The Abilities Associated With Verbal Fluency ...

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The Abilities Associated With Verbal Fluency Performance in a Young, Healthy Population Are Multifactorial and Differ Across Fluency Variants a

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Claudine Kraan , Rene J. Stolwyk & Renee Testa a

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School of Psychology and Psychiatry, Monash University, Melbourne, Australia

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School of Psychology and Psychiatry, Monash University, and Melbourne Neuropsychiatry Center, The University of Melbourne, Melbourne, Australia Published online: 05 Feb 2013.

To cite this article: Claudine Kraan , Rene J. Stolwyk & Renee Testa (2013): The Abilities Associated With Verbal Fluency Performance in a Young, Healthy Population Are Multifactorial and Differ Across Fluency Variants, Applied Neuropsychology: Adult, DOI:10.1080/09084282.2012.670157 To link to this article: http://dx.doi.org/10.1080/09084282.2012.670157

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APPLIED NEUROPSYCHOLOGY: ADULT, 0: 1–10, 2013 Copyright # Taylor & Francis Group, LLC ISSN: 0908-4282 print=1532-4826 online DOI: 10.1080/09084282.2012.670157

The Abilities Associated With Verbal Fluency Performance in a Young, Healthy Population Are Multifactorial and Differ Across Fluency Variants Claudine Kraan and Rene J. Stolwyk

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School of Psychology and Psychiatry, Monash University, Melbourne, Australia

Renee Testa School of Psychology and Psychiatry, Monash University, and Melbourne Neuropsychiatry Center, The University of Melbourne, Melbourne, Australia

Numerous variants of verbal fluency tasks exist within clinical and research domains that purport to measure ‘‘executive function.’’ However, to date, there has been a paucity of research examining what specific abilities are measured by these tasks. In this study, the relationships between a select group of cognitive constructs and phonemic, semantic, alternating, and excluded-letter verbal fluency tests were examined in 93 young healthy individuals (aged 18 to 35 years old). Forward-selection multiple regression analyses were performed for each fluency task. Phonemic fluency was associated with verbal intellectual function and processing speed; semantic fluency was associated with working memory and semantic word retrieval; excluded-letter fluency was associated with processing speed; and alternating fluency was associated with semantic word retrieval. These results highlight verbal intellectual function, processing speed, and semantic word-retrieval contributions to verbal fluency performances. The main conclusion from this study is that the abilities associated with verbal fluency performance in a young healthy population are multifactorial and differ across fluency variants. These findings progress our theoretical understanding of what is measured by different verbal fluency tasks and will assist interpretation of performance.

Key words:

ability=abilities, alternating fluency, excluded-letter fluency, executive function, healthy population, neuropsychological assessment, phonemic fluency, semantic fluency, verbal fluency

INTRODUCTION Verbal fluency is a popular neuropsychological assessment tool widely used across clinical and research settings (Strauss, Sherman, & Spreen, 2006). All verbal fluency tasks follow the same basic paradigm of measuring an individual’s ability to rapidly generate words Address correspondence to Rene J. Stolwyk, School of Psychology and Psychiatry, Monash University, Building 17, Clayton Campus, Melbourne 3800, Australia. E-mail: [email protected]

within a fixed 60-second timeframe. Fluency tasks have sensitivity to frontal-lobe, fronto-striatal and temporal-lobe dysfunction (Henry & Crawford, 2004a; Iudicello et al., 2008). Thus, they are frequently administered across a range of clinical populations including Parkinson’s disease, Alzheimer’s disease, traumatic brain injury, schizophrenia, and multiple sclerosis, among others (Becker et al., 2010; Henry & Crawford, 2004a, 2004b; Troyer, Moscovitch, Winocur, Leach, & Freedman, 1998). Today, verbal fluency tasks hold a prominent place in numerous cognitive test batteries,

