Neural Correlates for Roughness Choice in Monkey Second Somatosensory Cortex (SII)

J. R. Pruett, Jr., R. J. Sinclair, and H. Burton

Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri 63110


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Pruett, J. R., Jr., R. J. Sinclair, and H. Burton. Neural Correlates for Roughness Choice in Monkey Second Somatosensory Cortex (SII). J. Neurophysiol. 86: 2069-2080, 2001. This experiment explored the relationship between neural firing patterns in second somatosensory cortex (SII) and decisions about roughness of tactile gratings. Neural and behavioral data were acquired while monkeys made dichotomous roughness classifications of pairs of gratings that differed in groove width (1.07 vs. 1.90 and 1.42 vs. 2.53 mm). A computer-controlled device delivered the gratings to a single immobilized finger pad. In one set of experiments, three levels of contact force (30, 60, and 90 g) were assigned to these gratings at random. In another set of experiments, three levels of scanning speed (40, 80, and 120 mm/s) were assigned to these gratings at random. Groove width was the intended variable for roughness. Force variation disrupted the monkeys' groove-width (roughness) classifications more than did speed variation. A sample of 32 SII cells showed correlated changes in firing (positive or negative effects of both variables) when groove width and force increased. While these cells were recorded, the monkeys made roughness classification errors, confusing wide groove-width gratings at low force with narrow groove-width gratings at high force. Three-dimensional plots show how some combinations of groove width and force perturbed the monkeys' trial-wise classifications of grating roughness. Psychometric functions show that errors occurred when firing rates failed to distinguish gratings. A possible interpretation is that when asked to classify grating roughness, the monkeys based classifications on the firing rates of a subset of roughness-sensitive cells in SII. Results support human psychophysical data and extend the roughness range of a model of the effects of groove width and force on roughness. One monkey's SII neural sample (21 cells) showed significant correlation between firing rate response functions for groove width and speed (both correlations either positive or negative). Only that monkey showed a statistically significant interaction between groove width and speed on roughness classification performance. This additional finding adds weight to the argument that SII cell firing rates influenced monkey roughness classifications.


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

The present study examines how tactile roughness is coded in the firing patterns of neurons in the second somatosensory cortex (SII) and whether the correlations between neural and behavioral data explain abilities to classify surface roughness. In a prior study, Sinclair and Burton (1991b) explored the relationship between neural firing patterns and roughness perception. They showed that neurometric functions created from firing patterns in areas 3b and 1 of monkey primary somatosensory cortex (SI) correlated with psychometric assessments of roughness discrimination performance in the same subjects during active touch. No analyses of this kind exist for SII or for passive touch with controlled stimuli. In addition, the relationship between neural firing patterns and perceptual decisions has not been explored for stimuli constructed from multiple physical independent variables.

For tactile gratings, roughness is defined typically in operational terms as variations in the spatial parameters of groove width or spatial period, an appropriate definition if contact force and scanning speed are held constant. However, Lederman and colleagues (Lederman 1974; Lederman and Taylor 1972; Taylor and Lederman 1975) found that force, and to a lesser degree scanning speed, affected human magnitude estimates of roughness. This raises the possibly that contributions of spatial factors and force cannot be separated in the perception of roughness (Foard and Kemler-Nelson 1984; Garner 1974).

In the present study, monkeys learned to classify passively applied surfaces that differed in roughness (tactile grating groove width) while stimulus scanning speeds or contact forces were manipulated independently. We previously described the responses of SII neurons to these parametrically manipulated stimuli (Pruett et al. 2000). While monkeys performed roughness classifications, individual neurons in a sample from SII showed correlated changes in average firing rate with increasing groove width and force (and to a lesser degree, with groove width and speed) (Pruett et al. 2000). If the monkeys had based their classification decisions on the activity of these SII cells, they would have confused narrow groove-width gratings at high force with wide groove-width gratings at low force. Results show performance consistent with this prediction. For some individual neurons in SII, average firing rate changes with groove width and force correlated with monkey mean accuracy levels and with trial-wise correct and incorrect responses. The correlation of behavioral classification and neural response patterns in the present study supports the conclusion that roughness is an integration of predominantly groove width and force represented as a single continuum of neural activity in a subset of SII cells.


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Our earlier paper (Pruett et al. 2000) described the procedures for passive application of tactile gratings, sequences of events in the monkeys' tasks, single-unit recordings in SII, basic data analyses, and histological identification of recording sites. All surgical, preoperative, and postoperative procedures were in accord with the National Institutes of Health's Guide for the Care and Use of Laboratory Animals.

Stimuli

Tactile gratings consisting of alternating ridges and grooves were manufactured by photoetching polyamide plastic (Nyloprint; Process Color Plate, Chicago, IL). Grating groove width was manipulated to create the variable for roughness. Four gratings (1.07-, 1.42-, 1.90-, and 2.53-mm groove widths) were used. Ridge width was a constant 0.25 mm. Each grating strip consisted of a 22 × 70 mm smooth start pad, followed by a 22 × 50 mm grating, with ridges and grooves oriented across the short dimension perpendicular to the direction of motion.

Task

The 1.07- and the 1.42-mm groove-width gratings were designated as smooth, and the 1.90- and 2.53-mm groove-width gratings as rough. A smooth and a rough grating were paired (1.07 vs. 1.90 or 1.42 vs. 2.53 mm) in each block of trials (see following text) for both the speed/groove-width and force/groove-width experiments. The groove-width differences for the pairs were chosen to be nearly equally discriminable on the basis of previous experiments (Sinclair and Burton 1991a). Blocks of trials with these different grating pairs were alternated to characterize roughness response functions for each cell. Each grating of a pair was presented randomly an equal number of times in each block.

