Abstract
Categorizing objects into superordinate classes (e.g., animals, plants) based on their visual appearance is challenging. Often, items of very different appearance need to be grouped together (e.g., elephants and mice) whereas items of more similar appearance don’t (e.g., twigs and insects). Plants and animals are a particularly salient example: as living things, both typically have rich shape structure consisting of multiple limbs, whose properties and configurations differ systematically on account of growth regularities peculiar to their biological category (e.g., animals tend to have symmetrical pairs of limbs, whereas plants don’t). We propose that these growth regularities lead to different perceptual organizations of object parts, namely their spatial arrangement and relations, creating potent cues for differentiating the two. To test this, we used a generative algorithm based on shape skeletons to create many novel object-pairs that differed in their part-structure but were otherwise very similar. We found that participants reliably judged shapes with certain part organizations to be systematically more like plants rather than animals (and vice versa). Based on these results, we generated another 110 sequences of shapes morphing from animal- to plant-like appearance by manipulating part structure in terms of three features: sprouting parts, curvedness of parts and symmetry of part pairs. Judgements of a different group of participants showed that all three parameters are highly predictive of human object classifications along the animal/plant continuum. This shows that the perceptual organization of parts along with part-based features like curvedness — both of which can be visually quite subtle — are powerful cues for superordinate categorization.