JOURNALOFNEUROPHYSIOLOGY Vol. 65. No. 3. March 190 I.

Yt*itlrrd it1I’.S..

I.

Human Tactile Pattern Recognition: Active Versus Passive Touch, Velocity Effects, and Patterns of Confusion FRANCISCO

VEGA-BERMUDEZ,

KENNETH

0. JOHNSON,

Philip Bard Laboratories ~~‘Ne~lrophy.riol~)~y, Department School of Medicine, Baltimore, Maryland 2120.5 SUMMARY

AND

AND

of’Neuroscience, .

STEVEN

S. HSIAO

The Johns Hopkins

University

CONCLUSIONS

ture (Johnson and Phillips 1981; Phillips et al. 1983), employed active touch and stationary touch to characterize the human capacity for tactile letter recognition. They were designedto determine the range of letter sizesthat would be appropriate for neurophysiological experimentation and to map the confusions that occur when the stimuli are near the limits of acuity. Those experiments showed that letter sizes ranging from 3 to 8 mm in height yield recognition levels ranging from 25 to 75% correct judgments. The spatial acuity needed for this task is consistent with the acuity measured using a grating orientation task, in which performance levels begin to rise from chance behavior at spatial cycle lengths > 1.Omm (gaps and bars 500 pm wide) (Johnson and Phillips 1981) and also with the acuity that would be predicted based on the mean spacing between primary afferent fibers in man and monkey, which is close to 1.0 mm (Darian-Smith and Kenins 1980; Johansson and Vallbo 1979). These earlier psychophysical studies and similar studies by Loomis ( 198 1, 1982, 1985) show that the somatosensory system processesspatial information from the fingers with very high precision. If it is accepted as a general principle that experimental precision needsto be at least an order of magnitude better than the phenomenon being investigated, then the primary implication for neurophysiological studies is that the skin-stimulus interaction needs to be measured with a precision measured in tens of microns. Precision of that order is practicable when a stimulus is delivered to a passive restrained hand but not when the hand moves actively over the stimulus. Indeed, neurophysiological studies in which embossedletters are delivered to the passive, restrained hand of a monkey show that peripheral and cortical neurons respond with a precision measured in microns and milliseconds (Johnson and Phillips 1988; Phillips et al. 1988). The experiments reported here serve two purposes. First, they characterize human performance using the samestimulus conditions as in neurophysiological experiments where letters embossedupon the surface of a rotating drum (Johnson and Phillips 1988) are scanned acrossthe skin of a restrained fingerpad at velocities ranging from 20 to 80 mm/s. Second, they addressthe question of the relevance of neurophysiological investigations employing a passive, reINTRODUCTION strained hand for active tactual pattern recognition: can The psychophysical study reported here represents part neural mechanisms identified in such experiments be preof a seriesof psychophysical and neurophysiological studies sumed to operate in active touch? The question of whether there are sensory or perceptual aimed at the mechanisms of tactile pattern recognition. The first studies in this series,which were psychophysical in na- differences between active and passive touch has occupied

1. Subjects without any previous experience in a tactile psychophysics task participated in a study of tactile letter recognition employing active and passive touch. In the active task, subjects reached through a curtain and examined embossed letters with horizontal, unidirectional finger strokes. In the passive task, subjects sat with their arms and hands immobilized while a rotating drum stimulator pressed the embossed letters onto the right index finger. The stimulus conditions in the passive task were identical to those used in neurophysiological experiments with monkeys. 2. A survey of 40 naive subjects who were not screened in any way showed a wide range of performance levels. There was no difference between the subjects in the active and passive tasks, either in overall mean percent correct scores, which were 49.0 and 50.7%, respectively or in the percent correct scores for individual letters whose product-moment correlation coefficient was 0.94. The active and passive groups, which contained 25 and 15 members, respectively, had no members in common. 3. Videotapes of the finger movements of eight subjects in the active task showed a characteristic V-shaped velocity profile (velocity vs. lateral position) starting at - 100 mm/s at the left-hand edge of the plate containing the embossed letter, decelerating to a minimum when the center of the finger was directly over the letter, and then accelerating away from the letter. The average minimum scanning velocity was 17 mm/s. 4. Scanning velocity had no significant effect on performance in the passive task between 20 and 40 mm/s. An increase to 80 mm/s produced a 16% decline in percent correct identifications. 5. Learning effects were evident across sessions even though subjects were given no feedback or training. The increase in mean percent correct j udgments a.veraged 4% per session, wh ich lasted for -1 h. 6. Data from 64 subjects were pooled for detailed co lmparison of identification patterns in active and passive touch. The results were analyzed and found to be consistent with the hypothesis that the identification and confusion probabilities are identical in the two modes. We conclude that there is no difference between active and passive touch in form recognition when the stimulus pattern is smaller than a finger pad. 7. Data from all experiments were pooled to produce a single confusion matrix with 324 presentations per letter. The majority of erroneous responses are grouped in a small number of confusion pairs and the majority of those confusion pairs are strongly asymmetric. The probable neural mechanisms of some confusion patterns are discussed.

OO22-X)77/9

1 $ I SO Copyright

c

199 I The American

Physiological

Society

531

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532

F. VEGA-BERMUDEZ,

K. 0. JOHNSON.

an important position in the literature of tactile sensation since the publication of Gibson’s (1962) important paper on the subject. The question addressed by Gibson was whether it makes a difference whether “the impression is brought about by the perceiver himself. . . [or] . . . by some outside agency.” The question is not one of motor activity or its lack but rather whether the perceiver initiates and controls the acquisition of sensory information (Sherrick and Craig 1982). Gibson argued that “the difference is very important” (Gibson 1962) and substantiated the claim with experimental results that showed a significant difference between active and passive touch in a tactual pattern recognition task. Although Gibson’s experimental results are of questionable validity, his views have had a wide influence in psychological and neurophysiological research (Gordon 1978). If tactual pattern recognition is, in fact, degraded under the conditions of passive touch, then there are two possible explanations. The simplest explanation is simply that the experimenter has failed to provide the passive subject with equivalent sensory information. In the active mode the subject may select contact forces, scanning velocities, and contact geometries that are difficult to duplicate in a passive task. A second explanation, which has serious implications for the interpretation of neurophysiological data obtained from experiments in which the hand is restrained, is that the degraded performance is due to some important difference in the sensory neural mechanisms underlying tactual pattern recognition behavior. It is possible that the somatosensory neural pathways are affected through extrinsic inputs, descending neural pathways and feedback connections whose anatomic and physiological presence is well documented but whose function is unknown (Jones and Powell 1969; Nelson 1984; Towe 1973). If passive touch were to prove inferior to active touch even after extensive efforts to equate stimulus conditions, then neurophysiological results obtained from the restrained hand would have to be interpreted with the proviso that the neural mechanisms might be substantially different in active touch. In this paper we report the results of psychophysical experiments in which the conditions of passive touch are identical to those used previously in neurophysiological experiments (Phillips et al. 1988). In the active touch experiments subjects were instructed to make horizontal, unidirectional scanning movements to make the sensory information in the active and passive tasks as similar as possible. The hands of some active subjects were videotaped to determine the movement patterns and velocities that were actually used. METHODS

The stimuli in all experiments consisted of embossed sans-serif, Helvetica capital letters 6.0 mm in height and ranging in width from 0.5 mm for the letter I to 7.0 mm for the letter W. The Helvetica font is illustrated in Figs. 7- 1 1. The stroke width was 0.5 mm and the letters were raised above the background by 0.5 mm. A height of 6.0 mm was chosen because it yields performance levels of 40-60s correct judgments in most subjects (Phillips et al. 1983). The patterns were constructed from a nylon polymer that is water soluble until exposed to ultraviolet light. The stimulus patterns were produced by UV exposure through a photographic nega-

