Feature-processing deficits following brain injury

BRAIN

AND

COGNITION

4, 338-355 (1985)

Feature-Processing I. Overselectivity

Deficits following Brain Injury

in Recognition

SUSAN WAYLAND The University

Memory for Compound

Stimuli

AND JOHN E. TAPLIN of New

South

Wales

A previous experiment (S. Wayland & J. E. Taplin, 1982, Brain and Language, 16, 87-108) demonstrated that aphasic subjects had particular diiculty performing a categorization task, which for normals involves abstraction of a prototype from a set of patterns and sorting of other patterns with reference to this prototype. This study extended the investigation to a recognition memory task similarly organized in categorical structure. The aim was to replicate the previous findings and to delineate the precise nature of aphasics’ difficulties with such tasks. Aphasics were again found to be aberrant in performing this task in comparison with normal subjects, nonaphasic brain-injured control subjects also demonstrating a departure from normality. The results suggest that the problem for brain-injured subjects is one of overselectivity in terms of the features of the stimuli to which they respond rather than a difficulty with prototype abstraction itself. o 1985 Academic

Press, Inc.

Although aphasia can be seen as primarily a linguistic deficit, it appears that many aphasics have accompanying difficulties at what might be called a prelinguistic level. Of particular interest to the present authors is the suggestion that the word retrieval problems that many aphasics encounter may be linked to underlying conceptual difficulties. Caramazza and Berndt (1978) have argued that at least some aphasics who have difficulty in finding words are operating from a disrupted semantic system. This view is supported by research demonstrating that fluent aphasics, and to some extent nonfluent aphasics, are limited in their knowledge of relationships between concepts, be they verbal (Goodglass & Baker, 1976; Howes, 1967; Lhermitte, Derouesne, & Lecours, 1971; Zurif, Caramazza, Myerson, & Galvin, 1974) or nonverbal (Cohen, Kelter, & Woll, 1980; Gainotti, Miceli, & Caltagirone, 1979; Kelter, Cohen, Engel, List, The 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. 338 0278-2626185 $3.00 Copyright D 1985 by Academic Press, Inc. AU rights of reproduction in any form reserved.

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& Strohner, 1977). In addition, aphasics have been found to display decreased knowledge of the functional and perceptual features of objects (De Renzi, Faglioni, Scotti, & Spinnler, 1972; De Renzi, Pieczuro, & Vignolo, 1968; Faglioni, Spinnler, & Vignolo, 1969; Tsvetkova, 1975), which would likewise be predicted if their semantic memory representations were deficient. In accordance with these findings, Wayland and Taplin (1982) hypothesized that aphasics are defective in their organization of semantic featural information, and suggested that a categorization task which involved the abstraction of a prototype from a set of patterns, and classification of other patterns with reference to this prototype, should be particularly sensitive to their problem. Underlying this proposal was the claim made by Rosch (1973, 1975) and others that performance on such pattern recognition tasks reflects the manner in which normal individuals organize categorical semantic information. Aphasic subjects therefore were divided into a fluent group and a nonfluent group, and were compared with a nonaphasic brain-injured control group. As predicted, fluent aphasics showed a significant deficit in performance on the categorization task as compared with nonfluent aphasics and with nonaphasic control subjects. It was thus suggested that the category structures formed by aphasics were different from those of the controls, and also different from what would be expected of normal subjects. However, data relating to the aphasics’ knowledge of the typicality structure of the categories were equivocal. Fluent aphasics alone did not sort prototypes more accurately than other items, suggesting a failure on their part to relate to typicality structure. On the other hand, all subjects sorted items which were highly typical of the categories more accurately than items of low typicality. Although the difference between high and low was less marked for the aphasic group than for the control group, this interaction was not significant. A significant correlation between naming ability and categorization task scores was found for fluent aphasics, supporting the hypothesis that some difficulty in categorizing feature sets is related to anomia in these cases. However, given the relatively small, highly variable group of subjects in this initial study, replication of these findings is necessary. Further, it is not immediately apparent exactly which aspect of the task led to the poor performance of fluent aphasics. Possibly their difficulty lay in monitoring the frequency distribution of features in such a manner as to produce a veridical memorial representation of their occurrence within the categories. Such a defect in responding to typicality structure would almost inevitably result in an inability to abstract prototypes for the categories. On the other hand, the performance of some aphasic subjects suggested that a potential reason for their failure may have been their inability to combine individual features into a composite whole. That is, a number of subjects appeared to make decisions about category mem-

