- Email: [email protected]
Cognitive processing of tokens and their description in aphasia
BRAIN
AND
LANGUAGE
25, 37-51 (1985)
Cognitive Processing of Tokens and Their Description in Aphasia KLEMENS
GUTBROD,
BARBARA MAGER, University
of Konstanz,
ERWIN MEIER, AND RUDOLF COHEN West Germany
In a dual-reaction time task aphasics (N = 21) and right-hemisphere (RH) controls (N = 24) had to decide whether a list of features given verbally or pictorially correctly described the picture of a token. Although the error rates were extremely low, aphasics made significantly more errors than RH controls. There were no significant differences between the groups in latencies when pictures of tokens were presented; the groups differed drastically, however, when confronted with lists of features. The findings are interpreted as indicating a general deficit in the short-term storage of highly specific information. D 19x5 Academic
Press, Inc.
INTRODUCTION
The Token Test (TT), developed by DeRenzi and Vignolo (1962), has been shown to be extremely powerful in discriminating aphasics from other groups of brain-damaged patients (e.g., Cohen, Kelter, Engel, List, & Strohner, 1976; DeRenzi & Faglioni, 1978; Orgass, 1976a, 1976b; PeckSwisher & Taylor-Sarno, 1969; Van Dongen & van Harskamp, 1972). The percentage of correct classifications with respect to this dichotomy among brain-damaged patients varies in different studies from 77 to about 90%. The TT consists of five parts in which the subject is requested to touch (parts I to IV) or manipulate (part V) tokens of five different colors, two shapes, and sometimes two sizes, according to progressively more complex verbal commands. There is general agreement on the excellent psychometric qualities of the TT according to classical test theory, and Willmes (1981) has shown that parts I to IV as well as part V also fit the basic two-parameter Rasch model of probabilistic test theory. According to this analysis the syntactically more complex subtest V requires different abilities than the This research was supported by a grant from the Deutsche Forschungsgemeinschaft (Sonderforschungsbereich 99). We are most grateful to the staff and the patients of the Neurologisches Rehabilitationskrankenhaus fur Kinder und Jugendliche, Jugendwerk e. V., Gailingen, for their kind and generous cooperation. 37 0093-934X/85 $3.00 Copyright 0 1985 by Academic Press. Inc. All rights of reproduction in any form reserved.
38
GUTBROD
ET AL.
highly homogeneous parts I to IV. Surprisingly, this linguistically more demanding subtest does not increase the discriminative power of the whole test (Orgass, 1976b; Woll, Naumann, Cohen, & Kelter, 1976). Similarly, there were 91% correct classifications in the standardization sample of the Aachener Aphasie Test (Willmes, 1980), whether one took the ‘IT alone or the whole set of linguistically highly sophisticated subtests and ratings of this outstanding test battery without the Token Test. DeRenzi and Vignolo (1962) had introduced the TT as a sensitive method to detect aphasic disturbances in auditory language comprehension. But, strangely enough, the test is about equally powerful in identifying patients with Broca’s aphasia as it is in detecting Wernicke’s and global aphasics (Orgass & Poeck, 1966; Poeck, Kerschensteiner, & Hartje, 1972; Cohen et al., 1976). Concordantly, the correlations of the TT with tests of language production are just as high as those with tests of language comprehension (Cohen et al., 1976; Huber, Poeck, Weniger, & Willmes, 1983; Peck-Swisher & Taylor-Sarno, 1969). The TT obviously requires more than auditory language comprehension. Poeck and Hartje (1979) found that the sensitivity of the test is about the same whether the commands are presented orally or visually. The TT discriminates aphasics from nonaphasic brain-damage patients even if the verbal instructions are replaced by visual presentation of the targets or by pictorial representations of the features (Cohen, Kelter, 8z Schafer, 1977; Cohen, Lutzweiler, & Woll, 1980b). Thus, even if nothing is being said by either the patient or the experimenter, the test discriminates aphasics from nonaphasics. On the basis of these findings and of high correlations between the TT and various nonverbal matching tasks (Cohen, Kelter, & Woll, 1980a; Cohen & Wall, 1981), Cohen et al. (1976, 1977, 1980b) suggested that the TT is not so much a test of language comprehension as of analytical competence in the cognitive handling of individual features of objects or concepts. A somewhat contrasting view has been proposed by DeRenzi, Faglioni, and Previdi (1978), in stating that aphasics are possibly deficient in organizing or integrating the verbally presented information into a visual image. These views differ in two aspects: (1) analysis vs. integration and (2) general-cognitive vs. specific-verbal. The experiment presented here was designed to provide at least some evidence in favor of one or the other of these conjectures. In a dual-reaction time task, comparable to the sentence-picture verification paradigm by Clark and Chase (1972), subjects had to decide whether the picture of a single token was correctly described by a list of features. In the first part of each trial (inspection phase), while being confronted with the first slide, patients had to indicate their readiness to view the second slide by pressing a button. In the second phase (decision phase) they had to decide whether the information on this
COGNITIVE
PROCESSING
OF TOKENS
39
second slide corresponded with the information of the preceding slide in this trial. In different blocks of trials either the tokens or the lists of features were presented first. The lists of features described the token by giving either three words or three pictorial representations defining its size (large/small), color (blue/red), and shape (rectangle/circle). Since this task should be even easier than part I of the Token Test, aphasics could be expected to make few errors requiring response latencies instead of errors as the dependent variable. If aphasics have special difficulties in analyzing a whole into its constituent elements (analytic deficit), they can be expected to have longer latencies than the controls in processing the tokens. If they have specific difficulties in organizing or integrating individual features into a visual image (integrative deficit), their latencies should increase especially when confronted with lists of features. In addition, we thought it worthwhile to vary instructions to see if patients compensated for their specific deficits when relieved from time pressure. To this end subjects were instructed either to minimize their latencies in the inspection phase or to minimize their latencies in the decision phase. METHOD Subjects. Twenty-one aphasics (12 male, 9 female) and 24 right-hemisphere (RH) braindamaged controls (16 male, 8 female) served as subjects. All patients were native German speakers, with an age range of 14 to 25 and a mean age of 18.7 years. Subjects who were or had been either left-handed or ambidextrous according to the Edinburgh Inventory (Oldfield, 1971) were excluded. Diagnoses were taken from hospital records comprising information from clinical examinations, CT scans, and EEG records. For the aphasic group only those patients were accepted who, in addition, satisfied the criteria of the Aachener Aphasie Test (Huber et al., 1983). According to this test 7 patients were classified as amnesics, 12 as Broca’s, and 2 as Wemicke’s aphasics. Patients of the RH control group were screened according to clinical records, the Token Test, and the subtest “Written Language” from the Aachener Aphasie Test for not giving any indication of aphasic symptoms at any time. Background information for the aphasic and RH control group is given in Table I. Both groups were comparable with respect to sex, age, education, etiology, duration of illness, duration of post-traumatic unconsciousness, their performance in Form A of the Trail Making Test (Reitan, 1959), and a simple visual reaction task. Thus, there is no indication of one group being generally more impaired than the other. Stimuli. Each task involved the successive presentation of two slides, one showing a token and the other a list of features. The list of features described a token according to size (large/small), color (red/blue), and shape (rectangle/circle), by presenting either three written words or three pictorial representations of the features. The information was given in a vertical array (size-color-shape) in the same order throughout, corresponding to the order in the Token Test. The pictorial representation for size showed a vertical bar with a concave indentation either 6.7 (“large”) or 1.8 cm (“small”) high, and the symbol for shapes showed either a 3.6 x 1.8-cm rectangle or a circle with a diameter of 2.9 cm. The outline form for the presentation of color was taken from a “random shape” (Vanderplas & Garvin, 1959) with low associative value. The slides for the tokens showed rectangles of 10.8 x 14.4 cm (large) or 4.8 x 7.2 cm (small) and circles with a diameter of 12.4 or 5.5 cm.
40
GUTBROD
ET AL.
TABLE BACKGROUND
Etiology Traumatic Other Duration of illness (months) Median Range Unconsciousness (days) Median Range Age Median Range Sex Male Female Education Volksschule Realschule Gymnasium Trail making test (se4 Median Range Baseline reaction time (set) Median Range
INFORMATION
1
ON APHASICS
Aphasics (N = 21)
Controls (N = 24)
14 7
AND
RH
CONTROLS
Statistics”
P
18 6
x2 (1) = 0.08
>0.20
12 4-60
9 4-27
z = 1.09
BO.20
10 O-42
9.5 O-56
z = 0.02
>0.20
19.67 14-25
17.79 14-23
z = 1.62
>O.lO
12 9
16 8
x* (1) = 0.12
>0.20
2 7 12
5 13 6
x2 (2) = 4.90
=0.09
52 30-91
44 21-97
z = 1.59
>o. 10
0.20 0.14-0.40
0.22 0.16-0.33
z = 0.93
>0.20
0 z values based on Mann-Whitney
U test.
