Phonemic Fluency, Semantic Fluency, and Difference Scores: Normative Data for Adult Hebrew Speakers

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Journal of Clinical and Experimental Neuropsychology, 27:690–699, 2005Copyright © Taylor & Francis Ltd.ISSN: 1380-3395DOI: 10.1080/13803390490918499

NCEN1380-3395Journal of Clinical and Experimental Neuropsychology, Vol. 27, No. 06, April 2005, pp. 0–00Journal of Clinical and Experimental Neuropsychology

Phonemic Fluency, Semantic Fluency, and Difference Scores: Normative Data

for Adult Hebrew Speakers

Hebrew Fluency NormsG. Kavé GITIT KAVÉ

Department of Communication Disorders, Tel Aviv University, Israel

Norms for Hebrew semantic and phonemic fluency were collected in a sample of 369participants, ranging in age from 18 to 85. Two hundred and sixty nine persons com-pleted both tests and the rest completed only the semantic test. Phonemic fluency wasassessed with the use of three letters (bet, gimel, and shin) and semantic fluency withthe use of three categories (animals, fruits and vegetables, and vehicles). Scores ofindividual letters and categories, sum scores, as well as the difference between thesemantic and phonemic sum scores are presented for four age groups (18–30, 31–50,51–70, and 71–85). Results show that age had the greatest effect on fluency perfor-mance, level of education was positively correlated to sum scores but contributed littleto its prediction beyond the contribution of age, and gender had no significant effect.

Introduction

Word fluency tests are included in almost every neuropsychological evaluation (Lezak,1995), and they are sensitive to cognitive impairment from a variety of etiologies.Fluency measures are useful in detecting dementia (Barr & Brandt, 1996; Binetti, Magni,Cappa, Padovani, Bianchetti, & Trabucchi, 1995; Epker, Lacritz, & Cullum, 1999), in dif-ferentiating among types of dementia (Rosser & Hodges, 1994), and in pointing out fron-tal lobe involvement (Stuss et al., 1998). As a general rule, deficits in phonemic fluencyare more sensitive to frontal brain damage, as opposed to deficits in semantic fluency,which are more sensitive to temporal damage (Troyer, Moscovitch, Winocur, Alexander, &Stuss, 1998).

Several cognitive abilities underlie fluency performance. Word generation is moststrongly correlated with measures of vocabulary and auditory attention (Ruff, Light,Parker, & Levin, 1997), as well as with articulation speed (Hughes & Bryan, 2002). Rela-tively automatic processes of clustering that rely on word storage, and relatively effortfulprocesses of shifting that rely on strategic search and mental flexibility further influenceperformance (Troyer, 2000; Troyer, Moscovitch, & Winocur, 1997). The task involves

Part of this work was supported by grants from the Brookdale Institute of Gerontology andHuman Development, Eshel—The Association for the Planning and Development of Services forthe Aged in Israel, and the Israel Foundations Trustees, which the author received while completinga doctorate degree at the Hebrew University of Jerusalem. I am grateful to Limor Assayag, NaomiBarancik, Sharon Malka, Maya Marcus, Amos Raber, Diana Tsimkin, and Daphna Tzur for theirhelp in data collection.

Address correspondence to Gitit Kavé, Ph.D., Department of Communication Disorders, SacklerFaculty of Medicine, Tel Aviv University, Sheba Medical Center, Tel Hashomer, Israel, 52621.E-mail: [email protected]

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initiation of numerous searches of subcategories, retrieval of words within these subcate-gories, and efficient shifting from one subcategory to another (Troyer et al., 1997; 1998).

Demographic characteristics such as age, education level, and gender have beenshown to affect fluency performance, yet findings are somewhat inconsistent, most likelydue to variance in both population sampling and analysis methods. The majority of studiesfound a progressive age-related decrease in the number of generated words (Loonstra,Tarlow, & Sellers, 2001; Lucas et al., 1998; Tombaugh, Kozak, & Rees, 1999). However,more significant age effects were found in semantic fluency than in phonemic fluency(Gladsjo et al., 1999; Kozora & Cullum, 1995; Mathuranath, George, Cherian, Alexander,Sarma & Sarma, 2003; Troyer et al., 1997). An increase in word production has beenfound with higher levels of education (Crossley, D’Arcy, & Rawson, 1997; Gladsjo et al.,1999; Tombaugh et al., 1999), especially for the phonemic fluency task (Ratcliff et al.,1998; Ruff, Light, Parker, & Levin, 1996). Women may slightly outperform men on pho-nemic fluency tests (Capitani, Laiacona, & Basso, 1998; Crossley et al., 1997; Loonstraet al., 2001), whereas gender affects performance on certain semantic categories but noton others (Capitani, Laiacona, & Barbarotto, 1999). However, when these three demo-graphic variables are considered together, age and education have a much greater effect onfluency than does gender, and most normative studies do not stratify fluency data accord-ing to gender (Ivnik, Malec, Smith, Tangalos, & Peterson, 1996; Tombaugh et al., 1999).

