Acta Psychologica 57 (1984) 165-192
North-Holland
165
VARIATION WITHIN CATEGORIES E.A.
DAS-SMAAL and J.H. DE SWART *
Free Unit'ersity, The Netherlands
Accepted December 1983
Two aspects of variation within categories, relating to different models of categorization, were
investigated - frequency of dimensional values and typicality differences within values. The influence
of range of typicality experienced during learning and of informational value of feedback was also
studied. Finally, differential forgetting of values was examined.
In the experiment subjects learned to categorize faces, and then performed a classification test task
and pairwise comparisons of faces. A variety of dependent variables was employed, including the
galvanic skin response (GSR).
Typicality and frequency of values appeared to influence categorization performance independent
of each other. It was concluded that both prototype distance models and frequency models explain
different aspects of variation within the same categories, and that models of classification should
account for frequency of values in contrasting categories. Results showed furthermore (1) the
influence of typicality range on the extension of a category; (2) no influence of specific feedback
regarding representativeness of a face; (3) less decay with more important values; and (4) a positive
relationship between uncertainty reduction and GSR.
Traditional work on categorization treated all exemplars of a category as
equally good examples of the category. This view has been criticized a.o.
by Rosch (1973) and by Das-Smaal and De Swart (1981), who argued
that categorization models must be capable of representing variation
among exemplars within a category. Up to now, however, it is not clear
what aspects of within-category variation are represented, nor how
specifically they are represented (Palmer 1978). The present study
investigates two forms of variation within categories. The first form
concerns the similarity of a dimensional value variant to a prototypical
value (typicality of variants). Exemplars having the same dimensional
values may differ with respect to how typically they exhibit these
* Mailing address: E.A. Das-Smaal, Vakgroep Functieleer en Methodenleer, Free University, De
Boelelaan 1115, 1081 HV Amsterdam, The Netherlands.
0001-6918/84/$3.00 © 1984, Elsevier Science Publishers B.V. (North-Holland)
values. For example, a basket full of big red apples will not usually
contain apples all of which are equal in size and colour. The values big
and red have variants that differ in typicality. The second form of variation
relates to the frequency of values, irrespective of the variants with which
they occur. For example, the frequency of the value red among the
category of apples. The general purpose of this paper is to extend the
knowledge about the effects of different aspects of withincategory
variation.
Two kinds of categorization models that are often contrasted are
relevant to these aspects, prototype-distance models (Posner and Keele
1968; Reed 1972) and feature frequency models (Reitman and Bower
1973; Neumann 1974; Hayes-Roth and Hayes-Roth 1977). Prototypedistance models assume that subjects abstract a central representation
(prototype) from the presented exemplars of a category; subsequent
classification is based on distance from this prototype in a multidimensional space. On the other hand, frequency models assume that subjects
encode the frequency with which dimensional values occur among
members of a category, and that they base their classification judgments
on these frequency measures. This paper aims to show that both types of
within-category variation affect categorization independently, and that
prototype-distance models and feature frequency models are
complementary. Besides, this paper examines several related issues.
Typicality differences within values
Das-Smaal and De Swart (1981) showed that typicality of variants
influenced learning performance in a traditional, welt-defined categorization task. The influence, however, differed for relevant and irrelevant
dimensional values. Typicality of relevant values facilitated category
learning, whereas typicality of irrelevant values did not affect performance.
To further examine the typicality effects, the present experiment
comprised besides a learning task also a test following learning. Influence
of value typicality on classification was investigated in two ways. Firstly,
typicality of value variants was varied. Exemplars composed of highly
typical variants were predicted to be classified following learning more
easily than exemplars with less typical variants. Secondly, the range of
typicality variation experienced during learning
was varied. Learning exemplars were composed of either a small range of
typical variants only, or a broad range of both typical and atypical
variants. Das-Smaal and De Swart (1981) found that instances composed
of atypical variants slow down the learning rate. It is however possible,
that experience with atypical instances results in a better representation of
the category boundaries. That is, it may enhance the capability to
correctly separate instances from non-instances in this area. With dot
patterns, high instance variability has already been shown to enhance
transfer to new exemplars (Posner and Keele 1968; Homa and Vosburgh
1976). In these studies, however, instance variability and number of
learning trials experienced by the subjects were confounded. In the
present experiment, this was avoided. It was predicted that small as
compared with broad typicality range facilitates learning performance,
whereas broad typicality range facilitates classification of new atypical
exemplars.
If broad range experience enhances transfer, the question further arises
as to whether enhanced transfer implies only an extension of the
boundary of the focal category, or additionally, a more fine-grained
distinction between exemplars on both sides of a boundary. In the former
case only a decrease in number of false negative errors has to be
expected, whereas in the latter case a decrease in both false negative and
false positive errors will appear with boundary exemplars. This issue was
also investigated in the present study.
Frequency
Frequency of occurrence may be expressed in terms of cue validity,
defined as the frequency with which a cue, or a value, is associated with
one category, divided by the total frequency of that cue across all
categories. Cue validity takes into account the resemblance within a
category as well as distinctiveness from contrasting categories. Distinctiveness from other categories is accounted for in some models of
categorization but not in others. For example, the property-set model
proposed by Hayes-Roth and Hayes-Roth (1977) does, whereas Neumann's (1974) attribute-frequency model does not include this factor. In
the present experiment, frequency and cue validity were investigated
apart. The hypothesis was that categorization is facilitated by increasing
simple frequency and by increasing cue validity of the values; that is,
not only by a high frequency in the focal category, but also by low
frequency in the contrasting category.
Common and distinctive values and differential forgetting
The relative importance of common and distinctive values to categorization is an issue closely related to frequency of occurrence. In abstracting the prototype, both common and distinctive values are involved, with
distinctive values having the greater weight (Homa and Chambliss 1975).
Following Homa and Chambliss, common values are defined as values
which are found among many exemplars in one category, and distinctive
values as values which are common within one category but do not occur
in the contrasting category. In the present study, the relative influence of
these two types of values was determined.
Delay of testing has been shown to deteriorate performance on old
non-prototypical instances but not on new and prototypical ones (Posner
and Keele 1970; Strange et al. 1970; Homa et al. 1973). Homa et al.
(1973) suggested that this could be attributed to a higher resistance to
decay of the more frequent values, which compose the prototypes. In the
present study this view was extended to the hypothesis that values which
appear to be most important to classification are also the most resistant to
forgetting. Because distinctive values were supposed to receive greater
weight than common values, these values were expected to show less
decay.
Type of feedback
In studies of category learning, subjects learn by feedback that an
exemplar either belongs or does not belong to a particular category.
Feedback usually gives no information about the degree of membership.