340

WAYLAND

AND

TAPLIN

bership on the basis of variations in just one feature, usually the mouth; the stimuli used in this task were faces. Possibly they adopted this strategy because they had difficulty in processing the entire set of features simultaneously. In other words, these subjects may have been displaying a limited capacity in processing compound featural information, and their response to this limited capacity was one of overselectivity, the feature or features responded to probably being selected in terms of their salience. (The mouth can be seen as the most salient feature of these faces, in that it provides a clear distinction between happy and sad faces.) Recent literature provides some tentative support for the latter of these two interpretations; that is, that aphasics may have difficulty in dealing with compound featural stimuli. For example, Grober, Perecman, Kellar, and Brown (1980) have shown that aphasics are sensitive to the typicality structures of natural (i.e., nonartificial) semantic categories (e.g., clothing, vehicles, furniture, and tools). Thus, it would seem unlikely that in the categorization task used by Wayland and Taplin (1982) aphasics were unable to respond to the typicality structure of the categories as would be implied by the former account. Further, there is a degree of support for the view that abnormal responding may be explained by overselective processing of features. In this regard, Caramazza, Berndt, and Brownell (1982) have suggested that a partial disruption of the feature list associated with a concept underlies the disorders described for aphasics both in the processing of words (e.g., Goodglass & Baker, 1976) and in processing visual information. Moreover, Caramazza and his co-workers (Caramazza et al, 1982; Whitehouse, Caramazza, & Zurif, 1978) have demonstrated that certain aphasic subjects base their identification of objects on one perceptual feature, and do not take into account either the combination of perceptual features, or the combination of perceptual and functional features in categorizing the objects. Caramazza et al. (1982) have further proposed that the component of the naming process that may be impaired by this deficit is a modality-specific analysis which consists of a semantically constrained parsing of the perceptual input. As referenced previously, a number of studies point to difficulties for aphasic subjects in perceiving relationships between concepts, both verbal and nonverbal. Again, as Caramazza et al. (1982) have argued, such difficulties would be predicted if subjects based their judgments of similarity on only partial feature information. That the disruption in comparing individual features of objects is not, however, complete was evidenced by Cohen et al. (1980). They concluded that aphasics are equal to controls as long as they can base their judgments on a “global” rather than an “analytical” comparison. It seems possible that the “global” comparisons were able to be made on the basis of one salient feature, whereas the “analytical’ ’ comparisons required knowledge either of minor features or of combinations of features. In a furthur study, Cohen and Woll(l981)

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341

found that aphasics have difficulty in ordering a series of pictures according to a single feature, particularly if that criteria1 feature has to be inferred from the depicted objects rather than being an overt perceptual feature. It seems likely that the perceptual features were single salient features (one example being length of hair) whereas the inferred features (for example, age of persons) depended on observation of combinations of features. This reasoning can also be extended to the demonstration of decreased knowledge of functional and perceptual features of objects in aphasics (De Renzi et al., 1968, 1972; Faglioni et al., 1969). A further prediction that this theoretical position makes is that aphasics should have less difficulty matching a feature to an object if the feature is particularly salient for that object. For example, the color of an apple may be said to be more salient than is the color of a pear, shape being the most salient feature of the latter. Grober et al. (1980) showed that in verifying the membership of pictured objects in superordinate categories, aphasics demonstrated reasonably normal typicality structures for the categories, although they made an unusually large number of errors on semantically related nonmembers. This would be predicted if subjects were responding in terms of a limited number of features. 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 related category “vegetable.” In such category decision studies it has been argued that the object is first identified, then a search through semantic memory for the featural description of that category is carried out (see Jolicoeur, Kosslyn, & Gluck, 1984). If this is so, then it could be during this search of semantic features that errors in feature matching arise. Therefore, the Grober et al. (1980) study may be interpreted as extending difficulties in operating on feature sets to an entirely abstract level. Normal subjects also have difficulty rejecting related nonmembers of categories (Collins & Quillian, 1972; Schaeffer & Wallace, 1970). This, of course, presents no difficulties for the present argument, there being no necessary reason to assume that the behavior of brain-injured subjects is discontinuous with that of normals. There is, in fact, evidence that normal subjects under the stress of speeded experimental tasks also operate on minimal feature information (Easterbrook, 1959). Thus, it appears that the overselectivity hypothesis can account for a number of experimental findings, and may well have contributed to the problems aphasics encountered on the original study of categorization by Wayland and Taplin (1982). In order to address this possibility an attempt was made to replicate and extend the findings of the previous

342

WAYLAND

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TAPLIN

study using a slightly different type of prototype abstraction task. In this manner it was aimed to evaluate both the knowledge that brain-injured subjects acquired about the typicality structure of categories and also the degree of selectivity they demonstrated in responding to the features of the stimuli. The original categorization task was changed partly so as to reduce the problem-solving nature of the earlier task which could have presented difficulties of an intellectual nature for aphasics. Instead, a recognition memory test was designed, based largely on a procedure devised by Neumann (1974, 1977). In Neumann’s pattern recognition task a learning phase involved presentation of a subset of members of a category. Following this normal subjects were asked to view further patterns, and to estimate how confident they were that these patterns had occurred in the learning phase. Their level of confidence was typically highest for the prototype, or central tendency of the set of patterns, and higher for patterns of high family resemblance than for patterns of low family resemblance. The major departure from this procedure in the current study was to substitute reaction-time measures for confidence judgments, which were considered to be potentially problematic for aphasic subjects. That is, in the test phase subjects were asked to judge whether or not they had seen the patterns before, and the time taken to make these decisions was measured. In an initial experiment the performance of normal subjects was observed in order to demonstrate that predicted typicality effects could be obtained, and in particular to establish that the pattern of reaction-time data did in fact mirror that of confidence judgments. Following this, the performance of aphasic and nonaphasic brain-injured subjects was examined. EXPERIMENT

1

Method

Normal

Subjects

Fourteen first-year psychology students from the University of New South Wales participated in the experiment as part of their course requirement.

