Feature-processing deficits following brain injury

BRAIN

AND

4, 356-376 (1985)

COGNITION

Feature-Processing Deficits following Brain Injury II. Classification Learning, Categorical Decision Making, and Feature Production SUSAN WAYLAND AND JOHN

E. TAPLIN

The University of New South Wales The claim that overselectivity in feature processing underlies the disorders that aphasics display in processing both visual and verbal material was directly tested by exploring the relationships between the behavior of brain-injured subjects on three experimental tasks: classification learning, categorical decision making, and feature production. From each of these tests a score selected as being indicative of overselective responding was entered into a principal components analysis, together with measures of visual recognition and memory, visual reasoning, naming skills, and severity of aphasia. This analysis supported the assumption that feature-processing disability is a specific and separable deficit, although related both to naming ability and to severity of aphasia. The relevance of the overselectivity hypothesis to naming difficulties following brain injury is discussed. 0 1985 Academic

Press. Inc.

Aphasics have been shown to be deficient in all of the major functional components of language processing: syntactic, phonological, and semantic (Caramazza & Berndt, 1978; Saffran, 1982). As the difficulties which subjects display in the comprehension and expression of language may be caused by damage to one or more of these processes, it is not easy to isolate the effects of individual factors. Similarly, in experimental procedures, disabilities in separate areas may overlap and interact with one another, obscuring the cause of failure on particular tasks. Further, brain-injured subjects may have peripheral visual or auditory processing defects, or short-term memory problems, that may further complicate interpretation of behavior. However, notwithstanding these difficulties, judgments are frequently made concerning the cause of abnormal perThe authors thank Prince Henry and Royal South Sydney hospitals for providing subjects for the study. Correspondence and requests for reprints should be addressed to Susan Wayland, School of Psychology, The University of New South Wales, Kensington, New South Wales. Australia. 356 0278-2626185$3.OO Copyright All rights

0 1985 by Academic Press, Inc. of reproduction in any form reserved.

FEATURAL

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formance. For instance, based on displayed difficulties in performing experimental tasks involving the processing of both visual and verbal material, it has been hypothesized that certain aphasics have problems specific to the semantic component of language (e.g., Caramazza & Berndt, 1978; Caramazza, Berndt, & Brownell, 1982). It seems that a prerequisite for claiming that abnormal responding in these areas has a common base would be a demonstration that performance on these tasks is in some way related. Thus, the aim of this set of experimental investigations is to explore the relationships between behavior on a number of such tasks. Caramazza and Berndt (1978) have argued that the naming errors and comprehension loss that some aphasics display may be related to a disruption at the level of the semantic representation of language. The studies which they cited in support of this view generally indicate a reduced, or disordered, knowledge of relationships between lexical items (Howes, 1967; Goodglass & Baker, 1976; Lhermitte, Derouesne, & Letours, 1971; Zurif, Caramazza, Myerson, & Galvin, 1974). Further, they have suggested that the hypothesis that the disruption is at the level at which semantic information is represented is supported by the demonstration that some aphasics have difficulty in integrating perceptual and functional stimulus information (Whitehouse, Caramazza, & Zurif, 1978). This view has been extended (Caramazza et al., 1982) to suggest that difficulties with the above-mentioned tasks may be explained by a partial disruption of the feature list associated with a concept. Wayland and Taplin (1982, 1985) have similarly argued that abnormal responding in these experiments may relate to a difficulty in processing the features of the concepts presented. It was proposed that if aphasics are limited in their ability to process features then they should have particular difficulty in forming artificial nonverbal concepts from featural information provided about the structure of these concepts. The tasks selected to demonstrate this involved learning about categories of faces which differed on three dimensions: eyes, nose, and mouth. The first of the tasks required subjects to learn to classify the faces into two categories previously defined by the experimenters. The second was a recognition memory task, which again involved a test of the categorical knowledge that subjects had stored about the faces. In each task aphasic subjects, and to a lesser extent brain-injured control subjects, showed discrepant behavior from that of normals. Although some brain-injured subjects responded very similarly to normals, in other subjects two separate patterns of difficulty were observed. Some subjects, although able to perceive that the stimuli were faces, were unable to establish category membership on any basis. A larger number of subjects, however, responded by basing decisions about category membership on the value of one salient feature, usually the mouth. It was hypothesized that these latter subjects were