The present experiment investigated the effects of information about
degree of membership on learning and representation of categories. The
feedback was either specific or non-specific. Specific feed-back informed
the subjects about the representativeness of an exemplar, as determined
by cue validity. Specific feedback was expected to facilitate category
learning and to improve performance on subsequent test tasks.
Physiological activity
In previous studies on category learning, De Swart and Das-Smaal (1976,
1979a, b) and Das-Smaal and De Swart (1981) found a positive
relationship between galvanic skin response (GSR) and informational
value of the feedback. The studies suggested that during category
learning, GSR primarily reflects uncertainty reduction about alternative
hypotheses, and not information processing activities per se. The results
were related to Sokolov's (1969) model of the orienting reflex (OR). The
present study gathered additional information about this issue in two
different ways. Firstly, the specific feedback offered an opportunity to
gather further evidence about this relationship in a new way. The
discrepancy between expected and actual feedback indicates the informational value of the feedback. The discrepancy between expected and
actual feedback increases with increasing cue validity of a misclassified
instance. A concomitant rise in GSR was predicted. Secondly, in
replication of De Swart and Das-Smaal (1979 b), a relationship was
predicted between GSR and the subjects' certainty estimation about their
classification response.
Experiment
Method
Subjects
Ss were 48 male student volunteers. Ss who were tested immediately after learning
received Dfl. 25. Those who had to return after 1 week were paid Dfl. 30 each.
Stimuli
Stimuli were faces, mounted on slides. The faces were constructed from Photo-Fit
materials, European type (see fig. 1). Lower frame contour was the same for all faces. The
faces were further composed from seven variants selected in a preliminary study [1] for
each of three values of the dimensions eyes, nose and mouth. Table 1 gives mean
[1] In a preliminary study, degrees of typicality for variants of dimensional values were determined. In
a free-classification task, subjects partitioned sets of faces into groups showing the same family trait
(dimensional value). In each set, one dimension varied. Subjects also judged the typicality of each
variant within a group. Typicality appeared to be reliably rated in eight of the nine groups. The group
with non-significant inter-subject agreement (eyes value 3) was made non-characteristic in the
present study.
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Table 1
Mean typicality ratings of variants to their values.
Variant
Eyes
Nose
Value
Value
1
1
2
3
4
5
6
7
1.89
3.11
3.61
3.67
4.33
4.61
6.78
2
1.94
2.33
3.50
3.78
4.78
5.50
6.17
Mouth
Value
3
1
2
3
1
2
3
3.11
3.61
3.67
4.11
4.22
4.39
4.89
1.67
2.61
3.00
4.11
4.56
5.67
6.39
2.78
3.06
3.39
3.89
4.28
4.83
5.56
2.89
3.33
3.61
3.78
4.28
4.78
5.00
1.78
2.56
3.39
4.00
4.89
5.39
6.06
2.22
2.94
3.39
4.28
4.44
4.94
5.72
2.94
3.50
3.61
3.78
4.00
4.50
5.67
typicality ratings of the variants to their values, as obtained from the preliminary study.
Variants were numbered 1 to 7, from most to least typical, respectively.
In the learning task, 18 different hairstyles were assigned randomly to the faces, using
each hairstyle equally often. Non-specific feedback slides contained a + or a - , indicating
that the preceding stimulus belonged to the focal or the contrasting category, respectively.
Specific feedback slides showed +, + +, + + +, - -, - - -, indicating a less good, moderate, or
good example of the focal category, or a less good, moderate, or good example of the
contrasting category, respectively.
In the test tasks, one hairstyle was used, which was the same as in the preliminary study.
The classification test task was composed of faces like in the learning task. Slides for the
pairwise comparison task contained two faces each. The left face was labelled A, the right
face was labelled B.
Apparatus
The S was seated in a dimly illuminated, soundproofed room. Slides were projected onto
a frosted-glass window in front of the S, via two carousel projectors located outside the
room. A response panel on the arm rest of the chair contained a starter button below and
two choice buttons above. The choice buttons could be labelled either + and -, or A and B
by means of an interchangeable strip above the buttons. A and B were used in the pairwise
comparison task, + and - in the classification tasks. A lever was fixed at the side of the
panel and could be moved forwards or backwards. The range of this lever represented a
continuum of certainty about the choice response. Response times (RT) were measured
from the beginning of stimulus presentation to the S's choice response. Choice responses,
certainty ratings and RT's were recorded automatically.
Basic level GSR was measured DC and specific GSR's were measured AC by a constant
0.5 V voltage bridge. AC responses were registered with a time constant of 3 seconds. The
greatest conductance change beginning between 1 and 4 seconds after the onset of feedback
projection in the learning task was used to calculate the change in log conductance (4 log
C). In order to reduce individual differences, J log C was taken as
a measure for GSR. Ag/AgC I electrodes, 7.5 mm in diameter, were attached to the volar
surface of the distal phalanx of the second and third finger of the S ' s non-preferréd
hand. Interface medium was K-Y jelly. GSR, choice responses. certainty ratings and
projector impulses from the timer were all recorded on a Beekman eight -channel
polygraph (type R 411 dynograph).
Design and procedure
Following a training ta sk, Ss learned to categorize faces int() two categories: a focal
( + ) category of faces from one family, and a contrasting ( -) category of faces from other
families. Subsequently, Ss were tested in a classification task and in a pairwise
comparison task.
The procedure was as follows: after informing the S about the experimental
equipment, recording leads were attached and the S was seated in the soundproofed room.
Ss were informed of the nature of the tasks, the means of responding and the meaning of
the feedback. They were told that they would be presented with faces from men of
different families, and that they had to learn to distinguish faces of one family from faces
of other families. It was stated that hairstyle was irrelevant to classification. In the specific
feedback condition Ss were told that the representativeness of a face for the family of the
focal category was indicated by the number of plusses or minussen, with decreasing
representativeness from + + + to - - -.
With the classification task, Ss who were tested immediately were instructed to
classify faces as in the learning task, the only difference being that this time no feedback
would be given. Ss who were tested with one week delay received the same instruction,
preceded by a short verba] re capitulation of the learning phase.
In the pairwise comparison task, Ss were instructed to choose the more representa tive
face for the focal category. The tasks were separated by a rest interval of about 5
minutes. After completing the test tasks, Ss were asked to describe the characteristic
features of the family of the focal category.
Trial composition
Trials of the learning task started with presentation of the stimulus. The stimulus
disappeared when the S pressed a choice button. If he did not respond within 20 seconds,
the stimulus also disappeared and the S had to wake a response immediately. Then th e S
had to give his certainty rating by pushing a lever to the point that corresponded with his
estimation. Four seconds after stimulus switch -off a little red lamp came on, indicating
that the response had to be completed. Again 4 seconds later, feedback was shown for 5
seconds. The red lamp remained on until feedback switch -off. Two seconds later, the next
stimulus was presented.