Materials The stimuli were line drawings of faces, projected from the rear onto a screen situated approximately 1 m from the subject. Thus projected, the faces measured approximately 15 cm vertically and 11 cm horizontally. The faces were varied along three dimensions: distance between the eyes, size of the nose, and shape of the mouth. Five equidistant, easily discriminable values were selected for each dimension. Stimuli were chosen according to the distribution of frequencies for each feature value illustrated in Fig. 1. Thus, a set of faces was created, distributed around a protytype face (representing the central tendency of the set) with individual faces having varying degrees of family resemblance to other set members and to the prototype. Figure 2 shows more examples of the faces produced. By summing frequencies of feature values on the three dimensions of variation, a family resemblance score for each stimulus item was obtained (cf. Rosch & Mervis, 1975). On

OVERSELECTIVITY

1

IN RECOGNITION

2 FEATURE

FIG.

1. Distribution

3

4

343

5

VALUES

of frequencies of feature values for schematic faces.

this basis stimuli were divided into those with high family resemblance and those with low family resemblance. A further division was made into new and old stimuli, old stimuli being those encountered during the learning phase and new stimuli being those that appeared initially in the test phase. New and old stimuli were matched for family resemblance.

Procedure The task was divided into two phases: a learning phase and a test phase. In the learning phase, subjects were exposed to a subset of the stimuli, which conformed to the above frequency distribution of features, but in which the prototype was omitted. Ten such stimuli were presented twice, with an exposure time of 5 set and order of presentation being randomized and counterbalanced. No response was required of the subjects at this stage. Subjects were instructed that in the test phase which followed they would have to identify faces as having been present, or having not been present, in the learning phase. No instructions were given as to the dimensions on which the faces differed. This information was given in the previous categorization task studied (Wayland and Taplin, 1982), but it was felt that this may have disadvantaged aphasic subjects who failed to comprehend the instructions. The test phase immediately followed the learning phase. The faces presented during the test phase included some old stimuli and some new stimuli, the prototype, some faces with high family resemblance and others with low family resemblance, and some faces which contained feature values which were outside the distribution of features presented in the learning phase; that is, they were clearly nonmembers of the original set. Exposure time was again 5 sec. After three practice items, 26 stimuli were presented containing the

FIG. 2. Examples of schematic faces presented as stimuli: prototype (left), high family resemblance (middle), and low family resemblance (right).

344

WAYLAND

AND TAPLIN

following stimulus types: prototype; old, high family resemblance; new, high family resemblance; old, low family resemblance; new, low family resemblance; and the six nonmember items. Order was again counterbalanced and randomized. The subjects were required to depress one of two response keys, labeled “yes” and “no.” They were instructed to press “yes” if they felt they had encountered the item in the learning phase and “no” if they had not. As each stimulus item was presented a reaction timer was started; the timer was stopped when the subject pressed a response key. This sequence of events, including the presentation of stimuli, was controlled by a Gerbrands timer box. Both reaction time, in milliseconds, and response type (“yes” and “no”) were recorded.

Measurement Responses to the six nonmember items were discarded. For the 20 items that conformed to the distribution of features in the learning phase, “yes” responses were scored as correct and “no” responses as errors. This was the case whether the responses were old or new items. Reaction-time data for the same 20 category items were also recorded.

RESULTS Reaction Time

For each subject the median reaction time for “correct” responses on each type of stimulus was calculated. Table 1 gives means and standard deviations for the reaction time to each type of stimulus in the test phase. A planned contrasts analysis of variance on these data examined the effects of family resemblance (high vs. low), stimulus type (old versus new), prototypicality (prototypes vs. other stimuli), and the linear trend on typicality (prototypes vs. high family resemblance versus low family resemblance). The familywise Type 1 error rate was set at CY = .05. Typicality effects were demonstrated by a significant linear trend, F(1, 13) = 9.687, MS, = 0.257; that is, prototypes were responded to faster than items of high family resemblance, which were in turn responded to faster than items of low family resemblance. No other contrasts reached the level of significance, although there was a trend for prototypes to be responded to faster than other stimuli, F(1, 13) = 5.635, MS, = 0.163, and for high family resemblance items to be responded to faster than low family resemblance items, F(1, 13) = 4.055, MS, = 0.728. Error Data

Table 2 gives means and standard deviations type of stimulus in the test phase. TABLE MEANS