AND

S. S. HSIAO

tive of the stimulus letters and washing away the unexposed background (Johnson and Phillips 1988). In the active task, the letters were mounted at the centers of flat plastic plates, 55 mm wide. Subjects scanned the test letters by flexion of the metacarpophalangeal joint and mediolateral movement of the fully extended finger of the right hand (regardless of handedness), using whatever combination of wrist and finger movements they desired. Horizontal movement was constrained by a window that was 70 mm wide, which allowed 55 mm of horizontal movement (for a finger 15 mm wide). The subject initially contacted the blank, left side of the plate containing the test letter, scanned the test letter, and then raised the finger for return to the left side of the plate. The subject was instructed to scan only from left to right at whatever speed seemed most comfortable, not to pause during the scan, and not to use anything but a smooth, continuous horizontal traverse. In the first experiment, Expt. 1, subjects were allowed up to 10 s for active scanning to match the time allowed for passive subjects. Subjects most frequently employed three scans in this period. In the remaining active experiments subjects were restricted to five scans to match the number of exposures at a scanning velocity of 20 mm/s in the passive task. The number actually used ranged from one for letters with highrecognition scores, e.g., I, to five for letters with low-recognition scores. In the passive task, the letters were mounted in 26 bands around the surfaces of 67 mm diam plastic drums. Within each band, the same letter was repeated 7 times at 30-mm intervals. The letters were oriented so that their bases lay along a circumferential line and their vertical axes were aligned with the axis of the drum. The subject sat with his right arm resting on a platform, the palm facing downward, and the index finger extending through a slot. The right index finger was used regardless of handedness. The finger axis was aligned with the drum axis and the letters swept a path from right to left across the finger pad, mimicking the contact that occurs when the finger is swept from left to right in the active task, i.e., the letter orientation and scanning trajectory relative to the distal pad were the same as in the active task. During each trial the rotating drum (Johnson and Phillips 1988) moved up to contact the finger with a force of 60 g, which was judged to be a comfortable force, and remained in contact until the subject responded verbally or the maximum contact time, 10 s, expired. The average response time ranged from 5 to 7 s. The time between the end of one trial and the beginning of the next ranged from 5 to 15 s, depending on the time required for the operator to enter the results and initiate the next trial. The constant spacing between letters, 30 mm, resulted in different numbers of letter presentations per trial for different scanning velocities. The maximum and mean numbers of letter presentations (based on 10 s maximum exposure and 6 s mean response time) were 6 and 4 at 20 mm/s, 13 and 8 at 40 mm/s, and 26 and 16 at 80 mm/s. In both passive and active tasks, subjects were told that each stimulus consisted of one embossed capital letter, that the order of presentation was random, that all letters were equally likely, and that they were required to respond verbally with 1 of the 26 letters of the alphabet. No training or feedback was given and responses such as “I don’t know” were not allowed. The letters were never identified for the subjects, even as a matter of familiarization. Before testing, subjects were asked to run through the alphabet mentally to minimize response biases of a spurious kind reflected in comments such as “I forgot to include X as a possibility.” One aspect of the experiment that was common to both tasks and required considerable care was proximal-distal adjustment of finger position between letter scans. Subjects were told to make proximal-distal adjustments of finger position between trials only to center the letter on the finger pad. They were asked not to make such movements to maximize their sensory input in any other way, e.g., to scan the top of the letter in one pass and the bottom of

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TACTILE LETTER RECOGNITION

533

the letter in another pass.It wasclearthat no suchstrategieswere usedin the passivetask. Once the best proximal-distal position wasfound, subjectsmadeno further finger movements.This control wasmore difficult to effect in the active task. Becauseof the variability of active movements,subjectshad to makesomesmall adjustmentsbetween trials. Subjects may have unconsciously madetheseadjustmentsto fill in uncertaintiesabout the top or bottom of the letter after the first or secondpass.Nevertheless,an effort was made to minimize gross,consciousstrategiesof that kind. The apparatusand test stimuli were hidden from view by a curtain in both tasks. Fingermovementswerevideotapedin Expt. 2 usinga JVC camera model No. GR-ClU. The camera was mounted above the finger. The field ‘of view was 70 mm wide and included the test pattern, the subject’sfinger, a stop watch, and a ruler oriented alongthe subjectsscanningpath. Timing wasbasedon the video frame rate; the stop watch wasusedonly for verification. A mark placedat the centerofthe distalend ofthe subject’snail servedasa reference.The data werethen transferredto a reel-to-reeltape for convenient stopped-frameviewing. The horizontal location of the subject’sfinger relative to the left edgeofthe ruler wasmeasuredat every third video frame, i.e., ‘/IO-Sintervals. Velocity was measuredasthe slopeof the distance-timegraph.

TABLE

Statistical methods

Active versuspassive touch in naive subjects

1.

Performancelevelsof naive subjects Maximum

Mean

SD

0.18 0.21 0.18 0.32 0.18

0.76 0.78 0.78 0.76 0.78

0.490 0.507 0.496 0.556 0.457

0.157 0.149 0.152 0.120 0.161

0.40

0.76

0.575

0.104

0.18

0.78

0.477

0.157

Minimum Subjects Active (n = 25) Passive (n = 15) Pooled (n = 40) Female (n = 16) Male (n = 24) Left-handed

(n = 8)

Right-handed (n = 32) SD, standard deviation.

RESULTS

Data were obtained from 64 subjects: 29 were tested in the passive task, 25 in the active task, and 10 in both tasks. Excepting two subjects in Expt. 4, none of the subjects had previous experience in tactile letter identification.

Three statisticsare usedto comparesubjects’performancein EXPERIMENT I. The first experiment in the series used forty the active and passivetasks. Two of them, the overall rate of correct responses and product-moment correlation of correct re- unpaid volunteers (16 female, 24 male; 32 right-handed, 8 sponsesfor individual letters, are discussed in the Appendix. The left-handed; ages 2 l-25 yr) who were not screened in any responsefrequenciesevoked by individual lettersand the frequen- way. Twenty-five subjects participated in the active task ciesof falseidentifications are comparedusingthe Kolmogorov- and 15 in the passive task. The difference in numbers of Smirnov testwith ties(Hollander and Wolfe 1973).SED and SEM subjects was due to the relatively longer time required to standfor standarderror of the differenceand standarderror of the run the passive task. Each letter was presented three times mean. to each subject for a total of 78 trials per subject.

40

1

0

20

II 40

Performance FIG. I.

q Active w Passive

60

d80

100

level (% correct)

Histogram of performance levels for active (n = 25) and passive

(n = 15. velocitv = 20 mm/s) subiects. Subiects had no prior experience in ihe task, were &en no training o”r feedback, and were not screened in any way.

Although the frequency of correct judgments varied widely between subjects, there was no significant difference between the mean performance levels in the active and passive tasks. A histogram of the performance levels of subjects in the two tasks is illustrated in Fig. 1. The average performance levels for the active and passive tasks were 49.0 and 50.7% correct identifications, respectively. Neither the means nor the distributions of performance levels between subjects were significantly different in the two tasks @ED = 5%, t = 0.35, P = 0.73; Kolmogorov-Smirnov D = 0.15, P> 0.10). Because there was no significant difference in mean performance levels between active and passive touch, data from active and passive sessions were pooled for other comparisons, which are illustrated in Table 1. Female subjects performed better than males and the difference was marginally significant (t = 2.10, 38 df, P = 0.045). The difference between left- and right-handed subjects was as large as the female-male difference, but the difference did not reach statistical significance because of the small number of lefthanded subjects (t = 1.67, 38 df, P = 0.11). These results suggest that there is no difference between active and passive touch in tactile letter recognition but they are not conclusive. They might fail to reveal true differences for a number of reasons. First, the scanning velocities used by active subjects might have been quite different from the scanning velocity used in the passive task. The main possibility is that subjects in the passive experiment were constrained to use a scanning velocity (20 mm/s) that was far from optimum. The scanning velocity actually used

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F. VEGA-BERMUDEZ,

534

K. 0. JOHNSON, AND S. S. HSIAO

by active subjects needs to be determined and the effects of changing scanning velocity on passive subjects needs to be studied. Second, although the observed mean difference in performance levels between subjects in the active and passive experiments is small (1.7%) the 99% confidence interval for the true difference is 13%; i.e., a true difference of up to 13% between active and passive touch within single subjects might have produced the results of Expt. 1. The relatively wide confidence interval is due to the large variability in differences between subjects. Active and passive touch needs to be compared in single subjects. Third, a difference between active and passive touch might be manifest in the patterns of identification rather than the overall percent correct responses. The remainder of the paper is concerned with these three possibilities.