Two adult (~9 kg) rhesus monkeys (Macacca mulatta) performed a one-interval, two-alternative, forced-choice roughness classification task during passive application of the gratings. One grating was presented per trial and classified by the monkey as rough or smooth (see following text). Monkeys sat in a modified primate chair facing a computer monitor that supplied cues during execution of the task. A partition blocked view of the gratings. On each trial, a robotic stimulator moved a grating from proximal to distal (the long axis) across a single finger pad at one of three levels of force (30, 60, or 90 g) and speed (40, 80, or 120 mm/s). The stimulated finger was inserted, ventral pad down, through a hole in a plastic hand restraint. The distal tip of the finger engaged a microswitch. The distal pad of the finger contacted the stimulus surface. The other fingers of the stimulated hand were channeled to microswitches in the hand restraint. Opening of any switch aborted the trial and initiated a penalty time-out period. Monkeys used the foot contralateral to the stimulated hand to operate a pedal with a three-position microswitch. Software monitored foot pedal position, controlled cuing and stimulation, and recorded neural and behavioral data.

Figure 1 shows the sequence of cues, stimulation, and example neural responses during trials on a grating with groove width of 1.07 mm at 60 g force and 80 mm/s speed. Thin vertical bars on the rasters and histogram demarcate the period of neural discharge used to compute average firing rate (AFR), force, and speed, which were used in statistical analyses. This interval excluded transient responses to transition from the smooth surface that preceded the grating and to the point at which the fingerpad left the grating at the end of the stroke. This window corresponded to the last 40 mm of each 50-mm grating. AFR was computed as the number of spikes divided by window duration. Stimulation and window duration varied with speed (1,000-, 500-, and 333-ms windows for 40, 80, 120 mm/s, respectively). Monkeys controlled the pace of stimulation.



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Fig. 1. Trial event timing and example raster and histogram for 1 grating. Thin vertical bars (7 and 8) on the rasters (A) and histogram (B) demarcate the period of neural discharge used to compute average firing rate (AFR) for statistical analyses. During this data window, the fingerpad is completely in contact with the grating, which is moving at constant velocity. This window period excluded response transients visible in the rasters and histograms that corresponded to initial and trailing contact with the grating. The gray trace in the histogram represents average force level during these 7 trials. Force rose as the grating pressed against the finger (4), and transiently at grating contact (6). Rasters and histogram represent data obtained on a grating with 1.07-mm groove width at 60 g force. C: the intertrial interval begins at the interval labeled 1 and ends with the white ready box (2). The white box prompts the monkey to bring the foot pedal to the middle (neutral) position (3) and close all finger switches. A green cue box (3) signals that the finger switches are closed and stimulation will begin in 200 ms. The grating begins to move at the time labeled 5. The filled portion of the grating box represents grating contact. The unfilled portion represents contact with the smooth surface starting pad. There is no surface contact after the stimulated finger leaves the grating. The grating decelerates and stops at the time labeled 9. The animal must wait for offset of the green cue light (10) before making a behavioral response. Behavioral discrimination (smooth or rough) is made by positioning the foot pedal (up or down) (11). Bin size was 30 ms. Numbers in A-C correspond to the same events.

A trial began when the computer presented a white cue box on the monitor. This cue prompted the monkey to bring the foot pedal to the middle (neutral) position and close all finger switches, which guaranteed finger contact with the smooth pad of the grating strip. Following this, a green cue box on the monitor signaled that stimulation would begin in 200 ms. The robotic stimulator moved gratings across the long axis of the fingertip. A linear potentiometer measured the position of the grating. A force transducer under the grating measured applied force level. These data were recorded throughout the trial.

Behavioral discrimination (smooth or rough) was signaled by manipulation of the foot pedal from neutral into one of the other two positions (up or down). A reward of ~1 ml of fruit-flavored liquid followed correct classifications of groove width. A red monitor screen, an alarm tone, and a 10-s inter-trial penalty time identified incorrect classifications.

Speed was manipulated in one set of experiments. In these speed/groove-width experiments, monkeys had to ignore scanning speed to classify groove width correctly. Force was manipulated in another set of experiments. In these force/groove-width experiments, monkeys had to ignore force to make correct classifications about groove width. Each block of trials was a factorial cross of two gratings (rough and smooth), either three target force levels or three speeds depending on the experiment, and two different response time delays (700 and 1,500 ms: these delays provided additional behavioral control and will not be considered further). Blocks of trials consisted of several random shuffles of these 12 stimulus conditions, except trial sequences requiring the same operant response more than four times in a row were not allowed. Blocks of 24 or 36 trials were alternated between each pair of gratings. This design allowed assessment of effects of groove width, force (speed), and interactive effects of groove width and force (groove width and speed) for both monkey roughness classification performance and single-unit AFRs. Recording isolation times (~60 min) typically allowed only data collection for the speed/groove-width or force/groove-width experiment for each cell. A speed of 80 mm/s was used in all force experiments. A force of 60 g was used in all speed experiments.

Single-neuron recording procedures

Neural action potentials were recorded using a commercial hardware and software system (DataWave, Longmont, CO) with 100-µs resolution. In addition, acceptance pulses from a dual amplitude and time window discriminator (BAK, Rockville, MD) were recorded.

Cells were often isolated for study while the animals worked. We selected cells that appeared to be engaged by the task. When recording stability permitted, spatial extent of receptive field, traditional response adaptation characteristics, and submodalities were assessed with manually applied punctate indentation with calibrated nylon filaments (Stoelting).

Analyses

Behavioral analyses explored the ways that force and speed affected grating roughness classification performance. First, a two-way ANOVA (proc GLM; SAS v6.12) tested for effects of experiment type (force/groove width or speed/groove width), grating pair discriminability (1.07 vs. 1.90 or 1.42 vs. 2.53 mm), and the experiment type by grating pair interaction. Percent correct for each grating pair block (>19 trials) in the different experiments was the dependent variable. Additional analyses examined accuracy in force/groove-width and speed/groove-width experiments, separately. Percent correct for each stimulus condition (e.g., for each groove width by force combination) was the dependent variable for recording experiments with >= 40 trials. Two-way ANOVA tested the effects of groove width and force or speed and for groove width by force or groove width by speed interactions on roughness classification performance. Both analyses pooled data from many recording sessions to create residual error terms for ANOVAs because of the low number of trials in any one recording experiment. The two monkeys behaved slightly differently, which required separate analysis of their performance data.

