Natural scenes in tactile texture

Perceived Realism of Virtual Textures Rendered by a Vibrotactile Wearable Ring Display

Wearable haptic displays that relocate feedback away from the fingertip provide a much-needed sense of touch to interactions in virtual reality, while also leaving the fingertip free from occlusion for augmented reality tasks. However, the impact of relocation on perceptual sensitivity to dynamic changes in actuation during active movement remains unclear. In this work, we investigate the perceived realism of virtual textures rendered via vibrations relocated to the base of the index finger and compare three different methods of modulating vibrations with active finger speed. For the first two methods, changing finger speed induced proportional changes in either frequency or amplitude of vibration, and for the third method did not modulate vibration. In psychophysical experiments, participants compared different types of modulation to each other, as well as to real 3D-printed textured surfaces. Results suggest that frequency modulation results in more realistic sensations for coarser textures, whereas participants were less discerning of modulation type for finer textures. Additionally, we presented virtual textures either fully virtually in midair or under augmented reality in which the finger contacted a flat surface; while we found no difference in experimental performance, participants were divided by a strong preference for either the contact or non-contact condition.

EEG-based functional connectivity for tactile roughness discrimination

Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09876-1.

Crossmodal associations between visual textures and temperature concepts

Visual textures are critical in how individuals form sensory expectations about objects, which include somatosensory properties such as temperature. This study aimed to uncover crossmodal associations between visual textures and temperature concepts. In Experiment 1 (N = 193), we evaluated crossmodal associations between 43 visual texture categories and different temperature concepts (via temperature words such as cold and hot) using an explicit forced-choice test. The results revealed associations between striped, cracked, matted, and waffled visual textures and high temperatures and between crystalline and flecked visual textures and low temperatures. In Experiment 2 (N = 247), we conducted six implicit association tests (IATs) pairing the two visual textures most strongly associated with low (crystalline and flecked) and high (striped and cracked) temperatures with the words cold and hot as per the results of Experiment 1. When pairing the crystalline and striped visual textures, the results revealed that crystalline was matched to the word cold, and striped was matched to the word hot. However, some associations found in the explicit test were not found in the IATs. In Experiment 3 (N = 124), we investigated how mappings between visual textures and concrete entities may influence crossmodal associations with temperature and these visual textures. Altogether, we found a range of association strengths and automaticity levels. Importantly, we found evidence of relative effects. Furthermore, some of these crossmodal associations are partly influenced by indirect mappings to concrete entities.

Effects of Sensing Tactile Arrays, Shear Force, and Proprioception of Robot on Texture Recognition

In robotics, tactile perception is important for fine control using robot grippers and hands. To effectively incorporate tactile perception in robots, it is essential to understand how humans use mechanoreceptors and proprioceptors to perceive texture. Thus, our study aimed to investigate the impact of tactile sensor arrays, shear force, and the positional information of the robot's end effector on its ability to recognize texture. A deep learning network was employed to classify tactile data from 24 different textures that were explored by a robot. The input values of the deep learning network were modified based on variations in the number of channels of the tactile signal, the arrangement of the tactile sensor, the presence or absence of shear force, and the positional information of the robot. By comparing the accuracy of texture recognition, our analysis revealed that tactile sensor arrays more accurately recognized the texture compared to a single tactile sensor. The utilization of shear force and positional information of the robot resulted in an improved accuracy of texture recognition when using a single tactile sensor. Furthermore, an equal number of sensors placed in a vertical arrangement led to a more accurate distinction of textures during exploration when compared to sensors placed in a horizontal arrangement. The results of this study indicate that the implementation of a tactile sensor array should be prioritized over a single sensor for enhanced accuracy in tactile sensing, and the use of integrated data should be considered for single tactile sensing.

Electromechanical model for object roughness perception during finger sliding

Touch allows us to gather abundant information in the world around us. However, how sensory cells embedded in the fingers convey texture information into their firing patterns is still poorly understood. Here, we develop an electromechanical model for roughness perception by incorporating main ingredients such as voltage-gated ion channels, active ion pumps, mechanosensitive channels, and cell deformation. The model reveals that sensory cells can convey texture wavelengths into the period of their firing patterns as the finger slides across object surfaces, but they can only convey a limited range of texture wavelengths. We also show that an increase in sliding speed broadens the decoding wavelength range at the cost of reduction of lower perception limits. Thus, a smaller sliding speed and a bigger contact force may be needed to successfully discern a smooth surface, consistent with previous psychophysical observations. Moreover, we show that cells with slowly adapting mechanosensitive channels can still fire action potentials under static loadings, indicating that slowly adapting mechanosensitive channels may contribute to the perception of coarse textures under static touch. Our work thus provides a new theoretical framework to study roughness perception and may have important implications for the design of electronic skin, artificial touch, and haptic interfaces.

Learning to Feel Textures: Predicting Perceptual Similarities From Unconstrained Finger-Surface Interactions

Whenever we touch a surface with our fingers, we perceive distinct tactile properties that are based on the underlying dynamics of the interaction. However, little is known about how the brain aggregates the sensory information from these dynamics to form abstract representations of textures. Earlier studies in surface perception all used general surface descriptors measured in controlled conditions instead of considering the unique dynamics of specific interactions, reducing the comprehensiveness and interpretability of the results. Here, we present an interpretable modeling method that predicts the perceptual similarity of surfaces by comparing probability distributions of features calculated from short time windows of specific physical signals (finger motion, contact force, fingernail acceleration) elicited during unconstrained finger-surface interactions. The results show that our method can predict the similarity judgments of individual participants with a maximum Spearman's correlation of 0.7. Furthermore, we found evidence that different participants weight interaction features differently when judging surface similarity. Our findings provide new perspectives on human texture perception during active touch, and our approach could benefit haptic surface assessment, robotic tactile perception, and haptic rendering.

Roughness perception: A multisensory/crossmodal perspective

Roughness is a perceptual attribute typically associated with certain stimuli that are presented in one of the spatial senses. In auditory research, the term is typically used to describe the harsh effects that are induced by particular sound qualities (i.e., dissonance) and human/animal vocalizations (e.g., screams, distress cries). In the tactile domain, roughness is a crucial factor determining the perceptual features of a surface. The same feature can also be ascertained visually, by means of the extraction of pattern features that determine the haptic quality of surfaces, such as grain size and density. By contrast, the term roughness has rarely been applied to the description of those stimuli perceived via the chemical senses. In this review, we take a critical look at the putative meaning(s) of the term roughness, when used in both unisensory and multisensory contexts, in an attempt to answer two key questions: (1) Is the use of the term 'roughness' the same in each modality when considered individually? and (2) Do crossmodal correspondences involving roughness match distinct perceptual features or (at least on certain occasions) do they merely pick-up on an amodal property? We start by examining the use of the term in the auditory domain. Next, we summarize the ways in which the term roughness has been used in the literature on tactile and visual perception, and in the domain of olfaction and gustation. Then, we move on to the crossmodal context, reviewing the literature on the perception of roughness in the audiovisual, audiotactile, and auditory-gustatory/olfactory domains. Finally, we highlight some limitations of the reviewed literature and we outline a number of key directions for future empirical research in roughness perception.