Velocity eflects 2. Eight subjects (7 female, 1 male; 7 righthanded, 1 left-handed; ages 22-30 yr) participated in an experiment aimed at gauging the effects of scanning velocity on passive letter recognition, at measuring the scanning velocities actually used by active subjects, and at comparing active and passive touch within single subjects. Subjects participated in seven separate sessions, each lasting l-2 h and separated from the previous session by at least 24 h. Each letter was presented four times to each subject in each session for a total of 104 trials per session. In the first three sessions subjects performed the passive recognition task at scanning velocities of 20, 40, and 80 mm/s in that order, these being the velocities used in our primate neurophysiological experiments. In the fourth session subjects performed the active task as in Expt. 1. To judge whether there might have been a serial effect, e.g., learning, the passive conditions were repeated in sessions 5 through 7. During the active-touch experiments the subjects’ finger movements were videotaped. Finger movements over the letters D, H, and Z were chosen for detailed analysis. These particular letters were chosen because they appeared to be representative of different letter clusters; D, H, and Z are confused with other letters but rarely with each other (see Fig. 10). The videotapes showed a characteristic scanning profile, which is illustrated in Fig. 2A for five scans of the letter D in a single subject. Subjects began at the left side of the block containing the letter with a relatively high scanning velocity, decelerated rapidly as the finger contact patch moved toward the letter, reached a minimum when the contact patch was centered over the letter, and then accelerated away from the letter. The result is a V-shaped velocity profile whose minimum coincides with the centering of the letter within the contact patch. Sometimes there was a plateau of constant velocity at or near the minimum and sometimes not. A histogram of minimum velocities for 10 I scans from eight subjects and three letters (D, H, and Z) is shown in Fig. 2B. The mean minimum velocities for individual subjects were distributed from 6.2 to 30.7 mm/s and the differences were statistically significant (1;(7,93) = 4.66, P < 0.001). The overall mean minimum velocity was 17 mm/s. The strong skew is due to one subject who produced most of the velocities above 35 mm/s. In general, subjects

A 120

-

100

-

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60-

-!% x C

60-

:: 5>

40-

20 -

EXPERIMENT

OI

. 0

, IO

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, 20

I

, 30

.

, 40

.

( 50

Finger position (mm) 50

6

1 40

10

0

5

15

25

35

45

55

65

75

Velocity (mmkec) FIG. 2. Scanning velocities used in the active task. A: velocity profiles for 5 active scans of the letter D (6.0 mm high, 5.0 mm wide) obtained from 1 subject. The abscissa represents the horizontal location of a point at the center of the tip of the index finger. At 25-30 mm the center of the finger was directly over the letter being scanned. B: distribution of minimum active scanning velocities for 101 presentations of the letters D, H, and 2.

obeyed the instruction not to pause over the letter. In 3 of 10 1 instances the finger appeared to pause: two of those came from the subject with the lowest mean velocity. The letters D, H, and Z, which are relatively difficult to identify, were examined with an average of 4.2 scans per trial. The mean performance level in each of the seven sessions is shown in Fig. 3. The result was a decline in performance at 80 mm/s relative to 20 and 40 mm/s and an increase in performance in sessions 5-7 relative to sessions 1-3. A three-way analysis of variance (subjects, velocity, and serial

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TACTILE 100

LETTER

1 J

80

-

20

-

0

20

40

Velocity FIG. 3. Mean percent of 8 subjects in the passive 20, 40, and 80 mm/s, in sessions 5, 6, and 7. The whose result is plotted at subjects in session 4. Error

-

Sessions

0 -w-w

Session

60

1-3 4

Sessions

5-7

80

700

(mmkec)

correct vs. scanning velocity. The performance touch experiment were measured at 3 velocities, sessions 1, 2, and 3, respectively, and again in same subjects used active touch in session 4, 17 mm/s, the mean scanning velocity used by bars represent 1 SEM.

order) in the passive task showed that there were significant differences due to all three factors (P < 0.00 1). The statistically significant velocity effect was entirely due to the decrement from 40 to 80 mm/s. The small mean drop in nerformance level from 20 to 40 mm/s between sessions i and 2 and between 5 and 6, which averaged 2.0%, was not statistically significant (SEM = 1.67%, t = 1.20, P = 0.25). However, the drop from 40 to 80 mm/s, which averaged 12.75%, was significant (SEM = 0.84%, t = 15.2, P < 0.001). There was no suggestion of a relationship between the scanning velocity used by subjects in the active task and their performance level in either the active or passive task. It would be reasonable to suppose that subjects that used higher scanning velocities in the active task would exhibit better performance levels at 40 than at 20 mm/s. However, no such relationship was evident. The subject with the highest mean scanning velocity, 30.7 mm/s, happened to show the steepest decline from 20 to 40 mm/s (- 10.5%). The learning and active-passive effects were confounded in this task, which makes it difficult to compare the active and passive performance levels closely. These effects are separated in Expt. 5 (see below). EXPERIMENT 3. The learning and velocity effects were confounded in Expt. 2. If the learning effect transfers across scanning velocities, then the actual changes in performance levels between velocities might have been larger than was observed in Expt. 2. In Expt. 3, 14 naive volunteers were tested in the passive letter recognition task at velocities of 20 and 80 mm/s. Seven subjects (the 20-80 group) performed the task at 20 mm/s during the first 52 letter presentations of a single session and then switched to 80 mm/s for

RECOGNITION

535

the remaining 52 presentations. The other seven subjects (SO-20 group) performed the task in the reverse order. The results are shown as open and filled squares in Fig. 4. Each point represents the difference between a subject’s percent correct judgments at 20 and 80 mm/s. The mean decrement from 20 to 80 mm/s was 15.6% for the 20-80 group and 20.0% for the 80-20 group, yielding an overall mean decrement of 17.8% (1,456 trials, SEM = 2.6%, P < 0.0001). The results from Expt. 2 are shown as open and filled circles for comparison. The mean decrement between 20 and 80 mm/s in Expt. 2, 14.9%, was 2.9% less than the mean effect observed in this experiment, which is consistent with the learning effects that are presumed to have occurred in that experiment. EXPERIMENT 4. Another factor that could have affected the observed decrement from 20 to 80 mm/s in the passive experiment was the number of letter presentations. In previous experiments subjects were allowed up to 10 seconds of exposure, which means that the number of letter presentations was proportional to the scanning velocity. Speed settings of 20 and 80 mm/s allowed 6 and 26 letter presentations per trial, respectively. The much larger number of letter presentations at 80 mm/s could have offset a part of the decrement due to scanning velocity. Two subjects were tested at 6, 13, 20, and 26 presentations per trial at 20 and 80 mm/s using four trials per letter (i.e., 104 trials per combination of velocity and number of presentations per trial). The results illustrated in Fig. 5 show a difference between 20 and 80 mm/s that is independent of the number of letter presentations per trial. The slopes of linear regressions of performance on number of presentations shown in Fig. 5 were both zero to within one-tenth of a percentage point. Thus, the-. number of letter presentations per trial was not a confounding factor.

Exp 3

ww 0

0

w

w

ma

www

20-80

0

80-20

cl

Exp 2

0.0

aem

oca300

0

0

l-3

0

5-7

I

-40

-30

Change

-20

in percent

-10

correct

0

I

I

10

1

20

from 20 to 80 mmkec

FIG. 4. Difference in performance between 20 and 80 mm/s under 4 experimental conditions. Squares represent results for 14 naive subjects in kk~t. 3. Seven subjects (filled squares) were tested at 20 mm/s in session 1 and then at 80 mm/s in session 2; the other 7 (open squares) were tested in the reverse order. Circles represent results from individual subjects in E-x-pt. 2 (means are illustrated in Fig. 3). Filled circles represent performance differences between sessions 1 and 3 at 20 and 80 mm/s, respectively. Open circles represent performance difference between sessions 5 and 7 at 20 and 80 mm/s, respectively.

