Journal of Clinical and Experimental Neuropsychology, 2015 Vol. 37, No. 1, 70–83, http://dx.doi.org/10.1080/13803395.2014.988125

The cognitive abilities associated with verbal fluency task performance differ across fluency variants and age groups in healthy young and old adults Renerus Stolwyk, Bavani Bannirchelvam, Claudine Kraan, and Katrina Simpson School of Psychological Sciences, Monash University, Melbourne, VIC, Australia (Received 27 February 2014; accepted 10 November 2014) Despite their widespread use in research and clinical practice, the cognitive abilities purportedly assessed by different verbal fluency task variants remain unclear and may vary across different healthy and clinical populations. The overarching aim of this study was to identify which cognitive abilities contribute to phonemic, semantic, excluded letter, and alternating verbal fluency tasks and whether these contributions differ across younger and older healthy adults. Method: Ninety-six younger (18–36 years) and 83 older (65–87 years) healthy participants completed measures of estimated verbal intelligence, semantic retrieval, processing speed, working memory, and inhibitory control, in addition to phonemic, semantic, excluded letter, and alternating fluency tasks. Eight hierarchical multiple regressions were conducted across the four fluency variants and two age groups to identify which cognitive variables uniquely contributed to these fluency tasks. Results: In the younger group, verbal intelligence and processing speed contributed to phonemic fluency, semantic retrieval to semantic fluency, processing speed and working memory to excluded letter fluency, and semantic retrieval to alternating fluency. In contrast, in the older group, verbal intelligence contributed to phonemic fluency, no cognitive variables contributed to semantic fluency, inhibition to excluded letter fluency, and verbal intelligence to alternating fluency. Conclusions: Our findings highlight that both lower and higher order cognitive skills contribute to verbal fluency tasks; however, these contributions vary considerably across fluency variants and age groups. The heterogeneity of cognitive determinants of verbal fluency, across variants and age, may explain why older people performed less proficiently on semantic and excluded letter fluency tasks while no age effects were found for phonemic and alternating fluency. Interpretation of verbal fluency performances need to be tailored according to which verbal fluency variant and age group are used. Keywords: Verbal fluency; Aging; Executive function; Neuropsychological assessment; Healthy population.

Verbal fluency tasks are a group of neuropsychological assessment tools that require examinees to orally generate words under time pressure in accordance with orthographic or semantic rules. These tasks are quick to administer, easy to use, and sensitive to cognitive impairment across a range of clinical disorders including Alzheimer’s disease (Henry, Crawford, & Phillips, 2004), Parkinson’s disease (Henry & Crawford, 2004b), and depression (Henry & Crawford, 2005). Consequently, verbal fluency tasks are among the most widely used neuropsychological assessment tools (Rabin, Barr, & Burton, 2005; Sullivan

& Bowden, 1997) and are incorporated within numerous neuropsychological assessment batteries (Strauss, Sherman, & Spreen, 2006). Commonly used verbal fluency tasks include phonemic fluency, which requires the generation of words beginning with a specified initial letter (e.g., “F”: “fantastic, famous … ”), and semantic fluency, where exemplars of a given category are produced (e.g., “animals”: “cat, dog … ”). Two more recently developed variants are excluded letter fluency, where produced words must not contain a specified vowel (e.g., “A”: “friend, monkey … ”), and alternating fluency, in which examinees

Address correspondence to: Renerus Stolwyk, School of Psychological Sciences Monash University, Building 17, Clayton Campus, Melbourne 3800, Australia (E‐mail: [email protected]).

© 2015 Taylor & Francis

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produce words switching between two semantic categories (e.g., “fruit and furniture”: “grape, chair … ”). Despite their high sensitivity and widespread use, there is significant debate regarding which cognitive abilities are assessed by various verbal fluency measures. Verbal fluency tasks have been primarily interpreted as measures of “executive functioning,” because they purport to assess the executive aspects of verbal skills (Lezak, Howieson, Bigler, & Tranel, 2012). However, “executive functioning” is an umbrella term encompassing multiple higher order cognitive abilities that supervise, control, and regulate lower order cognitive processes to enable goaldirected action (Jurado & Rosseli, 2007; Miyake et al., 2000). Thus, theories of verbal fluency concede that both executive processes (i.e., the inhibition of inappropriate responses) and lower order abilities (i.e., associative word activation) are required for successful fluency performance (Rosen & Engle, 1997; Troyer, Moscovitch, & Winocur, 1997). In an effort to identify which cognitive abilities are measured by various verbal fluency tasks, most researchers in this field have used correlational techniques to examine the converging and diverging relationships of verbal fluency with other established measures of specific cognitive abilities. These studies have been conducted across a range of healthy and clinical populations. Phonemic fluency is widely interpreted as a measure of strategic search and retrieval within orthographic or phonological networks (Benton, 1968; Troyer et al., 1997). Consistently, studies have reported performance to reflect a number of higher order functions including working memory, inhibition, switching, and set-shifting in schizophrenia (Ojeda et al., 2010), dementia (Laisney et al., 2009), and clinically heterogeneous (Nutter-Upham et al., 2008) populations. Others have highlighted the significant contributions of verbal intelligence and speed of information processing to phonemic fluency performance in healthy populations (Bolla, Lindgren, Bonaccorsy, & Bleeker, 1990; Bryan, Luszcz, & Crawford, 1997; Hughes & Bryan, 2002; Ruff, Light, Parker, & Levin, 1997). Semantic fluency has been traditionally viewed to reflect both executive and associative retrieval mechanisms particularly reliant upon intact semantic networks (Henry & Crawford, 2004a; Newcombe, 1969). Studies have reported that semantic fluency performance measures higher order abilities, specifically working memory (Fournier-Vicente, Larigauderie, & Gaonac’h, 2008; Ojeda et al., 2010), and, less consistently,

