Picture Confrontation Oral Naming: Performance Differences between Aphasics and Normals

BRAIN AND LANGUAGE ARTICLE NO.

53, 105–120 (1996)

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Picture Confrontation Oral Naming: Performance Differences between Aphasics and Normals GE´RARD DELOCHE Universite´ Louis Pasteur, Strasbourg, France

DIDIER HANNEQUIN Service de Neurologie, Hoˆpital Charles Nicolle, Rouen, France

MONIQUE DORDAIN INSERM, Hoˆpital Fontmaure, Chamalie`res, France

DANIELLE PERRIER Hoˆpital Bretonneau, Tours, France

BRIGITTE PICHARD Hoˆpital de Brive, Brive, France

SYLVIANE QUINT Service des Convalescents, CHU de Lille, Lille, France

MARIE-NOE¨LLE METZ-LUTZ INSERM, Hospices Civils de Strasbourg, Strasbourg, France

HELGARD KREMIN CNRS, La Salpeˆtrie`re, Paris, France AND

DOMINIQUE CARDEBAT INSERM 230, Hoˆpital Purpan, Toulouse, France

This research was supported by EC-BIOMED 1 and INSERM grants. Thanks are due to two anonymous reviewers for their valuable comments and suggestions, to the teams of the language rehabilitation centers having participated in the study with aphasic patients (Brive, Chamalie`res, Lille, Paris, Rouen, Strasbourg, Toulouse, Tours), and to those having contributed to collecting normative data (Lie`ge (M. van der Linden), Marseille (M. Bunel), Paris (C. Larroque)). Address reprint requests to G. Deloche, 12 rue Goethe, 67000 Strasbourg, France. 105 0093-934X/96 $18.00 Copyright  1996 by Academic Press, Inc. All rights of reproduction in any form reserved.

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Oral confrontation naming was compared in 108 normal subjects controlled for education, age, and gender and in 18 aphasic patients for the same set of 115 pictures. Demographic variables influenced both normals’ and aphasics’ performance. However, the nature of aphasics’ misnamings on the one hand and the differential effects of characteristics of pictures and words on normals’ and aphasics’ responses on the other indicated specific deficits in patients. The classical hypothesis that aphasics’ misnamings and the production of word associations by normals should rely on similar mechanisms (Rinnert & Whitaker, 1973) is questioned. Nondominant responses observed in normals accounted for a larger proportion of verbal errors than associates to target words.  1996 Academic Press, Inc.

INTRODUCTION

Word-finding difficulty is certainly one of the most common features in aphasia. It has been particularly studied in picture confrontation naming tasks. This experimental situation is probably favored due to the fact that the target word that the patient is looking for (the name of the presented picture) is (supposedly) known to the examiner without any ambiguity, which is far from always being the case in the study of word-finding difficulties in the spontaneous speech of anomic or jargonaphasic aphasics (for a review, see Kremin, 1988). It must, however, be pointed out that since pictures generally elicit more than one name in groups of normal subjects, there is no absolute certainty as regards which particular lexical entry the patient is searching for (Snodgrass & Vanderwart, 1980; Kremin, Deloche, MetzLutz, Hannequin, Dordain, Perrier, Cardebat, Ferrand, Larroque, Naud, Pichard, and Bunel, 1991). Information processing models generally distinguish three main steps in picture naming: (i) perceptual analysis of the picture for extracting a presemantic structural description, (ii) access to stored semantic information from the preceding structural knowledge, (iii) selection of the output verbal representation of the name to be produced (Morton & Patterson, 1980; Morton, 1985; Howard & Orchard-Lisle, 1984; Kirshner, Webb, & Kelly, 1984; Riddoch & Humphreys, 1987; Hillis & Caramazza, 1991; Semenza, Bisiacchi, & Romani, 1992). Within such a theoretical framework, naming failures may occur at different levels (perceptual, semantic, lexical). Further distinctions can thus be drawn. For instance, Whitehouse, Caramazza, and Zurif (1978) considered selective deficits in conceptual representations on the one hand and in impaired lexical selections on the other hand. Pease and Goodglass (1978) distinguished semantic, lexical, and production naming disorders. Category-specific naming disorders were also reported (Hillis & Caramazza, 1991; Sartori, Job, Miozzo, Zago, & Marchiori, 1993). Some factors related to either pictures or to their names appeared to influence normal subjects’ and aphasics’ performance (Bisiach, 1966; Corlew & Nation, 1975; Wyke & Holgate, 1973). Significant effects have been reported as regards name frequency (Newcombe, Oldfield, & Wingfield, 1965; Goodglass, Theurkauf, & Wingfield, 1984; Snodgrass & Vanderwart, 1980; How-