The neural basis of tactile texture perception

Running our fingers across a textured surface gives rise to two types of skin deformations, each transduced by different tactile nerve fibers. Coarse features produce large-scale skin deformations whose spatial configuration is reflected in the spatial pattern of activation of some tactile fibers. Scanning a finely textured surface elicits vibrations in the skin, which in turn evoked temporally patterned responses in other fibers. These two neural codes-spatial and temporal-drive a spectrum of neural response properties in somatosensory cortex: At one extreme, neurons are sensitive to spatial patterns and encode coarse features; at the other extreme, neurons are sensitive to vibrations and encode fine features. While the texture responses of nerve fibers are dependent on scanning speed, those of cortical neurons are less so, giving rise to a speed invariant texture percept. Neurons in high-level somatosensory cortices combine information about texture with information about task variables.

The spatial profile of skin indentation shapes tactile perception across stimulus frequencies

Multiple human sensory systems exhibit sensitivity to spatial and temporal variations of physical stimuli. Vision has evolved to offer high spatial acuity with limited temporal sensitivity, while audition has developed complementary characteristics. Neural coding in touch has been believed to transition from a spatial to a temporal domain in relation to surface scale, such that coarse features (e.g., a braille cell or corduroy texture) are coded as spatially distributed signals, while fine textures (e.g., fine-grit sandpaper) are encoded by temporal variation. However, the interplay between the two domains is not well understood. We studied tactile encoding with a custom-designed pin array apparatus capable of deforming the fingerpad at 5 to 80 Hz in each of 14 individual locations spaced 2.5 mm apart. Spatial variation of skin indentation was controlled by moving each of the pins at the same frequency and amplitude, but with phase delays distributed across the array. Results indicate that such stimuli enable rendering of shape features at actuation frequencies up to 20 Hz. Even at frequencies > 20 Hz, however, spatial variation of skin indentation continues to play a vital role. In particular, perceived roughness is affected by spatial variation within the fingerpad even at 80 Hz. We provide evidence that perceived roughness is encoded via a summary measure of skin displacement. Relative displacements in neighboring pins of less than 10 µm generate skin stretch, which regulates the roughness percept.

Naturalistic stimuli in touch research

Neural mechanisms of touch are typically studied in laboratory settings using robotic or other types of well-controlled devices. Such stimuli are very different from highly complex naturalistic human-to-human touch interactions. The lack of scientifically useful naturalistic stimuli hampers progress, particularly in social touch research. Vision science, on the other hand, has benefitted from inventions such as virtual reality systems that have provided researchers with precision control of naturalistic stimuli. In the field of touch research, producing and manipulating stimuli is particularly challenging due to the complexity of skin mechanics. Here, we review the history of touch neuroscience focusing on the contrast between strictly controlled and naturalistic stimuli, and compare the field to vision science. We discuss new methods that may overcome obstacles with precision-controlled tactile stimuli, and recent successes in naturalistic texture production. In social touch research, precise tracking and measurement of naturalistic human-to-human touch interactions offer exciting new possibilities.

Skin and Mechanoreceptor Contribution to Tactile Input for Perception: A Review of Simulation Models

We review four current computational models that simulate the response of mechanoreceptors in the glabrous skin to tactile stimulation. The aim is to inform researchers in psychology, sensorimotor science and robotics who may want to implement this type of quantitative model in their research. This approach proves relevant to understanding of the interaction between skin response and neural activity as it avoids some of the limitations of traditional measurement methods of tribology, for the skin, and neurophysiology, for tactile neurons. The main advantage is to afford new ways of looking at the combined effects of skin properties on the activity of a population of tactile neurons, and to examine different forms of coding by tactile neurons. Here, we provide an overview of selected models from stimulus application to neuronal spiking response, including their evaluation in terms of existing data, and their applicability in relation to human tactile perception.

Artificial SA-I, RA-I and RA-II/vibrotactile afferents for tactile sensing of texture

Robot touch can benefit from how humans perceive tactile textural information, from the stimulation mode to which tactile channels respond, then the tactile cues and encoding. Using a soft biomimetic tactile sensor (the TacTip) based on the physiology of the dermal-epidermal boundary, we construct two biomimetic tactile channels based on slowly adapting SA-I and rapidly adapting RA-I afferents, and introduce an additional sub-modality for vibrotactile information with an embedded microphone interpreted as an artificial RA-II channel. These artificial tactile channels are stimulated dynamically with a set of 13 artificial rigid textures comprising raised-bump patterns on a rotating drum that vary systematically in roughness. Methods employing spatial, spatio-temporal and temporal codes are assessed for texture classification insensitive to stimulation speed. We find: (i) spatially encoded frictional cues provide a salient representation of texture; (ii) a simple transformation of spatial tactile features to model natural afferent responses improves the temporal coding; and (iii) the harmonic structure of induced vibrations provides a pertinent code for speed-invariant texture classification. Just as human touch relies on an interplay between slowly adapting (SA-I), rapidly adapting (RA-I) and vibrotactile (RA-II) channels, this tripartite structure may be needed for future robot applications with human-like dexterity, from prosthetics to materials testing, handling and manipulation.

Data-Driven Playback of Natural Tactile Texture Via Broadband Friction Modulation

We used broadband electroadhesion to reproduce the friction force profile measured as a finger slid across a textured surface. In doing so, we were also able to reproduce with high fidelity the skin vibrations characteristic of that texture; however, we found that this did not reproduce the original perception. To begin, the reproduction felt weak. In order to maximize perceptual similarity between a real texture and its friction force playback, the vibratory magnitude of the latter must be scaled up on average ≈ 3X for fine texture and ≈ 5X for coarse texture samples. This additional gain appears to correlate with perceived texture roughness. Additionally, even with optimal scaling and high fidelity playback, subjects could identify which of two reproductions corresponds to a real texture with only 71 % accuracy, as compared to 95 % accuracy when using real texture alternatives. We conclude that while tribometry and vibrometry data can be useful for texture classification, they appear to contribute only partially to texture perception. We propose that spatially distributed excitation of skin within the fingerpad may play an additional key role, and may thus be able to contribute to high fidelity texture reproduction.