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inhibition, switching, and shifting (Laisney et al., 2009; McDowd et al., 2011; Nutter-Upham et al., 2008; van Beilan et al., 2004). At a more basic level, semantic retrieval, vocabulary, and processing speed have been associated with higher performance in healthy and clinically heterogeneous populations (Kave & Mashal, 2012; NutterUpham et al., 2008); however, findings have been inconsistent across other clinical populations (Bowie et al., 2004; Ojeda et al., 2010). Excluded letter fluency was constructed as a more demanding analogue of phonemic fluency and is widely viewed as a measure of inhibition and strategic search abilities within an orthographic network (Bryan et al., 1997). Hughes and Bryan (2002) reported performance to be related with working memory, inhibition, verbal intelligence, and processing speed. Alternating fluency has been conceptualized as a task requiring the retrieval of words from a semantic network while placing added demands on switching and mental flexibility (Delis, Kaplan, & Kramer, 2001). In support, studies have reported fluid intelligence (Henry & Phillips, 2006), switching, shifting, and inhibition (McDowd et al., 2011; Nutter-Upham et al., 2008) to predict higher alternating fluency performance. At the level of fundamental abilities, processing speed has been associated with alternating fluency performance, while the involvement of semantic retrieval and vocabulary remains contentious across clinical (Nutter-Upham et al., 2008) and healthy (Henry & Phillips, 2006; McDowd et al., 2011) populations. To summarize the above, cognitive abilities including verbal intelligence, semantic retrieval, processing speed, working memory, inhibition, switching, and shifting have been found to relate with various verbal fluency variants to some extent; however, findings have been inconsistent. A number of inherent methodological limitations are likely to have contributed to these inconsistent findings. First, researchers have used different cognitive measures across studies. For example, McDowd et al. (2011) allowed three minutes for verbal fluency tasks, while most other researchers used the standard one minute per trial. Furthermore, working memory has been measured using different tools across studies including Digit Span Forwards (Laisney et al., 2009), Digit Span Backwards (Ojeda et al., 2010), and Letter– Number Sequencing (Ross et al., 2007). Secondly, different clinical and healthy populations have been used across verbal fluency studies. It is now acknowledged that cognitive tools may measure different cognitive constructs across populations, depending on their cognitive profile

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(Donders, 2005). This issue has not been comprehensively delineated within the verbal fluency literature. Thirdly, many previous studies have used a correlation rather than regression approach to statistical analyses (Henry & Phillips, 2006; Kave & Mashal, 2012; Nutter-Upham et al., 2008). Performances across measures of cognitive function share at least some shared variance, which can confound findings when using multivariate statistics. One advantage of using regression analyses is that shared variance can be controlled, and unique contributions of cognitive abilities to verbal fluency task performance can be identified (e.g., Fallows & Hilsabeck, 2012). A recent study addressed many of the methodological issues discussed above (Kraan, Stolwyk, & Testa, 2013). Within a sample of 93 healthy young people aged 18–35 years, the relative and unique contributions of five cognitive abilities (verbal intelligence, semantic retrieval, processing speed, working memory, and inhibition) to four different verbal fluency tasks (phonemic, semantic, excluded letter, and alternating fluency) were investigated using identical cognitive measures and stepwise regression procedures across fluency variants. The results found phonemic fluency to be uniquely predicted by verbal intelligence and processing speed, semantic fluency by semantic retrieval and working memory, excluded letter fluency by processing speed, and alternating fluency by semantic retrieval. It was surprising that alternating and excluded letter fluency, which were designed to place additional demands on “executive” skills, were not significantly predicted by constructs such as working memory or inhibition. It was postulated that alternating and excluded fluency may place significant demands on these executive constructs in clinical populations where higher level cognitive functioning is compromised, but not in healthy younger people. Whilst this study provided valuable information regarding which cognitive abilities uniquely predict verbal fluency performance in a younger healthy population and how the pattern of predictors varies across different fluency variants, we cannot assume that these findings will generalize to other populations. Understanding which cognitive abilities are measured by verbal fluency tasks is particularly relevant in healthy older adults. Older adults are at risk of developing a range of neurocognitive disorders such as Alzheimer’s disease and other dementias, and verbal fluency tasks have been shown to play an important role in delineating normal and pathological aging trajectories.

