This article was downloaded by: [University of Western Ontario]

On: 11 May 2013, At: 11:37

Publisher: Routledge
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer
House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of Clinical and Experimental

Neuropsychology
Publication details, including instructions for authors and subscription information:
http://www.tandfonline.com/loi/ncen20

Normative Data for Clustering and Switching on

Verbal Fluency Tasks
Angela K. Troyer
Published online: 09 Aug 2010.

To cite this article: Angela K. Troyer (2000): Normative Data for Clustering and Switching on Verbal Fluency Tasks, Journal
of Clinical and Experimental Neuropsychology, 22:3, 370-378

To link to this article: http://dx.doi.org/10.1076/1380-3395(200006)22:3;1-V;FT370

PLEASE SCROLL DOWN FOR ARTICLE

Full terms and conditions of use: http://www.tandfonline.com/page/terms-and-conditions

This article may be used for research, teaching, and private study purposes. Any substantial or systematic
reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to
anyone is expressly forbidden.

The publisher does not give any warranty express or implied or make any representation that the contents
will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses
should be independently verified with primary sources. The publisher shall not be liable for any loss, actions,
claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or
indirectly in connection with or arising out of the use of this material.
 Journal of Clinical and Experimental Neuropsychology 1380-3395/00/2203-370$15.00
2000, Vol. 22, No. 3, pp. 370-378 © Swets & Zeitlinger

Normative Data for Clustering and Switching on Verbal

Fluency Tasks*
Angela K. Troyer
Baycrest Centre for Geriatric Care, Toronto, Canada

ABSTRACT
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

Normative data for clustering and switching on verbal fluency tasks are provided. Four hundred and eleven
healthy adults between the ages of 18 and 91 were given tests of phonemic fluency (FAS or CFL) and
semantic fluency (Animals and Supermarket). Raw scores were corrected for demographic (i.e., age, edu-
cation, and sex) and test (i.e., fluency form) variables that were determined to make sizable contributions
to fluency performance. These normative data should be useful for clinicians and researchers in determin-
ing the nature of the fluency impairment in any given individual.

Tests of verbal fluency or word-list generation fluency, and is thought to be a relatively auto-
are frequently used in clinical and experimental matic process. Switching involves cognitive
examinations of cognitive function. The most flexibility in shifting from one subcategory to
commonly used score from verbal fluency tests another and is a relatively effortful process.
is the total number of words generated. How- These components of fluency performance
ever, this score provides little information about are differentially affected by various neurologi-
the cognitive processes underlying fluency per- cal disorders. Clustering is related to temporal-
formance and does not answer the question as to lobe functioning, as indicated by impaired per-
why a particular patient group or experimental formance among patients with temporal lobec-
manipulation is associated with reduced test per- tomy for intractable epilepsy (Troyer, Mos-
formance. Additional information is needed to covitch, Winocur, Alexander, & Stuss, 1998)
examine the behavioral components that deter- and patients with Alzheimer’s disease (Troyer,
mine fluency performance. Moscovitch, Winocur, Leach, & Freedman,
Optimal fluency performance involves gener- 1998). Clustering is unaffected by focal frontal
ating words within a subcategory and, when a lesions (Troyer, Moscovitch, Winocur, Alexan-
subcategory is exhausted, switching to a new der, et al., 1998). Switching, on the other hand,
subcategory. These behavioral components were is related to frontal functioning. That is, switch-
identified (Bousfield & Sedgewick, 1944; Grue- ing is specifically impaired among patients with
newald & Lockhead, 1980) and operationalized left dorsolateral and superior medial frontal-lobe
as clustering and switching, respectively lesions (Troyer, Moscovitch, Winocur, Alexan-
(Troyer, Moscovitch, & Winocur, 1997). Clus- der, et al., 1998) and is decreased under condi-
tering involves phonemic analysis on phonemic tions of divided attention (Troyer et al., 1997),
fluency and semantic categorization on semantic an experimental model of frontal dysfunction

*
Thanks to Morris Moscovitch and Kathryn Stokes for conceptual input and comments on the manuscript; Jill
B. Rich, Nicole D. Anderson, and Don Stuss for the provision of fluency protocols; Malcolm Binns for statistical
assistance; and Katy Kamkar for assistance with scoring and data entry.
Address correspondence to: A. Troyer, Psychology Department, Baycrest Centre for Geriatric Care, 3560
Bathurst Street, Toronto, ON, M6A 2E1, Canada. E-mail: a.troyer@utoronto.ca.
Accepted for publication: October 19, 1999.
 NORMATIVE FLUENCY DATA 371

