Abstract
No two snowflakes are alike — each one is unique, with its own distinct properties. But our visual experience often fails to reflect this: If we view an array of snowflakes, for example, it may be difficult to recognize the distinctness of each individual, even if they truly are unique at bottom. What, then, *does* make an object look unique — i.e., most different from its peers? Here, 5 experiments (N=336) explore a deep connection between visual complexity and the experience of uniqueness. We algorithmically generated random-looking shapes across varying complexity levels, based on their skeletal surprisal. In E1, subjects dragged-and-dropped the shapes to place similar objects near one another and dissimilar objects far from one another. Remarkably, both highly simple and highly complex shapes were placed closer to one another on average, whereas medially-complex shapes showed the greatest overall dispersion; in other words, sets of medially-complex objects were judged as having the most unique members. To explore the nature of this quadratic pattern, four follow-up experiments targeted different stages of cognitive processing. We found this same quadratic pattern in another conceptually-laden task (E2), in which subjects were shown a named object and decided which other objects would have the same name; we found that medially-complex objects were least likely to be given similar names, as compared to highly complex objects and highly simple objects. Intriguingly, however, this quadratic pattern did *not* appear in more rapid perceptual processes, including tests of discrimination (E3), change detection (E4), and visual search (E5) — which all showed linear relationships between complexity and performance. The findings suggest a role for complexity in visual uniqueness that evolves through the hierarchy of cognitive processing, whereby medially-complex objects are seen as the most unique objects in higher-level processes, whereas simple objects are most distinctive in lower-level processes.