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ard, Patterson, Franklin, Morton, & Orchard-Lisle, 1984; Kay & Ellis, 1987; Feyereisen, Van der Borght, & Seron, 1988), word length (Howard et al., 1984), age of acquisition (Snodgrass & Vanderwart, 1980; Feyereisen et al., 1988), operativity (Gardner, 1973; Feyereisen et al., 1988), familiarity, visual complexity, and image agreement (Snodgrass & Vanderwart, 1980; Whitehouse et al., 1978). Some individual characteristics were also reported as relevant factors in confrontation naming by normals: age (Albert, 1980; Nicholas, Obler, Albert, & Goodglass, 1985; La Barge, Edwards, & Knesevich, 1986), educational background (Borod, Goodglass, & Kaplan, 1980; Reis, Guerreiro, & Castro-Caldas, 1994), and gender (Kindlon & Garrison (1984) for children; Rosselli, Ardila, Florez, & Castro (1990) in adults). However, the influence of such demographic variables on picture naming is not clearly defined. Whereas some studies reported main effects of the three variables and some interactions (e.g., Kremin et al., 1991), others observed no age (e.g., Flicker, Ferris, Crook, & Bartus, 1987), education, or gender (La Barge, Edwards, & Knesenich, 1986) effects. As concerns the different types of misnamings, particular attention has been devoted to the study of semantic paraphasias produced by aphasic patients (Caramazza & Hillis, 1990) and elderly normals (Goodglass, 1980). An interpretation of the production of semantic errors has been sought by relating the intended (target)-substituted (error) word pairs to norms of word association pairs (Rinnert & Whitaker, 1973; Goodglass, 1980; Huber, 1981; Butterworth, Howard, & McLoughlin, 1984; Howard et al., 1984). The general issue of concern is whether the two production mechanisms follow similar lexico-semantic organizational patterns, or resort to different linguistic performance strategies. Results are not clear-cut. The proportion of misnamings which were indeed associates of target words varied from 8% (Butterworth, Howard, & McLoughlin, 1984) to 72% (Rinnert & Whitaker, 1973). Such discrepancies might be due to differences in the collection of naming errors on the one hand and in the definitions of error types and associates on the other. According to Huber (1981), associative words produced by normals in response to pictures (which corresponds to the confrontation naming task administered to patients) have only 30% in common with associative words produced in response to words. Moreover, semantic paraphasias observed in aphasics have only about 20% in common with associates produced by normals in response to words or pictures (Huber, 1981). Given the variety of picture characteristics that have been reported to influence naming, but without clear information on their relative importance, the lack of precise knowledge of the main effects and interactions due to individual differences, and the discrepancies between the results reported in the literature on the associate words as a source of misnamings in aphasics, the aim of this study was to examine in a more detailed and methodologically controlled way four issues. First, the effects of factors characterizing pictures and words on the perfor-