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including the Woodcock-Johnson-Third Edition, Delis-Kaplan Executive Function System (D-KEFS), and Frontal Assessment Battery. These batteries are designed to test cognition across multiple domains and thus highlight specific cognitive strengths and weaknesses; however, the role of verbal fluency in these batteries is somewhat unclear. Since 1938, when Thurstone introduced word fluency as one of the seven primary mental abilities, verbal fluency performance has been interpreted in many different ways (Strauss et al.). Despite its longstanding frequent use within clinical and research settings, very little is known about the abilities that underlie performance on the verbal fluency task, not to mention the differences between the many variants available for use. Thus, in the present study, we have selected four verbal fluency variants (shown in Table 1) for analysis and discussion. Although verbal fluency performance has consistently been identified as a sensitive indicator of brain dysfunction, controversy remains regarding what cognitive abilities underpin verbal fluency tasks. Verbal fluency has previously been interpreted in terms of ‘‘executive functioning’’ alone; however, this umbrella term lacks the specificity required to make clear interpretations of research and clinical data (Strauss et al., 2006). Others purport verbal fluency to measure a range of discrete cognitive functions (e.g., initiation, self-monitoring, cognitive flexibility), and it has been noted that performance is dependent on a range of ‘‘foundation skills’’ (e.g., verbal intellectual function, processing speed, and semantic retrieval abilities; Ardila, Galeano, & Rosselli, 1998; Laisney et al., 2009; Libon et al., 2010; Nutter-Upham et al., 2008; Rodriguez-Aranda & Sundet, 2006; Ruff, Light, Parker, & Levin, 1997). There is a general lack of consensus with regard to what abilities are associated with verbal fluency performance, and

TABLE 1 Verbal Fluency Tests Used in Clinical and Research Domains Verbal Fluency Test

Instructions

Phonemic fluency

Rapid generation of words beginning with a specific letter. Typically, F, A, and S are used as stimulus letters. Semantic fluency Rapid generation of words from within a semantic category. ‘‘Animal names’’ and ‘‘supermarket items’’ are commonly used. Alternating Rapid generation of words by alternating between fluency two semantically unrelated categories. ‘‘Fruit and furniture’’ are often used. Excluded-letter Rapid generation of words that do not contain fluency a specified vowel (e.g., the letter ‘‘A’’). Note. Each trial is 60 seconds in duration, and repetitions, wrong words, proper nouns, and variations of the same word do not count toward the raw score total (Strauss et al., 2006).

there is limited empirical evidence to support these conceptualizations. This heterogeneous definition of verbal fluency is problematic. Poor verbal fluency performance may be interpreted differently depending on how an individual or group conceptualizes this measure. This in turn can compromise the reliability and validity of research findings and clinical neuropsychological evaluations. To further complicate this issue, as noted earlier, different variants of verbal fluency exist. Again, these different variants have been purported to measure different abilities. For example, semantic fluency has been suggested to be particularly sensitive to semantic memory ability (Henry, Crawford, & Phillips, 2004), alternating fluency to working memory function (Iudicello et al., 2008), and excluded-letter fluency to inhibitive control (Hughes & Bryan, 2002). However, again, there is limited and equivocal empirical data supporting these suppositions. Keeping in mind that there may be articles that were not found or not yet published when this article was produced, Table 2 presents key studies examining relationships between different abilities and phonemic, semantic, alternating, and excluded-letter fluency. Importantly, only studies that employed tools commonly used within clinical and research settings with adequate reliability and validity indexes (as reviewed in Lezak, Howieson, & Loring, 2007; Strauss et al., 2006) were selected for inclusion. Most of these studies have used correlation and regression approaches, a frequent choice in studies aiming to clarify the abilities that are measured by various neuropsychological tools (Bate, Mathias, & Crawford, 2001; Peters, Giesbrecht, Jelicic, & Merckelbach, 2007). For example, Sa˜nchez-Cubillo et al. (2009) used regression analysis to show that performance on Part A of the Trail-Making Test (TMT) requires mainly visuoperceptual skills, while performance on Part B mostly reflects working-memory and task-switching abilities. With regard to phonemic fluency, foundation skills such as verbal knowledge (Ruff et al., 1997) and processing speed (Boone, Ponton, Gorsuch, Gonzales, & Miller, 1998) have been associated with performance across a range of healthy and clinical populations. The contribution of working memory to phonemic fluency has also been demonstrated (Ross et al., 2007). This is expected considering the need to actively monitor for errors=repetitions while remembering the rules of the task. The association between inhibition and phonemic fluency is less clear. Although it is logical that the suppression of prepotent word responses in favor of the specified criteria (e.g., words that begin with F) is important for phonemic fluency performance, there is only limited evidence to support this (Ross et al., 2007). As with the phonemic variant, semantic fluency has also been associated with verbal knowledge and working memory (Ardila, Galeano, &

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ABILITIES ASSOCIATED WITH VERBAL FLUENCY TABLE 2 Abilities Previously Associated With Different Verbal Fluency Variants Verbal Fluency Variant Phonemic