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displaying a limited capacity in processing compound featural information, and that their response to this limited capacity was one of overselectivity. It was further argued that performance on other tasks can be explained in like manner. These include the demonstration of poor knowledge of relationships between verbal concepts that Caramazza and Berndt (1978) referred to, and also the demonstration of disordered perception of relationships between nonverbal concepts (Cohen, Kelter, & Woll, 1980; Gainotti, Miceli, & Caltagirone, 1979; Kelter, Cohen, Engel, List, $ Strohner, 1977). Decreased knowledge of functional and perceptual features of objects can similarly be explained (De Renzi, Faglioni, Scotti, & Spinnler, 1972; De Renzi, Pieczuro, & Vignolo, 1968; Faglioni, Spinnler, & Vignolo, 1969). Thus, it was assumed that performance on a wide variety of tasks could be understood by positing what might be called a “featureprocessing” deficit. (It should be noted that such a deficit could potentially involve all or some of the stages of encoding, storage, and response. In contrast, Caramazza et al, 1982, appear to be arguing rather more strongly that it is memorial storage that is involved, as they suggest that it is a semantically constrained parsing of the perceptual input that is affected.) As has been discussed earlier, one test of an hypothesis that abnormal responding on a number of tasks has a common base would be a test of the relationship between some aspect of performance on these tasks. For this purpose, three specific tasks were selected, on the basis that each could be hypothesized to demonstrate feature-processing problems, but otherwise is dissimilar from the other tasks in a number of ways. The first task selected was the categorization task initially carried out by Wayland and Taplin (1982). It involves a learning phase in which subjects classify faces into two categories with the help of feedback from the experimenter, followed by a test phase in which they sort more faces, without feedback. As the categories are carefully structured in terms of instance typicality, this task allows evaluation of the knowledge subjects gain about the typicality structures of the categories. It also provides an opportunity to assess overselectivity in terms of the features of the stimuli to which the subjects respond. Although it has been argued by Rosch (1975a) and others that such tasks represent analogously the manner in which individuals store information about natural semantic categories, it seems clear that this task also has a strong visuo-perceptual orientation. Thus, it may be reflecting difficulties of a perceptual categorization stage, rather than a semantic categorization stage (cf. Wanington & Taylor, 1978; Warrington, 1982). A further reason for inclusion of this task was that our initial categorization study (Wayland & Taplin, 1982)demonstrated that aphasic subjects made an overall greater number of errors on this task than did brain-injured control subjects, whereas, on the later recognition memory task (Wayland & Taplin, 1985), although aphasics tended

FEATURAL

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to be less accurate than other subjects, statistically signficant betweengroup differences were not obtained. As this current set of tests employed the same group of subjects as were involved in the recognition memory task, it provided an opportunity to examine whether this variation in outcome was due to a difference between the tasks or differences between the participating subjects. The second task was a category decision task modeled very closely to that of Grober, Perecman, Kellar, and Brown (1980). This involved the verification of membership of pictured objects in superordinate categories. Some of the objects were highly typical of the categories and others less typical (according to the ratings given by Rosch, 1975b). It was hoped to replicate the finding of Grober et al. (1980) that while demonstrating reasonably normal typicality structures for the categories, aphasics make an extremely large number of errors on semantically related nonmembers of the categories. Wayland and Taplin (1985) have argued that this result would be predicted if subjects were responding in terms of a limited number of features. That is, supposing the stimulus item to be an apple, and the salient feature abstracted from it being that it is edible, subjects would correctly respond “yes” if asked whether it was a member of the category “fruit.” However, on this basis they would also respond “yes” to membership of the category “vegetable.” The third task, chosen to display a reduced knowledge of the features of objects, was a coloring task, designed in a similar fashion to that of De Renzi et al. (1972). An interesting aspect of this task is that whereas the categorization task and the category decision task involve comparing a stimulus item with a memorial representation in order to make a response, this task necessitates the sampling of memory in order to produce a remembered feature. That is, it examines the production as well as the encoding of features. For this reason, this task was called the featureproduction task. As difficulties with feature processing have been hypothesized to be linked with anomia (Caramazza et al. 1982; Wayland & Taplin, 1985), a naming test was given to all subjects. Likewise, as other authors have commented that feature analysis is linked to aphasia (for example, Birchmeier, 1980), it was deemed necessary to examine the relative strength of the relationship between the featural tasks and aphasia, in general, as against anomia, in particular. To determine the severity of aphasia the Western Aphasia Battery (Kertesz, 1982) was given to all aphasic subjects. In order to be able to relate performance to visual recognition memory and visual reasoning ability, tests of these were also included. Following validation of the category decision and the feature-production tasks on a group of normal subjects, the series of tests was given to both aphasic and nonaphasic brain-injured control subjects. As other investigators have demonstrated varied performance for fluent and nonfluent

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aphasics (Zurif et al, 1974; Wayland & Taplin, 1982; Grober et al., 1980), the aphasic subjects were divided into a fluent and a nonfluent group. It was predicted that a relationship would be found between the three “feature-processing” tasks-categorization, category decision, and feature production-and that these tasks would be related to naming ability and possibly also to severity of aphasia. Although some relationship between visual recognition memory and visual reasoning was expected, it was anticipated that these tasks would be shown to be relatively orthogonal to the feature-processing tasks. If this should not be the case, any relationship between the feature processing tasks could be associated with some other factor, for example, a visuo-perceptual deficit or the degree of brain injury. If, on the other hand, these tasks were shown to represent a separate aspect of task performance, a specific feature-processing impairment would be further supported. METHOD Subjects The 25 brain-injured subjects were those who had earlier been employed in the recognition memory task. For method of selection and description of subjects see Wayland and Taplin (1985). Six normal subjects also participated in the category decision and feature-production tasks. They were first-year psychology students from the University of New South Wales, who took part in the experiment as part of their course requirement.

Procedure Tests were administered individually to the subjects by the experimenter. For the braininjured subjects testing was divided into three sessions of approximately 40 min, carried out on 3 consecutive days whenever possible. Tests were usually given in the order: (1) Naming test. (2) Visual recognition and memory test. (3) Visual reasoning test. (4) Feature-production task. (5) Category decision task. (6) Classification task.