Texture is encoded in precise temporal spiking patterns in primate somatosensory cortex

Humans are exquisitely sensitive to the microstructure and material properties of surfaces. In the peripheral nerves, texture information is conveyed via two mechanisms: coarse textural features are encoded in spatial patterns of activation that reflect their spatial layout, and fine features are encoded in highly repeatable, texture-specific temporal spiking patterns evoked as the skin moves across the surface. Here, we examined whether this temporal code is preserved in the responses of neurons in somatosensory cortex. We scanned a diverse set of everyday textures across the fingertip of awake macaques while recording the responses evoked in individual cortical neurons. We found that temporal spiking patterns are highly repeatable across multiple presentations of the same texture, with millisecond precision. As a result, texture identity can be reliably decoded from the temporal patterns themselves, even after information carried in the spike rates is eliminated. However, the combination of rate and timing is more informative than either code in isolation. The temporal precision of the texture response is heterogenous across cortical neurons and depends on the submodality composition of their input and on their location along the somatosensory neuraxis. Furthermore, temporal spiking patterns in cortex dilate and contract with decreases and increases in scanning speed, respectively, and this systematic relationship between speed and patterning may contribute to the observed perceptual invariance to speed. Finally, we find that the quality of a texture percept can be better predicted when these temporal patterns are taken into consideration. We conclude that high-precision spike timing complements rate-based signals to encode texture in somatosensory cortex.

Neuroscientists seek to understand how neuronal signals carry information and drive perception. Here, the authors show that millisecond-level spike timing in somatosensory cortex is informative about texture and shapes the evoked sensory experience.

Fabric Classification Using a Finger-Shaped Tactile Sensor via Robotic Sliding

Tactile sensing endows the robots to perceive certain physical properties of the object in contact. Robots with tactile perception can classify textures by touching. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors can also be identified through exploratory robotic movements like sliding. To study the problem of fine texture classification, we design a robotic sliding experiment using a finger-shaped multi-channel capacitive tactile sensor. A feature extraction process is presented to encode the acquired tactile signals (in the form of time series) into a low dimensional (≤7D) feature vector. The feature vector captures the frequency signature of a fabric texture such that fabrics can be classified directly. The experiment includes multiple combinations of sliding parameters, i.e., speed and pressure, to investigate the correlation between sliding parameters and the generated feature space. Results show that changing the contact pressure can greatly affect the significance of the extracted feature vectors. Instead, variation of sliding speed shows no apparent effects. In summary, this paper presents a study of texture classification on fabrics by training a simple k-NN classifier, using only one modality and one type of exploratory motion (sliding). The classification accuracy can reach up to 96%. The analysis of the feature space also implies a potential parametric representation of textures for tactile perception, which could be used for the adaption of motion to reach better classification performance.

Unsupervised learning of haptic material properties

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.

The neural mechanisms of manual dexterity

The hand endows us with unparalleled precision and versatility in our interactions with objects, from mundane activities such as grasping to extraordinary ones such as virtuoso pianism. The complex anatomy of the human hand combined with expansive and specialized neuronal control circuits allows a wide range of precise manual behaviours. To support these behaviours, an exquisite sensory apparatus, spanning the modalities of touch and proprioception, conveys detailed and timely information about our interactions with objects and about the objects themselves. The study of manual dexterity provides a unique lens into the sensorimotor mechanisms that endow the nervous system with the ability to flexibly generate complex behaviour.

Bilaterally Shared Haptic Perception for Human-Robot Collaboration in Grasping Operation

Tactile sensations are crucial for achieving precise operations. A haptic connection between a human operator and a robot has the potential to promote smooth human-robot collaboration (HRC). In this study, we assemble a bilaterally shared haptic system for grasping operations, such as both hands of humans using a bottle cap-opening task. A robot arm controls the grasping force according to the tactile information from the human that opens the cap with a finger-attached acceleration sensor. Then, the grasping force of the robot arm is fed back to the human using a wearable squeezing display. Three experiments are conducted: measurement of the just noticeable difference in the tactile display, a collaborative task with different bottles under two conditions, with and without tactile feedback, including psychological evaluations using a questionnaire, and a collaborative task under an explicit strategy. The results obtained showed that the tactile feedback provided the confidence that the cooperative robot was adjusting its action and improved the stability of the task with the explicit strategy. The results indicate the effectiveness of the tactile feedback and the requirement for an explicit strategy of operators, providing insight into the design of an HRC with bilaterally shared haptic perception.

Building a Navigable Fine Texture Design Space

Friction modulation technology enables the creation of textural effects on flat haptic displays. However, an intuitive and manageably small design space for construction of such haptic textures remains an unfulfilled goal for user interface designers. In this paper, we explore perceptually relevant features of fine texture for use in texture construction and modification. Beginning with simple sinusoidal patterns of friction force that vary in frequency and amplitude, we define irregularity, essentially a variable amount of introduced noise, as a third building block of a texture pattern. We demonstrate using multidimensional scaling that all three parameters are scalable features perceptually distinct from each other. Additionally, participants' verbal descriptions of this 3-dimensional design space provide insight into their intuitive interpretation of the physical parameter changes.

The interaction between motion and texture in the sense of touch

Besides providing information on elementary properties of objects, like texture, roughness, and softness, the sense of touch is also important in building a representation of object movement and the movement of our hands. Neural and behavioral studies shed light on the mechanisms and limits of our sense of touch in the perception of texture and motion, and of its role in the control of movement of our hands. The interplay between the geometrical and mechanical properties of the touched objects, such as shape and texture, the movement of the hand exploring the object, and the motion felt by touch, will be discussed in this article. Interestingly, the interaction between motion and textures can generate perceptual illusions in touch. For example, the orientation and the spacing of the texture elements on a static surface induces the illusion of surface motion when we move our hand on it or can elicit the perception of a curved trajectory during sliding, straight hand movements. In this work we present a multiperspective view that encompasses both the perceptual and the motor aspects, as well as the response of peripheral and central nerve structures, to analyze and better understand the complex mechanisms underpinning the tactile representation of texture and motion. Such a better understanding of the spatiotemporal features of the tactile stimulus can reveal novel transdisciplinary applications in neuroscience and haptics.

Of mice and monkeys: Somatosensory processing in two prominent animal models

Our understanding of the neural basis of somatosensation is based largely on studies of the whisker system of mice and rats and the hands of macaque monkeys. Results across these animal models are often interpreted as providing direct insight into human somatosensation. Work on these systems has proceeded in parallel, capitalizing on the strengths of each model, but has rarely been considered as a whole. This lack of integration promotes a piecemeal understanding of somatosensation. Here, we examine the functions and morphologies of whiskers of mice and rats, the hands of macaque monkeys, and the somatosensory neuraxes of these three species. We then discuss how somatosensory information is encoded in their respective nervous systems, highlighting similarities and differences. We reflect on the limitations of these models of human somatosensation and consider key gaps in our understanding of the neural basis of somatosensation.

