Each fingertip hosts thousands of nerve fibers that allow us to handle objects with great dexterity. These fibers relay the amount of friction between the skin and the item, and the brain uses this sensory feedback to adjust the grip as necessary. Yet, exactly how tactile nerve fibers encode information about friction remains largely unknown.

Previous research has suggested that friction might not be recorded per se in nerve signals to the brain. Instead, fibers in the finger pad might be responding to localized ‘partial slips’ that indicate an impending loss of grip. Indeed, when lifting an object, fingertips are loaded with a tangential force that puts strain on the skin, resulting in subtle local deformations. Nerve fibers might be able to detect these skin changes, prompting the brain to adjust an insecure grip before entirely losing grasp of an object.

However, technical challenges have made studying the way tactile nerve fibers respond to slippage and skin strain difficult. For the first time, Delhaye et al. have now investigated how these fibers respond to and encode information about the strain placed on fingertips as they are loaded tangentially. A custom-made imaging apparatus was paired with standard electrodes to record the activity of four different kinds of tactile nerve fibers in participants who had a fingertip placed against a plate of glass. The imaging focused on revealing changes in skin surface as tangential force was applied; the electrodes measured impulses from individual nerve fibers from the fingertip. While all the fibers responded during partial slips, fast-adapting type 1 nerves generated strong responses that signal a local loss of grip. Recordings showed that these fibers consistently encoded changes in the skin strain patterns, and were more sensitive to skin compressions related to slippage than to stretch.

These results show how tactile nerve fibers encode the subtle skin compressions created when fingers handle objects. The methods developed by Delhaye et al. could further be used to explore the response properties of tactile nerve fibers, sensory feedback and grip.

The most fundamental requirement for dexterous manipulation is the ability to handle objects without slippage and dropping of the object. To ensure that an object is held safely in the hand, one must apply a sufficient amount of force to the object’s surface to counteract the forces tending to make it slip, for example, the object’s weight and inertia (Johansson and Flanagan, 2009). Exerting an excessive grip is inefficient and can result in crushing the object. Inversely, a minimal amount of force is required to avoid slip and is dictated by friction: a stronger grip is required for slippery surfaces and a looser grip is sufficient for sticky surfaces. A good strategy is then to adjust the grip to friction with an amount of force slightly above the minimum. Previous work has suggested that humans can quickly and accurately adapt to changes in friction (Westling and Johansson, 1984; Cadoret and Smith, 1996). Importantly, tactile feedback is necessary for grip adjustments to take place as disruption of this feedback abolishes the normal, fine-tuned grasp control and results in frequent object dropping despite excessive compensatory grip forces (Westling and Johansson, 1987; Augurelle et al., 2003; Witney et al., 2004). However, how information about friction is encoded by tactile afferents is largely unknown.

Friction information might be partly available at the initial contact. Anecdotal evidence suggests that when contacting surfaces of different frictions the strength of the initial burst of activity of the afferents varies as the surface is changed (Johansson and Westling, 1987). In these experiments, however, different frictional conditions were associated with different surface textures and thus do not necessarily imply that the afferent responses specifically represented friction between the fingerpads and contact surface. Friction might not be encoded per se, but instead, tactile afferents might respond to short and localized slip events that imply impending slip. There is indeed evidence that small, short-lasting slips occur during manipulation (Johansson and Westling, 1984). Those slips trigger strong afferent responses and elicit grip force adjustments (Johansson and Westling, 1987). In addition to the context of object manipulation, it has also been suggested that tactile slip detection plays an important role in a range of tactile tasks involving movements of surfaces relative to the skin (Gueorguiev et al., 2016; Schwarz, 2016).