There were eight blocks of trials with 60 items, i.e., pairs of slides. Half of the trials consisted of the list of features correctly describing the token presented. In the other half there was a discrepancy relating to size, color, or shape equally often (N = 10) distributed. In addition there were 24 pairs of stimuli to familiarize the patients with the task. The difference between the practice stimuli and the experimental stimuli was that two features of a token had to be compared with each other, one being presented verbally and the other pictorially. Apparatus. The slides were rear-projected onto a translucent screen located 1 m in front of the subjects. After being initiated by the experimenter, each series of trials was automatically controlled. The presentation of a slide started with the opening of a concentric shutter located in front of the projector. Coincident with the onset of the slide was the onset of a timer. The subject had a movable board with three keys marked “same” (=), “next slide,” and “different” (f). The depression of one of the keys closed the shutter and stopped the timer.
COGNITIVE
PROCESSING
OF TOKENS
41
Procedure. The subjects were tested individually in four sessions each lasting approximately 1 hr. In the beginning of the first session all patients were given a simple visual reactiontime task (baseline). They were instructed to respond by button press as soon as the picture of a token appeared on the screen (25 trials). Otherwise, the order of the eight experimental blocks was randomized across subjects with the restriction that within a session the same instruction would hold for both blocks of trials. All the trials within a block were homogeneous in that (1) the token either preceded or followed the list of features, (2) the lists of features were presented either verbally or pictorially, and (3) the instructions required subjects to minimize either the “inspection” or the “decision” phase. The intertrial interval was 5 sec. Latencies were measured to the nearest l/IO0 set on a digital counter from the onset of the first and second stimulus slides to the responses of the subjects. Subjects were given eight practice trials for each condition. They were instructed to press the key marked “next slide” when they had viewed the first slide long enough. Immediately after the key had been pressed, the first slide was removed, and the second slide appeared on the screen approximately 0.5 set after the subject pressed the button for the first time. By pressing either the key marked “same” or the key marked “different,” the subject indicated if there was a mismatch with respect to any of the three dimensions. Subjects were asked always to use the same hand, whichever they preferred. In both groups about three-quarters of the subjects were hemiplegic, leading to the use of the nondominant hand in the aphasics. In ail conditions it was emphasized that errors were to be avoided. In two of the four sessions subjects were instructed to minimize the inspection phase, i.e., to respond as quickly as possible to the presentation of the first slide and then to decide without time pressure whether the information of the second slide corresponded to the information of the first slide. In the other two sessions subjects were instructed to minimize the decision phase, i.e., to decide as quickly as possible whether the information of the second slide matched the information of the first slide which they could inspect as long as they wanted. Half of the subjects of each group began with the instruction “minimize inspection phase” and the other half with “minimize decision phase.”
Error Rates
RESULTS
As intended, the error rates were extremely low. Nevertheless, aphasics made significantly more errors (Mdn = 3.2%) than right-hemisphere controls (Mdn = 1.5%), according to Mann-Whitney U tests (z = 2.95, p < .Ol). The highest error rate of a single patient was 7.5%. The difference was most pronounced when lists of features preceded the tokens, whether the features were described verbally (z = 2.75, p < .Ol) or pictorially (z = 2.07, p < .05); the difference was somewhat smaller when the lists of features followed the tokens, with z = 1.06 for the verbal and z = 1.91 for the pictorial version (p > .05). For both groups the differences between the experimental conditions do not reach an acceptable level of significance according to the Friedman test. A speed-accuracy trade-off cannot account for the relation between error rates and the inspection or decision latencies. Correlations between error scores and latencies were in the range - .04 s r s + .57. Only 2 of the 16 correlations were significant (r 2 .50), indicating that faster responders tended to make the least number of errors.
42
GUTBROD
ET AL.
Overall Latencies Under all experimental conditions the average latencies for correct responses of the aphasics were longer than the latencies of the RH controls [11.27 s F(1, 43) 6 11.32; p < .Ol]. This result does not reflect a general retardation of the aphasics: in the simple reaction-time task, when subjects had to press a button whatever token was presented, the two groups showed about equal latencies with M = .20 set for the aphasics and M = .22 set for the RH controls. Figure 1 presents the means of the median latencies of correct responses for processing lists of features and tokens, separately for the two groups, the two phases within a trial, and the two modes for defining the features. According to four-way ANOVAs (groups x instruction x representation of features x material), aphasics differ from controls in overall latencies both for the inspection [F( 1, 43) = 11.27; p < .Oll and for the decision
INSPECTION
DECISION
Pr.s.n+.t,on of f.a,ur.s Slid.
u.rb.1
LIST
pact.
“.,b.l
p*ct.
OF FERTURES
PHRSE
PHFlSE
“.rb.l
Y.,b.l
pict.
p*ct.
TOKEN
FIG. 1. Mean latencies of aphasics and RH controls to lists of features and tokens.
COGNITIVE
PROCESSING
OF TOKENS
43
phase [F(l) 43) = 11.32; p < .Ol]: Aphasics have longer latencies than RH controls (inspection: M (A) = 1.83 set, M (C) = 1.21 set; decision: M (A) = 1.61 set, M (C) = 1.20 set). The different instructions had only a negligible influence on the latencies. Only for the inspection phase (IP) there is a significant main effect for instruction [F(l, 43) = 4.54; p < .05], with M = 1.40 set vs. M = 1.63 sec. For the decision phase (DP) the two instructions led to nearly identical latencies [F(l, 43) = 0.291, with M = 1.43 set and M = 1.39 sec. All interactions of instruction with groups or experimental conditions are nonsignificant. For both phases within a trial the latencies are significantly affected by the difference between verbal and pictorial representation of features [IP: F(1, 43) = 9.93; p < .Ol; DP: F(1, 43) = 28.21, p < .OOl]. The pictorial representations (MIp = 1.41 set; MDp = 1.31 set) resulted in shorter latencies than the verbal representation (M,, = 1.62 set; MDP = 1.51 set). Finally, there is a strong main effect in both phases for material [IP: F(1, 43) = 138.53; p < .OOl; DP: F(1, 43) = 250.66; p < .OOl], showing that the latencies are longer for lists of features (MIp = 1.95 set; MDp = 1.74 set) than for tokens (Mlp = 1.09 set; MDP = 1.05 set). Significant interactions with groups are found for representation of features [IP: F(1, 43) = 7.33;~ < .Ol; DP: F(1, 43) = 17.81;~ < .OOl], for material [IP: F(1, 43) = 11.87; p < .Ol; DP: F( 1, 43) = 6.83; p < .05], and for the triple interaction with lists of features and material [IP: F(1, 43) = 5.90; p < .05; DP: F(1, 43) = 8.71; p < .Ol]. Scheffe tests (Winer, 1971) with a critical level of p = .05 were conducted to unravel these interactions. As was to be expected from the strong main effect for material, all post hoc comparisons led to significant differences between lists of features and tokens for both groups, both phases, and both modes of representation. There is not a single significant difference between the groups or between the experimental conditions in the processing time for the tokens. Both inspection and decision times for tokens are about the same for aphasics and RH controls. However, the groups differ drastically when confronted with lists of features: For RH controls it does not matter whether the features are described verbally (MIp = 1.52 set, MDp = 1.53 set) or pictorially (MIp = 1.52 set, MDp = 1.46 set), but for the aphasics latencies for verbal lists of features (MIp = 2.68 set, MDp = 2.29 set) are significantly longer than for pictorial ones (MIp = 2.09 set, MDp = 1.77 set). The difference between the two groups is highly significant for both phases when verbal lists of features are presented, while the post hoc comparisons between the groups do not reach the critical level for the pictorial lists of features. Since the Scheffk test is known to be conservative, separate two-way ANOVAs (groups x instruction) were run for the
44
GUTBROD
ET AL.
pictorial lists of features only. These ANOVAs led to a significant group effect [F(l, 43) = 6.35; p < .05] for the inspection phase, but not for the decision phase [F( 1, 43) = 3.85; p > .05]. Splitting the group of aphasics into those with an AAT diagnosis of Broca’s aphasia (N = 12) and those with an AAT diagnosis of amnesic or Wernicke’s aphasia (N = 9) did not reveal any further interactions. The trends are the same in both subgroups, with the Broca’s having longer latencies throughout. Decision Latencies for Correct “Different”
Responses
In this section the latencies of correct “different” responses will be analyzed for possible differences between the three stimulus dimensions. It might be recalled that half of the stimuli within a trial differed with respect to either size, color, or shape. Figure 2 presents the means of the median latencies for correct “different” decisions in processing either lists of features or tokens as the second stimulus within a trial. Three-way ANOVAs (groups x representation of features x dimension) were carried out separately for lists of features and tokens. Since the decision latencies for correct “same” and correct “different” responses 3.6
LIST
OF FEFlTURES
TOKEN
DilB*“sl*” PreSentatlO” of fertur*s
size
Color
ForIn
si7.e
Color
Form
size
Color
verbal FIG.