Verbal fluency tasks are sensitive to cognitive impairment associated with neurodegenerative processes such as Alzheimer’s disease and Parkinson’s disease (Henry & Crawford, 2004b; Salmon et al., 2002) and are included in best practice assessment guidelines to assist identification of cognitive impairment in these disorders (Litvan et al., 2012; Paajanen et al., 2014). Healthy older adults demonstrate a significantly distinct cognitive profile from the young. Pertinently to verbal fluency, older adults tend to perform better than young adults on crystallized cognitive variables such as vocabulary measures (Schaie, 2005; Verhaeghen, 2003). However, they tend to be less proficient than young adults on more fluid cognitive variables such as word retrieval (Zec, Burkett, Markwell, & Larsen, 2007), psychomotor speed (Schaie, 2005), working memory (Bopp & Verhaeghen, 2005), and inhibition (Wecker, Kramer, Wisniewski, Delis, & Kaplan, 2000). Due to these different profiles, cognitive abilities measured by verbal fluency tasks in older adults may differ from those measured in young adults (Donders, 2005). A limited number of studies have investigated which cognitive abilities are associated with verbal fluency tasks in healthy older adults, and even fewer have adequately compared these across healthy younger and older adults. Hughes and Bryan (2002) reported that phonemic fluency reflected verbal intelligence in both young and old adult groups; however, processing speed was a significant correlate only in the elderly. It was suggested that cognitive functions such as processing speed that decline in older people are more likely to be taxed by verbal fluency tasks and thus emerge as contributors to performance. Semantic retrieval and inhibitory control have also been associated with phonemic fluency performance in older adults (Kave & Mashal, 2012; McDowd et al., 2011). Studies report significant associations of verbal intelligence (Barnes, Tager, Satariano, & Yaffe, 2004; Rodriguez-Aranda, Waterloo, Sparr, & Sundet, 2006), semantic retrieval (Kave & Mashal, 2012), and processing speed and inhibition (McDowd et al., 2011) with semantic fluency performance in the elderly. Hughes and Bryan (2002) reported excluded letter fluency to be correlated with verbal intelligence, processing speed, and working memory in both young and older groups. Inhibition only contributed in the older group, again likely due to relative weakness of inhibition in the elderly compared to younger participants. Finally, regarding alternating fluency, both processing speed and inhibition have been associated with this variant in older adults (McDowd et al., 2011).

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The present study aimed to compare healthy young and healthy older adults regarding which cognitive abilities uniquely predict phonemic, semantic, excluded letter, and alternating fluency performance. Overall, we hypothesized that contributions of cognitive abilities including verbal intelligence, semantic retrieval, processing speed, working memory, and inhibition would vary across verbal fluency variants. We also hypothesized that due to disparate cognitive profiles between younger and older adults, these contributions would differ across age groups. In particular, due to age-related decline, fluid cognitive abilities may contribute to verbal fluency tasks more significantly in older than in younger adults. Regarding younger people, we utilized the same participant group as that in our previous study (Kraan et al., 2013). Thus, despite utilizing modified statistical analyses (see Method section), we expected that findings from this earlier study would be replicated. Specifically, phonemic fluency would be predicted by estimated verbal intelligence and processing speed, semantic fluency by semantic retrieval and working memory, excluded letter fluency by processing speed, and alternating fluency by semantic retrieval. With respect to our older group, based on previous studies described above, we hypothesized that both phonemic fluency and semantic fluency would be predicted by verbal intelligence, semantic retrieval, processing speed, and inhibitory control; excluded letter fluency by verbal intelligence, processing speed, working memory, and inhibition; and alternating fluency by processing speed and inhibition.

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METHOD All procedures in this study were approved by the Monash University Human Research Ethics Committee.

Participants Two cohorts of participants, 96 young healthy adults aged 18–36 years and 83 older healthy adults aged 65–87 years, were included in this study. Demographic information for both cohorts is summarized in Table 1. Data for the young healthy adult sample were taken from a previous study (Kraan et al., 2013). Young adults were recruited from a university undergraduate student pool (in exchange for course credit) and the wider community. Older adults were recruited through announcements and dissemination of flyers in independent living retirement villages and various community clubs including senior citizen social societies. Inclusion criteria for all participants were fluency of the English language and no history of major neurological or psychiatric disorders, learning disabilities, substance abuse, color blindness, functionally significant motor or sensory impairment, or use of medications known to affect cognitive function. Older adults were additionally screened for cognitive decline using a structured interview where participants were asked to report any recent change to their cognitive function in addition to the MiniMental State Examination (MMSE; Folstein,

TABLE 1 Mean, standard deviation, range and difference between groups for age, education, cognitive predictors, and verbal fluency tasks in young and old adults Young adults (N = 96) Demographic, cognitive and verbal fluency variables

Mean

SD Range

Gender (M/F) Age (years) Years of education Verbal intelligence Semantic retrieval Processing speed Working memory Inhibition Phonemic fluency Semantic fluency Excluded letter fluency Alternating fluency

35/61 26.00 15.69 102.67 18.63 64.72 10.55 5.53 13.57 25.79 16.74 14.39

5.39 2.39 8.11 3.71 9.72 2.20 8.39 3.50 4.76 4.51 2.40

Min

Old adults (N = 83) Max

Mean

SD Range

Min

Max

χ2

t

d

27/56 0.30 18.00 18.00 36.00 73.39 6.41 22.00 65.00 87.00 12.00 11.00 23.00 14.13 3.14 16.00 8.00 24.00 3.74*** 0.56 34.00 84.00 118.00 113.00 7.52 34.00 90.00 124.00 –8.79*** 1.32 19.00 7.00 26.00 21.70 4.10 19.00 10.00 29.00 –5.27*** 0.79 52.00 38.00 90.00 48.49 8.11 40.00 26.00 66.00 12.02*** 1.82 10.00 6.00 16.00 9.40 1.96 10.00 4.00 14.00 3.68*** 0.55 45.54 –22.52 23.02 –4.53 6.26 28.75 –20.82 7.94 8.96*** 1.37 20.00 5.00 25.00 13.57 3.67 19.50 5.50 25.00 –0.009 0.00 24.00 14.00 38.00 22.06 3.83 21.00 12.50 33.50 5.71*** 0.87 23.00 6.00 29.00 13.94 4.32 23.00 4.00 27.00 4.22*** 0.63 13.00 9.00 22.00 13.64 2.70 15.00 7.00 22.00 1.96 0.29

Note. As no transformed variables were used, higher scores on all cognitive and verbal fluency variables indicate better performance. M = male; F = female. ***p ≤ .001.

