Literature DB >> 28114494

Shape, motion, and optical cues to stiffness of elastic objects.

Vivian C Paulun1, Filipp Schmidt2, Jan Jaap R van Assen3, Roland W Fleming4.   

Abstract

Nonrigid materials, such as jelly, rubber, or sponge move and deform in distinctive ways depending on their stiffness. Which cues do we use to infer stiffness? We simulated cubes of varying stiffness and optical appearance (e.g., wood, metal, wax, jelly) being subjected to two kinds of deformation: (a) a rigid cylinder pushing downwards into the cube to various extents (shape change, but little motion: shape dominant), (b) a rigid cylinder retracting rapidly from the cube (same initial shapes, differences in motion: motion dominant). Observers rated the apparent softness/hardness of the cubes. In the shape-dominant condition, ratings mainly depended on how deeply the rod penetrated the cube and were almost unaffected by the cube's intrinsic physical properties. In contrast, in the motion-dominant condition, ratings varied systematically with the cube's intrinsic stiffness, and were less influenced by the extent of the perturbation. We find that both results are well predicted by the absolute magnitude of deformation, suggesting that when asked to judge stiffness, observers resort to simple heuristics based on the amount of deformation. Softness ratings for static, unperturbed cubes varied substantially and systematically depending on the optical properties. However, when animated, the ratings were again dominated by the extent of the deformation, and the effect of optical appearance was negligible. Together, our results suggest that to estimate stiffness, the visual system strongly relies on measures of the extent to which an object changes shape in response to forces.

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Mesh:

Year:  2017        PMID: 28114494     DOI: 10.1167/17.1.20

Source DB:  PubMed          Journal:  J Vis        ISSN: 1534-7362            Impact factor:   2.240


  13 in total

1.  Object stiffness recognition using haptic feedback delivered through transcutaneous proximal nerve stimulation.

Authors:  Luis Vargas; Henry Shin; He Helen Huang; Yong Zhu; Xiaogang Hu
Journal:  J Neural Eng       Date:  2019-12-05       Impact factor: 5.379

2.  Visual perception of joint stiffness from multijoint motion.

Authors:  Meghan E Huber; Charlotte Folinus; Neville Hogan
Journal:  J Neurophysiol       Date:  2019-04-24       Impact factor: 2.714

3.  Soft like velvet and shiny like satin: Perceptual material signatures of fabrics depicted in 17th century paintings.

Authors:  Francesca Di Cicco; Mitchell J P van Zuijlen; Maarten W A Wijntjes; Sylvia C Pont
Journal:  J Vis       Date:  2021-05-03       Impact factor: 2.240

4.  Dynamic Visual Cues for Differentiating Mirror and Glass.

Authors:  Hideki Tamura; Hiroshi Higashi; Shigeki Nakauchi
Journal:  Sci Rep       Date:  2018-05-30       Impact factor: 4.379

5.  Identifying shape transformations from photographs of real objects.

Authors:  Filipp Schmidt; Roland W Fleming
Journal:  PLoS One       Date:  2018-08-16       Impact factor: 3.240

6.  A Computational Mechanism for Seeing Dynamic Deformation.

Authors:  Takahiro Kawabe; Masataka Sawayama
Journal:  eNeuro       Date:  2020-04-24

7.  Expectations affect the perception of material properties.

Authors:  Lorilei M Alley; Alexandra C Schmid; Katja Doerschner
Journal:  J Vis       Date:  2020-11-02       Impact factor: 2.240

8.  Perceptual Properties of the Poisson Effect.

Authors:  Takahiro Kawabe
Journal:  Front Psychol       Date:  2021-01-22

9.  Visual assessment of causality in the Poisson effect.

Authors:  Takahiro Kawabe
Journal:  Sci Rep       Date:  2019-10-18       Impact factor: 4.379

10.  The causal future: The influence of shape features caused by external transformation on visual attention.

Authors:  Yunyun Chen; Yuying Wang; Sen Guo; Xuemin Zhang; Bihua Yan
Journal:  J Vis       Date:  2021-10-05       Impact factor: 2.240

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