Healthy individuals tend to provide more words by semantic categories than by letters,whereas brain pathology can lead to the opposite pattern of performance (Cerhan, Ivnik,Smith, Tangalos, Petersen, & Boeve, 2002). For example, individuals with Alzheimer’sdisease often, though not always, show greater difficulties in semantic fluency relative tophonemic fluency (Cerhan et al., 2002; Diaz, Sailor, Cheung, & Kuslansky, 2004; Monschet al., 1994; Sherman & Massman, 1999). Disproportionate semantic fluency impairmentis also reported in schizophrenia (Bokat & Goldberg, 2003; Kremen, Seidman, Faraone, &Tsuang, 2003). Thus, the difference in performance between the two fluency tests couldbe utilized in neuropsychological assessment of specific disorders. Discrepancy measuresare available for healthy English speakers (e.g., Delis, Kaplan, & Kramer, 2001) and oneof the aims of the current study is to provide such norms for Hebrew.

In addition to the difference between semantic and phonemic fluency measures, rawscores for one letter combination are not directly comparable to raw scores for anothercombination (Ruff et al., 1996), and the sum of exemplars provided for the same lettercombination varies across languages (Tombaugh et al., 1999). English speakers provideboth grammatical and content words but Spanish speakers provide only the latter (Rosselli,Ardila, Salvatierra, Marquez, Matos, & Weekes, 2002). The FAS letter combination, com-monly used in English, cannot be used in Hebrew, in which the sounds of /f/ and /s/ do nothave a one-to-one correspondence to any given letter, and in which phonological rulespreclude word initial /f/. Clearly, phonemic fluency norms from English are inapplicableto Hebrew, yet currently published Hebrew measures are available for only 34 individualswith a mean age of 31 years (Axelrod, Tomer, Fisher, & Aharon-Peretz, 2001). Thecurrent work aims to extend these measures to cover the entire adult age range.

The need for language specific norms may be less apparent when semantic fluency istested, as similar conceptual categories exist across languages. Yet, raw scores on animalfluency vary when speakers of different languages, such as Spanish (Benito-Cuadrado,Esteba-Castillo, Bohm, Cejudo-Bolivar, & Pena-Casanova, 2002), Italian (Capitani,Laiacona, & Barbarotto, 1999), Cantonese (Chan & Poon, 1999; Lee, Yuen, & Chan,2002), or Hindi (Ratclliff et al., 1998), are tested. In a direct comparison of animal fluencyperformance of Vietnamese and Spanish immigrants to California, Kempler, Teng, Dick,

692 G. Kavé

and Taussig (1998) found significant differences that were explained neither by age andeducation nor by vocabulary size. Instead, Kempler et al. (1998) suggest that word lengthbest accounts for the difference, since participants provide mostly monosyllabic responsesin Vietnamese but mostly multisyllabic responses in Spanish. These findings highlight thedifficulties of relying on data from one language when evaluating speakers of otherlanguages.

Phonemic fluency is most typically assessed through a sum score of responses tothree letters, yet this is not always the case for semantic fluency, which is commonly eval-uated with the single category of ‘animals’. Test-retest reliability of the three-letter version isbetter than any one letter (Harrison, Buxton, Husain, & Wise, 2000), most likely becausesampling a greater range of behavior improves validity of results. Furthermore, fluencymeasures differ between natural things, such as animals or fruits, and man-made things,such as tools or vehicles, in both normal individuals (Capitani et al., 1999; Chan & Poon,1999) and in individuals with brain damage (e.g. Laiacona, Capitani, & Barbarotto, 1997;Moss & Tyler, 2000). Hence, the present study collected norms for three semantic categoriesthat included both living (animals, fruits and vegetables) and non-living things (vehicles).

The aim of the current study, then, is to provide Hebrew norms for phonemic andsemantic verbal fluency and for the difference between them.