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Folstein, & McHugh, 1975) where a cutoff score of 24 was utilized. Materials Verbal fluency tasks Verbal fluency tasks were administered according to instructions given by Strauss et al. (2006). In all fluency tasks, participants were instructed to generate as many words as possible in accordance with the task restrictions without repeating items. Orthographic fluency tasks (i.e., phonemic and excluded letter fluency) additionally prohibited grammatical variants of a previously said word (e.g., “eat, eating, eaten”) and proper nouns. Phonemic and semantic fluency were each assessed across two trials, where the raw score was the average across two trials. Excluded letter and alternating fluency were assessed with one trial each. For phonemic fluency, participants produced words beginning with “F” in the first trial and “A” in a second trial (Tombaugh, Kozak, & Rees, 1999). For semantic fluency, exemplars of “animals” were required in the first trial and “supermarket items” in the second (Tombaugh et al., 1999). For excluded letter fluency, words not containing the letter “A” were required (Shores, Cairstairs, & Crawford, 2006). Finally, for alternating fluency, participants were instructed to generate exemplars switching between types of “fruit” and “furniture” (Delis et al., 2001). One minute was allowed for each trial, and the number of total correct responses was recorded. Incorrect responses were ignored. Participants were asked to clarify any repeated words that may have been homonyms at the end of the trial. Cognitive abilities Previous studies have investigated a range of cognitive abilities that may be associated with verbal fluency performance. However, investigating all of these abilities was beyond the scope of this paper. In their investigation of younger healthy people, Kraan et al. (2013) conducted a review of previous verbal fluency research and selected constructs for investigation based on common theoretical conceptualizations of verbal fluency variants and constructs most consistently shown to be associated with verbal fluency performance within previous literature. Tools used to measure these abilities were selected based on the following criteria: (a) The tool had been validated for use in a healthy cohort (Strauss et al., 2006); (b) the tool had demonstrated adequate reliability and validity

(Strauss et al., 2006); (c) the tool was commonly used within clinical and research settings; and (d) there had been previous use of the tool within this research field. To ensure consistency of methodology between the younger and older groups, the same abilities and measures were used in this study. Verbal intelligence Verbal intelligence was estimated from errors on the National Adult Reading Test–R (NART–R; Nelson & Willison, 1991). The NART requires participants to read aloud 50 words spelt incongruently from their true pronunciation, with the rationale that these words must be known in order to be pronounced correctly (e.g., “facade, placebo”). This test demonstrates excellent internal consistency, test–retest reliability, interreliability, and construct validity against measures of verbal intelligence, reading, and general intelligence (Crawford, Stewart, Cochrane, Parker, & Besson, 1989). Estimated verbal intelligence scores may range from 70 (indicated by 50 errors) to 127 (indicated by zero errors). Semantic retrieval Semantic retrieval was measured using the Graded Naming Test (GNT; McKenna & Warrington, 1980). This task requires participants to name 30 individually presented line drawings of objects and animals in ascending difficulty within 15 seconds. A response is only recorded as correct if the specific name is given. If participants provide alternate names or name nontarget areas of the picture, they are prompted to think of other possible names, or are directed to the target part of the picture. This test has demonstrated adequate test– retest reliability (Roberts, 2003) and high construct validity against other measures of semantic retrieval (McKenna & Warrington, 1980). The raw score of total correct responses was used in this study, and scores may range from a minimum of zero to a maximum of 30. Processing speed Processing speed was assessed using the oral version of the Symbol Digits Modalities Test (SDMT; Smith, 1991). This task presents a key in which the numbers one to nine are each paired with different symbols, followed by a randomly ordered page of these symbols without their related numbers. Participants are required to call out the associated numbers of each symbol as quickly as

COGNITIVE ABILITIES ASSOCIATED WITH VERBAL FLUENCY

possible, and the total number of correct responses made within 90 seconds is recorded. This task demonstrates acceptable test–retest reliability (Smith, 1991) and excellent construct validity against other measures of processing speed (Crowe et al., 1999; Morgan & Wheelock, 1995). The raw score of total correctly paired items was used in this study, and scores may range from a minimum of zero to a maximum of 110. Inhibition Inhibition was measured using the Stroop Color and Word Test (Golden, 1978). In this test, examinees are presented with a word page, color page, and color–word page, which consist of color-congruent words, colored “XXXX”s, and incongruent color words, respectively. Examinees are required to read (word page) or name the ink color of (color and color–word pages) as many words possible within 45 seconds per page. If an item is incorrectly read or named, participants are prompted to return to the item and correctly respond. A predicted color–word page score reflecting processing speed is calculated from the word and color pages, and the Stroop interference score controls for individual differences in processing speed by subtracting the predicted color–word page score from the actual color–word page score. The Stroop interference score demonstrates acceptable test–retest reliability (Franzen, Tishelman, Sharp, & Friedman, 1987) and high construct validity against other measures of selective attention and inhibition (Fournier-Vicente et al., 2008; Miyake et al., 2000). The Stroop interference score was used in this study, with higher scores reflecting better inhibitory control. Working memory Working memory was assessed using the Digit Span Sequencing Test (DSST) from the Wechsler Adult Intelligence Scale–IV (WAIS–IV; Wechsler, 2008). In this task, the examinee reads aloud a randomly ordered sequence between two and nine numbers in length. Participants are instructed to recall and repeat the sequence, rearranging the numbers in ascending order from smallest to largest. There are eight items in total, each consisting of two trials. The task is stopped when a participant responds incorrectly to both trials in one item. The Digit Span subtest demonstrates high test– retest reliability (Matarazzo & Herman, 1984) and high construct validity against other measures of auditory–verbal working memory and attention (MacDonald, Almor, Henderson, Kempler, &