Tactile slip is not instantaneous but develops progressively (Levesque and Hayward, 2003; Tada et al., 2006; André et al., 2011; Terekhov and Hayward, 2011; Delhaye et al., 2014; Barrea et al., 2018). As the surface-tangential force, that is, traction, of the fingerpad increases, ‘local’ slips begin at the periphery of the contact and progress toward the center until the last central ‘stuck’ point finally slips, that is, the instant of a ‘full slip’ or a global slip. We refer to the period between the beginning of the tangential loading and the instant of a full slip as the partial slip phase. Such a phenomenon gives rise to substantial local strain patterns in the slipping regions, near the boundary between the stuck and the slipping points (Delhaye et al., 2016). We hypothesized that information about these local deformations is carried by tactile afferents that inform the central nervous system about the contact state.

Single-unit recordings of primary tactile afferents, both in humans and monkeys, have shown that type I afferents respond strongly to local skin deformation (Johansson et al., 1982; Sripati et al., 2006; Saal et al., 2017). Those responses contain information about local geometric features such as edge orientation (Pruszynski and Johansson, 2014; Suresh et al., 2016; Delhaye et al., 2019). The most common stimuli used to evoke deformation of the skin are indentations and scanning with embossed geometric patterns or textures. Applying such stimuli makes it possible to relate the strength or the timing of the response to the topography or the statistics of the stimulus itself, but does not provide a mechanistic understanding of the nature of the response with respect to the local skin deformation at the mechanoreceptors themselves. Moreover, it is mostly unknown how tactile afferents respond to surface strains, that is, strains acting tangentially to the surface (as opposed to features indented perpendicularly to the skin surface). Afferent recordings in the hairy skin of the human hand have shown that afferents of all types strongly respond to local skin stretch and that the fast-adapting type I (FA-I) afferents also strongly respond when the stretch is released (Edin, 1992; Edin, 2004). Slowly-adapting type II (SA-II) afferents are also known to be sensitive to skin stretch, but relating their response to the exact local stretch pattern is complex given the large size of their receptive fields. How glabrous skin afferents, that is, those engaged in the contact with objects during manipulation, respond to local skin strain has, to our knowledge, never been studied. This is mainly due to the difficulties of applying well-controlled mechanical stimuli and measuring the strain at the same time.

To address this, we took advantage of a recently developed imaging system that can measure fingertip skin strain through a transparent material during tangential loading of the fingertip until slip occurs (Delhaye et al., 2014; Delhaye et al., 2016). While recording local strains with this system, we simultaneously recorded the activity of human tactile afferents innervating the fingertip (Figure 1A). This way we were able to relate local strains to responses of afferents with receptive fields inside the fingerpad contact area. We found that all tactile afferents in the fingertip responded to slip events, but that FA-I afferents in particular faithfully signaled local skin compressions related to the progression of slips. We suggest that FA-I afferents are primarily responsible for detecting changes in surface strains and that their discharges are a primary source of information for the central nervous system to, for instance, quickly adjust fingertip forces to different levels of friction.

Figure 1

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Slips were applied to the fingerpad using a robotic platform holding a transparent plate of glass that was either plain, yielding high friction, or covered with a hydrophobic coating yielding a lower friction. The plate first made contact with the fingerpad ('contact' in Figure 2A, C) and then moved tangentially at constant velocity until full slip in one of four different directions: ulnar, distal, radial, and proximal, and then moved back in the opposite direction until full slip occurred again (forward and backward, respectively, Figure 1B, Figure 2A, C). The normal component of the force was servo-controlled to be kept at 4 N, and the tangential component was developed as a consequence of the tangential movement of the plate. At the same time, we imaged the fingerpad contact and tracked numerous features on fingertip ridges as the slip progressed (Figure 1C). Finally, the plate was moved down ('release' in Figure 2A, C). We were able to precisely monitor skin strains from frame to frame (i.e., change in strain or strain rates) in the contact area during the transition from a fully stuck contact to a fully slipping contact (see also Delhaye et al., 2016). The skin strains were measured in the contact plane and expressed in terms of three independent components: two axial components aligned to the plate movements ($e_{xx}$ and $e_{yy}$) and one shear component ($e_{xy}$, Figure 1C). Importantly noted, the presence of local strains also indicates that the skin is locally slipping. That is, the limit between the stuck and slipping region is just preceding the front of the strain waves.