Trials of both test tasks were composed like the trials in the learning task, excluding
the 5 seconds feedback period. In the p airwise comparison task Ss had to start each slide
projection themselves by pressing the starter button.
Training task. The training task consisted of 15 Afro -Asian faces, composed arbitrarily
from different hairstyles, eyes, noses and mouths. Feedback of either the specific or the
non-specific type was given randomly.
The learning task was composed of 80 trials. Stimuli are given in table Al
of the Appendix. Typicality of variants was varied, employing variants 1 to 6. Variants 1
to 3 were "typical", variants 4 to 6 were "atypical". Typicality range varied between Ss.
Either a smal] range of faces with typical variants was learned, or a broad range of faces
with both typical and atypical variants. Variants are indicated in table Al of the Appendix.
In the small range condition, variants were chosen randomly, with the restriction that the
three variants allowed in this condition had to be used in about equal numbers. In the
broad range condition, 50% from the variants of the small range condition were replaced
by atypical variants 4 to 6.
Learning task.
The three relevant dimensions (A, B and C) differed with respect to the frequency
distribution of their values (see table 2). Values Al, Bl and Cl were characteristic of the
focal category. Al was distinctive because of its non-occurrence in the contrasting
category, BI and Cl were common values because of their high frequency in the focal
category. The focal category was formed by faces containing Al and/or BI + Cl. All other
faces belonged to the contrasting category. The particular assignment of the dimensions
eyes, nose and mouth to A, B and C varied across Ss and was counterbal anced in three
learning task problems, employing each dimension once as A, B or C. Each problem had
its own accessory classification and comparison task. Frequency of the 27 combinations
possible with 3 three-valued dimensions, varied from 5-15% within a category. The order
of presentation was the same for each S and was randomized.
Cue validities for each dimensional value for the focal category were computed by
dividing the frequency of a dimensional value x in the focal category by the total
frequency of x in the focal and the contrasting category. From table 2 it can be seen that
Al and BI occur with equal frequencies in the focal category, but differ with respect to
their cue validity. In contrast, BI and Cl have the same cue validity, but differ in
frequency.
Total cue validities (TCV's) of faces were computed by summation of the cue validities
of their composing values. TCV's are given in table Al of the Appendix. With specific
feedback, information was given about the TCV of a face. Therefore, in the
Table 2
Frequency of occurrence and cue validity of dimensional values.
Dimension
Frequency in
category
+
Cue validity
A
B
Value
1
Value
2
28
0
6
20
1.00
0.23
3
6
20
0.23
C
Value
1
2
3
6
13
20
10
15
10
15
0.32
0.67
1
2
3
28
6
13
0.67
0.32
14
10
0.40
0.40
focal and in the contrasting category three groups were distinguished according to TCV.
The focal category contained five different TCV's. To equalize the frequencies of the stimuli
within each group as much as possibble, one group was formed by the two highest TCV's
(H), one group by two medium validities (M), and one group by the lowest validity (L).
TCV's for the contrasting category were computed in the same way as described for the
focal category and classified equally into a high, a medium and a low validity group. L, M
and H groups of the focal and the contrasting category were indicated by one, two or three
plusses or minusses, respectively.
Classification task. The classification task was composed of 29 faces. The first two trials
were habituation trials. The last three faces were faces of the highest TCV with most
typical value variants, leaving out either distinctive value dimension A, or common value
dimension B or C. The omitted value was replaced by a black rectangle. The trials in
between consisted of three sets of eight faces. The eight faces represented different TCV's,
five for the focal category (2 H, 2 M and 1 L) and three for the contrasting category (H, M
and L). Each of these faces was chosen randomly from faces with the same TCV. One set
of eight faces was composed of variants 2, which were typical and familiar to all Ss.
Another set contained atypical variants 5, which were familiar to Ss in the broad typicality
range condition, and novel to Ss in the small range. The third set contained atypical
variants 7, which were novel for all Ss. The order of presentation of the trials in between
was the same for each S and determined at random. The order of the last three trials
varied between Ss and was counterbalanced.
Pairwise comparisons. The comparison task consisted of 10 pairwise combinations of five
faces, preceded by an habituation trial. The five faces which had to be compared
represented the five TCV's for the focal category. They were all composed of highly typical
variants (variants 1). The task was preceded by a training task of five pairs not used in the
test task. The order of presentation of the stimuli was determined at random. The order
was the same for each S. However, the place of the faces on a slide (left or right) was
reversed for half of the Ss.
Variables
The experiment comprised four between-Ss variables. These were type of feedback,
typicality range, time of testing, and learning task problem. Within-S variables in the
learning task were learning phase and TCV. In the classification test task, typicality of
variants, TCV, and omission of a dimension in a face varied within Ss, as did TCV in the
pairwise comparison task. Dependent variables were number of errors (NE), certainty
estimate (CE), reaction time (RT), and GSR. In the classification task also type of error
(false positive or false negative) was measured.
Ss were assigned randomly to one of the conditions formed by combination of the four
between-Ss variables. To each possible combination of two types of feedback, two kinds of
typicality range, two times of testing, and three learning task problems, two Ss were
assigned.
Resuits
Certainty estimate, reaction time and number of errors
Multivariate analyses of variance (MANOVA's) were run on certainty estimate (CE),
reaction time (RT) and number of errors (NE) data. The analysis used is described by
Finn and Mattson (1978).
Step-down results were computed in the order CE-RT-NE, because it was presumed
that low certainty would result in slow reactions with a high error probability. When
appropriate, data were collapsed across problems, across order of the three classification
test exemplars that lacked one dimension, and across place of faces on the slides of the
pairwise comparisons task. Post-hoc comparisons on interactions were carried out with
Tukey's test, adjusted as per Cicchetti (1972). For all analyses, the level of statistic al
significance was p < 0.05.
Learning task
Table 3 summarizes mean performance scores apart for beginning and end of the task.
The table shows an improved performance at the end of learning in all conditions on all
dependent variables. Nevertheless, learning proved difficult, with an average of 28%
errors in the last quarter of the task.