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Andersen, 2001; Robertson, Ward, Ridgeway, & Nimmo-Smith, 1996). The raw score of total correctly repeated sequences was used in the present study. Scores may range from a minimum of zero to a maximum of 16. Procedure After signing consent forms and providing demographic information, volunteers were screened for the inclusion criteria during a structured interview. Older adults were additionally administered the MMSE. Those who met the inclusion criteria completed the cognitive tasks in a pseudorandomized order. Participants were encouraged to take rest breaks between tasks if needed. All sessions were conducted by two student researchers under the supervision of a clinical neuropsychologist in quiet private rooms primarily at a university, retirement villages, and public libraries. Data analysis The data were analyzed using Statistical Package for Social Sciences (SPSS) Version 20. To provide background context to key analyses, demographic variables—namely, gender and years of education—were compared between young and old healthy adult groups using chi-square and between-group t tests. Performance on the cognitive tests and verbal fluency tasks were also compared using between-group t tests. To investigate whether estimated verbal intelligence, semantic retrieval, processing speed, working memory, or inhibition (independent variables) uniquely predicted phonemic, semantic, excluded letter, and alternating fluency (dependent variables), eight hierarchical multiple regression analyses were conducted for all four verbal fluency tasks in both young and old age groups. In our previous study (Kraan et al., 2013), stepwise regression was employed. However, stepwise procedures have been critiqued because inclusion of variables is reliant on purely statistical criteria (Tabachnick & Fidell, 2013). Thus, hierarchical multiple regression was utilized in this study. This facilitated controlling for demographic variables (within-group age, gender, education) in Step 1 and then ascertaining the degree of unique verbal fluency variance explained by the cognitive predictors in Step 2. Within the second step, estimated verbal intelligence, semantic retrieval, processing speed, working memory, and inhibition were entered together. This was due to insufficient

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evidence to guide hierarchical ordering of individual cognitive predictors into the regression models across both age groups. It also ensured consistent methodology across all eight regression models to facilitate later comparison of results across the analyses. Following preliminary analysis, two cases were excluded because of missing data, one case was also excluded as a multivariate outlier (casewise diagnostic), and three univariate outliers with z scores above 3.29 were replaced with a value one more than the next extreme (Tabachnick & Fidell, 2013). An analysis of regression residuals indicated that data satisfied the assumptions of the primary regression analysis (multicollinearity, normality, homoscedasticity, independence of errors, absence of multivariate outliers; Tabachnick & Fidell, 2013). As the normality of the independent variables is additionally recommended (Tabachnick & Fidell, 2013), these were assessed separately in young and old adults. Accordingly, a reflected square root transformation was applied to verbal intelligence, semantic retrieval, and inhibition. With the exception of working memory, all transformed independent variables were normally distributed, and these were used in the primary regression analyses. As non-normality of independent variables does not violate the regression assumptions (Tabachnick & Fidell, 2013), and no transformation facilitated normality for working memory, the original working memory variable was used in all analyses.

RESULTS The means, standard deviations, minimum, maximum, range, and difference between groups for the raw scores of control (gender, education), independent (cognitive predictors), and dependent (verbal fluency) variables are summarized in Table 1. As can be seen, all measures demonstrated adequate range. There was no significant difference in gender across younger and older groups; however, female gender was overrepresented in both groups. Younger participants had completed significantly more years of education than older participants. Regarding cognitive predictor variables, older participants performed significantly higher on estimated verbal intelligence and semantic retrieval measures, while the younger group performed significantly higher on processing speed, working memory, and inhibition measures. There was no significant between-groups difference on phonemic and alternating fluency performances; however, younger participants performed significantly higher on semantic and excluded letter fluency measures.

Cognitive predictors of verbal fluency tasks in both young and old adults Results from hierarchical multiple regression analyses are presented in Table 2 for the younger group and Table 3 for the older group. All eight

TABLE 2 Cognitive abilities predicting phonemic, semantic, excluded letter, and alternating verbal fluency in the younger group Verbal fluency task Semantic

Phonemic Independent variables Step 1 Gender Education Age Step 2 Verbal intelligencea Semantic retrievala Processing speed Working memory Inhibitiona Total R2

β

ΔR

2

β

.02 .14 –.08 –.01

ΔR

Excluded letter 2

.12* .15 .29** –.03

.18** –.33* .17 .31* .14 .15

ΔR

.23***

β

ΔR2 .14**

.31** .15 .12 .20***

.01 –.18 .35** .18* .10 .34***

Alternating

.16* .10 .22 –.29*

.12 –.48*** –.03 .21* –.08 .20*

β

2

.20*** .04 –.36** .18 .15 .09

.32***

.33***

Note. N = 95. a Reflected variable where higher scores indicated worse performance. Negative regression coefficients for these variables in Step 2 indicate positive relationships where better scores on the cognitive ability predicted higher verbal fluency performance. *p ≤ .05. **p ≤ .01. ***p ≤ .001.

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TABLE 3 Cognitive abilities predicting phonemic, semantic, excluded letter, and alternating verbal fluency in older group Verbal fluency task Phonemic Independent variables Step 1 Gender Education Age Step 2 Verbal intelligencea Semantic retrievala Processing speed Working memory Inhibitiona Total R2

β

Semantic β

ΔR2 .12*

.14 .21 –.15

ΔR2

β

.15**

.24***

β

ΔR2 .11*

.23* –.12 –.22 .12*

–.25 .08 –.13 .13 –.21* .24**

Alternating

.30***

.09 –.14 –.16 .03 .19 .08

.37***

ΔR2

–.03 .21* .44***

.01 .02 –.38**

–.43*** .12 .23 .17 .08

Excluded letter

.15* –.34** .04 .16 .11 .14

.42***

.27**

Note. N = 81. Reflected variable where higher scores indicated worse performance. Negative regression coefficients for these variables in Step 2 indicate positive relationships where better scores on the cognitive ability predicted higher verbal fluency performance. *p ≤ .05. **p ≤ .01. ***p ≤ .001. a

models provided satisfactory fit with limited autocorrelation among residuals. Durbin–Watson scores were reported between 1.8 and 2.5 across all eight regression models (Field, 2013). Verbal intelligence, semantic retrieval, and inhibition were reflected, therefore negative regression coefficients indicate a positive predictive relationship (i.e., better performance on the respective cognitive task predicts higher verbal fluency performance) for these variables.