Associated Ability

Population

Verbal IQ=Knowledge

Healthy younger and older Healthy younger Healthy younger Healthy older Amnestic Mild Cognitive Impairment (MCI) Amnestic MCI Behavioural=dysexecutive disorder Semantic Dementia Healthy older Healthy older Amnestic MCI Mixed Clinical Group Healthy younger Healthy younger and older Healthy younger and older Healthy younger Traumatic brain injury Behavioural=dysexecutive disorder Semantic dementia Frontotemporal dementia Semantic dementia Frontotemporal dementia Healthy younger Healthy younger and older Healthy younger Amnestic MCI Frontotemporal dementia Semantic dementia Amnestic MCI HIV Behavioural=dysexecutive disorder Progressive nonfluent aphasia Semantic dementia Amnestic MCI Healthy younger HIV Behavioural=dysexecutive disorder Semantic dementia Semantic dementia Frontotemporal dementia Semantic dementia Amnestic MCI Amnestic MCI HIV Amnestic MCI HIV Healthy younger Healthy older Healthy younger Healthy older Healthy older Healthy older

Semantic word retrieval

Processing speed

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Working memory

Cognitive flexibility Inhibition Semantic

Verbal IQ=Knowledge Semantic memory Semantic word retrieval

Processing speed Working memory

Cognitive flexibility Alternating

Excluded

Verbal IQ=Knowledge Semantic word retrieval Processing speed Working memory Verbal IQ=Knowledge Processing speed

Inhibition

Cognitive Test WAIS-R Vocab WAIS Vocab (Spanish) WAIS-III VIQ NART WAIS-III Vocab Boston Naming Test Boston Naming Test Boston Naming Test WAIS-R Digit Symbol WAIS-R Digit Symbol WAIS-III Digit Symbol WAIS-R Digit Symbol WAIS-III Letter Number Sequencing WAIS-R Digit Span WAIS-R Digit Span Backwards WAIS-III WMI WAIS-III WMI WAIS-R Digits Backwards WAIS-R Digits Backwards Digit Span Forwards Backwards visuospatial Span TMT Part B Stroop colour-word Stroop colour-word WAIS Vocab (Spanish) WAIS–III Vocab Semantic Knowledge Test Semantic Knowledge Test Boston Naming Test Boston Naming Test Boston Naming Test Boston Naming Test Boston Naming Test WAIS-III Digit Symbol Operation span WAIS-III Digits Backwards WAIS-R Digits Backwards WAIS-R Digits Backwards Backwards visuospatial span TMT part B TMT part B WAIS-III Vocabulary Boston Naming Test Boston Naming Test WAIS-III Digit Symbol WAIS-III Digits Backwards NART NART WAIS-R Digit Symbol WAIS-R Digit Symbol WAIS-R Digit Symbol Stroop Colour-Word

Reference 1 2 3 4 5 5 13 13 7 4 5 12 6 1 8 3 3 13 13 10 10 10 6 8 2 5 10 10 5 9 13 13 13 5 11 9 13 13 10 10 10 5 5 9 5 9 7 4 7 7 4 7

References: (1) Ruff et al., 1997; (2) Ardila et al., 1998; (3) Bittner & Crowe, 2007; (4) Bryan, Luszcz, & Crawford, 1997; (5) Nutter-Upham et al., 2008; (6) Ross et al., 2007; (7) Hughes & Bryan, 2002; (8) Rodriguez-Aranda & Sundet, 2006; (9) Iudicello et al., 2008; (10) Laisney et al., 2009; (11) Rosen & Engle, 1997; (12) Boone et al., 1998; (13) Libon, McMillan, Gunawardena, Powers, Massimo, Khan et al., 2010.