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mance of normals and aphasics with misnaming errors need to be clarified using the same set of pictured material. Second, the effects of individual differences also need to be clarified in normals since systematic bias might have obscured some previous studies contrasting groups of patients with noncomparable groups of normals (as regards for instance age and education). Third, the hypothesis proposed by Rinnert and Whitaker (1973) concerning similar mechanisms in misnamings by aphasics and in the production of target associates by normals has to be carefully studied. Fourth, categories of words, other than associates, as possible sources for aphasics misnamings should be considered (e.g., nondominant responses produced by normals, i.e., names distinct other than the one given by the majority of subjects (Deloche, Kremin, Metz-Lutz, & Lota, 1991)). Finally, as regards the last two issues, some information should be obtained from this study with respect to the view that aphasics’ misnamings in terms of global disturbances simply magnify the level of errors produced incidentally by normals (i.e., quantitative differences but without qualitative differences). MATERIALS AND METHODS Procedure and data analysis. Pictures consisted of 115 line drawings, 72 of which were adapted from Snodgrass and Vanderwart (1980). Items were presented on the monitor of a microcomputer and were selected on the basis of relatively high name agreement (..80) in a population of normal subjects (see below and Deloche, Ferrand, Metz-Lutz, Dordain, Kremin, Hannequin, Perrier, Pichard, Quint, Larroque, Cardebat, Naud, Bergego, Pradat-Diehl, & Tessier, 1992). Oral responses were tape-recorded and further transcribed. Multiple responses to a given picture presenting several nouns at the same category level were treated as separate responses and scored independently (e.g., ‘‘orange, lemon’’ considered as two different responses). Due to these multiple responses, the number of tokens per picture might thus be greater than the number of subjects (108). In fact, it varied between 108 and 115. Responses where nouns were flanked by adjectives (e.g., ‘‘old canon’’), by verbal developments (e.g., ‘‘a tap which leaks water’’), or by a superordinate (e.g., ‘‘this animal, ant’’) were treated as nouns alone (e.g., ‘‘canon, tap, ant,’’ respectively). Empty words or comments, circumlocutions or definitions, no responses, or negated responses (e.g., ‘‘not a magnet’’) were considered no responses. Responses submitted to further analysis were thus either verbal lexical entries or no responses. The frequency of occurrence of the different nouns (types) was computed for each picture. The dominant response was defined as the one produced by a majority of the 108 normal subjects. Name agreement (NA) was computed by dividing the number of occurrences of the dominant response by the total number of responses (tokens) produced by the 108 subjects for that particular picture. Types refer to the number of distinct lexical entries, whereas tokens correspond to the cumulated frequency of occurrence of the types. Depending on their relation to dominant responses, nondominant responses were classified into: (i) verbal deviation, further categorized according to typologies proposed by Goodglass (1980) and Snodgrass and Vanderwart (1980) into synonyms, coordinates (e.g., ‘‘tractor’’ for ‘‘truck’’), superordinates (e.g., ‘‘tool’’ for ‘‘screw-driver’’), and subordinates (e.g., ‘‘viper’’ for ‘‘snake’’); (ii) both verbal and visual deviations further categorized into coordinates (e.g., ‘‘tomato’’ for ‘‘pimento’’) or noncoordinates; (iii) visual deviations (e.g., ‘‘handbag’’ for ‘‘padlock’’); and (iv) miscellaneous (see Table 2).

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TABLE 1 Characteristics of Pictures and Their Dominant Names N Transformed word frequency a No. of phonemes Level in age of acquisition b Familiarity c Visual complexity d Image agreement d

115 115 108 72 72 72

Range .90, 2, 3, 1.52, 1.00, 2.27,

4.95 9 37 4.90 4.78 4.62

Mean

Standard deviation

3.15 4.51 16.44 3.24 3.01 3.80

.69 1.56 6.16 1.02 1.02 .53

a

Decimal log of the relative frequency of occurrence on a total of 23,505,451 occurrences. According to norms, level 37 is mastered by more than 90% of 14-year-old children, and level 3 by more than 75% of 6-year-old children. c From the 5-point scale of Snodgrass and Vanderwart (1980), where 1 refers to a very unfamiliar picture and 5 to a very familiar one. d Five-point scale used by Snodgrass and Vanderwart (1980). b

