Neuronal correlates of tactile speed in primary somatosensory cortex

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.

Dynamic causal modeling of sensorimotor networks elicited by saltatory pneumotactile velocity in the glabrous hand

BACKGROUND AND PURPOSE

The effective connectivity of neuronal networks during passive saltatory pneumotactile velocity stimulation to the glabrous hand with different velocities is still unknown. The present study investigated the effectivity connectivity elicited by saltatory pneumotactile velocity arrays placed on the glabrous hand at three velocities (5, 25, and 65 cm/second).

METHODS

Dynamic causal modeling (DCM) was used on functional MRI data sampled from 20 neurotypical adults. Five brain regions, including the left primary somatosensory (SI) and motor (M1) cortices, bilateral secondary somatosensory (SII) cortices, and right cerebellar lobule VI, were used to build model space.

RESULTS

Three velocities (5, 25, and 65 cm/second) of saltatory pneumotactile stimuli were processed in both serial and parallel modes within the sensorimotor networks. The medium velocity of 25 cm/second modulated forward interhemispheric connection from the contralateral SII to the ipsilateral SII. Pneumotactile stimulation at the medium velocity of 25 cm/second also influenced contralateral M1 through contralateral SI. Finally, the right cerebellar lobule VI was involved in the sensorimotor networks.

CONCLUSIONS

Our DCM results suggest the coexistence of both serial and parallel processing for saltatory pneumotactile velocity stimulation. Significant contralateral M1 modulation promotes the prospect that the passive saltatory pneumotactile velocity arrays can be used to design sensorimotor rehabilitation protocols to activate M1. The effective connectivity from the right cerebellar lobule VI to other cortical regions demonstrates the cerebellum's role in the sensorimotor networks through feedforward and feedback neuronal pathways.

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.

Neurophysiology of slip sensation and grip reaction: insights for hand prosthesis control of slippage

Sensory feedback is pivotal for a proficient dexterity of the hand. By modulating the grip force in function of the quick and not completely predictable change of the load force, grabbed objects are prevented to slip from the hand. Slippage control is an enabling achievement to all manipulation abilities. However, in hand prosthetics, the performance of even the most innovative research solutions proposed so far to control slippage remain distant from the human physiology. Indeed, slippage control involves parallel and compensatory activation of multiple mechanoceptors, spinal and supraspinal reflexes, and higher-order voluntary behavioral adjustments. In this work, we reviewed the literature on physiological correlates of slippage to propose a three-phases model for the slip sensation and reaction. Furthermore, we discuss the main strategies employed so far in the research studies that tried to restore slippage control in amputees. In the light of the proposed three-phase slippage model and from the weaknesses of already implemented solutions, we proposed several physiology-inspired solutions for slippage control to be implemented in the future hand prostheses. Understanding the physiological basis of slip detection and perception and implementing them in novel hand feedback system would make prosthesis manipulation more efficient and would boost its perceived naturalness, fostering the sense of agency for the hand movements.

"Shooting pain" in lumbar radiculopathy and trigeminal neuralgia, and ideas concerning its neural substrates

Patients with radicular low back pain (radicular LBP, sciatica) frequently describe their pain as "shooting" or "radiating." The dictionary meaning of these words implies rapid movement, and indeed, many sufferers report feeling pain moving rapidly from the lower back or buttock into the leg. But, others do not. Moreover, the sensation of movement is paradoxical; it is neither predicted nor accounted for by current ideas about the pathophysiology of radicular LBP. We have used a structured questionnaire to evaluate the sensory qualities associated with "shooting" and "radiating" in 155 patients, 98 with radicular LBP and 57 with trigeminal neuralgia, a second chronic pain condition in which shooting/radiating are experienced. Results indicated a spectrum of different sensations in different people. Although many sciatica patients reported rapid downward movement of their pain, even more reported downward expansion of the area of pain, some reported upward movement, and for some, there was no spatial dynamic at all. The velocity of movement or expansion was also variable. By cross-referencing sensations experienced in the sciatica and trigeminal neuralgia cohorts with known signal processing modes in the somatosensory system, we propose testable hypotheses concerning the pathophysiology of the various vectorial sensations reported, their direction and velocity, and the structures in which they are generated. Systematic evaluation of qualitative features of "shooting" and "radiating" pain at the time of diagnosis can shed light on the pain mechanism in the individual patient and perhaps contribute to a better therapeutic outcomes.