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536

F. VEGA-BERMUDEZ,

100

K. 0. JOHNSON,

--w--w

-IT -------------------

S. S. HSIAO

any difference between active and passive touch in single subjects, all other things being equal. The mean overall performance levels for active and passive touch in the first eight sessions were 52.1 and 50.6%, respectively. The difference, 1.5%, is not statistically significant (P = 0.55, SED = 2.5%, 1,664 trials). There is a suggestion that subjects learn during the passive trials and that the learning is expressed in the active trials but not vice versa; over the first eight trials the mean jump in performance from passive to active is 6.0%, whereas the mean jump from active to passive is only 1.3%. However, the difference, 4.7%, is not statistically significant (SED = 5.2%, P = 0.37).

1 V

AND

0 0

Patterns of true and false identiJication 0 20 mm/set o 80 mmlsec

0

:

0

I

I

I

10

Presentations

I

20

I

1

30

per trial

FIG. 5. Effect of number of letter presentations per trial in passive letter recognition. Abscissa represents the number of times each letter was scanned across the finger before a recognition judgment was required. Ordinate represents the percent of correct judgments. Sessions run at 20 and 80 mm/s are represented by squares and circles, respectively. Each dashed line represents a linear regression through the results at 1 scanning velocity.

Active and passive touch in single subjects 5. The learning effect in Expt. 2 interfered with one of the objectives of that experiment, which was close comparison of active and passive touch in single subjects. Experiment 5 employed two naive subjects with relatively low initial performance levels, so that differences between active and passive touch would not be masked by a ceiling effect. One subject began with the active task and one with the passive task. Then, each subject alternated between active and passive touch in successive sessions. The point of the design is that after both subjects have undergone an equal, even number of sessions, any differences between subjects and effects of serial order are equally distributed between the active and passive tasks. The matched subjects were obtained by testing naive subjects until two were found with similar, low scores and different first-testing modes. The points at session 1 in Fig. 6 come from those screening trials. Each letter was presented four times to each subject in each session for a total of 104 trials per session. The scanning velocity in the passive task was 20 mm/s. The results summarized in Fig. 6 show a mild continual increase in performance for both subjects as exposure to the task increased even though no feedback was given. The mean overall performance of the two subjects over the first eight sessions where the history effects are completely balanced is 5 1.4% correct judgments. The mean performance rose from 38% correct on the first session to 64% correct on the eighth session. The primary result is the obvious lack of EXPERIMENT

The results have so far shown no difference between mean percent correct performance levels in the active and passive tasks. However, percent correct is an incomplete measure of performance in a pattern-recognition task. Each letter produces a complex and highly repeatable pattern of true and false responses. In this section the patterns of correct and false identifications are analyzed. First, the response patterns in active and passive touch are compared and are found to be indistinguishable (excepting the responses to a single letter that are traceable to an experimental artifact). Then, the data are pooled for an analysis of the true and false identification patterns. Subjects’ responses are represented by 26 X 26 confusion matrices. Each element of a confusion matrix, cii, represents the number of times that the ith stimulus evokes the jth response. The analysis presented here is based on all of the data from subjects that performed the active task (35 subjects, 147 presentations per letter, 53.8% correct judgments) and from subjects that performed the passive task at 100 -

80 -

?2 k E E l!i 5 L

60 -

40 -

l

20 -

0

.

0

;,

h

Active Passive

. 12

Session

number

FIG. 6. Performance versus session number for 2 subjects that alternated bet ween active and passive touch (at 20 mm/s). Sessions lasted I h and were separated by 224 h.

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!=IG. 7. Pooled confusion matrices for subjects in the active and passive tasks. Each entry, e.g., the entry in row i and columnj, represents the number oftimes that stimulus S, evoked the response R,. The right-hand column represents the total number of presentations of each letter. The number beneath each column represents the total number of times each letter was given as a response. The bold diagonal entries represent correct responses. The Helvetica letters identifying the rows and columns are identical in form to the letters used in the psychophysical tasks. A: pooled confusion matrix for active tasks (n = 35 subjects). B: pooled confusion matrix for all subjects in passive tasks run at 20 mm/s (n = 39 subjects). Ten subjects contributed data to both the active and passive pooled matrices.

537

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F. VEGA-BERMUDEZ.

538

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K. 0. JOHNSON, AND S. S. HSlAO

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signed to each letter. The average relative false identification rate for each response category is 3.85% (i.e., l/26). The absolute false identification rate for any specific letter is obtained by multiplying the relative rate for that letter 80 and the absolute overall rate; e.g., the absolute rate for the letter 0 in the active touch experiments was 3.53% (7.65 of 46.2%). As in Fig. 8, the shaded region represents the area within which 99% of points would be expected to fall if there was no difference in the underlying response probabilities. Most of the false identification rates are nearly identical (rho = 0.9 1). Two points, H and R, lie outside the 99% region, but direct comparison of the false identification rates showed no significant difference (Kolmogorov-Smirnov D = 0.025 1, P > 0.10). Specific false-identification rates are compared in Fig. 9B, which shows confusion fre20 quencies for all off-diagonal cells in which the frequency exceeded l/25 in either matrix. The percentages in this plot are percentages of all responses to a specific stimulus. For example, the letter Q was called 0 on 44% (64/147) of ac04 1 tive trials and 53% (94/177) of passive trials. 0 20 40 SO 100 The preceding analyses, which showed that the confusion Active (% correct) matrices for active and passive touch are nearly identical, suggest that the differences are no greater than would be FIG. 8. Correlation of percent correct for individual letters in the active expected if the underlying response probabilities were idenand passive tasks (see Fig. 7). Each letter is centered over the point that represents its correct identification percentages in the 2 tasks. The shaded tical and the differences were solely due to chance variaregion represents the 99% confidence region within which points are ex- tions. In fact if it is assumed that each response has a certain pected to occur when the underlying, true probabilities of correct identifia priori probability independent of previous responses, then cation are identical in the 2 tasks. the chance variations are completely described by binomial statistics. In the APPENDIX, binomial distribution theory is a scanning velocity of 20 mm/s (39 subjects, 177 presentaused to calculate expected correlation coefficients between tions per letter, 54.4% correct judgments). The pooled con- rates of correct responses based on the null hypothesis that fusion matrices for these two groups are shown in Fig. 7, A there is no difference between active and passive touch. and B. Table 2 contains a summary of observed and expected The similarity of the subjects’ performance in the two correlations for all of the experiments reported in this modes can be seen by examining the matrices on a cell-bypaper. The expected correlation is the average correlation cell basis. The rank order of letters from least to most fre- that would be observed if the underlying probabilities of quently correct is SNBQRZMGHCPXDKVYFETWAUcorrect identification were identical in the active and pasOJLI in the active task and BNSQRMPGZCDHFKYEsive tasks and the study was repeated many times. The final TXVAWUJOLI in the passive task. The majority of letters column displays the standard deviation. It can be seen that ( 15 of 26) differ by 0 or 1 place in the rank order. The most the observed and expected correlation values are nearly disparate is X, which differs by six places in the two rankidentical in every case; for Expt. 1, in which the subjects in ings. The correlation of percent correct judgments for indithe active and passive experiments were different; for vidual letters is illustrated in Fig. 8. The shaded area repre- Expts. 2 and 5, in which the subjects were the same; and for sents the area within which 99% of the pairs from the two the pooled data. The largest deviation between observed and expected correlations, 1.4 SDS in Expt. 2, is not statistimatrices would be expected to fall if there was no difference cally significant (P = 0.16). between the underlying probabilities of correct judgments in the two matrices (see APPENDIX). The fact that the letter The responses to a single letter, which are contained in a S lies outside the 99% region does not, in itself, signify that single row of a confusion matrix, constitute a complete, the difference in correct identification rates are significantly sampled probability function; the fractions of responses in different in the two matrices: the probability that all 26 each cell of a single row sum to 1.O. The Kolmogorov-Smirpoints will be confined to the 99% region is only 0.77 (i.e., nov test, which tests for differences between two cumula0.9926). However, the letter S lies well outside the confi- tive probability functions, was used to compare the redence region and it will be seen later the responses to this sponses to single letters between the active and passive conletter were significantly different in the active and passive fusion matrices. Excepting responses evoked by the letter S tasks. (see below) the largest difference in cumulative probabiliSome aspects of subjects’ false response behavior are il- ties between responses to the same letter in the active and lustrated in Fig. 9. The overall active and passive false idenpassive experiments was 0.135, which is not significant at tification rates were 46.2 (1,765 out of 3,822 responses) and the 0.10 level. The letter S yielded a maximum difference of 45.6% (2,099 out of 4,602 responses), respectively. Figure 0.232 (P < 0.001). The stimulus letter S was identified correctly 15% less 9A shows the relative false identification rates for individual letters, i.e., the percentage of all false identifications as- often in the active task than in the passive task (see Fig. 8). Downloaded from www.physiology.org/journal/jn by ${individualUser.givenNames} ${individualUser.surname} (163.015.154.053) on August 26, 2018. Copyright © 1991 American Physiological Society. All rights reserved.