2.
Mean latencies of correct “different”
rornl
pit
SIZS
torial
decisions.
color
Form
COGNITIVE
PROCESSING
OF TOKENS
4.5
are about the same for all experimental conditions, the overall picture for the effects of groups, material, lists of features, and their interactions is by and large the same as reported above. Thus, only the differences between the dimensions (size, color, shape) and the interactions of these factors will be reported here. Lists of features. Significant effects were found for dimension [F(2, 86) = 45.78; p < .OOl], dimension x group [F(2, 86) = 7.61; p < .OOl], representation of features x dimension [F(2, 86) = 15.12; p < .OOl], and representation of features x dimension x group [F(2, 86) = 3.21; p < .05].
In general, latencies are longer for shape (M = 1.95 set) than for size M = 1.58 set) or color (M = 1.54 set), although the pattern of medians is different for verbal and pictorial lists of features. According to Scheffe tests, the difference between shape and size as well as between shape and color reaches the 5% level for aphasics when verbal lists of features were presented, with M = 2.62 set vs. M = 1.78 set vs. M = 2.01 set, while for the pictorial lists of features only the difference between shape and color is significant in this group, with M = 1.90 set vs. M = 1.31 sec. The difference between groups is significant only for the lists of features if the mismatch refers to shape of an earlier presented token. Tokens. Post hoc comparisons revealed that the only significant interaction, dimension x group [F(2, 86) = 5.08; p < .Ol], is due to longer latencies for both shape and size than for color among the aphasics. Otherwise, there is no indication that the tokens are being processed differently in the two groups. Intercorrelations
It was the aim of this study to shed some light on processes critical for the poor performance of aphasics in the Token Test (TT) and especially in the subtests I-IV of this test. Thus, performance measures in this experiment were correlated with the error scores of the TT and other measures from the Aachener Aphasie Test. Inspection and decision latencies for lists of features correlate (.39 G r c .70) with TT I-IV and (.39 6 r c .67) with the total TT. The highest correlations are found between the verbal lists of features and TT I-IV (IP: r = .70; DP: r = .45, p < .05). The correlation between the latencies for the pictorial lists of features and TT I-IV in the inspection phase (r = .42) falls just short of significance; the remaining correlations are nonsignificant. Due to a strong bottom effect the error rates from this experiment could not be expected to show any covariance with the TT (.08 c r s .34). The latencies from the eight experimental conditions and the simple reaction time task were intercorrelated and subjected to a principal component analysis with subsequent varimax rotation separately for aphasics
46
GUTBROD
ET AL.
and RH controls. For both groups two components have Eigenvalues > 1.00, explaining 80.2 and 75.5% of the total variance in the two groups. The varimax-rotated loadings of the two structures are shown in Table 2, and the correlations of the factor scores of the aphasics with the Token Test and the subtests of the Aachener Aphasie Test are given in Table 3. For both groups the latencies from the inspection phase have high loadings on Factor I, ranging .83 s u s .91 for the aphasics and .46 s a < .95 for the RH controls. The latencies from the decision phase as well as the latencies in the simple reaction time task are primarily represented by Factor II, ranging 58 s a s .96 in the aphasics, and .67 6 a s .89 in the controls. Apparently, Factor I is more related to information intake and storage while Factor II can be thought of as representing a more general speed component. Factor I in the aphasics’ structure covers almost all the variance of the latencies from the inspection phase. It is this factor which also correlates significantly with the Token Test (r = .51), as well as with the “Naming on Confrontation” (r = - .52) and the “Comprehension” subtests (r = - .55) of the Aachener Aphasie Test. DISCUSSION
There are several reasons to consider the results of this dual-reaction time task as being meaningfully related to the performance deficit of aphasics in the Token Test: (1) the latency medians of various conditions are significantly correlated with the error scores in the standard Token Test, with the highest correlation found for the condition resembling most closely the standard Token Test; (2) even though the aphasics were capable of understanding the meaning of the words and of the pictorial representations, as evidenced by their low error rates, they still made significantly more errors (M&r = 3.2%) than the RH controls. The most conspicuous finding of this experiment is the total lack of any significant differences between aphasics and RH controls whenever tokens were presented. All the differences between the two groups are found under those conditions in which the patient is being confronted with a list of features. This result is clearly at variance with the suggestion that “the analytic ability to isolate and identify . . . individual features of objects . . . might also be a primary requirement in the Token Test” (Cohen et al., 1980a, p. 345). There is no evidence in our data indicating that aphasics were particularly impaired in the cognitive handling of these stimuli: It is the processing of the lists of features which differentiated aphasics from the controls. This conclusion must also raise doubts about whether it really is the “abstract nature” of the tokens that makes the test so sensitive to aphasic impairments. The better performance of aphasics, when offered pictures of familiar objects as opposed to tokens (Kreindler, Gheorghita, & Voinescu, 1971; Martino, Pizzamiglio, & Razzano, 1976; Naumann, Kelter, & Cohen, 1980), might only indicate that
37.0
.74
.83 .96 .58 .I5
.16 .43 .06 .26
II
Aphasics
80.2
.56
.80 .96 .71 .87
.73 .90 .81 .88
h’
a Stimuli that preceded or followed the critical stimuli within a trial in brackets.
43.2
.33 .20 .61 .56
% Total variance
token token verbal list pictorial list
-
.83 .84 .90 .91
- .Ol
(token) (token) (verbal list) (pictorial list)
-
I
Simple reaction time
Inspection phase Verbal list Pictorial list Token Token Decision phase (Verbal list) (Pictorial list) (Token) (Token)
TABLE 2
31.6
-.06
.45 .23 .34
.4l
.46 .58 .95 .91
I
ROTATEDFACTORLOADINGSOF THE LATENCY MEASURES
.82 .84 .86
.78 .89 .86
44.8
76.4
.45
.84
.80
.67
.58 .70 .91 .89
h’
.60 .60 .13 .24
II
RH controls
!z 5
3
%
3
g F: !z z
s
48
GUTBROD
ET AL.
TABLE CORRELATIONS LATENCY
BETWEEN MEASURES,
THE AACHENER AND
Token Test Written language Comprehension Naming on confrontation Repetition Errors in experiment Factor I Factor II
3
APHASIE
THE ERROR
TEST,
SCORE
THE FACTOR
SCORES
FROM
FROM THE EXPERIMENTAL
TASKS
6
1
2
3
4
5
-.93** - .68** - .73** -.51* .31 .51* .03
.70** .67** .56** -.I8 - .38 .Ol
.77** .42 - .33 - .52* -.18
.58** -.12 -.55** -.12
-.Ol -.I6 .I5
THE
7
8
.36 .44* 0.0 -
a Aphasics only, N = 21. * p < .05. ** p < .Ol.
representations of familiar objects possibly allow for an easier and/or more reliable encoding and storage of the target description. Such an interpretation would also be in line with the hypothesis of DeRenzi et al. (1978) that aphasics are deficient in organizing or integrating the verbally presented information into a visual image. Obviously, their hypothesis can be reconciled with our data much more easily than the hypothesis of a deficit in the competence for handling the tokens analytically. The hypothesis of a deficit in organizing the verbally as well as the pictorially presented information into a visual image is further supported by the triple interaction showing that the difference between the groups in handling the lists of features is most pronounced when they are given not in the decision but in the inspection phase. The formation of a visual image to be compared with the picture of a token should be most advantageous if the features are described in the first phase of a trial and it is in this phase where the aphasics’ performance is poorest. When the features are described in the second phase it should be at least equally effective to follow a self-terminating sequential comparison process. Such a sequential comparison was apparently the most common strategy in this condition for both groups. Both groups show the longest latencies for correct decisions about shape, the feature defined at the bottom of the list. This increase in latency is most pronounced in the aphasics when the features were described verbally. When it was possible to detect a mismatch by considering only size and color the differences between groups are negligible. While some results discussed so far support the notion of DeRenzi et al. (1978) that aphasics were deficient in integrating verbally presented informations into a visual image, other results suggest that such a deficit should be considered only as one component of a more general deficit in the encoding and storage of highly specific information.