Electrotactile Feedback for the Discrimination of Different Surface Textures Using a Microphone

Most commercial prosthetic hands lack closed-loop feedback, thus, a lot of research has been focusing on implementing sensory feedback systems to provide the user with sensory information during activities of daily living. This study evaluates the possibilities of using a microphone and electrotactile feedback to identify different textures. A condenser microphone was used as a sensor to detect the friction sound generated from the contact between different textures and the microphone. The generated signal was processed to provide a characteristic electrical stimulation presented to the participants. The main goal of the processing was to derive a continuous and intuitive transfer function between the microphone signal and stimulation frequency. Twelve able-bodied volunteers participated in the study, in which they were asked to identify the stroked texture (among four used in this study: Felt, sponge, silicone rubber, and string mesh) using only electrotactile feedback. The experiments were done in three phases: (1) Training, (2) with-feedback, (3) without-feedback. Each texture was stroked 20 times each during all three phases. The results show that the participants were able to differentiate between different textures, with a median accuracy of 85%, by using only electrotactile feedback with the stimulation frequency being the only variable parameter.

Adaptive Equalization of Vibrotactile Actuators

In order to provide convincing artifical touch sensations, humans should be presented with high quality haptic stimuli. In the vibrotactile domain, signals are usually displayed through mechanical actuators. Current high quality actuators exhibit a high dynamic range and have the ability to display a wide range of frequencies. However, fundamentally all actuators introduce distortions into the displayed signals. These distortions are usually nonlinear with additive noise components and they can be detrimental to some vibrotactile application scenarios that require high signal playback precision. To neutralize these distortions, we propose a signal-based equalization setup with adaptive filtering. Such a setup is very general and can be applied to any actuator in a straightforward manner. We introduce a novel adaptive filter based on Volterra and bilinear filter models that is nonlinear and more robust than previous approaches. In simulations and experiments, we show that our filter model is able to consistently outperform existing adaptive filter models and equalize vibrotactile actuators efficiently.

Rendering Spatiotemporal Haptic Effects Via the Physics of Waves in the Skin

A major challenge in haptic engineering has been to design practical methods to efficiently stimulate distributed areas of skin. Here, we show how to use a single actuator to generate vibrotactile stimuli which cause sensations of temporally varying spatial extent. Through optical vibrometry methods, we show that vibrational stimuli applied at the fingertip elicit waves in the finger that propagate proximally toward the hand and show how the frequency-dependent damping behavior of skin causes propagation distances to decrease rapidly with increasing frequency of stimulation. Utilizing these results, we design haptic stimuli applied through a single actuator that produces wavefields that expand or contract in size. In a perception experiment, participants accurately (median $>$95%) identified these stimuli as expanding or contracting without prior exposure or training. As a potential application, we used these effects as haptic cues for interactions in virtual reality. We show through a second experiment that the spatiotemporal haptic stimuli were rated as significantly more engaging than conventional vibrotactile stimuli. These findings demonstrate how the physics of waves in skin can be utilized to excite spatiotemporal tactile effects over large surface areas with a single actuator, and inform methods to utilize the effects in practical applications.

Wearable Artificial Fingers With Skin Vibration and Multi-Axis Force Sensors

Tactile sensations are based on stimulation elicited on the skin through mechanical interactions between the skin and an object. Hence, it is important to consider skin properties as well as objects. In this article, we aim to develop wearable artificial fingers for quantitative evaluations reflecting individual differences in human fingers. In a previous study, a wearable skin vibration sensor was attached to artificial fingers and it was demonstrated that the skin vibrations differed according to the dimension of surface ridge and the artificial finger is useful for roughness evaluation. This article improved the artificial finger to measure the contact force and friction in addition to the skin vibration. A small three-axis force sensor was embedded into the base of the finger, and normal and friction forces were estimated via a multi-regression method. Furthermore, artificial fingers with different hardness were prepared and six different textures were used to investigate tactile evaluation. Experimental results showed that the artificial fingers could measure normal and friction forces along with the skin vibration and were useful to evaluate textures. Resulting distributions of the vibration intensity and friction coefficient were different for the soft and hard artificial fingers, indicating the complex influence of skin properties on tactile sensations.

Burst gap code predictions for tactile frequency are valid across the range of perceived frequencies attributed to two distinct tactile channels

Perceived frequency of vibrotactile stimuli can be divided into two distinctive cutaneous sensations-flutter (<60 Hz) and vibratory hum (>60 Hz), mediated by two different tactile afferent types [fast adapting type I (FA1) and fast adapting type II (FA2), respectively]. We recently demonstrated a novel form of neural coding in the human tactile system, where frequency perception of stimulus pulses grouped into periodic bursts in the flutter range depended on the duration of the silent gap between bursts, rather than the periodicity or mean impulse rate. Here, we investigated whether this interburst interval could also explain the perceived frequency of electrocutaneous pulse patterns delivered at frequencies above the flutter range. At stimulus rates of 50 to 190 pulses/s, the burst gap model correctly predicted the perceived frequency. This shows that the burst gap code represents a general coding strategy that spans the range of frequencies traditionally attributed to two different tactile channels.NEW & NOTEWORTHY We present evidence for a generalized frequency processing strategy on tactile afferent inputs that is shared across a broad range of frequencies extending beyond the flutter range, supporting the notion that spike timing has an important role in shaping tactile perception.

