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date: 15 October 2019

Neural Mechanisms of Tactile Texture Perception

Summary and Keywords

The ability to identify tactile objects depends in part on the perception of their surface microstructure and material properties. Texture perception can, on a first approximation, be described by a number of nameable perceptual axes, such as rough/smooth, hard/soft, sticky/slippery, and warm/cool, which exist within a complex perceptual space. The perception of texture relies on two different neural streams of information: Coarser features, measured in millimeters, are primarily encoded by spatial patterns of activity across one population of tactile nerve fibers, while finer features, down to the micron level, are encoded by finely timed temporal patterns within two other populations of afferents. These two streams of information ascend the somatosensory neuraxis and are eventually combined and further elaborated in the cortex to yield a high-dimensional representation that accounts for our exquisite and stable perception of texture.

Keywords: skin, touch, roughness, somatosensory, nerve, spatial, temporal, vibration, coding, cortex


We can identify objects by the way they feel. As we move our fingers over objects in our pocket or purse, we can easily pick out keys or a phone from an assortment of other items. This ability to identify objects relies in part on the tactile perception of texture (Klatzky, Lederman, & Metzger, 1985). Using touch, we can reliably distinguish satin from silk, or perceive subtle differences between kinds of denim. This extraordinarily rich tactile experience of texture requires the integration of information across a wide span of spatial scales, ranging across six orders of magnitude (from tens of nanometers to tens to millimeters [Skedung et al., 2013]), and gives rise to a complex sensory space that comprises at least four primary perceptual axes—roughness, hardness, stickiness, and warmth (Hollins, Faldowski, Rao, & Young, 1993; Hollins, Bensmaia, Karlof, & Young, 2000a; Picard, Dacremont, Valentin, & Giboreau, 2003; Guest et al., 2011; Okamoto, Nagano, & Yamada, 2013), and many more nameable and unnameable ones.

When fingers run over a surface, the skin is deformed, which in turn results in stresses and strains in the tissue, ultimately driving spatiotemporal patterns of electrical activity in several populations of mechanoreceptors (Sripati, Bensmaia, & Johnson, 2006; Saal, Delhaye, Rayhaun, & Bensmaia, 2017). Patterns on different spatial scales are encoded through parallel mechanisms: Larger spatial patterns of skin deformation (on the order of millimeters) engage spatial encoding mechanisms that suggest compelling analogies with vision (Pack & Bensmaia, 2015; Yau, Hollins, & Bensmaia, 2016), while finer spatial patterns (on the order of microns) drive perceptually relevant skin vibrations (Hollins & Risner, 2000; Bensmaia & Hollins, 2003; Bensmaia & Hollins, 2005; Yau et al., 2009; Saal, Wang, & Bensmaia, 2016) that are reproduced faithfully by temporally precise spiking patterns (Mackevicius, Best, Saal, & Bensmaia, 2012; Weber et al., 2013). These two streams of information are then carried by tactile nerve fibers to the brain, where they are integrated and transformed through successive stages of neural processing to ultimately culminate in a tactile percept of texture.

Texture Perception

What Is Texture?

Classically, the term “texture” refers to the feel or appearance of an object’s surface. And yet, not every aspect of a surface is considered texture—for example, buttons on a video-game controller are shapes rather than features. So what are the qualities that separate a shape from a texture? One distinction is that textural features are generally distributed across the extent of a surface, and their precise pattern is meaningless, while shape features tend to be more discrete and their configuration is typically meaningful. In touch, another distinction is the relevant spatial scale: A feature that is larger than the size of the fingerpad is not a textural one.

Over the last two decades, much progress has been made to quantitatively describe the stimulus parameters that define visual and auditory texture (Portilla & Simoncelli, 2000; McDermott, Schemitsch, & Simoncelli, 2013). These parameters can be described as statistical distributions that are consistent between different samples of a given texture, but vary between different kinds of texture. In other words, textures are defined by a set of statistics that characterize non-identical stimuli that are perceptually equivalent, known as metamers (Julesz, 1962, 1981). This framework has been quite fruitful for the visual system, where textures can be defined by their statistics (Portilla & Simoncelli, 2000), which can then be used to produce new images (metamers) that appear to have the same visual texture (Portilla & Simoncelli, 2000; Freeman, Ziemba, Heeger, Simoncelli, & Movshon, 2013; Ziemba, Freeman, Movshon, & Simoncelli, 2016; Ziemba, Freeman, Simoncelli, & Movshon, 2018). A similar approach has been fruitfully applied to generate auditory textures, such as the sound of a burbling stream or crackling fire (McDermott & Simoncelli, 2011; McDermott et al., 2013; McWalter, McDermott, McWalter, & McDermott, 2018).

This approach cannot straightforwardly be applied to tactile texture, however, because early processing—well established for vision and audition and crucial for the generation of metamers—has not been established for touch. This challenge stems in part from the biomechanical interactions between skin and surface, which complicate the relationship between stimulus, neural response, and percept. Furthermore, despite advances in 3D printing, to generate arbitrary tactile surfaces is uniquely difficult compared to creating visual or auditory textures. Not only must surfaces match on a micron-level of three-dimensional detail, but tactile objects also contain difficult-to-characterize material properties such as compliance and viscosity. Facsimiles of textural percepts have been created through the clever use of vibrating surfaces (Winfield, Glassmire, Colgate, & Peshkin, 2007; Bau, Poupyrev, Israr, & Harrison, 2010; Chubb, Colgate, & Peshkin, 2010), but no simulation approach is currently able to elicit a natural textural percept. Thus, little is known about the mapping between surface features and the tactile experience to which they give rise.

The Perceptual Space of Texture

When prompted, subjects will consistently describe different textures as rough or smooth, or hard and soft (Hollins et al., 1993). Quantitative ratings of these characteristics are consistent across individuals (Connor, Hsiao, Phillips, & Johnson, 1990; Friedman, Hester, Green, & LaMotte, 2008b) as are ratings of the dissimilarity of pairs of textures (Hollins et al., 2000a). In fact, these two sets of ratings are linked: Dissimilarity ratings of texture pairs consistently yield a perceptual space that is well described by a small number of dimensions, and these dimensions generally map onto nameable textural continua, including rough/smooth, hard/soft, sticky/slippery, and warm/cool (Figure 1) (Hollins et al., 1993, 2000a; Picard et al., 2003; Bergmann Tiest & Kappers, 2006; Okamoto et al., 2013).

