Material appearance affects object recognition

Poster Presentation: Saturday, May 17, 2025, 2:45 – 6:45 pm, Pavilion
Session: Color, Light and Materials: Surfaces and materials

Fatma Kilic1 (), Filipp Schmidt1,2, Celine Aubuchon1, Emily A-Izzeddin1, Zoe R. Goll1, Roland W. Fleming1,2; 1Justus-Liebig-Universität-Giessen, Germany, 2Center for Mind, Brain and Behavior, Universities of Marburg, Giessen, and Darmstadt

It is widely recognized that shape and contours serve as crucial cues for object recognition. Several studies have also highlighted the impact of color on our ability to recognize and classify certain objects, with diagnostic colors. Yet the role of broader material appearance in object recognition remains surprisingly underexplored. Here, we sought to measure the cognitive and perceptual impact of mismatches between an object’s shape and its material in object recognition tasks. We rendered images of 3D scans of animals and vegetables that were outfitted with various natural or manmade materials (e.g., cardboard). Participants performed a Go/No-Go task in which they had to respond as rapidly as possible to an image of the specified superordinate category (i.e., Animal in one block, Vegetable in another) while ignoring stimuli of other categories. The results showed that people responded more slowly to vegetables in incongruent materials than those in natural materials, while there was no such effect for animals. The observed difference in identification processes between vegetables and animals may stem from the fact that vegetable shapes were less complex and therefore less diagnostic (e.g., carrot vs. fish). Thus the resulting ambiguity might be resolved through additional information on the material or texture to aid in accurate identification. In contrast, the more distinct and recognizable shapes of animals facilitate their recognition, reducing the reliance on external characteristics for identification purposes.

Acknowledgements: This research was funded by the European Research Council (ERC) Advanced Award ‘STUFF’ (ERC-ADG-2022- 101098225)