Naming

Test

Stimulus items were 30 pictures of objects, projected from the rear onto a screen situated approximately 1 m from the subject. The names of all objects were two- or three-syllable words, syllable length and concreteness being controlled throughout. The items were arranged on a continuum of word frequency from high to low. Word-frequency estimates were taken from the Kensington Word Pool (Stewart, 1980), which is based on a number of word-frequency counts of written language. Procedure. Subjects spoke the name of the presented picture into a microphone. As each stimulus item was presented a reaction timer was started; the timer was stopped by activation of a voice key. This sequence of events, including the presentation of stimuli, was controlled by a Gerbrands timer box. Both reaction time, in hundredths of a second, and response given were recorded. Scoring. Naming responses were scored for both accuracy and delay. Three points were scored for an accurate and immediate response, two points for an accurate response given

FEATURAL

OVERSELECTIVITY

361

within 3-10 sec. Subjects scored zero if they failed to respond within 10 set or if their response was inaccurate.

Visual Recognition and Memory The test used was the visual recognition subtest of the British Ability Scales (1977). This test consists of the selection of a previously seen item (pictures of objects or shapes) from a set of similar items. It ranges from simple picture matching of common objects to the matching of abstract designs, requiring a complex level of visual analysis and memory. This test was scored and administered according to the test manual instructions.

Visual Reasoning Test Raven’s Coloured Progressive Matrices (1977) were used. Scoring and administration were according to the test manual.

Feature Production Nineteen line drawings of objects were presented to the subjects together with 24 colored pencils. The subjects were instructed to color in the drawings with the pencil they felt most appropriate. If, by reason of physical disability, they were unable to color adequately, they were permitted to select a pencil and hand it to the experimenter, who colored in the picture. Two independent judges rated the colorings. Two points were given for choosing the typical color for the object, one was given for choosing a nontypical but appropriate color, and zero was given for an inappropriate color. In order to assist the judges the six normal subjects also colored in the drawings. The judges were allowed to peruse their responses before rating the colorings of the brain-injured subjects. Following independent ratings, discrepancies between the raters were resolved by conference. A high degree of interrater reliability was attained, 426) = .91, p < .OOl.

Category Decision Task The stimuli were pictures of objects chosen from five categories: vehicles, clothing, furniture, fruit, and vegetables. From each category eight high-typical and eight low-typical members were selected from the category norms for goodness of example given by Rosch (1975b). The stimuli were arranged in blocks of 16 items, eight members of a category and eight nonmembers, both members and nonmembers being evenly divided into high- and low-typical items. Procedure. The stimuli were projected from the rear onto a screen approximately 1 m from the subjects. Prior to their presentation a category name was displayed at the top of the screen. This name was read to the subjects and they were instructed that they were to decide whether or not the forthcoming stimuli were members of the category. The relevant category name continued to be displayed throughout stimulus presentation. The subjects were instructed to press a response key labeled “yes” if they thought the stimulus item was a member of the displayed category, or the one labeled “no” if they thought it was not. Both reaction time, in hundredths of a second, and response type, “yes” or “no,” were recorded. The initial block of 16 items served as a training period and results from this were discarded. If subjects failed to perform above chance level in the training period the task was discontinued. Three aphasic subjects, all nonfluent, were thus excluded.

Categorization Task In this task subjects had to classify line drawings of faces into two categories. The two types, or categories, of faces were distributed around a prototype face (representing the central tendency of the set), individual faces having varying degrees of family resemblance

362

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to other category members and to the prototype. The task was divided into phases: a learning phase, in which feedback about the appropriateness of the subjects’ responses was given by the experimenter; and a test phase, in which 50 more faces were sorted without feedback being provided. Each response was scored as correct or incorrect. Error data were analyzed from both learning-phase and test-phase data. (For a complete description of the stimuli and procedure see Wayland and Taplin, 1982.)

RESULTS Naming

Table 1 shows group means and standard deviations on the picturenaming task. For the purpose of distinguishing between naming performance for high- and low-frequency words, items were divided equally into two groups. Those in the high-frequency group had word frequencies (occurrences per million words) of 81 to 733, and those in the low-frequency group, 19 to 80. A planned contrasts analysis of variance was performed on this data. The effects of type of aphasia (fluent vs. nonfluent), presence of aphasia (aphasics vs. controls), and frequency of occurrence of stimulus item (high vs. low) were examined. The decisionwise Type 1 error rate was set at LY= .05. There was a significant difference between the naming ability of aphasics and controls, F(1, 22) = 23.721, MS, = 285.187; and fluent aphasics were significantly more successful at naming than were nonfluent aphasics, F(1, 22) = 9.533, MS, = 285.187. Performance on high-frequency items was better overall than that on low-frequency items, F(1, 22) = 9.318, MS, = 9.865. This effect held for all groups, there being no significant group by frequency interactions. Visual Recognition

and Memory

Table 2 shows the means and standard deviations for groups on the visual recognition and memory test. Again an analysis of variance was carried out on these data, with planned contrasts written to examine the group effects of type of aphasia (fluent vs. nonfluent) and presence of aphasia (aphasic vs. controls). With the decisionwise error rate set at (Y = .05, the visual recognition and memory ability of the controls was TABLE 1 PICTURENAMING Low frequency