Multiplexed Processing of Vibrotactile Information in the Mouse Primary Somatosensory Cortex

The primary somatosensory (S1) cortex plays a key role in distinguishing different sensory stimuli. Vibrotactile touch information is conveyed from the periphery to the S1 cortex through three major classes of mechanoreceptors: slowly adapting type 1 (SA1), rapidly adapting (RA), and Pacinian (PC) afferents. It has been a long-standing question whether specific populations in the S1 cortex preserve the peripheral segregation by the afferent submodalities. Here, we investigated whether S1 neurons exhibit specific responses to two distinct vibrotactile stimuli, which excite different types of mechanoreceptors (e.g., SA1 and PC afferents). Using in vivo two-photon microscopy and genetically encoded calcium indicator, GCaMP6s, we recorded calcium activities of S1 L2/3 neurons. At the same time, static (<1 Hz) and dynamic (150 Hz) vibrotactile stimuli, which are known to excite SA1 and PC, respectively, were pseudorandomly applied to the right hind paw in lightly anesthetized mice. We found that most active S1 neurons responded to both static and dynamic stimuli, but more than half of them showed preferred responses to either type of stimulus. Only a small fraction of the active neurons exhibited specific responses to either static or dynamic stimuli. However, the S1 population activity patterns by the two stimuli were markedly distinguished. These results indicate that the vibrotactile inputs driven by excitation of distinct submodalities are converged on the single cells of the S1 cortex, but are well discriminated by population activity patterns composed of neurons that have a weighted preference for each type of stimulus.

Tactile direction discrimination in humans after stroke

Sensing movements across the skin surface is a complex task for the tactile sensory system, relying on sophisticated cortical processing. Functional MRI has shown that judgements of the direction of tactile stimuli moving across the skin are processed in distributed cortical areas in healthy humans. To further study which brain areas are important for tactile direction discrimination, we performed a lesion study, examining a group of patients with first-time stroke. We measured tactile direction discrimination in 44 patients, bilaterally on the dorsum of the hands and feet, within 2 weeks (acute), and again in 28 patients 3 months after stroke. The 3-month follow-up also included a structural MRI scan for lesion delineation. Fifty-nine healthy participants were examined for normative direction discrimination values. We found abnormal tactile direction discrimination in 29/44 patients in the acute phase, and in 21/28 3 months after stroke. Lesions that included the opercular parietal area 1 of the secondary somatosensory cortex, the dorsolateral prefrontal cortex or the insular cortex were always associated with abnormal tactile direction discrimination, consistent with previous functional MRI results. Abnormal tactile direction discrimination was also present with lesions including white matter and subcortical regions. We have thus delineated cortical, subcortical and white matter areas important for tactile direction discrimination function. The findings also suggest that tactile dysfunction is common following stroke.

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.

Functional Connectivity Evoked by Orofacial Tactile Perception of Velocity

The cortical representations of orofacial pneumotactile stimulation involve complex neuronal networks, which are still unknown. This study aims to identify the characteristics of functional connectivity (FC) evoked by three different saltatory velocities over the perioral and buccal surface of the lower face using functional magnetic resonance imaging in twenty neurotypical adults. Our results showed a velocity of 25 cm/s evoked stronger connection strength between the right dorsolateral prefrontal cortex and the right thalamus than a velocity of 5 cm/s. The decreased FC between the right secondary somatosensory cortex and right posterior parietal cortex for 5-cm/s velocity versus all three velocities delivered simultaneously (“All ON”) and the increased FC between the right thalamus and bilateral secondary somatosensory cortex for 65 cm/s vs “All ON” indicated that the right secondary somatosensory cortex might play a role in the orofacial tactile perception of velocity. Our results have also shown different patterns of FC for each seed (bilateral primary and secondary somatosensory cortex) at various velocity contrasts (5 vs 25 cm/s, 5 vs 65 cm/s, and 25 vs 65 cm/s). The similarities and differences of FC among three velocities shed light on the neuronal networks encoding the orofacial tactile perception of velocity.