Mean SD

AND

STANDARD

DEVIATIONS

for “errors”

on each

1

FOR REACTION NORMALS (in

TIMES

ON EACH

TYPE

OF STIMULUS

FOR

Set)

Prototype

Old HIFR

New HIFR

Old LOFR

New LOFR

2.230 0.888

2.169 0.440

2.405 0.674

2.756 1.178

2.137 1.082

OVERSELECTIVITY

MEANS

AND STANDARD

DEVIATIONS

IN RECOGNITION

345

TABLE 2 FORERRORSON EACH TYPE OF STIMULUS FORNORMALS

Prototype

Old HIFR

New HIFR

Old LOFR

New LOFR

0.143 0.350

0.380 0.783

0.857 0.826

1.571 0.821

1.286 0.795

Mean SD

The same planned contrasts analysis was carried out as for the reactiontime data. For error there was a clear typicality effect. The linear trend, F(1, 13) = 56.160, MS, = 0.275, demonstrated that more errors were made on low family resemblance items than high family resemblance items, with fewest errors being made on prototypes. The effect of prototypicality was also signficant, F(1, 13) = 48.162, MS, = 0.180, as was high vs. low family resemblance, F(1, 13) = 13.763, MS, = 0.667. No other contrasts were significant. Thus the error data, more so than the reaction-time data, provided clear-cut results. It was hoped that reaction-time measures would mirror the typicality effects demonstrated by Neumann (1977) using confidence judgments. However, as only one of the contrasts relating to typicality reached the level of significance, it may be that reaction time is not as sensitive a measure of typicality effects as is level of confidence. For this reason, it was decided to not examine reaction times in the experiment with brain-injured subjects. EXPERIMENT 2 Method

Brain-Injured

Subjects

Aphasic and nonaphasic control subjects were drawn from two Sydney rehabilitation centers.

Aphasic Subjects Speech pathologists were asked to refer subjects for inclusion in the experimental group. In order to maximize generalizability of results to a normal clinical population speech pathologists were urged to submit all possible aphasic patients currently undergoing therapy. This necessitated the inclusion of subjects with varying etiologies, leading to a heterogeneous group in terms of concomitant defects, intellectual ability, etc. While this may have produced a subject population in some ways different from that examined in previously cited experiments, it enabled the relevance of other cognitive factors in performing this task to be explored; this issue is studied further in Wayland and Taplin (1985). Thus, 20 aphasic subjects were considered. Three of these, all global aphasics, were dropped from the study because their extremely poor comprehension and unreliable yes/no responses made it impossible to administer the task. In order to assign aphasic subjects to the fluent and nonfluent groups, a phrase-length ratio for each subject was derived by the procedure proposed by Goodglass, Quadfasel, and Timberlake (1965). This produced a clear division of subjects into a fluent group and a nonfluent group. The subjects were assessed independently on the Western Aphasia

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AND TAPLIN

Battery (Kertesz, 1982) by their individual speech pathologists. As this also contains a fluency rating the division of groups on the two measures was compared. Only one subject had discrepant results, being rated as fluent by her speech pathologist according to the Western Aphasia Battery criterion, but being placed in the nonfluent group on phraselength ratio. As this subject had the highest fluency rating of the nonfluent group and as the experimenters agreed that she met the criterion for fluency set by the Western Aphasia Battery, this subject was included in the fluent group. Aphasic classification and Aphasia Quotient, according to the Western Aphasia Battery, phrase-length ratio, and neurological history for these subjects are given in the Appendix.

Nonaphasic

Control

Subjects

These subjects were drawn from the same rehabilitation center population. The criterion for inclusion of the control subjects was that they should have sustained cortical injury and have shown no evidence in their medical records of being aphasic. Nine subjects were submitted, but one was dropped from the study because her extremely poor eyesight made viewing of the stimuli impossible. Data analysis was carried out on the following three brain-injured groups: (a)Jluent aphasics: II subjects, 7 male and 4 female, average age 52 years. (b) nonfruenr aphasics: 6 subjects, 1 male and 5 female, average age 54 years. (c) nonnphasic conrrols: 8 subjects, 6 male and 2 female, average age 55 years.

Procedure Testing was administered individually to the subjects by the experimenter. Materials, procedure, and measurement of error for the brain-injured subjects were precisely as described for normal subjects.