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We focused the analyses on tangential loading movements, from the moment when the plate started to move tangentially and until it completed a forward or a backward movement. Each tangential loading movement was split into three sequential phases defined as follows: (1) the movement onset phase arbitrarily defined as the initial 100 ms; (2) the partial slip phase that lasted until the tangential force reached a plateau and the finger fully slipped; and (3) the plateau phase during which the finger was fully slipping against the glass surface and that lasted until the end of the tangential movement (Figure 2A, C). Strain changes were observed in the contact area during the partial slip phase in the form of two waves of opposite signs (Figure 2B, D). Remember that the strain wavefront is where the slip starts to occur. Those waves started moving at the onset of movement from opposite sides and from the periphery of the contact toward the center and disappeared at the point of full slip (Delhaye et al., 2016). Once the full slip was reached, the changes in strain faded away. The components of the strain changes corresponding to the direction of the plate movement were the largest in amplitude (Figure 2B, D, circled by a black box). Different movement directions elicited different patterns (i.e., compression, stretch, or shear) at all points in the contact area. For instance, the receptive field of a given afferent could be stretched along the proximal-distal axis for movements in the distal direction but compressed along the same axis for movements in the opposite (i.e., proximal) direction (Figure 2B, $e_{yy}$).

Tactile afferents strongly respond during partial slip

We used microneurography (Vallbo and Hagbarth, 1968) to record the activity of single units whose receptive fields were located at the fingertip (Figure 1A). We focused on FA-I and SA-I afferents, which respond to local deformation events and have small, well-defined receptive fields (Johansson and Vallbo, 1983). We also recorded from a few type II afferents (FA-II and SA-II). Sufficient recordings for data analysis (three out of five repetitions of each plate move direction) were obtained from 22 afferents (13 FA-I, 6 SA-I, 2 SA-II, and 1 FA-II). The locations of the receptive fields of all afferents are depicted in Figure 2—figure supplement 1. As expected, the afferents responded vigorously to contact (Figure 2, ‘contact’), but also responded in a variety of ways to the tangential loading (Figure 2, ‘forward’ and ‘backward’). First, we looked at the overall discharge pattern of the afferents. FA-I units showed a phasic response, with a burst of activity at the instant of contact, and another one during the tangential loading (e.g., Figure 2A). However, a majority of FA-I afferents responded mostly only during the partial slip phase, showing no or almost no response during the start (‘onset’ phase) and the end (‘plateau’ phase) of the tangential movement (for U115-04 in Figure 2A, there was one spike at the onset phase during one repeat). SA-I units instead presented a rather tonic response beginning at the initial contact that changed during the tangential loading phase by increasing or decreasing their firing rates (e.g., Figure 2C). The discharge patterns of all recorded afferents for all directions and frictions are reported in Figure 2—figure supplement 1. Video 1 and Video 2 show image recordings of the fingerpad during one trial, together with spike sound associated with the afferent responses, for the two example units shown in Figure 2.

Video 1

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Video 2

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Note that the tangential movement of the plate led to slight fluctuations in the normal force that could not be suppressed by the force controller (see Materials and methods). Those fluctuations did not evoke strong afferent responses. Indeed, the discharge rates were neither correlated to the normal force nor to its derivative (Figure 2—figure supplement 2). In fact, we considered a causal relationship untenable observing in Figure 2A that the afferent discharge seemed to follow the normal force fluctuation by ~100 ms in the distal direction but preceded it by ~200 ms in the proximal direction.