3.77, p = .004; R = .61, R2 = .37, F(5, 72) = 5.50, p < .001, respectively], accounting for an additional 18% and 24% of phonemic fluency performance variance, respectively. Individually, higher verbal intelligence was a unique significant predictor of better phonemic fluency in both younger and older groups, and higher processing speed provided unique significant prediction for better performance, but only for the younger group. See Table 2 for all standardized coefficients (beta), R statistics, and levels of significance.

Phonemic fluency Total regression models for phonemic fluency were significant in both younger and older groups [F(94) = 2.68, p = .011; F(80) = 5.19, p < .001, respectively], accounting for 20% and 37% of phonemic fluency performance variability, respectively. At Step 1, demographic variables together did not predict a significant amount of phonemic fluency variance in younger participants, R = .16, R2 = .02, F(3, 91) = 0.75, p = .53; however, they did predict significant amount of variance in the older group, R = .35, R2 = .12, F(3, 77) = 3.62, p = .017, accounting for 2% and 12% of phonemic fluency performance variability, respectively. Individually, age, gender, nor education significantly contributed to phonemic fluency performance in either age group. At Step 2, together, the cognitive variables significantly contributed to phonemic fluency performance in both younger and older groups [R = .45, R2 = .20, F(5, 86) =

Semantic fluency Total regression models for semantic fluency were significant in both younger and older groups [F(94) = 5.53, p < .001; F(80) = 2.87, p = .008, respectively], accounting for 34% and 24% of semantic fluency performance variability, respectively. At Step 1, demographic variables together significantly predicted semantic fluency variance in both younger and older age groups [R = .34, R2 = .12, F(3, 91) = 3.91, p = .011; R = .39, R2 = .15, F(3, 77) = 4.57, p = .005, respectively], accounting for 12% and 15% of semantic fluency performance variability, respectively. Higher education level significantly contributed to better semantic fluency performance in younger participants, and lower age was a significant contributor to better performance in the older age group. At Step 2, together the cognitive variables significantly contributed to semantic fluency performance in younger but not

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older groups [R = .58, R2 = .34, F(5, 86) = 5.87, p < .001; R = .49, R2 = .24, F(5, 72) = 1.72, p = .140, respectively], accounting for an additional 23% and 9% of semantic fluency variance, respectively. Individually, higher semantic retrieval and working memory significantly predicted better semantic fluency in the younger group; however, individually no cognitive variables significantly predicted semantic fluency in the older group. Excluded letter fluency Total regression models for phonemic fluency were significant in both younger and older groups [F(8, 86) = 5.02, p < .001; F(8, 72) = 6.48, p < .001, respectively], accounting for 32% and 42% of excluded letter fluency performance, respectively. At Step 1, demographic variables together significantly predicted excluded letter fluency variance in both younger and older age groups [R = .34, R2 = .16, F(3, 91) = 3.92, p = .011; R = .55, R2 = .30, F(3, 77) = 11.15, p < .001, respectively], accounting for 16% and 30% of excluded letter fluency performance, respectively. Individually, lower age significantly contributed to better excluded letter fluency performance in younger participants. In older participants, both higher education and lower age contributed to better performance. At Step 2, together, the cognitive variables significantly contributed to excluded letter fluency performance in both younger and older groups [R = .56, R2 = .32, F(5, 86) = 5.14, p < .001; R = .65, R2 = .42, F(5, 72) = 2.87, p = .02, respectively], accounting for an additional 20% and 12% of excluded letter fluency performance variance, respectively. Individually, higher processing speed and working memory significantly predicted better excluded letter fluency in the younger group while higher inhibitory control significantly predicted better performance in the older group. Alternating fluency Total regression models for alternating fluency were significant in both younger and older groups [F(8, 86) = 5.37, p < .001; F(8, 72) = 3.24, p = .003, respectively], accounting for 33% and 27% of alternating fluency performance variability, respectively. At Step 1, demographic variables together predicted a significant amount of alternating fluency variance in both younger and older groups [R = .36, R2 = .14, F(3, 77) = 3.25, p = .026; R = .37, R2 = .11, F(3, 91) = 4.80, p = .004, respectively], accounting for 14% and 11% of alternating fluency performance variability, respectively. Individually, gender significantly contributed to alternating

fluency in both younger and older age groups, with females recording higher levels. At Step 2, together, the cognitive variables significantly contributed to alternating letter fluency performance in both younger and older groups [R = .58, R2 = .33, F(5, 86) = 5.07, p < .001; R = .51, R2 = .27, F(5, 72) = 2.98, p = .017, respectively], accounting for an additional 20% and 15% of alternating letter fluency performance variance, respectively. Individually, higher semantic retrieval significantly predicted better alternating fluency in the younger group while higher verbal intelligence significantly predicted better performance in the older group.