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Figure 1. The sensory space of texture is multidimensional. Projection of the multidimensional space of texture, estimated from ratings of dissimilarity, onto two dimensions for an example subject. Textures are further apart to the extent that they are perceived as more dissimilar. The subjective dimensions—including rough/smooth, hard/soft, sticky/slippery, and warm/cool—can be projected onto the space.

Adapted from (Hollins et al., 2000a).

The dominant and most studied perceptual dimension of texture is roughness (Lederman & Taylor, 1972; Hollins et al., 1993, 2000a). Perceived roughness is a consistent property of surface microstructure: human observers overwhelmingly agree on the relative roughness of textured surfaces (Stevens & Harris, 1962; Lederman, 1983; Sathian, Goodwin, John, & Darian-Smith, 1989; Connor et al., 1990; Connor & Johnson, 1992; Blake, Hsiao, & Johnson, 1997a). However, the relationship between the spatial geometry of surface features and roughness is not always straightforward. For example, while the roughness of periodic gratings increases with increases in groove width (Taylor & Lederman, 1975; Sathian et al., 1989) and the roughness of sandpaper increases as grit size increases (Harper & Stevens, 1964), the roughness of embossed dot patterns is a complex function of dot spacing (Connor et al., 1990; Connor & Johnson, 1992; Sutu, Meftah, & Chapman, 2013; Tymms, Zorin, & Gardner, 2017), dot width (Blake et al., 1997a), and dot height (Figure 2) (Sutu et al., 2013). In fact, unlike most other sensory continua, there is no established physical determinant of perceived roughness. One possible determinant of roughness is the magnitude of time-varying fluctuations in the surface friction (Smith, Chapman, Deslandes, Langlais, & Thibodeau, 2002), but even this quantity cannot account for the fact that surfaces can be judged along the roughness continuum in the absence of motion between skin and surface (Hollins & Risner, 2000). Roughness is only loosely associated with any one surface property but rather is defined by aspects of the response in the somatosensory nerves (see “Neural mechanisms of texture—Roughness”).

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Figure 2. Roughness does not have a clear physical determinant. A| When dots are thin, taller dots feel much rougher than shorter dots. When dots are wide, roughness is not strongly affected by dot height. Adapted from (Blake et al., 1997a). B| Taller dots feel rougher for increasingly large spacings, while shorter dot patterns feel smoother at larger spacings. In both cases, texture roughness is not easily explained by a single physical parameter.

Adapted from (Sutu et al., 2013b).

The second major axis of texture perception is hardness/softness (Klatzky, Lederman, & Reed, 1989; Hollins et al., 2000a), which closely tracks surface compliance (Figure 3) (Harper & Stevens, 1964; Yoshioka, Bensmaia, Craig, & Hsiao, 2007; Friedman et al., 2008b). When touching a surface, differences in surface compliance lead to different distributions of force across the surface of the finger, both during initial contact and static grip. These forces provide multiple cues—in their spatial distribution and temporal progression—which can be used to extract information about compliance. Indeed, we reliably perceive hardness during static grip, a mode that minimizes temporal cues (though these are still present at the onset of the grip) (Friedman et al., 2008b), but we can also perceive the hardness of a surface explored through a rigid probe, an ability that relies on temporal cues from vibrations transmitted through the probe (LaMotte, 2000). When tactile cues are eliminated altogether through digital anesthesia, we can still under certain circumstances perceive hardness using only kinesthetic cues, signaling the movement of the fingers into the object (Srinivasan & LaMotte, 1995). Thus, the perception of hardness relies on the ability to integrate multiple streams of somatosensory information to reconstruct the physical compliance of a surface.

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Figure 3. Softness depends on surface compliance. Subjects explored a series of surfaces that only varied in compliance, using a variety of methods: tapping with a stylus using 1 finger or 2 fingers, active tapping with the finger pad, active pressing with the finger pad, or passive presentation of the surface onto the finger pad. In all cases, perceived softness reliably increased with increasing compliance.

Adapted from (Friedman et al., 2008b).

The third axis of texture is stickiness, which tracks the friction between skin and surface (Smith & Scott, 1996; Yoshioka et al., 2007). For natural textures, perceptual judgments of stickiness are often correlated with judgments of roughness (Hollins et al., 2000a), but this relationship can be broken if friction is controlled (Taylor & Lederman, 1975). While stickiness is a far weaker determinant of the perceptual experience of texture than are roughness or hardness (Hollins et al., 2000a), the ability to sense friction contributes to texture discrimination (Gueorguiev, Bochereau, Mouraux, Hayward, & Thonnard, 2016) and is critical to the determination of the force required to grasp an object (Cadoret & Smith, 1996; Augurelle, Smith, Lejeune, & Thonnard, 2003).

A fourth, modest axis of texture is warmth (Hollins et al., 2000a), associated with the thermal conductivity of the surface material. Indeed, at room temperature, objects are colder than is the body, so heat will tend to flow out of the skin and into the object. The rate of this heat transfer depends on the thermal conductivity of the material and determines the warmth of the surface, with higher heat transfer leading to cooler surfaces (Ho & Jones, 2006). In addition to being used to judge the material properties of surfaces, thermal conductivity is also associated with the tactile perception of surface wetness (Filingeri, Redortier, Hodder, & Havenith, 2013, 2015).