Results Error Data Table 3 gives group means and standard deviations for error for braininjured subjects. The planned contrasts analysis of variance on these data examined both group effects-fluent versus nonfluent aphasics, and aphasics versus TABLE GROUP

MEANS

AND

3

STANDARD DEVIATIONS FOR THE NUMBER OF ERRORS STIMULUS (BRAIN-INJURED SUBJECTS)

Fluent Mean SD

Nonfluent Mean SD

Control Mean SD

Means across groups

ON EACH

TYPE OF

Prototype

Old HIFR

New HIFR

Old LOFR

New LOFR

0.636 0.881

0.242 0.513

1.018 1.029

1.636 1.226

1.545 1.233

0.500 0.764

0.665 1.016

1.200 0.400

1.333 0.471

0.333 0.471

0.250 0.433 0.480

0.166 0.440 0.319

0.400 0.400 0.864

1.375 1.218 1.480

1.ooo 1.323 1.080

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IN RECOGNITION

controls-together with the same repeated measures contrasts assessing the effects of family resemblance, stimulus type, prototypicality, and linear trend. Again a familywise Type 1 error rate set at CY= .05 was used to determine the significance of individual contrasts. Typicality effects were demonstrated for linear trend, F(l, 22) = 8.688, MS, = 0.993; for prototypicality, F(1, 22) = 6.624, MS, = 0.569; and for family resemblance (high versus low), F(1, 22) = 7.528, MS, = 1.083. There were no group x family resemblance interactions; that is, on this task, aphasic subjects, as well as nonaphasic brain-injured controls, showed the same pattern of results as normals, whereby prototypes were responded to more accurately than items of high family resemblance, which were in turn responded to more accurately than items of low family resemblance. Aphasics tended to make more errors than controls but this contrast was not significant, F(l, 22) = 1.092, MS, = 1.798, and there was no significant difference between fluent and nonfluent aphasics, F < 1. The absence of significant group differences superficially appears not to replicate the findings of our previous study of categorization (Wayland & Taplin, 1982). To some extent this may be due to the fact that the performance of the most aberrant subjects did not enter into the analysis. Two subjects, one aphasic and one control, responded “yes” to all faces whether members or nonmembers of the original category. Thus, although apparently having extreme difficulty in establishing category membership, these subjects were scored as correct on all items (nonmember items being ignored for the purpose of the analysis). This casts some doubt on the validity of concluding that in general brain-injured subjects were responding normally to this task. Further, there was a significant interaction between stimulus type (old vs. new) and family resemblance, F(1, 22) = 9.643, MS, = 0.614, that did not appear in the data of the normal subjects. This interaction demonstrates that overall more errors were made on new than old items of high family resemblance, whereas more errors were made on old than new items of low family resemblance (see Table 4). Inspection of the stimuli in these conditions reveals that this interaction could result from subjects responding on the basis of the TABLE PEARSON CORRELATION COEFFICIENTS FOR INDIVIDUAL FEATURES AND WITH AND

NONAPHASIC

BRAIN-INJURED

Normals Aphasics Nonaphasic controls

4

FOR MEAN NUMBER OF ERRORS WITH TYPICALITY SCORES FAMILY RESEMBLANCE SCORES FOR NORMAL, APHASIC,

SUBJECTS

Eyes

Nose

- .4208*” - .2022 - .2293

- .4615* .0799 - .3780*

Mouth - .5739* - .7629* - .645-o*

a * a significant, that is, nonzero, correlation (n = 20, p = .05).

Family resemblance - .7160* - .4457* - .6223*

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WAYLAND

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TAPLIN

typicality value of the mouth alone rather than the family resemblance of the face as a whole. In order to establish the relationship between the subjects’ responses and the typicality values of individual features, as distinct from the overall family resemblance score, a further analysis was carried out. For each stimulus the family resemblance score was partitioned into three typicality scores for each feature (eyes, nose, and mouth) separately. That is, each typicality score represented the frequency with which that feature occurred in the initial category distribution. For each group separately, the mean number of errors was calculated for each stimulus. Correlations between error and the typicality scores for the three feature dimensions, as well as the overall family resemblance scores for the stimuli, were calculated for normal subjects (from Experiment l), aphasic subjects, and nonaphasic brain-injured controls. As there was no evidence that fluent and nonfluent subjects had behaved differently, these two groups were collapsed into the one aphasic group. In this manner it was hoped to provide further information about the basis for the subjects’ responses, whether they were responding to the typicality of the face as a whole (as is presumed for normal subjects) or whether they were responding selectively to one feature of the face. The results of this analysis can be seen in Table 4. This analysis provided confirmation for the hypothesis that aphasics were performing this task differently from normal subjects. For normal subjects the best single predictor of their performance was the overall family resemblance score, accounting for 51% of the variance. There were significant correlations for each of the three features alone, adding weight to the suggestion that normal subjects take into account the whole face. (It should be noted, however, that these correlations for individual features could simply represent a confounding with family resemblance. As the family resemblance score is the sum of the typicality values for single features, a degree of confounding is unavoidable. The correlations between family resemblance and constituent features for the stimuli in this experiment were family resemblance with eyes, r(20) = .69, p < .OOl; family resemblance with nose, ~(20) = .65, p < .OOl; and family resemblance with mouth, r(20) = .71, p < .OOl.) For aphasics, on the other hand, the best single predictor was the typicality score of the mouth, accounting for 58% of the variance. There was a significant though lower correlation also with family resemblance, but this again reflects a confounding with the typicality value of the mouth, since neither typicality values for eyes nor nose were significantly correlated with their responses. The picture for controls was less clear-cut, family resemblance and mouth alone being equally good predictors of performance. For controls, the correlation of error with typicality of nose was also significant, r(20) = - .378, p < .05, whereas that with eyes was not, r(20) = - .229, p = .17. Clearly the brain-injured control subjects were not performing normally