Information about contact force and surface texture is mixed in the firing rates of cutaneous afferent neurons

Cutaneous mechanoreceptors in our hands gather information about the objects we handle. Tactile fibers encode mixed information about contact events and object properties. Neural coding in tactile afferents is typically studied by varying a single aspect of tactile stimuli, avoiding the confounds of real-world haptic interactions. We instead record responses of small populations of dorsal root ganglia (DRG) neurons to variable tactile stimuli and find that neurons primarily respond to force, though some texture information can be detected. Tactile nerve fibers convey information about many features of haptic interactions, including the force and speed of contact, as well as the texture and shape of the objects being handled. How we perceive these object features is relatively unaffected by the forces and movements we use when interacting with the object. Because signals related to contact events and object properties are mixed in the responses of tactile fibers, our ability to disentangle these different components of our tactile experience implies that they are demultiplexed as they propagate along the neuraxis. To understand how texture and contact mechanics are encoded together by tactile fibers, we studied the activity of multiple neurons recorded simultaneously in the cervical DRG of two anesthetized rhesus monkeys while textured surfaces were applied to the glabrous skin of the fingers and palm using a handheld probe. A transducer at the tip of the textured probe measured contact forces as tactile stimuli were applied at different locations on the finger-pads and palm. We examined how a sample population of DRG neurons encode force and texture and found that firing rates of individual neurons are modulated by both force and texture. In particular, slowly adapting (SA) neurons were more responsive to force than texture, and rapidly adapting (RA) neurons were more responsive to texture than force. Although force could be decoded accurately throughout the entire contact interval, texture signals were most salient during onset and offset phases of the contact interval.NEW & NOTEWORTHY Cutaneous mechanoreceptors in our hands gather information about the objects we handle. Tactile fibers encode mixed information about contact events and object properties. Neural coding in tactile afferents is typically studied by varying a single aspect of tactile stimuli, avoiding the confounds of real-world haptic interactions. We instead record responses of small populations of DRG neurons to variable tactile stimuli and find that neurons primarily respond to force, though some texture information can be detected.

Sensing the structural characteristics of surfaces: texture encoding by a bottom-dwelling fish

The texture of contacted surfaces influences our perception of the physical environment and modulates behavior. Texture perception and its neural encoding mechanisms have traditionally been studied in the primate hand, yet animals of all types live in richly textured environments and regularly interact with textured surfaces. Here we explore texture sensation in a different type of vertebrate limb by investigating touch and potential texture encoding mechanisms in the pectoral fins of fishes, the forelimb homologs. We investigated the pectoral fins of the round goby (Neogobius melanostomus), a bottom-dwelling species that lives on substrate types of varying roughness and whose fins frequently contact the bottom. Analysis shows that the receptive field sizes of fin ray afferents are small and afferents exhibit response properties to tactile motion that are consistent with those of primates and other animals studied previously. In response to a periodic stimulus (coarse gratings), afferents phase lock to the stimulus temporal frequency and thus can provide information about surface texture. These data demonstrate that fish can have the capability to sense the tactile features of their near range physical environment with fins.

Tactile perception of randomly rough surfaces

Most everyday surfaces are randomly rough and self-similar on sufficiently small scales. We investigated the tactile perception of randomly rough surfaces using 3D-printed samples, where the topographic structure and the statistical properties of scale-dependent roughness were varied independently. We found that the tactile perception of similarity between surfaces was dominated by the statistical micro-scale roughness rather than by their topographic resemblance. Participants were able to notice differences in the Hurst roughness exponent of 0.2, or a difference in surface curvature of 0.8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {mm}^{-1}$$\end{document}mm-1 for surfaces with curvatures between 1 and 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {mm}^{-1}$$\end{document}mm-1. In contrast, visual perception of similarity between color-coded images of the surface height was dominated by their topographic resemblance. We conclude that vibration cues from roughness at the length scale of the finger ridge distance distract the participants from including the topography into the judgement of similarity. The interaction between surface asperities and fingertip skin led to higher friction for higher micro-scale roughness. Individual friction data allowed us to construct a psychometric curve which relates similarity decisions to differences in friction. Participants noticed differences in the friction coefficient as small as 0.035 for samples with friction coefficients between 0.34 and 0.45.

Temporal patterns in electrical nerve stimulation: Burst gap code shapes tactile frequency perception

We have previously described a novel temporal encoding mechanism in the somatosensory system, where mechanical pulses grouped into periodic bursts create a perceived tactile frequency based on the duration of the silent gap between bursts, rather than the mean rate or the periodicity. This coding strategy may offer new opportunities for transmitting information to the brain using various sensory neural prostheses and haptic interfaces. However, it was not known whether the same coding mechanisms apply when using electrical stimulation, which recruits a different spectrum of afferents. Here, we demonstrate that the predictions of the burst gap coding model for frequency perception apply to burst stimuli delivered with electrical pulses, re-emphasising the importance of the temporal structure of spike patterns in neural processing and perception of tactile stimuli. Reciprocally, the electrical stimulation data confirm that the results observed with mechanical stimulation do indeed depend on neural processing mechanisms in the central nervous system, and are not due to skin mechanical factors and resulting patterns of afferent activation.

Auditory and tactile frequency representations are co-embedded in modality-defined cortical sensory systems

Sensory information is represented and elaborated in hierarchical cortical systems that are thought to be dedicated to individual sensory modalities. This traditional view of sensory cortex organization has been challenged by recent evidence of multimodal responses in primary and association sensory areas. Although it is indisputable that sensory areas respond to multiple modalities, it remains unclear whether these multimodal responses reflect selective information processing for particular stimulus features. Here, we used fMRI adaptation to identify brain regions that are sensitive to the temporal frequency information contained in auditory, tactile, and audiotactile stimulus sequences. A number of brain regions distributed over the parietal and temporal lobes exhibited frequency-selective temporal response modulation for both auditory and tactile stimulus events, as indexed by repetition suppression effects. A smaller set of regions responded to crossmodal adaptation sequences in a frequency-dependent manner. Despite an extensive overlap of multimodal frequency-selective responses across the parietal and temporal lobes, representational similarity analysis revealed a cortical "regional landscape" that clearly reflected distinct somatosensory and auditory processing systems that converged on modality-invariant areas. These structured relationships between brain regions were also evident in spontaneous signal fluctuation patterns measured at rest. Our results reveal that multimodal processing in human cortex can be feature-specific and that multimodal frequency representations are embedded in the intrinsically hierarchical organization of cortical sensory systems.

Secondary somatosensory area is involved in vibrotactile temporal-structure processing: MEG analysis of slow cortical potential shifts in humans

Purpose: Temporal-structure discrimination is an essential dimension of tactile processing. Exploring object surface by touch generates vibrotactile input with various temporal dynamics, which gives diversity to tactile percepts. Here, we examined whether slow cortical potential shifts (SCPs) (<1 Hz) evoked by long vibrotactile stimuli can reflect active temporal-structure processing.Materials and methods: Vibrotactile-evoked magnetic brain responses were recorded in 10 right-handed healthy volunteers using a piezoelectric-based stimulator and whole-head magnetoencephalography. A series of vibrotactile train stimuli with various temporal structures were delivered to the right index finger. While all trains consisted of identical number (15) of stimuli delivered within a fixed duration (1500 ms), temporal structures were varied by modulating inter-stimulus intervals (ISIs). Participants judged regularity/irregularity of ISI for each train in the active condition, whereas they ignored the stimuli while performing a visual distraction task in the passive condition. We analysed the spatiotemporal features of SCPs and their behaviour using the minimum norm estimates with the dynamic statistical parametric mapping.Results: SCPs were localized to contralateral primary somatosensory area (S1), contralateral superior temporal gyrus, and contralateral as well as ipsilateral secondary somatosensory areas (S2). A significant enhancement of SCPs was observed in the ipsilateral S2 (S2i) in the active condition, whereas such effects were absent in the other regions. We also found a significant larger amplitude difference between the regular- and irregular-stimulus evoked S2i responses during the active condition than during the passive condition.Conclusions: This study suggests that S2 subserves the temporal dimension of vibrotactile processing.