Method

Participants

The sample consisted of 369 community-dwelling volunteers, aged 18 to 85. Participantswere recruited through places of employment, psychology classes, senior citizen centers,and word of mouth. Any person with a known history of neurological disease, headtrauma, or stroke was excluded. Individuals over age 65 had to score within the normalrange (27–30) on the Mini-Mental Status Exam (Folstein, Folstein, & McHugh, 1975) inorder to be included in the sample. All participants were either born in Israel or immi-grated to Israel as children, having acquired formal education in Hebrew, and having usedHebrew as their primary language throughout their life.

Of this sample, 269 persons completed both the phonemic and the semantic fluencytests, and the remaining 100 completed only the semantic fluency test. Recruitment of thetwo groups followed identical criteria, but individuals in the sub-sample were either under30 or over 65 years old. In order to have data on the phonemic test from a minimum of 30individuals in each cell, the entire sample was divided into four age groups (see Table 1).With very few exceptions, participants in the current study also took part in a study ofnaming abilities (Kavé, 2005), and the sub-sample participated in a more extensiveresearch of Hebrew morphology as well (Kavé & Levy, 2003a; 2003b; 2004; in press).

Procedure

In both tests responses were recorded verbatim and repetitions, the same word with a dif-ferent ending, or perseverations were subsequently excluded from the total score. When ahomonym was provided, its second mention was counted only when the person explicitlypointed out the alternate meaning (i.e., gamal ‘camel’, ‘repaid’). Words provided in bothmasculine and feminine forms (e.g., gever ‘mister’ and gevert ‘mistress’; par ‘bull’ andpara ‘cow’) were counted as one, whereas an animal and its offspring were counted as sep-arate words (e.g., para ‘cow’ and egel ‘calf’). In the semantic test, names of subcategories

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(e.g., bird) were not given credit if specific items within that subcategory (e.g., dove,eagle) were also provided. When a questionable response was provided, clarificationswere invited at the end of the one-minute interval. Slang terms were generally acceptable,as well as commonly used foreign words.

The order of administration was constant across participants both within the phone-mic test (bet, gimel, shin) and within the semantic test (animals, fruits and vegetables, andvehicles), and individuals who completed both fluency tests were first asked to providewords by letters and then by semantic categories.

Phonemic Fluency Test. The number of words generated in one minute for the letters bet(/b/), gimel (/g/), and shin (/š /) was obtained (these letters were chosen because they arecommonly used in neuropsychological evaluations in Israel). Instructions were as follows:“I want you to say as many Hebrew words as possible that begin with a certain letter. Youmay say any word except for names of people and places, such as Tomer or Tel Aviv. Also,you should use different words rather than the same word with a different ending. Forexample, if you say tapuz (‘orange’), don’t also say tapuzim (‘oranges’). If you say a verb,use the simplest form halax (‘he went’) and not halaxti (‘I went’) or holex (‘he goes’).Please don’t say words that are attached to other words, such as mi-shamayim (‘from thesky’) or la-kise (‘to the chair’)”.

Semantic Fluency Test. The number of words generated in one minute for each of the fol-lowing three semantic categories: animals, fruits and vegetables, and vehicles wasobtained. Fruits and vegetables were treated as one category in order to avoid the ambigu-ity between botanical definitions and common usage (as in ‘avocado’). It was specifiedthat for the category of vehicles only types of transportation should be provided whilebrand names were unacceptable.

Results

Mean scores of phonemic and semantic fluency are presented according to the differentage groups, education level, and gender (Tables 2 and 3), and percentiles for the sum scoreare shown in Table 4. Inspection of the data by decades showed that dividing the sample intofour groups did not mask any significant age effects. Following Cerhan et al. (2002), adifference score was computed for each individual by subtracting the phonemic sum score

Table 1Demographic characteristics of the entire population

Age Education levela Gender

Range n mean sd mean sd < 13 yrs 12 > yrs Women Men

18–30 96 24.1 3.4 13.4 1.7 42 (44%) 54 (56%) 56 (58%) 40 (42%)31–50 107 39.4 5.9 14.9 2.7 29 (27%) 78 (73%) 52 (49%) 55 (51%)51–70 92 59.7 6.1 13.9 2.8 34 (37%) 58 (63%) 58 (63%) 34 (37%)71–85 74 76.3 3.8 12.5 2.6 42 (57%) 32 (43%) 48 (65%) 26 (35%)Total 369 47.9 19.8 13.8 2.6 147 (40%) 222 (60%) 214 (58%) 155 (42%)

a Education level is divided into high school or less (<13) versus continuing graduate studies(12>), in accordance with the Israeli schooling system.

694 G. Kavé

from the semantic sum score (Table 5). Only eight individuals (3%) scored higher on thephonemic fluency test than on the semantic fluency test, thus receiving a negative differencescore.