DISCUSSION The primary aims of this study were to investigate which cognitive abilities uniquely contribute to phonemic, semantic, excluded letter, and alternating fluency performance and whether these predictors vary across younger and older healthy people. Overall, as hypothesized, both foundation cognitive abilities, such as processing speed and semantic retrieval, and higher order cognitive abilities, such as verbal intelligence, working memory, and inhibition contributed to various verbal fluency variants. Also consistent with expectations, cognitive contributions to verbal fluency tasks differed across fluency variants and age groups. To provide background context to our study, performance on cognitive predictor and verbal fluency measures were compared between young and old groups. Our older participants performed higher on the measure of verbal intelligence that was estimated through word knowledge. This finding is consistent with previous research showing that vocabulary, along with other abilities of crystallized verbal intelligence, improves until at least the age of 55 and is maintained until a gradual decrease beginning in the mid to late 60s (Albert, Heller, & Milberg, 1988; Salthouse, 2014; Schaie, 2005; Verhaeghen, 2003). Also as expected, younger participants demonstrated more proficient performance on fluid cognitive measures such as processing speed, working memory, and inhibitory control (Bopp & Verhaeghen, 2005; Wecker et al., 2000). In contrast with previous research (Zec et al., 2007), performance on our measure of semantic retrieval was higher in the older group, which was likely facilitated by their superior word knowledge. With regard to verbal fluency performances, young and older groups performed similarly on phonemic and alternating letter fluency. In contrast, performance on semantic and excluded letter fluency was significantly higher in the

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younger group. There are a number of reasons why semantic and excluded letter fluency may be more sensitive to age effects than phonemic and alternative fluency. One reason may be that different verbal fluency tasks tap different cognitive functions across age groups, which is discussed below. Phonemic fluency has traditionally been conceptualized as a measure of executively mediated strategic search ability. However, as hypothesized, in this study estimated verbal intelligence was the strongest predictor of performance in both younger and older groups. Phonemic fluency conceptually requires the nonhabitual retrieval of words from orthographic and phonological networks. Our results demonstrate that the magnitude and sophistication of these networks, as reflected by verbal intelligence, are of great importance. It is not clear why processing speed was required for phonemic fluency performance in the younger group only. One potential explanation is that younger participants were required to draw on additional processing speed resources to compensate for relatively weaker verbal intelligence and word knowledge. In contrast to previous studies, semantic retrieval, processing speed, and inhibitory control did not contribute to phonemic fluency performance in our older group. This contrasting result may reflect that shared contributions of cognitive predictors to verbal phonemic fluency performance were controlled in this study, which did not consistently occur in previous research. Interestingly, working memory was not a significant predictor of performance in either group, which contrasts with findings from clinical populations where working memory, rather than processing speed or verbal intelligence, predicted phonemic fluency (Ojeda et al., 2010; van Beilen et al., 2004). Working memory has been shown to be significantly compromised in many neurological and psychiatric populations (Lezak et al., 2012). As a result, working memory may be taxed by phonemic fluency in clinical, but not healthy, populations. Semantic fluency is often conceptualized as a measure of associative and retrieval mechanisms within a semantic network (Henry & Crawford, 2004b; Kave & Mashal, 2012). This conceptualization was supported within our younger group, but not our older group. The reason for this difference across age groups is not clear. It is noted that younger participants performed lower on our semantic retrieval measure than did older individuals. As a result, it is possible that semantic retrieval functions may have been more taxed in younger adults during semantic fluency task performance and thus was a significant contributor only in this younger group. Interestingly, working

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memory also contributed to semantic fluency performance in our younger group only. This activation of working memory in the younger group is consistent with previous research (Rende, Ramsberger, & Miyake, 2002; Rosen & Engle, 1997) and may have facilitated higher overall semantic fluency performance in the younger group, despite lower semantic retrieval ability. In contrast to younger participants, no cognitive variables included in this study significantly contributed to semantic fluency performance in our older group. Even together, cognitive variables did not significantly contribute to the semantic fluency regression model. Further research is required to investigate what other cognitive and noncognitive variables contribute to semantic fluency performance in older people. Excluded letter fluency is widely conceptualized as a measure of strategic search and inhibition abilities within orthographic networks. In younger participants, consistent with our previous study (Kraan et al., 2013), processing speed was again found to be a strong contributor to this fluency variant. However, utilizing our modified statistical approach, working memory also emerged as a significant contributor. This may reflect the concurrent demands of excluded letter fluency, including holding the exclusion rule in mind, generating target words, and scanning words for accuracy. Consistent with previous research (Bryan et al., 1997), we found inhibition to contribute to excluded letter fluency in our older group only. Our older group exhibited reduced inhibitory control compared to our younger participants. Thus, inhibition may have been relatively more taxed by excluded letter fluency in our older participants, resulting in a stronger contribution. With regard to alternating fluency, as hypothesized, semantic retrieval uniquely contributed to this fluency variant in young adults. However, as with semantic fluency, this finding did not emerge in our older group. Again, it is possible that alternating fluency placed greater demands on semantic retrieval abilities in young adults than in older adults, as semantic retrieval performance was lower in young adults. In contrast, it appears the older people were utilizing their strengths in verbal intelligence, and specifically word knowledge, to complete this fluency variant. Alternating fluency has been postulated to place additional demands on executive abilities such as mental flexibility and switching (Delis et al., 2001). Thus, the lack of significant contributions of higher order variables such as working memory and inhibition in this study was surprising, particularly in older groups where age-related decline in these functions occur.

