The study achieved an 81.3% predictive accuracy for anger, higher than the 71.3% overall average. Future research can build on these results by improving technological methods to quantify more aspects of drawing content. Investigating a more comprehensive array of emotions and examining factors influencing emotional drawing styles will further our understanding of visual-emotional communication.