The aim was to elucidate how the population of digital nerve afferents signals information about the shape of objects in contact with the fingerpads during fine manipulations. Responses were recorded from single mechanoreceptive afferent fibers in median nerves of anesthetized monkeys. Seven spherical surfaces were used, varying from a highly curved surface (radius, 1.44 mm; curvature, 694 m-1) to a flat surface (radius, infinity; curvature, 0 m-1). These were applied to the fibers' receptive fields, which were located on the central portion of a fingerpad. When the objects were located at the centers of the receptive fields, the responses of the slowly adapting fibers (SAIs) increased as the curvature of the surface increased and as the contact force increased. All SAIs behaved in the same way, differing only by a scaling factor (the sensitivity of the individual afferent). Responses of the rapidly adapting afferents were small and did not vary systematically with the stimulus parameters, and most Pacinians did not respond at all. Stimuli were applied at different positions in the receptive fields of SAIs to define the response profiles of the afferents (response as a function of position on the fingerpad). All SAIs had similarly shaped profiles for the same surface curvature and the shape differed for different curvatures. These profiles reflected the shape of the stimulus. An increase in contact force scaled these profiles upward. Thus, the population of digital nerve fibers signals unambiguous information about the shape and contact force of curved surfaces contacting the fingerpad.

For humans to manipulate an object successfully, the motor control system must have accurate information about parameters such as the shape of the stimulus, its position of contact on the skin, and the magnitude and direction of contact force. The same information is required for perception during haptic exploration of an object. Much of these data are relayed by the mechanoreceptive afferents innervating the glabrous skin of the digits. Single afferent responses are modulated by all the relevant stimulus parameters. Thus, only in complete population reconstructions is it clear how each of the parameters can be signaled to the brain independently when many are changing simultaneously, as occurs in most normal movements or haptic exploration. Modeling population responses reveals how resolution is affected by neural noise and intrinsic properties of the population such as the pattern and density of innervation and the covariance of response variability.

We measured the ability of humans to discriminate the positions of spherical objects passively contacting the fingerpad. The discrimination threshold averaged 0.55 mm for a moderately curved sphere (radius 5.80 mm) and decreased to 0.38 mm for a more curved sphere (radius 1.92 mm); since the receptor density is about 1 per mm2, these values are substantially smaller than those predicted by the sampling theorem (referred to as hyperacuity). To elucidate the underlying neural mechanisms, responses to the same spheres and random sequences of stimuli were recorded from single Merkel afferents (SAIs) and Meissner afferents (RAs) in anesthetized monkeys. For multiple applications of identical stimuli, coefficients of variation of responses were around 3%. Profiles of responses across the SAI population were "hill-shaped." A change in position of the stimulus on the skin resulted in a matching shift of the profile, evident over the whole profile for the more curved sphere but ony at the skirts for the less curved sphere. The shift in response profiles, relative to the standard deviations, increased as the change in position increased, and was more reliable for the more curved sphere. Responses were measured over four time frames: 0.2, 0.3, 0.5, and 1.0 sec. Although responses increased with an increase in integration time, so, too, did their standard deviations, so that signal-to-noise ratios or the resolution in the SAI population was bout the same at 0.2 sec as at 1.0 sec. Only half the RAs responded; responses were small, but signalled reliable information about the position of the stimulus.

The aim of this study was to determine the acuity of the peripheral tactile system for gaps and to determine how stimulus orientation may impact on this. We quantified the ability of humans to discriminate small differences in gap width using a forced-choice task. Stimuli were presented passively to the distal fingerpad in a region where the skin ridges all run approximately in the same direction. Two standard gap widths were used (2 and 2.9 mm), and the comparison gap widths were larger than the standard gaps. With the gap axis parallel to the skin ridges, the average difference limen was approximately 0.2 mm for both standards. We examined the effect of stimulus orientation by asking subjects to discriminate between a smooth surface and a grating (ridge width, 1.5 mm; groove width, 0. 75 mm). They were able to discriminate the two surfaces when the axis of the grooves was parallel to the skin ridges, but performance was below threshold in the orthogonal orientation. The underlying neural mechanisms were investigated using the gap stimuli to activate single slowly adapting type I mechanoreceptive afferents (SAIs) innervating the fingerpads of anesthetized monkeys. The edges of the gap produced response peaks, and the gap resulted in a trough in the receptive field profiles. The response magnitude at the peaks was greater, and at the troughs was smaller, for larger gap widths and also when the axis of the gap was parallel to the skin ridges as compared with the orthogonal orientation. Simulated SAI population responses showed that response profiles were distorted by variation in afferent sensitivity and by neural noise. Using signal detection theory, based on a neural measure of the gaps computed over the active population, the acuity of the SAIs under realistic population conditions was compared with human performance. These analyses showed how parameters like afferent sensitivity, the pattern and density of innervation, and noise impact on performance and why their impact is different for the two stimulus orientations investigated. The greatest limitation was imposed by noise that is independent of response magnitude, and this effect was greater for stimuli oriented orthogonal to the skin ridges than for the parallel orientation.