Patients’ oral namings were analyzed with the same conventions as for normals. Aphasics’ responses were scored correct if and only if they corresponded to the dominant responses observed in the group of 108 normal subjects. Phonemic paraphasias and neologisms were counted errors, whereas phonetic distortions were not. From a lexical point of view, patients’ responses were thus nouns, nonwords (phonemic paraphasias, neologisms) or no responses. Verbal paraphasias were analyzed with respect to the same classification as for controls. Phonemic paraphasias that incidentally resulted in another legal word were not treated as verbal errors but were considered to be phonemic errors (e.g., ‘‘crapaud’’/kRapo/(toad) for ‘‘drapeau’’/dRapo/(flag)). The analyses were performed on aphasics and normals following the same plan with reference to the issues of concern, but separately. Due to the extreme discrepancies in the order of magnitude of the dependent variables between the two groups (e.g., mean percentage of nondominant responses about 4% in normals and 51% in aphasics; see below), the data were not pooled for single analysis designs (e.g., ANOVAs). Information characterizing pictures and names. Some information concerning target names and pictures was available from different published sources; other information was collected for the purpose of this study. We considered: (i) name frequency (values of the occurrences in the second part of the 20th century (Imbs, 1971) were transformed by log 10); (ii) number of phonemes; (iii) age of acquisition (norms were available on 108 of the 115 items only for written words, in the form of 43 graded levels in age of acquisition (Ters, Mayer, & Reichenbach, 1975)); (iv) familiarity, visual complexity, and image agreement (we adopted the measures of Snodgrass and Vanderwart (1980) for the 72 pictures common to the two studies). The distribution of these variables is shown in Table 1. In addition, we recorded norms for association words. Data were collected on a specific group of normals (N 5 60) equally distributed as regards their individual characteristics (gender, age (20–39, 40–59, 60–75), education (9 years or less versus more than 9 years)). We defined close associates as those produced by at least 10% of normals in oral response to pictures. Population. Normal subjects were recruited by psychologists and speech therapists. Subjects with a history of central nervous system disease, psychiatric illness, or suggestion of dementia were not included in the study. Subjects with vision problems corrected by glasses were not excluded. Subjects were controlled for education (two classes: 9 years of schooling or less,

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more than 9 years), gender (two classes), and age (three classes: 20–39, 40–59, 60–75). There were 9 subjects in each of the 12 experimental cells (total: 108 normals). Teams from seven different sites (Lie`ge, Marseille, Paris, Rouen, Strasbourg, Toulouse, Tours) contributed to standardization; the population of normals could thus be taken as representative of the Frenchspeaking community in France and Wallonia. Subjects were asked to name the pictures as accurately and using as few words as possible. There was no time constraint, but comments were rare during the naming task. There were 18 aphasic patients. They were engaged in a study of the effectiveness of a computerized rehabilitation program for naming disorders involving the participation of eight different language rehabilitation centers in France (Deloche et al., 1992). There were 12 males and 6 females, age ranged from 22 to 76 years (median: 57.5 years), median postonset time was 10.5 months (range: 5 months to 13 years). There were 14 ischemic and 4 hemorrhagic strokes. Patients presented Wernicke’s (5 cases), anomic (4), global (4), Broca’s (2), and conduction (2) aphasias (one case due to thalamic hematoma remained unclassified).

RESULTS

We will first report on oral naming performance by the group of 108 normal subjects. Analyses will concern the statistical distribution of name agreement (NA), the role of demographic variables on NA, the effects of the characteristics of pictures and names on NA, and the study of nondominant responses. We will then analyze the performance of the group of 18 aphasic patients in the same way: distribution of error rates, role of picture and name variables, possible origin of aphasics’ misnamings with respect to some lexical processes at work in normals (association words, non dominant oral naming responses). Oral Naming Responses in Normal Subjects