A MANOVA was computed for stimuli from the first (begin) and the last (end) 20
trials, for the variables mentioned in table 3. Six H, six M and six L TCV items were
analyzed for each learning phase. Learning phase and TCV were both significant ( F (3,
42) = 44.66, and F (6, 39) = 10.03). The effect of learning phase represented an improved
performance at the end. The TCV effect reflected facilitation by high TCV. Type of
feedback was marginally significant (F (3, 42) = 2.65, p < 0.06), due to both a lower CE
and a lower NE with specific feedback. Typicality range showed no multivariate main
effect. However, this variable affected CE significantly, CE being higher with a small
range of variants than with a broad range. The Learning phase X TCV interaction was
significant (F (6, 39) = 9.77), as was the Typicality range x Learning phase X TCV
interaction (F (6,39) = 2.42). At the end of the task, CE increased with increasing TCV
more than in the beginning. This effect was stronger in
Table 3
Mean performance scores in different phases of the learning task.
Dependent Learning
variable
phase
CE
RT
NE (%)
Begin
End
Begin
End
Begin
End
Typicality range Type of feedback
Total cue validity
Smal]
H
0.50
0.73
9.55
7.40
39.3
28.2
Broad
Specific
0.44
0.43
0.63
0.64
11.33
7.89
44.7
27.5
Non-spec.
M
0.43
11.08
9.81
10.36
8.22
39.7
26.7
7.08
6.96
7.61
44.5
34.3
13.2
42.7
0.50
0.63
10.43
8.38
49.0
26.7
43.8
29.2
0.74
0.48
0.66
L
0.50
0.72
10.54
the broad range condition (post-hoc analysis). None of the other interactions approached
significance.
Classification task
Table 4 shows mean performance scores on trial 3-26. A MANOVA was performed on
these trials for the variables summarized in table 4. Overall results of the MANOVA will be
presented first, followed by the results on the main topics of interest.
All main effects of the MANOVA were significant, except for Type of feedback.
Typicality range (F(3, 38)=3.30) reflected a significantly higher NE for the small range
condition. Delay of testing (F(3, 38) = 2.90) affected only RT significantly, with higher RT
for the delayed condition. Typicality of variants (F(6, 35) = 11.63) represented a significant
decrease in CE and increase in both RT and NE from variants 2 to 5 to 7. Finally, TCV
(F(6,35) = 6.95) indicated a significant decrease in CE and increase in RT and NE with
decreasing TCV. The interaction between Typicality range x Type of feedback x TCV was
significant (F(6, 35) = 2.49). This was due to a relatively high NE for H TCV in the small
range and non-specific feedback condition. Finally, the highest interaction was significant
(F(4, 160) = 3.84), due to the NE variable. The estimated proportion of variance accounted
for by this interaction, however, was one percent. None of the other interactions was
significant.
Table 4
Mean performance during the classification test.
Condition
Dependent variable
Typicality range
Smal]
Broad
Feedback
Specific
Non-specific
Time of testing
Immediately
Delayed
Variant typicality
Variant 2
5
7
Total cue validity
H
M
L
CE
RT
NE (%)
0.66
0.62
8.05
7.61
31.6
23.4
0.61
0.67
8.29
7.38
26.9
28.1
0.64
0.64
6.64
9.02
29.5
25.5
0.70
0.63
0.59
7.26
7.90
8.34
21.4
25.8
35.4
0.68
0.63
0.58
7.46
7.76
8.38
21.1
31.7
35.8
T y p i c a l i t y . From the MANOVA, high typicality of value variants appeared to facilitate
classification performance. However, this effect could have been confounded with novelty,
because experience with the variants varied. Although all faces in the classification task
were new to the Ss, lome of the variants were shown during learning in other combinations.
Thus, variants 2 were old in the small, and variants 2 and 5 were old in the broad range
condition. The unconfounded effect of variant typicality was analyzed within each category
and typicality range condition. In the focal category, a typicality effect showed up in the
small but not in the broad range condition. In the smal] range condition less errors were
made with variant 5 than with variant 7 exemplars (sign test, ( p < 0.02). RT and CE were
not significantly affected (Wilcoxon matched-pairs signed ranks test). In the broad range
condition variant 2 and 5 exemplars were compared, and these showed no significant
differences in CE, RT or NE (Wilcoxon matched-pairs signed ranks test and sign test,
respectively). In the contrasting category, a typicality effect showed up in the broad but not
in the small range condition. Variant 5 and 7 exemplars in the small range condition did not
differ significantly in CE, RT or NE. However, variant 2 and 5 exemplars in the broad
range condition did show the expected differences. CE was significantly higher and RT was
faster with variants 2 than with variants 5 (Wilcoxon matched-pairs signed ranks test, both
p < 0.01). NE tended to be smaller with variants 2 than with variants 5 (sign test, p < 0.09) .
Typicality range. The extension of the focal category appeared to be larger following broad
than following small range experience. More + responses were given in the broad than in
the small range condition (Mann-Whitney U-test, U= 380.5, p < 0.03). To get more insight
in the category extension, the boundary (cut-off) between both categories was determined.
The cut-off was defined as the point at which faces were categorized 50% of the cases in
the focal category. With small range experience, the cut-off turned out to fall between
variants 5 and 7 of the focal category, whereas with broad range experience the cut -off was
placed between variants 7 of both categories.
Type of errors were analyzed to test the hypotheses of better classification of new
atypical focal exemplars and of new atypical contrasting category exemplars in the broad
than in the small range condition. Type of errors were analyzed by an ANOVA for the
variables Typicality range, Type of feedback, Delay of testing, Typicality of variants, TCV
and Type of classification error (false positive or false negative). The number of false
positives and false negatives possible was made equal by taking mean NE both over the two
H and over the two M TCV focal exemplars. The interaction Typicality range x Typicality
of variants x Type of error (see fig. 2) was relevant to the hypotheses. This interaction was
significant (F(2, 80) = 3.20). Regarding the focal exemplars, no differences between small
and broad range condition were found for the typical exemplars (variants 2; t = 0 , df = 80).
However, the number of false negatives was significantly higher with the small than with
the broad range condition for the atypical exemplars (both variants 5 and 7; t = 2.85 and t =
2.24, dj= 80, p < 0.01 and p < 0.025, respectively). Fig. 2 also shows that typicality range
does not affect the number of misclassifications of atypical contrasting category items. The
number of false positives did not differ significantly for the three typicality levels nor for
both typicality range conditions (post-hoc analysis).
The ANOVA on errors showed furthermore the following results with respect to
type of error. Type of error was significant (F(1, 40) = 19.91). False negative responses
were given more often than false positive ones. Type of feedback interacted significantly
with type of error (F(1, 40)=12.84). Post-hoc analysis indicated that this interaction
reflected significantly more errors on focal than on contrasting category exemplars in the
non-specific feedback condition ( p < 0.01), but a similar number of false positives and
false negatives with specific feedback. No other interactions with type of error were
significant. Apart from effects of type of error, the same results as with NE in the
MANOVA mentioned above were obtained.