(Moscovitch, 1994). Decreased switching is also were required either to: (a) obtain scores of 25 or
seen in patient groups with frontal dysfunction higher on the Mini Mental Status Examination
in the context of additional brain dysfunction, (Folstein, Folstein, & McHugh, 1975), or (b) score
within the normal range on an episodic memory
including Parkinson’s disease (Tröster et al.,
test (Anderson, Craik, & Naveh-Benjamin, 1998).
1998; Troyer, Moscovitch, Winocur, Leach, et
al., 1998), Huntington’s disease (Rich, Troyer, Fluency Tasks
Bylsma, & Brandt, 1999), multiple sclerosis All testing was conducted in English. Each partic-
(Tröster et al., 1998), and schizophrenia (Robert ipant was administered three trials of a phonemic
et al., 1998). fluency task and one or two trials of a semantic
Overall, research with these patient groups fluency task. For phonemic fluency, participants
were given either the letters F, A, and S (n = 257)
has consistently shown distinctions between pa- or C, F, and L (n = 154). Consistent with standard
tient groups with predominant temporal versus instructions (e.g., Spreen & Strauss, 1998), partici-
frontal dysfunction. Some inconsistencies have
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

pants were asked to generate as many words as

been obtained, however. Patients with more possible in 60 seconds that began with each letter,
widespread brain dysfunction may show impair- excluding proper names and repetitions of the
ments in both clustering and switching, although same word with different endings.
one impairment tends to predominate. For exam- For semantic fluency, most participants were
given Animal fluency (n = 407), and a subset of
ple, patients with Alzheimer’s disease were im- participants was also given Supermarket fluency (n
paired in both clustering and switching on se- = 156). On animal fluency, participants were in-
mantic fluency and in only clustering on phone- structed to name as many different animals as pos-
mic fluency; furthermore, the clustering impair- sible in 60 seconds. On supermarket fluency, par-
ment was more severe than any switching im- ticipants were instructed to name as many items as
pairment (Troyer, Moscovitch, Winocur, Leach, possible that one can find or buy in a supermarket,
as part of the Dementia Rating Scale (Mattis,
et al., 1998). Despite this complication, on bal-
1988). Responses were recorded for the full 60
ance, measures of clustering and switching pro- seconds. For tests of both phonemic and semantic
vide useful information about the ongoing cog- fluency, all errors, including repetitions and intru-
nitive processes during performance on tests of sions, were recorded along with correct words in
fluency, including information for differential the order in which they were generated.
diagnosis. To increase the usefulness of these Three scores were obtained from each fluency
fluency indices in clinical and research settings, task, including the mean cluster size, the raw num-
ber of switches, and the total number of correct
there is a need for normative data from a large
words generated. Detailed rules for scoring cluster
group of healthy adults. The purpose of this pa- size and switches for phonemic and animal fluency
per is to provide these data. have been provided previously (Troyer et al.,
1997) and are reprinted in the Appendix. Briefly,
on phonemic fluency, clusters were defined as suc-
METHODS cessively generated words that began with the
same first two letters, differed only by a vowel
sound, rhymed, or were homonyms. On animal
Participants
fluency, clusters were successively generated
Participants were 411 healthy, community-dwell-
words belonging to the same semantic subcatego-
ing adults recruited from university-based subject
ries, such as African animals, Australian animals,
pools, a hospital database of outpatients, senior
North American wild animals, pets, and individual
centres, and advertisements posted in the commu-
zoological categories, such as birds, canine, in-
nity. Ages ranged from 18 to 91 years (M = 59.8,
sects, primates, and so forth. On supermarket flu-
SD = 20.7), and level of education ranged from 5
ency, clusters included fruits, vegetables, dairy
to 21 years (M = 13.9, SD = 2.9). Seventy percent
products, meats, and so forth. These categories
of participants were women. All participants were
were determined based on the naturally-occurring
fluent in English. Participants were screened for
clusters in the participants’ protocols. The clusters
neurologic and psychiatric disorders that could
used for supermarket fluency are consistent with a
affect cognitive function. To screen for general
previous scoring system (Tröster, Salmon,
cognitive decline, participants aged 60 and older
McCullough, & Butters, 1989). On phonemic flu-
 372 ANGELA K. TROYER

