BRAIN
AND
LANGUAGE
39, %-lo6 (19%))
Typicality Effects in Artificial Categories: Is There a Hemisphere Difference? LORIE
G. RICHARDS AND CHRISTINE CHIARELLO Syracuse
University
In category classification tasks, typicality effects are usually found: accuracy and reaction time depend upon distance from a prototype. In this study, subjects learned either verbal or nonverbal dot pattern categories, followed by a lateralized classification task. Comparable typicality effects were found in both reaction time and accuracy across visual fields for both verbal and nonverbal categories. Both hemispheres appeared to use a similarity-to-prototype matching strategy in classification. This indicates that merely having a verbal label does not differentiate classification in the two hemispheres. Q 1990 Academic press, IIIC.
Human perception, learning, and memory are influenced by the manner in which we organize our knowledge of the world. Hemisphere specialization may play an important part in such organization (Zaidel, 1987). Rosch and Mervis (1975) have suggested that the framework of this knowledge structure consists of categories or concepts having internal structure. As each hemisphere has been shown to deal with various types of stimuli differently, it is possible that hemispheric specialization affects the learning and storage of these categories. Smith and Medin (1981) discuss several types of category organization. One type of structure is family resemblance (Rosch & Mervis, 1975). With such organization each category consists of items whose functional and perceptual attributes overlap with the other members of the category. There are no critical attributes required for membership. Extent of membership varies from the prototype through nonmembers who share more attributes with contrasting categories than they share with members of the current category (Rosch & Mervis, 1975). A second type of category structure also posits a prototype. With these categories, the prototype This research was supported by NIMH Grant MH43868 to C.C. We thank Paul Gelling for hardware and software support. Reprint requests should be addressed to Lorie Richards, Department of Psychology, Syracuse University, Syracuse NY i3244-2340. 90 0093-934x&o $3.00 Copyright All rights
0 I990 by Academic Press, Inc. of reproduction in any form reserved.
HEMISPHERIC
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is the center of the category and the category is ordered in terms of physical distance from the prototype in multidimensional space. A third type of category has definitive membership criteria. All of the above structures result in typicality effects (typical exemplars are responded to faster and more accurately than atypical exemplars) in performance on a variety of tasks using both word and picture stimuli, including classification (Posner & Keele, 1968; Rosch, 1975; Rosch & Mervis, 1975; Rosch, Simpson, & Miller, 1976), recognition (Franks & Bransford, 1971), cued recall (Rosch et al., 1976), category matching (KoemedaLutz, Cohen, & Meier, 1987), generation of category members (Grossman, 1981), and typicality ratings (Rosch, 1975; Rosch & Mervis, 1975; Rosch et al., 1976). Few studies have investigated category performance in the two cerebral hemispheres. With normal subjects, Zaidel (1987) employed a category matching task where subjects heard the name of the category followed by a picture of an exemplar of the category in central vision. Subjects then saw another picture lateralized to the right visual field (RVF) or left visual field (LVF) and manually indicated whether or not both items were members of the spoken category. She found that the performance in the RVF/left hemisphere (LH) did not demonstrate typicality effects. The LVF/right hemisphere (RH) did show such effects, with faster reaction times for the items of high typicality. Zaidel (1987) suggested that in the LH classification is accomplished using a logical/definitional process (one relying on defining features) while in the RH classification proceeds via similarity-to-prototype matching. With highly typical items, the RH’s matching strategy is fast while the LH’s logical strategy is slow. With atypical items, however, the RH’s similarity-to-prototype matching is inefficient and leads to very slow classification. The LH’s logical strategy does not differentiate by typicality and classifies both types of exemplars at the same rate. Classification of atypical items in the RH is much slower than in the LH. In this way, typicality structure is evidenced in the RH, but not the LH. While possible, Zaidel’s (1987) explanation seems too simplistic. If such a dichotomy existed, it would imply that accurate classification could not occur in the LH when defining features were absent. Correspondingly, when classifying categories whose membership is delineated only by a defining feature, the right hemisphere would not be able to classify accurately. It remains possible that Zaidel’s (1987) findings result from task and stimulus-dependent processes. Other classification procedures may be available to the cerebral hemispheres in other situations. The literature on unilateral brain injury may offer some evidence regarding this hypothesis. Research in this field indicates that LH-damaged individuals demonstrate typicality effects. Grober, Perecman, Kellar, and Brown’s (1980) LH-damaged patients, like normal subjects, obtained
92
RICHARDS
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typicality effects when classifying exemplars of natural categories (Rosch & Mervis, 1975) in both picture and word form. Wayland and Taplin (1985a,b) also found that LH-damaged patients demonstrated typicality effects in classifying categories of schematic faces. Further analysis, however, revealed that these patients, unlike normal controls, were classifying items on the basis of one salient feature. On items where this feature was equivalent, no typicality effect was demonstrated by the LHdamaged subjects. This type of classification strategy is similar to classifying by defining features, which according to Zaidel(l987) should only occur in the LH. Thus, this finding may provide counterevidence to Zaidel’s claim. Grossman (1978, 1981) likewise found that LH-damaged subjects demonstrated a typicality effect when generating exemplars of a category. However, their performance was not identical to that of normal subjects. Anterior lesions resulted in generation of exemplars heavily clustered in the highly typical region of possible exemplars, while posterior lesions led to many out of category generations. Normal subjects generated no out of category exemplars, and, although they generated a large number of exemplars in the high typicality range, their generations were spread more evenly throughout the entire typicality range of the category. RHdamaged subjects in Grossman’s study (1981) generated exemplars in a manner similar to that used by normal controls. This again counters Zaidel’s (1987) hypothesis. She claims that typicality information is only available to the RH. If true, it would be unlikely to obtain normal typicality effects with RH damage. Consequently, both Grossman (1981) and Wayland and Taplin (1985a,b) provide evidence counter to Zaidel’s (1987) hypothesis about classification in the two hemispheres. Based on the studies above, it is unclear just how typicality is represented in the two cerebral hemispheres. Zaidel (1987) suggests that typicality effects occur only in the RH by use of a similarity-to-prototype matching procedure. She further suggests that the LH does not have access to typicality information and relies on a logical process (defining features) to classify. As no other study has directly compared classification in the two hemispheres and there are conflicting data in the braindamaged literature, this hypothesis requires further testing. In addition, little is known about the variables affecting these hemisphere classification processes. Zaidel’s (1987) categories were learned informally and had existed in long term memory for an extended period of time. Hence, the role of typicality information in the acquisition of categories in the hemispheres is not known. In addition, the categories Zaidel(1987) used were linguistic in the sense that, although her stimuli were pictures, the category itself and each exemplar of the category had a unique verbal label. Since verbal ability is one variable known to differentiate the hemispheres, the hemispheres may use different classification procedures
HEMISPHERIC
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93
for verbal and nonverbal items. Task parameters may also alter processing strategies in the cerebral hemispheres (Uricuioli, Klein, & Day, 1981). Zaidel’s (1987) task allowed category access prior to presentation of the targets and then required matching two targets to that category. It is unknown whether changing the classification task would modify hemisphere classification strategies. The study reported here investigated the effect of stimulus parameters on category acquisition and classification in the two cerebral hemispheres. Subjects learned to classify members of two centrally presented artificial categories. Following this acquisition phase, new and old exemplars of these categories were presented to the RVF or LVF for classification. Artificial categories of dot patterns served as stimuli since both verbal and nonverbal categories could be created that were equivalent in terms of levels of typicality, length of time in memory, and the amount of abstract information available. In addition, Posner and Keele (1968) demonstrated typicality effects on a similar task using dot pattern categories presented in central vision. The primary variable of interest was the verbal versus nonverbal nature of the category. Verbal categories were defined as those for which subjects should have a preexisting verbal label for the prototype. Two types of categories served as the verbal stimuli: alphanumeric and common object. Alphanumeric categories were those whose prototypes were letters or numbers. Letters and numbers are overlearned stimuli and people experience them in a myriad of physical distortions, from typed characters to handwriting. Because of this, we were concerned that these categories would be easier to classify than the nonverbal categories. Thus, common object verbal categories, where the prototype was a common object, were also used. These objects are not experienced in the myriad of forms as are letters and numbers. As such, they should be more akin to the nonverbal categories. Nonverbal categories were those whose prototypes had no preexisting verbal label. Because of the visual-spatial nature of the stimuli, an overall LVF/RH advantage was expected. However, our critical predictions concerned differences in typicality effects across hemispheres. Since the experimental categories were constructed in terms of distance from a prototype, the existence of one defining feature distinguishing one category from another was precluded, especially for the atypical exemplars. Consequently, it would be difficult for classification to occur efficiently using a logical process. Our manipulation of category type was an attempt to determine if merely having a verbal label was sufficient to allow the LH to use a logical process in classification. Our predictions were as follows. If classification in the LH can be accomplished using a logical process when verbal labels are present, the verbal patterns presented to the RVF/LH should be classified without typicality effects. It is unclear
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exactly what to expect from the LH during the classification of the nonverbal categories. If the logical process is the only one available to the LH, it should not be able to accurately classify nonverbal patterns. However, it has not been demonstrated that this process is the only one available to the LH. Typicality effects for nonverbal patterns would be expected in the RVF/LH if similarity-to-prototype matching is used as a default option when verbal labels are unavailable. However, if Zaidel (1987) is correct that classification in the RH relies on similarity-toprototype matching, typicality effects will be demonstrated in the LVF/RH with both verbal and nonverbal categories. EXPERIMENT
1
Methods Design. A split-plot design was used to vary type of category (alphanumeric verbal, common object verbal, or nonverbal) and response hand between subjects. Visual field of presentation and typicality level varied within subjects. Three levels of typicality were defined in terms of distance from the prototype. Dependent measures were percentage correct and median reaction time for correct responses. Subjects. Forty-eight right-handed, native speakers of English (24 female and 24 male) participated. They were undergraduates in an introductory psychology class receiving course credit for participating. All had normal or corrected to normal vision. For each subject, handedness was assessed by a five-item handedness questionnaire based on hand use in common activities (Bryden, 1982). Handedness scores ranged from + .3 to + 1.0 with a mean of + .75. (Positive values indicate right hand preference.) None had a history of familial sinistrality. Stimuli. Prior to selecting the stimuli for the experimental categories, a verbalization rating task was undertaken by 18 naive subjects. Dot patterns were presented in the center of a computer graphics screen and subjects were required to name them as quickly as possible. Naming latencies were used to measure ease of verbal label retrieval. A similarity rating task was also performed on pairs of dot patterns used in the verbalization rating task. Twenty-two new subjects were presented with all possible pairs of dot patterns (verbal patterns were paired only with other verbal patterns and nonverbal patterns were paired only with other nonverbal patterns) and were asked to rate their similarity on a scale ranging from one (1) (completely different) to seven (7) (identical). Subjects for these tasks did not participate in the main experiments. Based on the results of these tasks, 12 dot patterns were chosen to represent the prototypes of the experimental categories. Verbal prototypes were named in under 1700 msec while nonverbal prototypes had naming latencies of over 4000 msec. The 12prototypes were paired such that their similarity to each other was balanced across pairs. The paired prototypes can be seen in Fig. 1, and their mean similarity scores and naming latencies are given in Table 1. Each prototype was composed of fifteen dots arranged within a rectangular (length greater than width) matrix whose dimensions were 13 x 19 squares. (Ten-squares-to-the-inch graph paper was used to construct the prototypes.) From each of the common object and nonverbal prototypes, a category of dot patterns was created as follows. Each prototype was distorted so as to create three levels of distortions (and, thus, three levels of typicality). Level 1 distortions were created by moving two dots, level 2 by moving four dots, and level 3 by moving six dots. A pilot experiment had indicated that alphanumeric dot patterns created as above were much easier to classify than were the nonverbal categories. Thus, to avoid ceiling effects for the alphanumeric categories, these patterns were distorted to a greater degree. Distortion levels 1, 2, and 3
HEMISPHERIC Nonverbal
TYPICALITY
Prolotyp~
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:. . . . .