High frequency

(total 45)

(total 45) Fluents Nonfluents Controls

Mean

SD

Mean

SD

27.364 9.333 42.875

14.182 10.765 3.219

24.121 5.333 41.125

16.772 7.630 2.666

FEATLJRAL

363

OVERSELECTIVITY

TABLE 2 VISUAL REC~GNUION AND MEMORY (POSSIBLE TOTAL 17)

Fluents Nonfluents Controls

Mean

SD

9.545 9.667 13.375

2.675 2.427 1.798

shown to be significantly higher than that of the aphasics, F(1, 22) = 11.791, MS, = 139.936, but there was no difference between fluent and nonfluent aphasics, F < 1. Visual Reasoning

Table 3 gives group means and standard deviations for scores on Raven’s Coloured Progressive Matrices. Again the’ decisionwise error rate was set at CY= .05. Neither of the planned between-group contrasts was significant-fluent vs. nonfluent aphasics, F(l, 20) = .070, MS, = 63.238, or aphasics vs. controls, F(l, 20) = 2.754, MS, = 63.238. Feature Production

Group means and standard deviations for scores on the coloring task are given in Table 4. Once more a planned contrasts analysis of variance was carried out on these data, the decisionwise Type 1 error rate being set at (Y = .05. There was again no difference between fluent and nonfluent aphasics, F(l, 22) = 1.654, MS, = 21.334, but control subjects did significantly better than aphasics, F(l, 22) = 7.353, MS, = 21.334. Category Decision

Task

Both normal subjects and brain-injured subjects participated in this task. The initial analysis of data was carried out separately for normals and for the brain-injured group. Reaction time-Normals. A planned contrasts analysis of variance was performed on these data. Repeated-measures contrasts examined the TABLE

3

GROUP MEANS AND STANDARD DEVIATIONS FOR SCORESON RAVEN’S COLOURED PR~GREWVE MATWCES (POSSIBLE TOTAL 36)

Fluents Nonfluents Controls

Mean

SD

22.111 21.oocl 27.375

7.680 8.775 5.830

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AND TAPLIN

TABLE 4 GROUPMEANS AND STANDARD DEVIATIONS FOR SCORES ON THE FEATURE PRODUCTION TASK (POSSIBLE TOTAL 38)

Fluents Nonfluents Controls

Mean

SD

32.182 29.167 36.125

5.254 5.014 1.364

effects of typicality (high vs. low) and category membership (members vs. nonmembers). The familywise Type 1 error rate was set at a! = .05. Table 5 gives means and standard deviations for reaction time on the category decision task. High-typical items were responded to significantly faster than lowtypical items, F(1, 6) = 8.992, MS, = 0.004, but there was no significant difference between members and nonmembers of categories, F < 1. A significant interaction between typicality and category membership, F(l, 6) = 10.219, MS, = 0.003, indicated that the difference between high- and low-typicality items held only for members, not nonmembers, of categories. Error-Normals. Again an analysis of variance on these data was carried out, examining the same repeated-measures contrasts as for the reaction-time data. Table 6 gives means and standard deviations for error on the category decision task. The same pattern of results was found as for the reaction-time data. High-typical items were sorted more accurately than low-typical items, F(1, 6) = 15.909, MS, = 2.750, but there was no significant difference between members and nonmembers of categories, F(l, 6) = 2.821, MS, = 5.583. Again a significant interaction between typicality and category membership demonstrated that the difference between high- and low-typical items applied only to members of categories, F(1, 6) = 15.000, MS, = 1.488. Reaction time-Brain-injured subjects. Table 7 gives group means and standard deviations for reaction time on the category decision task. In TABLE 5 MEANS

AND STANDARD

DEVIATIONS FOR REACTION TIME (in DECISION TASK (NORMAL SUBJECTS)

set)

ON THE CATEGORY

Nonmembers

Members

Mean SD

High

Low

High

Low

0.807 0.255

0.943 0.309

0.873 0.269

0.876 0.270

FEATURAL

365

OVERSELECTIVITY TABLE 6

MEANS

AND STANDARD

DEVIATIONS FOR ERROR ON THE CATEGORY DECISION TASK (NORMAL SUBJECTS)

Nonmembers

Members High typical

Low typical

High typical

Low typical

0.714 0.700

5.000 2.330

1.000 0.926

1.714 1.750

Mean SD

the analysis of variance on these data planned contrasts were written to examine the group effects of type of aphasia (fluent vs. nonfluent aphasics) and presence of aphasia (aphasics vs. controls). The same repeatedmeasures contrasts as for the normal subjects analysis, examining the effects of typicality (high vs. low) and category membership (members vs. nonmembers) were written. With the familywise Type 1 error rate set at (Y = .05, there were no significant group contrasts, in each case, F < 1. The repeated-measures contrasts showed a similar pattern to that of the normal subjects’ data. Highly typical items were responded to faster than low-typicality items, F(1, 19) = 21.739, MS, = 0.079, with no signitkant group x typicality interactions. Again there was no significant difference between members and nonmembers of categories. The interaction between typicality and category membership was, once more, significant, F(l, 19) = 15.863, MS, = 0.115, revealing that for brain-injured subjects also the typicality effect held only for members of categories. This was true for all groups, neither of the group x typicality x category membership interactions being significant, F < 1 in all cases. TABLE 7 GROUP MEANS