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Specifically, we did not see that the additional task requirements of alternating fluency increased demands on working memory relative to the other fluency tasks. Some authors have drawn analogies between verbal fluency and free recall tasks, suggesting that in comparison to free recall, providing a cue in verbal fluency gives participants added external support for searching and retrieving words (Hedden, Lautenschlager, & Park, 2005). Following this line of reasoning, providing two cues in alternating fluency would provide even greater external support in searching for and “recalling” target words. This is consistent with our results generally showing slightly decreased demands on working memory in alternating fluency, relative to the other fluency tasks. It is also acknowledged that previous studies suggest that other cognitive functions not considered here, such as attention switching and shifting, may also contribute to alternating fluency performance (McDowd et al., 2011; Nutter-Upham et al., 2008). As discussed above, semantic and excluded letter fluency variants were less proficiently performed in older people than in young. In contrast, no age group differences were observed for phonemic and alternating fluency. It is important to consider why this might be the case. By examining the pattern of cognitive contributors across different verbal fluency variants and across age groups, it becomes clear that verbal intelligence, including word knowledge, which is a significant strength for older people, was the sole significant contributor to phonemic and alternating fluency performance in the older group. In contrast, no cognitive strengths for older people were identified as contributing to semantic fluency, and a cognitive weakness was solely contributing to excluded letter fluency in our older groups. Thus, we suggest that fluency variants such as semantic fluency and excluded letter fluency that do not tap cognitive strengths in older people, or in fact tap cognitive weaknesses in this group, are more likely to be sensitive to healthy age-related decline in performance. In turn, fluency variants such as phonemic and alternating fluency that tap cognitive strengths in older people, such as verbal intelligence and word knowledge, are more resistant to age-related performance decline. This supposition is similar to that of Bryan and colleagues (1997) who also suggested that cognitive skills contributing to verbal fluency may influence age-related decline on these tasks. However, possibly due to methodological differences (e.g., older adult only sample used) they concluded that age-related processing speed decline was most likely responsible for reduced verbal fluency performance in healthy older adults.

Further research is warranted to explore this issue further. More, specifically it would be interesting to further delineate the contributions of premorbid intelligence and other markers of cognitive reserve, in addition to current verbal intelligence, to investigate whether these factors help protect against healthy age-related declines in fluency performances. Although this study provided important contributions to the understanding of verbal fluency tasks in healthy young and old adults, some methodological limitations are acknowledged. With regard to demographic issues, older adults within our sample had completed significantly fewer years of education than the younger adult group. However, this is typical of the normal population, and in fact higher education was slightly overrepresented in our older group compared to their age-related peers (see Australian Bureau of Statistics, 2011). While no significant gender differences were noted between groups, females were generally overrepresented across the study sample, and we note significant age ranges within our older and younger groups. These findings support our decision to control for these demographic factors in the initial step of all regression analyses. It is also acknowledged that the NART–R was utilized to estimate verbal intelligence in this study. While previous research has shown a robust relationship between the NART–R and concurrent full and verbal intelligence (Schretlen, Buffington, Meyer, & Pearlson, 2005), we recommend the NART–R be used to measure premorbid intelligence and the WAIS–IV Verbal Comprehension Index as a more comprehensive measure of concurrent verbal intelligence in future studies. Another issue to highlight is the relatively small proportion of verbal fluency performance explained by the demographic and cognitive variables, which may be attributed to two methodological issues. First, a limitation of the regression approach is that the variance in the outcome variable shared by multiple simultaneously entered independent predictors is not assigned to any of these predictors (Tabachnick & Fidell, 2013). The unexplained variance in our regression models may reflect this issue; however, unfortunately we did not have a clear evidence base to guide individual entry of our cognitive predictors for this current study. Secondly, this study was limited to investigating the cognitive abilities most commonly associated with verbal fluency. However, there are likely to be many other cognitive and noncognitive variables that also explain performance on these tasks. In any case, due to the limited variance accounted for, results from this study need to be

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treated with some caution. Finally, we acknowledge that our protocol to screen for cognitive decline has limited sensitivity to mild cognitive impairment and would have benefited from a more stringent screening process. Based upon the findings and limitations of the present study, future research should now extend the current understanding of verbal fluency tasks by investigating the contributions of other cognitive abilities such as cognitive switching and shifting to fluency performance. Further, this study has given a strong indication of the primary and secondary cognitive predictors of fluency tasks in healthy adult populations. Future research can use present results to countermand the issue regarding shared variance in simultaneous entry regression to guide hierarchical entry order of cognitive predictors. Finally, the current study has provided a baseline indicator of the cognitive abilities reflected by successful verbal fluency performance in healthy adult populations. Future research should now investigate the cognitive abilities reflected by impaired verbal fluency performance in clinical populations and whether these deviate from the abilities measured in healthy populations. Our findings have some implications for the current uses and interpretation of verbal fluency tasks across young and old healthy adult populations in both research and clinical settings. First, verbal fluency tasks reflect multiple lower and higher cognitive abilities, and an accurate interpretation of verbal fluency performance requires that clinicians and researchers make these distinctions. Secondly, contextualizing these findings within the current widespread interpretation of verbal fluency as an executive measure, our findings indicate that in healthy adult populations, performance on some verbal fluency variants such as phonemic fluency may more strongly reflect lower order cognitive abilities such as word knowledge and processing speed. Furthermore, there is only limited evidence that newer fluency variants specifically constructed to place added demands on executive functioning reach this aim in healthy populations. Specifically, no executive functions included in this study significantly predicted alternating fluency, and inhibition predicted excluded letter fluency only in our older group. Therefore, at a practical level, it is imperative that individual differences in abilities such as verbal intelligence, processing speed, and semantic retrieval are considered if these tasks are to be used as measures of executive functioning. Finally, there are several specific but important differences in the construct validity of verbal fluency tasks between young and older adults, and

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clinicians and researchers need to appreciate these distinctions.

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