Alphanumeric
Verbal
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1. Nonverbal, alphanumeric verbal, and common object verbal prototypes.
TABLE SIMILARITY
Pairs
.
. .
pllr5
FIG.
95
EFFECTS
RATINGS AND NAMING
1
LATENCIES
(IN
msec)
FOR THE PROTOTYPE PAIRS
Similarity Ratings
Verbalization RT
Nonverbal Pair 1
3.58
Pair 2
4.00
Pair 3
3.58
Pair 4
4.00
Proto Proto Proto Proto
a b c d
4143 4467 4677 4302
Proto Proto Proto Proto
e f g h
653 851 785 739
Proto Proto Proto Proto
i j k 1
1471 1607 1218 1467
Alphanumeric Verbal
Common Object Verbal Pair 5
4.21
Pair 6
3.71
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RICHARDS AND CHIARELLO
were created from the alphanumeric prototypes by moving three, five, or seven dots, respectively. This was done in an attempt to equate classification ease between the three types of categories. Twenty unique distortions were created at each level, as described below. Each prototype pattern was divided into four quadrants. For each distortion, the proportion of dots moved in that quadrant was approximately equal. All dots were moved outward two squares from their original position in the prototype. All of the distortions fit into a rectangular (length greater than width) matrix whose dimensions were I7 x 23 squares. Examples of prototypes and their distortions at each level for the common object categories can be seen in Fig. 2. Examples of distortions of alphanumeric prototypes are seen in Fig. 3. Apparatus. The experiment took place in a sound-attenuated room. Subjects sat I75 cm in front of a Hewlett-Packard l310B vector graphics display equipped with a fast decay phosphor. Stimulus presentation and timing was controlled by an LSI 11/23 computer. The computer also recorded subjects’ responses which were made via a response key held in either the right or left hand. Response hand was counterbalanced over subjects. Head position was maintained by the use of a modified Tektronix viewing hood.
Procedure Acquisition
task. For each subject, stimuli consisted of one pair of categories of a similar
type (either the common object verbal, the alphanumeric verbal, or the nonverbal). For the acquisition task, IO distortions from each level of each category (60 patterns in all) were randomly selected by the computer for each subject and were presented in central vision. Patterns subtended a visual angle of 1.5” in height and 1.3” in width. Prototypes were not presented in the acquisition task. Subjects were instructed that in this part of the experiment they were to learn the categories into which they would later classify patterns. They were told they would see a pattern appear in the middle of the screen, and that this pattern would be a member of the category that corresponded to the button under their index fingers. Subjects were to look at the pattern as long as they wished and to push the button under their index fingers when they were ready to see the next pattern of that category. Patterns belonging to the
.
. . . . . . . . ..
.
. . . . .
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Level
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.
.
. . . . . . . . . . . . . Dlstortlon
.
. . . . . . . . .
Prototype
.
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Level
.
2
.
. . . . . . . . . .
Dlstortlon
Level
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3
FIG. 2. Examples of a prototype and distortions for the common object verbal categories.
HEMISPHERIC
TYPICALITY
. . . .
.
. . .
. mm
.
.
Level
.
.
.
I
.
.
l
.
.
.
.
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FIG. 3.
. .
Dlstortlon
. . .
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97
. . . . . . . . .
.
Prototylx
.
.
.
. . . . . . . . . .
EFFECTS
.
Level
2
DIstortIon
. .
Level
3
Examples of a prototype and distortions for the alphanumeric verbal categories.
first category appeared on the screen in this manner until all 30 patterns of that category had appeared. Subjects were then told that they would now see the patterns belonging to the category corresponding to the button under their middle fingers. The 30 patterns of this category appeared on the screen as they had for the first category. When all 60 patterns from the two categories had been seen, subjects viewed the same patterns randomly mixed and were instructed to classify them into the categories they had just learned. Responses were made with the middle and index fingers by pushing one of the two buttons corresponding to the two categories. At the beginning of each trial, a fixation cross appeared in the center of the screen accompanied by a 50-msec 3.75kHz tone. After 500 msec, the fixation cross disappeared and a dot pattern was centrally presented for 50 msec. Auditory feedback in the form of a 500-msec 3.75-kHz tone signaled an incorrect response. Intertrial interval was 5 sec. The stimuli were randomly divided into two blocks of 30 trials each, and were presented with the constraint that no more than three members of one category were shown consecutively. The blocks were repeated until criterion within a block of 75% correct had been reached. Accuracy feedback was provided to each subject after each block. Transfer task. Following a short break, each subject proceeded to the transfer task. In this task, all of the 122 dot patterns from the two experimental categories were presented. The stimuli consisted of the 60 old patterns (those used in the acquisition task: IO from each distortion level in each category), 60 new patterns (IO from each distortion level in each category), and the 2 prototypes. Stimuli were presented once in each visual field resulting in a total of 244 trials (presented in four blocks of 61 trials each). Stimuli were presented pseudo-randomly with the constraints that no more than three patterns from the same category were shown in succession and no more than three patterns were presented to the same visual field successively. At the start of each trial, a SO-msec3.75-kHz warning tone was presented accompanying the fixation cross which was shown in the center of the screen. Subjects were instructed to keep their gaze focused on the cross when it was on the screen. After 500 msec elapsed, a dot pattern was presented for 50 msec to either the right or left visual field. Patterns were presented 2.3” from central fixation. The fixation cross remained on the screen for 200 msec following the offset of the dot pattern.
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RICHARDS AND CHIARELLO
Subjects were told that this part of the experiment explored how well they could classify dot patterns they were not directly looking at. They were instructed to make their classification response as before.
RESULTS
Acquisition tusk. Differences in the amount of time needed to learn the categories emerged among the category types. All 16 subjects who were presented with the letter/number verbal categories learned the categories to criterion within one acquisition block. Of those subjects receiving the nonverbal categories, 2 required two acquisition blocks while a third required three acquisition blocks. Five of the 16 subjects receiving the common object verbal categories required more than one acquisition block. Two of these subjects learned the categories to criterion in two blocks while 3 required three blocks. Transfer tusk. Preliminary analyses indicated that neither sex nor stimulus history (whether the stimulus was a new pattern or had been seen in the acquisition task) produced significant effects or interactions. Preliminary analyses also indicated that the only significant effect of response hand was that subjects responded faster with their right hand than their left hand in the common object verbal categories, F(1, 14) = 4.25, p < .06. Therefore, for the remaining analyses, the data was pooled over response hand, sex, and stimulus history. Levene’s test for homogeneity of variance indicated that heterogeneity of variance existed in both response measures. Reaction time data was transformed using logarithmic transformations and accuracy data using reciprocal transformations (Kirk, 1982). These transformations did not produce homogeneity for either response measure. Therefore, separate analyses were run in each of the three category type conditions, since homogeneity of variance only has relevance for between-subjects factors and the only between-subjects factor in this study was category type. Our primary predictions concern the presence or absence of typicality effects within each category type rather than a direct comparison among these categories. Thus, we were able to test our predictions despite the lack of homogeneity of variance among the data for the different categories. 2(visual field) x 3(distortion level) repeated measures ANOVAs were performed separately for each category type and each response measure. Since subjects were presented with each prototype only once per visual field, the data for this level of distortion were based on only 2 scores per subject. The analysis for the other levels were based on 40 scores per visual field. (Each distortion level consisted of 20 patterns in each category of the presented pair. Analyses were collapsed across these pairs.) Thus, interpreting the prototype scores in relation to the scores for the other levels is problematic. Because of this, the ANOVA was performed on distortion levels one through three. However, mean values for the prototypes can be found in Tables 2 and 3.