Distribution of NA Dominant responses accounted for at least 83% of the responses to each of the 115 pictures. Mean NA was slightly higher in our study (.96 6 .04) than in Feyereisen et al. (1988) (.91 6 .10) and Snodgrass and Vanderwart (1980) (.86 6 .14). There was greater homogeneity (lower standard deviation) despite higher heterogeneity of subjects as regards age and education (mean age 24 years in Feyereisen et al. (1988), psychology students in Snodgrass and Vanderwart (1980)). However, since the number of pictures differed in the three studies, direct comparisons between these values are hazardous (Howes, 1971). Role of Demographic Variables on NA Data were submitted to a three-way analysis of variance with the frequency of dominant responses as the dependent variable, whereas age of subjects, gender, and education were the independent variables. A principal effect of each individual variable was found. Name agreement decreased with age (.96 under 40 years, .94 between 40 and 59; .92 above 60 years;

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F(2, 228) 5 28, p , .001). NA was higher in educated subjects (.95 and .92, respectively; F(1, 114) 5 33.2, p , .001) and in women (.95 and .93, respectively; F(1, 114) 5 9.8, p , .01). There were significant interactions: education and gender (F(1, 114) 5 12.5, p , .001), education and age (F(2, 228) 5 4.4, p , .05), age and gender (F(2, 228) 5 5.7, p , .01). These results were similar to the ones already reported on a subset of 88 pictures that showed the three main effects and an education and gender interaction, but not the last two interactions (Kremin et al., 1991). Effects of Characteristics of Pictures and Names on NA Pearson’s correlation coefficients were computed between NA on the one hand and the six variables (see Table 1) characterizing pictures and their names on the other. The significant negative correlation between NA and age of acquisition (r 5 2.27; p , .01) is in agreement with findings by Feyereisen et al. (1988). The positive correlations between NA and the frequency of the names of pictures (r 5 .18; p , .05) and with familiarity (r 5 .38; p , .001) could reflect lexico-semantic properties: concepts, the names of which are frequent and their visual representations familiar, should be less ambiguous and thus more suitable to be given a highly dominant name (if not unique) than less frequent and more vague concepts. Another interpretation might refer to models of the lexicon where frequency is a principal factor in the activation of words in the output phonological lexicon (Morton, 1980; Riddoch & Humphreys, 1987). No other statistically significant correlation was observed. Multiple regression analysis was performed with NA as the dependent variable, three characteristics of pictures (image agreement, familiarity, visual complexity), and three characteristics of names (age of acquisition, frequency of occurrence, number of phonemes) as independent variables (stepwise forward analysis). Results showed that only two variables, familiarity of pictures (partial r 2 of the variable: .14) and image agreement contributed for more than 1% to the variance of NA. Taken together, these variables accounted for 18% of total variance. The linguistic characteristics of names contained no significant additional information. Nondominant Responses Distribution of error types. The 12,420 items (108 3 115) administered to normals yielded 540 nondominant responses. They were either no-responses (16% of the total of responses, observed on 48 different pictures) or nondominant responses (84% of the total, i.e., 455 tokens for 258 types). Classification of nondominant responses. Results (Table 2) showed more purely verbal (N 5 151) than purely visual (N 5 85) nondominant responses. Globally, coordinates were twice as frequent as superordinates and subordinates pooled together. The same tendency was reported by Snodgrass and

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TABLE 2 Distribution of Nondominant Naming Responses in Normals Tokens Verbal Synonyms Coordinates Superordinates Subordinates Verbal and visual Noncoordinates Coordinates Visual Miscellaneous Total

151 37 22 44 48 201 8 193 85 18 455

Tokens % (n 5 455) 33 8 5 10 11 44 2 42 19 4

Types 75 16 11 22 26 110 5 105 60 13 258

Vanderwart (1980). Misnamings of visual type accounted for 19% of nondominant responses. They are generally reported in elderly normals (Goodglass, 1980; Metz-Lutz et al., 1991). Verbal errors and associates. There were 278 different close associates (types). Of the 258 nondominant responses (types), only 9 were close associates (3.5%). This is at variance with Goodglass (1980) who found that close associates of target words accounted for 70% of nondominant responses produced by two groups of elderly normals. Oral Naming Responses in Aphasic Subjects