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Rosselli, 1998; Rosen & Engle, 1997); however, its association to processing speed has only been shown in a clinical population (Nutter-Upham et al., 2008). The semantic fluency variant is often purported to load more significantly on semantic activation and wordretrieval functions (Henry & Crawford, 2004a), and this has been demonstrated within a number of clinical populations (Libon et al., 2009; Nutter-Upham et al.). Only two studies have examined the abilities associated with alternating fluency performance, which is considered to place more demand on working-memory functions compared with traditional phonemic and semantic variants. Iudicello et al. (2008) reported significant correlations between alternating fluency and working memory and semantic word retrieval. Nutter-Upham et al. (2008) reported correlations with processing speed and verbal knowledge. Although few researchers have examined the abilities underlying excluded-letter fluency performance, a higher load on inhibitive processes is expected because of the need to monitor and suppress words containing a common letter. Hughes and Bryan (2002) found that excluded-letter fluency performance was significantly correlated with verbal intellectual functions. Interestingly, inhibition was found to correlate in an older, but not a younger, healthy cohort. In addition to these findings, Troyer et al. (1997) proposed a dual-component model to explain the underlying cognitive abilities that are associated with verbal fluency performances. The underlying premise of this model is that we frequently rely on naturalistic language mechanisms to retrieve words within a semantic category (a process termed clustering); however, we are required to employ executive control processes to activate search strategies and switch categories (termed switching). This notion has implications for our interpretation of different types of verbal fluency tests. That is, fluency performances mediated by active word-search retrieval strategies might load heavily on an ‘‘executive domain’’ (e.g., as in phonemic and excluded-letter fluency). Alternatively, fluency performances that are largely mediated by naturalistic semantic retrieval functions would have a more prominent ‘‘semantic component’’ (e.g., as in semantic fluency). This research suggests that the abilities associated with verbal fluency performance differ according to task type. However, due to inconsistent results across studies, it is not clear what these abilities are. There are four main issues that need to be addressed to progress this research field. The first issue is that many studies have not directly compared the different verbal fluency variants. Some studies have partially addressed this issue. For example, Bryan, Luszcz, and Crawford (1997) found that processing speed correlated with excluded-letter fluency more strongly than with phonemic fluency (unfortunately,

semantic and alternating fluency were not included to allow a more comprehensive analysis). To date, direct comparisons across variants have not been conducted systematically or comprehensively. The second issue relates to numerous gaps within the literature. There is a particular paucity of research investigating the abilities associated with alternating and excluded-letter fluency. The third issue relates to the different tools used to measure potential abilities that are associated with verbal fluency performance. For example, some studies have used the raw Stroop Color–Word score to measure inhibitive control (Ross et al., 2007; Rodriguez-Aranda & Sundet, 2006), while others have used the interference score (considered to be less confounded by processing speed; Hughes & Bryan, 2002). This variability, both in tools selected and their interpretation, is a significant weakness of previous research. Until a single valid tool has been used to measure the same ability across verbal fluency variants in the same cohort, comments about relative contributions of different abilities will be limited. Although not directly addressed in this study, the fourth issue relates to the use of different populations between studies. Due to different neuropsychological profiles across healthy and clinical populations, it is likely that the abilities contributing to verbal fluency performance will vary. For example, as noted earlier, Hughes and Bryan (2002) found that inhibitive control was significantly associated with excluded-letter fluency in an older, but not a younger, healthy population. The overall aim of this research is to address the methodological issues summarized here. To address the first and second issues, we will perform a comprehensive and systematic examination of the abilities associated with the most commonly used verbal fluency variants— phonemic, semantic, alternating, and excluded-letter verbal fluency. As shown in Table 2, numerous studies have reported a range of cognitive abilities that correlate with verbal fluency performance. For the purpose of the present article, five of these most commonly reported abilities have been selected for further examination— inhibition, working memory, processing speed, word retrieval, and verbal intellectual function. To address the third issue, only commonly used tools with adequate psychometric properties will be selected to measure the cognitive abilities of interest. An effort will also be made to maximize consistency with tools used in previous studies to optimize comparative analyses. Although the fourth issue is not directly addressed in this study, we will comprehensively investigate what cognitive abilities are associated with verbal fluency function in a normal, healthy population. This will provide helpful comparative baseline data for future studies investigating verbal fluency dysfunction across a range of clinical populations. Our overarching hypothesis is that in a young healthy population, verbal intellectual function, semantic word

ABILITIES ASSOCIATED WITH VERBAL FLUENCY

retrieval, processing speed, working memory, and inhibitory functions will all be associated with verbal fluency performance to a degree; however, the strengths of these associations will vary across fluency variants. Despite the inconsistent findings and gaps of knowledge within this research field, some tentative specific hypotheses can be made based on previous research. We predict that phonemic and excluded-letter fluency will be particularly associated with verbal intellectual function, processing speed, working memory, and inhibition. Semantic and alternating fluency will be particularly associated with verbal intellectual function, semantic word retrieval, and working memory.