HEMISPHERIC
TYPICALITY
99
EFFECTS
TABLE 2 ACCURACY IN PERCENTAGE CORRECT FOR THE ALPHANUMERIC VERBAL, COMMON OBJECT VERBAL, AND NONVERBAL CATEGORIES IN EACH VISUAL FIELD FOR EXPERIMENT I”
Common object verbal
Alphanumeric verbal
Nonverbal
Distortion level
LVF
RVF
LVF
RVF
LVF
RVF
0 (prototype) I
lo@ 97.5 (3.0) 96.9
100 97.5 (2.7) 97.6 (4.0) 96.5 (4.3)
100 92.7
96.9 92.8 (7.1) 91.1 (7.9) 84.8 (10.5)
93.8 90.7 (7.1) 87.1 7.9 83.0 (12.2)
100 93.1 (6.9) 89.9
2
(3.6) 3
95.1 (4.9)
(6.0) 90.6 (7.4) 86.6
(8.9)
(6.6) 88.0 (10.5)
” Standard deviations are given in parentheses. ’ Standard deviations are not given for the prototypes since there were only two data points for the prototype level per category in each visual field.
Accuracy. Accuracy means are given in Table 2. A significant main effect of distortion level was present for all three category types (common object verbal: F(2, 75) = 14.39, p < .OOl; alphanumeric verbal: F(2, 75) = 3.93, p < .03; nonverbal: F(2, 75) = 10.05, p < .OOl). The effect of visual field was not significant in the common object or alphanumeric categories. However, for the nonverbal categories there was a main effect of visual field, F(1, 75) = 8.29, p < .Ol, with greater accuracy in the RVF. Distortion level did not interact with visual field in any of the categories, all Fs < 1. Reaction time. Average reaction times are given in Table 3. Responses TABLE 3 MEAN REACTION TIMES (IN msec) FOR THE ALPHANUMERIC VERBAL, COMMON OBJECT VERBAL, AND NONVERBAL CATEGORIES BY VISUAL FIELD FOR EXPERIMENT 1”
Alphanumeric verbal
Common object verbal
Nonverbal
Distortion level
LVF
RVF
LVF
RVF
LVF
RVF
0 (prototype) 1
653’ 650 (131) 679 (159) 689
624
833 711
731 706
744 694
(E, 668 (119) 678 (154)
(132)
(12%
730 (135) 758 (107)
738 (155) 752
758 699 (159) 733
2 3
(168)
(163
a Standard deviations are given in parentheses. b Standard deviations are not given for the prototypes since there points for the prototype level per category in each visual field.
(162) 718
mw
(176)
732 (175)
(166)
were
only
715
two
data
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varied reliably with distortion level in each category type (common object verbal: F(2, 75) = 7.05, p < .Ol; alphanumeric verbal: F(2, 75) = 6.22, p < .Ol; nonverbal: F(2, 75) = 4.41, p < .02. The effect of visual field was not significant in any of the categories nor did visual field interact with distortion level. Prototype information. The prototype was always classified more accurately than exemplars of the other distortion levels. Forty-four of 48 subjects obtained this result. However, the average reaction times for the prototypes did not always represent the fastest classification. The prototype was classified more quickly than the exemplars of the other distortion levels only in the alphanumeric verbal category condition. Thirteen subjects in the letter/number category condition had faster response latencies to the prototype than to the other distortion levels. In the remaining conditions, only 14 subjects responded faster to the prototype. DISCUSSION
The results of Experiment 1 are clear cut. Response latency and accuracy varied with distortion level. Items of high typicality were classified faster and more accurately than atypical items. This effect occurred in both visual fields with both verbal and nonverbal categories. Thus, we found no evidence of differential classification in the two cerebral hemispheres. It is strange that our subjects did not always classify the prototype with the greatest speed. This was unexpected based on previous research which found the shortest RTs to prototype patterns (Posner & Keele, 1968; Franks & Bransford, 1971; Knapp & Anderson, 1984). One possible explanation is that although the subjects were abstracting a prototype to use in further classification, the prototype that they abstracted may not have been the patterns we designated as the prototype. In creating the distortions around the prototypes, dots were moved only outward from the original pattern. Averaging dot positions over all of the distortions (Breen & Schvaneveldt, 1986) might result in a central pattern that was different from the one we had designated as the prototype. If such were the case, the experimenter’s prototype would not be expected to receive the fastest and most accurate classifications. Some unknown pattern might actually be the prototype of each category and would be expected to be easiest to classify. A second factor may have contributed to slower classification of our prototypes. For each subject the computer randomly determined which distortions they obtained in acquisition. It is possible that the center of the acquisition set was not the center of the entire category. If such were the case, subjects would have been abstracting a prototype other than our prototype. Again, classification would not be expected to be
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faster and more accurate for our prototype in that case. In support of this, Breen and Schvaneveldt (1986) found that experimenter-defined prototypes (those residing in the center of their categories) were classified less accurately than empirically derived prototypes (those in the center of acquisition categories). Experiment 2 was undertaken to correct for these confounds. In Experiment 2, prototypes were distorted by moving dots both inwards and outwards in order to ensure that the prototypes were actually in the middle of their categories. In addition, an acquisition list for each pair of categories was constructed such that its prototypes were also the prototype of each category. This acquisition list was seen by all subjects. EXPERIMENT
2
Subjects. Twenty-two subjects (IO males/l2 females) participated in Experiment 2. Subject recruitment and requirements were identical to those of Experiment 1. Stimuli. The common object and nonverbal prototype pairs of Experiment I were used to create the categories used in Experiment 2. These prototypes were distorted as in Experiment 1 except that dots were moved either two squares inward or two squares outward from the original pattern. The number of dots moved inward or outward in a quadrant was balanced in each distortion and in each level of distortion. Ten patterns at each level of distortion from each category of the pair formed the acquisition list. Again, prototypes were excluded from the acquisition stimuli. These patterns were chosen such that the number of dots moved inwards or outwards in each quadrant was equated across the acquisition list and the entire category. Thus, for each category, the acquisition prototype should be equivalent to the prototype of the entire category. Procedure. The apparatus and procedure were identical to those of Experiment 1.
RESULTS
Heterogeneity of variance continued to be problematic for the accuracy data in Experiment 2. Again transformation (both logarithmic and reciprocal) of the data failed to rectify this situation. Thus, for the accuracy data, 3 (dlev) x 2 (vf) repeated measures ANOVAS were performed separately within each category type. Because heterogeneity of variance did not exist for the reaction time data, a 2 (category type) x 3 (dlev) x 2 (vf) split plot ANOVA was performed with this response measure. As in Experiment 1, our major findings were not affected by stimulus history, response hand, or sex. Thus, the data was pooled over these variables. Accuracy. Accuracy means are given in Table 4. As in Experiment 1, there was a significant main effect of distortion level in both the verbal and nonverbal categories (common object verbal, F(2, 50) = 36.67, p < .OOl; nonverbal, F(2, 50) = 20.58, p < .OOl). There was also a main effect of visual field in both types of categories. With the common object verbal categories, accuracy was greater in the RVF, F(1, 50) = 4.26, p < .05. The LVF was more accurate than the RVF with the nonverbal
102
RICHARDS AND CHIARELLO TABLE 4 ACCURACY IN PERCENTAGE CORRECT FOR THE COMMON OBJECT VERBAL AND THE NONVERBAL CATECXXIES IN EACH VISUAL FIELD FOR EXPERIMENT 2
Common object verbal Distortion level
LVF
0 (prototype) I 2
100b 95.4 (5.1) 81.8
3
82.3
(8.5) (9.8)
Nonverbal
RVF 96.2 95.6 (4.4) 91.3 (6.3) 83.8 (6.4)
LVF 81.8 86.9 (9.6) 82.2 (9.9) 75.6
(8.5)
RVF 95.5 84.9 (11.1) 15.2 (9.3) 69.7 (9.3)
a Standard deviations are given in parentheses. b Standard deviations are not given for the prototypes since there were only two data points for the prototype level per category in each visual field.