TACTILE

LETTER

RECOGNITION

539

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FIG. 9. Correlation of false identification rates in active and passive touch. A: overall false identification rates for each response category. Abscissa represents the percent of all false identifications in the active task that were assigned to a specific response. Ordinate represents the same quantity for the passive task. For example, the letter K was named falsely 1 I7 and I25 times in the active and passive experiments, which constitute 6.63 ( I l7/ 1,765) and 5.96% ( I25/2,099) ofall false reports, respectively. B: specific false identification rates exceeding 4% in either the active or passive task. Each point represents the rate of a specific stimulus-response pair in the active and passive tasks expressed as a percent of all presentations of the specific stimulus letter. Some of the higher rates have been labeled; e.g., the point labeled Z-X shows that Z was called X on 33% (48/147) of Z presentations in the active task and 29% (52/177) of Z presentations in the passive task.

In Fig. 9A it can be seen that this difference is not due to a Pooled patterns of true and false identification shift in preference for S between the active and passive tasks. The false identification rate for S was identical in The primary goal of the psychophysical research reported active and passive touch. Thus the difference is isolated to here is to obtain pattern recognition performance data the responses evoked by the letter S itself. A microscopic under exactly the same stimulus conditions that have been experiments (Phillips et al. examination of the stimulus surfaces showed a clear struc- used in neurophysiological tural defect in the upper left corner of the embossed letter S 1988). Strictly speaking, those are the data from the passive used in the active experiments. The location of the defect is experiments reported earlier. However, we have concluded consistent with the overall shift in responses towards letters that there is no statistically discernible difference between with sharp upper left corners (BDEPR) in the active relative the data from the active and passive experiments and that to the passive data (see Fig. 7 and DISCUSSION). there is no reason not to combine the data. Doing so roughly halves the variance of the estimated response probabilities. The pooled data (excluding responses evoked by the letter S in the active experiments) are represented in Fig. 10. TABLE 2. Correlationsbetweenactive and passive Each element, p,,, of the matrix illustrated in Fig. 10 reprecorrectjudgments sents the percentage of presentations of the stimulus S, that resulted in the response R,. The standard error of each pv is Presentations per Letter Correlation Values given by the square root ofp, ( 100 - pi,)/n, which, based on 324 presentations per letter, amounts to 0.5% forp, = 1% or Active Passive Observed Expected SD 99%, 1.7% for pi, = 10% or 90%, and 2.8% for pi, = 50%. The confusion data are characterized by highly strucExpt. I 75 45 0.938 0.936 0.020 tured patterns of true and false identification rates for each Expt. 2 32 64 0.952 0.917 0.025 Expt. 5 letter. The false identification rates illustrated in Fig. 10 Subject I I6 I6 0.867 0.805 0.056 exhibit two main characteristics. First, 50% of all erroneous Subject 2 I6 I6 0.794 0.805 0.056 responses ( 1,942 out of 3,864 erroneous responses) in Fig. Pooled data I47 177 0.966 0.976 0.008 10 are concentrated in 7% of all possible confusion pairs (22 SD, standard deviation. out of 325 letter pairs), which are enclosed in boxes in Fig. Downloaded from www.physiology.org/journal/jn by ${individualUser.givenNames} ${individualUser.surname} (163.015.154.053) on August 26, 2018. Copyright © 1991 American Physiological Society. All rights reserved.

F. VEGA-BERMUDEZ,

540

K. 0. JOHNSON, AND S. S. HSIAO

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FIG. 10. Pooled confusion matrix for all subjects (n = 64). Excepting that the individual entries represent percentages and not absolute response numbers, the matrix structure is the same as in Fig. 7. Each entry, p,,, represents the percent oftrials on which stimulus S, evoked the response R,. The mean rate of correct responses for the pooled data is 54.3%. Boxes represent letter pairs whose mean confusion rates exceed 8%. For example, the mean confusion rate for B and G is 8% based on the fact that G is called B on 1 1% of trials in which G is presented and B is called G on 5% of trials in which B is presented.

10. Each of those confusion pairs has a mean confusion rate NTZCS for total responses, ILJOUWAVTEYXKFDHof 8% or higher. Second, specific confusions are highly CPGZMRQSBN for correct responses, and ORKWMBfor false identifications; asymmetric. In all but five (XK, KE, RN, HN, VU) of those NDGQXYHUFPESVZCATJLI 22 confusion pairs the probability that symmetric confu- the Spearman rank correlations of these orderings with the sion probabilities would produce such an asymmetric result rank order in American English are 0.03,O. 18, and -0.05, is co.00 1. For example, B is called D on 27% of trials but D respectively, none of which is statistically significant (Siegel is called B on only 2% of trials, which results in a BD confu1956). These results suggest that subjects’ responses were not influenced by the relative frequency of letters in comsion ratio of 13.5:1. Among the 17 pairs (out of 22 total) with highly significant asymmetries the confusion ratios mon usage. range from 2.1: 1 (PF) to 16.3: 1 (QO) with a mean of 5.5: 1. Seventy percent (83/l 19) of all letter pairs with a mean DISCUSSION confusion rate of 2 1% (119 pairs) have an asymmetry of 22: 1. Mechanisms that might underlie the asymmetries are The results of this study are of two kinds. The first group considered in DISCUSSION. of results is concerned with subjects’ performance in the One final item concerns the possibility that subjects were active and passive letter recognition tasks. The second influenced by the frequency of letters in common usage. To group is concerned with the details oftactile pattern recognitest that possibility, the frequency of letters in American tion and the data that need to be explained by any theory English were compared with total response frequency, the linking pattern recognition to observed neural responses. frequency of correct judgments, and the frequency of false identifications (total response frequency minus frequency Active versus passive touch of correct identification) for individual letters. The rank The studies presented in this paper yield a clear, simple order in American English from most to least frequent is ETAHOSRNIDLWUMCGFYPBVKJXQZ (Kucera and result, which is that there is no statistically discernible difFrancis 1967). In the confusion matrix illustrated in Fig. ference between subjects’ performance in active and passive tactile letter recognition tasks. One apparent exception was 10, the rank order is OWKUDXRYMIGEFLVJAHBQPDownloaded from www.physiology.org/journal/jn by ${individualUser.givenNames} ${individualUser.surname} (163.015.154.053) on August 26, 2018. Copyright © 1991 American Physiological Society. All rights reserved.