Differential effects of the mode of touch, active and passive, on experience-driven plasticity in the S1 cutaneous digit representation of adult macaque monkeys

This study compared the receptive field (RF) properties and firing rates of neurons in the cutaneous hand representation of primary somatosensory cortex (areas 3b, 1, and 2) of 9 awake, adult macaques that were intensively trained in a texture discrimination task using active touch (fingertips scanned over the surfaces using a single voluntary movement), passive touch (surfaces displaced under the immobile fingertips), or both active and passive touch. Two control monkeys received passive exposure to the same textures in the context of a visual discrimination task. Training and recording extended over 1–2 yr per animal. All neurons had a cutaneous receptive field (RF) that included the tips of the stimulated digits (D3 and/or D4). In area 3b, RFs were largest in monkeys trained with active touch, smallest in those trained with passive touch, and intermediate in those trained with both; i.e., the mode of touch differentially modified the cortical representation of the stimulated fingers. The same trends were seen in areas 1 and 2, but the changes were not significant, possibly because a second experience-driven influence was seen in areas 1 and 2, but not in area 3b: smaller RFs with passive exposure to irrelevant tactile inputs compared with recordings from one naive hemisphere. We suggest that added feedback during active touch and higher cortical firing rates were responsible for the larger RFs with behavioral training; this influence was tempered by periods of more restricted sensory feedback during passive touch training in the active + passive monkeys.

NEW & NOTEWORTHY We studied experience-dependent sensory cortical plasticity in relation to tactile discrimination of texture using active and/or passive touch. We showed that neuronal receptive fields in primary somatosensory cortex, especially area 3b, are largest in monkeys trained with active touch, smallest in those trained with passive touch, and intermediate in those trained using both modes of touch. Prolonged, irrelevant tactile input had the opposite influence in areas 1 and 2, favoring smaller receptive fields.

Emergence of an Invariant Representation of Texture in Primate Somatosensory Cortex

A major function of sensory processing is to achieve neural representations of objects that are stable across changes in context and perspective. Small changes in exploratory behavior can lead to large changes in signals at the sensory periphery, thus resulting in ambiguous neural representations of objects. Overcoming this ambiguity is a hallmark of human object recognition across sensory modalities. Here, we investigate how the perception of tactile texture remains stable across exploratory movements of the hand, including changes in scanning speed, despite the concomitant changes in afferent responses. To this end, we scanned a wide range of everyday textures across the fingertips of rhesus macaques at multiple speeds and recorded the responses evoked in tactile nerve fibers and somatosensory cortical neurons (from Brodmann areas 3b, 1, and 2). We found that individual cortical neurons exhibit a wider range of speed-sensitivities than do nerve fibers. The resulting representations of speed and texture in cortex are more independent than are their counterparts in the nerve and account for speed-invariant perception of texture. We demonstrate that this separation of speed and texture information is a natural consequence of previously described cortical computations.

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.

Illusory Motion Reversal in Touch

Psychophysical visual experiments have shown illusory motion reversal (IMR), in which the perceived direction of motion is the opposite of its actual direction. The tactile form of this illusion has also been reported. However, it remains unclear which stimulus characteristics affect the magnitude of IMR. We closely examined the effect of stimulus characteristics on IMR by presenting moving sinusoid gratings and random-dot patterns to 10 participants' fingerpads at different spatial periods, speeds, and indentation depths. All participants perceived a motion direction opposite to the veridical direction some of the time. The illusion was more prevalent at spatial periods of 1 and 2 mm and at extreme speeds of 20 and 320 mm/s. We observed stronger IMR for gratings and much weaker IMR for a random-dot pattern, indicating that edge orientation might be a major contributor to this illusion. These results show that the optimal parameters for IMR are consistent with the characteristics of motion-selective neurons in the somatosensory cortex, as most of these neurons are also orientation-selective. We speculate that these neurons could be the neural substrate that accounts for tactile IMR.

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.

Somatosensory deficits

The analysis and interpretation of somatosensory information are performed by a complex network of brain areas located mainly in the parietal cortex. Somatosensory deficits are therefore a common impairment following lesions of the parietal lobe. This chapter summarizes the clinical presentation, examination, prognosis, and therapy of sensory deficits, along with current knowledge about the anatomy and function of the somatosensory system. We start by reviewing how somatosensory signals are transmitted to and processed by the parietal lobe, along with the anatomic and functional features of the somatosensory system. In this context, we highlight the importance of the thalamus for processing somatosensory information in the parietal lobe. We discuss typical patterns of somatosensory deficits, their clinical examination, and how they can be differentiated through a careful neurologic examination that allows the investigator to deduce the location and size of the underlying lesion. In the context of adaption and rehabilitation of somatosensory functions, we delineate the importance of somatosensory information for motor performance and the prognostic evaluation of somatosensory deficits. Finally, we review current rehabilitation approaches for directing cortical reorganization in the appropriate direction and highlight some challenging questions that are unexplored in the field.

