Texture from touch
|Sliman Bensmaia (2009), Scholarpedia, 4(8):7956.||doi:10.4249/scholarpedia.7956||revision #149771 [link to/cite this article]|
Texture from touch refers to the processing of information about surface material and microgeometry obtained from tactile exploration. Though textural information can be obtained both visually (Heller, 1989) and auditorily (Lederman, 1979), touch yields much finer and more complex textural information than do the other sensory modalities. Humans can distinguish textures whose elements differ in size by tens of nanometers or whose spatial periods differ by hundreds of nanometers (Skedung et al., 2014). When we run our fingers across a surface, we may perceive the surface as being rough, like sandpaper, or smooth, like glass; the surface may also vary along other sensory continua, such as hardness (e.g., stone) vs. softness (e.g. moist sponge), stickiness (e.g., tape) vs. slipperiness (e.g., soap). Also, whether a texture is thermally isolating (e.g., leather) or thermally conductive (like metal) contributes to the textural percept (Hollins et al., 1993; Hollins et al., 2000; Bensmaia and Hollins, 2005). Tactile texture perception plays a role in the tactile recognition of objects (Klatzky et al., 1987) as most natural objects differ not only in shape but in texture as well. Furthermore, certain types of texture information are essential in order to properly manipulate objects (Johansson and Flanagan, 2009). Texture is represented at the somatosensory periphery in the spatiotemporal pattern of activity in populations of receptors embedded in the skin and in cortex by different populations of neurons with different response properties.
Neural basis of texture perception in the nerve
Three types of mechanoreceptive afferents contribute to the tactile perception of texture, namely slowly adapting type 1 (SA1), rapidly adapting (RA), and Pacinian (PC) afferents, which innervate Merkel cells, Meissner corpuscles, and Pacinian corpuscles, respectively. Coarse textural features, with element sizes on the order of millimeters, are encoded in the responses of SA1 afferents. Specifically, the spatial configuration of coarse surface elements is reflected in the spatial pattern of SA1 activation. However, this encoding mechanism is limited by the innervation density of these receptors (which are spaced about 1 mm apart; cf. Vallbo and Johansson, 1979). To perceive fine textural features, motion between skin and surface is required: When the exploring finger scans a fine texture, small vibrations are produced in the skin, which reflect both the microgeometry of the surface and that of the fingerprint (Hollins et al., 1998; Hollins et al., 2001; Hollins et al., 2002; Bensmaia and Hollins, 2003; Bensmaia and Hollins, 2005; Manfredi et al., 2014). These texture-elicited vibrations are transduced and processed by two other populations of vibration-sensitive fibers, namely RA and PC afferents (Weber et al., 2013). These fibers respond to textures by producing highly repeatable temporal spiking patterns, which are sufficiently informative about texture identify to mediate our ability to identify and discriminate tactile textures (Weber et al., 2013). This vibrotactile coding scheme is analogous to that is observed in the vibrissal system of rodents (see http://www.scholarpedia.org/article/Vibrissal_texture_decoding).
Neural basis of texture perception in cortex
As discussed above, tactile texture perception relies on two distinct mechanism: a spatial mechanism mediated by SA1 fibers, and a temporal mechanism mediated by RA and PC fibers. In cortex, a subpopulation of neurons exhibit receptive field properties that are well suited to analyze the spatial image of a texture conveyed by SA1 afferents. To investigate the receptive field properties of these neurons, random dot patterns (DiCarlo et al., 1998; DiCarlo and Johnson, 1999; DiCarlo and Johnson, 2000) or spatiotemporal white noise stimuli (Sripati et al., 2005) were presented to the glabrous skin of the distal finger pads while neuronal responses in primary somatosensory cortex (SI) were recorded. The spatial and spatiotemporal receptive fields (SRFs and STRFs) of SI neurons were computed from these measurements using reverse correlation (DiCarlo et al., 1998; DiCarlo and Johnson, 1999; DiCarlo and Johnson, 2000; Sripati et al., 2005). A subpopulation of neurons, whose SRFs or STRFs comprise excitatory and inhibitory sub-regions and are thus highly analogous to neurons in primary visual cortex, are ideally suited to analyze the spatial image conveyed by SA1 neurons. The texture signal carried in the responses of RA and PC afferents likely drives the responses of another population of SI neurons that exhibits highly patterned responses to skin vibrations (Harvey et al., 2013). Indeed, these neurons receive input from both RA and PC afferents (Saal et al., 2014) and produce entrained responses to simple and complex (texture-like) vibrations across the range of frequencies that are experienced during tactile texture exploration. How texture information from these two streams – spatial and temporal – is combined to culminate in a holistic percept of texture remains to be elucidated.
