Haptic discrimination of 3D-printed patterns based on natural visual textures

Abstract

We perceive spatial textures mainly through eyes and hands. In contrast to visual texture perception, computational mechanisms of haptic texture perception remain poorly understood. Here we measured haptic texture discrimination ability of 3D printed surfaces transcribed by visual images of five natural textures (stones, leaves etc.). For each texture image, the intensity map was converted into the 3D surface height modulation of a 40×40mm plate. The textures look sufficiently different and the maximum modulation depth (2mm) was well above the haptic detection threshold. Nevertheless, observers (n=10) could not accurately discriminate some texture pairs. In the main experiment, the observer passively touched the plate swept on their stabilized index finger (passive scan mode), but even when we tested other touching modes, the performance was improved only slightly (active scan), or not at all (static touch, vibration only). Since the amplitude spectra of natural visual textures are similar to each other (fall by a factor of f-a), we hypothesized that haptic texture discrimination may rely solely on the difference in amplitude spectra, or on the spatial-frequency/orientation subband histograms. In agreement with this, the discrimination performance we obtained could be explained by a multivariate linear regression based on the amplitude difference between the paired textures. In an additional experiment, we directly tested this hypothesis by matching the subband histogram of each texture using a texture synthesis algorithm (Heeger & Bergen, 1995). Haptic discrimination of these textures was found to be nearly impossible, although visual discrimination remains feasible due to differences in higher-order statistics (joint subband statistics to which V2 neurons are sensitive, or more complex phase information detectable by attentive foveal vision). These findings suggest that haptic texture processing may be qualitatively different from visual texture processing in that it simply relies on lower-order image statistics.