Perceptual Invariance of Texture

Remarkably, the perception of texture is stable across broad changes in the way textures are explored. While everyday surfaces are generally explored through active movements of the hand, textures that are passively presented to the skin are perceived to be similar to their actively explored counterparts (Lederman, 1981; Lamb, 1983; Verrillo, Bolanowski, & McGlone, 1999; Friedman et al., 2008b; Yoshioka et al., 2011). This perception is robust to changes in scanning speed (Lederman, 1974, 1983; Yoshioka et al., 2011; Boundy-Singer, Saal, & Bensmaia, 2017) and force (Lederman & Taylor, 1972; Lederman, 1981), which is surprising given the strong dependence of skin responses on these exploratory parameters (Delhaye, Hayward, Lefèvre, & Thonnard, 2012; Manfredi et al., 2014). In this sense, the construction of textural percepts draws a strong analogy with the context-invariant perception of objects in the visual (DiCarlo, Zoccolan, & Rust, 2012) and auditory (Marozeau, de Cheveigné, McAdams, & Winsberg, 2003) systems.

Skin Response during Texture Exploration

Contact between the skin and a textured surface results in a spatial pattern of loads (or forces) on the skin’s surface (Taylor & Lederman, 1975; Dandekar, Raju, & Srinivasan, 2003; Sripati, Bensmaia, et al., 2006; Kim, Sripati, & Bensmaia, 2010; Saal, Delhaye, et al., 2017). These loads directly reflect the spatial layout of the surface percept. Importantly, these forces are not a simple recapitulation of the surface structure: Loads are greater at edges and corners and weaker or nil for small internal features. These forces then propagate through the skin, producing patterns of stresses and strains at the location of the mechanoreceptors, which transduce these skin deformations and convert them into neural signals. Skin mechanics has two important consequences on the perception of surface features. First, it distorts them, amplifying certain features (corners, edges) and obscuring others (small internal ones). Second, as these forces propagate deeper into the skin, their spatial patterning becomes increasingly blurred, which causes further masking of fine spatial features. Thus, while these spatial patterns of deformation account for some aspects of texture perception (Taylor & Lederman, 1975), they do not account for the perception of fine surface features because these are filtered out. Indeed, while coarse surface features (>1 mm) can be perceived by pressing the surface against the fingertip—thereby producing these spatially patterned skin deformations—finer features cannot (Van Boven & Johnson, 1994; Sathian & Zangaladze, 1996; Hollins & Risner, 2000).

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Figure 4. Scanning a surface drives texture-specific skin vibrations. The power spectral densities of skin vibrations produced when eight different textures are scanned over the fingertip. The frequency content of vibrations depends on the texture.

Adapted from (Bensmaia & Hollins, 2005).

The perception of fine textural features requires movement between the surface and the skin (Katz, 1925; Hollins & Risner, 2000). Unlike static touch, active exploration of a texture drives vibrations in the skin (Scheibert, Leurent, Prevost, & Debregeas, 2009), and the structure of these vibrations depends critically on surface microgeometry (Figure 4) (Bensmaia & Hollins, 2003; Bensmaia & Hollins, 2005; Delhaye et al., 2012; Manfredi et al., 2014). Furthermore, these vibrations are perceptually relevant: Differences in the texture-driven vibrations predict how dissimilar textures feel (Bensmaia & Hollins, 2005), vibrations imposed onto textures make textures feel rougher (Hollins, Fox, & Bishop, 2000b), and adaption of vibration-sensitive nerve fibers impairs subjects’ ability to discriminate fine but not coarse textures (Lederman, Loomis, & Williams, 1982; Hollins, Bensmaia, & Washburn, 2001). These results form the basis of the duplex theory of tactile texture perception (Katz, 1925; Hollins & Risner, 2000): The perception of coarse features relies on spatial patterns of skin deformation, and the perception of fine features relies on skin vibrations elicited during texture scanning.

Neural Mechanisms of Texture

Primer on the Neural Basis of Touch

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Figure 5. Mechanoreceptors in the skin. SA1 afferents innervate clusters of Merkel cell-neurite complexes, located in the basal layer of the epidermis. RA afferents innervate groups of Meissner corpuscles, located in the dermal papillae. PC afferents innervate large, onion-shaped Pacinian corpuscles, located deep within the dermis of the glabrous skin. SA2 afferents are thought to be associated with Ruffini endings, but this association is still controversial.

Adapted from (Abraira & Ginty, 2013).

Fine discriminative touch with the palmar surface of the hand relies on three main classes of tactile fibers (Abraira & Ginty, 2013): slowly adapting type-1 (SA1) fibers, which innervate Merkel cells, rapidly adapting (RA) fibers, which innervate Meissner corpuscles, and PC fibers, which terminate in Pacinian corpuscles (Figure 5, Table 1). A fourth type of nerve fiber, slowly adapting type-2 fibers (SA2), innervates Ruffini end-organs, a mechanoreceptor that only sparsely innervates the glabrous skin of humans and is absent in non-human primates (Paré, Smith, & Rice, 2002; Paré, Behets, & Cornu, 2003) so less is known about their responses to texture.

Table 1. Cutaneous Mechanoreceptors. Typical response properties of the three main mechanoreceptors in the glabrous skin. While different afferent types respond differently to skin deformations, they are typically all activated during every day interactions with objects.

Nerve fiber

End organ

RF size

Optimal Stimulus

Response Properties

Slowly adapting type 1 (SA1)

Merkel cell



Neural Mechanisms of Tactile Texture Perception

Rapidly adapting (RA)

Meissner corpuscle


Flutter, slip

Neural Mechanisms of Tactile Texture Perception

Pacinian corpuscle-associated (PC)

Pacinian corpuscle



Neural Mechanisms of Tactile Texture Perception

Table adapted from (Johansson & Vallbo, 1983; Abraira & Ginty, 2013)

Signals from these three populations of fibers carried by the somatosensory nerves through the spinal cord and synapse (ipsilaterally) onto neurons in the cuneate nucleus, in the medulla oblongata of the brainstem. Neurons from this nucleus send axons across the midline to the contralateral ventral posterolateral nucleus of the thalamus (VPL). The few studies of tactile responses within these two nuclei suggest that their patterns of activity look qualitatively similar to those of peripheral afferents (Douglas, Ferrington, & Rowe, 1978; Ghosh, Turman, Vickery, & Rowe, 1992; Zhang et al., 2001), but texture representations have not been systematically investigated there.