Correlation analyses indicated that performance on each component of a fluency test(i.e., a letter or a category) was highly correlated with the other components of that test andthat the two fluency tests were significantly and positively correlated (Table 6). In addition,

Table 2Mean scores of phonemic fluency by age, education level, and gender

Bet Gimel Shin Total

n mean sd mean sd mean sd mean sd

Age18–30 46 13.5 3.7 14.4 3.5 15.2 4.0 43.1 9.931–50 106 11.8 3.6 12.9 3.5 14.2 3.9 38.5 10.251–70 84 11.1 3.5 12.1 3.1 13.3 3.7 36.5 8.971–85 33 9.5 2.9 10.4 3.6 10.9 3.2 30.8 8.2

Education8–12 89 10.7 3.5 12.1 3.7 13.2 4.2 36.0 9.0

13–24 180 12.0 3.7 12.9 3.5 13.9 3.8 38.6 10.1

GenderWomen 154 11.7 3.8 12.7 3.7 13.7 3.9 38.1 9.8Men 115 11.5 3.6 12.4 3.4 13.7 4.1 37.6 9.9

Total 269 11.6 3.7 12.6 3.6 13.7 4.0 37.7 10.1

Table 3Mean scores of semantic fluency by age, education level, and gender

AnimalsFruits &

vegetables Vehicles Total

n mean sd mean sd mean sd mean sd

Age18–30 96 24.8 5.2 23.2 4.5 15.1 3.4 63.1 10.431–50 107 22.8 5.5 23.1 4.6 15.4 3.9 61.3 11.751–70 92 19.8 5.1 21.1 4.3 13.6 3.6 54.5 10.571–85 74 17.9 4.7 18.0 4.4 11.7 3.0 47.6 10.3

Education8–12 147 20.5 5.6 20.9 5.1 13.2 3.7 54.6 12.0

13–24 222 22.3 5.8 22.1 4.6 14.8 3.8 59.2 12.1

GenderWomen 214 21.0 5.3 21.9 4.8 13.8 3.6 56.7 11.6Men 155 22.4 6.3 21.2 5.0 14.6 4.0 58.2 13.2

Total 369 21.6 5.8 21.6 4.9 14.1 3.8 57.3 12.3

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Table 4Cumulative percentiles of sum scores, by age group

Age

18–30 31–50 51–70 71–85 Total

Percentile phoa semb pho sem pho sem pho sem pho sem

≤ 10 ≤30 ≤ 48 ≤ 24 ≤ 46 ≤ 23 ≤ 39 ≤ 18 ≤ 35 ≤ 24 ≤ 4111–25 31–36 49–54 25–31 47–52 24–29 40–47 19–24 36–39 25–30 42–4726–50 37–41 55–62 32–39 53–59 30–36 48–54 25–29 40–47 31–37 48–5651–75 42–49 63–70 40–44 60–70 37–42 55–60 30–36 48–52 38–43 57–6476–100 50+ 71+ 45+ 71+ 43+ 61+ 37+ 53+ 44+ 65+Range 22–71 43–89 19–63 31–93 17–57 31–89 17–49 25–76 17–71 25–93

a pho = phonemic; b sem = semantic.

Table 5Cumulative percentiles of difference scores, by age group

Age

Percentile 18–30 31–50 51–70 71–85 Total

≤ 10 ≤ 2 ≤ 9 ≤ 3 ≤ 1 ≤ 511–25 3–10 10–14 4–10 2–6 6–1026–50 11–15 15–21 11–17 7–11 11–1751–75 16–25 22–27 18–23 12–15 18–2476–100 26+ 28+ 24+ 16+ 25+Mean 17.4 22.3 17.8 12.3 18.8sd 11.4 10.2 9.8 8.5 10.6Range −3–45 2–56 −6–47 −3–31 −6–56

Table 6Pearson correlationsa between fluency scores and age and education level

/b/ /g/ /š /Phonemic

sum AnimalsFruits &

vegetables VehiclesSemantic

sum

/g/ .67/š/ .63 .67Phonemic sum .87 .88 .88Animals .37 .41 .40 .42Fruits & vegetables .33 .41 .43 .42 .62Vehicles .40 .49 .46 .47 .56 .54Semantic sum .43 .51 .50 .51 .89 .86 .79Age −.31 −.31 −.30 −.33 −.47 −.40 −.35 −.49Education .15 .17 .13 .16 .16 .15 .23 .21

a All correlations were statistically significant at the .05 level.

696 G. Kavé

all measures of fluency performance were negatively correlated with age and positively cor-related with level of education, using age and education as continuous variables.

