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Material category of visual objects computed from specular image structure

Author

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  • Alexandra C. Schmid

    (Justus Liebig University Giessen)

  • Pascal Barla

    (Inria)

  • Katja Doerschner

    (Justus Liebig University Giessen)

Abstract

Recognizing materials and their properties visually is vital for successful interactions with our environment, from avoiding slippery floors to handling fragile objects. Yet there is no simple mapping of retinal image intensities to physical properties. Here, we investigated what image information drives material perception by collecting human psychophysical judgements about complex glossy objects. Variations in specular image structure—produced either by manipulating reflectance properties or visual features directly—caused categorical shifts in material appearance, suggesting that specular reflections provide diagnostic information about a wide range of material classes. Perceived material category appeared to mediate cues for surface gloss, providing evidence against a purely feedforward view of neural processing. Our results suggest that the image structure that triggers our perception of surface gloss plays a direct role in visual categorization, and that the perception and neural processing of stimulus properties should be studied in the context of recognition, not in isolation.

Suggested Citation

  • Alexandra C. Schmid & Pascal Barla & Katja Doerschner, 2023. "Material category of visual objects computed from specular image structure," Nature Human Behaviour, Nature, vol. 7(7), pages 1152-1169, July.
    • Handle: RePEc:nat:nathum:v:7:y:2023:i:7:d:10.1038_s41562-023-01601-0
    DOI: 10.1038/s41562-023-01601-0
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    References listed on IDEAS

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    1. Masataka Sawayama & Shin'ya Nishida, 2018. "Material and shape perception based on two types of intensity gradient information," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-40, April.
    2. Katherine R. Storrs & Barton L. Anderson & Roland W. Fleming, 2021. "Unsupervised learning predicts human perception and misperception of gloss," Nature Human Behaviour, Nature, vol. 5(10), pages 1402-1417, October.
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    1. Katherine R. Storrs & Barton L. Anderson & Roland W. Fleming, 2021. "Unsupervised learning predicts human perception and misperception of gloss," Nature Human Behaviour, Nature, vol. 5(10), pages 1402-1417, October.

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