categories, F(1, 50) = 8.74, p < .005. However, in neither category did distortion level interact with visual field. Reaction time. Average reaction times are given in Table 5. RT also varied with distortion level, F(2, 40) = 14.61, p < .OOl. The main effect of visual field did not reach significance, nor were there any significant interactions. Thus, comparable typicality effects were found for both categories in both visual fields. Prototype information. As in Experiment 1, the prototype was not always the most quickly classified pattern. Only in the LVF with the nonverbal categories was the prototype classified with the greatest speed. As in Experiment 1, the prototypes were generally classified the most accurately. Only in the LVF during classification of nonverbal category exemplars was this not the case. These results suggest that with the prototype patterns, a speed accuracy trade off was occurring. GENERAL DISCUSSION
The major results of the first experiment were replicated. Response latencies and accuracy varied with distortion level. In accordance with much of the literature on category learning and classification (Posner & Keele, 1968; Franks & Bransford, 1971; Kellogg, 1981; Neumann, 1977), items of high typicality were classified more accurately and faster than were items of low typicality. This occurred in both hemispheres for both verbal and nonverbal stimuli. Thus, contrary to Zaidel’s (1987) results, we obtained no evidence for hemisphere differences in typicality for any of our artificial categories. In order to classify items into a category correctly, each item has to
HEMISPHERIC
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EFFECTS
TABLE 5 MEAN REACTION TIMES (IN msec) FOR THE COMMON OBJECT VERBAL AND NONVERBAL CATEGORIESBY VISUAL FIELD FOR EXPERIMENT 2
Common object verbal Distortion level 0 (prototype) 1 2 3
Nonverbal
LVF
RVF
LVF
RVF
713h 677 ( 97) 706 ( 97) 758
703 680 ( 84) 711 ( 76) 749
706 712 ( 99) 736 (109) 779
786 728
(118)
(111)
(121)
(106) 761
(127) 765
(110)
u Standard deviations are given in parentheses. ’ Standard deviations are not given for the prototypes since there were only two data points for the prototype level per category in each visual field.
be compared to the representation of the category in memory. There are two general ways to do this. One is to store individual members of the category in memory and to compare each new item to those stored items (Smith & Medin, 1981). If the new item closely matches one of the stored items, it is classified into the category. If such a strategy is used, previously encountered items should be classified more accurately and faster than new items. The current results demonstrating that the old exemplars were not classified more easily than the new exemplars argue against the use of an exemplar matching strategy to classify items in this study. Another way to classify items is to compare each item to the prototype of the category. According to this account of classification, when learning a category, subjects do not store each individual member of the category but instead average these members to abstract a prototype, or best example of the category (Evans, 1967). Each item to be classified is compared to this prototype. The more similar the item is to the prototype, the faster and more accurately it is classified as a member of the category. Those items that are very different from the prototype are classified as nonmembers. It is suggested that this is how the subjects classified the dot patterns in the current experiment. Since typicality effects were obtained in both visual fields, it appears that both hemispheres are capable of and do use similarity-to-prototype matching under some conditions. The absence of conclusive hemisphere differences in classification was unexpected. In Experiment 1, an overall visual field difference emerged only in accuracy with the nonverbal categories. This RVF/LH advantage contrasted with our prediction of a LVF/RH advantage for our visual-
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spatial stimuli. In Experiment 2, a visual field difference was obtained for both the verbal and nonverbal categories. Contrary to Experiment 1, there was a LVF/RH advantage for the nonverbal categories in Experiment 2. For the verbal categories, a RVF/LH advantage emerged. It is possible that these differences in hemisphere effects resulted from the different methods of stimulus construction used in the two experiments. We also found no evidence for hemisphere differences in typicality effects. In addition, we obtained these typicality effects for both verbal and nonverbal categories. This result was replicated over the two experiments. In the current study, then, both hemispheres appeared to perform the classification task in the same manner with both verbal and nonverbal categories. Each appeared to be classifying on the basis of a similarity-to-prototype matching strategy. Why did we obtain no hemisphere differences in typicality effects while Zaidel (1987) did? One difference between Zaidel’s (1987) stimuli and ours is that our stimuli were constructed to preclude the existence of a defining feature differentiating the categories. Subjects could have used a logical (i.e., defining feature) strategy to classify Zaidel’s (1987) stimuli, but they could not have done so with our stimuli. Thus, our results indicate that the presence of a verbal label for the prototype of the category is not sufficient to allow classification to proceed via a logical process in the LH. Our results further suggest that similarity-to-prototype matching may be the default classification process used in each hemisphere when no defining features differentiate categories. A second difference between our study and Zaidel’s is the type of tasks used. Uricuioli et al. (1981) found that the type of categorization task determined whether or not hemisphere differences were found. Using word stimuli in a category membership task (e.g., “Is stimulus 2 a member of category l?“), no hemisphere differences were detected. However, a category matching task (e.g., “Are the stimuli members of the same category?“), again using word stimuli, did demonstrate a RVF/LH hemisphere advantage. Zaidel’s (1987) paradigm was a category matching task while the paradigm used in the current study was more similar to the category membership task. She found hemisphere differences in typicality; we did not. This continues to suggest that the use of typicality information by the two hemispheres is strategic and depends upon task and stimulus parameters. Our results clearly show, as suggested by clinical data, that both hemispheres have access to typicality information. Previous research has indicated that prototypes are classified more easily than other members of the category (Posner & Keele, 1968; Franks & Bransford, 1971; Knapp & Anderson, 1984). Similarly, accuracy was generally highest for our prototypes. It remains strange, then, that in
HEMISPHERIC
TYPICALITY
EFFECTS
10.5
both experiments our subjects did not always classify the prototype with the greatest speed. However, because of our reliable and robust findings of decreasing accuracy and increasing RT over distortion levels 1, 2, and 3, we are confident in explaining our results in terms of typicality effects. In summary, we found no difference in hemisphere processing of the typicality structure of our artificial categories. Both hemispheres appeared to utilize a similarity-to-prototype matching process to classify. This implies that a simple dichotomy of hemispheric classification strategies, as suggested by Zaidel (1987), is not sufficient to describe classification in the two cerebral hemispheres. Both hemispheres are capable of acquiring and accessing typicality information. However, since task and stimulus parameters determine the processing strategies used in classification, typicality effects may or may not be obtained. The results of this study further indicate that merely having a verbal label does not lead to differential hemisphere storage or processing of categorical information. REFERENCES Anderson, J., & Bower. G. 1973. Human associative memory. Washington. DC: Hemisphere Press. Baddeley, A., & Hitch, G. 1974. Working memory. The fyschology of Learning and Motivation, 8, 47-89. Breen, T., & Schvaneveldt, R. 1986. Classification of empirically derived prototypes as a function of category experience. Memory and Cognition, 14, 313-320. Bryden, M. 1982. Luterality: Ftmctional asymmetr.y in the intact bruin. New York: Academic Press. Das Smaal, E., & De Swart, J. 1986. Effects of contrasting category, conjoint frequency and typicality on categorization. Acta Psychologicu, 12, 15-40. Evans, S. 1967. A brief statement of schema theory. Psychonomic Science, 8, 87-88. Franks, J.. & Bransford, J. 1971. Abstraction of visual patterns. Journal of Experimental Psychology, 90, 65-74. Grober, E.. Perecman, E.. Kellar, L, & Brown, J. 1980. Lexical knowledge in anterior and posterior aphasics. Bruin and Lunguage, 10, 318-330. Grossman, M. 1978. The game of the name: An examination of linguistic reference after brain damage. Brain and Longuage,6, 112-I 19. Grossman, M. 1981. A bird is a bird is a bird: Making reference within and without superordinate categories. Bruin und Language, 12, 313-331. Kellogg, R. 1981. Feature frequency in concept learning: What is counted? Memory und Cog&ion, 9, 1.57-163. Kirk, R. (1982). Experimental design. Belmont: Brooks/Cole Publishing Company. Knapp, A., & Anderson, J. 1984. Theory of categorization based on distributed memory. Journul of Experimental Psychology: Learning, Memory, and Cognition, 10, 616-637. Koemeda-Lutz, M., Cohen, R., & Meier, E. 1987. Organization of and access to semantic memory in aphasia. Bruin and Language, 20, 321-337. Kosslyn, S., Pinker, S., Smith, G., & Shwartz, S. 1979. On the demystification of mental imagery. The Behuvioral and Brain Sciences, 2, 535-581. Levy, J., Trevarthen, C., & Sperry, R. 1972. Perception of bilateral chimeric figures following hemispheric disconnection. Brain, 95, 61-78.
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Neumann, P. 1977. Visual prototype formation with discontinuous representation of dimensions of variability. Memory and Cognition, 5, 187-197. Posner, M., & Keele, S. 1968. On the genesis of abstract ideas. Journal of Experimental Psychology,
77, 353-363.
Rosch, E. 1975. Cognitive representations of semantic categories. Journal of Experimental Psychology,
104, 192-233.
Rosch, E., & Mervis, C. 1975. Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573-605. Rosch, E., Simpson, C., & Miller, R. 1976. Structural bases of typicality effects. Journal of Experimental Psychology: Human Perception and Performance, 2, 491-502. Smith, E. & Medin, D. 1981. Categories and Concepts. Cambridge, MA: Harvard Univ. Press. Springer, S., & Deutsch, G. 1985. Left brain, right brain. New York: Freeman. Tulving, E. 1972. Episodic and semantic memory. In Tulving & Donaldson (Eds.), Organization of memory. New York: Academic Press. Uricuioli, P., Klein, R., & Day, J. 1981. Hemispheric differences in semantic processing: Category matching is not the same as category membership. Perception and Psychophysics, 29, 343-35 1. Wayland, S., & Taplin, J. 1985a. Feature-processing deficits following brain injury I. Brain and Cognition,
4, 338-355.
Wayland, S., & Taplin, J. 1985b. Feature-processing deficits following brain injury II. Brain and Cognition,
4,356-376.