AND STANDARD DEVIATIONS FOR REACTION TIME (in DECISION TASK (BRAIN-INJURED SUBJECTS)

Members

Fluent (n = 11) Mean SD

Nonfluent (n = 3) Mean SD

Control (n = 8) Mean SD

Means averaged across groups

set)

ON THE CATEGORY

Nonmembers

High

Low

High

Low

2.076 0.647

2.822 0.948

2.104 0.600

2.065 0.531

1.820 0.171

2.203 0.571

2.167 0.246

2.147 0.293

1.783 0.471 1.935

2.626 0.988 2.666

1.943 0.564 2.054

1.971 0.544 2.042

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Error-Brain-injured subjects. Table 8 gives group means and standard deviations for error on the category decision task. Analysis of variance examined the same group and repeated-measures contrasts as for the reaction-time data, and the familywise Type 1 error rate was again set at (Y = .05. There were no between-group differences on overall number of errors, fluent vs. nonfluent aphasics, F(1, 19) = 513, MS, = 5.023; aphasics vs. controls, F(1, 19) = .931, MS, = 5.023. More errors were made on low-typical items than high-typical items, F(1, 19) = 112.982, with no significant group by typicality interactions. For the brain-injured subjects, unlike normals, significantly more errors were made on members than on nonmembers of categories, F(1, 19) = 8.300, MS, = 4.859, this effect holding across groups. There was again a significant interaction between typicality and category membership, F(1, 19) = 27.528, MS, = 2.120, indicating that there was a greater difference between highand low-typical items for members than for nonmembers of categories. These results constitute a replication of the findings of Grober et al. (1980), in that they demonstrate relatively normal responses to the typicality structures of categories by aphasic and nonaphasic brain-injured subjects. All brain-injured groups had particular difficulty responding to low-typical members of categories (see Table 8), but it should be noted that this was also the case for normal subjects. Grober et al. (1980) reported that posterior aphasics had problems with “fuzzy” instances of categories. They defined “fuzzy” instances as being atypical members plus related nonmembers of categories (e.g., VEGETABLE, pineapple). Although unable to obtain a significant group (anterior vs. posterior) x type of instance (fuzzy vs. other) interaction, they did demonstrate that posterior aphasics made more errors on fuzzy instances than did anterior aphasics. The lack of any group x typicality interaction in our data may suggest TABLE 8 GROUP MEANS AND STANDARD DEVIATIONS FOR ERROR ON THE CATEGORY DECISION TASK (BRAIN-INJURED SUBJECTS) Nonmembers

Members

Fluent Mean SD Nonfluent Mean SD Control Mean SD Means averaged

across groups

High

Low

High

Low

0.909 0.900

4.273 2.562

1.364 1.432

2.364 1.823

1.667 1.700

5.ooo 1.414

2.667 0.471

3.667 1.247

1.250 1.561 1.136

6.500 2.345 6.182

0.750 1.299 1.318

1.375 1.218 2.182

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OVERSELECTIVITY

367

that our aphasic subjects were not having unusual difficulty with atypical items. However, a further analysis was carried out to examine performance on related nonmembers of categories. In verifying the two categories FRUIT and VEGETABLE 16 nonmember instances were encountered, 8 of which were related (e.g., FRUIT, peas), and 8 unrelated (e.g., VEGETABLE, table). Error data from this subset of items were analyzed (no reaction-time analysis was attemped as there were so few correct responses to related items). Again the group effects of type of aphasia (fluent vs. nonfluent), and presence of aphasia (aphasics vs. controls), were considered along with the repeated-measureseffect of type of instance (related vs. unrelated). For this subset of items aphasics made significantly more errors than controls, F(1, 19) = 5.367, MS, = 3.007, but there was no difference between fluent and nonfluent aphasics, F(1, 19) = 1.764, MS, = 3.007. Across groups significantly more errors were made on related than unrelated instances, F(1, 19), = 46.702, MS, = 2.224, and there was a significant interaction between presence of aphasia and type of instance, F(1, 19) = 6.274, MS, = 2.242, confirming that aphasics have particular problems in rejecting members of related categories. Up to this point no direct comparison between brain-injured and normal subjects has been made, there being theoretical reasons for separate analysis of their results. First, it may be that the underlying cause of error, or delayed reaction time, might be fundamentally different for brain-injured subjects than for normals. Second, between-subject variability is frequently greater for brain-injured than for normal subjects, thus violating the assumption of homogeneity of variance. However, as the recognition memory task raised questions as to whether this group of brain-injured control subjects was performing in an aberrant manner, in this case it seemed profitable to compare brain-injured subjects with normals. It would be expected that normal subjects would similarly show a higher proportion of related to unrelated errors as other investigators have demonstrated that they also have difficulty in rejecting related nonmembers of categories (Collins & Quillian, 1972; Schaeffer & Wallace, 1970). Thus, a further analysis of variance was carried out on the subset of nonmember items from the categories of FRUIT and VEGETABLE, comparing the performance of normal subjects with that of brain-injured controls. The group effect of brain injury (brain-injured controls vs. normals) and the repeated-measures effect of type of instance were examined. This analysis showed that brain-injured controls were not overall more error prone than normals on this restricted set of items, F < 1. Further, the ratio of errors on related to unrelated items was similar for the two groups, the group x type of instance interaction yielding F < 1. Table 9 illustrates the number of errors on related and unrelated instances for all four groups.