OVERSELECTIVITY

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IN RECOGNITION

on this task. Just what they were doing is not certain, but two possibilities can be suggested: (a) they may have been responding to two rather than three features (nose and mouth); or (b) two types of responding may have been combined, some subjects responding like normals in terms of family resemblance, and some like aphasics in terms of a single feature, the mouth. In order to further evaluate the contribution of individual features of stimuli to the responses made by normal and brain-injured subjects, standardized regression weights for features in predicting probability of error were calculated for each group. (These weights can be seen in Table 5). This demonstrated that for all groups the mouth was weighted most heavily, but again whereas the normal subjects gave relatively equal weight to all features, the aphasic subjects, and to a lesser extent the brain-injured controls, placed particular weight on the typicality value of the mouth. DISCUSSION

From the pattern of results of this experiment it appears that aphasic subjects did have particular difficulty with the prototype abstraction task, and furthermore, the nature of their problem is now much clearer. As, in general, aphasics were responding with reasonable accuracy to the typicality value of the mouth, it seems that they were able to observe, store in memory, and respond to the typicality structure of one feature. That is, their problem appears not to be with prototype abstraction per se, as they were apparently abstracting a prototypical mouth, but rather their ditficulty lies in processing the entire set of features in a simultaneous fashion. Of interest in these data was the lack of differentiation between fluent and nonfluent aphasics, given that some previous studies (Lhermitte et al., 1971; Zurif et al., 1974) have indicated that the structure of semantic categories is less likely to be affected in nonfluent aphasics. Other experimenters have, however, reported difficulties for nonfluent, as well as fluent, aphasics on tasks of a conceptual nature (for example, Caramazza et al., 1982; Gainotti et al., 1979). It seems possible that difficulties for TABLE STANDARDIZED

WEIGHTS

FOR FEATURES

APHASIC,

Aphasics Controls Normals

AND

5

IN PREDICTING

NONAPHASIC

PROBABILITY

BRAIN-INJURED

OF AN ERROR

(NORMAL,

SUBJECTS)

Eyes

Nose

Mouth

-.03 -.02 - .24

.20 -.29 - .35

-.79 - .60 - .46

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TAPLIN

nonfluent subjects are more likely to appear if the group is unselected, possibly because nonfluent subjects with mixed anterior/posterior lesions are then included. Although aphasics again were the most aberrant group in performance on this task, controls were also having trouble, one control in fact appearing unable to establish category membership on any basis. It has been argued that prototype abstraction tasks model the manner in which categorical information is represented in memory, and that this applies both to the storage of perceptual features of stimuli, as in pattern recognition tasks such as the one reported here, and to the storage of information about natural semantic categories, that is, semantic featural information. Most theories of object recognition and naming propose only one categorical stage in processing (for example, Caramazza et al., 1982; Sutherland, 1973). That is, it is assumed that both perceptual and semantic information about objects are stored together in semantic memory. If this is so, it is hard to explain the failure of right-hemisphere-damaged subjects on this task, it being presumed that their semantic memory is intact. Visual acuity deficiency could perhaps provide an explanation, but none of the subjects employed in the study had marked visual difficulties. This was confirmed by a test of visual recognition, the description of which is included in Wayland and Taplin (1985). All subjects were successful on this test at the level of selecting previously seen pictures of common objects from a set of similar items. It is probable, however, that as subjects with mixed etiologies were included, some may have sustained bilateral injury. This, of course, could explain the failure of some primarily right-hemisphere-lesioned subjects. Nevertheless, the performance of an individual subject suggests that this conclusion is at least partially unwarranted. On the recognition memory task this subject (see control subject 5 in the Appendix) responded “yes” to all but one of the stimuli presented (a prototypical instance of the category), indicating that he was unable to distinguish between members and nonmembers of the category. On a later classification learning task (see Wayland & Taplin, 1985) he was again unable to learn about category membership-his score (25 out of 50 items correctly sorted in the test phase) being at chance level. Recent CT scan information was available for this subject. This showed no evidence of left-hemisphere damage, suggesting that the locus of his categorization dimculties lay in his posterior right-hemisphere lesion. This outcome could be explained with reference to the two-stage model of object recognition proposed by Warrington and Taylor (1978; Warrington, 1982). This model specifies two serially ordered phases. The input to the first categorical stage, the perceptual categorization system, is from a primary visual analysis carried out by the left- and right-hemisphere visual cortices. The perceptual categorization system is held to be lateralized

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IN RECOGNITION

351

to the right hemisphere, the critical anatomical structure being within the right posterior cortex. The categorized output from this system goes to the semantic categorization system, situated in the posterior part of the left hemisphere. This two-stage categorical model is based on experimental data which suggest that right-hemisphere-damaged patients do badly on tasks that maximize perceptual categorization by physical identity, whereas left-hemisphere-damaged patients are impaired on tasks which maximize semantic categorization by functional identity. If this two-stage categorical model is accepted, then it follows that both leftand right-hemisphere-damaged groups should have some difficulty with tasks involving prototype abstraction, as appeared to be the case in this study. CONCLUSION