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METHOD Participants and Background Measures Participants were sourced through university advertisements and across other public domains. The total sample of 93 healthy Australian individuals had an average predicted Full-Scale IQ as measured by the National Adult Reading Test-Revised (NART-R; M ¼ 104.30, SD ¼ 8.62) and were aged 18 to 35 years old (M ¼ 26.20 years, SD ¼ 5.34 years); 58 were women. All participants used in this research were native English-speaking individuals with no previous history of neurological disorder, psychiatric disorder, speech problems, learning disability, color blindness, use of medications known to affect cognitive function, or alcohol=drug dependencies. Verbal Fluency Tasks In accordance with administration instructions noted in Strauss et al. (2006), each verbal fluency trial was 60 seconds in duration. Repetitions, wrong words, proper nouns, and variations of the same word did not count toward the raw score total. In the phonemic verbal fluency task (Tombaugh, Kozak, & Rees, 1999), participants completed two trials in which they were asked to orally generate words beginning with the letters F and A. Total phonemic fluency score was created by adding scores from both trials. Two trials of the semantic verbal fluency task (Tombaugh et al., 1999) were completed—one in which the participant was asked to generate ‘‘animal names’’ and another where they named ‘‘things you can buy at the supermarket.’’ Scores from both trials were added to create a total semantic fluency score. To complete the excluded-letter verbal fluency task (Shores, Carstairs, & Crawford, 2006), all participants were given one trial in which they were asked to generate words that did not contain the letter ‘‘A.’’

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The alternating verbal fluency task (D-KEFS; Delis, Kaplan, & Kramer, 2001) was administered once, and participants were asked to alternate between the categories ‘‘fruit’’ and ‘‘furniture.’’ Neuropsychological Tasks As noted in the Introduction, the cognitive abilities included for examination in this study were determined by findings from previous research. Selection of the tools used to measure these cognitive abilities was guided by the following principles: (i) Previous research had validated the tool as a measure of that purported ability in a healthy cohort (Strauss et al., 2006); (ii) previous research had demonstrated adequate reliability and validity indicators (Strauss et al.); (iii) the tool had been commonly used within clinical and research settings; and (iv) the tool had been previously used within this research field (see Table 2).

Verbal intellectual function. Verbal intellectual function was estimated using the NART-R (Nelson & Willison, 1991). The NART-R is a good predictor of Wechsler Adult Intelligence Scale (WAIS) Verbal IQ variance, reading ability, and general intelligence in a healthy population (Crawford, Stewart, Parker, Besson, & Cochrane, 1989; Nelson, 1982). To complete the test, each participant read out loud 50 irregularly spelled words (e.g., facade). In the present study, NART-R errors were used to estimate Verbal IQ. The highest possible estimated Verbal IQ score (0 errors) is 127, and the lowest possible score (indicated by 50 errors) is 70. Semantic word retrieval. Semantic word-retrieval mechanisms were assessed using the Graded Naming Test (GNT; McKenna & Warrington, 1983). Although the Boston Naming Test (BNT) has been used before in verbal fluency research, it was replaced with the GNT in this study to overcome problems with ceiling effects in a healthy population. Participants were asked to name 30 objects based on a visual cue. Item difficulty in this test varies from easy (e.g., kangaroo) to difficult (e.g., mitre). A maximum of 10 seconds was permitted to name the object. The total number of correct names was scored from 0 to 30, with 30 being the highest possible score. Processing speed. Processing speed was measured using the oral version of the Symbol–Digit Modalities Test (Smith, 1991). This test is derived from the Digit– Symbol Substitution Test (DSST) previously used in the army beta test on healthy individuals. Performance

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is closely related to an individual’s ability to integrate numerous cognitive processes, and it is sensitive to the scanning and tracking aspects of attention. To perform the test, participants were presented with a list of nine symbol–digit pairs and then a large list of unordered symbols. After a brief practice, the participant was given 90 seconds to read out loud as many of the corresponding digits as possible, and each response was recorded by the administrator on a separate sheet. The total number of correct items was scored. The highest score to be obtained was 110 and the lowest was 0. Working memory. To assess working memory, Digit Span Sequencing from the WAIS-Fourth Edition (WAIS-IV) was administered (Wechsler, 2008). Digits Sequencing was selected over Digits Forwards or Backwards (both of which have previously been used) because of its purported increased working-memory demand. This task is sensitive to the integrity of the cognitive mechanisms needed to temporarily store and manipulate information (e.g., the phonological loop and central executive) in a healthy population. Participants were asked to verbally generate increasingly long lists of numbers after mentally rearranging them into ascending order. The raw total number of correct trials was scored. The maximum possible score is 16. Inhibition. Inhibition was assessed using the Stroop Color and Word Test (Golden, 1978; Golden & Freshwater, 2002). This test is widely used in cognitive and psychological research with healthy populations and is moderately correlated with other measures of attentional control (Strauss et al., 2006). The Stroop Color and Word Test, which is based on the principle that a participant must selectively respond to the color of the word and not the actual word printed, is sensitive to an individual’s ability to inhibit an automatic response. The calculated Stroop Interference score was used (Golden, 1978). Procedure The study was approved by the Monash University Human Research Ethics Committee. Participants were tested in a quiet room. Testing sessions were approximately 45 minutes in duration, and participants were welcome to rest breaks between tests. All tests were administered in random order and were scored according to their respective administration and scoring manuals. The administrator had an undergraduate psychology degree and was trained by a registered clinical neuropsychologist who observed testing on two separate occasions to ensure correct administration and scoring.