The dimensions of texture
The tactile exploration of a surface has been shown to yield a multidimensional textural percept that includes sensations of roughness/smoothness, hardness/softness, stickiness/slipperiness, warmth/coolness (Hollins et al., 1993; Hollins et al., 2000; Bensmaia and Hollins, 2005). The overall textural percept of a surface is strongly determined by three of these texture properties, namely roughness, hardness and stickiness. Of all textural continua, the study of roughness has been the most extensive and has yielded important insights into neural coding in the somatosensory system.
The subjective sense of roughness seems to vary along a single dimension and has been shown to vary predictably with surface properties. In psychophysical studies, the perceived roughness of sandpapers increases as a power function of particle size (exponent ≈ 1.5), the roughness of gratings increases linearly with spatial period (Lederman and Taylor, 1972; Chapman et al., 2002), and that of embossed dots increases monotonically with inter-element spacing up to a spatial period of about 2 mm, then decreases with further increases in spatial period (Morley et al., 1983; Connor et al., 1990). For gratings, however, the spatial period does not seem to be the relevant stimulus property. For instance, changing the groove width and ridge width of gratings has opposite effects on perceived roughness (Lederman and Taylor, 1972; Sathian et al., 1989). The main determinant of perceived roughness seems to be the spatial pattern of deformation of the skin (Taylor and Lederman, 1975) although temporal cues (Cascio and Sathian, 2001; Gamzu and Ahissar, 2001) and tangential forces (Smith et al., 2002) also play a role. In a series of studies pairing human psychophysics with macaque neurophysiology, Johnson and colleagues set out to establish the peripheral neural code underlying roughness perception (Connor et al., 1990; Connor and Johnson, 1992; Blake et al., 1997; Yoshioka et al., 2001). Their approach consisted of devising and testing a set of hypotheses linking the activity evoked by textured surfaces in populations of mechanoreceptive afferents, measured in macaque monkeys, to estimates of their perceived roughness, measured in human observers. The stimuli consisted of embossed dot patterns, varying in their spatial properties, presented passively to the skin using a rotating drum stimulator (Johnson and Phillips, 1988). The roughness estimates, obtained for a variety of dot patterns, were plotted against predictions derived from each putative neural code. A hypothesis was eliminated if it failed to account for roughness estimates under any single experimental condition. The putative neural codes for roughness included (1) the mean firing rate elicited in a given population of mechanoreceptive afferents fibers; (2) the temporal variability in the firing of a given population of mechanoreceptive afferent fibers; (3) the spatial variability in the firing of a given population of mechanoreceptive afferent fibers. The spatial variability in the responses of slowly adapting type 1 afferents was found to account for perceived roughness of all the textures tested.
In the aforementioned studies, however, textured surfaces tended to be relatively coarse, ranging in inter-element spacing from 0.5 mm to 5 mm (with the exception one or two stimuli in Yoshioka et al., 2001). A first hint that SA1 afferents were not solely responsible for roughness coding was provided in a study that showed that low-frequency vibratory adaptation (at 10 Hz), which would primarily desensitize RA and SA1 fibers (Bensmaia et al., 2005), had no effect on fine texture discrimination (with elements sized in the tens of microns), whereas high-frequency adaptation, targeting PC fibers, abolished subjects ability to discriminate fine textures (Hollins et al., 2001). When roughness coding was investigated with a wide range of textures, it was found that the spatial code mediated by SA1 fibers could not account for the perceived roughness over the range of tangible textures. Rather, roughness judgments could be accounted for based on a combination of spatial variation in SA1 (following Johnson and colleagues) and temporal variation in RA and PC fibers (Weber et al., 2013). That is, a texture is rough to the extent that the response it evokes in SA1 afferents is spatially inhomogeneous and the response it evokes in RA and PC afferents is temporally inhomogeneous.