Neurons in VPL primarily project to anterior parietal cortex (APC) (Jones, 1975, 1983; Jones & Burton, 1976; Jones & Friedman, 1982; Padberg et al., 2009)—located on the posterior bank of the central sulcus and on the postcentral gyrus—which comprises four cortical modules, namely Brodmann’s areas 3a, 3b, 1, and 2, progressing posteriorly (Geyer, Schleicher, & Zilles, 1999). Although all cortical fields in APC receive direct projections from thalamus, cutaneous neurons in thalamus project primarily to areas 3b, 1, and 2, while proprioceptive ones project to areas 3a and 2 (Friedman & Jones, 1981). Only area 3b receives thalamocortical input that is characteristic of primary sensory areas, and so this area is considered to be primary somatosensory cortex proper (Kaas, 1983).

From APC, somatosensory signals diverge along two parallel streams, dorsal and ventral (Gardner, 2010; Sathian, 2016). Little is known about the elaboration of texture signals in the dorsal stream, which includes posterior parietal cortex (Brodmann’s areas 5 and 7), an area generally associated with visuo-motor transformations (Andersen & Buneo, 2002; Gardner, 2010; Gharbawie, Stepniewska, Qi, & Kaas, 2011). Rather, the conscious experience of tactile texture is thought to rely on propagation of texture signals along the ventral stream (Ledberg, O’Sullivan, Kinomura, & Roland, 1995; Roland, O’Sullivan, & Kawashima, 1998; Kitada et al., 2005; Stilla & Sathian, 2008; Sathian et al., 2011; Simões-Franklin, Whitaker, & Newell, 2011; Kaas, van Mier, Visser, & Goebel, 2013; Sathian, 2016). This stream includes the lateral parietal cortex (LPC), consisting of areas PV and S2, and is known as the parietal operculum in humans (Krubitzer, Clarey, Tweedale, Elston, & Calford, 1995; Sathian, 2016). Because the LPC contains projections to regions of insular cortex that eventually connect to the amygdala and hippocampus (Friedman et al., 1986), the ventral pathway is thought to underlie tactile object recognition, perception, and memory.

Spatial code

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Figure 6. Spatial resolution of tactile nerve fibers. Responses from an example SA1, RA, and PC fiber as a grating pattern was repeatedly pressed into the skin, each time at a lateral displacement of 0.2 mm, plotted along the abscissa. Firing rate is plotted along the ordinate. Note that SA1 responses exhibit clear modulation even for spatial patterns as small as 1 mm. RA responses only exhibit modulation starting with the 3 mm gap, and PC responses exhibit no spatial modulation at all.

Adapted from (Johnson, 2001).

The perception of textural features on the coarse end of the tangible spectrum (~1 mm or greater) is mediated by the spatial pattern of activation across tactile fibers innervating the skin. Only two populations of fibers have the necessary spatial resolution to carry this information: (1) SA1 fibers, characterized by small receptive fields (2–3 mm diameter on the macaque fingertip, [Vega-Bermudez & Johnson, 1999]) and dense innervation the skin, with as many as 1.4/mm2 in the fingertip (Johansson & Vallbo, 1979; Darian-Smith & Kenins, 1980), and (2) RA fibers, characterized by slightly larger receptive fields (3–5 mm diameter on the macaque fingertip, [Vega-Bermudez & Johnson, 1999]) and slightly denser innervation in the dermal papillae (1.7/mm2 on the fingertips than SA1 fibers [Johansson & Vallbo, 1979; Darian-Smith & Kenins, 1980]). Of these two classes of nerve fibers, SA1 afferents most faithfully carry the shape of surface features. SA1 fibers persistently fire in response to statically indented tactile shape, and their response is more spatially selective than that of RA or PC afferents (Figure 6) (Phillips & Johnson, 1981; Goodwin, Browning, & Wheat, 1995; Goodwin, Macefield, & Bisley, 1997; Wheat, Goodwin, & Browning, 1995; Bisley, Goodwin, & Wheat, 2000). Individual SA2 afferents also exhibit shape-selective responses (Jenmalm, Birznieks, Goodwin, & Johansson, 2003), but the SA2 population lacks the dense innervation of SA1 afferents (Johansson, 1978). During active touch, all populations of SA1, RA, and PC afferents are all robustly activated, but the spatial image relayed by SA1 afferents is sharper than that of its RA counterpart (Figure 7) (Johnson & Lamb, 1981; Phillips, Johnson, & Hsiao, 1988; Connor et al., 1990; LaMotte, Lu, & Srivasan, 1996; Blake, Johnson, & Hsiao, 1997b). Thus, SA1 fibers encode spatial patterns in a manner analogous to photoreceptors in the retina and their spatial resolution (around 0.5–1 mm, [Phillips & Johnson, 1981]) is thought to mediate performance on standard tests of tactile acuity (Loomis, 1980; Johnson & Phillips, 1981).

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Figure 7. Spatial image in population responses of tactile nerve fibers. Responses from an example SA1 and RA afferent are plotted in response to a series of squares scanned over the skin. While both afferents carry spatial information—the spatial pattern of SA1 or RA activation faithfully reproduces the spatial features of the stimulus—SA1 signals are more acute than their RA counterparts. Note that the edges of the square are enhanced in the neural representation due to skin mechanical effects.

Adapted from (Blake et al., 1997b).

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Figure 8. Spatial and receptive fields in APC. Spatial receptive fields from eight example neurons in area 3b, as measured from spiking responses to a randomly distributed dot pattern scanned over the skin. In each 10 mm × 10 mm patch, darker colors represent excitatory regions and lighter colors represent inhibitory regions.

Adapted from (DiCarlo et al., 1998).