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TABLE 9 MEANS

AND STANDARD DEVIATIONS FOR ERROR ON NONMEMBER INSTANCES OF RELATED UNRELATED CATEG~IUES (BRAIN-INJURED AND NORMAL SUBJECTS)

Related

Fluents Nonfluents Controls Normals

vs.

Unrelated

Mean

SD

Mean

SD

3.364 5.667 1.875 2.143

2.568 0.471 1.763 1.807

0.182 0.004 0.000 0.143

0.136 0.000 0.000 0.350

Thus, the present analysis indicates that although aphasics are not performing abnormally on atypical instances they have a specific difficulty with related category nonmembers. On the whole brain-injured controls are behaving like normal subjects, but it should be noted that at least one control subject made a large number of related errors (six out of a total of eight). As the making of related errors has been argued to be consistent with a feature-processing deficit hypothesis, and as this score discriminated aphasics from nonaphasics, the number of related errors subjects made was entered into the final factor analysis. Categorization

Task

Learning phase. Table 10 gives group means and standard deviations for errors on stimuli of high and low family resemblance in the learning phase. The analysis of variance on these data examined the group effects of type of aphasia (fluent vs. nonfluent) and presence of aphasia (aphasics vs. controls) as well as the effect of family resemblance (high vs. low). The familywise Type 1 error rate was set at (I! = .05. Overall more errors were made on items of low family resemblance than on those with high family resemblance, F(1, 22) = 71.217, MS, = 5.755. There were no significant group x family resemblance interactions. TABLE 10 LEARNING

PHASE: GROUP MEANS AND STANDARD DEVIATIONS HIGH AND Low FAMILY RESEMBLANCE

FOR ERRORS ON STIMULI

Low FR

High FR

Fluents Nonfluents Controls

OF

(FR)

Mean

SD

Mean

SD

2.909 2.500 2.750

2.575 3.041 4.815

8.909 8.833 8.125

2.712 3.387 3.887

FEATURAL

369

OVERSELECTIVITY

As was the case in our original classification study (Wayland & Taplin, 1982) no difference was demonstrated in the learning phase between fluent and nonfluent aphasics, nor between aphasic subjects and braininjured controls, F < 1 in each case. Test phase. Group means and standard deviations for errors on each type of stimulus in the test phase can be seen in Table 11. The planned contrasts analysis of variance on these data examined both group effects, and the effects of family resemblance (high vs. low) stimulus type (old vs. new), prototypicality (prototypes vs. other stimuli), and the linear trend on typicality (prototypes vs. high family resemblance items vs. low family resemblance items). As for the learning phase, the Type 1 error rate was set at (Y = .05. Typicality effects were demonstrated on some contrasts. Prototypes were sorted more accurately than other items, F( 1, 22) = 24.785, MS, = 0.609, and the linear trend was significant, F(l, 22) = 29.301, MS, = 0.471; items of high family resemblance, however, were not sorted more accurately than items of low family resemblance, F(l, 22) = 0.611, MS, = 0.359. There were no significant group x family resemblance interactions. Unlike the outcome of our previous experiment using this task, no group effects were demonstrated. In overall performance nonfluent aphasics could not be distinguished from fluent aphasics, nor could aphasics be separated from controls, F < 1 in each case. (This illuminates the failure to find between-group differences on the recognition memory categorization task (Wayland & Taplin, 1985). The same subjects were employed in the recognition memory task and in the presently reported categorization task, and in each case group differences failed to emerge. Clearly the presence or absence of group differences is thus tied to subject differences, rather than task differences.) TABLE 11 TEST PHASE: GROUP MEANS AND STANDAFCD DEVIATIONS

FOR ERRORS ON EACH TYPE

OF STIMULUS

Prototype Fluent Mean SD Nonfluent Mean SD Control Mean SD Means averaged across groups