First, we describe how the tactile afferent responded with respect to ‘global’ stimulus parameters such as the movement phase or direction. Afferents were more active during the partial slip phase than during the two other tangential loading phases (i.e., onset and plateau), suggesting that this period is key to the afferent responses (Figure 3A). Indeed, for all four afferent types, the fraction of trials during which the afferent responded with at least one spike was higher for the partial slip phase than for the two others (one-way ANOVA with repeated measures, F(2,24) = 47.05, p<0.001 for FA-I units; F(2,10) = 13.97, p=0.001 for SA-I units; all p<0.05 for paired comparisons; for FA-II and SA-II afferents, we did not have enough data for such analysis). FA-I units were silent during the plateau, except when a stick-and-slip phenomenon was observed, in which case the firing phase was locked to the stick-and-slip events (e.g., see U109-04 during plateau phase in Figure 2—figure supplement 1).

Figure 3

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For each afferent, we defined its preferred global direction (i.e., ulnar, distal, radial, or proximal), namely, the plate movement direction for which the highest mean firing rate was observed during partial slip. Figure 3B shows the mean firing rate in the preferred global direction (labeled ‘North’) and in the remaining other directions with respect to the preferred one. We found that different movement directions elicited different responses, confirming previous observations (Birznieks et al., 2001). Some FA-I units tended to have a preferred-opposite pattern (5 out of 13), that is, the preferred (‘N’ for North in Figure 3B) and opposite directions (‘S’ for South in 3B) tended to elicit a stronger response than the two other directions. This was not observed for the other afferent types, which tended to discharge less in the direction opposite to the preferred direction. The distribution of the preferred global direction covered the four directions tested; however, we observed overall a slight preference for the proximal-distal axis (Figure 3C). Firing rates during partial slips were consistent across forward and backward (Figure 3D; correlation r = 0.97, n = 176, p<0.001), suggesting similar directional preference when the skin was already under tension (backward movement). While the firing rates were strongly correlated across friction levels (Figure 3E; correlation r = 0.95, n = 176, p<0.001), the firing rates across the whole population tended to be slightly lower for low friction (paired t-test, t(171) = 7.57, p<0.001) and the ratio of the mean firing rate during low and high friction condition was 0.82 ± 0.55 (mean ± std).

FA-I afferents respond to local skin strain rates

Next, we sought to relate the afferent discharge to the strain pattern, that is, the local events taking place inside the afferent receptive field. We observed that the discharge of FA-I units was strongly coupled to the compressive strain changes taking place in their receptive fields during the partial slip phase (Figure 4). This was particularly clear for U104-02, whose receptive field (depicted with a gray circle in the fingerpad heatmaps of Figure 4) was located on the proximal side of the contact area. Indeed, during a proximal movement (Figure 4A, left, and B, right), the plate movement elicited a compressive wave of strain changes (in red) along the y-axis (proximal-distal axis) moving from the proximal side of the contact area toward the center and crossing the afferent’s receptive field. This crossing elicited a strong discharge burst. Moreover, the low friction surface exhibited full slip at a lower level of tangential force, and therefore also earlier than the high friction surface, with respect to the movement onset. As a consequence, the compressive strain wave moved across the afferent receptive field earlier in this case and, strikingly, the discharge burst of the afferent also took place earlier, coinciding with the strain changes. This is even easier to observe for the backward movement (Figure 4B, right). In this case, due to the previous loading, the movement of the compressive wave came even earlier when the low friction condition was used and the discharge burst of the afferent coincided. Finally, we observed that the response evoked by a stretch wave, generated by a distal movement (Figure 4A, right, and B, left), was much weaker than its compressive counterpart. Still, the timing of the response was perfectly synchronized with the occurrence of the stretch in the receptive field. Note that a short burst was elicited at the onset of the movement in the distal direction in the backward case (Figure 4A, right). Such transient burst cannot be explained by our strain measurements and occurred in a small fraction of the trials and only in a few afferents (Figure 3A). Also note that part of the unit receptive field lost contact during the partial slip phase in the high friction case.