ency, only phonemic clusters were counted, and on sex-specific and age-specific names of the same
semantic fluency, only semantic clusters were animal species were considered to be the same ani-
counted. The size of the cluster was counted begin- mal (e.g., hen and rooster, cat and kitten). In order
ning with the second word in each cluster. The to provide consistency between the semantic flu-
mean cluster size was calculated by summing the ency tasks, on supermarket fluency, no credit was
size of each cluster and dividing by the number of given for subcategory labels (e.g., fruits) if spe-
clusters. Switches were calculated as the number cific exemplars were also given (e.g., apple, ba-
of transitions between clusters, including single nana).
words. The number of switches, therefore, is the On phonemic fluency, scores from the three tri-
same as the number of clusters minus the number als (i.e., F, A, and S, or C, F, and L) were com-
of trials administered (e.g., three trials on phone- bined into a single score for each participant. Simi-
mic fluency and two trials on semantic fluency). larly, on semantic fluency, scores on the two trials
For several reasons, the raw number of switches (i.e., Animals and Supermarket) were combined
was chosen as the switching index rather than the into a single variable. The benefit of these com-
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

number of switches corrected for number of words bined scores is that they are based on a higher
generated. Conceptually, the raw number of swit- number of responses and are thus presumed to be
ches is the behavior of interest, as it indicates the more valid and reliable measures of fluency abil-
number of times an individual can generate a new ity. Because a large number of participants re-
cluster of responses. This is similar to the raw in- ceived animal but not supermarket fluency, animal
dex used for the number of words generated. scores alone were also retained.
Knowing the percent of the words generated that
were correct does not provide as much information
as knowing the raw number of words generated RESULTS
(e.g., 90% could indicate 9 out of 10 or 36 out of
40). As well, because switching is thought to deter-
mine, in part, the number of words generated Raw total mean scores obtained by the entire
(Troyer et al., 1997), correcting switches for total sample were 42.5 (SD = 11.7) for FAS and CFL,
words generated would be tantamount to correct- 19.5 (SD = 5.3) for Animals, and 22.9 (SD =
ing a cause for its effect. Another reason that raw 5.8) for Supermarket fluency. These scores are
switches were used is because previous experience consistent with previously published scores
has indicated that a corrected switching score does
(e.g., Bolla, Lindgren, Bonaccorsy, & Bleecker,
not produce meaningful information. No patient
groups, including those with mild to moderate de- 1990; Kozora & Cullum, 1995; Spreen &
mentia, have been found to be impaired in compar- Strauss, 1998).
ison to controls on a corrected switching score, To determine the contributions of demo-
despite large group impairments on other fluency graphic and administrative factors to fluency
measures (Troyer, 1997; Tröster et al., 1998). performance, a series of regression equations
Importantly, repetitions and intrusions were was performed using age, education, sex, and
included in calculations of cluster size and swit-
ches because they provide information about the
phonemic-fluency test form (i.e., CFL or FAS)
ongoing strategy. Any protocol on which the ex- to predict cluster size, switches, and total words
aminer failed to record repetitions or intrusions in generated on the fluency tasks. Effect sizes (i.e.,
the order in which they were generated was dis- f; Cohen, 1988) based on the partial regression
carded from the analyses (six protocols). This was coefficients (Bs) were calculated to determine
considered to be important because, otherwise, which variables made meaningful contributions
both clustering and switching would be under- to fluency scores. According to Cohen (1988), fs
reported in patient populations in which perse-
verations are frequent. of .10, .25, .40 correspond to small, medium,
Repetitions and intrusions were excluded from and large effect sizes, respectively.
the number of correct words generated. On phone-
mic fluency, consistent with the instructions given Age
to participants, proper names and repetitions of the Age showed a small effect size as a predictor of
same word with a different ending were also ex- switching on phonemic fluency, B = 0.04, t =
cluded. On animal fluency, no credit was given for
–2.05, p = .041, f = .10, and a large effect size as
subcategory labels (e.g., bird) if specific exemplars
were also given (e.g., robin, canary). In addition, a predictor of switching on semantic fluency, B
 NORMATIVE FLUENCY DATA 373