TACTILE

LETTER

found to be explainable by a small defect in the embossed letter S used in the active task (see later). These results cannot prove that there is no difference in the central processing of tactile spatial form related to active and passive touch but they suggest strongly that this is so. In tasks like tactile letter recognition where proprioceptive information provides little or no useful information, e.g., Braille character identification and texture discrimination, active touch might be thought to provide an advantage in that it allows the subject to optimize the acquisition of sensory information. Even so, all studies involving such tasks have found no significant difference between active and passive touch (Grunwald 1966; Lamb 1983; Lederman 1981, 1983). All studies that have reported a difference between active and passive touch (of which we are aware) have employed tasks wherein proprioceptive information would appear to be a critical component of the sensory information on which identification is based. The majority of studies in this category have used a variant of Gibson’s original experiment in which no attempt was made to duplicate the proprioceptive information in the passive task. The task in those experiments was to identify a raised outline (a cookie cutter pattern) whose dimensions were considerably larger than a finger-pad (Cronin 1977; Gibson 1962; Heller 1984; Heller and Myers 1983; Schwartz et al. 1975). In the active task the subject traced the pattern using a combination of arm, hand, and finger movements. Excepting Gibson’s experiment, the cookie cutter patterns were presented to the same skin region in the two tasks but the hand was immobilized in the passive task, thus depriving the subjects of what would appear to be essential sensory information. Even so, some of the investigators found no difference (Cronin 1977; Schwartz et al. 1975). In Gibson’s original experiment the pattern was presented to the fingertip in the active experiment and to the palm in the passive task. In a very carefully controlled study, in which care was taken to preserve proprioceptive information in the passive task, Magee and Kennedy (1980) obtained the surprising result that subjects’ pattern recognition performance using raised line patterns was significantly better in the passive mode. The available published data present a relatively clear picture of tactile, spatial pattern recognition. Experiments that have attempted to preserve equal sensory opportunity have so far shown no perceptual advantage related to active touch. In fact, taking the results of Magee and Kennedy (1980) and of experiments like the ones reported in this paper, it would be easier to make the contrary argument that active touch imposes an additional load that may reduce performance in a difficult pattern-recognition task. In experiments like the ones reported here, the active subjects were free to optimize contact pressure and velocity and even to vary them between trials, but that bought them no advantage, not even a few percentage points. Perhaps (so goes this contrary argument) the advantages of active touch were offset by the additional burden of motor control. However, the more parsimonious interpretation of our results is that there is no difference in the tactual recognition of small surface patterns related to active and passive touch. These arguments do not mean that there is no difference between active and passive touch or that the distinction is

RECOGNITION

541

unimportant. The observation that there is a difference between touching and being touched (Gibson 1962) is too compelling to be easily dismissed. However, if there is a difference it is not manifested as a decrement in performance in tactile pattern recognition of the kinds that have been studied so far.

DzJhwzcc’s hct wwn szhjccts . The first experiment showed that there are large differences in tactile acuity between subjects when they are not screened in any way and that the performance of female subjects is slightly better than male subjects. Previous studies using embossed letters -6 mm high have also reported a wide range of performance levels (30-86% correct judgments in Johnson and Phillips 198 1; Loomis 198 1, 1982; Phillips et al. 1983). The wide differences between subjects has no ready explanation. It could have been due to differences in skin mechanics, differences in primary neural mechanisms (e.g., innervation density), or psychological differences. All of the subjects reported that the task was difficult. It is possible that-the differences are due to differences in response criteria and not to differences in acuity. That is, it might be that the confusion matrices of subjects with low performance levels (low numbers in the diagonal entries) are still highly structured. However, an assessment of that kind would require more data per subject than were collected in this study.

Vdocit -~7e//&s .. The second experiment showed that subjects use a scanning velocity near 20 mm/s in the active task and that scanning velocity had only a small effect on subjects’ performance in the passive task. In the passive task, subjects’ overall performance levels at 20,40, and 80 mm/s were 65, 63, and 50%, respectively. The decline of 2% between 20 and 40 mm/s in Edxpt. 2 was not statistically significant. Learning effects may have elevated the results at 40 and 80 mm/s relative to 20 mm/s in Expt. 2. Experiment 3, which was designed to eliminate learning as a confounding factor, verified the result and showed that a fourfold increase in scanning velocity from 20 to 80 mm/s caused an 18% decline in overall performance levels. By comparing the results of Expts. 2 and 3 it is estimated that the learning effects between 20 and 80 mm/s (2 sessions) elevated the subjects’ performance at 80 mm/s by only 3%. The fact that subjects perform reliably at 80 mm/s carries implications for the underlying neural mechanisms. A previous study showed that 50% correct responses to 6-mm letters is roughly equivalent to threshold level discrimination for gratings with 2-mm spatial periods (i.e., l-mm gaps and bars) (Johnson and Phillips 198 1). Spatial periods of 2 mm translate to temporal periods of 25 ms at a scanning velocity of 80 mm/s, which implies that the mechanisms responsible for tactile letter recognition must have a temporal bandpass extending to at least 40 Hz.

Learning eficts . The learning effect, which was first evident in Expt. 2, was shown in Expt. 5 to amount to a 4% gain in perfor-

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F. VEGA-BERMUDEZ,

542

K. 0. JOHNSON,

mance per session.Previous studies that have included serial effects in their statistical analyses have reported no statistically significant effects (Johnson and Phillips 198 1; Phillips et al. 1983). Examination of the original data from those earlier experiments shows a small overall gain between sessionsthat does not reach statistical significance. However, that result included subjects with high initial performance levels which left no significant room for improvement. This ceiling effect was screened out in Expt. 5 by accepting only subjects with low initial performance levels. When subjects in the Johnson and Phillips experiments are screened post hoc so that subjects with high initial performance levels are eliminated, then the result is the same, i.e., a mean gain of -4% per session. Epstein et al. (1989) have reported the results of experiments whose designsare quite like the ones usedin Expts. 2 and 5 and which are aimed directly at the question of perceptual learning. They used completely naive subjects in a tactile pattern identification task in which no feedback or training were given and they reported large differences between subjects, a decline in performance with increased scanning velocity, and significant learning between sessions for a majority of subjects. Epstein et al. advance Gibson’s view that the improvement is attentional in nature, “involving a greater noticing of the critical differences” (Gibson 1966). Later, when the probable neural mechanisms are discussed,a similar explanation is advanced for learning in this study.

Patterns of confitsion . The frequencies of correct identification and the patterns of correct and incorrect judgments vary greatly between letters. In general, the erroneous responsesto a single letter are confined to a small number of response alternatives. Error patterns are highly repeatable between active and passive touch and between subjects. Most of the data in the active and passive confusion matrices shown in Fig. 7, A and B, come from different subjects as well as different experimental designs. The systematic error patterns demonstrated in these experiments must be accounted for by any mechanistic explanation of tactile pattern recognition. A fundamental question is whether and to what extent subjects’ responsescan be accounted for by cognitive mechanisms as opposed to basic sensory mechanisms. Basic sensory mechanisms include transduction, transmission, and transformation, i.e., mechanisms that affect the neural representations of letters. Cognitive mechanisms include general response bias, i.e., relative increasesand decreasesin responserates for specific letters regardlessof the stimulus. An example of a general responsebias based on cognitive mechanisms would be an unconscious preference for certain responsesbased on the frequency of letters in English text. We tried to minimize such effects by asking subjects to run through the alphabet mentally, so that all of the letters would be equally fresh in memory, but there is no guarantee that this precaution had any effect. Whatever the cause, sensory or cognitive, strong biasesare a dominant property of the recognition data illustrated in the confusion matrices (Figs. 7 and 10). For example, the letter 0 is reported almost twice as often as it is

AND S. S. HSIAO

presented ( 189%, Fig. 10) and is reported three times more often than the letter C (66%). Another example involves the letters B and D: B is mistaken for D 1 1 times more often than D is mistaken for B. Letter frequency in American English was examined (see RESULTS) and was shown to bear no statistically significant relationship to total response rates, hit rates (percent correct), or false alarm rates (summed rates for each responsecategory excluding correct responses)for individual letters. However, general response bias based on letter frequency in ordinary text is only one of many possible kinds of cognitive bias. One could test many specific possibilities and still not exclude the possibility that a cognitive bias played a major role in the subjects’ responses. Loomis (1982) has considered the question of response bias in a more general way. Central to his discussion is the relationship between false alarm and hit rates. Figure 11 illustrates the false alarm and hit rates for each letter in our study. Loomis argues that if general responsebias of any kind was a major factor in the structure of the matrix, it should affect both hit rates and false alarm rates and they should be positively correlated. In fact, they are negatively correlated; the correlation between false alarm and hit rate in Fig. 11 is -0.40 [it averaged -0.33 in seven studies of tactual and visual letter recognition that were analyzed by Loomis (1982)]. These findings contradict the hypothesis that general response bias of some kind is a major determinant of response rates. An interpretation of this negative correlation advanced by Loomis is that stimuli with high legibility are unlikely to be the named falsely becausethey are so distinct. For example, after once experiencing the letters I, J, and L asstimuli,