Figure 4

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To test the hypothesis that the responses of the tactile afferents are caused by specific ‘local’ strain patterns taking place inside the contact area, we took two different approaches. In the first model-free approach, we looked at the strain pattern observed at the time of each spike across all stimulus directions and frictions and computed a ‘spike-triggered average’ (STA, see Materials and methods) for all recorded FA-I and SA-I units. If the afferents with a receptive field in the contact area responded to local strains, we expected to observe a clear strain pattern associated with these units’ discharges. In contrast, for afferents with a receptive field outside the contact area, we expected no clear strain pattern at all. First, we used the strain rate norm (||e||) as a variable to estimate the STA. We found that, indeed, the average strains causing spikes in all recorded FA-I afferents had a clear, more or less annular (ring-like) pattern (Figure 5A). Such an annular pattern is expected from the stimulus, which is a strain wave in the form of an annulus and does not reflect the shape of the afferent receptive field but rather the correlations present in the strain patterns (Materials and methods). Importantly, however, the pattern overlapped the afferent’s receptive field and often peaked inside it. Furthermore, as expected, we did not observe such a clear pattern for the afferents having their receptive field outside the contact area (Figure 5, middle). Strikingly, clear patterns did not emerge for the SA-I afferents, suggesting that those afferents are less sensitive to the local surface strain changes (Figure 5A, bottom). Note that we repeated the same analysis using the total (cumulative) strain instead of the strain changes, and that again, we did not observe any clear pattern. The peak values of the STA are shown in Figure 5B in orange and show strong values for FA-I units inside the contact and much weaker values for other afferents, confirming the previous observations. The same STA analysis was repeated using the two principal strain components, one compressive and one tensile, separately to build two STA maps (see Materials and method). The results obtained are consistent with the STA obtained with strain rate norm, that is, that a clear pattern emerges only for FA-I afferents and that the STA peaks in the afferent receptive field (Figure 5—figure supplement 1). Moreover, we found that the compressive STA peaks were generally larger and more often found in the afferent receptive field than their tensile counterpart, suggesting that FA-I afferents are more sensitive to compression. This finding will be further supported in the next section.

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In the second, model-based approach, we aimed to predict the afferent discharge rate from the skin strain measured in the contact area. Inspection of the data led us to assume that, first, strains of opposite signs might not contribute in the same manner to the discharge as skin stretch seemed to evoke weaker responses than skin compression (Figure 4). Second, multiple components might be needed to explain responses in all directions. Therefore, we first set out to test if the afferents’ firing patterns could be reliably predicted for the skin strains using a model with six distinct predictors, that is, the three strain components each half-wave rectified, using both the positive (stretch) and negative part (compression). The simplest model possible, a multiple linear regression including an intercept, was used. Our method was cross-validated, such that the regression models were fit on one friction condition and tested on the other (Materials and methods). In the subsequent validations, we used data from different directions and forward vs. backward movements and observed quantitatively similar results (see Figure 5—figure supplement 2). The results obtained using the model-based approach revealed similar trends as the first model-free method. The linear model could predict the discharge pattern of the FA-I afferents, but not the SA-I afferents. Heatmaps built from the cross-validated R² (Figure 5—figure supplement 2) were qualitatively similar to those built from the STA. An example unit is shown in Figure 5C for an afferent with a substantial R² (0.66). This unit maximally responded in the proximal direction, when a compressive wave was observed in its receptive field. The maximal values of the R² found within the receptive field (or anywhere for the afferents outside the contact area) are shown in Figure 5B in green. As with the model-free approach, only the firing rates of FA-I inside the contact area could be predicted from the strain. The high R² value for the SA-I afferent outside the contact area is because this particular afferent was either active at a constant firing rate or silent, generating two separate clouds of data points and thus driving up the R² (Figure 2—figure supplement 1).

In summary, we observed that FA-I afferents respond to local strain patterns generated during partial slips.