= –.11, t = –5.07, p < .001, f = .42, and animal Test Form

fluency, B = –.05, t = –8.28, p < .001, f = .413. Small differences were present in the scores
Age had a minimal effect size on clustering on obtained on the two forms of phonemic fluency
all tasks, including phonemic, B = 0.001, t = after controlling for demographic variables.
1.65, p = .099, f = .08, semantic, B = 0.001, t = Larger cluster sizes were produced on CFL than
0.47, p = .640, f = .03, and animal fluency, B = on FAS, B = 0.097, t = 3.63, p < .001, f = .18. In
0.002, t = 1.12, p = .262, f = .06. For total num- contrast, number of switches was higher on FAS
ber of words generated, age showed a minimal than on CFL, B = –2.79, t = –2.99, p = .003, f =
effect size as a predictor of phonemic fluency, B .15. The total number of words generated on the
= –0.04, t = –1.48, p = .141, f = .07, but a large two forms was similar, B = –2.14, t = –1.70, p =
effect size for both semantic, B = –0.23, t = .090, f = .08.
–5.73, p < .001, f = .47, and animal fluency, B =
–0.09, t = –8.29, p < .001 , f = .41. In general, Calculation of Normative Data
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

increasing age was associated with slightly Generally, age, education, and fluency form (but
larger cluster sizes and with reduced switches not sex) were important predictors of fluency
and words generated. performance. Thus, regression analyses were
performed that included only these three predic-
Education tor variables. Corrections were then determined
Number of years of formal education showed by the B’s, and these corrections were applied to
minimal to small effect sizes as a predictor of the original scores in order to calculate norma-
clustering on phonemic fluency, B = 0.015, t = tive test data. The corrections are presented at
3.34, p = .001, f = .17, semantic fluency, B = the top of Table 1. Age and education correc-
0.012, t = 0.75, p = .454, f = .06, and animal flu- tions were applied for each year of age or formal
ency, B = 0.024, t = 2.31, p = .021, f = .12. Edu- education, respectively, and form corrections
cation had a small effect size as a predictor of were applied for FAS. Percentiles were then cal-
switching on phonemic, B = 0.39, t = 2.53, p = culated based on the actual distribution of the
.012, f = .13, semantic, B = 0.25, t = 1.67, p = corrected scores (e.g., 16% of participants in our
.097, f = .14, and animal fluency, B = 0.17, t = sample obtained scores at the 16th percentile or
3.49, p = .001, f = .17. Education showed the less). This method was chosen because some
largest effect size (small to medium) as a predic- score distributions (i.e., cluster size) were
tor of total number of words generated, includ- slightly skewed. Means, standard deviations,
ing phonemic, B = 1.06, t = 5.06, p < .001, f = and percentiles for the corrected scores are pre-
.25, semantic, B = 0.74, t = 2.80, p = .006, f = sented in the Table.
.23, and animal fluency, B = 0.51, t = 6.12, p < Percentiles for individual raw scores can be
.001, f = .31. Higher levels of education were obtained by adding any relevant corrections and
always associated with better fluency perfor- looking up the corresponding corrected score.
mance. Consider, for example, a hypothetical 50-year-
old woman with 13 years of education. If she
Sex produced a cluster size of 0.35 on FAS, her cor-
Sex showed a minimal effect size as a predictor rected score would be calculated as 0.35 +
of every phonemic, semantic, or animal fluency 50(–.001) + 13 (–0.015) + 0.094 = 0.20. This
variable, t’s = 0.08 to 1.25, p’s = .21 to .94, f’s = places her at approximately the 50th percentile.
.00 to .06, with the exception of a small effect If this same individual generated a total of 35
size on total number of words generated on se- words on semantic fluency, her corrected score
mantic fluency, B = –2.32, t = –1.55, p = .123, f would be 35 + 50(0.23) + 13 (–0.74) = 36.8,
= .13. which places her between the 5th and 16th per-
centiles.
 374 ANGELA K. TROYER

Table 1. Corrections, Demographically Corrected Descriptive Data, and Percentiles for Fluency Scores.