Introduction of a New In-Situ Measurement System for the Study of Touch-Feel Relevant Surface Properties

The touch-feel sensation of product surfaces arouses growing interest in various industry branches. To entangle the underlying physical and material parameters responsible for a specific touch-feel sensation, a new measurement system has been developed. This system aims to record the prime physical interaction parameters at a time, which is considered a necessary prerequisite for a successful physical description of the haptic sensation. The measurement setup enables one to measure the dynamic coefficient of friction, the macroscopic contact area of smooth and rough surfaces, the angle enclosed between the human finger and the soft-touch surfaces and the vibrations induced in the human finger during relative motion at a time. To validate the measurement stand, a test series has been conducted on two soft-touch surfaces of different roughness. While the individual results agree well with the literature, their combination revealed new insights. Finally, the investigation of the haptics of polymer coatings with the presented measuring system should facilitate the design of surfaces with tailor-made touch-feel properties.

Effect of scanning speed on texture-elicited vibrations

To sense the texture of a surface, we run our fingers across it, which leads to the elicitation of skin vibrations that depend both on the surface and on exploratory parameters, particularly scanning speed. The transduction and processing of these vibrations mediate the ability to discern fine surface features. The objective of the present study is to characterize the effect of changes in scanning speed on texture-elicited vibrations to better understand how the exploratory movements shape the neuronal representation of texture. To this end, we scanned a variety of textures across the fingertip of human participants at a variety of speeds (10-160 mm s^-1) while measuring the resulting vibrations using a laser Doppler vibrometer. First, we found that the intensity of the vibrations-as indexed by root-mean-square velocity-increases with speed but that the skin displacement remains constant. Second, we found that the frequency composition of the vibrations shifts systematically to higher frequencies with increases in scanning speed. Finally, we show that the speed-dependent shift in frequency composition accounts for the speed-dependent change in intensity.

Compression of dynamic tactile information in the human hand

A key problem in the study of the senses is to describe how sense organs extract perceptual information from the physics of the environment. We previously observed that dynamic touch elicits mechanical waves that propagate throughout the hand. Here, we show that these waves produce an efficient encoding of tactile information. The computation of an optimal encoding of thousands of naturally occurring tactile stimuli yielded a compact lexicon of primitive wave patterns that sparsely represented the entire dataset, enabling touch interactions to be classified with an accuracy exceeding 95%. The primitive tactile patterns reflected the interplay of hand anatomy with wave physics. Notably, similar patterns emerged when we applied efficient encoding criteria to spiking data from populations of simulated tactile afferents. This finding suggests that the biomechanics of the hand enables efficient perceptual processing by effecting a preneuronal compression of tactile information.

A Tactile Virtual Reality for the Study of Active Somatosensation

Natural exploration of textures involves active sensing, i.e., voluntary movements of tactile sensors (e.g., human fingertips or rodent whiskers) across a target surface. Somatosensory input during moving tactile sensors varies according to both the movement and the surface texture. Combining motor and sensory information, the brain is capable of extracting textural features of the explored surface. Despite the ecological relevance of active sensing, psychophysical studies on active touch are largely missing. One reason for the lack of informative studies investigating active touch is the considerable challenge of assembling an appropriate experimental setup. A possible solution might be in the realm of virtual tactile reality that provides tactile finger stimulation depending on the position of the hand and the simulated texture of a target surface. In addition to rigorous behavioral studies, the investigation of the neuronal mechanisms of active tactile sensing in humans is highly warranted, requiring neurophysiological experiments using electroencephalography (EEG), magnetoencephalography (MEG) and/or functional magnetic resonance imaging (fMRI). However, current neuroimaging techniques impose specific requirements on the tactile stimulus delivery equipment in terms of compatibility with the neurophysiological methods being used. Here, we present a user-friendly, MEG compatible, tactile virtual reality simulator. The simulator consists of a piezo-electric tactile stimulator capable of independently protruding 16 plastic pistons of 1 mm diameter arranged in a 4 × 4 matrix. The stimulator delivers a spatial pattern of tactile stimuli to the tip of a finger depending on the position of the finger moving across a 2-dimensional plane. In order to demonstrate the functionality of the tactile virtual reality, we determined participants’ detection thresholds in active and passive touch conditions. Thresholds in both conditions were higher than reported in the literature. It could well be that the processing of the piston-related stimulation was masked by the sensory input generated by placing the finger on the scanning probe. More so, the thresholds for both the active and passive tasks did not differ significantly. In further studies, the noise introduced by the stimulator in neuromagnetic recordings was quantified and somatosensory evoked fields for active and passive touch were recorded. Due to the compatibility of the stimulator with neuroimaging techniques such as MEG, and based on the feasibility to record somatosensory-related neuromagnetic brain activity the apparatus has immense potential for the exploration of the neural underpinnings of active tactile perception.

Spectral hallmark of auditory-tactile interactions in the mouse somatosensory cortex

To synthesize a coherent representation of the external world, the brain must integrate inputs across different types of stimuli. Yet the mechanistic basis of this computation at the level of neuronal populations remains obscure. Here, we investigate tactile-auditory integration using two-photon Ca2+ imaging in the mouse primary (S1) and secondary (S2) somatosensory cortices. Pairing sound with whisker stimulation modulates tactile responses in both S1 and S2, with the most prominent modulation being robust inhibition in S2. The degree of inhibition depends on tactile stimulation frequency, with lower frequency responses the most severely attenuated. Alongside these neurons, we identify sound-selective neurons in S2 whose responses are inhibited by high tactile frequencies. These results are consistent with a hypothesized local mutually-inhibitory S2 circuit that spectrally selects tactile versus auditory inputs. Our findings enrich mechanistic understanding of multisensory integration and suggest a key role for S2 in combining auditory and tactile information.