RESULTS The computer software Statistical Package for the Social Sciences Version 18 was used for all statistical analyses. Prior to regression analysis, scores on all variables were examined for missing data, outliers, skewness, and kurtosis. The data set was found to be complete, and all five univariate outliers were changed to one value from the second most extreme point (Tabachnick & Fidell, 2007). Estimated verbal intellectual function was negatively skewed; thus, its square root was taken and it was reflected. A positive skew on excluded-letter fluency was reduced by taking its square root. The more powerful parametric regression method was used because assumptions were met after only minimal transformation. See Table 3 for a summary of performances across verbal fluency and cognitive measures. The mean, standard deviations and range data indicate no issues with floor or ceiling effects of performance within our study sample. Regression Analyses For the statistical analysis, separate stepwise regression analyses were performed for each dependent variable (phonemic, semantic, alternating, and excluded-letter fluency scores). Although a hierarchical approach would have been appropriate for the well-studied phonemic fluency task, there is limited research on the remaining three tasks (especially alternating and excluded-letter fluency) to support its use in this study. Thus, due to a lack of clarity in past research, the data-driven approach of stepwise regression was deemed more appropriate than was the theoretically driven approach of hierarchical regression. Moreover, stepwise methods use statistical calculations to exclude independent variables that do not significantly contribute to the model; thus, they provide a way of investigating only those variables that significantly contribute to variance of the dependent

TABLE 3 Means and Standard Deviations for Dependent and Independent Variables

Phonemic fluency Semantic fluency Alternating fluency Excluded-letter fluency Estimated verbal IQ Semantic word retrieval Processing speed Working memory Inhibition

Mean

Standard Deviation

Range

26.87 51.88 14.41 16.81 103.04 18.98 64.73 10.55 5.78

6.52 9.28 2.33 4.49 7.91 3.18 9.74 2.23 8.03

13–42 28–72 9–20 8–29 84–117 12–26 38–90 6–16 –15.17–23.02

ABILITIES ASSOCIATED WITH VERBAL FLUENCY

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TABLE 4 Forward-Selection Multiple Regression Statistics Showing Beta Values for Abilities That Significantly Predict Phonemic, Semantic, Alternating, and Excluded-Letter Verbal Fluency Performance Phonemic

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VIQ SWR PS WM I

Semantic

b

t

SE

b

.23
— .25
— —

2.32 — 2.50 — — R2 ¼ .15 Adjusted R2 ¼ .13 R ¼ .38

F ¼ 7.60



.62 — .07 — —

— .42

— .21
—

t — 4.57 — 2.24 — R2 ¼ .24 Adjusted R2 ¼ .22 R ¼ .49

F ¼ 14.11



Alternating SE

b

— .27 — .39 —

— .44

— — —

t — 4.65 — — — R2 ¼ .19 Adjusted R2 ¼ .18 R ¼ .44

F ¼ 21.65



Excluded SE

b

— .07 — — —

— — .46

— —

t — — 5.00 — — R2 ¼ .21 Adjusted R2 ¼ .20 R ¼ .46

F ¼ 24.45



SE — — .04 — —

VIQ ¼ predicted Verbal IQ based on NART; SWR ¼ semantic word retrieval as measured by the Graded Naming Test; PS ¼ processing speed as measured by the Symbol–Digit Modalities Test; WM ¼ working memory as measured by Digits Sequencing from WAIS-IV; I ¼ inhibition as measured by the Stroop Color–Word Test.
p < .05.

p < .01.

variables (Tabachnick & Fidell, 2007). All independent variables were entered together, and the forward method was selected. As shown in Table 4, many of these abilities did not significantly predict performance of the dependent variable and thus were not entered into the regression model. The results from the regression analyses show that when all other independent variables are controlled, verbal intellectual function and processing speed predict a significant proportion of the variance in phonemic fluency performance. In stark contrast, semantic word retrieval and working memory were found to have significant prediction strengths when semantic fluency was the dependent variable. Alternating fluency performance was significantly predicted by semantic word retrieval. Excluded-letter fluency performance was significantly predicted by processing speed. Our measure of inhibition did not associate with any fluency task strongly enough to enter into the regression models.

