Journal
ELIFE
Volume 11, Issue -, Pages -Publisher
eLIFE SCIENCES PUBL LTD
DOI: 10.7554/eLife.64876
Keywords
touch; natural textures; materials; unsupervised deep learning; haptic perception; efficient coding; Human
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Funding
- Deutsche Forschungsgemeinschaft [222641018 -SFB/TRR 135]
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When touching an object, its spatial structure is translated into vibrations on the skin, allowing the perceptual system to distinguish between different materials. The study demonstrates that a deep neural network trained with unsupervised learning can classify materials based on the vibratory patterns elicited by human exploration. The compressed representation shows similarities to perceptual distances, indicating a similar coding mechanism.
When touching the surface of an object, its spatial structure translates into a vibration on the skin. The perceptual system evolved to translate this pattern into a representation that allows to distinguish between different materials. Here, we show that perceptual haptic representation of materials emerges from efficient encoding of vibratory patterns elicited by the interaction with materials. We trained a deep neural network with unsupervised learning (Autoencoder) to reconstruct vibratory patterns elicited by human haptic exploration of different materials. The learned compressed representation (i.e., latent space) allows for classification of material categories (i.e., plastic, stone, wood, fabric, leather/wool, paper, and metal). More importantly, classification performance is higher with perceptual category labels as compared to ground truth ones, and distances between categories in the latent space resemble perceptual distances, suggesting a similar coding. Crucially, the classification performance and the similarity between the perceptual and the latent space decrease with decreasing compression level. We could further show that the temporal tuning of the emergent latent dimensions is similar to properties of human tactile receptors.
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