The present thesis is based on three studies that research different aspects of fine texture perception. The goal is to better understand the mechanisms involved in haptic perception of textures below 200 µm, also known as microtextures. Study I was conducted to establish a friction measurement model and relating the friction measurements to perceived coarseness of fine textures. A set of printing papers was used as stimulus material. In Study II an expanded set, including the set of Study I, was used as stimuli in a multidimensional scaling (MDS) experiment of haptic fine texture perception. Through scaling of perceptual attributes and similarities, a three dimensional space was found to best describe the data and the dimensions were interpreted as rough-smooth, thick-thin and distinct-indistinct. In Study III a series of model surfaces were manufactured with a systematically varied sinusoidal pattern, spanning from 300 nm to 80 µm. As in Study II, a similarity experiment was conducted and a two dimensional space was chosen, the dimensions of which were explained well through friction and the wavelength. Together these three studies form a better picture of fine texture perception. The dimensionality found with paper stimuli was very similar to the corresponding spaces for marcrotextures of everyday materials, even though a different perceptual system is used for fine texture perception. Regardless if the information is coded through the spatial or the vibratory sense, the perception does not seem to differ in dimensionality. Further, the largest among the microtextures seem to have been perceived as carrying spatial information. On the systematically varied, rigid, textures, the MDS space did not come out in a similar fashion to those of everyday materials but instead similar to the physical properties that characterizes the change in the textures. It was further found that the participants in Study III successfully discriminated textures with an amplitude of 13 nm from the unwrinkled surfaces. From these studies the main conclusions are (a) haptically measured friction and surface roughness are important contributors to fine texture perception, (b) even at microscales, spatial information is retrieved haptically, probably through vibrations, and (c) persons can haptically discriminate textures at a nanoscale.

Traditionally, human texture perception has been studied using artificial textures made of random-dot patterns or abstract structured elements. At the same time, computer algorithms for the synthesis of natural textures have improved dramatically. The current study seeks to unify these two fields of research through a psychophysical assessment of a particular computational model, thus providing a sense of what image statistics are most vital for representing a range of natural textures. We employ Portilla and Simoncelli’s 2000 model of texture synthesis for this task (a parametric model of analysis and synthesis designed to mimic computations carried out by the human visual system). We find an intriguing interaction between texture type (periodic v. structured) and image statistics (autocorrelation function and filter magnitude correlations), suggesting different processing strategies may be employed for these two texture families under pre-attentive viewing.

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