In anterior parietal cortex, neurons exhibit strong selectivity for spatial patterns impinging upon the skin. Specifically, the receptive fields of neurons in area 3b consist of excitatory and inhibitory subfields (Figure 8) (DiCarlo, Johnson, & Hsiao, 1998; Sripati, Yoshioka, Denchev, Hsiao, & Johnson, 2006; Lieber & Bensmaia, 2018) in different configurations, which confers to each neuron a preference for a specific spatial feature. This receptive structure is highly analogous to that of neurons in primary visual cortex and can be approximated by a Gabor function (Bensmaia et al., 2008). Importantly, the implied spatial differentiation computation that these neurons implement is precisely that predicted from studies of the peripheral representation of embossed dot patterns (Connor et al., 1990; Connor & Johnson, 1992) (see “Neural mechanisms of texture—Roughness”), providing converging evidence of the role of spatial computations in texture processing. In fact, individual neurons in APC respond robustly to textured surfaces scanned across their receptive fields, and show graded responses to textures with varying coarse spatial structure (e.g., gratings or dot patterns that vary in spatial period) (Darian-Smith et al., 1982; Sinclair & Burton, 1991; Burton & Sinclair, 1994; Tremblay et al., 1996). While individual neurons in APC tend to receive convergent input from multiple classes of tactile fibers (Pei et al., 2009a; Saal, Harvey, & Bensmaia, 2015; Lieber & Bensmaia, 2018), neurons that best differentiate coarse textural features tend to receive strong input from SA1 fibers (Figure 9) (Lieber & Bensmaia, 2018).

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Figure 9. Subpopulations within APC preferentially encode coarse and fine features. A| The population averaged Fourier amplitude of texture evoked responses is plotted for two subpopulations within APC: SA1-like neurons (green) and PC-like cells (orange). These spectra are plotted for the responses to 3 different textures: dots spaced 7.7 mm apart, a 1 mm-period grating, and a superposition of the dots with the grating. PC-like cells exhibit high-frequency phase-locking even when the dots are present, while SA1-like cells do not. B| Discriminability (d') of textures based on the firing rates they evoke in SA1-like and PC-like neurons (green and orange, respectively). PC-like neurons are better at discriminating textures with differing fine features, while SA1-like neurons are better at discriminating textures with differing coarse features. C| Firing rates across subpopulations of neurons within APC predict the perceived dissimilarity of pairs of textures, measured in human subjects. Firing rates in SA1-like neurons (green) better predict the dissimilarity of textures with different coarse features, while firing rates in PC-like neurons (orange) better predict the dissimilarity of textures with different fine structure.

Adapted from (Lieber & Bensmaia, 2018).

Temporal code

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Figure 10. Spatial coding cannot explain fine texture perception. A| Surface profiles of four different textures, two with coarse spatial features (dots and hucktowel) and two with fine spatial features (nylon and chiffon). B| Spatial patterns of activation (spatial event plots) averaged over 15 SA1 afferents, in response to the four textures. While coarse textures evoke patterns of activity that preserve the spatial structure in the stimulus, the fine textures do not. C| Average firing rate evoked by twelve different textures, average across the SA1 afferent population. These textures are ordered from least rough (left) to most rough (right). Finer textures do not evoke significant spiking in SA1 afferents. D| Standard deviation across all frequencies the SEP spatial power spectrum (a measure of spatial patterning), plotted for all 12 textures. Responses to coarse textures show more spatial patterning than fine textures. E| Correlation between the surface profiles and the SEPs, plotted for all 12 textures. The structure of coarse textures is faithfully reflected in the SA1 population response, while the structure of fine textures is not.

Adapted from (Weber et al., 2013).

While the spatial image carried by SA1 fibers is effective for relaying information about coarse textural features, it lacks the resolution to encode finer features (smaller than ~1 mm), due to limits set by the mechanical filtering by the skin and the innervation density of tactile nerve fibers (Figure 10) (Phillips & Johnson, 1981; Sripati, Bensmaia, et al., 2006; Weber et al., 2013) (see “Skin response during texture exploration”). In fact, fine features can be discerned only during dynamic contact, when the relative motion between the finger and surface drives texture-specific skin vibrations (Bensmaia & Hollins, 2003; Bensmaia & Hollins, 2005; Delhaye et al., 2012; Manfredi et al., 2014). These characteristic skin vibrations cannot be encoded by SA1 fibers, as these afferents do not respond to skin vibrations in the relevant frequency range (>50 Hz, e.g., for small features, <1 mm, even moving at a relatively slow speed, 50 mm/s) (Talbot et al., 1968; Bensmaia et al., 2005; Mackevicius et al., 2012; Callier et al., 2015). Rather, these vibrations drive precise, repeatable temporal spiking patterns in RA and PC afferents that are highly texture-specific and thus informative about the fine features of a surface (Figure 11) (Weber et al., 2013).

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Figure 11. Fine textures evoke temporally patterned spiking responses. A| Surface profiles for three finely textured materials. B| The spiking responses of a single PC afferent to 42 presentations of each of the three textures. Each texture evokes a consistent pattern of response. C| The power spectrum of each corresponding single trial neural response. PC responses are highly repeatable and their texture responses exhibit consistent spectral signatures. D| The power spectral densities in response to a single texture (averaged across all trials and PC afferents) are plotted in orange, the densities of texture-elicited vibrations are shown in black. The frequency composition of the neural response matches that of the vibrations elicited in the skin (plotted in black).

Adapted from (Weber et al., 2013).

In anterior parietal cortex, the amplitude and fine timing of these afferent spiking patterns are reflected in the responses of their downstream recipients (Mountcastle et al., 1969; Salinas et al., 2000; Harvey et al., 2013). Vibratory amplitude is encoded in firing rates across the frequency range. Vibratory frequency is encoded in both temporal patterning and firing rates at low vibratory frequencies (<50 Hz) but only in temporal patterning (and not in firing rates) at high vibratory frequencies (>50 Hz). Indeed, at these high vibratory frequencies, firing rates are dependent on amplitude but not frequency. The net result is that precisely timed spiking patterns in a subpopulation of APC neurons convey texture information (Figure 12) (Lieber & Bensmaia, 2018). These neurons, which tend to receive strong input from PC fibers and exhibit phase-locked spiking when periodic textures are scanned across their receptive fields. While this timing signal could, in principle, contribute to the processing and perception of fine textural features, this texture information is redundantly carried in firing rates, so the role of temporal patterning in texture signaling in cortex remains to be demonstrated. Neurons that exhibit a sensitivity to fine surface features tend to be found within area 1, where functional and anatomical evidence has suggested a preponderance of neurons with strong PC input (Paul et al., 1972; Hyvärinen & Poranen, 1978; Merzenich et al., 1978; Hyvärinen et al., 1980; Padberg et al., 2009; Lieber & Bensmaia, 2018). Notably, lesions to area 1 seem to lead to specific deficits in texture perception (Randolph & Semmes, 1974; Carlson, 1981) though this area does not seem to carry a more elaborate representation of texture across the tangible range than do its neighbors (Lieber & Bensmaia, 2018).