COGNITIVE
PROCESSING
OF TOKENS
49
No doubt the difference between aphasics and RH controls is largest whenever verbal lists of features are presented, but there is also a tendency toward increased latencies for the nonverbal lists of features, favoring our model of a more general, i.e., not purely verbal, deficit. Depending on the particular way of testing the difference, this tendency reaches or just fails the 5% level of significance. This finding cannot be reduced to possible difficulties in translating the symbols for size, color, and form into verbal codes for memory storage. Such translations should lead to longer latencies for the pictorial than for verbal lists, and not to the considerably shorter latencies actually found for the aphasics. Moreover, although the patients of both groups made few errors throughout and thus gave good evidence that they understood the meaning of the words and of the pictorial representations, aphasics make significantly more errors even with the nonverbal lists of features. This finding corroborates earlier results of Cohen et al. (1977, 1980b) that aphasics are still poorer than controls when nonverbal instead of verbal instructions are given in the Token Test. A similar result has been reported by Grtisser, von Hartrott, Heeschen, and Reischies (1982) for Broca’s aphasics only. In our experiment no such difference between Broca’s and a mixed group of amnesic and Wemicke’s aphasia could be found. Possibly the discrepancy in the results is due to differences in the populations tested: while most of their aphasics were of cerebrovascular origin, most of our patients were adolescent and victims of traumatic accidents. A principal components analysis of the latency measures led to one factor which was significantly correlated with the performance in the standard Token Test as well as with the subtests ‘Comprehension” and “Naming on Confrontation” of the Aachener Aphasie Test. This factor accounts for 69 to 83% of the variance of all the latencies from the inspection phase, whether verbal lists, pictorial lists, or tokens had been presented, while the latencies from the decision phase have most of their loadings on a second factor which has no relation to the number of errors in the Token Test. This close relationship between the latencies from the inspection phase (.64 c Y s .92) in the aphasics has no resemblance in the correlation matrix of the RH controls (. 16 s r < 64). The correlations between the latencies from the decision phase on the other hand are in the same range (.71 =Z r s .85) for both groups. Considering this high correlation between all the latencies from the inspection phase of the aphasics, and the significant correlation of this factor with the performance in the Token Test, we are hesitant to accept the notion of a particular deficit confined to the comparison of verbally presented information with the visual stimulus of a token. In the factorial structure obtained, such a deficit appears embedded in a more general deficit in the storage of highly specific information.
50
GUTBROD
ET AL.
There is one disquieting problem in all these considerations. Throughout this paper we accepted both error rates and prolonged latencies as indicators of difficulty. This is common practice in psychology. But how do these two indicators of difficulties relate to each other? As shown earlier, there is no evidence of a speed-accuracy trade-off on our data. What does it mean that aphasics need more time to process the description? In line with our latency data Liles and Brookshire (1975) as well as Poeck and Pietron (1981), have shown that the number of errors in the Token Test decreases when the instructions are given at a reduced rate of speech. It is unlikely that the higher error rates and increased latencies were the results of fluctuations in vigilance. Since we took the medians and not the means, our latency measure cannot be seriously affected by such fluctuations; furthermore we do not know of any evidence that aphasics have more severe fluctuations in vigilance than do RH controls. The simplest way to cover both the higher error rates and the increased latencies would be to consider the association between a word or a symbol with a specific meaning not as an all-or-none matter but as a probabilistic relationship. Obviously, with an error rate of only about 3.5% our aphasics understood the meaning of the words and symbols most of the time, but the association between words and meanings was clearly not perfect and not as good as found in the controls. It is not unreasonable to assume that aphasics know about their loose connections between words and meanings. Such a knowledge might well lead to an increased tendency to double-check and thus to prolonged latencies. Such a simple model can definitely not account for all the questions raised by the amazingly high discrimination power of the Token Test. But it might cover some “mechanics of verbal ability” (Hunt, 1978) which appear impaired in different forms of aphasia over and above their purely linguistic deficits. REFERENCES Clark, H. H., & Chase, W. G. 1972. On the process of comparing sentences against pictures. Cognitive Psychology, 3, 472-517. Cohen, R., Kelter, S., Engel, D., List, G., & Strohner, H. 1976. Zur Vahditlt des Token Tests. Nervenarzt, 47, 357-361. Cohen, R., Kelter, S., & Schafer, B. 1977. Zum Einfluss des Sprachverstandnisses auf die Leistungen im Token Test. Zeitschrif fir Klinische Psychologie, 6, 1-14. Cohen, R., Kelter, S., & Wall, G. 1980. Analytical competence and language impairment in aphasia. Brain and Language, 10, 331-347. (a) Cohen, R., Lutzweiler, W., & Wall, G. 1980. Zur Konstruktvaliditat des Token Tests. Nervenarzt, 51, 30-35. (b) Cohen, R., & Woll, G. 1981. Facets of analytical processing in aphasia: A picture ordering task. Cortex, 17, 557-570. DeRenzi, E., & Faglioni, P. 1978. Normative data and screening power of a shortened version of the Token Test. Cortex, 14, 41-49. DeRenzi, E., Faglioni, P., & Previdi. 1978. Increased susceptibility of aphasics to a distractor task in the recall of verbal commands. Brain and Language, 6, 14-21.
COGNITIVE
PROCESSING
OF TOKENS
51
DeRenzi, E., & Vignolo, L. 1962. The Token Test: A sensitive test to detect receptive disturbances in aphasics. Bruin, 85, 665-678. Griisser, O., von Hartrott, H ., Heeschen, C., & Reischies, F. 1982. A note on a nonverbal version of the Token Test. Berlin: Physiol. Inst., Free University. Huber, W., Poeck, K., Weniger, D., 62 Willmes, K. 1983. Der Aachener Aphasie Test. Gottingen: Hogrefe. Hunt, E. 1978. Mechanics of verbal ability. Psychological Review, 85, 109-130. Kreindler, A., Gheorghita, N., & Voinescu, I. 1971. Analysis of verbal reception of a complex order with three elements in aphasics. Bruin, 94, 375-386. Liles, B., & Brookshire, R. 1975. The effects of pause time on auditory comprehension of aphasic subjects. Journal of Communication Disorders, 8, 221-235. Martino, A., Pizzamiglio, L., & Razzano, C. 1976. A new version of the Token Test for aphasics: A concrete objects form. Journal of Communication Disorders, 9, l-5. Naumann, E., Kelter, S., & Cohen, R. 1980. Zum Einfluss mnestischer, semantischer und konzeptueller Faktoren auf die Leistungen aphasischer Patienten im Token Test. Archiv. fir Psychiatie und Nervenkrunkheiten, 228, 317-328. Oldfield, R. 1971. The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychologiu,
2, 97- 113.
Orgass, B. 1976. Eine Revision des Token Tests. I. Vereinfachung der Austwertung, Itemanalyse und Einftlhrung einer Alterskorrektur. Diagnostica, 22, 70-87. (a) Orgass, B. 1976. Eine Revision des Token Tests. II. Validitatsnachweis, Normierung und Standardisierung. Diagnostica, 22, 141-156. (b) Orgass, B., & Poeck, K. 1966. Clinical validation of a new test for aphasia: An experimental study on the Token Test. Cortex, 2, 222-243 Peck-Swisher, L., & Taylor-Sarno, M. 1969. Token Test scores of three matched patient groups: Left-brain-damaged with aphasia, right-brain-damaged without aphasia, nonbrain-damaged. Cortex, 5, 264-273. Poeck, K., & Hartje, W. 1979. Performance of aphasic patients in visual versus auditory presentation of the Token Test: Demonstration of a supramodal deficit. In F. Boiler & M. Dennis (Eds.), Auditory comprehension. Clinical and experimental studies with the Token Test. New York: Academic Press. Pp. 107-117. Poeck, K., Kershensteiner, M. & Hartje, W. 1972. A quantitative study on language understanding in fluent and non-fluent aphasia. Cortex, 8, 299-304. Poeck, K., & Pietron, H. 1981. The influence of stretched speech presentation on Token Test performance of aphasic and right brain damaged patients. Neuropsychologiu, 19, 133-136. Reitan, R. 1959. A manual for the administration and scoring of the Trail-Making-Test. Bloomington, Indiana Univ. Press. Vanderplas, J., & Garvin, E. 1959. The association value of random shapes. Journal of Experimental
Psychology,
57, 147-154.
Van Dongen, H., & van Harskamp, F. 1972. The Token Test: A preliminary evaluation of a method to detect aphasia. Psychiatriu, Neurologiu, Neurochirurgiu, 75, 129-134. Willmes, K. 1980. Die psychometrischen Eigenschaften eines Aphusie-Tests-dargestellt am Beispiel des Auchener Aphusie-Tests. Paper presented at the Kolloquium Sprachpathologie of the Deutsche Forschungsgemeinschaft, Berlin, Sept. 1980. Willmes, K. 1981. A new look at the Token Test using probabilistic test models. Neuropsychologia,
19, 631445.
Winer, B. 1971. Statistical principles in experimental design. New York: McGraw-Hill. Woll, G., Naumann, E., Cohen, R., & Kelter, S. 1976. Kreuzvalidierung der Revision des Token Tests durch Orgass. Diugnostica, 22, 157-162.