Brain encoding of saltatory velocity through a pulsed pneumotactile array in the lower face

Processing dynamic tactile inputs is a primary function of the somatosensory system. Spatial velocity encoding mechanisms by the nervous system are important for skilled movement production and may play a role in recovery of sensorimotor function following neurological insult. Little is known about tactile velocity encoding in mechanosensory trigeminal networks required for speech, suck, mastication, and facial gesture. High resolution functional magnetic resonance imaging (fMRI) was used to investigate the neural substrates of velocity encoding in the human orofacial somatosensory system during unilateral saltatory pneumotactile stimulation of perioral and buccal hairy skin in 20 neurotypical adults. A custom multichannel, scalable pneumotactile array consisting of 7 TAC-Cells was used to present 5 stimulus conditions: 5cm/s, 25cm/s, 65cm/s, ALL-ON synchronous activation, and ALL-OFF. The spatiotemporal organization of whole-brain blood oxygen level-dependent (BOLD) response was analyzed with general linear modeling (GLM) and fitted response estimates of percent signal change to compare activations associated with each velocity, and the main effect of velocity alone. Sequential saltatory inputs to the right lower face produced localized BOLD responses in 6 key regions of interest (ROI) including; contralateral precentral and postcentral gyri, and ipsilateral precentral, superior temporal (STG), supramarginal gyri (SMG), and cerebellum. The spatiotemporal organization of the evoked BOLD response was highly dependent on velocity, with the greatest amplitude of BOLD signal change recorded during the 5cm/s presentation in the contralateral hemisphere. Temporal analysis of BOLD response by velocity indicated rapid adaptation via a scalability of networks processing changing pneumotactile velocity cues.

Study of single and multidigit activation in monkey somatosensory cortex using voltage-sensitive dye imaging

Toward the goal of understanding cutaneous sensory integration during manual behavior, we used voltage-sensitive dye (VSD) imaging to study the organization and dynamics of anesthetized monkey primary somatosensory cortex (SI) in response to single and multidigit tactile stimulation. We find that in both macaque and squirrel monkey SI, VSD reveals clear focal digit topography consistent with previous electrophysiological and intrinsic signal imaging studies. VSD also reveals interactions in SI in response to multidigit stimulation. With a tactile funneling paradigm in areas 3b and 1 in squirrel monkeys, VSD reveals two-digit induction of subthreshhold influences, consistent with lateral intracortical inhibition. In response to tactile apparent motion stimuli, VSD reveals preferential response to motion stimuli over static tactile stimuli in both areas 1 and 3b. Comparison of the response at different digit locations to "toward digit" stimuli suggests the presence of direction-selective response in area 1; however, further study is needed. These exciting results indicate that VSD constitutes a powerful tool for studying somatosensory cortical processing in nonhuman primates and should be further developed for future somatosensory studies in awake behaving monkeys.

The tactile motion aftereffect suggests an intensive code for speed in neurons sensitive to both speed and direction of motion

Neurophysiological studies in primates have found that direction-sensitive neurons in the primary somatosensory cortex (SI) generally increase their response rate with increasing speed of object motion across the skin and show little evidence of speed tuning. We employed psychophysics to determine whether human perception of motion direction could be explained by features of such neurons and whether evidence can be found for a speed-tuned process. After adaptation to motion across the skin, a subsequently presented dynamic test stimulus yields an impression of motion in the opposite direction. We measured the strength of this tactile motion aftereffect (tMAE) induced with different combinations of adapting and test speeds. Distal-to-proximal or proximal-to-distal adapting motion was applied to participants' index fingers using a tactile array, after which participants reported the perceived direction of a bidirectional test stimulus. An intensive code for speed, like that observed in SI neurons, predicts greater adaptation (and a stronger tMAE) the faster the adapting speed, regardless of the test speed. In contrast, speed tuning of direction-sensitive neurons predicts the greatest tMAE when the adapting and test stimuli have matching speeds. We found that the strength of the tMAE increased monotonically with adapting speed, regardless of the test speed, showing no evidence of speed tuning. Our data are consistent with neurophysiological findings that suggest an intensive code for speed along the motion processing pathways comprising neurons sensitive both to speed and direction of motion.