As already indicated, patients’ responses were scored correct when they corresponded to the dominant responses produced by normals. Distribution of Correct Responses (CR) For the 18 aphasic patients, CR to the 115 pictures ranged from .06 to .83; median .50; mean .49; and standard deviation .17. The shape of the distribution was approximately normal, which was not the case for control subjects. Effects of Characteristics of Pictures and Names on Correct Response Rates Correlations were studied between CR and information characterizing either pictures or their names (Table 3). Significant correlations were found with all variables except image agreement and visual complexity of pictures. The lack of effect of these two characteristics replicated findings reported by Newcombe et al. (1971) and by Goodglass (1980). It confirmed that the group of 18 aphasic patients presented almost no deficit in perceptual analy-

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TABLE 3 Correlations between Correct Responses and Picture and Name Characteristics in Aphasics Item dimensions

N

Transformed name frequency Age of acquisition No. of phonemes Type/token ratio in normals Name agreement in normals Familiaritya Visual complexity a Image agreement a

115 108 115 115 115 72 72 72

r .52 2.44 2.43 2.42 .38 .33 2.14 .10

p, p, p, p, p, p, ns ns

.001 .001 .001 .001 .001 .01

a Values of item characteristics taken from Snodgrass and Vanderwart (1980).

sis of pictures (first level of the naming models mentioned above). Correct response rates were more sensitive to word frequency in aphasics (r 5 .52) than in normals (r 5 .18) and this correlation was situated in the range of other studies (r 5 .33 for Howard et al. (1984); r 5 .73 for Feyereisen et al. (1988)). The correlation between CR and age of acquisition was higher (2.44) in aphasics than in normals (2.27), but without reaching the level (2.76) reported in Feyereisen et al. (1988). Correlation between CR and word length (number of phonemes) was greater (2.43) than in English (2.15; Howard et al. 1984). Significant correlations between CR in aphasics, on the one hand, and the diversity of responses in normals (indicated by type/token ratio (r 5 2.42)) or by name agreement in normals (r 5 .38) on the other hand, confirmed similar findings reported by Mills, Knox, Juola, and Salmon (1979) but not replicated by Feyereisen et al. (1988). Multiple regression analysis was then performed with oral misnaming rate as the dependent variable and the eight picture and name characteristics as the independent variables; results of the stepwise forward analysis provided a combination of variables for the statistical model (Table 4). The frequency of the names of pictures, the diversity of nondominant responses (type/token ratio) produced by normals, and to a lesser degree the number of phonemes appeared to be the relevant variables. This finding is in contrast to the results in normals, where two factors were found (familiarity, image agreement). It should be noted that picture naming in aphasics was influenced by the diversity of nondominant responses in normals (type/ token ratio) rather than the level of name agreement on dominant responses. Misnamings: Where Do Aphasics’ Erroneous Responses Come from? Two possible sources for aphasics’ misnamings will be briefly considered: close associates to target words and nondominant responses.

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TABLE 4 Multiple Regression on Misnaming Rates in Aphasics Variables entered

r2

Partial r2

F

Transformed frequency Type/token ratio in normals No. of phonemes Age of acquisition Visual complexitya Image agreementa Familiarity a Name agreement in normals

.28 .40 .46 .48 .48 .48 .48 .48

.28 .12 .06 .01 — — — —

24.74 21.29 13.94 14.09 11.21 9.28 7.87 6.78

a Values of item characteristics taken from Snodgrass and Vanderwart (1980).

Misnamings and associates. For this particular analysis, phonemic paraphasias produced by aphasics were corrected into words when they unambiguously referred to clearly recognizable lexical entries. Taken together (verbal paraphasias and such corrected phonemic paraphasias), there were 151 types (distinct words) corresponding to 226 tokens (cumulated word occurrences). Only 15 types, corresponding to 20 tokens, were close associates. In addition, 3 of the 151 types (corresponding to 10 tokens) belonged to the 9 words which were both close associates and nondominant responses. Associates thus accounted for only 9% of aphasics’ misnamings (the percentage increased to 13% if associates which are also nondominant responses were included), which is lower than the results reported in other studies (Rinnert & Whitaker, 1973; Goodglass, 1980; Huber, 1981). A methodological point could account for this discrepancy. In cases where several nouns were produced in response to a given picture, we recorded the more precise noun, independently of its place, whereas only first responses, without self-corrections, were analyzed by Huber (1981). However, a reanalysis of our data showed 322 (tokens) misnamings in first responses, 22% of which were nondominant responses, 11% were associates, and an additional 2% belonged to both categories. Thus associates still accounted for a lower proportion of misnamings than in other studies. Misnamings and nondominant responses. Among the 226 misnamings (tokens) there were 61 that belonged to the 249 nondominant responses. Since the number of close associates produced by normals (269) was in the same order of magnitude, direct comparisons can be made; only a few misnamings (9% of tokens, 10% of types) were close associates, whereas 27% (tokens or types) were nondominant responses. DISCUSSION