Zaidel, D. 1987. Hemispheric asymmetry in long-term semantic relationships. Cognitive Neuropsychology, 4, 321-332.
AND
LANGUAGE
39, %-lo6 (19%))
Typicality Effects in Artificial Categories: Is There a Hemisphere Difference? LORIE
G. RICHARDS AND CHRISTINE CHIARELLO Syracuse
University
In category classification tasks, typicality effects are usually found: accuracy and reaction time depend upon distance from a prototype. In this study, subjects learned either verbal or nonverbal dot pattern categories, followed by a lateralized classification task. Comparable typicality effects were found in both reaction time and accuracy across visual fields for both verbal and nonverbal categories. Both hemispheres appeared to use a similarity-to-prototype matching strategy in classification. This indicates that merely having a verbal label does not differentiate classification in the two hemispheres. Q 1990 Academic press, IIIC.
Human perception, learning, and memory are influenced by the manner in which we organize our knowledge of the world. Hemisphere specialization may play an important part in such organization (Zaidel, 1987). Rosch and Mervis (1975) have suggested that the framework of this knowledge structure consists of categories or concepts having internal structure. As each hemisphere has been shown to deal with various types of stimuli differently, it is possible that hemispheric specialization affects the learning and storage of these categories. Smith and Medin (1981) discuss several types of category organization. One type of structure is family resemblance (Rosch & Mervis, 1975). With such organization each category consists of items whose functional and perceptual attributes overlap with the other members of the category. There are no critical attributes required for membership. Extent of membership varies from the prototype through nonmembers who share more attributes with contrasting categories than they share with members of the current category (Rosch & Mervis, 1975). A second type of category structure also posits a prototype. With these categories, the prototype This research was supported by NIMH Grant MH43868 to C.C. We thank Paul Gelling for hardware and software support. Reprint requests should be addressed to Lorie Richards, Department of Psychology, Syracuse University, Syracuse NY i3244-2340. 90 0093-934x&o $3.00 Copyright All rights
0 I990 by Academic Press, Inc. of reproduction in any form reserved.
HEMISPHERIC
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is the center of the category and the category is ordered in terms of physical distance from the prototype in multidimensional space. A third type of category has definitive membership criteria. All of the above structures result in typicality effects (typical exemplars are responded to faster and more accurately than atypical exemplars) in performance on a variety of tasks using both word and picture stimuli, including classification (Posner & Keele, 1968; Rosch, 1975; Rosch & Mervis, 1975; Rosch, Simpson, & Miller, 1976), recognition (Franks & Bransford, 1971), cued recall (Rosch et al., 1976), category matching (KoemedaLutz, Cohen, & Meier, 1987), generation of category members (Grossman, 1981), and typicality ratings (Rosch, 1975; Rosch & Mervis, 1975; Rosch et al., 1976). Few studies have investigated category performance in the two cerebral hemispheres. With normal subjects, Zaidel (1987) employed a category matching task where subjects heard the name of the category followed by a picture of an exemplar of the category in central vision. Subjects then saw another picture lateralized to the right visual field (RVF) or left visual field (LVF) and manually indicated whether or not both items were members of the spoken category. She found that the performance in the RVF/left hemisphere (LH) did not demonstrate typicality effects. The LVF/right hemisphere (RH) did show such effects, with faster reaction times for the items of high typicality. Zaidel (1987) suggested that in the LH classification is accomplished using a logical/definitional process (one relying on defining features) while in the RH classification proceeds via similarity-to-prototype matching. With highly typical items, the RH’s matching strategy is fast while the LH’s logical strategy is slow. With atypical items, however, the RH’s similarity-to-prototype matching is inefficient and leads to very slow classification. The LH’s logical strategy does not differentiate by typicality and classifies both types of exemplars at the same rate. Classification of atypical items in the RH is much slower than in the LH. In this way, typicality structure is evidenced in the RH, but not the LH. While possible, Zaidel’s (1987) explanation seems too simplistic. If such a dichotomy existed, it would imply that accurate classification could not occur in the LH when defining features were absent. Correspondingly, when classifying categories whose membership is delineated only by a defining feature, the right hemisphere would not be able to classify accurately. It remains possible that Zaidel’s (1987) findings result from task and stimulus-dependent processes. Other classification procedures may be available to the cerebral hemispheres in other situations. The literature on unilateral brain injury may offer some evidence regarding this hypothesis. Research in this field indicates that LH-damaged individuals demonstrate typicality effects. Grober, Perecman, Kellar, and Brown’s (1980) LH-damaged patients, like normal subjects, obtained
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typicality effects when classifying exemplars of natural categories (Rosch & Mervis, 1975) in both picture and word form. Wayland and Taplin (1985a,b) also found that LH-damaged patients demonstrated typicality effects in classifying categories of schematic faces. Further analysis, however, revealed that these patients, unlike normal controls, were classifying items on the basis of one salient feature. On items where this feature was equivalent, no typicality effect was demonstrated by the LHdamaged subjects. This type of classification strategy is similar to classifying by defining features, which according to Zaidel(l987) should only occur in the LH. Thus, this finding may provide counterevidence to Zaidel’s claim. Grossman (1978, 1981) likewise found that LH-damaged subjects demonstrated a typicality effect when generating exemplars of a category. However, their performance was not identical to that of normal subjects. Anterior lesions resulted in generation of exemplars heavily clustered in the highly typical region of possible exemplars, while posterior lesions led to many out of category generations. Normal subjects generated no out of category exemplars, and, although they generated a large number of exemplars in the high typicality range, their generations were spread more evenly throughout the entire typicality range of the category. RHdamaged subjects in Grossman’s study (1981) generated exemplars in a manner similar to that used by normal controls. This again counters Zaidel’s (1987) hypothesis. She claims that typicality information is only available to the RH. If true, it would be unlikely to obtain normal typicality effects with RH damage. Consequently, both Grossman (1981) and Wayland and Taplin (1985a,b) provide evidence counter to Zaidel’s (1987) hypothesis about classification in the two hemispheres. Based on the studies above, it is unclear just how typicality is represented in the two cerebral hemispheres. Zaidel (1987) suggests that typicality effects occur only in the RH by use of a similarity-to-prototype matching procedure. She further suggests that the LH does not have access to typicality information and relies on a logical process (defining features) to classify. As no other study has directly compared classification in the two hemispheres and there are conflicting data in the braindamaged literature, this hypothesis requires further testing. In addition, little is known about the variables affecting these hemisphere classification processes. Zaidel’s (1987) categories were learned informally and had existed in long term memory for an extended period of time. Hence, the role of typicality information in the acquisition of categories in the hemispheres is not known. In addition, the categories Zaidel(1987) used were linguistic in the sense that, although her stimuli were pictures, the category itself and each exemplar of the category had a unique verbal label. Since verbal ability is one variable known to differentiate the hemispheres, the hemispheres may use different classification procedures
HEMISPHERIC
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93
for verbal and nonverbal items. Task parameters may also alter processing strategies in the cerebral hemispheres (Uricuioli, Klein, & Day, 1981). Zaidel’s (1987) task allowed category access prior to presentation of the targets and then required matching two targets to that category. It is unknown whether changing the classification task would modify hemisphere classification strategies. The study reported here investigated the effect of stimulus parameters on category acquisition and classification in the two cerebral hemispheres. Subjects learned to classify members of two centrally presented artificial categories. Following this acquisition phase, new and old exemplars of these categories were presented to the RVF or LVF for classification. Artificial categories of dot patterns served as stimuli since both verbal and nonverbal categories could be created that were equivalent in terms of levels of typicality, length of time in memory, and the amount of abstract information available. In addition, Posner and Keele (1968) demonstrated typicality effects on a similar task using dot pattern categories presented in central vision. The primary variable of interest was the verbal versus nonverbal nature of the category. Verbal categories were defined as those for which subjects should have a preexisting verbal label for the prototype. Two types of categories served as the verbal stimuli: alphanumeric and common object. Alphanumeric categories were those whose prototypes were letters or numbers. Letters and numbers are overlearned stimuli and people experience them in a myriad of physical distortions, from typed characters to handwriting. Because of this, we were concerned that these categories would be easier to classify than the nonverbal categories. Thus, common object verbal categories, where the prototype was a common object, were also used. These objects are not experienced in the myriad of forms as are letters and numbers. As such, they should be more akin to the nonverbal categories. Nonverbal categories were those whose prototypes had no preexisting verbal label. Because of the visual-spatial nature of the stimuli, an overall LVF/RH advantage was expected. However, our critical predictions concerned differences in typicality effects across hemispheres. Since the experimental categories were constructed in terms of distance from a prototype, the existence of one defining feature distinguishing one category from another was precluded, especially for the atypical exemplars. Consequently, it would be difficult for classification to occur efficiently using a logical process. Our manipulation of category type was an attempt to determine if merely having a verbal label was sufficient to allow the LH to use a logical process in classification. Our predictions were as follows. If classification in the LH can be accomplished using a logical process when verbal labels are present, the verbal patterns presented to the RVF/LH should be classified without typicality effects. It is unclear
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RICHARDS AND CHIARELLO
exactly what to expect from the LH during the classification of the nonverbal categories. If the logical process is the only one available to the LH, it should not be able to accurately classify nonverbal patterns. However, it has not been demonstrated that this process is the only one available to the LH. Typicality effects for nonverbal patterns would be expected in the RVF/LH if similarity-to-prototype matching is used as a default option when verbal labels are unavailable. However, if Zaidel (1987) is correct that classification in the RH relies on similarity-toprototype matching, typicality effects will be demonstrated in the LVF/RH with both verbal and nonverbal categories. EXPERIMENT
1
Methods Design. A split-plot design was used to vary type of category (alphanumeric verbal, common object verbal, or nonverbal) and response hand between subjects. Visual field of presentation and typicality level varied within subjects. Three levels of typicality were defined in terms of distance from the prototype. Dependent measures were percentage correct and median reaction time for correct responses. Subjects. Forty-eight right-handed, native speakers of English (24 female and 24 male) participated. They were undergraduates in an introductory psychology class receiving course credit for participating. All had normal or corrected to normal vision. For each subject, handedness was assessed by a five-item handedness questionnaire based on hand use in common activities (Bryden, 1982). Handedness scores ranged from + .3 to + 1.0 with a mean of + .75. (Positive values indicate right hand preference.) None had a history of familial sinistrality. Stimuli. Prior to selecting the stimuli for the experimental categories, a verbalization rating task was undertaken by 18 naive subjects. Dot patterns were presented in the center of a computer graphics screen and subjects were required to name them as quickly as possible. Naming latencies were used to measure ease of verbal label retrieval. A similarity rating task was also performed on pairs of dot patterns used in the verbalization rating task. Twenty-two new subjects were presented with all possible pairs of dot patterns (verbal patterns were paired only with other verbal patterns and nonverbal patterns were paired only with other nonverbal patterns) and were asked to rate their similarity on a scale ranging from one (1) (completely different) to seven (7) (identical). Subjects for these tasks did not participate in the main experiments. Based on the results of these tasks, 12 dot patterns were chosen to represent the prototypes of the experimental categories. Verbal prototypes were named in under 1700 msec while nonverbal prototypes had naming latencies of over 4000 msec. The 12prototypes were paired such that their similarity to each other was balanced across pairs. The paired prototypes can be seen in Fig. 1, and their mean similarity scores and naming latencies are given in Table 1. Each prototype was composed of fifteen dots arranged within a rectangular (length greater than width) matrix whose dimensions were 13 x 19 squares. (Ten-squares-to-the-inch graph paper was used to construct the prototypes.) From each of the common object and nonverbal prototypes, a category of dot patterns was created as follows. Each prototype was distorted so as to create three levels of distortions (and, thus, three levels of typicality). Level 1 distortions were created by moving two dots, level 2 by moving four dots, and level 3 by moving six dots. A pilot experiment had indicated that alphanumeric dot patterns created as above were much easier to classify than were the nonverbal categories. Thus, to avoid ceiling effects for the alphanumeric categories, these patterns were distorted to a greater degree. Distortion levels 1, 2, and 3
HEMISPHERIC Nonverbal
TYPICALITY
Prolotyp~
. .. . .