Frequencv. Table 4 shows that CE increased and that RT and NE decreased with
increasing TCV. Main effect of TCV in the MANOVA was significant, and all mutual
differences between H, M and L TCV were significant for CE, RT and NE. H-L differences
were also significant within the non-specific feedback condition (post-hoc analyses).
Common and distinctive values and differential forgetting. The last three faces of the
classification task were faces in which either dimension A (with distinctive value Al), or
dimension B or C (with common value BI or Cl) was omitted. Performance was
significantly worse when the distinctive than when a common value was omitted; NE was
15, 7 and 9 with omission of A, B and C, respectively (Cochran Q-test; Q = 7.13, dj= 2, p <
0.05). RT mirrored these results (Friedman two-way ANOVA; x2 = 9.28,
F A L S E- , S M AL L
~ - -F A L S E - , BR O AD
F A L S E+ , SM A LL
FALSE+, BROAD c---
2
5
7
TYPICALITY
Fig. 2. Type of error as a joint function of typicality range during learning (small and broad) and
variant typicality (variants 2, 5 and 7) in the classification task.
d/= 2, p < 0.01). CE differences were non-significant (Friedman two-way ANOVA; x2 =
3.21, df = 2). Differential forgetting did occur. With immediate testing, no significant
differences were found for either NE, or RT, or CE (Cochran Q-test; Q = 1.56, and Friedman
two-way ANOVA; x2 = 1.00 and x2 = 1.94, respectively). However, with delayed testing,
significant differences were found for NE (Cochran Q-test; Q = 7.14, df = 2, p < 0.05). NE
was 8, 3 and 3 with omission of A, B and C, respectively. Again RT mirrored these results,
while CE differences were marginally significant (Friedman two-way ANOVA; x2 = 11.09
and x2 = 4.75, df = 2, p < 0.01 and p < 0.10, respectively).
Type of feedback. The expected facilitation of classification performance in the specific
feedback condition did not show up. Main effect in the MANOVA was non-significant.
However, as said before, the type of error analysis showed that Ss in the specific feedback
condition made less errors in the focal but more in the contrastang category than Ss in the
non-specific feedback condition.
Pairwise comparison task
From the pairwise comparisons the ranking of the five faces of different TCV's was
established for each S. Agreement among rankings was significant (Kendall's Coefficient of
Concordance, W = 0.38, x2 = 71.95, df = 4, p < 0.001). The mean ranking was the same as the
ordering to TCV (experimenter-determined), except for the order of the two faces with the
lowest TCV (see table 5). Faces of the type 2 1 1, which contained the two characteristic
common values BI and Cl, were ranked higher than 1 3 3 type faces, containing only one
characteristic, distinctive value (Al).
Mean rankings were also calculated for the specific and the non-specific feedback
condition separately (see table 5). In both conditions Ss agreed significantly in their rankings
(W = 0.31 and W = 0.32, x2 = 29.77 and x2 = 30.56 for specific and nonspecific feedback,
respectively, df = 4, p < 0.001). The order in the specific feedback condition was the same as
the overall mean ranking. In the non-specific feedback
Table 5
Objective and consensual rankings according to representativeness of five types of faces in the
pairwise comparison task. Each face was composed of one value (1, 2 or 3) for each of three
dimensions (A, B and C). Values Al, BI and Cl were characteristic of the focal category. Al was a
distinctive value, BI and Cl were common values.
Faces
Total cue validity
Objective ranking
Consensual mean rankings
Overall
Specific feedback
Non-specific feedback
AIB1CI
A1BIC3
AIB2C1
AIB3C3
A2BICI
2.34
1
2.07
2
1.99
3
1.72
4
1.57
5
1.76
1.78
1.75
2.55
2.48
2.63
3.28
3.29
3.27
4.10
4.02
4.19
3.30
3.44
3.17
condition, faces of the type 2 1 1 were ranked higher than both 1 3 3 and 1 2 1 type faces.
Effect of TCV was further determined by examinating whether performance was
influenced by the distance in TCV between two faces in a comparison. Therefore, a
MANOVA was run on CE and RT data, with the main factors of typicality range, type of
feedback, delay of testing and distance. The 10 distantes among the five faces were
classified into three groups. The small distance group contained the four smallest, the
medium the three intermediate, and the large distance group the three largest differences.
Distance was significant (F(4, 37) = 4.20). The distance effect represented a significantly
increasing CE and a significantly decreasing RT with increasing distance between the
faces. Typicality range and Type of feedback were both non-significant. Delay of testing
was significant ( F (2, 39) = 3.72), reflecting a higher RT in the delayed condition. There
were no significant interactions.
After completing the test tasks, Ss were asked to describe the characteristic features of
the family of the focal category. The number of times a value was mentioned rightly was
43, 37 and 27 out of 48 each for the characteristic values Al, Bl and Cl, respectively.
These numbers differed significantly (Cochran Q-test; Q = 16.33, d j - 2, p<0.001).
GSR
To test the first hypothesis regarding GSR, an ANOVA was performed on GSR data for
misclassifications in the specific feedback condition. To avoid differences in GSR caused
by disparity in frequency and habituation, the learning task was divided into four equal
parts of 20 trials. From each part, an equal amount of H, M and L TCV disconfirming
feedback trials were taken, which were the first to occur within each part. Main factors in
the analysis were typicality range, delay of testing, and TCV. TCV was significant (F(2, 40)
= 4.51), representing the predicted decrease in GSR from H to M to L TCV. The interaction
Typicality range X Delay of testing was significant (F(1, 20) = 4.80). This reflected for the
broad range condition a higher GSR with delay than with immediate testing, but for the
small range condition the reverse. None of the other main effects or interactions
approached significance.
The second hypothesis on the relationship between GSR and informational value of
feedback was tested by the relationship between GSR and CE on trials with confirming
and on trials with disconfirming feedback. On the average, informational value is smaller
with confirming than with disconfirming feedback (De Swart and Das-Smaal 1979a, b).
Furthermore, it was assumed that informational value of confirming feed-back was
smaller when it followed a high CE than a low CE, whereas the opposite would hold with
disconfirming feedback. An ANOVA was run for the variables of typicality range, type of
feedback, delay of testing, and CE with confirming and disconfirming feedback. The latter
factor contained four levels: CE > 0.50 before confirmation (high conf), CE 0.50 before
confirmation (low conf), CE 0.50 before disconfirmation (low disc), and CE> 0.50 before
disconfirmation (high disc). It was hypothesized that GSR would rise in this order. The
analysis showed that the only significant main factor was CE with confirming and
disconfirming feedback (F(3, 120) = 10.02). Tukey's test indicated that GSR on high conf =
low conf < low disc < high disc.