Phase Difference Between Normal and Shear Forces During Tactile Exploration Represents Textural Features

Contact forces and skin deformation induced during tactile exploration have been investigated in the frequency domain to understand finger-material interaction. Their power spectra are one of the representative feature quantities that have been associated with the surface properties of materials. However, thus far, the phase information of these quantities has not been studied. Furthermore, most previous studies focused on uni-dimensional signals such as forces in either the normal or tangential directions. We investigated the phase differences between normal and shear forces induced during tactile exploration. The results showed that the phase differences between these two axial forces differ among materials and that they exhibit features different from their power spectra. These results indicate that the phase difference between two axial forces should be taken into account to understand the finger-material interactions during tactile exploration.

Closed Loop Application of Electroadhesion for Increased Precision in Texture Rendering

Tactile displays based on friction modulation offer wide-bandwidth forces rendered directly on the fingertip. However, due to a number of touch conditions (e.g., normal force, skin hydration) that result in variations in the friction force and the strength of modulation effect, the precision of the force rendering remains limited. In this paper we demonstrate a closed-loop electroadhesion method for precise playback of friction force profiles on a human finger and we apply this method to the tactile rendering of several textiles encountered in everyday life.

Spinal signalling of C-fiber mediated pleasant touch in humans

C-tactile afferents form a distinct channel that encodes pleasant tactile stimulation. Prevailing views indicate they project, as with other unmyelinated afferents, in lamina I-spinothalamic pathways. However, we found that spinothalamic ablation in humans, whilst profoundly impairing pain, temperature and itch, had no effect on pleasant touch perception. Only discriminative touch deficits were seen. These findings preclude privileged C-tactile-lamina I-spinothalamic projections and imply integrated hedonic and discriminative spinal processing from the body.

Auditory Frequency Representations in Human Somatosensory Cortex

Recent studies have challenged the traditional notion of modality-dedicated cortical systems by showing that audition and touch evoke responses in the same sensory brain regions. While much of this work has focused on somatosensory responses in auditory regions, fewer studies have investigated sound responses and representations in somatosensory regions. In this functional magnetic resonance imaging (fMRI) study, we measured BOLD signal changes in participants performing an auditory frequency discrimination task and characterized activation patterns related to stimulus frequency using both univariate and multivariate analysis approaches. Outside of bilateral temporal lobe regions, we observed robust and frequency-specific responses to auditory stimulation in classically defined somatosensory areas. Moreover, using representational similarity analysis to define the relationships between multi-voxel activation patterns for all sound pairs, we found clear similarity patterns for auditory responses in the parietal lobe that correlated significantly with perceptual similarity judgments. Our results demonstrate that auditory frequency representations can be distributed over brain regions traditionally considered to be dedicated to somatosensation. The broad distribution of auditory and tactile responses over parietal and temporal regions reveals a number of candidate brain areas that could support general temporal frequency processing and mediate the extensive and robust perceptual interactions between audition and touch.

Controlled emotional tactile stimulation during functional magnetic resonance imaging and electroencephalography

BACKGROUND

Tactile stimulation used to induce emotional responses is often not well-controlled. Replicating the same tactile stimulations across studies is difficult, compared to replicating visual and auditory modalities, which have standardized stimulus sets. Standardizing a stimulation method by replicating stimuli across studies is necessary to further elucidate emotional responses in neuroscience research using tactile stimulation.

THE NEW METHOD

We developed a tactile stimulation device. The device's ultrasonic motor and optical force sensor have the following criteria: (1) controls the physical property of stimuli, pressure, and stroking speed; (2) measures actual touch timing; (3) is safe to use in a magnetic resonance imaging (MRI) scanner; and (4) produces low noise in electroencephalography (EEG) and MRI.

RESULTS

The noise level of the device's drive was sufficiently low. For the EEG experiment, we successfully used signal processing to diminish the commercial power supply noise. For functional MRI (fMRI) scans, we found <5% signal loss occurred during device rotation.

COMPARISON WITH EXISTING METHOD(S)

We found no previous report about the noise level of a tactile stimulation device used to induce emotional responses during EEG and fMRI recordings. The signal loss rate was comparable with that of other robotic devices used in MRI scanners. Emotional feelings induced by this stimulation method were comparable with those elicited in other sensory modalities.

CONCLUSIONS

The developed device could be used for cognitive-affective neuroscience research when conducting EEG and fMRI scans. The device should aid in standardizing affective tactile stimulation for research in psychology and cognitive neuroscience.

Feeling fooled: Texture contaminates the neural code for tactile speed

Motion is an essential component of everyday tactile experience: most manual interactions involve relative movement between the skin and objects. Much of the research on the neural basis of tactile motion perception has focused on how direction is encoded, but less is known about how speed is. Perceived speed has been shown to be dependent on surface texture, but previous studies used only coarse textures, which span a restricted range of tangible spatial scales and provide a limited window into tactile coding. To fill this gap, we measured the ability of human observers to report the speed of natural textures—which span the range of tactile experience and engage all the known mechanisms of texture coding—scanned across the skin. In parallel experiments, we recorded the responses of single units in the nerve and in the somatosensory cortex of primates to the same textures scanned at different speeds. We found that the perception of speed is heavily influenced by texture: some textures are systematically perceived as moving faster than are others, and some textures provide a more informative signal about speed than do others. Similarly, the responses of neurons in the nerve and in cortex are strongly dependent on texture. In the nerve, although all fibers exhibit speed-dependent responses, the responses of Pacinian corpuscle–associated (PC) fibers are most strongly modulated by speed and can best account for human judgments. In cortex, approximately half of the neurons exhibit speed-dependent responses, and this subpopulation receives strong input from PC fibers. However, speed judgments seem to reflect an integration of speed-dependent and speed-independent responses such that the latter help to partially compensate for the strong texture dependence of the former.

Our ability to sense the speed at which a surface moves across our skin is highly unreliable and depends on the texture of the surface. This study shows that speed illusions can be predicted from the responses of a specific population of nerve fibers and of their downstream targets; because the skin is too sparsely innervated to compute tactile speed accurately, the nervous system relies on a heuristic to estimate it.

Somatosensory interactions reveal feature-dependent computations

Our ability to perceive and discriminate textures is based on the processing of high-frequency vibrations generated on the fingertip as it scans across a surface. Although much is known about the processing of vibration amplitude and frequency information when cutaneous stimulation is experienced at a single location on the body, how these stimulus features are processed when touch occurs at multiple locations is poorly understood. We evaluated participants’ ability to discriminate tactile cues (100–300 Hz) on one hand while they ignored distractor cues experienced on their other hand. We manipulated the relative positions of the hands to characterize how limb position influenced cutaneous touch interactions. In separate experiments, participants judged either the frequency or intensity of mechanical vibrations. We found that vibrations experienced on one hand always systematically modulated the perception of vibrations on the other hand. Notably, bimanual interaction patterns and their sensitivity to hand locations differed according to stimulus feature. Somatosensory interactions in intensity perception were only marked by attenuation that was invariant to hand position manipulations. In contrast, interactions in frequency perception consisted of both bias and sensitivity changes that were more pronounced when the hands were held in close proximity. We implemented models to infer the neural computations that mediate somatosensory interactions in the intensity and frequency dimensions. Our findings reveal obligatory and feature-dependent somatosensory interactions that may be supported by both feature-specific and feature-general operations.