AND
LANGUAGE
25, 37-51 (1985)
Cognitive Processing of Tokens and Their Description in Aphasia KLEMENS
GUTBROD,
BARBARA MAGER, University
of Konstanz,
ERWIN MEIER, AND RUDOLF COHEN West Germany
In a dual-reaction time task aphasics (N = 21) and right-hemisphere (RH) controls (N = 24) had to decide whether a list of features given verbally or pictorially correctly described the picture of a token. Although the error rates were extremely low, aphasics made significantly more errors than RH controls. There were no significant differences between the groups in latencies when pictures of tokens were presented; the groups differed drastically, however, when confronted with lists of features. The findings are interpreted as indicating a general deficit in the short-term storage of highly specific information. D 19x5 Academic
Press, Inc.
INTRODUCTION
The Token Test (TT), developed by DeRenzi and Vignolo (1962), has been shown to be extremely powerful in discriminating aphasics from other groups of brain-damaged patients (e.g., Cohen, Kelter, Engel, List, & Strohner, 1976; DeRenzi & Faglioni, 1978; Orgass, 1976a, 1976b; PeckSwisher & Taylor-Sarno, 1969; Van Dongen & van Harskamp, 1972). The percentage of correct classifications with respect to this dichotomy among brain-damaged patients varies in different studies from 77 to about 90%. The TT consists of five parts in which the subject is requested to touch (parts I to IV) or manipulate (part V) tokens of five different colors, two shapes, and sometimes two sizes, according to progressively more complex verbal commands. There is general agreement on the excellent psychometric qualities of the TT according to classical test theory, and Willmes (1981) has shown that parts I to IV as well as part V also fit the basic two-parameter Rasch model of probabilistic test theory. According to this analysis the syntactically more complex subtest V requires different abilities than the This research was supported by a grant from the Deutsche Forschungsgemeinschaft (Sonderforschungsbereich 99). We are most grateful to the staff and the patients of the Neurologisches Rehabilitationskrankenhaus fur Kinder und Jugendliche, Jugendwerk e. V., Gailingen, for their kind and generous cooperation. 37 0093-934X/85 $3.00 Copyright 0 1985 by Academic Press. Inc. All rights of reproduction in any form reserved.
38
GUTBROD
ET AL.
highly homogeneous parts I to IV. Surprisingly, this linguistically more demanding subtest does not increase the discriminative power of the whole test (Orgass, 1976b; Woll, Naumann, Cohen, & Kelter, 1976). Similarly, there were 91% correct classifications in the standardization sample of the Aachener Aphasie Test (Willmes, 1980), whether one took the ‘IT alone or the whole set of linguistically highly sophisticated subtests and ratings of this outstanding test battery without the Token Test. DeRenzi and Vignolo (1962) had introduced the TT as a sensitive method to detect aphasic disturbances in auditory language comprehension. But, strangely enough, the test is about equally powerful in identifying patients with Broca’s aphasia as it is in detecting Wernicke’s and global aphasics (Orgass & Poeck, 1966; Poeck, Kerschensteiner, & Hartje, 1972; Cohen et al., 1976). Concordantly, the correlations of the TT with tests of language production are just as high as those with tests of language comprehension (Cohen et al., 1976; Huber, Poeck, Weniger, & Willmes, 1983; Peck-Swisher & Taylor-Sarno, 1969). The TT obviously requires more than auditory language comprehension. Poeck and Hartje (1979) found that the sensitivity of the test is about the same whether the commands are presented orally or visually. The TT discriminates aphasics from nonaphasic brain-damage patients even if the verbal instructions are replaced by visual presentation of the targets or by pictorial representations of the features (Cohen, Kelter, 8z Schafer, 1977; Cohen, Lutzweiler, & Woll, 1980b). Thus, even if nothing is being said by either the patient or the experimenter, the test discriminates aphasics from nonaphasics. On the basis of these findings and of high correlations between the TT and various nonverbal matching tasks (Cohen, Kelter, & Woll, 1980a; Cohen & Wall, 1981), Cohen et al. (1976, 1977, 1980b) suggested that the TT is not so much a test of language comprehension as of analytical competence in the cognitive handling of individual features of objects or concepts. A somewhat contrasting view has been proposed by DeRenzi, Faglioni, and Previdi (1978), in stating that aphasics are possibly deficient in organizing or integrating the verbally presented information into a visual image. These views differ in two aspects: (1) analysis vs. integration and (2) general-cognitive vs. specific-verbal. The experiment presented here was designed to provide at least some evidence in favor of one or the other of these conjectures. In a dual-reaction time task, comparable to the sentence-picture verification paradigm by Clark and Chase (1972), subjects had to decide whether the picture of a single token was correctly described by a list of features. In the first part of each trial (inspection phase), while being confronted with the first slide, patients had to indicate their readiness to view the second slide by pressing a button. In the second phase (decision phase) they had to decide whether the information on this
COGNITIVE
PROCESSING
OF TOKENS
39
second slide corresponded with the information of the preceding slide in this trial. In different blocks of trials either the tokens or the lists of features were presented first. The lists of features described the token by giving either three words or three pictorial representations defining its size (large/small), color (blue/red), and shape (rectangle/circle). Since this task should be even easier than part I of the Token Test, aphasics could be expected to make few errors requiring response latencies instead of errors as the dependent variable. If aphasics have special difficulties in analyzing a whole into its constituent elements (analytic deficit), they can be expected to have longer latencies than the controls in processing the tokens. If they have specific difficulties in organizing or integrating individual features into a visual image (integrative deficit), their latencies should increase especially when confronted with lists of features. In addition, we thought it worthwhile to vary instructions to see if patients compensated for their specific deficits when relieved from time pressure. To this end subjects were instructed either to minimize their latencies in the inspection phase or to minimize their latencies in the decision phase. METHOD Subjects. Twenty-one aphasics (12 male, 9 female) and 24 right-hemisphere (RH) braindamaged controls (16 male, 8 female) served as subjects. All patients were native German speakers, with an age range of 14 to 25 and a mean age of 18.7 years. Subjects who were or had been either left-handed or ambidextrous according to the Edinburgh Inventory (Oldfield, 1971) were excluded. Diagnoses were taken from hospital records comprising information from clinical examinations, CT scans, and EEG records. For the aphasic group only those patients were accepted who, in addition, satisfied the criteria of the Aachener Aphasie Test (Huber et al., 1983). According to this test 7 patients were classified as amnesics, 12 as Broca’s, and 2 as Wemicke’s aphasics. Patients of the RH control group were screened according to clinical records, the Token Test, and the subtest “Written Language” from the Aachener Aphasie Test for not giving any indication of aphasic symptoms at any time. Background information for the aphasic and RH control group is given in Table I. Both groups were comparable with respect to sex, age, education, etiology, duration of illness, duration of post-traumatic unconsciousness, their performance in Form A of the Trail Making Test (Reitan, 1959), and a simple visual reaction task. Thus, there is no indication of one group being generally more impaired than the other. Stimuli. Each task involved the successive presentation of two slides, one showing a token and the other a list of features. The list of features described a token according to size (large/small), color (red/blue), and shape (rectangle/circle), by presenting either three written words or three pictorial representations of the features. The information was given in a vertical array (size-color-shape) in the same order throughout, corresponding to the order in the Token Test. The pictorial representation for size showed a vertical bar with a concave indentation either 6.7 (“large”) or 1.8 cm (“small”) high, and the symbol for shapes showed either a 3.6 x 1.8-cm rectangle or a circle with a diameter of 2.9 cm. The outline form for the presentation of color was taken from a “random shape” (Vanderplas & Garvin, 1959) with low associative value. The slides for the tokens showed rectangles of 10.8 x 14.4 cm (large) or 4.8 x 7.2 cm (small) and circles with a diameter of 12.4 or 5.5 cm.
40
GUTBROD
ET AL.
TABLE BACKGROUND
Etiology Traumatic Other Duration of illness (months) Median Range Unconsciousness (days) Median Range Age Median Range Sex Male Female Education Volksschule Realschule Gymnasium Trail making test (se4 Median Range Baseline reaction time (set) Median Range
INFORMATION
1
ON APHASICS
Aphasics (N = 21)
Controls (N = 24)
14 7
AND
RH
CONTROLS
Statistics”
P
18 6
x2 (1) = 0.08
>0.20
12 4-60
9 4-27
z = 1.09
BO.20
10 O-42
9.5 O-56
z = 0.02
>0.20
19.67 14-25
17.79 14-23
z = 1.62
>O.lO
12 9
16 8
x* (1) = 0.12
>0.20
2 7 12
5 13 6
x2 (2) = 4.90
=0.09
52 30-91
44 21-97
z = 1.59
>o. 10
0.20 0.14-0.40
0.22 0.16-0.33
z = 0.93
>0.20
0 z values based on Mann-Whitney
U test.