Torsional loads are ubiquitous during everyday dextrous manipulations. We examined how information about torque is provided to the sensorimotor control system by populations of tactile afferents. Torsional loads of different magnitudes were applied in clockwise and anticlockwise directions to a standard central site on the fingertip. Three different background levels of contact (grip) force were used. The median nerve was exposed in anaesthetized monkeys and single unit responses recorded from 66 slowly adapting type-I (SA-I) and 31 fast adapting type-I (FA-I) afferents innervating the distal segments of the fingertips. Most afferents were excited by torque but some were suppressed. Responses of the majority of both afferent types were scaled by torque magnitude applied in one or other direction, with the majority of FA-I afferent responses and about half of SA-I afferent responses scaled in both directions. Torque direction affected responses in both afferent types, but more so for the SA-I afferents. Latencies of the first spike in FA-I afferent responses depended on the parameters of the torque. We used a Parzen window classifier to assess the capacity of the SA-I and FA-I afferent populations to discriminate, concurrently and in real-time, the three stimulus parameters, namely background normal force, torque magnitude and direction. Despite the potentially confounding interactions between stimulus parameters, both the SA-I and the FA-I populations could extract torque magnitude accurately. The FA-I afferents signalled torque magnitude earlier than did the SA-I afferents, but torque direction was extracted more rapidly and more accurately by the SA-I afferent population.

When humans manipulate objects, the sensorimotor system coordinates three-dimensional forces to optimize and maintain grasp stability. To do this, the CNS requires precise information about the magnitude and direction of load force (tangential to skin surface) plus feedback about grip force (normal to skin). Previous studies have shown that there is rapid, precise coordination between grip and load forces that deteriorates with digital nerve block. Obviously, mechanoreceptive afferents innervating fingerpad skin contribute essential information. We quantify human capacity to scale tangential and normal forces using only cutaneous information. Our paradigm simulated natural manipulations (a force tangential to the skin superimposed on an indenting force normal to the skin). Precisely controlled forces were applied by a custom-built stimulator to an immobilized fingerpad. Using magnitude estimation, subjects (n ϭ 8) scaled the magnitude of tangential force (0.25-2.8 N) in two experiments (normal force, 2.5 and 4 N, respectively). Performance was unaffected by normal force magnitude and tangential force direction. Moreover, when both normal (2-4 N) and tangential forces were varied in a randomized-block factorial design, the relationship between applied and perceived tangential force remained near linear, with a minor but statistically significant nonlinearity. Our subjects could also discriminate small differences in tangential force, and this was the case for two different reference stimuli. In both cases, the Weber fraction was 0.16. Finally, scaling functions for magnitude estimates of normal force (1-5 N) were also approximately linear. These data show that the cutaneous afferents provide a wealth of precise information about both normal and tangential force.

The capacity of a population of primary afferent fibers to signal information about a sphere indenting the fingerpad is limited by factors such as the inhomogeneity of sensitivity among the afferents, the pattern and density of innervation, and the effects of noise (response variability). Using experimental data recorded from single slowly adapting type I afferents (SAIs), we simulated the response of the SAI population to such a stimulus. The human ability to discriminate stimulus curvature, location, and force has been quantified previously. We devised three neural measures, treating them as surrogates for the real neural measures underlying human performance, and explored how population parameters usually overlooked in neural coding studies affect such measures. Variation in sensitivity among SAIs is large; this distorts population response profiles markedly but has no significant impact on the neural measures. Two classes of noise were introduced, one dependent on and the other independent of the level of neural activity. Resolution of the model was compared with discrimination in humans. Correlation of noise among neurons had different effects for the different measures. An increase in correlation decreased resolution in the measure for force but improved resolution in the measure for position. Increasing innervation density (1) always increased resolution for position and (2) increased resolution for force if noise was uncorrelated but had diminishing effects as correlation increased. Correlation and innervation density had complex effects on the measure for curvature, depending on the class of noise. Nonuniformity in the pattern of innervation had negligible effects on resolution.

The central part of the fingerpad in anesthetized monkeys was stimulated by spheres varying in curvature indented into the skin. Responses were recorded from single slowly adapting type I primary afferent fibers (SAIs) innervating the sides and end of the distal segment of the stimulated finger. Although these afferents had receptive field centers that were remote from the stimulus, their responses were substantial. Increasing the curvature of the stimulus resulted in an increased response for most afferents. In general, responses increased most between stimuli with curvatures of 0 (flat) and 80.6 m(-1), with further increases in curvature having progressively smaller effects on the response. We calculated an index of sensitivity to changes in curvature; this index varied widely among the afferents but for most it was less than the index calculated for afferents innervating the fingerpad in the vicinity of the stimulus. Responses of all the SAIs increased when the contact force of the stimulus increased. An index of sensitivity to changes in contact force varied widely among the afferents but in all cases was greater than the index calculated for SAIs from the fingerpad itself. Neither the curvature sensitivity nor the force sensitivity of an afferent was related in any obvious way to the location of its receptive field center on the digit. There was only a minor correspondence between an afferent's sensitivity to force and its sensitivity to curvature. The large number of afferents innervating the border regions of the digit do respond to stimuli contacting the central fingerpad; they convey some information about the curvature of the stimulus and substantial information about contact force.