Information about roughness is encoded in SI as evidenced by the fact that the responses of neurons in this brain area are sensitive to changes in surface properties that determine perceived roughness, namely the spatial period of embossed dot patterns and the groove width of tactile gratings (Darian-Smith et al., 1982; Darian-Smith et al., 1984; Sinclair and Burton, 1991; Tremblay et al., 1996; Sinclair et al., 1996; Chapman et al., 2002). Lesions in SI, particularly in areas 3b and 1, lead to severe impairments in roughness discrimination (Randolph and Semmes, 1974). The second somatosensory cortex (SII) has also been implicated in the processing of surface roughness as it contains neurons that are sensitive to the relevant surface properties (Jiang et al., 1997; Pruett, Jr. et al., 2000) and lesions in SII cause impairments in roughness discrimination (Murray and Mishkin, 1984). Finally, the lateral parietal opercular cortex (Roland et al., 1998; Stilla and Sathian, 2008) is selectively activated when human subjects perform a roughness discrimination task, as are the posterior insula and the medial occipital cortex (Stilla and Sathian, 2008), indicating that they too are involved in the cortical processing of roughness information. Spatial variability in the peripheral response – which drives the perception of roughness for coarse textures – is likely computed by the subpopulation of neurons, described above, whose receptive fields comprise excitatory and inhibitory sub-regions. In fact, a subset of these neurons exhibit responses to embossed dot patterns that match their perceived roughness: Their response increases for inter-element spacings up to about 2mm, then decreases (Arun Sripati, personal communication); this population of neurons is thus ideally suited to extract roughness information from spatial patterns of SA1 activation. The roughness signal carried in the responses of RA and PC afferents likely drives responses in another population of SI neurons, also described above, that are highly sensitive to skin vibrations over a wide range of frequencies (Harvey et al., 2013) and receive input from both RA and PC fibers (Saal et al., 2014).
Hardness/softness is the subjective continuum associated with the compliance of an object: Hardness ratings have been shown to be inversely proportional to softness ratings; these ratings are, in turn, related to surface compliance following Stevens’s power law (Harper and Stevens, 1964). Softness perception has been shown to rely primarily on cutaneous cues: eliminating kinesthetic information has no effect on subjects’ ability to discriminate softness (Srinivasan and LaMotte, 1995). As the hand is pressed up against a compliant object, it conforms to the contour of the hand in proportion to the contact force. The compliance (and the softness) of the object may be signaled by the growth of the area over which the skin contacts the object as the contact force increases, as well as the increase in the force exerted by the object on the skin across the contact area. Softness perception likely relies on signals from SA1 fibers (Srinivasan and LaMotte, 1996): First, PC fibers are too sparse and their RFs too large to play a significant role in softness perception. Second, the response of RA fibers to a surface indented into the skin is not modulated by the compliance of the surface whereas the response of SA1 fibers is (Srinivasan and LaMotte, 1996). Although the evidence suggests that SA1 fibers are implicated in softness perception, the neural code for softness is unclear: the rate of indentation (in addition to surface compliance) modulates the discharge rate in individual SA1 afferents whereas softness perception is independent of the rate with which a surface is (passively) indented into the skin. Thus, the firing rate in a single SA1 fiber does not unambiguously encode the compliance of an object. One possibility is that the average pressure exerted across the contact area is predictive of compliance and invariant with respect to indentation velocity; it may then be this quantity – average pressure – that is encoded in the population response of SA1 fibers.
Stickiness/slipperiness is the sensory continuum associated with the friction between skin and surface. Indeed, magnitude estimates of stickiness have been shown to closely match the measured kinetic friction between skin and surface, i.e. the ratio between the force exerted normal to the surface to that exerted parallel to the plane of the surface (Smith and Scott, 1996). Furthermore, when judging stickiness, subjects do not substantially vary the normal forces they apply on the surface, but the applied tangential forces tend to vary across surfaces, suggesting that tangential forces are critical in the perception of stickiness. As slowly adapting type 2 fibers are sensitive to skin stretch (Witt and Hensel, 1959; Iggo, 1966; Knibestöl, 1975), this population of mechanoreceptive afferent fibers may provide the peripheral signals underlying stickiness perception, although recent evidence suggests that other mechanoreceptive afferents also convey information about tangential forces exerted on the skin (Birznieks et al., 2001).
Because ambient temperatures are generally cooler than the temperature of the skin, objects in the environment tend to conduct heat out of the skin when contacted. The perceived warmth or coolness of a surface is determined by how slowly or rapidly heat is conducted out of the skin (Ho and Jones, 2006; Ho and Jones, 2008). The perception of the thermal quality of a surface is likely mediated by thermoreceptors in the skin (Darian-Smith et al., 1973; Johnson et al., 1973; Darian-Smith et al., 1979; Johnson et al., 1979).
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