DISCUSSION The aim of this study was to examine what cognitive abilities are associated with performance on different verbal fluency variants in a young, healthy population. As expected from previous research, current results show verbal intellectual function, semantic word retrieval, processing speed, and working memory are associated with verbal fluency performances, but these associations differ according to verbal fluency variant. Key findings for each verbal fluency variant are discussed in the following section. As hypothesized, current findings show that verbal intellectual function and processing speed significantly contribute to phonemic fluency performance. This

finding is consistent with previous research using a healthy population (Bittner & Crowe, 2007; Bryan et al., 1997; Hughes & Bryan, 2002; Ruff et al., 1997). Interestingly, our current data suggest that verbal intelligence significantly contributes to phonemic fluency performance but not the other fluency variants. Our findings are inconsistent with previous studies that have found a significant correlation between working memory and phonemic fluency (Bittner & Crowe, 2007; Rodriguez et al., 2006; Ross et al., 2007; Ruff et al.) and also question the common assumption that phonemic fluency has a high inhibitory load. Two studies have previously reported significant correlations between Stroop and phonemic fluency performance in a healthy population; however, their use of the raw color–word score may have been confounded by processing speed (Rodriguez-Aranda & Sundet, 2006; Ross et al.). Although word retrieval has been shown to correlate significantly with phonemic fluency performance in a clinical population (Libon et al., 2009; NutterUpham et al., 2008), this result was not found in our healthy sample. Consistent with past research and our hypotheses, we have shown that processing speed is an important factor to consider when examining excluded-letter fluency (Hughes & Bryan, 2002). In fact, nearly half of the variance of excluded-letter fluency performance in our young, healthy sample was explained by processing speed alone. Interestingly, verbal intellectual function, working memory, and inhibition did not significantly associate with excluded-letter performance. In contrast to the present data, a significant correlation between verbal intellectual function and excluded-letter fluency has previously been reported in a healthy young population (Hughes & Bryan). One suggestion for this discrepancy is that the previously reported correlation

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was moderated by other variables that were controlled for in the present study’s regression analysis. It was surprising that inhibition did not significantly contribute to excluded-letter fluency performance in this study. Many assume that excluded-letter fluency has a high inhibitory component because performance relies on suppression of words containing a common letter. Indeed, a previous study did report an association between excluded-letter fluency and inhibition using an older population (Hughes & Bryan). Thus, it is possible that excluded-letter fluency performance places significant inhibition demands on some clinical and older populations where inhibition is reduced, but not healthy, younger ones where inhibition is relatively intact. Further research is required to explore this issue. In support of our hypotheses, a significant association was found between alternating fluency and semantic word retrieval, which highlights the important contribution of semantically based word-retrieval mechanisms for this fluency variant. A surprising result was the lack of a significant contribution of working-memory ability to alternating fluency performance. Empirical methods have previously demonstrated this correlation within an HIV-positive population (Iudicello et al., 2008), and there is a common assumption that alternating fluency places demands on working-memory functions as a result of the cognitive-switching aspects of the task. The fact that working memory was found to significantly correlate in a clinical, but not a healthy population again suggests that it may only impact on alternating fluency performance in populations with reduced cognitive ability. Indeed, this may also be the case for verbal intellectual function and processing speed, which were found to significantly correlate with alternating fluency performance in a population of older individuals with mild cognitive impairment (Nutter-Upham et al., 2008), but not in the present study. In support of our hypotheses, semantic word retrieval and working memory significantly contributed to semantic fluency performance. The lack of correlation between semantic fluency and processing speed is interesting. Numerous studies have shown that semantic fluency deficits in clinical populations disappear after controlling for the variable of processing speed (Delaloye et al., 2009; Henry & Crawford, 2005; Henry, Crawford, & Phillips, 2005), and a significant correlation between processing speed and semantic fluency was previously reported in a population of older individuals with mild cognitive impairment (Nutter-Upham et al., 2008). Thus, processing speed may be a variable that interferes with semantic fluency performance in populations with brain impairment and slowed cognition, but it may have little to do with performance in a healthy population.