. *.. - . ... . *.. - .
:. . . . .
Alphanumeric
Verbal
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. . . . . . * . . . . . . .
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1. Nonverbal, alphanumeric verbal, and common object verbal prototypes.
TABLE SIMILARITY
Pairs
.
. .
pllr5
FIG.
95
EFFECTS
RATINGS AND NAMING
1
LATENCIES
(IN
msec)
FOR THE PROTOTYPE PAIRS
Similarity Ratings
Verbalization RT
Nonverbal Pair 1
3.58
Pair 2
4.00
Pair 3
3.58
Pair 4
4.00
Proto Proto Proto Proto
a b c d
4143 4467 4677 4302
Proto Proto Proto Proto
e f g h
653 851 785 739
Proto Proto Proto Proto
i j k 1
1471 1607 1218 1467
Alphanumeric Verbal
Common Object Verbal Pair 5
4.21
Pair 6
3.71
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RICHARDS AND CHIARELLO
were created from the alphanumeric prototypes by moving three, five, or seven dots, respectively. This was done in an attempt to equate classification ease between the three types of categories. Twenty unique distortions were created at each level, as described below. Each prototype pattern was divided into four quadrants. For each distortion, the proportion of dots moved in that quadrant was approximately equal. All dots were moved outward two squares from their original position in the prototype. All of the distortions fit into a rectangular (length greater than width) matrix whose dimensions were I7 x 23 squares. Examples of prototypes and their distortions at each level for the common object categories can be seen in Fig. 2. Examples of distortions of alphanumeric prototypes are seen in Fig. 3. Apparatus. The experiment took place in a sound-attenuated room. Subjects sat I75 cm in front of a Hewlett-Packard l310B vector graphics display equipped with a fast decay phosphor. Stimulus presentation and timing was controlled by an LSI 11/23 computer. The computer also recorded subjects’ responses which were made via a response key held in either the right or left hand. Response hand was counterbalanced over subjects. Head position was maintained by the use of a modified Tektronix viewing hood.
Procedure Acquisition
task. For each subject, stimuli consisted of one pair of categories of a similar
type (either the common object verbal, the alphanumeric verbal, or the nonverbal). For the acquisition task, IO distortions from each level of each category (60 patterns in all) were randomly selected by the computer for each subject and were presented in central vision. Patterns subtended a visual angle of 1.5” in height and 1.3” in width. Prototypes were not presented in the acquisition task. Subjects were instructed that in this part of the experiment they were to learn the categories into which they would later classify patterns. They were told they would see a pattern appear in the middle of the screen, and that this pattern would be a member of the category that corresponded to the button under their index fingers. Subjects were to look at the pattern as long as they wished and to push the button under their index fingers when they were ready to see the next pattern of that category. Patterns belonging to the
.
. . . . . . . . ..
.
. . . . .
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.
.
.
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. . . . . . . . .
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.
. .
Level
.
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FIG. 2. Examples of a prototype and distortions for the common object verbal categories.
HEMISPHERIC
TYPICALITY
. . . .
.
. . .
. mm
.
.
Level
.
.
.
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.
.
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.
.
.
.
.
.
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. .
Dlstortlon
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97
. . . . . . . . .
.
Prototylx
.
.
.
. . . . . . . . . .
EFFECTS
.
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Level
3
Examples of a prototype and distortions for the alphanumeric verbal categories.
first category appeared on the screen in this manner until all 30 patterns of that category had appeared. Subjects were then told that they would now see the patterns belonging to the category corresponding to the button under their middle fingers. The 30 patterns of this category appeared on the screen as they had for the first category. When all 60 patterns from the two categories had been seen, subjects viewed the same patterns randomly mixed and were instructed to classify them into the categories they had just learned. Responses were made with the middle and index fingers by pushing one of the two buttons corresponding to the two categories. At the beginning of each trial, a fixation cross appeared in the center of the screen accompanied by a 50-msec 3.75kHz tone. After 500 msec, the fixation cross disappeared and a dot pattern was centrally presented for 50 msec. Auditory feedback in the form of a 500-msec 3.75-kHz tone signaled an incorrect response. Intertrial interval was 5 sec. The stimuli were randomly divided into two blocks of 30 trials each, and were presented with the constraint that no more than three members of one category were shown consecutively. The blocks were repeated until criterion within a block of 75% correct had been reached. Accuracy feedback was provided to each subject after each block. Transfer task. Following a short break, each subject proceeded to the transfer task. In this task, all of the 122 dot patterns from the two experimental categories were presented. The stimuli consisted of the 60 old patterns (those used in the acquisition task: IO from each distortion level in each category), 60 new patterns (IO from each distortion level in each category), and the 2 prototypes. Stimuli were presented once in each visual field resulting in a total of 244 trials (presented in four blocks of 61 trials each). Stimuli were presented pseudo-randomly with the constraints that no more than three patterns from the same category were shown in succession and no more than three patterns were presented to the same visual field successively. At the start of each trial, a SO-msec3.75-kHz warning tone was presented accompanying the fixation cross which was shown in the center of the screen. Subjects were instructed to keep their gaze focused on the cross when it was on the screen. After 500 msec elapsed, a dot pattern was presented for 50 msec to either the right or left visual field. Patterns were presented 2.3” from central fixation. The fixation cross remained on the screen for 200 msec following the offset of the dot pattern.
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Subjects were told that this part of the experiment explored how well they could classify dot patterns they were not directly looking at. They were instructed to make their classification response as before.
RESULTS
Acquisition tusk. Differences in the amount of time needed to learn the categories emerged among the category types. All 16 subjects who were presented with the letter/number verbal categories learned the categories to criterion within one acquisition block. Of those subjects receiving the nonverbal categories, 2 required two acquisition blocks while a third required three acquisition blocks. Five of the 16 subjects receiving the common object verbal categories required more than one acquisition block. Two of these subjects learned the categories to criterion in two blocks while 3 required three blocks. Transfer tusk. Preliminary analyses indicated that neither sex nor stimulus history (whether the stimulus was a new pattern or had been seen in the acquisition task) produced significant effects or interactions. Preliminary analyses also indicated that the only significant effect of response hand was that subjects responded faster with their right hand than their left hand in the common object verbal categories, F(1, 14) = 4.25, p < .06. Therefore, for the remaining analyses, the data was pooled over response hand, sex, and stimulus history. Levene’s test for homogeneity of variance indicated that heterogeneity of variance existed in both response measures. Reaction time data was transformed using logarithmic transformations and accuracy data using reciprocal transformations (Kirk, 1982). These transformations did not produce homogeneity for either response measure. Therefore, separate analyses were run in each of the three category type conditions, since homogeneity of variance only has relevance for between-subjects factors and the only between-subjects factor in this study was category type. Our primary predictions concern the presence or absence of typicality effects within each category type rather than a direct comparison among these categories. Thus, we were able to test our predictions despite the lack of homogeneity of variance among the data for the different categories. 2(visual field) x 3(distortion level) repeated measures ANOVAs were performed separately for each category type and each response measure. Since subjects were presented with each prototype only once per visual field, the data for this level of distortion were based on only 2 scores per subject. The analysis for the other levels were based on 40 scores per visual field. (Each distortion level consisted of 20 patterns in each category of the presented pair. Analyses were collapsed across these pairs.) Thus, interpreting the prototype scores in relation to the scores for the other levels is problematic. Because of this, the ANOVA was performed on distortion levels one through three. However, mean values for the prototypes can be found in Tables 2 and 3.