Means for significant omnibus ANOVA effects were compared post hoc using Tukey's studentized range (HSD) test. Means for interactive effects of groove width and force or groove width and speed were compared post hoc in a similar way. Thus the 12 levels of the groove width by force or groove width by speed interaction became nominal levels of a condition variable. A one-way ANOVA (model: percent correct = condition) with Tukey's HSD Test assessed the significance of differences in mean percent-corrects for levels of this condition variable.

The number of trials available for analysis was limited because inter-trial grating repositioning with our robotic stimulator was slow, which dictated a stimulation rate of two to three trials per minute (Pruett et al. 2000). Therefore we could not use standard multivariate statistical analyses of force and groove-width effects on performance and AFR. For similar reasons of limited trial data per recording session, we could not determine the receiver operating characteristics (Green and Swets 1988) to calculate conditional probabilities of rough/smooth decisions as functions of AFR. Instead, plots of trial-wise roughness classification choices versus groove width, force, and average firing rate provided evidence of the relationship between SII cell AFRs and roughness classification decisions.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Data sample

Analysis of performance data was restricted to that collected during recordings from 86 cells histologically verified to be located in SII and tested with >= 40 trials of grating stimuli. The effects of parametric manipulations of groove width, force, and speed were examined for effects on roughness classifications before correlations were sought between possible classification patterns and stimulus effects on neural responses. The number of cells contributed by each animal to each experiment are shown in Table 1.1


                              
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Table 1. Number of SII cells with data collected from >= 40 trials, in each experiment for each monkey

Effects of groove width, force, and speed on performance

Both monkeys performed equally well on trials testing 1.07- versus 1.90-mm gratings and those testing 1.42- versus 2.53-mm gratings. However, the two animals differed in their sensitivity to force and speed variables. In MM23, mean percent correct for force/groove-width experiments (83%) was significantly lower than that for speed/groove-width runs (90%) [F(1, 599) = 17.22, P = 0.0001]. MM26 showed no significant difference between force and speed experiment types (force/groove-width, 92.4%; speed/groove-width, 90%). Neither monkey showed a grating set by experiment type interaction, indicating, for monkey MM23, that impairment in performance for the force experiments was uniform across the grating pairs. As intended, both monkeys appeared to find the grating pairs nearly equally discriminable.

A more detailed analysis was performed for each of the 12 groove width by force combinations or each of the 12 groove width by speed combinations. As shown in Fig. 2, left,, error patterns were very similar in the force/groove-width experiments, even though MM26 performed better than MM23. Performances on the roughest (2.53 mm) and smoothest (1.07 mm) gratings were generally higher than those for the middle two gratings. These performance differences between gratings were confirmed by significant main effects of groove width [MM23: F(3, 227) = 13.72, P = 0.0001; MM26: F(3, 179) = 10.31, P = 0.0001].



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Fig. 2. Left: performance in the force/groove-width experiments. Bar graphs show mean percent corrects during all 12 groove width by force conditions for each monkey. Hollow and filled bars represent accuracy in classifying smooth and rough gratings, respectively. Monkey MM26 (right, average of 15 cells) performed better than monkey MM23 (left, average of 19 cells). However, both monkeys showed similar interaction between groove width and force on performance. They both made many errors on the 1.42-mm grating at 90 g and the 1.90-mm grating at 30 g. Right: performance in the speed/groove-width experiments. The 2 animals differed in the pattern of errors. Monkey MM26 (right, average of 21 cells) had a significant interactive effect of speed and groove width on performance, while MM23 (left, average of 31 cells) did not. Error bars are 1 SE.

Force had different effects on the four gratings and had a greater effect on the middle two, 1.42 and 1.90 mm [MM23: F(2, 227) = 18.91, P = 0.0001; MM26: F(2, 179) = 3.93, P = 0.01]. In fact, performance was worse on the 1.90-mm groove-width grating at 30 g and on the 1.42-mm grating at 90 g. These performance differences partly explain the significant groove width by force interactions in the ANOVAs [MM23: F(6, 227) = 14.86, P = 0.0001; MM26: F(6, 179) = 4.60, P < 0.001]. These results are consistent with the monkeys' perceiving the rough grating as smooth at low force and the smooth grating as rough at high force.

Post hoc tests for pair-wise differences in mean percent correct at the 12 stimulus combinations were run on nominal re-coded levels of the interaction term (see METHODS). The results of Tukey's HSD test on these mean percent-corrects are listed in Table 2 for monkey MM23 and Table 3 for monkey MM26, which confirm the low performance scores for both monkeys (high to low performance reads top to bottom in these tables) on the 1.42 mm/90 g and 1.90/30 g combinations (* in tables). In these tables mean percent corrects with the same letter in any column on the left are not significantly different (e.g., AB is significantly different from CD, but ABC is not significantly different from CD). For example, Table 2 shows that monkey MM23 performed significantly worse on the 1.90-mm grating at 30 g than on the same grating at 60 and 90 g.


                              
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Table 2. Tukey's studentized range (HSD) test for percent correct at all combinations of groove width and force:monkey MM23


                              
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Table 3. Tukey's HSD test for percent correct at all combinations of groove width and force: monkey MM26

In the speed/groove-width experiments (Fig. 2, right), monkey MM23 performed better on the 1.07- and 2.53-mm groove-width gratings than on the intermediate ones and showed a significant effect of groove width [F(3, 371) = 10.07, P = 0.0001]. The post hoc analysis of the differences in mean performance for MM23 found significantly higher accuracy on the 1.07-mm grating (94.9% correct) than on the 1.42 (87.8%) and 1.90 mm gratings (83.3%). The effect of speed and the groove width by speed interaction were not significant. The absence of a groove width by speed interaction for monkey MM23 contrasts with the groove width by force interaction shown in Fig. 2, left.