Phonemic Semantic Animals

Cluster Switches Total Cluster Switches Total Cluster Switches Total

Age (years) –0.001 +0.05 +0.04 –0.001 +0.11 +0.23 –0.002 +0.05 +0.09
Education (years) –0.015 –0.38 –1.06 –0.012 –0.25 –0.74 –0.023 –0.17 –0.51
Form (FAS) +0.094 –2.67 –2.18 NA NA NA NA NA NA

Mean 0.24 23.9 28.6 0.94 23.4 46.9 0.75 9.8 18.1
SD 0.23 8.2 11.1 0.47 4.4 7.9 0.57 2.7 4.6

1st percentile –0.16 6.6 4.3 0.24 13.4 28.3 –0.24 3.9 8.3
5th percentile –0.06 10.2 11.4 0.40 16.2 34.4 0.01 5.8 10.9
16th percentile 0.01 15.6 17.0 0.60 18.9 39.4 0.23 7.3 13.5
25th percentile 0.08 18.7 20.6 0.66 20.5 40.7 0.40 7.9 14.9
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

50th percentile 0.19 23.3 28.7 0.91 22.7 46.3 0.64 9.6 17.9
75th percentile 0.35 29.7 36.6 1.18 26.5 52.5 1.12 11.6 21.2
84th percentile 0.44 32.3 39.3 1.44 27.5 56.6 1.39 12.4 22.8
95th percentile 0.73 37.6 47.6 2.02 31.3 60.7 1.89 14.7 26.7
99th percentile 0.97 43.2 57.4 2.37 34.0 62.4 2.43 16.7 29.3

DISCUSSION Crossley et al., 1997). There were no differences

in the number of words generated on FAS versus
These normative data can be used when standard CFL, consistent with previous reports (Lacy et
fluency instructions and recording procedures al., 1996).
are used. However, it is important that the exam- The effect of age on clustering and switching
iner record repetitions and intrusions – as well was examined in a previous report (Troyer et al.,
as correct words – in the order that they are gen- 1997). In contrast to previous findings, in the
erated in order to use these data. This is crucial present study, increasing age was not associated
because repetitions and intrusions were included with significantly larger cluster sizes on phone-
in the normative calculations of cluster size and mic fluency. A trend in that direction was ob-
switches, and because a failure to do so will re- tained, but the effect size was not meaningful (p
sult in an underestimate of both cluster size and = .099, f = .08). Thus, although older adults have
switches, especially in groups in which repeti- a tendency to produce slightly larger clusters
tions and intrusions occur frequently. than younger adults, in large groups of partici-
The present findings regarding the total num- pants, this is an extremely small effect. Findings
ber of words generated are consistent with sev- regarding switching are consistent with our pre-
eral other reports. That is, age was more vious report. That is, increasing age was associ-
strongly related to the number of words gener- ated with decreased switching on animal flu-
ated on semantic fluency than phonemic fluency ency.
(Crossley, D’Arcy, & Rawson, 1997; Kozora & An examination of the demographic correc-
Cullum, 1995). Education was related to the tions suggests that both clustering and switching
number of words generated on both phonemic influence the number of words generated; simi-
and semantic fluency (Crossley et al., 1997; larly, neither index alone accounts for the num-
Kempler, Teng, Dick, Taussig, & Davis, 1998). ber of words generated. A higher level of educa-
We found no sex differences on the number of tion, for example, was associated with a larger
words generated on phonemic or semantic flu- cluster size and more words generated on phone-
ency (Kozora & Cullum, 1995), although there mic fluency. Presumably, those participants pro-
have been some reports of better performance by ducing larger cluster sizes consequently gener-
women on phonemic fluency (Bolla et al., 1990; ated more words overall. In contrast, higher edu-
 NORMATIVE FLUENCY DATA 375

cation was associated with more switches and centile) fluency. This pattern is not consistent
more words generated on animal fluency. In this with Alzheimer’s disease, and instead implicates
case, more frequent switching presumably re- frontal dysfunction, perhaps related to vascular
sulted in more words generated. Thus, both clus- or frontal dementia. Indeed, vascular dementia
tering and switching are necessary to account for was the most likely diagnosis for LS, given her
variations in the number of words generated. history (stable impairment over the last year)
The usefulness of these fluency scores in as- and brain imaging (periventricular white matter
sessment and diagnosis can be demonstrated changes on CT). Thus, an examination of clus-
with the following sample patient profiles. SR is tering and switching scores may be useful in
a 56-year-old woman with a university-level discriminating between different causes of cog-
education and a one- to two-year history of nitive impairment.
memory decline. A comparison of her demo-
graphically-corrected fluency component scores
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

