Social cues, which function as extensions of emotions, play a critical role in facilitating interpersonal communication. However, existing tools for augmented emotion representation face limitations in terms of usability and computational complexity. We propose a visual tool design that represents five basic emotions and examines the minimum number of parameters necessary to model them. Informed by a previous workshop, we focus on modeling emotions by adjusting the irregularity and complexity of shapes. We found that the parameters could be reduced to noise and pace through logistic regression analysis, as supported by their distributions for selected parameter values. We found that pace captured arousal, while noise captured only certain aspects of valence. This study concludes that a third parameter is necessary to fully distinguish positive and negative emotions.
LEE et al. (Thu,) studied this question.