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TACTILE

LETTER

no other stimuli can be mistaken for I, J, and L. However, this hypothesis provides a poor description of the rest of the data. For example, the letter 0 is highly legible (has a high hit rate) but is also the strongest target of false alarms. When the I, J, and L data points are removed, the correlation coefficient between false alarm and hit rate (-0.06) is not statistically significant. That is, the legibility hypothesis may account for I, J, and L because they are so distinctive but not for the rest of the data. An alternative sensory explanation for asymmetries and the low correlation between hit and false alarm rates is considered here. When embossed letters are scanned across the skin, the individual segments of the letters evoke structured activity in the underlying cutaneous slowly and rapidly adapting (SA and RA) afferent fiber populations, which innervate the skin of the distal phalangeal pad with a mean spacing of - 1 mm (Darian-Smith and Kenins 1980; Johansson and Vallbo 1979). The result is two isomorphic neural images of the moving stimulus, one in the SA and one in the RA afferent population (Phillips et al. 1988). In our working model of the neural events underlying tactile pattern recognition, these neural images are transmitted in parallel through the somatosensory system and transformed in some unknown way at each successive stage until, finally, they are compared with previously stored images (templates) and classified as 1 of the 26 letters of the alphabet. The responses of a peripheral monkey SA neuron, which is assumed to be similar to responses of primary SA afferents in man, to 6-mm embossed letters scanned at 20 mm/s are illustrated in Fig. 12 (methods are described in Johnson and Phillips 1988; Phillips et al. 1988). Because the SA (and RA) afferents innervate the skin densely and the responses of individual afferents are all very similar to one another, the spatial event plot illustrated in Fig. 12 approximates the neural image conveyed to the brain by the peripheral SA afferent population response. The primary distortions, which occur in the responses of both SA and RA afferents, are enhanced responses to leading edges and diminished responses to internal horizontal elements. Some of the psychophysical response asymmetries can be explained by the primary afferent fiber response properties

RECOGNITION

543

exhibited in Fig. 12. For example, B is often called D but D is rarely called B. The loss of internal and trailing structure makes the neural representation of a B look like a D. Since subjects base their judgments on these neural representations, the basis for the asymmetry between B and D responses seems clear. Our hypothesis is that the subjects compare these representations with complete, well-formed templates for the letters B and D based on visual memory. In fact, they had little other choice in the beginning since they were given no training or feedback (see below). Other asymmetries can be explained in a similar way. For example, the confusion between G and Q is strongly biased towards Q (GQ 18%, QG 7%). As before, the explanation is that the neural image of G is more like a Q than vice versa. To take another example, H is called U six times more often than U is called H. The weak response to the middle bar in the H allows it to be mistaken for M, N, R, and U. However, the relatively strong response to the bottom of the U (which produces a strong subjective impression) makes it quite distinct from an H. U is most frequently mistaken for a V. Other confusions and asymmetries like R-H, T-Y, DU, and E-R can be explained in a similar manner. A reason for the lack of strong correlation between hit and false alarm rates can be seen by examining the neural responses to 0 and J. 0 and J have nearly identical hit rates but very different false alarm rates. The neural data illustrated in Fig. 12 suggest that 0 is highly legible (has a high hit rate) because it evokes a peripheral neural image that looks like an 0 and that it has a high false alarm rate because other stimuli, e.g., C, evoke neural representations that can be mistaken for an 0. By the same arguments, J has a high hit rate because its peripheral neural representation resembles a J and a low false alarm rate because no letter excepting J evokes a neural representation with a strong lower leading component. Similar explanations can be found for other letters with identical hit rates but very different false alarm rates, e.g., T and K. This explanation, which places all of the responsibility for strong response biases and asymmetries on primary neural mechanisms, is consistent with (indeed it assumes) a decision model that is free of bias. Bias and asymmetry in subjects’ responses are attributed to a mismatch between

I

1

1 cm FIG. 12. Spatial event plots of action potentials in a peripheral SA afferent fiber evoked by scanned stimulus letters like those used in this study: letter height 6.0 mm, scanning velocity 20 mm/s, contact force 35 g.

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544

F. VEGA-BERMUDEZ,

K. 0. JOHNSON,

the neural representations and the templates against which the representations are compared. Consider a decision model in which the templates for letters are high-fidelity representations of the letters in a decision space and unbiased responses are represented by decision boundaries that lie midway between the templates (Johnson 1980a,b). Because subjects were given no training or feedback and had no prior experience of small tactile letters, their judgments must have been based on comparisons of tactile impressions with visual memories of letters. Since the neural representation of a tactile, scanned B is more like a D than a B, the mean neural representation of B actually lies on the D side of an unbiased decision boundary. Thus B is called D more often than it is called B. If training and feedback were given, subjects would (we predict) rapidly learn the characteristic responses evoked by each of the letters and the asymmetries would be reduced dramatically. Subjects would soon learn that B and D feel nearly the same and to concentrate on the presence or absence of internal detail and a trailing deficit, which signals the cusp in the B. To put it differently, with training the subjects’ templates would be readjusted to coincide with the mean responses evoked by each letter and the decision boundaries would move with them. The learning that we have reported in this paper is due, we believe, to a slow readjustment of internal templates and decision boundaries to coincide with the actual responses evoked by the letters. An example of the mechanism that we are proposing can be seen by considering the letter U. The gradual appreciation of the strong response at the bottom of the U relative to other letters with vertical sides would result in a gradual movement of its template. A prediction of this working model is that letters that are frequently mistaken for U but evoke no more activity at the bottom than at the top (B, D, and H) would exhibit declining rates of false U responses. We do not have sufficient longitudinal data to test such hypotheses. This discussion is not meant to exhaust the possible comparisons between neural and behavioral data nor does it represent the way in which the two kinds of data should ultimately be brought together. It is meant to be a qualitative assessment of the kind that must precede quantitative studies. There are two possible outcomes of quantitative studies. One possibility is that the peripheral neural data provide a complete explanation for the observed pattern recognition data. If that is the outcome, the implication would be that the central nervous system processes and transforms spatial form without further distortion. The other possible outcome is that the peripheral data account for some but not all of the recognition responses. In that case, the remaining lack of fit should point to effects that need to be explained by central mechanisms. APPENDIX

This appendix develops formulas for calculating the expected correlation between the diagonal elements (correct responses, hits) of two confusion matrices when the subjects contributing to the two matrices have identical response probabilities. Under those circumstances the diagonal elements of the two matrices will be similar but rarely identical; correspondingly, the correlation will be high but rarelv 1.0. Even though” the subiects contributing ” to the J v---r’---