We review successively the main issues of concern raised in the introduction. Some comparisons with results reported by Feyereisen et al. (1988) in

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aphasic patients may be made since the groups of subjects were of equal size in both studies (N 5 18). Effects of Characteristics of Pictures and Names on Normals’ and Aphasics’ Naming Performance Familiarity of pictures and image agreement were the relevant factors in normals on multiple regression analysis. Word frequency, diversity of nondominant responses, and number of phonemes were significant in aphasics. Word frequency thus seemed to play a more important role in pathological than in normal subjects’ naming process. Within the theoretical framework provided by models of lexical processing that have modality-specific lexicons, this should be viewed as the result of deficits in the phonological output lexicon since the activation of its lexical entries is supposed to be sensitive to frequency of usage (Morton, 1980; Morton & Patterson, 1980; Riddoch & Humphreys, 1987; Miceli, Giustolisi, & Caramazza, 1991). As pointed out by Rapp and Caramazza (1993), further distinctions attributing the frequency effect to an access or to a storage deficit (Kay & Ellis, 1987; Ellis & Young, 1988; Kremin, 1994) would depend on the particular architecture of subcomponents (see Butterworth, Howard, & McLoughlin (1984) for models with single phonological lexicon addressed for both comprehension and production (Allport & Funnell, 1981) or models with a semantic lexicon linking phonological forms to semantic representations. See also Warrington and Shallice (1984) for an interpretation of frequency effects as an indicator of a degradation deficit at the semantic level). The global results of our group study, however, do not allow for in depth analysis of these different theories for which multiple single cases would be more appropriate (Howard et al., 1984). There was a discrepancy between our results and those reported by Feyereisen et al. (1988) on the role of age of acquisition, name frequency, and picture familiarity in aphasics. Moreover, the diversity of names given by normals to a given picture (uncertainty) had a much lower contribution to aphasics’ error rates in the multiple regression analysis of Feyereisen et al. (1988) than in our study (partial r 2, .04 and .12, respectively). The latter study might have minimized the role of the diversity of names since some aphasics’ responses were scored correct precisely when they were nondominant responses ‘‘given by a small proportion’’ (unspecified) of normal subjects. In our study, the diversity of nondominant responses (type/token ratio), rather than name agreement was the relevant factor for aphasics. Patients thus had problems when target names had to be selected from a large number of ‘‘normal’’ plausible candidates. It could indicate some disturbances in semantic processing (second level of information processing in naming models) since most nondominant responses were coordinates, and coordinates were considered by Rosch, Mervis, Gray, Johnson, and Boyes-Bream (1976)