Based on the pattern of current data, verbal fluency performance in younger, healthy people appears to be particularly mediated by processing speed and semantic word-retrieval abilities. Interestingly, the former appears to associate with performance on orthographictype tasks (such as phonemic and excluded-letter fluency) and the latter appears to associate with performance on semantic-type tests (such as semantic and alternating fluency). As hypothesized, current results from the semantic and alternating fluency analyses show strong associations with semantic word-retrieval functions. However, the orthographic-type fluency tasks did not show the expected associations with executive functions such as working memory and inhibition. This again may reflect our use of a healthy young population. Orthographic fluency tasks may place high demands on processing, as indicated by the significant correlations between both orthographic fluency variants and processing speed, but it may not place sufficient demand on executive systems in a young, healthy population. Alternatively, orthographic fluency tasks may be mediated by other executive functions not measured in this study (e.g., set shifting, strategy generation, etc.). An extension of this analysis in other clinical groups, employing a more extensive set of executive functions will help clarify this finding. In any case, an important finding from this study is that processing speed and verbal intelligence are also important for orthographic-type verbal fluency performance in a young healthy population. Results from this study have a number of implications pertinent to researchers and clinicians. With regard to future research using healthy, younger participants, verbal fluency tests may not be the most appropriate executive function measures in people with underlying word-retrieval or processing speed weaknesses. In addition, phonemic fluency performance should be interpreted in the context of overall verbal intellectual function due to the significant association between these two variables. Furthermore, a number of surprising results were noted that challenge common conceptualization of verbal fluency performances within a normal healthy population (e.g., no significant association between working memory and alternating fluency or between inhibition and excluded-letter fluency). With regard to clinical implications, comparisons between current and previous data (see Table 2) suggest that the abilities associated with verbal fluency may vary across healthy and clinical groups. If this were the case, it would mean different fluency variants might have different discriminatory functions across different clinical populations. Interestingly, this may explain why deficits found in semantic fluency performance occur in the presence of retained phonemic fluency performance in Alzheimer’s disease (Clark et al., 2009) and schizophrenia (Becker et al., 2010) cohorts. It would be

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ABILITIES ASSOCIATED WITH VERBAL FLUENCY

useful to employ our systematic and comprehensive methodology to investigate whether common conceptualizations of verbal fluency variants are valid across different clinical populations. The choice to use the NART-R to estimate verbal intellectual function is a limitation of this study. Although it is true that Crawford et al. (1989) found that 72% of the variance in WAIS Verbal IQ could be predicted by the NART-R, strong associations between recognition=recall and long-term memory retrieval (Haist, Shimamura, & Squire, 1992) present an important confound. Moreover, when based on NART-R errors, the maximum predicted verbal intelligence WAIS-IV score has a ceiling effect of 127. For this reason, future studies should use a more comprehensive test of verbal intellectual function, such as the WAIS-IV Verbal Comprehension Index. A second limitation to this study is that by using only one measure for each purported ability, we need to consider the likelihood that each selected measure was multifactorial (e.g., additional cognitive=motor=visual demands, etc.). Therefore, there are limitations involved in correlating performance on tests that may be multifactorial. Future studies can overcome these limitations by employing multiple measures of abilities. Third, because the variance of verbal fluency performances explained by the tasks used in this study were rather low (b range ¼ .21–.46), it is likely that other ‘‘executive’’ (such as set shifting and initiation) and ‘‘nonexecutive’’ abilities contribute to verbal fluency performance, and these could be examined in future studies. Finally, this study could have provided a more in-depth analysis of strategy development by scoring for clustering and switching (Troyer et al., 1997). It would have been interesting to see if the strengths of associations between cognitive abilities and fluency-task types differ with switching and clustering scores as the dependent variables of interest. Lastly, the sample size was slightly low, and the male-to-female ratio was slightly disproportionate. To conclude, the main finding from this study is that the abilities associated with verbal fluency performance in a young, healthy population are multifactorial and differ across fluency variants. This finding challenges the conceptualization of verbal fluency as merely a measure of executive function and highlights the numerous nonexecutive and executive contributors to performance. The use of a large healthy population has allowed us to investigate verbal fluency function, as opposed to dysfunction. This has laid a foundation for future studies to investigate performance across clinical populations, which will not only aid our theoretical understanding of this complex function but will also help improve the validity of clinical neuropsychological assessments.

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ACKNOWLEDGEMENTS The authors would like to thank the School of Psychology and Psychiatry at Monash University for funding this project. We also thank Monash University for assistance in recruiting participants.

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