September 2018
Volume 18, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2018
Shape scission: causal segmentation of shape
Author Affiliations
  • Filipp Schmidt
    Experimental Psychology, Justus Liebig University Giessen
  • Flip Phillips
    Psychology & Neuroscience, Skidmore College
  • Roland Fleming
    Experimental Psychology, Justus Liebig University Giessen
Journal of Vision September 2018, Vol.18, 1054. doi:10.1167/18.10.1054
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      Filipp Schmidt, Flip Phillips, Roland Fleming; Shape scission: causal segmentation of shape. Journal of Vision 2018;18(10):1054. doi: 10.1167/18.10.1054.

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

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Abstract

Research on shape perception usually focuses on the estimation of local surface geometry through cues like stereopsis, shading or texture. Here, we argue that observers use these shape estimates to infer other object properties such as material composition and the transformation processes that generated the observed shape from this matter. We call this separation of object shape into intrinsic and extrinsic object properties shape scission. We investigated shape scission in a series of experiments with different groups of participants responding to a set of 8 unfamiliar rendered objects, each transformed by 8 transformations (e.g., "melted", "cut", or "inflated"). Importantly, participants did never see the untransformed versions of objects. First, participants produced adjectives in a free naming task to describe what happened to the transformed objects. Second, participants classified the objects according to either (i) their original shape, or (ii) the transformation that had been applied to them. Third, participants marked those regions of the objects that were transformed away from the original shape. Finally, participants viewed objects at 5 different levels of transformation magnitude and provided perceptual ratings of deformation. We find that participants (i) are consistent in naming the transformations, (ii) can classify unfamiliar objects according to their original shape as well as the applied transformation, (iii) can identify regions of the objects that were transformed, and (iv) can to some extent perceive the magnitude of the transformation (when compared to objective mesh deformations). Thus, we can identify "objects" across transformations and "transformations" across objects, separating observed features by their causal origin (shape scission). We can use this perceptual understanding of the causal processes to make inferences about what other members of the same class might look like and about how objects have been altered by forces in their past.

Meeting abstract presented at VSS 2018

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