Aspects of the skin strain rates encoded by the FA-I afferents

Having demonstrated that the recorded FA-I afferents respond to local strain patterns, we then sought to uncover what aspects of the strains were responsible for these responses. To that end, we aimed to predict FA-I afferent discharge rates with a subset of strain predictors and to compare their performance to the full model with six predictors. The analysis was performed only on the FA-I afferents for which we had optical measurements of the strains, that is, those having their receptive fields inside the contact area (n = 6, all shown in the top line of Figure 5A). Since all models are cross-validated, they can be compared irrespective of the number of predictors. First, we selected three single predictors that were invariant to the choice of a particular reference frame. The strain norm, informative about the intensity but neither the orientation nor the sign of the deformation, and the two principal strain components separately, the compressive ($e_1$) and the tensile ($e_2$), obtained from single-value decomposition of the strain tensor (Materials and methods), informative about the intensity and the sign of the deformation, irrespective of the orientation of the deformation. All those three single predictor models performed worse than the full model, as could be expected (Figure 6A, left). However, we found that the compressive principal component always outperformed the tensile one, suggesting that the afferents are more sensitive to compression than to stretch.

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Next, we used each of the predictors of the full model separately. That is, the half-wave rectified positive and negative value of the three strain tensor components ($e_{xx}$, $e_{yy}$, and $e_{xy}$). Given that those components are dependent on the choice of a particular reference frame orientation, we repeated the fitting procedure for multiple rotations of the reference frame equally spaced from 0 to 90° (Materials and methods). The results are shown in Figure 6B for an exemplar afferent, with the shear component ignored. In this figure, the performance of the prediction (R²) based on a single strain component (compressive in red and tensile in blue) is shown as a function of the reference frame rotation. This afferent seemed to have a preferred strain orientation close to 90° with respect to the radial-ulnar axis (i.e., along the proximal-distal axis), where the compressive component peaks. That is, the afferent seemed to encode preferentially 'local' compressive strain rate along a particular orientation. Indeed, as already described in Figure 4, this unit was responding strongly in the proximal condition, where a compressive strain wave along the proximal-distal orientation passed through its receptive field. Perpendicular to that orientation, the tensile component peaked as well but with a lower R². This is expected since compression in one orientation generates stretch in the other at the same time because the volume is mostly conserved. The same plot as in Figure 6B is provided for all FA-I afferents and the three cross-validation methods in Figure 6—figure supplement 1. It is important to avoid the confusion between the units’ preferred direction described in Figure 3, which relates to the robot movement direction and the maximal firing rate of the afferent, and the strain orientation preference described here, which is related to the orientation of the local deformation in the afferent receptive field.

From these analyses, we draw two important conclusions. First, compressive strain change is a more effective stimulus than tensile strain change (Figure 6A, right), confirming the observation in the principal component analyses, namely, that the FA-I responses are mostly driven by compression. Second, FA-I afferents did not respond to compression in any orientation but rather to compression along a certain preferred strain orientation. The argument for this is twofold: (1) the performance of a single compressive component in a particular orientation was always higher than the performance of the compressive principal component $e_1$ (Figure 6A) and (2) models with one single component along its preferred strain orientation performed as well as the full model comprising all the components, suggesting that this component is mainly responsible for driving the afferent response. Note that the FA-I afferents’ preferred strain orientation seemed to coincide with the local fingerprint orientation, but more data is needed to confirm this trend (Figure 2—figure supplement 2, correlation r = 0.74, p=0.10, n = 6).

In sum, our analyses strongly suggest that the FA-I are sensitive to local skin strains, more so to changes in compressive strain than tensile strain and that they respond maximally along a preferred strain orientation.