Monkey MM26 had a more complicated performance pattern. This animal had significant effects of groove width [F(3, 251) = 14.86, P = 0.0001] and speed [F(2, 251) = 9.23, P = 0.0001] and had a significant groove width by speed interaction [F(6, 251) = 5.27, P = 0.0001]. This interaction was characterized by a reversal of the performance advantage of the 1.42- over the 1.90-mm grating at 40 and 80 versus 120 mm/s. The post hoc analysis of re-coded levels of the groove width by speed interaction term found that MM26 performed significantly better on 1.42 mm (96.4%) versus 1.90 mm (77.6%) at 80 mm/s but not at 120 mm/s (82.5 vs. 95.2%).

In summary, both monkeys performed equally well classifying the two rough-smooth pairs of gratings. Force variations disrupted the monkey's groove-width (roughness) classifications more than did speed variations. Force had a greater effect on classification accuracy of the 1.42- and 1.90-mm groove widths. In particular, classification accuracy was lowest on the 1.90-mm groove-width grating (rough) at 30 g and the 1.42- mm grating (smooth) at 90 g. These results are consistent with the monkeys perceiving the rough grating as smooth at low force and the smooth grating as rough at high force.

Correlations between stimulus effects on SII cell AFRs

Previously (Pruett et al. 2000) we found SII cells with positive or negative AFR response functions for increasing groove width, force, or speed. For example, the rasters in Fig. 4B show generally increasing responses with increases in groove width and force. Figure 5B shows the opposite relationship with decreasing AFR as groove width and force increase. A sample of 32/34 SII single units had significant groove width or force ANOVA effects. Response patterns of these cells were similar to those illustrated in Figs. 4B and 5B and showed strong linear relationships between single-unit average firing rates and groove width, force, or speed. The strengths of these linear relationships with the effects of the other variables removed were examined with calculation of partial Pearson correlation coefficients of AFR with groove width (rAFR/GW) and force (rAFR/FORCE) and speed (rAFR/SPEED). In this sample of 32 cells, rAFR/GW correlated with rAFR/FORCE (r = 0.63, P = 0.0001), and the slope of the regression equation (0.57 ± 0.13) predicted rAFR/FORCE from rAFR/GW. This linear relationship of AFR with groove-width and force was strong and individually consistent for each monkey. Values of rAFR/GW and rAFR/FORCE for each cell were plotted against each other for each monkey in Fig. 3, A and B, to illustrate how these stimulus parameters were positively or negatively correlated. Regression lines with 95% confidence intervals have been superimposed on the data. For both monkeys, rAFR/GW correlated with rAFR/FORCE (MM23: r = 0.57, P < 0.02, n = 18; MM26: r = 0.72, P < 0.005, n = 14), and the slopes of the regression lines predicting rAFR/FORCE from rAFR/GW were >0 (MM23: slope = 0.55 ± 0.20, T = 2.74, P < 0.02; MM26: slope = 0.57 ± 0.16, T = 3.56, P < 0.005).



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Fig. 3. Scatter plots of partial Pearson correlation coefficients of AFR with groove width, force, and speed. A and B: in these scatter plots, each cell was assigned a position based on its partial Pearson correlation coefficients between AFR and groove width, rAFR/GW, and AFR and force, rAFR/FORCE. The symbols generally scatter to the bottom left and top right quadrants. Note the consistency of the correlation across animals.  in A is the cell with negative response functions depicted in Fig. 5.  in A is an exceptional counter-example (see text).  in B is the example cell for Fig. 4. Only well-characterized second somatosensory cortex (SII) cells with significant ANOVA effects of groove width and/or force were included (n = 18 in A and 14 in B). C and D: each cell was assigned a position based on its partial Pearson correlation coefficients between AFR and groove width, rAFR/GW, and AFR and speed, rAFR/SPEED. The pattern is less consistent across animals than for the rAFR/GW's and rAFR/FORCE's. The correlation in D is weaker than those in A and B, and in C, it fails to reach significance. Only well-characterized (40+ trials across all 4 groove widths) SII cells with significant ANOVA effects of groove width and/or speed were included (n = 19 in C and 21 in D). Regression lines with 95% confidence intervals have been overlaid.

Values of rAFR/GW and rAFR/SPEED for each cell were plotted against each other for each monkey in Fig. 3, C and D. The partial correlation rAFR/GW and rAFR/SPEED correlated significantly [r = 0.42, P < 0.01]. The slope of the regression equation predicted rAFR/SPEED from rAFR/GW and differed from 0 (0.37 ± 0.13, T = 2.86, P < 0.01) (Pruett et al. 2000). The correlation between rAFR/GW's and rAFR/SPEED's failed to reach significance for monkey MM23, and the slope of the regression line predicting rAFR/SPEED from rAFR/GW was not significantly different from 0. For monkey MM26, rAFR/GW correlated with rAFR/SPEED (r = 0.46, P < 0.05, n = 21) and the slope of the regression equation predicting rAFR/SPEED from rAFR/GW was >0 (slope = 0.33 ± 0.14, T = 2.29, P < 0.05).

Neural and behavioral confusion matrices

The less than unity slopes for the regression equations relating rAFR/FORCE and rAFR/GW imply that the groove-width effect dominated the cells' responses. However, a positive correlation between rAFR/FORCE and rAFR/GW means, on average, AFR increased with both increasing groove width and increasing force or decreased as both of these variables increased. Examination of single units in both monkeys with positive or negative correlations between rAFR/FORCE and rAFR/GW showed that firing patterns at the single-unit level correlated with the monkeys' trial-wise roughness classification error patterns in the force/groove-width experiments.