Discussion
Within category variation
The present study established the effects of two forms of variation within
categories. One form follows from prototype-distance models and
concerns typicality differences within dimensional values; it is continuous
in character. The other form relates to the frequency of discrete values, in
conformity with frequency models. Frequency is not accounted for by
distance models. Both frequency and variant typicality were varied in the
same experiment on the same values. Each form of variation was shown
to affect categorization performance in its own way. The forms did not
interact. Therefore, it is concluded that prototype-distance and frequency
models are complementary to one another; they explain different aspects
of variation within the same categories. A viable model of categorization
must be able to account for both variant typicality and frequency effects,
and not for only one of them.
Typicality differences within values
Increasing typicality of variants highly improved classification performance following learning. There is a problem with the interpretation of
this result. Typicality and experience with the variants were confounded
in the experiment. To disentangle both variables, the effect of typicality
was further determined separately for old and novel variants. In
consequence of the experimental set-up, the effects for novel and old
variants could be tested only within the small and within the broad
typicality range condition of learning, respectively.
In the small range condition, medium typical focal exemplars were
classified better than atypical ones. This efffect did not show up in the
contrasting category. The width of the focal category could account for
the lack of effect in the contrasting category. Subjects in the small range
condition formed a small extension of the focal category. The small
extension, and the instruction to focus on the focal category, may have
induced categorization by default with items of the contrasting category,
resulting in a reduction of typicality effects in the contrasting category.
In the broad range condition, typical and medium typical focal
exemplars were classified equally, whereas in the contrasting category,
typical exemplars were classified faster and with more certainty than
medium typical ones. The Jack of typicality effect in the focal category
can be explained also by category width. In the broad range condition, the
focal category had a large extension. Subjects could have compressed the
focal category to obtain a category of a more manageable size, causing a
reduction of differences within the focal category. This explanation is
consistent with a model of stimulus discrimination proposed by Gravetter
and Lockhead (1973), which predicts that two stimuli are more likely to be
confused when they are part of a broad range of stimuli than when they
belong to a small range.
Das-Smaal and De Swart (1981) demonstrated that typicality variation
within values influenced ease of category learning with well-defined
conceptual rules. The results were taken as evidence in support of the
prototype view and against models of categorization that treat exemplars
of a category as equivalent in their degree of membership of the category.
The present results with ill-defined categories, amplify this support by
showing that categories, once learned, still allow for degrees of
membership. Classification is more difficult the less typical an exemplar
is, but the effect is dependent on category and typicality range experienced
during learning.
Typicality range
Regarding learning, small range experience did not show the expected
facilitating effect, although it resulted in higher response certainty. In
contrast, Das-Smaal and De Swart (1981) found faster learning with
typical rather than atypical instances. In the present experiment, learning
by typical instances was compared with a mixed condition of both typical
and atypical variants. If learning with this mixed condition is easier than
with atypical instances only, this can explain the smaller effect in the
present study.
Following learning, broad range experience resulted in less classification errors than small range experience, due to better classification of
both medium typical and (new) atypical focal faces in the broad range
condition. The result that small range subjects categorized the atypical
faces more often in the contrasting than in the focal category, clearly
indicates the smaller extension formed by these subjects. The results agree
with Posner and Keele (1968) and Homa and Vosburgh (1976), who found
that broad experience on dot patterns enhances transfer to new exemplars.
The present study, however, excluded the alternative explanation of their
results, that broad range subjects had more learn-
ing experience than small range subjects.
In the contrasting category, boundary exemplars were not classified
better with broad than with small range experience. However, with small
range experience, contrasting category items might have been rejected as
focal exemplars relatively easily by default. This could have veiled the
advantage of broad range experience. Categorization of atypical items of
a contrasting category may be improved by broad experience in a situation
where subjects are prevented from categorizing by default, for instance in
a classification task with more than two categories.
The present study demonstrated effects of typicality of variants that
cannot easily be accounted for by discrete feature frequency. At the same
time, the study indicates how category boundaries are affected by learning
experience. Broad range experience results in a larger extension of the
focal category, with better classification of atypical exemplars on the
focal side of the boundary but not on the contrasting side.
Frequency in focal and contrasting category
The second form of within-category variation implied variation among
dimensional values in frequency of occurrence both in the focal and in the
contrasting category, as expressed in TCV. Like predicted, high TCV
facilitated both category learning and classification after learning, and
this yielded also when non-specific feedback was given. The importance
of TCV is further evidenced by pairwise comparisons, from which rank
orderings of focal exemplars were inferred. Subjects showed significant
consensus on these rankings. The rankings followed the ordering
according to TCV except for the ranking of the lower TCV exemplars.
Furthermore, pairwise comparison performance improved with increasing
TCV distance between the exemplars.
The facilitating effect of increasing TCV implies that the exemplars
which are categorized best are the ones which have the most in common
with other exemplars of the same category, and at the same time share the
least with items outside the category. The findings support the idea of
Rosch (1973) that the categories which are formed are structured
according to the principle of maximization of cue validity.
Besides TCV, simple frequency of occurrence affected performance.
Two common values had the same TCV but different simple frequencies.
The more frequent value was mentioned more often as a characteristic
feature than the less frequent one, although no effect of simple
frequency was shown with classification of typical focal faces that misled
one dimension.
An important conclusion of the present study is that models of
categorization that do not account for frequencies outside the focal
category, are not supported by the differential performance on common as
compared with distinctive values. Classification performance on faces
appeared to be worse when the distinctive value dimension was left out
than when the common value dimension with the same focal frequency
distribution was omitted. This difference has to be attributed to different
frequency distributions of values in the contrasting category. Furthermore,
when asked to describe the characteristic features of the family of the
focal category, subjects more often mentioned the distinctive value than
the common values. This supports the idea that distinctive values are
weighted more heavily than common values (Homa and Chambliss 1975).
The results on delay of testing further specify this conclusion. With value
omission in the classification task, delay had less effect on distinctive than
on common values. This suggests that distinctive values become relatively
more important to classification with the passage of time.
The conclusion that models of classification should account for
frequency of values in contrasting categories is supported by two studies
on categories defined in terms of discrete values. Rosch and Mervis (1975)
found that the best examples of a category were related least to members
of other categories. Although Martin and Caramazza (1980) did not aim to
investigate the issue of frequency in a contrasting category, the effect can
be deduced from their results. The study employed binary values with
equal frequencies in category x. In the contrasting category, one value
occurred always, the other value never. The Jatter value facilitated
performance on category x. This can only be an effect of the differential
frequencies in the contrasting category. It can be argued that the effect is
limited to their experimental set-up. Definingness of one value of a
bi-valued dimension to one category, may induce the tendency to regard
the other value as relatively more important to the other category. This
objection was cancelled out in the present experiment, since discrete,
defining and binary values were not used. Nevertheless, effects of
frequency in the contrasting category were obtained.