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to be the level at which the semantic system is accessed. Such interpretation is in agreement with previous reports on semantic disorders in patients with naming difficulties (Howard et al., 1984; Gainotti, Silveri, Villa, & Miceli, 1986). Aphasics’ performance thus showed the two classical types of naming disturbances, defective lexical processes and semantic disorganisations (Luria, 1973; Gainotti et al., 1986), situated in the theoretical framework of selective disturbances introduced by pathology in the different levels of information processing models developed for normal ability. Effects of Demographic Variables on Normals’ Performance As in studies that experimentally controlled for individual differences, we replicated effects of education, age, and gender on normal picture naming (Bachy-Langedock, 1987; Rosselli et al., 1990; Kremin et al., 1991). This is probably one reason to explain certain discrepancies between our findings on aphasics’ performance and those from other studies (e.g., Huber, 1981; Feyereisen et al., 1988) which did not pay the same attention to individual variables when comparing patients to normative data. The interaction between age and educational background confirmed previous reports (Borod, Goodglass, & Kaplan, 1980; Rosselli et al., 1990) and suggestions (Van Gorp, Satz, Evans, Kiersch, & Henry, 1986). The interaction between education and gender, and the main effect of gender might result from socio-cultural factors, as in the study by Kremin et al. (1991) where statistically significant differences disappeared when some kitchen and household tools were removed from the test material. Aphasics’ Misnamings and Target Word Associates According to the hypothesis proposed by Rinnert and Whitaker (1973), the production of semantic paraphasias in aphasics and associative words in normals should be related. However, since associative words are not necessarily semantically linked to target names, the hypothesis should consider not only semantic errors but at least all misnamings of verbal type (i.e., neologisms and no-responses might be excluded). Since the diversity of associates is known to be greater in French than in English (Rosensweig, 1970), mean name agreement should be consequently lower in French. However, name agreement (NA) on the 72 pictures common to our study and to Snodgrass and Vanderwart (1980) seems to indicate the reverse pattern in normals (mean NA, .96 and .91 in French and English respectively; t 5 .44, df 5 71, ns). This finding suggests that, at least in normals, there is no direct relationship between the mechanisms subserving the production of picture associations on the one hand and picture naming on the other. Discrepancies concerning the proportions of associates in nondominant responses produced by normals when comparing our results (4%) to findings (70%) by Goodglass (1980) might be due to differences in the demographic characteristics of the

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two populations. Effects of such variables have indeed been reported on the production of associates (Kent–Rosanoff list) in different languages by Rosensweig (1970) as regards age and education. Normals were 20 women in their 80s in the study by Goodglass (1980), whereas our 108 controls ranged from 20 to 75 years and were balanced according to education and gender. Associates accounted for 9 to 13% of the total of verbal misnamings produced by aphasics. Considering only semantic paraphasias, as in the original paper by Rinnert and Whitaker (1973), showed 10 and 15% for associates that were not nondominant responses and nouns that were both associates and nondominant responses, respectively. The production of verbal errors in picture naming by aphasics on the one hand and the production of associative words by normals on the other thus seem to have little in common from a psycholinguistic point of view. Aphasics’ Misnamings and Nondominant Responses In aphasics, we found a significant correlation between error rates on pictures and the diversity of responses elicited by the same pictures in normals. The more different nondominant names (types) were produced by normals, and the higher their cumulated frequency (tokens), the less chance aphasics had to produce the dominant responses. Such a finding suggests that normal nondominant responses could constitute a potential lexical source for aphasics’ misnamings. This hypothesis was supported by the finding that nondominant responses accounted for about 27 to 31% of aphasics’ misnamings, thus more than associates. Considering semantic errors only, the percentage rose to 32 and 36%, depending on whether nouns that were both associates and nondominant responses were excluded or not. There were several pieces of information indicating that naming deficits observed in the group of aphasic patients should not be viewed as an abnormal augmentation of a phenomenon uncommon but incidentally observed in the group of normal subjects. First, the scores of the two populations were differentially sensitive to the characteristics of pictures and their names. Second, the curves of the distributions of scores were not comparable and directly deducible from one to the other by some kind of degree of difficulty factor. The parallel often made between aphasics and normals as concerns slips of the tongue (e.g., Buckingham, 1980) does not seem to extend to picture naming. Beyond the similarities between the two populations evidenced at a superficial behavioral level, there are some differences in the basic mechanisms, as pointed out for instance in the interpretations of word frequency effects by Howard et al. (1984). However, this study showed that a proportion of so-called ‘‘errors’’ in aphasics on confrontation naming tests could be tentatively related to two processes at work in normals. First, the process by which normals produce associates to target words accounted for about 10% of aphasics’ misnamings.

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