indicated consistently lower clustering than REFERENCES

switching performance. On phonemic fluency,
clustering was at the low end of average (score Anderson, N. D., Craik, F. I. M., & Naveh-Benjamin,
= 0.09, 25th percentile), and switching was aver- M. (1998). The attentional demands of encoding
and retrieval in younger and older adults: Evidence
age to high average (score = 25.3, 50th to 75th
from divided attention costs. Psychology and Ag-
percentile). On semantic fluency, clustering was ing, 13, 405-423.
impaired (score = 0.06, below the 1st percen- Bolla, K. I., Lindgren, K. N., Bonaccorsy, C., &
tile), and switching was average to high average Bleecker, M. L. (1990). Predictors of verbal flu-
(score = 24.9, 50th to 75th percentile). The total ency (FAS) in the healthy elderly. Journal of Clini-
cal Psychology, 46, 623-628.
number of words generated was average on pho- Bousfield, W. A., & Sedgewick, C. H. W. (1944). An
nemic fluency (corrected score = 25.2, 25th to analysis of restricted associative responses. Jour-
50th percentile) and impaired on semantic flu- nal of General Psychology, 30, 149-165.
ency (corrected score = 30.3, below the 1st per- Cohen, J. (1988). Statistical power analysis for the
centile). This pattern of poorer clustering than behavioral sciences (2nd ed.). Hillsdale, NJ: Law-
rence Erlbaum.
switching is consistent with Alzheimer’s dis- Crossley, M., D’Arcy, C., & Rawson, N. S. B. (1997).
ease, as indicated by previous research (Troyer, Letter and category fluency in community-dwell-
Moscovitch, Winocur, Leach, et al., 1998), and, ing Canadian seniors: A comparison of normal par-
indeed, SR’s history and performance on other ticipants to those with dementia of the Alzheimer
neuropsychological tests met NINCDS-ADRDA or vascular type. Journal of Clinical and Experi-
mental Neuropsychology, 19, 52-62.
criteria for Alzheimer’s disease (McKhann et Folstein, M. F., Folstein, S. E., & McHugh, P. R.
al., 1984). This profile can be contrasted with (1975). Mini-Mental State: A practical method for
LS, who is an 88-year-old woman with 12 years grading the cognitive state of patients for the clini-
of formal education and a six-year history of cian. Journal of Psychiatric Research, 12, 189-
memory impairment. Corrected fluency scores 198.
Gruenewald, P. J., & Lockhead, G. R. (1980). The
showed that clustering was generally better than free recall of category examples. Journal of Exper-
switching. On phonemic fluency, clustering was imental Psychology: Human Learning and Mem-
average (score = 0.26, 50th to 75th percentile) ory, 6, 225-240.
and switching was impaired (score = 10.2, 5th Kempler, D., Teng, E. L., Dick, M., Taussig, I. M., &
percentile). On animal fluency, clustering was Davis, D. S. (1998). The effects of age, education,
and ethnicity on verbal fluency. Journal of the In-
average to low average (score = 0.54, 25th to ternational Neuropsychological Society, 4, 531-
50th percentile), and switching was just below 538.
average (score = 7.4, 16th percentile). Total Kozora, E., & Cullum, C. M. (1995). Generative nam-
number of words generated was impaired on ing in normal aging: Total output and qualitative
both phonemic (score = 7.6, 1st to 5th percen- changes using phonemic and semantic constraints.
The Clinical Neuropsychologist, 9, 313-325.
tile) and animal (score = 11.8, 5th to 16th per-
 376 ANGELA K. TROYER

Lacy, M. A., Gore Jr., P. A., Pliskin, N. H., Henry, G. Tröster, A. I., Fields, J. A., Testa, J. A., Paul, R. H.,
K., Heilbronner, R. L., & Hamer, D. P. (1996). Blanco, C. R., Hames, K. A., Salmon, D. P., &
Verbal fluency task equivalence. The Clinical Neu- Beatty, W. W. (1998). Cortical and subcortical in-
ropsychologist, 10, 305-308. fluences on clustering and switching in the perfor-
Mattis, S. (1988). Dementia Rating Scale. Odessa, FL: mance of verbal fluency tasks. Neuropsychologia,
Psychological Assessment Resources. 36, 295-304.
McKhann, G., Drachmann, D., Folstein, M., Katzman, Tröster, A. I., Salmon, D. P., McCullough, D., & But-
R., Price, D., & Stadlan, E. M. (1984). Clinical ters, N. (1989). A comparison of the category flu-
diagnosis of Alzheimer’s disese. Neurology, 34, ency deficits associated with Alzheimer’s and
939-944. Huntington’s disease. Brain and Language, 37,
Moscovitch, M. (1994). Cognitive resources and dual- 500-513.
task interference effects at retrieval in normal peo- Troyer, A. K. (1997). [Alternate indices of verbal flu-
ple: The role of the frontal lobes and medial tem- ency clustering and switching]. Unpublished raw
poral cortex. Neuropsychology, 8, 524-534. data.
Rich, J. B., Troyer, A. K., Bylsma, F. W., & Brandt, J. Troyer, A. K., Moscovitch, M., & Winocur, G.
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