The experiment of Martin and Caramazza (1980) was aimed at comparing
several models of categorization. None of the models was
consistently supported by their data. However, the models were not
properly tested. In predicting from the Rosch model, they failed to take
into account frequency in the contrasting category. Furthermore, their
predictions did not account for which category the subjects considered the
focal one. Their results showed that the subjects focused on only one
category and categorized instances of the second category by default.
Task difference
In the pairwise comparison task, lower TCV faces were ranked higher
than expected. This result differs from that on the learning task and the
classification test task, which showed better performance with medium
than with low TCV faces. Task difference can explain the disagreement.
In a classification task, differences between categories are relevant, and
this stresses distinctive values. On the contrary, a withincategory
comparison task asks for similarity judgments, stressing common values
(Tversky and Gati 1978). This explains why medium TCV faces, with a
distinctive value and either one or no common values, were classified
better but were not ranked higher than the low TCV face, with two
common values but no distinctive value. In addition to task difference,
correlation of values could explain why face 2 1 1 was ranked even
slightly higher than face 1 2 1 in the non-specific feedback condition.
Importance of values to a category may be enhanced by conjoint
frequency (Rosch 1975; Hayes-Roth and Hayes-Roth 1977; Medin and
Schaffer 1978). In the learning task, B1 and Cl occurred together 40%,
and Al and Cl 20%. With otherwise equal frequencies, this could have
made face 2 1 1 more representative than face 1 2 1.
Task difference also explains why Kellogg (1980) found representativeness ratings to be a function of focal frequency, regardless of
frequency in a contrasting category. Distinction between categories - and
therefore frequency in t contrasting category - is not of primary
importance in judging representativeness of exemplars within one category.
Task dependency of the weights assigned to values, implies theoretically that the represented information about occurrence in a category has
to be available apart from information about occurrence in other
categories. It depends upon the task whether or not occurrence in other
categories is taken into account. It could be argued that for each value
relevant to a category two weights are stored, one accounting for
distinctiveness, the other only for commonality. However, this is unlikely
because distinctiveness depends upon which category is the contrasting
one. For instance, comparing dogs with Cats asks for other distinctive
values than comparing dogs with wolves. This is a matter of context.
Labov (1973) showed that weighting of values is influenced by context.
Context altered the degree to which a value was considered important in
naming cup-like objects. For "bowl" classification, more weight was
attached to the value "wide" when an object was imagined to be filled
with food than if it was judged without that imagination instruction.
If weights of values depend on task and context, it is unlikely that
weights per se are stored in memory. It would not be very parsimonious
to store different weights for various types of tasks and possible contexts.
It seems more likely that values, their frequency of occurrence in various
categories, and perhaps their co-occurrences, are registered. Different
operations are used to obtain and weigh the represented information that
is needed in a particular context or task. When distinction between
categories is relevant, weights are assigned that take into account
frequencies in contrasting categories. Otherwise, resemblance within a
category is weighted more heavily.
Type of feedback
The effect of a new, specific, kind of feedback was investigated in our
study. Specific feedback did not show the expected facilitatory effect,
either on learning, or on tests. In the learning phase, specific feedback
tended to decrease the number of errors. However, contrary to our
expectation, it also tended to make subjects less certain about the
correctness of their responses. The Jatter tendency can tentatively be
interpreted as an effect of confusion, which offers one explanation of the
lack of positive results on specific feedback. With specific feedback,
subjects have to evaluate their response and to process extra information
on representativeness. The time to process all information was fixed at 7
seconds for both kinds of feedback. In the highly informative specific
feedback condition this time might have been too short. Another
explanation is that evaluation of information concerning frequency of
occurrence is a relatively autonomous process. This process is not
influenced by whether the information is presented implicitly or explicitly. Hence, specific feedback will not improve performance. In fine
with this explanation is the suggestion of Kellogg et al. (1978) that
subjects automatically create differences in representativeness in
organizing a category. They found that typicality judgments were
positively related to frequency of values, and that these judgments were
not influenced by whether or not the subjects were specially instructed to
learn which faces were better examples than others.
An unexpected finding and potentially interesting result was the
interaction between type of feedback and type of error. Following specific
feedback, the number of categorization errors did not differ between the
focal and the contrasting category. Following non-specific feedback,
more errors were made on focal than on contrasting category items.
Specific feedback, therefore, may favour performance on the focal
category, but not on the contrasting category.
Physiological activity
Previous results of De Swart and Das-Smaal (1976, 1979a, b) and of
Das-Smaal and De Swart (1981) have demonstrated that increasing
informational value of feedback in concept learning tasks is accompanied
by increasing GSR activity. The specific feedback condition of our study
provided the opportunity to test the relationship in a new way.
Informational value of feedback was assumed to be positively related to
the discrepancy between expected and actual feedback. This discrepancy
increased with increasing representativeness of misclassified instances to
their category. Therefore, in the specific feedback condition, a rising GSR
was predicted with increasing TCV of a misclassified instance. The results
agree with this hypothesis, providing new evidence for the existence of a
positive relationship between GSR and informational value of feedback.
De Swart and Das-Smaal (1979b) found different relationships between
certainty about classification responses and GSR for different types of
feedback. The present study replicated these findings. It was assumed that
informational value of confirming feedback was lower with high than
with low CE. The reverse was assumed to hold with disconfirming
feedback. Furthermore, on the average, disconfirming feedback supplies
more information than confirming feedback. Hence, a higher GSR with
disconfirming than with confirming feedback was predicted. Therefore,
GSR was expected to increase from high to low CE confirmed responses,
and further from low to high CE disconfirmed
responses. GSR appeared to increase in this order, although the difference
between low and high CE confirmed responses was in the predicted
direction, but not significant. De Swart et al. (1981) found the same
relationship with an EEG response (the P300) as an indicator of
physiological activity.
In total, the results clearly support the idea that the amount of
uncertainty reduction in a category learning task is reflected in the change
in autonomic activity, measured by GSR.
Table Al
Values and variants of faces from the learning task for the smal] and broad typicality range
condition. With each dimension values are indicated first and variants are given next. Values are
numbered 1 to 3. Variants are numbered - 1 to - 6 .