The results shown in Fig. 4 present one of these examples from a cell recorded in monkey MM26 during a force/groove-width experiment. A  in Fig. 3B marks the location of this cell, which shows the strong correlation between rAFR/GW and rAFR/FORCE. This cell's neural data for 121 trials has been arranged in a 4 × 3 bar graph on the right in Fig. 4A and a raster matrix in Fig. 4B. The bar graph shows that AFR increased with groove width [F(3, 102) = 23.11, P = 0.0001, rAFR/GW = 0.67, P < 0.0001] and with force [F(4, 102) = 10.94, P = 0.0001, rAFR/FORCE = 0.40, P < 0.0001].2 The increasing density of raster tic marks moving from left to right (increasing groove width) and top to bottom (increasing force) in this neural raster matrix (Fig. 4B) illustrates the trial-wise consistency of these effects. A groove width by force interaction [F(11, 102) = 4.26, P = 0.0001] can be appreciated by noting the more rapid increase in raster tic density moving from top to bottom as one scans left to right across the matrix; i.e., force had a greater effect on AFR on wide groove-width gratings.



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Fig. 4. Neural and behavioral confusion matrices. A: bar graphs on the left show mean percent corrects at all 12 groove width by force conditions for monkey MM26, while the neural data shown in the right bar graphs and in B were recorded. The bar graphs on the right show firing rates averaged (AFR) over trials in each of the same 12 conditions. The monkey made many errors on the 1.42-mm (vs. 2.53 mm) grating at 90 g and the 1.90-mm (vs. 1.07 mm) grating at 30 g (circled for comparison with corresponding bars at right and rasters in B). B: the neural rasters from 1 cell (m26c15) show data for the same 12 groove width by force conditions. In each raster line, a tic marks the time of occurrence of an action potential during the time when the finger tip was in full contact with the grating. From top to bottom in each raster, trials occur in the time order that the monkey saw them. The vertical bars demarcate the interval used to compute AFR shown in the bar graphs at the top right. These same intervals were selected for statistical analysis of effects of groove width and force on AFR (raster tic density). This cell's AFR increased more steeply with increasing groove width as force increased. The rasters for 1.42 mm at 90 g and 1.90 mm at 30 g are circled to highlight correspondence with those same elements in the behavioral and neural data in A. C: an example of the oscilloscope trace demonstrates the recorded action potential isolation quality. Window discriminator acceptance pulses are visible below each spike. D: the box indicates the stimulated finger on the left (L) hand. Darker shading indicates more vigorous responses to manual stimulation in the receptive field. The cell responded to cutaneous stimulation of glabrous distal pads of digits 3-5. It also responded to a vibrating 256-Hz tuning fork, indicating a degree of Pacinian-receptor-like adaptation characteristics.

The 12-element dot-raster matrix parallels the performance matrix (Fig. 4A, left) created by graphing monkey MM26's percent corrects for these same trials at the 12 groove-width and force combinations. The performance matrix is arranged in the same format as the ones on the left side of Fig. 2 (except that there are no SE marks because each bar in Fig. 4A represents 1 data point). We call the bar graph on the left in Fig. 4A a behavioral confusion matrix because the circled points show where force confused this monkey's roughness classification decisions. At low force, MM26 often called the rough, 1.90-mm groove-width grating "smooth" (low percent correct), and at high force it, often called the smooth, 1.42-mm groove-width grating "rough" (low percent correct). In the right bar graph in Fig. 4A and the rasters in Fig. 4B, the circled elements of each neural matrix correspond to the circled elements of the behavioral confusion matrix shown in the left bar graph in Fig. 4A. As this cell's AFR increased with roughness (left to right in both panels), its AFR at 1.42 mm and 90 g was higher (denser raster element) than that for smooth gratings in other conditions due to the nonlinear effect of force. Likewise, its AFR at 1.90 mm and 30 g was lower (low raster tic density) than that for rough gratings in other conditions due to the effect of force. If the monkey based its roughness classification decisions on this cell's AFRs, it would make errors at these groove-width and force combinations.

Cells with negative AFR functions of increasing groove width and force similarly showed correspondence between neural and behavioral confusion matrices. Figure 5, A and B, shows behavioral and neural confusion matrices for a cell from monkey MM23. A  denotes the position of this cell in the bottom left part of the scatter plot in Fig. 3A. This cell had decreasing AFR functions for both groove width and force. The decreasing density of raster tics in Fig. 5B and the decreasing AFR shown in the right bar graph of Fig. 5A moving from 1.07 to 2.53 mm and 30 to 90 g illustrate this cell's negative main effects of groove width [F(3, 83) = 3.09, P = 0.03, rAFR/GW = -0.32, P = 0.001] and force [F(4, 83) = 16.94, P = 0.0001, rAFR/FORCE = -0.67, P < 0.0001]. The effect of groove width on AFR did not vary significantly with force [F(10, 83) = 1.85, P > 0.06], but the trend is visible in the neural matrix as a greater decrease in AFR with increasing force on rougher surfaces.



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Fig. 5. Negative neural and behavioral confusion matrices. A: the behavioral matrix of bar graphs on the left show mean percent corrects at all 12 groove width by force conditions for monkey MM23, while the neural data shown in the bar graphs on the right and in B were recorded. The bar graphs on the right show firing rates averaged (AFR) over trials in each of the same 12 conditions. B: the neural rasters from 1 cell (m23k10) show data for the same 12 groove width by force conditions. This cell's average firing rate decreased with groove width and force. For simplicity, 1 corresponding element in each of the neural and behavioral matrices has been circled but patterned similarity between the matrices extends across essentially all conditions. C: an example oscilloscope trace demonstrates isolation quality of recorded action potentials. D: the cell gave an intermediately adapting response to manipulation and flexion of all digit joints.