In order to examine the relative contribution of age, education level, and gender inpredicting fluency scores, a stepwise regression analysis was performed for each task withthe sum score as the dependent variable, age entered in the first step, level of educationnext, and then gender. Age accounted for 10.8% of the variance in phonemic fluencywhile the contribution of either level of education (1%) or gender (.7%) was not statisti-cally significant. In the semantic fluency test, age accounted for 23.5% of the variance,level of education added a statistically significant 2.0%, and the contribution of genderwas not statistically significant (Table 7).

Discussion

In order to measure language performance in speakers of different languages, tests of verbalabilities must be standardized in the language under examination. The current findingsshow that gender had no significant influence on fluency performance on either test, asseen in other studies (Benito-Cuadrado et al., 2002; Ivnik et al., 1996; Lucas et al., 1998;Tombaugh et al., 1999). Age was the best predictor of performance on both tasks, but itaccounted for a greater share of the variance in semantic than in phonemic fluency, in linewith previous reports of more significant age effects on the semantic test (Gladsjo et al.,1999; Troyer et al., 1997). The reason for the greater effect of age on semantic fluencymay reflect the fact that fluency performance is a function of the size of the set beingsearched and thus larger sets are more vulnerable to a slowdown in search processes (e.g.in Alzheimer’s disease, Diaz et al., 2004).

Education level was positively correlated with both fluency measures, replicating reportsof an increase in word generation with higher levels of education (Gladsjo et al., 1999;Loonstra et al., 2001; Tombaugh et al., 1999). Nevertheless, the contribution of educationlevel to the prediction of fluency sum scores was only marginal. This finding contrasts withprevious studies in which education was the best predictor of phonemic fluency (Ruff et al.,1996; Tombaugh et al., 1999) and had a significant effect on semantic fluency (Benito-Cuadrado et al., 2002; Chan & Poon, 1999). The inconsistency is most likely the result ofdemographic differences among participants of the normative populations in each of thestudies. While the lowest level of education in the current sample was 8 years, studies thatdocumented quite significant education effects included individuals with very low levels ofeducation or with no education at all (e.g. Crossley et al., 1997; Gladsjo et al., 1999; Ratcliff

Table 7Regression of age, education level, and gender on fluency sum scores

StepVariable entered R SEE

R Square change F df Sig. F

Phonemic 1 Age .329 9.54 .108 32.52 1, 268 .0002 Education .343 9.50 .010 2.91 1, 267 .0893 Gender .353 9.48 .007 2.04 1, 266 .155

Semantic 1 Age .485 10.75 .235 112.78 1, 367 .0002 Education .505 10.62 .020 9.68 1, 366 .0023 Gender .505 10.64 .000 .08 1, 365 .782

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et al., 1998). Other research in which individuals with low education levels accounted for asmall percentage of the population reported much less pronounced education effects (e.g.Lucas et al., 1998).

Crucially, when an effect of education is found, the assumption is that educationeither enhances the individual’s exposure to the necessary vocabulary, and/or that a per-son’s education level reflects his or her innate aptitude, which also contributes to perfor-mance. However, the second assumption may be arguable. For instance, many of theyoungest participants in the present sample were university students who would be verylikely to acquire further education later on, so that their low level of education at the timeof the study hardly reflected their true potential. Furthermore, the lower level of educationof the older participants could be due to circumstances that prevented that cohort fromattaining formal education and hence the actual aptitude of these individuals may havebeen higher than estimated by years of education.

As in previous research, performance on the two fluency tasks was highly corre-lated (Tombaugh et al., 1999), reflecting the fact that it draws on shared cognitiveabilities of auditory attention and articulation speed and is greatly affected by mea-sures of vocabulary (Hughes & Bryan, 2002; Ruff et al., 1997). In addition, 97% of thepopulation generated more words on the semantic than on the phonemic fluency task.This finding may stem from the fact that semantic fluency depends on existing seman-tic stores, whereas phonemic fluency may depend on the ability to analyze characteris-tics of words (Troyer et al., 1998) and group them in categories that are not stored assuch in the mental lexicon. However, since this pattern of performance is not alwaysfound in individuals with brain pathology (Bokat & Goldberg, 2003; Cerhan et al.,2002; Kremen et al., 2003; Monsch et al., 1994; Sherman & Massman, 1999), itshould be specifically assessed with the use of the normative difference scores pro-vided here. Taken together, the given norms are expected to assist neuropsychologistsand speech pathologists in the evaluation of language difficulties of adult Hebrewspeakers.

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