December 2022
Volume 22, Issue 14
Open Access
Vision Sciences Society Annual Meeting Abstract  |   December 2022
Part structure predicts superordinate categorization of animals and plants
Author Affiliations & Notes
  • Henning Tiedemann
    Justus Liebig University Giessen
  • Filipp Schmidt
    Justus Liebig University Giessen
  • Roland W Fleming
    Justus Liebig University Giessen
  • Footnotes
    Acknowledgements  Research funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)–project number 222641018–SFB/TRR 135 TP C1), by the European Research Council (ERC) Consolidator Award “SHAPE”–project number ERC-2015-CoG-682859 and by “The Adaptive Mind”
Journal of Vision December 2022, Vol.22, 3228. doi:
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    • Get Citation

      Henning Tiedemann, Filipp Schmidt, Roland W Fleming; Part structure predicts superordinate categorization of animals and plants. Journal of Vision 2022;22(14):3228.

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      © ARVO (1962-2015); The Authors (2016-present)

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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.


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