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Figure 12. Finely timed information in texture responses in APC. The population averaged Fourier amplitude of texture evoked responses is plotted for two subpopulations in APC: neurons whose texture evoked firing rate responses resemble SA1 afferents, and neurons whose responses resemble PC-like afferents. Across the six different textures, the PC-like subpopulation exhibited texture evoked phase-locking to frequencies between 50–200 Hz.

Adapted from (Lieber & Bensmaia, 2018).

Integration of spatial and temporal codes

These two encoding mechanisms for texture—spatial and temporal—provide a mechanistic underpinning for the duplex theory of tactile texture perception (Katz, 1925; Hollins & Risner, 2000): coarse features are encoded by the spatial pattern of activity across SA1 (and perhaps RA) fibers, fine features are encoded by temporally precise patterns of activity within RA and PC fibers. As most textures comprise both coarse and fine features, these two streams of information must eventually be combined. Within APC, this process has already begun. Functional evidence suggests that signals from all three populations of tactile fibers (SA1, RA, and PC) converge onto individual cortical neurons, as early as area 3b (Hyvärinen & Poranen, 1978; Pei et al., 2009b; Saal & Bensmaia, 2014; Lieber & Bensmaia, 2018). Specifically, most neurons exhibit sustained responses to a skin indentation, a signature of SA1 input (Pei et al., 2009b), and nearly all neurons also exhibit strong offset responses, a signature of RA and/or PC input (Sur et al., 1984; Pei et al., 2009b). Furthermore, cortical responses to complex vibration responses can only be explained by the integration of RA and PC signals (Saal et al., 2015). As might be expected, then, the responses of most neurons in APC encode both coarse and fine features, implying an integration of the spatial and temporal codes for texture (Lieber & Bensmaia, 2018).


The neural code for roughness—the primary axis of texture—was first established by comparing judgments of roughness of embossed dot patterns, obtained from human observers, to the responses evoked by these textures in the three populations of tactile fibers. In an elegant series of studies, Johnson and colleagues tested a set of hypotheses as to the neural determinants for roughness and eliminated all but one (Connor et al., 1990; Connor & Johnson, 1992; Blake et al., 1997a): across all conditions tested, the variation (or degree of inhomogeneity) across the responses of SA1 fibers could account, with remarkable precision, for roughness judgments (Figure 13). That is, to the extent that all SA1 fibers respond equally to a surface—indicating a lack of asperities—a surface will be perceived as smooth; to the extent that asperities are distributed across the surface, they will excite some SA1 fibers and not others, and the surface will be perceived as rough (Goodman & Bensmaia, 2017). The computation implied by this neural code for roughness can be implemented by a spatial filter consisting of a positively and negatively weighted lobe offset by 2–4 mm (a Gabor filter) passed across the spatial pattern of activation in this population of fibers (Connor & Johnson, 1992; Lieber et al., 2017). The friction between surface and finger also contributes to its perceived roughness (Smith et al., 2002), but a coarse texture can be perceived as rough even in the absence of movement between skin and surface (Hollins & Risner, 2000), highlighting the fact that there is no single well defined physical determinant of perceived roughness.

Neural Mechanisms of Tactile Texture PerceptionClick to view larger

Figure 13. Spatial variation code for perceived roughness. A| Reconstructed pattern of activation that would be elicited in a population of identical SA1 fibers (bottom row) by a series of dot patterns (shown above). Each line of action potentials corresponds to a single scan of the texture over the skin. Successive lines represent a 200 micron shift of the pattern, perpendicular to the scanning direction. Patterns with larger dot spacings elicit, on average, fewer action potentials per scan, and more spatial variation across the SA1 population. B| Human subjects rated the roughness of each of the dot patterns (left). Neither the firing rate nor the temporal variation of SA1 responses can account for how rough the surfaces feel. Rather, perceived roughness mirrors the spatial variation across SA1 responses. Note that while the spatial variation accounts for the roughness of dot patterns, it cannot account for the roughness of finer textures.

Adapted from (Connor & Johnson, 1992).

That SA1 fibers carry a faithful neural image of embossed dot patterns that accounts for roughness judgments was taken as evidence that SA1 fibers mediate tactile perception of texture across the tangible range (Yoshioka et al., 2001a). However, the spatial image carried by SA1 fibers lacks the resolution to encode fine features, and thus cannot account for roughness perception of finely textured surfaces, including sandpapers (Lieber et al., 2017). Rather, the peripheral code for fine texture roughness relies on temporally structured patterns of activity within RA and PC afferents. In parallel to the spatial computation, a computation of temporal variation in RA and PC responses—the extent to which responses of individual fibers are inhomogeneous in time—contributes to perceived roughness. The computation implied by the temporal variation code can be represented by a temporal Gabor filter (of width 10–20 ms) passed across the responses of individual RA and PC fibers (Weber et al., 2013; Saal et al., 2015; Lieber et al., 2017). In summary then, tactile roughness likely depends on two parallel codes: a spatial variation code for coarse features and a temporal variation code for finer features.