The results shown in Fig. 5 illustrate reversed logic from that considered in Fig. 4 because of the negative effects of groove width and force on AFR. In the left bar graph of Fig. 5A, at 30 g of force, the monkey made errors on both rough gratings, but more on the 1.90-mm surface, and at 60 g, it made errors on the 1.90-mm grating. At 60 g, it also sometimes incorrectly classified the 1.42-mm grating, and at 90 g, it showed a greater tendency to classify mistakenly both smooth gratings as rough; this results in lower performance than at 30 g on these same gratings. For simplicity of illustration, only the 1.90-mm, 30-g element is circled. In the raster matrix in Fig. 5B, force altered the expected pattern of decreasing tic density with increasing groove width at 1.90 mm at 30 g, 1.90 mm at 60 g, and 2.53 mm at 30 g. This is also clearly shown as higher AFR in the right bar graph. These were the same three stimulus conditions where monkey MM23 showed impaired performance for the rough gratings. Similarly, for the smooth gratings of 1.42 mm at 90 g, 1.07 at 90 g, and 1.42 mm at 60 g, lower than expected tic density and AFR was found given a negative effect of groove width for this cell. More performance errors also occurred on these same force/groove-width trials.

Examples of the trial-wise associations of neural responses and roughness classifications are shown in plots of AFR versus groove width and force in Fig. 6. Graphs on the left side show data from three cells with positive force and groove-width correlations. The top left graph shows data from the example cell in Fig. 4. Graphs on the right side show data from three cells with negative force and groove-width correlations. The top right graph shows data from the example cell in Fig. 5. In these graphs, each symbol represents AFR data from a single trial that was assigned a position in the horizontal plane based on the applied groove width and force during that trial. Each symbol is color-coded according to how the monkey classified the grating on that trial: green for smooth and red for rough. Red symbols on the 1.42- or 1.07-mm rows (smooth) and green symbols on the 1.90- and 2.53-mm rows (rough), therefore represent errors. The colors distribute according to the patterns that have been discussed in the preceding text, but the primary issue, here, is the relative height of the colored symbols representing error trials in these graphs. Note that error trials on rough gratings for the positive cells generally occurred when AFRs were low. Error trials on smooth gratings for these cells generally had high AFRs. Similarly, for the negative responding cells in the graph, errors tended to occur when AFRs were high on the rough gratings. Twenty-nine of 32 cells with significant ANOVA effects for groove width or force had either both positive (n = 16) or negative (n = 13) correlation of these two variables with AFR. Twenty-one of these cells had sufficient classification errors to show neural and classification confusion patterns similar to those illustrated in Fig. 6.



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Fig. 6. Plots of roughness choice vs. AFR, groove width, and force in 6 SII cells. These graphs plot monkey roughness classification choice (red for rough) and (green for smooth) as a function of groove width, force, and AFR. Graphs on the left show data for three cells with positive force and groove width correlations (cells m26c15, m26o03, and m23k03). Data in the top left graph came from the example cell in Fig. 4 (m26c15). Graphs on the right show data for three cells with negative force and groove width correlations (cells m23k10, m23j13, and m26d03). Data in the top right graph came from the example used in Fig. 5 (m23k10). For the cells with positive response functions (left), high AFR would mean rough. Note that errors on the 1.90- and 2.53-mm gratings ("rough") occur when AFRs are low. For the cells with negative response functions (right), high AFR would mean smooth. Note that errors on the 1.90- and 2.53-mm gratings ("rough") occur when AFRs are high.

The relationship between roughness classifications and neural discharges is summarized as average psychometric functions in Fig. 7. Figure 7A shows data averaged from ten cells with the strongest positive force and groove-width correlations [mean AFR = 51 ± 2.4 (SE) imp/s]. The percentage that an animal called a surface rough (% called rough) was computed for each cell and for each of the 12 combinations of force and groove width. Average firing rates for each of these combinations was normalized as a percentage of the maximum AFR (%Maximum AFR) for that cell, and then averaged across cells. Separate curves are shown for each grating groove width, and symbols identify force level. Several points fall in the middle range of percent-called-rough and correspond to combinations of force and groove width that were misclassified. In particular, a smooth grating groove width of 1.07 mm at 90 g evoked the same relative discharge rate as a rough grating of 1.90 mm at 30 g but quite different classification accuracy. This suggests a bias toward responding "smooth" with this particular firing rate in the sample of positive cells. Figure 7B shows similar findings for data derived from 10 cells with the strongest significant negative force and/or groove-width correlations (mean AFR =27.9 ± 2.4 imp/s). Figure 7, A and B, shows that the animals made mistakes when two combinations of groove width and force evoked the same normalized average discharge rate in this sample of cells. Further, note that AFR for the 1.42-mm gratings tested at 90 g is greater than that of the 1.90-mm grating tested at 30 g for positively correlated cells (Fig. 7A) and lower for negatively correlated cells (Fig. 7B). Most discrimination errors were made on these surfaces. These findings are consistent with the interpretation that the discrimination errors are related to the relative firing rates of these two populations of neurons.



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Fig. 7. Psychometric functions showing the relationship between roughness classification and neural discharge. Data averaged from 10 cells with significant positive force and/or groove width correlations (A; 4 cells from MM23 and 6 cells from MM26) and data from 10 cells with significant negative force and/or groove width correlations (B; 7 cells from MM23 and 3 cells from MM26) show that classification errors (% called rough) occurred when different combinations of groove width and force evoked similar normalized average firing rates (%Maximum AFR) in either cell subset. Note that the response functions of these 2 types of cells peak at opposite ends of the range of groove widths studied. C: average percent called rough (Ave % called rough) is plotted as a function of the difference in activity of cells with positive and negative response functions (%Maximum AFR difference). Classification errors occurred when positive and negative cell subsets were activated approximately equally. Separate curves are shown for each grating groove width (1.07, 1.42, 1.90, and 2.53 mm), and symbols identify force level. Linetype identifies grating pairs. Error bars represent 1 SE. Selected SEs shown for %Maximum AFR are referenced in text. See text for further details.