This is, to our knowledge, the first study that investigates the relationship between the responses of tactile afferents to slippage and the detailed surface strain patterns in glabrous skin. Specifically, the results from our analyses imply that the local compression induced by partial slip generates strong responses from FA-I but not SA-I units. Given that the FA-I from the fingertip are mostly silent when the fingerpad is stuck to the object and even at the onset and offset of the plate movement, their vigorous responses to the changes in compressive strain provide a particularly salient signal that enables contact stability. It is reasonable to suspect that FA-I afferents play a crucial role in maintaining grasp stability since they constitute more than 50% of all afferents in the glabrous skin of a human fingertips (Johansson and Vallbo, 1979). Our results show that while partial slips give rise to strong responses, the friction per se is poorly encoded by FA-I. Finally, given that cortical somatosensory neurons are strong edge detectors (DiCarlo et al., 1998; Delhaye et al., 2018), the particularly salient signal from the FA-I afferents will likely be amplified downstream.

A previous study showed that information about grip safety, that is, the increment of tangential force needed to reach full slip, is present in the response of local tactile afferents at the level of a population of afferents, irrespective of the level of friction (Khamis et al., 2014). Given that all afferents respond to progressing slips, and that the timing of each FA-I afferent’s burst is dependent on how far from the center of contact the afferent is, it is not surprising that a linear combination of multiple FA-I units can provide a good estimate of the grip safety. While this previous study demonstrated that the information is present in the response of the population, our approach provides a mechanistic explanation and underlines the saliency of the grip safety signal.

An essential component of this work was the use of two different frictions. By using the same protocol with two different frictions, we were able to disambiguate the relative contribution of the external force and the local strain measurements to the afferent response. Indeed, until an instant very close to full slip in the low friction condition, the low and high friction materials lead to very similar normal and tangential force profiles, whereas the surface strain patterns inside the contact area have different timing and their progressions differ very early on. Thanks to this contrast, uniquely provided by the difference in friction, the earlier afferent response in the case of the low friction cannot be attributed to the change in tangential force itself. The fact that the afferent responses, particularly the FA-I units, are related to local slip and are therefore poor predictors of friction per se suggests that humans could be exquisitely sensitive to detect slip while being poor at estimating friction. That is, estimating friction would require the occurrence of partial slips, and the absence of slips would make such estimates impossible.

The FA-I afferent responses were best predicted by compressive strain, rather than tensile strain. Moreover, the afferents were mostly sensitive to a particular strain orientation. This property might be explained by the shape of the afferent receptive fields, which is elongated (rather than circular) and contains numerous zones of high sensitivity (‘hot spots’, Johansson, 1978; Phillips et al., 1990; Jarocka et al., 2021). A particular arrangement of those hot spots might lead to increased sensitivity to a particular strain orientation. Indeed, it has been demonstrated that particular arrangements enable each afferent to differentiate the orientation of edges moving across the receptive fields from the strength or the fine timing of their response (Pruszynski and Johansson, 2014; Suresh et al., 2016). In fact, the moving strain wave related to partial slip observed in this study can be seen as an edge moving across the contact area.

All the data used to create the figures in the manuscript are available for download following this Zenodo link: https://zenodo.org/record/4818439.

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Author details

1. Benoit P Delhaye

   1. Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique de Louvain, Louvain-la-Neuve, Belgium
   2. Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   delhayeben@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3974-7921

2. Ewa Jarocka

   Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

   Contribution

   Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

3. Allan Barrea

   1. Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique de Louvain, Louvain-la-Neuve, Belgium
   2. Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium

   Contribution

   Data curation, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1094-4596

4. Jean-Louis Thonnard

   1. Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique de Louvain, Louvain-la-Neuve, Belgium
   2. Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

5. Benoni Edin

   Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

   Contribution

   Conceptualization, Supervision, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Philippe Lefèvre

   1. Institute of Information and Communication Technologies, Electronics and Applied Mathematics, Université catholique de Louvain, Louvain-la-Neuve, Belgium
   2. Institute of Neuroscience, Université catholique de Louvain, Brussels, Belgium

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2032-3635

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