NEW & NOTEWORTHY Little is known about the neural computations mediating feature-specific sensory interactions between the hands. We show that vibrations experienced on one hand systematically modulate the perception of vibrations felt on the other hand. Critically, interaction patterns and their dependence on the relative positions of the hands differed depending on whether participants judged vibration intensity or frequency. These results, which we recapitulate with models, imply that somatosensory interactions are mediated by feature-dependent neural computations.

High-dimensional representation of texture in somatosensory cortex of primates

The sense of touch affords a remarkable sensitivity to the microstructure of surfaces, affording us the ability to sense elements ranging in size from tens of nanometers to tens of millimeters. The hand sends signals about texture to the brain using three classes of nerve fibers through two neural codes: coarse features in spatial patterns of activation and fine features in precise temporal spiking patterns. In this study, we show that these nerve signals culminate in a complex, high-dimensional representation of texture in somatosensory cortex, whose structure can account for the structure of texture perception. This complexity arises from the neurons that act as idiosyncratic detectors of spatial and/or temporal motifs in the afferent input.

In the somatosensory nerves, the tactile perception of texture is driven by spatial and temporal patterns of activation distributed across three populations of afferents. These disparate streams of information must then be integrated centrally to achieve a unified percept of texture. To investigate the representation of texture in somatosensory cortex, we scanned a wide range of natural textures across the fingertips of rhesus macaques and recorded the responses evoked in Brodmann’s areas 3b, 1, and 2. We found that texture identity is reliably encoded in the idiosyncratic responses of populations of cortical neurons, giving rise to a high-dimensional representation of texture. Cortical neurons fall along a continuum in their sensitivity to fine vs. coarse texture, and neurons at the extrema of this continuum seem to receive their major input from different afferent populations. Finally, we show that cortical responses can account for several aspects of texture perception in humans.

Individual differences in cognitive processing for roughness rating of fine and coarse textures

Previous studies have demonstrated that skin vibration is an important factor affecting the roughness perception of fine textures. For coarse textures, the determining physical factor is much less clear and there are indications that this might be participant-dependent. In this paper, we focused on roughness perception of both coarse and fine textures of different materials (glass particle surfaces and sandpapers). We investigated the relationship between subjective roughness ratings and three physical parameters (skin vibration, friction coefficient, and particle size) within a group of 30 participants. Results of the glass particle surfaces showed both spatial information (particle size) and temporal information (skin vibration) had a high correlation with subjective roughness ratings. The former correlation was slightly but significantly higher than the latter. The results also indicated different weights of temporal information and spatial information for roughness ratings among participants. Roughness ratings of a different material (sandpaper versus glass particles) could be either larger, similar or smaller, indicating differences among individuals. The best way to describe our results is that in their perceptual evaluation of roughness, different individuals weight temporal information, spatial information, and other mechanical properties differently.

Reciprocal Interactions Between Audition and Touch in Flutter Frequency Perception

In both audition and touch, sensory cues comprising repeating events are perceived either as a continuous signal or as a stream of temporally discrete events (flutter), depending on the events' repetition rate. At high repetition rates (>100 Hz), auditory and tactile cues interact reciprocally in pitch processing. The frequency of a cue experienced in one modality systematically biases the perceived frequency of a cue experienced in the other modality. Here, we tested whether audition and touch also interact in the processing of low-frequency stimulation. We also tested whether multisensory interactions occurred if the stimulation in one modality comprised click trains and the stimulation in the other modality comprised amplitude-modulated signals. We found that auditory cues bias touch and tactile cues bias audition on a flutter discrimination task. Even though participants were instructed to attend to a single sensory modality and ignore the other cue, the flutter rate in the attended modality is perceived to be similar to that of the distractor modality. Moreover, we observed similar interaction patterns regardless of stimulus type and whether the same stimulus types were experienced by both senses. Combined with earlier studies, our results suggest that the nervous system extracts and combines temporal rate information from multisensory environmental signals, regardless of stimulus type, in both the low- and high temporal frequency domains. This function likely reflects the importance of temporal frequency as a fundamental feature of our multisensory experience.

Multisensory perceptual interactions between higher-order temporal frequency signals

Naturally occurring signals in audition and touch can be complex and marked by temporal variations in frequency and amplitude. Auditory frequency sweep processing has been studied extensively; however, much less is known about sweep processing in touch because studies have primarily focused on the perception of simple sinusoidal vibrations. Given the extensive interactions between audition and touch in the frequency processing of pure tone signals, we reasoned that these senses might also interact in the processing of higher-order frequency representations like sweeps. In a series of psychophysical experiments, we characterized the influence of auditory distractors on the ability of participants to discriminate tactile frequency sweeps. Auditory frequency sweeps systematically biased the tactile perception of sweep direction. Importantly, auditory cues exerted little influence on tactile sweep direction perception when the sounds and vibrations occupied different absolute frequency ranges or when the sounds consisted of intensity sweeps. Thus, audition and touch interact in frequency sweep perception in a frequency- and feature-specific manner. Our results demonstrate that audio-tactile interactions are not constrained to the processing of simple sinusoids. Because higher-order frequency representations may be synthesized from simpler representations, our findings imply that multisensory interactions in the temporal frequency domain span multiple hierarchical levels in sensory processing. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Neural Basis of Touch and Proprioception in Primate Cortex

The sense of proprioception allows us to keep track of our limb posture and movements and the sense of touch provides us with information about objects with which we come into contact. In both senses, mechanoreceptors convert the deformation of tissues-skin, muscles, tendons, ligaments, or joints-into neural signals. Tactile and proprioceptive signals are then relayed by the peripheral nerves to the central nervous system, where they are processed to give rise to percepts of objects and of the state of our body. In this review, we first examine briefly the receptors that mediate touch and proprioception, their associated nerve fibers, and pathways they follow to the cerebral cortex. We then provide an overview of the different cortical areas that process tactile and proprioceptive information. Next, we discuss how various features of objects-their shape, motion, and texture, for example-are encoded in the various cortical fields, and the susceptibility of these neural codes to attention and other forms of higher-order modulation. Finally, we summarize recent efforts to restore the senses of touch and proprioception by electrically stimulating somatosensory cortex. © 2018 American Physiological Society. Compr Physiol 8:1575-1602, 2018.