Abstract
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.
Highlights
With our sense of touch, we are able to discriminate a vast number of materials
We used unsupervised learning to reconstruct the vibratory signals that constitute the very input of the haptic system based on a highly compressed representation
Such representation shares similarities with the perceptual representation of natural materials: it allows for classification of material categories, as provided by human judgments, and crucially, distances between material categories in the latent space resemble perceptual distances
Summary
With our sense of touch, we are able to discriminate a vast number of materials. We usually slide the hand over the material’s surface to perceive its texture (Lederman and Klatzky, 1987). We explore whether the perceptual representations of our haptic world can be learned by encoding vibratory signals elicited by the interaction with textures. We computed the centroids of each category within the latent space and showed that the distances between these categories resemble perceptual distances, measured with a rating experiment. These results suggest that the latent representation produced by unsupervised learning is similar to the information coding of the haptic perceptual system. The similarity between the latent representation and perceptual representation increases with compression This suggests that perceptual representations emerge by efficient encoding of the sensory input signals
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