The response functions of these two types of cells peak at opposite ends of the range of groove widths studied. Positive correlated cells respond with highest AFRs at the rough end of the range. Negative correlated cells respond most at the smooth end. Together, the two cell subsets provide more information than either separately. Figure 7C shows the relationship between classifications and the difference in activity between cells with positive and negative response functions. The difference in firing rates between the cell subsets was computed by subtracting the mean normalized firing rates of negative from positive correlated cells for each of the 12 combinations of groove width and force.3 Roughness classification percentages were averaged over the two cell groups for each of these same combinations. Separate curves are shown for each groove width and with different symbols for each force level. Figure 7C shows that classification errors occurred when differences between the positive and negative cell subsets were small (i.e., closer to 0). For example, the mean difference in activation of the two cell subsets was the same for groove widths of 1.07 mm at 90 g and 1.9 mm at 30 g, respectively. Response bias was toward classifying both surfaces as smooth, suggesting reliance on the greater activation of the negative than positive cells. Groove widths of 1.42 mm at 90 g and 2.53 mm at 30 g were also confused. In this case, the mean differences in the AFRs for the two surfaces only approximately overlapped. Consistent with prediction, this separation was associated with less response bias, and more equal error rates on both surfaces. Performance accuracy was high when activation differences were above about +40% and below about -30%.


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

We found that force as well as groove width affected monkey roughness classifications, suggesting that the perception of roughness largely depends on integration of these two variables. The behavioral and neural confusion patterns seen with this passive touch roughness classification task support the conclusion that roughness (integrated groove-width and force) is represented as a single continuum of neural activity in a subset of SII cells.

Behavioral confusion

The most robust behavioral finding was the combined effects of grating groove width and force on behavioral responses. This was seen as a tendency to confuse narrow groove-width gratings at high force with wide groove-width gratings at low force. Monkeys in the present study performed generally according to predictions from the best available human model for roughness magnitude estimation of tactile gratings. Lederman studied human roughness magnitude estimation for actively touched gratings that varied in groove width, from 0.125 to 1.0 mm, under conditions of varying speed, from 10 to 250 mm/s, and force, from 28 to 448 g (Lederman 1974). She found a 4-dB increase in roughness magnitude estimates (perceived roughness) for each doubling of groove width, a 1- to 2-dB increase for a 9-fold increase in force, and only a 1-dB decrease for a 25-fold increase in speed. For 0.375- to 1.0-mm groove-width gratings, force and groove width changed perceived roughness as
force<SUP>4</SUP> = groove − width (1)
Perceived roughness was approximated as
<IT>k</IT>(<IT>force∗groove−width<SUP>4</SUP></IT>)<SUP><IT>1/4</IT></SUP> (2)
where k is an arbitrary (positive) constant (Lederman 1974). The most problematic stimulus combinations for monkeys in the present study were 1.42 mm groove width at 90 g and 1.90 mm at 30 g. This error pattern suggests tripling force had roughly the same effect on perceived roughness as multiplying groove width by about 1.3. This relationship approximates to Eq. 1 because 1.34 congruent  3 and extends the application of Lederman's model to a larger range of groove widths.

The behavioral data suggest that the monkeys employed slightly different strategies to solve the task. Both animals made more errors on some combinations of groove width and force. Only one monkey (MM26) made more errors when speed was varied. Likewise there was a significant positive correlation between partial Pearson correlation coefficients for average firing rate and speed and for average firing rate and groove width only for the SII cell population recorded in monkey MM26. Finding that the neural response and behavioral response patterns correlated, even for these differences between monkeys, adds weight to the contention that their classification decisions depended on the activity of these SII cells. Monkey MM23's lower performance in the force/groove-width experiments relative to that in the speed/groove-width experiments is also consistent with human psychophysical data that have shown force has a greater effect on roughness perception than speed (Lederman 1974; Lederman and Taylor 1972).

Neural confusion

If the monkeys relied on the average firing rates of SII cells to make their roughness classification decisions, they should have made errors on narrow groove-width gratings at high force and wide groove-width gratings at low force, as they did. Average firing rates in the SII sample showed correlated changes for increasing grating groove width and increasing contact force. For example, low force on wide groove-width gratings produced lower than expected average firing rates from cells with positive response functions of increasing groove width and force. The reverse held for cells with negative response functions. Not only was this general observation consistent, but for a subset of single cells with responses that were positive or negative functions of groove width, the pattern of mean firing rates, at the 12 groove width and force combinations used in this study, correlated with the pattern of mean percent-corrects for those same 12 stimulus conditions. Specifically, this meant that, for many cells, mean firing rates on individual trials predicted correct or incorrect roughness classification decisions (see Fig. 6). One interpretation is that the monkeys classified high firing rates from SII cells with positive response functions as rough gratings and low firing rates as smooth gratings (and the reverse from cells with negative response functions).

Neural substrates for roughness choice

A number of studies (reviewed in Parker and Newsome 1998) have described correlations between central and peripheral neural events and perceptual decisions with neural and behavioral data that were acquired simultaneously (Britten et al. 1996; Celebrini and Newsome 1994, 1995; Dubner et al. 1989; Hsiao et al. 1993; Johansson and Vallbo 1979; Logothetis and Schall 1989; Merchant et al. 1997; Mountcastle et al. 1990; Newsome et al. 1989; Romo et al. 1997, 1998; Salzman et al. 1990; Sinclair and Burton 1991b). Sinclair and Burton (1991b) found that "neural Weber fractions" for area 3b and area 1 cells correlated with Weber fractions for monkey grating roughness discriminations. Roughness discrimination errors occurred for trials with significantly smaller differences in firing rates for pairs of gratings. The present study demonstrates comparable physiological correlates for tactile roughness decisions in SII. Thus both SI and SII may be involved in decisions about roughness.

