Human visual clustering of point arrays

Although the importance of unsupervised learning has been recognized since William James's "blooming, buzzing confusion," it has received less attention in the literature than supervised learning. An important form of unsupervised learning is clustering, which involves determining the groups of distinct objects that belong together. Visual clustering is foundational for ensemble perception, numerosity judgments, spatial problem-solving, understanding information visualizations, and other forms of visual cognition, and yet surprisingly few researchers have directly investigated this human ability. In this study, participants freely clustered arrays that varied in the number of points (10-40) and cluster structure of the stimuli, which was defined based on the statistical distribution of points. We found that clustering is a reliable ability: Participants' clusterings of the same stimulus on two occasions were highly similar. With respect to the objective properties of the clusterings that people produce, points of individual clusters tend to follow a Gaussian distribution. With respect to processing, we identified five visual attributes that characterize the clusters that participants draw-cluster numerosity, area, density, and linearity and also percentage of points on the convex hull. We also discovered evidence for sequential strategies, with some attributes dominating when drawing the initial clusters of a stimulus and others guiding the final clusters. Collectively, these findings offer a comprehensive picture of human visual clustering and serve as a foundation for the development of new models of this important ability. (PsycInfo Database Record (c) 2025 APA, all rights reserved).