Neural Mechanisms of Tactile Texture PerceptionClick to view larger

Figure 14. Roughness perception depends on the convergence of three fiber types. A-C| Roughness ratings for 55 natural textures are plotted against the spiking response variation in SA1 (A), RA (B), and PC (C) afferents. Textures are split into three categories: coarse textures defined by features larger than 1 mm (grey), fine textures defined by features smaller than 1 mm (blue), and sandpapers (red). Lines of best fit are plotted in black. While SA1 responses can account for the perceived roughness of coarse textures (like dot patterns), they cannot explain the roughness of finer textures. Note that both SA1 and RA afferents underestimate the roughness of sandpapers, while PC afferents overestimate sandpaper roughness. D| Roughness ratings are plotted against the output of a multiple regression using the three afferent types. Roughness is almost perfectly explained (R2 = 0.94) when all afferent types are taken into account.

Adapted from (Lieber et al., 2017).

In everyday tactile experience, most surfaces comprise both fine and coarse features. Thus, the central nervous system cannot selectively listen to either a spatial or temporal code on a texture by texture basis; rather, it must combine signals from both of these encoding mechanisms to achieve a unified textural percept. Neurophysiological results show that the roughness of natural textures is well explained by a neural code that combines signals from SA1, RA, and PC afferents ((Weber et al., 2013; Lieber et al., 2017), though an opponency role has been proposed for SA1 signals, [see Gescheider et al., 2005; Gescheider & Wright, 2013, 2018]). While SA1 afferents best explain the roughness of dot patterns and gratings (Blake et al., 1997a; Yoshioka et al., 2001b; Lieber et al., 2017), RA responses account for differences in roughness across everyday fabrics, and PC afferents account for the characteristic roughness of sandpapers (Figure 14) (Lieber et al., 2017). Therefore, the neural code for roughness is determined by the combined activity of all three classes of tactile fibers, and requires the integration of the spatial signals encoding coarse features and temporal signals encoding fine features.

In anterior parietal cortex, these divergent streams of spatial and temporal information are combined and transformed into a more explicit code for roughness. Because SA1, RA, and PC signals converge onto individual neurons within APC, these neurons can simultaneously account for the perception of both fine and coarse features. Furthermore, the responses of APC neurons are well described by Gabor-like filters for both spatial patterns (DiCarlo & Johnson, 2000; Bensmaia, Denchev, Dammann, Craig, & Hsiao, 2008) and temporal spiking sequences (Saal et al., 2015), just as predicted by studies investigating the neural code for roughness in the somatosensory nerves (Connor et al., 1990; Lieber et al., 2017). Because cortical responses reflect the variation computations that drive perceived roughness, cortical firing rates can account for human judgments of roughness (Burton & Sinclair, 1994; Chapman et al., 2002; Bourgeon et al., 2016; Lieber & Bensmaia, 2018).

Other perceptual continua of texture

While most work on texture coding has focused on roughness, the neural determinants of other textural continua have also been investigated. The consensus from psychophysical and neurophysiological experiments is that perceived hardness, like roughness, depends on both spatial and temporal codes. Surfaces of differing compliance lead to different spatiotemporal patterns of activation when they make contact with the skin. For example, at equal contact force, the area of contact between skin and surface is wider for soft than for hard surfaces and forces drop off more progressively for soft than hard surfaces. As a result, the spatial distribution of SA1 responses is shaped, in part, by surface compliance (Figure 15): SA1 fibers are activated over a wider area and the drop off in their activation is more progressive when contacting a compliant surface (Condon et al., 2014; Hudson et al., 2015). However, hardness can also be perceived through rapid tapping of surface with a probe (LaMotte, 2000), which elicits hardness-dependent vibrations in the probe which are transduced primarily by RA and PC fibers.

Neural Mechanisms of Tactile Texture PerceptionClick to view larger

Figure 15. Hardness perception depends on SA1 afferents. The firing rate of an example SA1 afferent increases predictably as surfaces of decreasing compliance are indented into the skin (from 1, the most compliant surface, to 9, the least compliant).

Adapted from (Hudson et al., 2015).

The neural underpinnings for other sensory continua of texture have not been investigated as systematically as have those for roughness and hardness. Stickiness is thought to be driven by tangential forces that stretch the skin during texture scanning, and both SA1 and RA fibers respond to tangential forces in the skin (as do SA2 fibers) (Birznieks et al., 2001, 2010). However, stickiness perception has not been experimentally linked to specific patterns of afferent activation. The perception of warmth/coolness—associated with the thermal conductivity of the surface material—is thought to be encoded by thermoreceptive fibers (Johnson et al., 1973, 1979; Darian-Smith et al., 1979b, 1979a; Ho & Jones, 2006), but it is unknown where and how these signals are integrated with other texture signals.


Texture perception is largely independent of exploratory parameters used to scan a surface, such as the scanning speed or contact force. This perceptual invariance stands in contrast to responses of peripheral afferents, which are strongly modulated by changes in scanning speed (Goodwin & Morley, 1987a; Phillips et al., 1992; DiCarlo & Johnson, 1999; Lieber et al., 2018) and, to a lesser extent, exploratory force (Goodwin & Morley, 1987b; Phillips et al., 1992; Saal, Suresh, et al., 2017). To achieve perceptual invariance, exploration-invariant representations of texture must be extracted from spatial and temporal patterns of peripheral activity. Intriguingly, neurons across APC show a range of responses that lie between these two extremes—some neurons exhibit texture responses that, like afferents, are strongly affected by changes in scanning speed, while others show texture responses that are almost entirely invariant to speed (Sinclair & Burton, 1991; DiCarlo & Johnson, 1999; Dépeault et al., 2013; Lieber et al., 2018). As a result, the population response to texture within APC can simultaneously carry information about texture and about exploratory parameters.

Neural correlates of perceptual invariance can be explained, in part, by existing encoding mechanisms. For example, the spatial encoding mechanism for coarse textural features is naturally invariant to changes in exploratory parameters. Unlike afferent firing rates, the spatial patterning of texture responses is robust to changes in scanning speed (Johnson & Lamb, 1981; Phillips et al., 1992), as is the spatial variation code for coarse texture roughness (Connor et al., 1990; Lieber et al., 2017). The somatosensory system may also leverage large-scale spatial patterning to explicitly encode information about exploratory parameters, and then use that information to correct texture signals for any systematic biases. For example, while populations of afferents with receptive fields over the area of contact with an object encode its shape, fibers with receptive fields outside the contact area encode contact force (Bisley et al., 2000).