It has been argued that performance of brain-injured subjects on a recognition memory task models an underlying difficulty in processing featural information. The pattern of results suggests that a major problem for some subjects is their inability to respond to a combination of features in a complex multidimensional stimulus. Observation of subjects suggests that there are two levels of difficulty represented. Some subjects, although able to perceive that the stimuli were faces, were unable to establish category membership on any basis. A more common response, however, was to base decisions about category membership on the value of one salient feature. Presumably these difficulties reflect the problems brain-injured subjects have in dealing with perceptual and conceptual information, both of which arguably are represented in memory in a categorical manner. The parallel between performance on the experimental task reported here and on object-recognition tasks is fairly clear, and could be said to provide a model for recognition/classification difficulties following brain injury. As such it provides an explanation for the anemic errors produced by brain-injured subjects in parallel with failure to recognize the stimulus object. Following Warrington and Taylor’s two-stage model of categorical processing (1978; Warrington, 1982), it is suggested that two types of naming errors may thus be described: first, perceptual in-class errors in nonaphasic subjects, and second, semantic in-class errors in aphasic subjects. It would seem a logical extension of this argument to propose that retrieval of words is also affected by such a feature processing difficulty. This proposal assumes that lexical retrieval also involves mapping compound featural information onto a system which is organized in a categorical fashion. One question which this paper has not addressed is whether the featureprocessing deficit involves all stages of encoding, storage, and response. In the recognition memory task, if encoding is deficient then all three

352

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AND TAPLIN

stages must necessarily be affected as what is stored must be a function of what is encoded. In adult aphasics, however, it is possible that memorial storage remains intact, although it is equally plausible that memorial representations are now based on incomplete featural information. Our understanding of this would be enhanced if it could be established whether the defect is one of limited attentional capacity at encoding, or whether it is one of limited memory storage capacity, or both. An answer to this would seem to be closely linked to an increased understanding of lexical/semantic difficulties in aphasia. REFERENCES Caramazza, A., & Bemdt, R. S. 1978. Semantic and syntactic processes in aphasia: A review of the literature. Psychological Bulletin, 85, 898-918. Caramazza, A., Bemdt, R. S., & Brownell, H. H. 1982. The semantic deficit hypothesis: Perceptual parsing and object classification by aphasic patients. Brain and Language, 15, 161-189. Cohen, R., Kelter, S., & Woll, G. 1980. Analytical competence and language impairment in aphasia. Brain and Language, 10, 331-347. Cohen, R., & Woll, G. 1981. Facets of analytical processing in aphasia: A picture ordering task. Cortex, 17, 557-570. Collins, A. M., & Quillian, M. R. 1972. Experiments on semantic memory and language comprehension. In L. W. Gregg (Ed.), Cognition in /earning and memory. New York: Wiley. De Renzi, E., Faglioni, P., Scotti, G., & Spinnler, H. 1972. Impairment in associating colour to form, concomitant with aphasia. Brain, 8, 293-304. De Renzi, E., Pieczuro, A., & Vignolo, L. A. 1968. Ideational apraxia: A quantitative study. Neuropsychologia, 6, 41-52. Easterbrook, J. A. 1959. The effect of emotion on cue utilization and the organization of behaviour. Psychological Review, 66, 183-201. Faglioni, P., Spinnler, H., & Vignolo, L. A. 1969. Contrasting behavior of right and left hemisphere damaged patients on a discriminative and a semantic task of auditory recognition. Cortex, 5, 366-389. Gainotti, G., Miceli, G., & Caltagirone, C. 1979. The relationships between conceptual and semantic-lexical disorders in aphasia. International Journal of Neuroscience, 10, 45-50. Goodglass, H., & Baker, E. 1976. Semantic field, naming and auditory comprehension in aphasia. Brain and Language, 3, 359-374. Goodglass, H., Quadfasel, F. A., & Timberlake, W. H. 1965. Phrase length and the type and severity of aphasia. Cortex, 1, 133-153. Grober, E., Perecman, E., Kellar, L., & Brown, J. 1980. Lexical knowledge in anterior and posterior aphasics. Brain and Language, 10, 318-330. Howes, D. 1967. Some experimental investigations of language in aphasia. In K. Salzinger & S. Salzinger (Eds.), Research in verbal behavior and some neurophysiological implications. New York: Academic Press. Jolicoeur, P., Kosslyn, S., & Gluck, M. 1984. Pictures and names: Making the connection. Cognitive Psychology, 16, 243-275. Kelter, S., Cohen, R., Engel, D., List, G., & Strohner, H. 1977. The conceptual structure of aphasic and schizophrenic patients in a nonverbal sorting task. Journal of Psycholinguistic Research, 6, 279-301. Kertesz, A. 1982. The Western Aphasia Battery. New York: Grune & Stratton.