Stimulus
number
Typicality range
Total
Category
Small
cue
validity
label
Broad
Dimension
Dimension
A
B
C
A
B
C
1
2-2
1-3
1-1
2-5
1-6
1-4
1.57(L)
+
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
2-3
1-2
2-1
3-2
1-2
1-2
2-1
1-1
1-2
3-3
1-3
1-3
2-3
1-3
1-3
3-2
2-2
2-2
3-3
3-3
3-1
1-1
1-3
3-1
2-1
3-2
2-2
1-3
1-3
2-1
3-3
1-1
3-3
2-1
3-3
3-1
3-2
2-1
1-1
3-2
1-1
3-1
1-3
1-3
3-1
1-2
2-1
3-2
3-2
1-2
2-3
2-1
2-1
1-2
1-2
1-1
1-2
1-2
3-3
2-3
1-1
1-2
3-3
2-3
2-1
1-2
1-1
1-1
1-3
2-1
3-2
2-1
2-3
2-3
1-2
1-3
2-2
1-1
2-2
1-3
1-1
1-2
3-3
3-1
2-2
3-1
3-1
2-3
2-1
2-1
2-1
2-2
2-2
2-3
1-3
2-3
3-1
1-2
2-2
1-2
3-1
1-1
1-3
2-3
1-3
3-1
1-2
2-3
3-1
1-2
1-2
3-2
3-3
3-1
1-3
2-2
2-1
2-3
1-2
2-1
3-5
1-5
1-5
2-1
1-1
1-2
3-3
1-6
1-3
2-3
1-6
1-3
3-2
2-2
2-2
3-6
3-6
3-4
1-1
1-3
3-4
2-1
3-5
2-2
1-3
1-3
2-4
3-6
1-4
3-3
2-1
3-6
3-1
3-5
2-4
1-4
3-2
1-4
3-1
1-3
1-6
3-4
1-2
2-4
3-2
3-2
1-2
2-3
2-1
2-1
1-2
1-5
1-1
1-2
1-5
3-6
2-6
1-1
1-2
3-6
2-6
2-1
1-2
1-4
1-1
1-6
2-4
3-5
2-4
2-3
2-3
1-2
1-6
2-2
1-4
2-5
1-3
1-1
1-2
3-3
3-1
2-5
3-4
3-4
2-6
2-4
2-1
2-4
2-2
2-5
2-6
1-3
2-6
3-4
1-2
2-5
1-5
3-4
1-4
1-3
2-3
1-6
3-4
1-5
2-3
3-1
1-5
1-5
3-5
3-6
3-1
1-3
2-2
2-4
0.95(H)
2.34(H)
1.22(M)
1.57(L)
2.07(H)
1.72(M)
1.30(L)
1.72(M)
1.72(M)
0.95(H)
2.07(H)
1.72(M)
0.95(H)
1.72(M)
2.07(H)
1.30(L)
1.57(L)
1.30(L)
1.30(L)
1.22(M)
0.95(H)
2.34(H)
2.07(H)
1.22(M)
1.22(M)
0.95(H)
1.57(L)
2.07(H)
2.34(H)
1.30(L)
0.95(H)
1.99(M)
1.22(M)
0.95(H)
0.95(H)
1.30(L)
1.57(L)
0.95(H)
2.07(H)
+
+
+
+
+
+
+
+
+
+
+
-
+
+
+
+
+
+
-
+
+
Table Al (continued)
Stimulus
number
Typicality range
Small
Broad
Dimension
Total
cue
validity
C a tego rv
label
Dimension
A
B
C
A
41
2-1
2-2
1-3
2-4
B
2-5
C
1-3
1.22(M)
-
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
1-2
1-3
2-1
3-2
3-3
2-1
1-2
3-1
3-1
1-3
3-3
3-1
2-1
3-2
2-2
1-3
3-3
2-3
1-1
3-2
1-1
3-2
2-3
2-1
1-3
2-3
1-1
2-1
1-2
2-3
1-1
3-2
1-2
3-2
2-3
2-1
3-3
2-2
1-1
2-3
1-3
1-1
3-2
1-2
3-2
1-1
1-1
1-1
3-3
1-1
3-1
3-3
3-3
3-3
1-2
1-2
1-2
1-3
2-3
2-1
1-2
2-3
2-1
1-1
1-2
1-2
1-3
3-3
1-1
3-3
1-1
1-3
3-1
1-3
1-1
1-2
3-1
2-2
3-2
3-1
3-1
3-3
1-1
3-1
3-2
2-1
3-2
2-2
1-2
3-3
2-1
1-1
1-2
2-3
2-1
1-2
2-2
1-3
1-3
1-3
3-2
1-3
3-3
3-1
1-1
2-1
2-1
1-3
1-1
3-2
2-1
2-3
1-3
3-2
1-3
3-3
1-3
1-2
1-6
2-4
3-5
3-3
2-4
1-2
3-4
3-4
1-3
3-6
3-4
2-1
3-2
2-2
1-6
3-3
2-3
1-4
3-5
1-1
3-5
2-3
2-1
1-6
2-3
1-1
2-4
1-5
2-6
1-4
3-5
1-5
3-2
2-3
2-4
3-3
2-2
1-4
2-3
1-3
1-4
3-5
1-5
3-5
1-1
1-4
1-1
3-3
1-1
3-4
3-3
3-3
3-3
1-5
1-2
1-5
1-6
2-3
2-4
1-5
2-6
2-1
1-4
1-2
1-5
1-3
3-6
1-4
3-3
1-4
1-6
3-4
1-3
1-4
1-2
3-4
2-2
3-2
3-4
3-4
3-3
1-4
3-1
3-5
2-1
3-2
2-5
1-2
3-3
2-1
1-4
1-2
2-3
2-1
1-2
2-5
1-6
1-6
1-3
3-2
1-6
3-6
3-6
1-4
2-1
2-1
1-3
1-1
3-2
2-1
2-3
1-3
3-5
1-6
3-6
1-3
1.72(M)
2.07(H)
1.30(L)
0.95(H)
1.57(L)
0.95(H)
2.07(H)
1.30(L)
1.30(L)
1.72(M)
1.57(L)
0.95(H)
0.95(H)
1.22(M)
1.22(M)
2.07(H)
1.30(L)
1.57(L)
2.07(H)
1.22(M)
1.99(M)
1.57(L)
0.95(H)
1.22(M)
2.07(H)
1.30(L)
2.34(H)
1.30(L)
1.72(M)
1.59(L)
1.99(M)
0.95(H)
2.07(H)
0.95(H)
1.59(L)
1.30(L)
1.59(L)
0.95(H)
+
+
+
+
+
+
+
+
-
1.99(M)
+
+
+
+
+
-
+
+
+
+
+
+
+
Note: High, medium and low TCV are indicated in parentheses by H, M and L, respectively.
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