Old HIFR

New HIFR

Old LOFR

New LOFR

0.000 0.000

0.273 0.445

1.727 0.962

2.000 0.953

0.455 0.656

0.333 0.745

0.833 0.687

2.167 1.213

1.833 1.067

1.167 0.687

0.625 1.111 0.280

0.625 1.654 0.520

1.375 2.233 1.720

1.250 1.920 1.720

0.875 1.364 0.760

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AND TAPLIN

However, as was shown to be the case in the recognition memory task (Wayland & Taplin, 1985),the subjects did not appear to be responding entirely in a normal manner. This was indicated by the lack of a significant difference between responding to low- and high-category members and by a significant interaction between family resemblance and stimulus type (old vs. new), F(1, 22) = 22.269. The construction of the stimuli is such that an interaction of this nature could result from subjects sorting items on the basis of the mouth alone rather than the face as a whole. As analysis of responses from the recognition memory task demonstrated that such a strategy was used widely by this group of aphasics, a further analysis was carried out to assess possible selectivity in responding. Subjects could successfully pursue a strategy of sorting faces on the basis of the mouth alone for most of the stimuli. However, some stimuli had mouths (neither particularly happy, nor particularly sad) which lay on the borderline between the two categories. Feedback received in the learning phase for these feature values was equivocal, being dependent on the other features present in the face. If subjects were responding on the basis of family resemblance then faces containing the equivocal mouths should present no particular problems, whereas these items should prove particularly difficult for subjects relying on the mouth alone. Consequently a further analysis was carred out restricted to the group of items containing equivocal mouths. There were 24 such items in the learning phase (all stimuli with overall low family resemblance) and I2 such items in the test phase (some with high family resemblance and some with low). Learning phase-Equivocal mouths. Table 12 gives group means and standard deviations for errors on stimuli with equivocal mouths in the learning phase. A planned contrasts analysis of variance, with the familywise Type 1 error rate set at (Y = .05, revealed no difference between fluent and nonfluent aphasics, F(1,22) = .132, MS, = 8.284, nor between aphasic and nonaphasic subjects, F(1, 22) = 2.011, MS, = 8.284. For all groups, errors on these stimuli accounted for a high proportion of their errors in the learning phase. Stimuli with equivocal mouths represent only 60% of the low family resemblance items in the learning phase, but TABLE

12

LEARNING PHASE: GROUP MEANS AND STANDARD DEVIATIONS FOR ERRORS ON STIMULI WITH EQUIWXAL MOUTHS

Fluent Nonfluent Nonaphasic

control

Mean

SD

7.636 8.167 6.125

2.346 3.578 2.368

FEATURAL

371

OVERSELECTIVITY

on these stimuli fluent aphasics made 86% of their errors on low family resemblance items, nonfluents 93 and controls 76%. Test phase-Equivocal mouths. Table 13 gives group means and standard deviations for errors on stimuli of high and low family resemblance with equivocal mouths in the test phase. The planned contrasts analysis of variance on these data revealed that aphasic subjects made significantly more errors on items with equivocal mouths than did nonaphasics, F(1,22) = 7.138, MS, = 2.461, but there was no difference between fluent and nonfluent aphasics, F < 1. No typicality effect was demonstrated, the family resemblance (high vs. low) contrast yielding F( 1, 22) = 1.310, MS, = 0.555. There were no significant group x family resemblance interactions. Although a clear overall decrement in performance for aphasic subjects was not demonstrated for this group of subjects, an aberrant mode of response is strongly suggested. This is in line with the findings of the recognition memory task (Wayland & Taplin, 1985); that is, aphasic subjects appear to be overselective in terms of the features of the stimuli to which they respond. Again as the number of errors subjects made on equivocal mouths discriminated between aphasic and nonaphasic subjects, and could be taken to be an indication of overselective responding, this measure was included in the factor analysis. Factor Analysis

of Tasks

The scores on these foregoing tasks were entered into a factor analysis. The three feature-processing tasks were represented by (1) the overall score on the feature production (coloring) task, (2) the number of related errors on the category decision task, and (3) errors on equivocal mouths on the categorization task. As has been discussed previously, both of these last two scores can be interpreted as responses to only partial feature information, whereas the first task is seen to represent the inability to produce a specific feature of an object, namely, its color. TABLE 13 TEST PHASE: GROUP MEANS AND STANDARD DEVIATIONS FOR ERRORSON EQUIWCAI MOUTH ITEMS HIFR

Fluents Nonfluents Controls

LOFR

Mean

SD

Mean

SD

1.727 2.167 0.750

0.750 0.687 0.829

2.182 2.333 0.875

1.402 1.599 1.364

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Other scores entered into the analysis for each subject were aphasia quotient, naming score, visual recognition and memory score, and score on Raven’s Coloured Progressive Matrices. (It should be noted that only the last of these, the matrices score, failed to discriminate between aphasic and nonaphasic subjects.) A principal components analysis, with oblique rotation specifying three factors, was carried out. The results of this can be seen in Table 14. Factor 1 accounted for 56.9% of variance, Factor 2 accounted for 13.6% of variance, and Factor 3 accounted for 11.9% of variance. It appears that Factor 1, aphasia quotient and naming being highly correlated with it, can be interpreted alternatively as naming ability or severity of aphasia. As all tasks, with the exception of Raven’s matrices, discriminated between aphasic and nonaphasic subjects it is not surprising that this factor accounts for such a large proportion of the variance. Factor 2, with Raven’s matrices and visual recognition and memory correlated highly with it, can best be interpreted as “visual intelligence.” The visual recognition and memory task can be seen as jointly testing visuo-perceptual ability and memory, a component of intelligence. Performance on Raven’s matrices, although it is accepted as a test of visual reasoning, has also been shown to be related to visuo-spatial ability in brain-injured subjects (Kertesz & McCabe, 1975; Van Harskamp, 1974). Thus, both these tasks may tap a combination of visual and intellectual skills. Factor 3 can be interpreted as representing feature-processing disability. Most highly correlated with it are errors on equivocal mouths in the categorization task, followed by related errors on the category decision task and feature-production scores. An examination of the correlation between the three factors reveals that Factor 2 is only slightly correlated with Factor 1, r = .267, and with Factor 3, r = .277; whereas Factor 1 is moderately correlated with Factor 3, r = - .422. DISCUSSION