OVERSELECTIVITY

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Lhermitte, F., Derouesne, J., & Lecours, A. R. 1971. Contribution a l’etude des troubles semantiques dans l’aphasie. Revue Neurologique, 125, 81-101. Neumann, P. G. 1974. An attribute frequency model for the abstraction of prototypes. Memory & Cognition, 2, 241-248. Neumann, P. 1977. Visual prototype formation with discontinuous representation of dimensions of variability. Memory & Cognition, 5, 187-197. Rosch, E. 1973. On the internal structure of perceptual and semantic categories. In T. Moore (Ed.), Cognitive development and the acquisition of language. New York: Academic Press. Rosch, E. 197.5. Universals and cultural specifics in human categorization. In R. W. Brislin, S. Bochner, & W. J. Lonner (Eds.), Cross-cultural perspectives on learning. New York: Wiley. Rosch, E., & Mervis, C. B. 1975. Family resemblances: Studies in the internal structure of categories. Cognitive Psychology, 7, 573-605. Schaeffer, B., & Wallace, R. 1970. The comparison of word meanings. Journal of Experimental Psychology, 86, 144-152. Sutherland, N. S. 1973, Object recognition. In E. C. Carterette & M. P. Friedman (Eds.), Handbook of perception. New York: Academic Press. Vol. 3. Tsvetkova, L. S. 1975. The naming process and its impairment. In E. H. Lenneberg & E. Lenneberg (Eds.), Foundations of language development: A multidisciplinary approach. New York/London: Academic Press. Vol. 2. Warrington, E. K. 1982. Neuropsychological studies of object recognition. Philosophical Transactions

Warrington,

of the Royal

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of London,

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Wayland, S., & Taplin, J. E. 1982. Nonverbal categorization in fluent and nonfluent anemic aphasics. Brain and Language, 16, 87-108. Wayland, S., & Taplin, J. E. 1985. Feature-processing deficits following brain injury. II. Classification learning, categorical decision-making, and feature production. Brain and Cog&ion,

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Whitehouse, P., Caramazza, A., & Zurif, E. B. 1978. Naming in aphasia: Interacting effects of form and function. Bruin and Language, 6, 63-74. Zurif, E. B., Caramazza, A., Myerson, R., & Galvin, J. 1974. Semantic feature representations for normal and aphasic language. Brain and Language, 1, 167-187.

As

100.0 92.2 86.8 61.6

Anemic Anemic Anemic Anemic

1.25 1.00 0.58 0.06

20 65 61 64

8 9 10 11

67.8 56.4

Conduction Anemic

a

78 56

6 7

0.36

0.39

54

5

90.9

80.3

0.61

68

4 Anemic

86.5

0.81

92.8 61.9

18

Fluent

AQ

3

Conduction

Classification (Kertesz, 1982)

3.71 0.43

Phrase-length ratio

Neurological

history

Cerebral infarct of the left parietooccipital region Motor vehicle accident: craniotomy and evacuation of right parietotemporal haematoma (possible contrecoup injury) Motor vehicle accident: brain stem injury, widespread cerebral edema Cerebrovascular accident: Diagnosis: left-middle cerebral artery thrombosis Two cerebrovascular episodes: i. Left-middle cerebral artery thrombosis ii. Left internal carotid artery stenosis Cerebrovascular accident Aneurysm of distal portion of left-middle cerebral artery (clipped) Motor vehicle accident: generalized cerebral edema Posterior parietal internal carotid hematoma Left carotid endarterectomy Left frontoparietal infarct

DATA ON SUBJECTS

67 23

(Years)

DESCRIPTIVE

1 2

Subject

APPENDIX:

28

8

Broca’s Broca’s Broca’s Broca’s

Transcortical Broca’s 16.7 40.4 47.5 32.0

70.2 55.2

Basilar artery aneurysm (clipped) Posterior fossa tumor at level of midbrain (resected) Right cerebrovascular accident Right cerebrovascular accident CT scan: “low density area in the right temporal region. Appearances suggest an infarct in the region of the basal ganglia and internal capsule on the right” Right cerebrovascular accident Extensive infarct in the territory of the right-middle cerebral artery Motor vehicle accident: Trauma in the region of the brain stem and the right temporal lobe

Left frontal meningioma Aneurysm at bifurcation of anterior branch of middle cerebral artery (clipped) Left cerebrovascular accident Left cerebrovascular accident Left cerebrovascular accident Admitted to hospital in status epilepticus (CT scan showed moderate cortical atrophy and ventricular dilation without shift)

phonemic jargon.

Control

Nonfluent motor

a Phrase-length ratio not calculated as speech barely intelligible-largely

73 20

6 7

0 0 0.04 0

76 44 77 40

65 47 79 78 54

0 0

54 34

1 2 3 4 5

1 2

g 8 3 =I

2 2 Ki s 2 % 2 4 2