September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Inferring the deformation of unfamiliar objects
Author Affiliations
  • Filipp Schmidt
    Department of Psychology, Justus-Liebig-University Giessen
  • Flip Phillips
    Department of Psychology and Neuroscience, Skidmore College
  • Roland Fleming
    Department of Psychology, Justus-Liebig-University Giessen
Journal of Vision August 2017, Vol.17, 315. doi:10.1167/17.10.315
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      Filipp Schmidt, Flip Phillips, Roland Fleming; Inferring the deformation of unfamiliar objects. Journal of Vision 2017;17(10):315. doi: 10.1167/17.10.315.

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

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Abstract

When objects are deformed by external forces (e.g. a crushed can or twisted rag), the resulting shape is a complex combination of features from the original shape and those imparted by the transformation. If we observe only the resulting shape, distinguishing the origin of its various features is formally ambiguous. However, in many cases the transformation leaves distinctive signatures that could be used to infer how the object has been transformed. Here we investigated how well observers can identify the type and magnitude of deformations applied to unfamiliar 3D shapes. We rendered objects subjected to physical simulations of 12 shape-transforming processes (e.g., twisting, crushing, stretching). Observers rated the magnitude of object deformation at different stages of the transformation process (e.g., barely twisted vs. strongly twisted). Another group viewed one transformed object at a time and ranked other objects-which were submitted to the same or one of the 11 other transformations-according to their similarity to the test object in terms of the applied transformation. A third group viewed a subset of the objects and painted on the surface to indicate which regions appeared most informative about the type of transformation. We find that observers can estimate the magnitude of deformation of unfamiliar objects without knowing their pre-transformed shapes. They can infer specific causal origins from these deformations, reflected in their ability to identify other objects subjected to the same transformation. We also identify the shape features underlying these inferences by comparing the painting responses to the physical mesh deformations. Our findings show that observers can infer transformations from object shape. This ability to infer the causal origin of objects is potentially useful in estimating their physical properties (e.g., stiffness), predicting their future states, or judging similarity between different objects.

Meeting abstract presented at VSS 2017

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