The factor analysis supports the assumption that feature-processing disability is a specific and separable deficit, although the correlation TABLE

14

FACTORPATTERNMATRIX Factor FP VR FACE CAT MAT

AQ NAM

1

Factor 2

Factor 3

.42258 .46715 .10459 - .25674 - .12795 .77627 .92162

.19452 .71853 - .15839 - .09354 .85802 .21713 - .02366

- .60778 .15257 .91655 .65742 - .30857 - .15936 - .08183

FEATURAL

OVERSELECTIVITY

373

between Factors 1 and 3 suggests that it is related both to naming ability and severity of aphasia. It appears that the feature-processing deficit is not explicable in terms of visual or intellectual ability, as these emerge as a separate factor. The hypothesis that aphasic subjects are aberrant in performing tasks which require categorization behavior has been supported by this series of tests. Moreover, there is now a strong suggestion that aberrant performance across such tasks is related, and can be explained in terms of an inability to combine the features of a concept in order to respond. The latter part of this argument is based on the fact that the performance scores entered into the factor analysis for all three feature-processing tasks were selected on a priori grounds as being indicative of overselectivity. It remains to consider the relationship that overselectivity in featureprocessing bears to anomia. Caramazza et al. (1982) have proposed that anomia may result from breakdowns at two separate central processing stages. The first stage that may be impaired is a semantically constrained parsing of the perceptual input. The output of this stage they see as serving as the input to a recognition and naming algorithm. Hence, the production of some semantic in-class errors: for example, a bowl may be classified as and named a cup on the basis of one feature, even though other functional and perceptual features suggest that it is properly a bowl. This argument, though providing a clear explanation for some semantic paraphasias, appears to be limited to occasions on which the anomia is accompanied by an inability to recognize the object. For this reason, it would seem to account for only a small number of the anemic errors produced by aphasics. Many semantic paraphasias, or even total failures to name, appear to take place in the presence of ability to recognize the object, subjects frequently being able to give the function of the object (e.g., Rinnert & Whitaker, 1973). It is therefore necessary to postulate, as Caramazza et al. (1982) have done, that a later stage involving lexical retrieval is also disrupted. Generally, the explanation invoked for difficulties at this level is the “retrieval/arousal deficit” hypothesis (Goodglass 8z Geschwind, 1976). If this limited view of the role of a categorical feature analytic deficit in aphasia is accepted, then it is difficult to explain the relationship between feature analysis and aphasia in general (for example, Birchmeier, 1980), and between feature analysis and the comprehension (Gainotti et al. 1979) and production of lexical items in particular (Goodglass & Baker, 1976; Kelter et al., 1977; Wayland & Taplin, 1982). Certainly such conceptual difficulties appear widespread in aphasia. Gainotti et al. (1979), for example, found that 54 out of 74 aphasics obtained pathological scores on a task which tested knowledge of conceptual relationships between pictured objects. This same study also reported no significant relationship between conceptual impairment and severity of aphasia, but a strong

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relationship between conceptual disorder and a task which examined comprehension of lexical items. It seems reasonable on this basis, to assume that the difficulties in processing compound featural information may extend beyond the phase of processing that leads to the object’s recognition. One model proposed to account for naming difficulties in normal individuals suggests a manner in which such a processing difficulty may lead to semantic paraphasias (even though the object is correctly recognized). Brown (1979) asked subjects to provide low-frequency words (for example, “braille”) in response to a definition. He found that prior presentation of a semantically related word (for example, “shorthand”) delayed responding. It seemspossible that if incomplete feature information is supplied to the semantic memory network in aphasics then a similar blocking of the correct response may occur. That is, the initial salient feature of a presented object (for example, “something you eat with”) may arouse a set of lexical items (for example, “fork,” “knife,” “spoon”), one of which is randomly selected as the name. The function of the minor features may normally be to assist in inhibition of incorrect members of the semantic/lexical set, or alternatively to act in an additive manner in raising the activity level of a particular lexical item to a threshold at which the name is accessed. It is possible that this minor feature information does eventually arrive in order for the subject to recognize that his initial lexical response is incorrect. However, because an incorrect name has been accessed, blocking of the correct response may now occur. An additional issue is raised by the work of Blumstein and Milberg (Blumstein, Milberg, & Shrier, 1982; Milberg & Blumstein, 1981). They have shown that aphasic subjects demonstrate relatively normal semantic facilitation effects in both visual and auditory lexical decision tasks, while at the same time performing abnormally on semantic judgment tasks. They reconcile this apparently paradoxical finding by suggesting that whereas automatic activation of semantic information in memory is intact, conscious access to semantic information may be impaired (following Posner & Snyder, 1975). It seems possible that it is this difficulty in conscious access to semantic memory that the categorization task mirrors, the problem being specified as a reduced capacity in processing compound featural information. There is evidence from other sources that the integration of features in a stimulus requires attentional capacity in normal individuals. Thus Treisman and Gelade (1980), for example, reported that whereas the processing of integral stimuli does not require attentional capacity, the processing of stimuli with separable features does. As integration of perceptual or semantic information would seem to be a preliminary step in lexical retrieval, its disruption would seem likely to produce problems in later stages of the process.

FEATURAL

375

OVERSELECTIVITY

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