AND

S. S. HSIAO

two confusion matrices have identical response probabilities, the probabilistic nature of individual responses has a decorrelating effect. As the number of observations increases, the probabilistic fluctuations average out and their decorrelating effect diminishes. Systematic differences in the underlying response probabilities between the two confusion matrices have a further decor-relating effect. If the differences are great enough, the correlation will be significantly lower than could have resulted from two matrices with identical response probabilities. The null hypothesis considered here is that the underlying response probabilities are identical. The statistical model underlying all of the analyses presented here is a Bernoulli process in which each verbal response is independent of preceding responses to the same or different letters and the process is governed by a constant response-probability matrix P, in which each element pii represent the probability that stimulus Si will evoke response Rj (e.g., S4 = presentation of the letter D and R, = identification of the stimulus as D). The elements of each row constitute a discrete probability distribution whose values sum to 1.O. The diagonal terms, pii, represent the probabilities of correct identification. P is estimated from the experimental responsecount matrix, N, in which each entry, nij, represents the number of times the stimulus Si evoked the response Rj. Each entry, no, is a random variable drawn from a binomial distribution with parameters, pij, and ni (Bradley 1968) where ni represents the number of times that Si was presented. The response frequency matrix, F, in which.f;jj = n,ilni, provides an unbiased but variable estimate of the matrix P. Each cell,&, deviates from the true response probability for that cell, pii, by an amount pii whose mean and variance are zero and pu*( 1 - pij)lni, respectively. Since this analysis is concerned only with the diagonal probabilities, pii, the double subscript will be replaced by a single subscript, pi, and since all letters were presented an equal number of times, i.e., ni = n for all rows of N, the row subscript will be dropped from ni. Two factors affect the expected correlation between diagonal elements of two confusion matrices based on random processes with identical response probabilities. The first is random variation in the numbers of hits for individual letters. Since the underlying response probabilities for homologous diagonal cells in the two confusion matrices are equal, the contents of the two cells are independent samples from binomial distributions with equal means. When displayed as in Fig. 8, the bivariate mean for a particular letter will lie somewhere along the 45 O line of equal values and the pair of observed hit rates will lie somewhere nearby in a region whose size is inversely related to the number of stimulus presentations per letter. The larger the variation of observed hit rates around each bivariate mean the lower is the expected correlation between pairs of hit rates. The second factor is the range of hit rates between letters, i.e., the scatter of means up and down the diagonal line in Fig. 8. When the hit rates for individual letters are spread from near (0,O) to near (1, 1) on the diagonal line, as in Fig. 8, the expected correlation is high. If there were no spread, i.e., if the probability of correct identification were identical for all letters, then the expected correlation would be zero regardless of the number of observations per letter. For example, if the probability were 0.5 for all letters then all 26 bivariate points would be scattered around the point (0.5,0.5) without any tendency to covary and the correlation coefficient would vary around zero. Both of these factors, the random variability due to finite sampling and the systematic differences in hit rates between letters, can be assessed quantitatively and used to calculate the expected correlation. The correlation coefficient between the diagonal terms of two response frequency matrices, A and B, is computed with the formula r ab

=-

c

ab c .c L3a*3b

(AI)

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TACTILE where Cab represents the covariance standard deviations of the diagonal using the formulas Cab = -

I

LETTER

and Sa and Sb represent the terms. These are calculated (~2)

26

s,’ = L

C

26 i=l

(A3)

(ai - a)2

s,’ = -i6 f (bi - bJ2

W)

i I

sp2 = s,’

where aj and !Jj represent the hit rates for individual letters and a and b represent their grand means, i.e., the overall percent correct for the experiments represented by matrices A and B. The expetted correlation coefficient, r&,, will be derived by calculating the expected values for So, Sb, and Cab. The formulas for the expected values of S, and Sb are derived as follows: Sa depends on the individual values of aj and their mean, a. Each observed hit rate, aj, is in error by some unknown amount ej

ai = pi + 4i

(P, + ei> = P + &

I I

z 1

(ei) 1

E{ei} = 0

E{eiej} = 0 for

i and j not equal

W)

where E{ } represents expected value and n, represents the number of presentations per letter. The expected value of S,’ is obtained by substitution Of pi plus error terms for the values of aj in Eq. A.3 and A6 and then computing the expected value. The result (using Eqs. 7-9) is 25 26 E{Sa2> = Sp2 + -n *262 7

a

1=1

[Pi*('

where SP2 = 26’ i2= I

(Pi - PJ2

(AlO

represents the variance of hit probabilities between letters. The expected value of ,S’i is obtained in a similar manner and the result is the same as in Eq. AI0 excepting that n, is replaced by $,. Taking the same approach when calculating the expected value of cab, i.e., substitution of pi plus error terms for the values of aj and bj in Eq. A2 results in E{ Cab} = Sp2

(11)

This simple result emerges because all of the error terms are independent and the only covariance is the common variance of the underlying pi values. Thus E{Cab)

r = ab sqrt(E( S,‘} * E{Sb’}) =

wt[(sp2

+

Ss2/na)

* (s,’

+

-

Pi11

(Al3

-Pi)1 = 5 a

!

tai*C1

- ai))

VW

f-l

25 $ [a,*(1 - ai)] (n, - 1) * 262 i=i

(Al 7)

=

(A18

I

The significance of a difference between expected and observed correlation coefficients requires an estimate of the variability of the correlation values. Normal distribution theory cannot be used since the 26 bivariate points used to calculate the correlation coefficient are not drawn from a bivariate normal distribution (Wilks 1962). A computer simulation was constructed to obtain estimates of the standard deviations of the correlation values listed in Table 2 and to check the accuracy of Eq. AM. The hypothetical true response probabilities were taken from the confusion matrix illustrated in Fig. 10. Each experiment was simulated 10,000 times to obtain reliable estimates of the means and standard deviations of the correlation coefficients. The means of the simulated correlation coefficients were the same as the values computed by Eq. A I8 to three decimal places. The standard deviations are listed in Table 2. We are indebted to James Craig and to anonymous reviewers for criticisms that had a significant effect on this paper. This research was supported by National Institute of Neurological Disorders and Stroke Grant ROl NS-18787. Received 28 December 1989; accepted in final form 5 November 1990.

(Al3 ‘1

%’

[Pi*(l

Standard deviation of correlation

WO)

-Pi)]

r=l

0.049 0.19 0.19 sqrt 0.049 + 7 * 0.049 + nb I I( The expected correlation approaihes 1.O at very high values of n, and nb, as it should, but falls short for smaller values as the binomial variability degrades the correlation.

(Aa)

- PiJlna

g

a

and

Yab

(A7)

= Pi*(l

n*26225

The data from our experiments yielded values for SP2 and C [pj*( 1 - pi)] of 0.049 and 5.17. Th ese values were independent of the source, whether from the pooled active matrix, the pooled passive matrix, the grand pooled matrix, or from subsets of data like those from Expt. 1. The result for our experiments is

where p represents the overall mean probability of correct identification. The errors are independent, with binomial distributions and expected values

E{eiZ}

C[Pi*(l

sp2 = s,’ -

(A6) T& z

-

which leaves only the C [pi* ( 1 - pi)] to be estimated. The expected value of C [ai* ( 1 - ai)] can be computed by substituting pi + ej for aj as before. The result is that C [a,*( 1 - ai)] underestimates 2 [pi* ( 1 - pi)] by a factor of (n, - 1)/n,. Thus, unbiased estimates of C [pi * ( 1 - pi)] and SP2 are

(A3

where pi represents the subjects’ true hit rate. The grand mean, a, is related to the individual errors by the formula a = & z (ai) = 1I

545

represents the variance due to finite sampling from a Bernoulli process. The problem now is that the expected value of rab is expressed in terms of true probabilities and their variances. S,;! and 2 pj*( 1 pi) must be estimated from the observed hit rates aj and bj. From Eq. AI0 it can be seen that S,’ overestimates SP2 because it ineludes variance due to sampling as well as differences in hit rates between letters. Rearrangement of Eq. AI0 provides an unbiased estimated of SP2

26

2 (ai - a)*(bi - 6) 26 i=l

RECOGNITION

ss2hb)l

where

REFERENCES J. V. Distribution-Free Statistical Tests. Englewood Cliffs, NJ: Prentice-Hall, 1968. CRAIG, J. C. A confusion matrix for tactually presented letters. Percept. Psychophys. 26: 409-4 11, 1979.

BRADLEY,

25 26 s,’ = -262 F [Pi*(l - Pi)] 1 1

(Al4

)

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546

F. VEGA-BERMUDEZ,

K. 0. JOHNSON,

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Human tactile pattern recognition: active versus passive touch, velocity effects, and patterns of confusion.

1. Subjects without any previous experience in a tactile psychophysics task participated in a study of tactile letter recognition employing active and...
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