There were eight blocks of trials with 60 items, i.e., pairs of slides. Half of the trials consisted of the list of features correctly describing the token presented. In the other half there was a discrepancy relating to size, color, or shape equally often (N = 10) distributed. In addition there were 24 pairs of stimuli to familiarize the patients with the task. The difference between the practice stimuli and the experimental stimuli was that two features of a token had to be compared with each other, one being presented verbally and the other pictorially. Apparatus. The slides were rear-projected onto a translucent screen located 1 m in front of the subjects. After being initiated by the experimenter, each series of trials was automatically controlled. The presentation of a slide started with the opening of a concentric shutter located in front of the projector. Coincident with the onset of the slide was the onset of a timer. The subject had a movable board with three keys marked “same” (=), “next slide,” and “different” (f). The depression of one of the keys closed the shutter and stopped the timer.
COGNITIVE
PROCESSING
OF TOKENS
41
Procedure. The subjects were tested individually in four sessions each lasting approximately 1 hr. In the beginning of the first session all patients were given a simple visual reactiontime task (baseline). They were instructed to respond by button press as soon as the picture of a token appeared on the screen (25 trials). Otherwise, the order of the eight experimental blocks was randomized across subjects with the restriction that within a session the same instruction would hold for both blocks of trials. All the trials within a block were homogeneous in that (1) the token either preceded or followed the list of features, (2) the lists of features were presented either verbally or pictorially, and (3) the instructions required subjects to minimize either the “inspection” or the “decision” phase. The intertrial interval was 5 sec. Latencies were measured to the nearest l/IO0 set on a digital counter from the onset of the first and second stimulus slides to the responses of the subjects. Subjects were given eight practice trials for each condition. They were instructed to press the key marked “next slide” when they had viewed the first slide long enough. Immediately after the key had been pressed, the first slide was removed, and the second slide appeared on the screen approximately 0.5 set after the subject pressed the button for the first time. By pressing either the key marked “same” or the key marked “different,” the subject indicated if there was a mismatch with respect to any of the three dimensions. Subjects were asked always to use the same hand, whichever they preferred. In both groups about three-quarters of the subjects were hemiplegic, leading to the use of the nondominant hand in the aphasics. In ail conditions it was emphasized that errors were to be avoided. In two of the four sessions subjects were instructed to minimize the inspection phase, i.e., to respond as quickly as possible to the presentation of the first slide and then to decide without time pressure whether the information of the second slide corresponded to the information of the first slide. In the other two sessions subjects were instructed to minimize the decision phase, i.e., to decide as quickly as possible whether the information of the second slide matched the information of the first slide which they could inspect as long as they wanted. Half of the subjects of each group began with the instruction “minimize inspection phase” and the other half with “minimize decision phase.”
Error Rates
RESULTS
As intended, the error rates were extremely low. Nevertheless, aphasics made significantly more errors (Mdn = 3.2%) than right-hemisphere controls (Mdn = 1.5%), according to Mann-Whitney U tests (z = 2.95, p < .Ol). The highest error rate of a single patient was 7.5%. The difference was most pronounced when lists of features preceded the tokens, whether the features were described verbally (z = 2.75, p < .Ol) or pictorially (z = 2.07, p < .05); the difference was somewhat smaller when the lists of features followed the tokens, with z = 1.06 for the verbal and z = 1.91 for the pictorial version (p > .05). For both groups the differences between the experimental conditions do not reach an acceptable level of significance according to the Friedman test. A speed-accuracy trade-off cannot account for the relation between error rates and the inspection or decision latencies. Correlations between error scores and latencies were in the range - .04 s r s + .57. Only 2 of the 16 correlations were significant (r 2 .50), indicating that faster responders tended to make the least number of errors.
42
GUTBROD
ET AL.
Overall Latencies Under all experimental conditions the average latencies for correct responses of the aphasics were longer than the latencies of the RH controls [11.27 s F(1, 43) 6 11.32; p < .Ol]. This result does not reflect a general retardation of the aphasics: in the simple reaction-time task, when subjects had to press a button whatever token was presented, the two groups showed about equal latencies with M = .20 set for the aphasics and M = .22 set for the RH controls. Figure 1 presents the means of the median latencies of correct responses for processing lists of features and tokens, separately for the two groups, the two phases within a trial, and the two modes for defining the features. According to four-way ANOVAs (groups x instruction x representation of features x material), aphasics differ from controls in overall latencies both for the inspection [F( 1, 43) = 11.27; p < .Oll and for the decision
INSPECTION
DECISION
Pr.s.n+.t,on of f.a,ur.s Slid.
u.rb.1
LIST
pact.
“.,b.l
p*ct.
OF FERTURES
PHRSE
PHFlSE
“.rb.l
Y.,b.l
pict.
p*ct.
TOKEN
FIG. 1. Mean latencies of aphasics and RH controls to lists of features and tokens.
COGNITIVE
PROCESSING
OF TOKENS
43
phase [F(l) 43) = 11.32; p < .Ol]: Aphasics have longer latencies than RH controls (inspection: M (A) = 1.83 set, M (C) = 1.21 set; decision: M (A) = 1.61 set, M (C) = 1.20 set). The different instructions had only a negligible influence on the latencies. Only for the inspection phase (IP) there is a significant main effect for instruction [F(l, 43) = 4.54; p < .05], with M = 1.40 set vs. M = 1.63 sec. For the decision phase (DP) the two instructions led to nearly identical latencies [F(l, 43) = 0.291, with M = 1.43 set and M = 1.39 sec. All interactions of instruction with groups or experimental conditions are nonsignificant. For both phases within a trial the latencies are significantly affected by the difference between verbal and pictorial representation of features [IP: F(1, 43) = 9.93; p < .Ol; DP: F(1, 43) = 28.21, p < .OOl]. The pictorial representations (MIp = 1.41 set; MDp = 1.31 set) resulted in shorter latencies than the verbal representation (M,, = 1.62 set; MDP = 1.51 set). Finally, there is a strong main effect in both phases for material [IP: F(1, 43) = 138.53; p < .OOl; DP: F(1, 43) = 250.66; p < .OOl], showing that the latencies are longer for lists of features (MIp = 1.95 set; MDp = 1.74 set) than for tokens (Mlp = 1.09 set; MDP = 1.05 set). Significant interactions with groups are found for representation of features [IP: F(1, 43) = 7.33;~ < .Ol; DP: F(1, 43) = 17.81;~ < .OOl], for material [IP: F(1, 43) = 11.87; p < .Ol; DP: F( 1, 43) = 6.83; p < .05], and for the triple interaction with lists of features and material [IP: F(1, 43) = 5.90; p < .05; DP: F(1, 43) = 8.71; p < .Ol]. Scheffe tests (Winer, 1971) with a critical level of p = .05 were conducted to unravel these interactions. As was to be expected from the strong main effect for material, all post hoc comparisons led to significant differences between lists of features and tokens for both groups, both phases, and both modes of representation. There is not a single significant difference between the groups or between the experimental conditions in the processing time for the tokens. Both inspection and decision times for tokens are about the same for aphasics and RH controls. However, the groups differ drastically when confronted with lists of features: For RH controls it does not matter whether the features are described verbally (MIp = 1.52 set, MDp = 1.53 set) or pictorially (MIp = 1.52 set, MDp = 1.46 set), but for the aphasics latencies for verbal lists of features (MIp = 2.68 set, MDp = 2.29 set) are significantly longer than for pictorial ones (MIp = 2.09 set, MDp = 1.77 set). The difference between the two groups is highly significant for both phases when verbal lists of features are presented, while the post hoc comparisons between the groups do not reach the critical level for the pictorial lists of features. Since the Scheffk test is known to be conservative, separate two-way ANOVAs (groups x instruction) were run for the
44
GUTBROD
ET AL.
pictorial lists of features only. These ANOVAs led to a significant group effect [F(l, 43) = 6.35; p < .05] for the inspection phase, but not for the decision phase [F( 1, 43) = 3.85; p > .05]. Splitting the group of aphasics into those with an AAT diagnosis of Broca’s aphasia (N = 12) and those with an AAT diagnosis of amnesic or Wernicke’s aphasia (N = 9) did not reveal any further interactions. The trends are the same in both subgroups, with the Broca’s having longer latencies throughout. Decision Latencies for Correct “Different”
Responses
In this section the latencies of correct “different” responses will be analyzed for possible differences between the three stimulus dimensions. It might be recalled that half of the stimuli within a trial differed with respect to either size, color, or shape. Figure 2 presents the means of the median latencies for correct “different” decisions in processing either lists of features or tokens as the second stimulus within a trial. Three-way ANOVAs (groups x representation of features x dimension) were carried out separately for lists of features and tokens. Since the decision latencies for correct “same” and correct “different” responses 3.6
LIST
OF FEFlTURES
TOKEN
DilB*“sl*” PreSentatlO” of fertur*s
size
Color
ForIn
si7.e
Color
Form
size
Color
verbal FIG.