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TABLE 2 ACCURACY IN PERCENTAGE CORRECT FOR THE ALPHANUMERIC VERBAL, COMMON OBJECT VERBAL, AND NONVERBAL CATEGORIES IN EACH VISUAL FIELD FOR EXPERIMENT I”
Common object verbal
Alphanumeric verbal
Nonverbal
Distortion level
LVF
RVF
LVF
RVF
LVF
RVF
0 (prototype) I
lo@ 97.5 (3.0) 96.9
100 97.5 (2.7) 97.6 (4.0) 96.5 (4.3)
100 92.7
96.9 92.8 (7.1) 91.1 (7.9) 84.8 (10.5)
93.8 90.7 (7.1) 87.1 7.9 83.0 (12.2)
100 93.1 (6.9) 89.9
2
(3.6) 3
95.1 (4.9)
(6.0) 90.6 (7.4) 86.6
(8.9)
(6.6) 88.0 (10.5)
” Standard deviations are given in parentheses. ’ Standard deviations are not given for the prototypes since there were only two data points for the prototype level per category in each visual field.
Accuracy. Accuracy means are given in Table 2. A significant main effect of distortion level was present for all three category types (common object verbal: F(2, 75) = 14.39, p < .OOl; alphanumeric verbal: F(2, 75) = 3.93, p < .03; nonverbal: F(2, 75) = 10.05, p < .OOl). The effect of visual field was not significant in the common object or alphanumeric categories. However, for the nonverbal categories there was a main effect of visual field, F(1, 75) = 8.29, p < .Ol, with greater accuracy in the RVF. Distortion level did not interact with visual field in any of the categories, all Fs < 1. Reaction time. Average reaction times are given in Table 3. Responses TABLE 3 MEAN REACTION TIMES (IN msec) FOR THE ALPHANUMERIC VERBAL, COMMON OBJECT VERBAL, AND NONVERBAL CATEGORIES BY VISUAL FIELD FOR EXPERIMENT 1”
Alphanumeric verbal
Common object verbal
Nonverbal
Distortion level
LVF
RVF
LVF
RVF
LVF
RVF
0 (prototype) 1
653’ 650 (131) 679 (159) 689
624
833 711
731 706
744 694
(E, 668 (119) 678 (154)
(132)
(12%
730 (135) 758 (107)
738 (155) 752
758 699 (159) 733
2 3
(168)
(163
a Standard deviations are given in parentheses. b Standard deviations are not given for the prototypes since there points for the prototype level per category in each visual field.
(162) 718
mw
(176)
732 (175)
(166)
were
only
715
two
data
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varied reliably with distortion level in each category type (common object verbal: F(2, 75) = 7.05, p < .Ol; alphanumeric verbal: F(2, 75) = 6.22, p < .Ol; nonverbal: F(2, 75) = 4.41, p < .02. The effect of visual field was not significant in any of the categories nor did visual field interact with distortion level. Prototype information. The prototype was always classified more accurately than exemplars of the other distortion levels. Forty-four of 48 subjects obtained this result. However, the average reaction times for the prototypes did not always represent the fastest classification. The prototype was classified more quickly than the exemplars of the other distortion levels only in the alphanumeric verbal category condition. Thirteen subjects in the letter/number category condition had faster response latencies to the prototype than to the other distortion levels. In the remaining conditions, only 14 subjects responded faster to the prototype. DISCUSSION
The results of Experiment 1 are clear cut. Response latency and accuracy varied with distortion level. Items of high typicality were classified faster and more accurately than atypical items. This effect occurred in both visual fields with both verbal and nonverbal categories. Thus, we found no evidence of differential classification in the two cerebral hemispheres. It is strange that our subjects did not always classify the prototype with the greatest speed. This was unexpected based on previous research which found the shortest RTs to prototype patterns (Posner & Keele, 1968; Franks & Bransford, 1971; Knapp & Anderson, 1984). One possible explanation is that although the subjects were abstracting a prototype to use in further classification, the prototype that they abstracted may not have been the patterns we designated as the prototype. In creating the distortions around the prototypes, dots were moved only outward from the original pattern. Averaging dot positions over all of the distortions (Breen & Schvaneveldt, 1986) might result in a central pattern that was different from the one we had designated as the prototype. If such were the case, the experimenter’s prototype would not be expected to receive the fastest and most accurate classifications. Some unknown pattern might actually be the prototype of each category and would be expected to be easiest to classify. A second factor may have contributed to slower classification of our prototypes. For each subject the computer randomly determined which distortions they obtained in acquisition. It is possible that the center of the acquisition set was not the center of the entire category. If such were the case, subjects would have been abstracting a prototype other than our prototype. Again, classification would not be expected to be
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faster and more accurate for our prototype in that case. In support of this, Breen and Schvaneveldt (1986) found that experimenter-defined prototypes (those residing in the center of their categories) were classified less accurately than empirically derived prototypes (those in the center of acquisition categories). Experiment 2 was undertaken to correct for these confounds. In Experiment 2, prototypes were distorted by moving dots both inwards and outwards in order to ensure that the prototypes were actually in the middle of their categories. In addition, an acquisition list for each pair of categories was constructed such that its prototypes were also the prototype of each category. This acquisition list was seen by all subjects. EXPERIMENT
2
Subjects. Twenty-two subjects (IO males/l2 females) participated in Experiment 2. Subject recruitment and requirements were identical to those of Experiment 1. Stimuli. The common object and nonverbal prototype pairs of Experiment I were used to create the categories used in Experiment 2. These prototypes were distorted as in Experiment 1 except that dots were moved either two squares inward or two squares outward from the original pattern. The number of dots moved inward or outward in a quadrant was balanced in each distortion and in each level of distortion. Ten patterns at each level of distortion from each category of the pair formed the acquisition list. Again, prototypes were excluded from the acquisition stimuli. These patterns were chosen such that the number of dots moved inwards or outwards in each quadrant was equated across the acquisition list and the entire category. Thus, for each category, the acquisition prototype should be equivalent to the prototype of the entire category. Procedure. The apparatus and procedure were identical to those of Experiment 1.
RESULTS
Heterogeneity of variance continued to be problematic for the accuracy data in Experiment 2. Again transformation (both logarithmic and reciprocal) of the data failed to rectify this situation. Thus, for the accuracy data, 3 (dlev) x 2 (vf) repeated measures ANOVAS were performed separately within each category type. Because heterogeneity of variance did not exist for the reaction time data, a 2 (category type) x 3 (dlev) x 2 (vf) split plot ANOVA was performed with this response measure. As in Experiment 1, our major findings were not affected by stimulus history, response hand, or sex. Thus, the data was pooled over these variables. Accuracy. Accuracy means are given in Table 4. As in Experiment 1, there was a significant main effect of distortion level in both the verbal and nonverbal categories (common object verbal, F(2, 50) = 36.67, p < .OOl; nonverbal, F(2, 50) = 20.58, p < .OOl). There was also a main effect of visual field in both types of categories. With the common object verbal categories, accuracy was greater in the RVF, F(1, 50) = 4.26, p < .05. The LVF was more accurate than the RVF with the nonverbal
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RICHARDS AND CHIARELLO TABLE 4 ACCURACY IN PERCENTAGE CORRECT FOR THE COMMON OBJECT VERBAL AND THE NONVERBAL CATECXXIES IN EACH VISUAL FIELD FOR EXPERIMENT 2
Common object verbal Distortion level
LVF
0 (prototype) I 2
100b 95.4 (5.1) 81.8
3
82.3
(8.5) (9.8)
Nonverbal
RVF 96.2 95.6 (4.4) 91.3 (6.3) 83.8 (6.4)
LVF 81.8 86.9 (9.6) 82.2 (9.9) 75.6
(8.5)
RVF 95.5 84.9 (11.1) 15.2 (9.3) 69.7 (9.3)
a Standard deviations are given in parentheses. b Standard deviations are not given for the prototypes since there were only two data points for the prototype level per category in each visual field.