Neural Mechanisms of Tactile Texture PerceptionClick to view larger

Figure 16. Texture-driven temporal spiking patterns change systematically with changes in scanning speed. A| The timing of afferent responses (ISI distributions) was used to identify texture identity (55 textures total). Classification performance is plotted for SA1 (green), RA (blue), and PC (orange) fibers against the temporal resolution at which the spike timing is read out. Spike timing in RA and PC responses is most informative about texture identity at a temporal resolution of ~5 and ~2 ms, respectively. B| Classification performance of small populations of afferents, based on the distance between spike trains (Victor & Purpura, 1996) that have been dilated or contracted proportionally to their scanning speed. The temporal structure of spike trains, when scaled appropriately for speed, contains texture-specific information. C| Spiking responses evoked in example RA and PC afferents by two textures at three different speeds. Responses at 40 mm/s are compressed (twofold) and responses at 120 mm/s are expanded (by a factor of 1.5 so that they are placed on a common spatial scale.

Adapted from (Weber et al., 2013).

In contrast to the spatial code, the temporal code that carries information about fine textural features is particularly sensitive to changes in scanning speed. Specifically, the structure of texture-driven spiking patterns in RA and PC afferents are dependent not only on the microstructure of the surface but also on the speed at which the skin moves across the surface (Weber et al., 2013; Manfredi et al., 2014). Specifically, texture-evoked patterns dilate or contract systematically with decreases or increases in scanning speed, respectively. Notably, texture-specific temporal patterning within APC shows this same dilation/contraction effect (Lieber et al., 2018). These predictable shifts in patterning mean that texture-specific information can be extracted from temporal patterns of activity, but only when scanning speed is taken into account and corrected for (Figure 16). Thus, for surfaces with finer features, the extraction of an invariant texture representation draws a parallel to the auditory phenomenon of timbre invariance across changes in fundamental frequency (Marozeau et al., 2003; Yau et al., 2009; Saal et al., 2016). In both cases, the ability to extract meaningful information from a temporally patterned signal relies on the predictable transformation of that signal’s harmonic stack along the frequency axis. How the auditory and somatosensory systems accomplish this task is not understood.

High-level representations of texture

Neural Mechanisms of Tactile Texture PerceptionClick to view larger

Figure 17. Texture coding in secondary somatosensory cortex. A| The firing rate response of two example neurons in S2 when an animal scanned its hand over gratings of different groove widths (where wider groove widths are perceived as rougher). Unlike their counterparts in anterior parietal cortex, many neurons in S2 produce lower firing rates in response to rougher textures (in this case, corresponding to increased groove width) (adapted from Sinclair & Burton, 1993). B| Monkeys were trained to report whether the texture of a surface moving across the skin changed or remained constant. In APC, firing rates increase as the spatial period increases. In LPC, a subset of neurons shows a response that is not graded by roughness, but rather reflects the presence or absence of a change in texture.

Adapted from (Jiang et al., 1997).

The rich representation of texture within APC is further elaborated as it ascends the somatosensory neuraxis to lateral parietal cortex (LPC). Functional imaging studies have shown that LPC is strongly and selectively activated during texture related tasks (Ledberg et al., 1995; Roland et al., 1998; Kitada et al., 2005; Stilla & Sathian, 2008; Sathian et al., 2011; Simões-Franklin et al., 2011; Kaas et al., 2013; Sathian, 2016). At the single unit level, LPC responses differ from their APC counterparts in that LPC responses are alternately positively or negatively related to roughness whereas APC responses always increase with roughness (Figure 17A) (Sinclair & Burton, 1993; see also, a parallel result with vibrotactile stimuli, Salinas, Hernandez, Zainos, & Romo, 2000). This elaboration of the texture representation may be important for the extraction of higher-level features of texture, such as perceived hardness (Servos et al., 2001). LPC may also play a role in achieving a representation of texture that is invariant to changes in exploratory strategy: BOLD responses to texture in LPC are the same whether textures are actively scanned with the finger or passively presented by the experimenter (Simões-Franklin et al., 2011).

Unlike APC responses, which tend to encode a stimulus regardless of the task demands or behavioral context, LPC responses are highly task and context dependent (Jiang, Tremblay, & Chapman, 1997; Romo, Hernández, Zainos, Lemus, & Brody, 2002; Chapman & Meftah, 2005; Kitada et al., 2005; Meftah, Bourgeon, & Chapman, 2009). For example, when animals are trained to detect a change in surface texture, neurons within APC respond to the surface, independently of the task. By contrast, many neurons within LPC show “change-detection” response properties—that is, they signal a change in texture, regardless of the texture (Figure 17B) (Jiang et al., 1997). Functional imaging studies suggest that LPC may contain a persistent representation of tactile working memory in a texture task (Kaas et al., 2013; also see [de Lafuente & Romo, 2006]). LPC may also be a locus for more abstract representations of texture: functional imaging studies have shown that non-tactile inputs, such as spoken phrases suggestive of tactile texture, can drive activity within LPC (Lacey, Stilla, & Sathian, 2012).


Surfaces are ubiquitous and our sense of touch conveys exquisite information about their microstructure and material properties. Our perceptual experience of texture spans a wide range: surfaces can feel rough or smooth, hard or soft, velvety, fuzzy, etc. Texture percepts are consistent even as we move our hands over the surface in different ways. This rich and robust sense arises from different streams of neuronal activity: larger features of a surface are encoded in spatial patterns of activity in the nerve that mirror the large scale deformations they produce in the skin (on the order of millimeters), while finer features are encoded in temporal patterns of activity that reflect texture-specific skin vibrations. These two streams of information, spatial and temporal, are combined in cerebral cortex, where they give rise to a high dimensional representation of texture and thus to a complex perceptual space.


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