2.
Mean latencies of correct “different”
rornl
pit
SIZS
torial
decisions.
color
Form
COGNITIVE
PROCESSING
OF TOKENS
4.5
are about the same for all experimental conditions, the overall picture for the effects of groups, material, lists of features, and their interactions is by and large the same as reported above. Thus, only the differences between the dimensions (size, color, shape) and the interactions of these factors will be reported here. Lists of features. Significant effects were found for dimension [F(2, 86) = 45.78; p < .OOl], dimension x group [F(2, 86) = 7.61; p < .OOl], representation of features x dimension [F(2, 86) = 15.12; p < .OOl], and representation of features x dimension x group [F(2, 86) = 3.21; p < .05].
In general, latencies are longer for shape (M = 1.95 set) than for size M = 1.58 set) or color (M = 1.54 set), although the pattern of medians is different for verbal and pictorial lists of features. According to Scheffe tests, the difference between shape and size as well as between shape and color reaches the 5% level for aphasics when verbal lists of features were presented, with M = 2.62 set vs. M = 1.78 set vs. M = 2.01 set, while for the pictorial lists of features only the difference between shape and color is significant in this group, with M = 1.90 set vs. M = 1.31 sec. The difference between groups is significant only for the lists of features if the mismatch refers to shape of an earlier presented token. Tokens. Post hoc comparisons revealed that the only significant interaction, dimension x group [F(2, 86) = 5.08; p < .Ol], is due to longer latencies for both shape and size than for color among the aphasics. Otherwise, there is no indication that the tokens are being processed differently in the two groups. Intercorrelations
It was the aim of this study to shed some light on processes critical for the poor performance of aphasics in the Token Test (TT) and especially in the subtests I-IV of this test. Thus, performance measures in this experiment were correlated with the error scores of the TT and other measures from the Aachener Aphasie Test. Inspection and decision latencies for lists of features correlate (.39 G r c .70) with TT I-IV and (.39 6 r c .67) with the total TT. The highest correlations are found between the verbal lists of features and TT I-IV (IP: r = .70; DP: r = .45, p < .05). The correlation between the latencies for the pictorial lists of features and TT I-IV in the inspection phase (r = .42) falls just short of significance; the remaining correlations are nonsignificant. Due to a strong bottom effect the error rates from this experiment could not be expected to show any covariance with the TT (.08 c r s .34). The latencies from the eight experimental conditions and the simple reaction time task were intercorrelated and subjected to a principal component analysis with subsequent varimax rotation separately for aphasics
46
GUTBROD
ET AL.
and RH controls. For both groups two components have Eigenvalues > 1.00, explaining 80.2 and 75.5% of the total variance in the two groups. The varimax-rotated loadings of the two structures are shown in Table 2, and the correlations of the factor scores of the aphasics with the Token Test and the subtests of the Aachener Aphasie Test are given in Table 3. For both groups the latencies from the inspection phase have high loadings on Factor I, ranging .83 s u s .91 for the aphasics and .46 s a < .95 for the RH controls. The latencies from the decision phase as well as the latencies in the simple reaction time task are primarily represented by Factor II, ranging 58 s a s .96 in the aphasics, and .67 6 a s .89 in the controls. Apparently, Factor I is more related to information intake and storage while Factor II can be thought of as representing a more general speed component. Factor I in the aphasics’ structure covers almost all the variance of the latencies from the inspection phase. It is this factor which also correlates significantly with the Token Test (r = .51), as well as with the “Naming on Confrontation” (r = - .52) and the “Comprehension” subtests (r = - .55) of the Aachener Aphasie Test. DISCUSSION
There are several reasons to consider the results of this dual-reaction time task as being meaningfully related to the performance deficit of aphasics in the Token Test: (1) the latency medians of various conditions are significantly correlated with the error scores in the standard Token Test, with the highest correlation found for the condition resembling most closely the standard Token Test; (2) even though the aphasics were capable of understanding the meaning of the words and of the pictorial representations, as evidenced by their low error rates, they still made significantly more errors (M&r = 3.2%) than the RH controls. The most conspicuous finding of this experiment is the total lack of any significant differences between aphasics and RH controls whenever tokens were presented. All the differences between the two groups are found under those conditions in which the patient is being confronted with a list of features. This result is clearly at variance with the suggestion that “the analytic ability to isolate and identify . . . individual features of objects . . . might also be a primary requirement in the Token Test” (Cohen et al., 1980a, p. 345). There is no evidence in our data indicating that aphasics were particularly impaired in the cognitive handling of these stimuli: It is the processing of the lists of features which differentiated aphasics from the controls. This conclusion must also raise doubts about whether it really is the “abstract nature” of the tokens that makes the test so sensitive to aphasic impairments. The better performance of aphasics, when offered pictures of familiar objects as opposed to tokens (Kreindler, Gheorghita, & Voinescu, 1971; Martino, Pizzamiglio, & Razzano, 1976; Naumann, Kelter, & Cohen, 1980), might only indicate that
37.0
.74
.83 .96 .58 .I5
.16 .43 .06 .26
II
Aphasics
80.2
.56
.80 .96 .71 .87
.73 .90 .81 .88
h’
a Stimuli that preceded or followed the critical stimuli within a trial in brackets.
43.2
.33 .20 .61 .56
% Total variance
token token verbal list pictorial list
-
.83 .84 .90 .91
- .Ol
(token) (token) (verbal list) (pictorial list)
-
I
Simple reaction time
Inspection phase Verbal list Pictorial list Token Token Decision phase (Verbal list) (Pictorial list) (Token) (Token)
TABLE 2
31.6
-.06
.45 .23 .34
.4l
.46 .58 .95 .91
I
ROTATEDFACTORLOADINGSOF THE LATENCY MEASURES
.82 .84 .86
.78 .89 .86
44.8
76.4
.45
.84
.80
.67
.58 .70 .91 .89
h’
.60 .60 .13 .24
II
RH controls
!z 5
3
%
3
g F: !z z
s
48
GUTBROD
ET AL.
TABLE CORRELATIONS LATENCY
BETWEEN MEASURES,
THE AACHENER AND
Token Test Written language Comprehension Naming on confrontation Repetition Errors in experiment Factor I Factor II
3
APHASIE
THE ERROR
TEST,
SCORE
THE FACTOR
SCORES
FROM
FROM THE EXPERIMENTAL
TASKS
6
1
2
3
4
5
-.93** - .68** - .73** -.51* .31 .51* .03
.70** .67** .56** -.I8 - .38 .Ol
.77** .42 - .33 - .52* -.18
.58** -.12 -.55** -.12
-.Ol -.I6 .I5
THE
7
8
.36 .44* 0.0 -
a Aphasics only, N = 21. * p < .05. ** p < .Ol.
representations of familiar objects possibly allow for an easier and/or more reliable encoding and storage of the target description. Such an interpretation would also be in line with the hypothesis of DeRenzi et al. (1978) that aphasics are deficient in organizing or integrating the verbally presented information into a visual image. Obviously, their hypothesis can be reconciled with our data much more easily than the hypothesis of a deficit in the competence for handling the tokens analytically. The hypothesis of a deficit in organizing the verbally as well as the pictorially presented information into a visual image is further supported by the triple interaction showing that the difference between the groups in handling the lists of features is most pronounced when they are given not in the decision but in the inspection phase. The formation of a visual image to be compared with the picture of a token should be most advantageous if the features are described in the first phase of a trial and it is in this phase where the aphasics’ performance is poorest. When the features are described in the second phase it should be at least equally effective to follow a self-terminating sequential comparison process. Such a sequential comparison was apparently the most common strategy in this condition for both groups. Both groups show the longest latencies for correct decisions about shape, the feature defined at the bottom of the list. This increase in latency is most pronounced in the aphasics when the features were described verbally. When it was possible to detect a mismatch by considering only size and color the differences between groups are negligible. While some results discussed so far support the notion of DeRenzi et al. (1978) that aphasics were deficient in integrating verbally presented informations into a visual image, other results suggest that such a deficit should be considered only as one component of a more general deficit in the encoding and storage of highly specific information.