Tactile texture signals in primate primary somatosensory cortex and their relation to subjective roughness intensity

This study investigated the hypothesis that a simple intensive code, based on mean firing rate, could explain the cortical representation of subjective roughness intensity and its invariance with scanning speed. We examined the sensitivity of neurons in the cutaneous, finger representation of primary somatosensory cortex (S1) to a wide range of textures [1 mm high, raised-dot surfaces; spatial periods (SPs), 1.5-8.5 mm], scanned under the digit tips at different speeds (40-115 mm/s). Since subjective roughness estimates show a monotonic increase over this range and are independent of speed, we predicted that the mean firing rate of a subgroup of S1 neurons would share these properties. Single-unit recordings were made in four alert macaques (areas 3b, 1 and 2). Cells whose discharge rate showed a monotonic increase with SP, independent of speed, were particularly concentrated in area 3b. Area 2 was characterized by a high proportion of cells sensitive to speed, with or without texture sensitivity. Area 1 had intermediate properties. We suggest that area 3b and most likely area 1 play a key role in signaling roughness intensity, and that a mean rate code, signaled by both slowly and rapidly adapting neurons, is present at the level of area 3b. Finally, the substantial proportion of neurons that showed a monotonic change in discharge limited to a small range of SPs (often independent of response saturation) could play a role in discriminating smaller changes in SP.

The tactile speed aftereffect depends on the speed of adapting motion across the skin rather than other spatiotemporal features

After prolonged exposure to a surface moving across the skin, this felt movement appears slower, a phenomenon known as the tactile speed aftereffect (tSAE). We asked which feature of the adapting motion drives the tSAE: speed, the spacing between texture elements, or the frequency with which they cross the skin. After adapting to a ridged moving surface with one hand, participants compared the speed of test stimuli on adapted and unadapted hands. We used surfaces with different spatial periods (SPs; 3, 6, 12 mm) that produced adapting motion with different combinations of adapting speed (20, 40, 80 mm/s) and temporal frequency (TF; 3.4, 6.7, 13.4 ridges/s). The primary determinant of tSAE magnitude was speed of the adapting motion, not SP or TF. This suggests that adaptation occurs centrally, after speed has been computed from SP and TF, and/or that it reflects a speed cue independent of those features in the first place (e.g., indentation force). In a second experiment, we investigated the properties of the neural code for speed. Speed tuning predicts that adaptation should be greatest for speeds at or near the adapting speed. However, the tSAE was always stronger when the adapting stimulus was faster (242 mm/s) than the test (30-143 mm/s) compared with when the adapting and test speeds were matched. These results give no indication of speed tuning and instead suggest that adaptation occurs at a level where an intensive code dominates. In an intensive code, the faster the stimulus, the more the neurons fire.

The role of vibration in tactile speed perception

The relative motion between the surface of an object and our fingers produces patterns of skin deformation such as stretch, indentation, and vibrations. In this study, we hypothesized that motion-induced vibrations are combined with other tactile cues for the discrimination of tactile speed. Specifically, we hypothesized that vibrations provide a critical cue to tactile speed on surfaces lacking individually detectable features like dots or ridges. Thus masking vibrations unrelated to slip motion should impair the discriminability of tactile speed, and the effect should be surface-dependent. To test this hypothesis, we measured the precision of participants in discriminating the speed of moving surfaces having either a fine or a ridged texture, while adding masking vibratory noise in the working range of the fast-adapting mechanoreceptive afferents. Vibratory noise significantly reduced the precision of speed discrimination, and the effect was much stronger on the fine-textured than on the ridged surface. On both surfaces, masking vibrations at intermediate frequencies of 64 Hz (65-μm peak-to-peak amplitude) and 128 Hz (10 μm) had the strongest effect, followed by high-frequency vibrations of 256 Hz (1 μm) and low-frequency vibrations of 32 Hz (50 and 25 μm). These results are consistent with our hypothesis that slip-induced vibrations concur to the discrimination of tactile speed.

The neural basis of tactile motion perception

The manipulation of objects commonly involves motion between object and skin. In this review, we discuss the neural basis of tactile motion perception and its similarities with its visual counterpart. First, much like in vision, the perception of tactile motion relies on the processing of spatiotemporal patterns of activation across populations of sensory receptors. Second, many neurons in primary somatosensory cortex are highly sensitive to motion direction, and the response properties of these neurons draw strong analogies to those of direction-selective neurons in visual cortex. Third, tactile speed may be encoded in the strength of the response of cutaneous mechanoreceptive afferents and of a subpopulation of speed-sensitive neurons in cortex. However, both afferent and cortical responses are strongly dependent on texture as well, so it is unclear how texture and speed signals are disambiguated. Fourth, motion signals from multiple fingers must often be integrated during the exploration of objects, but the way these signals are combined is complex and remains to be elucidated. Finally, visual and tactile motion perception interact powerfully, an integration process that is likely mediated by visual association cortex.