categories, F(1, 50) = 8.74, p < .005. However, in neither category did distortion level interact with visual field. Reaction time. Average reaction times are given in Table 5. RT also varied with distortion level, F(2, 40) = 14.61, p < .OOl. The main effect of visual field did not reach significance, nor were there any significant interactions. Thus, comparable typicality effects were found for both categories in both visual fields. Prototype information. As in Experiment 1, the prototype was not always the most quickly classified pattern. Only in the LVF with the nonverbal categories was the prototype classified with the greatest speed. As in Experiment 1, the prototypes were generally classified the most accurately. Only in the LVF during classification of nonverbal category exemplars was this not the case. These results suggest that with the prototype patterns, a speed accuracy trade off was occurring. GENERAL DISCUSSION
The major results of the first experiment were replicated. Response latencies and accuracy varied with distortion level. In accordance with much of the literature on category learning and classification (Posner & Keele, 1968; Franks & Bransford, 1971; Kellogg, 1981; Neumann, 1977), items of high typicality were classified more accurately and faster than were items of low typicality. This occurred in both hemispheres for both verbal and nonverbal stimuli. Thus, contrary to Zaidel’s (1987) results, we obtained no evidence for hemisphere differences in typicality for any of our artificial categories. In order to classify items into a category correctly, each item has to
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TABLE 5 MEAN REACTION TIMES (IN msec) FOR THE COMMON OBJECT VERBAL AND NONVERBAL CATEGORIESBY VISUAL FIELD FOR EXPERIMENT 2
Common object verbal Distortion level 0 (prototype) 1 2 3
Nonverbal
LVF
RVF
LVF
RVF
713h 677 ( 97) 706 ( 97) 758
703 680 ( 84) 711 ( 76) 749
706 712 ( 99) 736 (109) 779
786 728
(118)
(111)
(121)
(106) 761
(127) 765
(110)
u Standard deviations are given in parentheses. ’ Standard deviations are not given for the prototypes since there were only two data points for the prototype level per category in each visual field.
be compared to the representation of the category in memory. There are two general ways to do this. One is to store individual members of the category in memory and to compare each new item to those stored items (Smith & Medin, 1981). If the new item closely matches one of the stored items, it is classified into the category. If such a strategy is used, previously encountered items should be classified more accurately and faster than new items. The current results demonstrating that the old exemplars were not classified more easily than the new exemplars argue against the use of an exemplar matching strategy to classify items in this study. Another way to classify items is to compare each item to the prototype of the category. According to this account of classification, when learning a category, subjects do not store each individual member of the category but instead average these members to abstract a prototype, or best example of the category (Evans, 1967). Each item to be classified is compared to this prototype. The more similar the item is to the prototype, the faster and more accurately it is classified as a member of the category. Those items that are very different from the prototype are classified as nonmembers. It is suggested that this is how the subjects classified the dot patterns in the current experiment. Since typicality effects were obtained in both visual fields, it appears that both hemispheres are capable of and do use similarity-to-prototype matching under some conditions. The absence of conclusive hemisphere differences in classification was unexpected. In Experiment 1, an overall visual field difference emerged only in accuracy with the nonverbal categories. This RVF/LH advantage contrasted with our prediction of a LVF/RH advantage for our visual-
104
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spatial stimuli. In Experiment 2, a visual field difference was obtained for both the verbal and nonverbal categories. Contrary to Experiment 1, there was a LVF/RH advantage for the nonverbal categories in Experiment 2. For the verbal categories, a RVF/LH advantage emerged. It is possible that these differences in hemisphere effects resulted from the different methods of stimulus construction used in the two experiments. We also found no evidence for hemisphere differences in typicality effects. In addition, we obtained these typicality effects for both verbal and nonverbal categories. This result was replicated over the two experiments. In the current study, then, both hemispheres appeared to perform the classification task in the same manner with both verbal and nonverbal categories. Each appeared to be classifying on the basis of a similarity-to-prototype matching strategy. Why did we obtain no hemisphere differences in typicality effects while Zaidel (1987) did? One difference between Zaidel’s (1987) stimuli and ours is that our stimuli were constructed to preclude the existence of a defining feature differentiating the categories. Subjects could have used a logical (i.e., defining feature) strategy to classify Zaidel’s (1987) stimuli, but they could not have done so with our stimuli. Thus, our results indicate that the presence of a verbal label for the prototype of the category is not sufficient to allow classification to proceed via a logical process in the LH. Our results further suggest that similarity-to-prototype matching may be the default classification process used in each hemisphere when no defining features differentiate categories. A second difference between our study and Zaidel’s is the type of tasks used. Uricuioli et al. (1981) found that the type of categorization task determined whether or not hemisphere differences were found. Using word stimuli in a category membership task (e.g., “Is stimulus 2 a member of category l?“), no hemisphere differences were detected. However, a category matching task (e.g., “Are the stimuli members of the same category?“), again using word stimuli, did demonstrate a RVF/LH hemisphere advantage. Zaidel’s (1987) paradigm was a category matching task while the paradigm used in the current study was more similar to the category membership task. She found hemisphere differences in typicality; we did not. This continues to suggest that the use of typicality information by the two hemispheres is strategic and depends upon task and stimulus parameters. Our results clearly show, as suggested by clinical data, that both hemispheres have access to typicality information. Previous research has indicated that prototypes are classified more easily than other members of the category (Posner & Keele, 1968; Franks & Bransford, 1971; Knapp & Anderson, 1984). Similarly, accuracy was generally highest for our prototypes. It remains strange, then, that in
HEMISPHERIC
TYPICALITY
EFFECTS
10.5
both experiments our subjects did not always classify the prototype with the greatest speed. However, because of our reliable and robust findings of decreasing accuracy and increasing RT over distortion levels 1, 2, and 3, we are confident in explaining our results in terms of typicality effects. In summary, we found no difference in hemisphere processing of the typicality structure of our artificial categories. Both hemispheres appeared to utilize a similarity-to-prototype matching process to classify. This implies that a simple dichotomy of hemispheric classification strategies, as suggested by Zaidel (1987), is not sufficient to describe classification in the two cerebral hemispheres. Both hemispheres are capable of acquiring and accessing typicality information. However, since task and stimulus parameters determine the processing strategies used in classification, typicality effects may or may not be obtained. The results of this study further indicate that merely having a verbal label does not lead to differential hemisphere storage or processing of categorical information. REFERENCES Anderson, J., & Bower. G. 1973. Human associative memory. Washington. DC: Hemisphere Press. Baddeley, A., & Hitch, G. 1974. Working memory. The fyschology of Learning and Motivation, 8, 47-89. Breen, T., & Schvaneveldt, R. 1986. Classification of empirically derived prototypes as a function of category experience. Memory and Cognition, 14, 313-320. Bryden, M. 1982. Luterality: Ftmctional asymmetr.y in the intact bruin. New York: Academic Press. Das Smaal, E., & De Swart, J. 1986. Effects of contrasting category, conjoint frequency and typicality on categorization. Acta Psychologicu, 12, 15-40. Evans, S. 1967. A brief statement of schema theory. Psychonomic Science, 8, 87-88. Franks, J.. & Bransford, J. 1971. Abstraction of visual patterns. Journal of Experimental Psychology, 90, 65-74. Grober, E.. Perecman, E.. Kellar, L, & Brown, J. 1980. Lexical knowledge in anterior and posterior aphasics. Bruin and Lunguage, 10, 318-330. Grossman, M. 1978. The game of the name: An examination of linguistic reference after brain damage. Brain and Longuage,6, 112-I 19. Grossman, M. 1981. A bird is a bird is a bird: Making reference within and without superordinate categories. Bruin und Language, 12, 313-331. Kellogg, R. 1981. Feature frequency in concept learning: What is counted? Memory und Cog&ion, 9, 1.57-163. Kirk, R. (1982). Experimental design. Belmont: Brooks/Cole Publishing Company. Knapp, A., & Anderson, J. 1984. Theory of categorization based on distributed memory. Journul of Experimental Psychology: Learning, Memory, and Cognition, 10, 616-637. Koemeda-Lutz, M., Cohen, R., & Meier, E. 1987. Organization of and access to semantic memory in aphasia. Bruin and Language, 20, 321-337. Kosslyn, S., Pinker, S., Smith, G., & Shwartz, S. 1979. On the demystification of mental imagery. The Behuvioral and Brain Sciences, 2, 535-581. Levy, J., Trevarthen, C., & Sperry, R. 1972. Perception of bilateral chimeric figures following hemispheric disconnection. Brain, 95, 61-78.
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Neumann, P. 1977. Visual prototype formation with discontinuous representation of dimensions of variability. Memory and Cognition, 5, 187-197. Posner, M., & Keele, S. 1968. On the genesis of abstract ideas. Journal of Experimental Psychology,
77, 353-363.
Rosch, E. 1975. Cognitive representations of semantic categories. Journal of Experimental Psychology,
104, 192-233.
Rosch, E., & Mervis, C. 1975. Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573-605. Rosch, E., Simpson, C., & Miller, R. 1976. Structural bases of typicality effects. Journal of Experimental Psychology: Human Perception and Performance, 2, 491-502. Smith, E. & Medin, D. 1981. Categories and Concepts. Cambridge, MA: Harvard Univ. Press. Springer, S., & Deutsch, G. 1985. Left brain, right brain. New York: Freeman. Tulving, E. 1972. Episodic and semantic memory. In Tulving & Donaldson (Eds.), Organization of memory. New York: Academic Press. Uricuioli, P., Klein, R., & Day, J. 1981. Hemispheric differences in semantic processing: Category matching is not the same as category membership. Perception and Psychophysics, 29, 343-35 1. Wayland, S., & Taplin, J. 1985a. Feature-processing deficits following brain injury I. Brain and Cognition,
4, 338-355.
Wayland, S., & Taplin, J. 1985b. Feature-processing deficits following brain injury II. Brain and Cognition,
4,356-376.
Zaidel, D. 1987. Hemispheric asymmetry in long-term semantic relationships. Cognitive Neuropsychology, 4, 321-332.