Roughly equal numbers of cells in the present sample reflected groove width and force as positive and negative response functions. In a simple average, the activity of these two cell groups cancels out. However, an analogy to color vision provides an alternate model. The relative proportions of activity in the red-green and blue-yellow opponent color systems determine hue (DeValois and Jacobs 1968). Similarly, animals might have compared the relative activation of the positive and negative SII cells, which were "tuned" to rough and smooth ends, respectively, of the roughness range used in the present study. The relative activations of these two subsets of cells correlated clearly with classification accuracy (see Fig. 7C). Animals made discrimination errors when both populations fired at moderate rates. These conclusions are based on correlative evidence. It is not possible to claim that only this cell population accounts for the animal's discrimination performance. However, the neurons described here are excellent candidates for participation in such sensory-based decisions.

Roughness as a combination of groove width and force

We intended groove width to be synonymous with roughness, but under conditions of varying force, groove width did not completely account for the monkeys' roughness classifications. The observed correlation between SII cell mean firing rates and roughness choice, therefore suggests two interpretations. First, it could reflect a neural substrate for the limits of roughness perceptual constancy in the face of varying force. Second, it may represent a neural substrate for integration of groove width and force into a "roughness" stimulus that the monkeys dichotomously classified along one intensive dimension. Both of these interpretations have merit. If the monkeys knew there were only four gratings and tried to ignore variations in force, these findings would pertain to perceptual constancy for roughness. If they perceived each feature combination as unique so that they classified many stimuli with two different foot pedal responses, the findings might better be considered in the context of integration for the features of roughness (see below). It is impossible to know what knowledge the monkeys had about these stimuli. The data, therefore, need to be considered in both contexts.

There are some similarities between the effects of groove width and force on SII cell response functions and descriptions of invariant responses in the visual system that may constitute neural substrates for some forms of visual perceptual constancy. For example, a cue-invariant MT cell's directional tuning function may peak at the same direction, whether motion is defined by luminance contrast (1st-order motion) or spatial texture (2nd-order motion) (Albright 1992). Since most MT cells would fire less for second order motion stimuli, the relative firing rate differences between subpopulations tuned at different directions reflect the direction of motion.

However, changes in SII cell peak response amplitudes for groove width caused by force pose problems for an invariant representation of groove width because many SII cell response functions have strong linear response function components for both variables (Pruett et al. 2000). There were no separate SII subpopulations tuned to 1.07, 1.42, 1.90, and 2.53 mm, for which peak amplitudes would all be diminished by lowered force. If the monkeys based roughness decisions on mean firing rates of cells such as the examples in Fig. 6, shifts in absolute firing rate levels produced by large changes in force would have confused roughness classifications. The present findings, therefore describe neural correlates for a percept of groove width or roughness that is only relatively constant. Groove width (roughness) perceptual constancy may exist over a range of force levels, but it may break down at the extremes; e.g., with no force there can be no roughness, and at extremely high force a texture becomes noxious. Humans and monkeys may optimize tactile exploration strategies to keep force in the range where roughness perceptual constancy holds. The threefold range of force used in this study may have exceeded this range.

Alternatively, the monkeys may never have experienced levels of groove width independent of force. The individual examples in Fig. 6, and the average functions illustrated in Fig. 7 suggest that animals could have read the response functions of the two subsets of cells correlated in a positive or negative fashion with groove width and force. Stimulus differences corresponding to the distances of the groove-width and force axes in Fig. 6 both produce measurable changes in average firing rate, but there are ranges of firing rates for both positive and negative cells where the force and groove-width coordinates are ambiguous. Clearly in Fig. 7, different combinations of groove width and force produce the same average firing rates. Average firing rates can be ordered on a single continuum that the monkeys may have used to make roughness choices. The present task required a dichotomous decision along this average firing rate scale, with adjustments of the average firing rate criterion for rough and smooth choices based on motivation, finger contact, and other factors.

Lederman and Taylor's experiments (Lederman 1974; Lederman and Taylor 1972; Taylor and Lederman 1975) on human roughness magnitude estimation apply to the integration of force and groove width. The ratio of exponents in Lederman's power function model of groove width and force effects on perceived roughness magnitude (Lederman 1974) suggests relative invariance for effects of groove width over reasonable ranges of force, but it also indicates integration for those dimensions because changing groove width or force has a relative multiplicative effect. The neural-behavioral correlation and the nature of the present task favor the interpretation that groove width and force combine to form a single perception of roughness.


    FOOTNOTES

Address for reprint requests: J. R. Pruett, Jr., Dept. of Anatomy and Neurobiology, Campus Box No. 8108, Washington University School of Medicine, 4566 Scott Ave., St. Louis, MO 63110.

1 Data were obtained from four cells in both the force and speed experiments (Pruett et al. 2000). Behavioral and neural data from these cells were assigned to only one experiment for analyses presented in this paper.

2 While three target or intended force levels were used, the raw data for force/groove-width experiments were parsed into 3-5 25-g force bins for greater sensitivity in the ANOVAs, hence the discrepancy between model degrees of freedom and figure raster force groupings (Pruett et al. 2000).

3 For example, for 1.07-mm groove width at 60 g force, %Maximum AFR was averaged separately for the 10 positive and 10 negative correlated cells used in Fig. 7, A and B. The negative average was subtracted from the positive average to derive a %Maximum AFR difference value for the abscissa of Fig. 7C. This was repeated for each combination of groove-width and force.

Received 19 May 2000; accepted in final form 21 May 2001.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

0022-3077/01 $5.00 Copyright © 2001 The American Physiological Society