COGNITIVE
PROCESSING
OF TOKENS
49
No doubt the difference between aphasics and RH controls is largest whenever verbal lists of features are presented, but there is also a tendency toward increased latencies for the nonverbal lists of features, favoring our model of a more general, i.e., not purely verbal, deficit. Depending on the particular way of testing the difference, this tendency reaches or just fails the 5% level of significance. This finding cannot be reduced to possible difficulties in translating the symbols for size, color, and form into verbal codes for memory storage. Such translations should lead to longer latencies for the pictorial than for verbal lists, and not to the considerably shorter latencies actually found for the aphasics. Moreover, although the patients of both groups made few errors throughout and thus gave good evidence that they understood the meaning of the words and of the pictorial representations, aphasics make significantly more errors even with the nonverbal lists of features. This finding corroborates earlier results of Cohen et al. (1977, 1980b) that aphasics are still poorer than controls when nonverbal instead of verbal instructions are given in the Token Test. A similar result has been reported by Grtisser, von Hartrott, Heeschen, and Reischies (1982) for Broca’s aphasics only. In our experiment no such difference between Broca’s and a mixed group of amnesic and Wemicke’s aphasia could be found. Possibly the discrepancy in the results is due to differences in the populations tested: while most of their aphasics were of cerebrovascular origin, most of our patients were adolescent and victims of traumatic accidents. A principal components analysis of the latency measures led to one factor which was significantly correlated with the performance in the standard Token Test as well as with the subtests ‘Comprehension” and “Naming on Confrontation” of the Aachener Aphasie Test. This factor accounts for 69 to 83% of the variance of all the latencies from the inspection phase, whether verbal lists, pictorial lists, or tokens had been presented, while the latencies from the decision phase have most of their loadings on a second factor which has no relation to the number of errors in the Token Test. This close relationship between the latencies from the inspection phase (.64 c Y s .92) in the aphasics has no resemblance in the correlation matrix of the RH controls (. 16 s r < 64). The correlations between the latencies from the decision phase on the other hand are in the same range (.71 =Z r s .85) for both groups. Considering this high correlation between all the latencies from the inspection phase of the aphasics, and the significant correlation of this factor with the performance in the Token Test, we are hesitant to accept the notion of a particular deficit confined to the comparison of verbally presented information with the visual stimulus of a token. In the factorial structure obtained, such a deficit appears embedded in a more general deficit in the storage of highly specific information.
50
GUTBROD
ET AL.
There is one disquieting problem in all these considerations. Throughout this paper we accepted both error rates and prolonged latencies as indicators of difficulty. This is common practice in psychology. But how do these two indicators of difficulties relate to each other? As shown earlier, there is no evidence of a speed-accuracy trade-off on our data. What does it mean that aphasics need more time to process the description? In line with our latency data Liles and Brookshire (1975) as well as Poeck and Pietron (1981), have shown that the number of errors in the Token Test decreases when the instructions are given at a reduced rate of speech. It is unlikely that the higher error rates and increased latencies were the results of fluctuations in vigilance. Since we took the medians and not the means, our latency measure cannot be seriously affected by such fluctuations; furthermore we do not know of any evidence that aphasics have more severe fluctuations in vigilance than do RH controls. The simplest way to cover both the higher error rates and the increased latencies would be to consider the association between a word or a symbol with a specific meaning not as an all-or-none matter but as a probabilistic relationship. Obviously, with an error rate of only about 3.5% our aphasics understood the meaning of the words and symbols most of the time, but the association between words and meanings was clearly not perfect and not as good as found in the controls. It is not unreasonable to assume that aphasics know about their loose connections between words and meanings. Such a knowledge might well lead to an increased tendency to double-check and thus to prolonged latencies. Such a simple model can definitely not account for all the questions raised by the amazingly high discrimination power of the Token Test. But it might cover some “mechanics of verbal ability” (Hunt, 1978) which appear impaired in different forms of aphasia over and above their purely linguistic deficits. REFERENCES Clark, H. H., & Chase, W. G. 1972. On the process of comparing sentences against pictures. Cognitive Psychology, 3, 472-517. Cohen, R., Kelter, S., Engel, D., List, G., & Strohner, H. 1976. Zur Vahditlt des Token Tests. Nervenarzt, 47, 357-361. Cohen, R., Kelter, S., & Schafer, B. 1977. Zum Einfluss des Sprachverstandnisses auf die Leistungen im Token Test. Zeitschrif fir Klinische Psychologie, 6, 1-14. Cohen, R., Kelter, S., & Wall, G. 1980. Analytical competence and language impairment in aphasia. Brain and Language, 10, 331-347. (a) Cohen, R., Lutzweiler, W., & Wall, G. 1980. Zur Konstruktvaliditat des Token Tests. Nervenarzt, 51, 30-35. (b) Cohen, R., & Woll, G. 1981. Facets of analytical processing in aphasia: A picture ordering task. Cortex, 17, 557-570. DeRenzi, E., & Faglioni, P. 1978. Normative data and screening power of a shortened version of the Token Test. Cortex, 14, 41-49. DeRenzi, E., Faglioni, P., & Previdi. 1978. Increased susceptibility of aphasics to a distractor task in the recall of verbal commands. Brain and Language, 6, 14-21.
COGNITIVE
PROCESSING
OF TOKENS
51
DeRenzi, E., & Vignolo, L. 1962. The Token Test: A sensitive test to detect receptive disturbances in aphasics. Bruin, 85, 665-678. Griisser, O., von Hartrott, H ., Heeschen, C., & Reischies, F. 1982. A note on a nonverbal version of the Token Test. Berlin: Physiol. Inst., Free University. Huber, W., Poeck, K., Weniger, D., 62 Willmes, K. 1983. Der Aachener Aphasie Test. Gottingen: Hogrefe. Hunt, E. 1978. Mechanics of verbal ability. Psychological Review, 85, 109-130. Kreindler, A., Gheorghita, N., & Voinescu, I. 1971. Analysis of verbal reception of a complex order with three elements in aphasics. Bruin, 94, 375-386. Liles, B., & Brookshire, R. 1975. The effects of pause time on auditory comprehension of aphasic subjects. Journal of Communication Disorders, 8, 221-235. Martino, A., Pizzamiglio, L., & Razzano, C. 1976. A new version of the Token Test for aphasics: A concrete objects form. Journal of Communication Disorders, 9, l-5. Naumann, E., Kelter, S., & Cohen, R. 1980. Zum Einfluss mnestischer, semantischer und konzeptueller Faktoren auf die Leistungen aphasischer Patienten im Token Test. Archiv. fir Psychiatie und Nervenkrunkheiten, 228, 317-328. Oldfield, R. 1971. The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychologiu,
2, 97- 113.
Orgass, B. 1976. Eine Revision des Token Tests. I. Vereinfachung der Austwertung, Itemanalyse und Einftlhrung einer Alterskorrektur. Diagnostica, 22, 70-87. (a) Orgass, B. 1976. Eine Revision des Token Tests. II. Validitatsnachweis, Normierung und Standardisierung. Diagnostica, 22, 141-156. (b) Orgass, B., & Poeck, K. 1966. Clinical validation of a new test for aphasia: An experimental study on the Token Test. Cortex, 2, 222-243 Peck-Swisher, L., & Taylor-Sarno, M. 1969. Token Test scores of three matched patient groups: Left-brain-damaged with aphasia, right-brain-damaged without aphasia, nonbrain-damaged. Cortex, 5, 264-273. Poeck, K., & Hartje, W. 1979. Performance of aphasic patients in visual versus auditory presentation of the Token Test: Demonstration of a supramodal deficit. In F. Boiler & M. Dennis (Eds.), Auditory comprehension. Clinical and experimental studies with the Token Test. New York: Academic Press. Pp. 107-117. Poeck, K., Kershensteiner, M. & Hartje, W. 1972. A quantitative study on language understanding in fluent and non-fluent aphasia. Cortex, 8, 299-304. Poeck, K., & Pietron, H. 1981. The influence of stretched speech presentation on Token Test performance of aphasic and right brain damaged patients. Neuropsychologiu, 19, 133-136. Reitan, R. 1959. A manual for the administration and scoring of the Trail-Making-Test. Bloomington, Indiana Univ. Press. Vanderplas, J., & Garvin, E. 1959. The association value of random shapes. Journal of Experimental
Psychology,
57, 147-154.
Van Dongen, H., & van Harskamp, F. 1972. The Token Test: A preliminary evaluation of a method to detect aphasia. Psychiatriu, Neurologiu, Neurochirurgiu, 75, 129-134. Willmes, K. 1980. Die psychometrischen Eigenschaften eines Aphusie-Tests-dargestellt am Beispiel des Auchener Aphusie-Tests. Paper presented at the Kolloquium Sprachpathologie of the Deutsche Forschungsgemeinschaft, Berlin, Sept. 1980. Willmes, K. 1981. A new look at the Token Test using probabilistic test models. Neuropsychologia,
19, 631445.
Winer, B. 1971. Statistical principles in experimental design. New York: McGraw-Hill. Woll, G., Naumann, E., Cohen, R., & Kelter, S. 1976. Kreuzvalidierung der Revision des Token Tests durch Orgass. Diugnostica, 22, 157-162.