(1999). Longitudinal analysis of phonemic cluster- (1997). Clustering and switching as two compo-
ing and switching during word list generation in nents of verbal fluency: Evidence from younger
Huntington’s disease. Neuropsychology, 13, 525- and older healthy adults. Neuropsychology, 11,
531. 138-146.
Robert, P. H., Lafont, V., Medecin, I., Berthet, L., Troyer, A. K., Moscovitch, M., Winocur, G., Alexan-
Thauby, S., Baudu, C., & Darcourt, G. (1998). der, M. P., & Stuss, D. (1998). Clustering and
Clustering and switching strategies in verbal flu- switching on verbal fluency: The effects of focal
ency tasks: Comparison between schizophrenic and frontal- and temporal-lobe lesions. Neuropsy-
healthy subjects. Journal of the International Neu- chologia, 36, 449-504.
ropsychological Society, 4, 539-546. Troyer, A. K., Moscovitch, M., Winocur, G., Leach,
Spreen, O., & Strauss, E. (1998). A compendium of L., & Freedman, M. (1998). Clustering and switch-
neuropsychological tests: Administration, norms, ing on verbal fluency tests in Alzheimer’s and Par-
and commentary (2nd ed.). New York: Oxford kinson’s disease. Journal of the International Neu-
University Press. ropsychological Society, 4, 137-143.
 NORMATIVE FLUENCY DATA 377

APPENDIX

Scoring Rules for Clustering and Switching1

Total number of correct words generated. This was calculated as the sum of all words produced,
excluding errors and repetitions.
Mean cluster size. Cluster size was counted starting with the second word in a cluster. That is, a
single word was given a cluster size of 0, two words had a cluster size of 1, three words had a cluster
size of 2, and so forth. Errors and repetitions were included. The mean cluster size was computed
across the three phonemic trials and across the one or two semantic trials.
Number of switches. This was calculated as the total number of transitions between clusters, in-
cluding single words, for the three phonemic trials combined and for the one or two semantic trials
combined. Errors and repetitions were included.
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

Phonemic fluency
Clusters on phonemic fluency trials consisted of successively generated words which shared any of
the following phonemic characteristics:
First letters: words beginning with same first two letters, such as ‘‘arm’’ and ‘‘art’’
Rhymes: words that rhyme, such as ‘‘sand’’ and ‘‘stand’’
First and last sounds: words differing only by a vowel sound, regardless of the actual spelling,
such as ‘‘sat,’’ ‘‘seat,’’ ‘‘soot,’’ ‘‘sight,’’ and ‘‘sought’’
Homonyms: words with two or more different spellings, such as ‘‘some’’ and ‘‘sum,’’ as indicated
by the participant

Semantic fluency
Clusters on semantic fluency trials consisted of successively generated words belonging to the same
subcategories, as specified below. Commonly generated examples are listed for each subcategory,
although listings are not exhaustive.

Animals
African animals: aardvark, antelope, buffalo, camel, chameleon, cheetah, chimpanzee, cobra,
eland, elephant, gazelle, giraffe, gnu, gorilla, hippopotamus, hyena, impala, jackal, lemur, leopard,
lion, manatee, mongoose, monkey, ostrich, panther, rhinoceros, tiger, wildebeest, warthog, zebra
Australian animals: emu, kangaroo, kiwi, opossum, platypus, Tasmanian devil, wallaby, wombat
Arctic/Far North animals: auk, caribou, musk ox, penguin, polar bear, reindeer, seal
Farm animals: chicken, cow, donkey, ferret, goat, horse, mule, pig, sheep, turkey
North America animals: badger, bear, beaver, bobcat, caribou, chipmunk, cougar, deer, elk, fox,
moose, mountain lion, puma, rabbit, raccoon, skunk, squirrel, wolf
Water animals: alligator, auk, beaver, crocodile, dolphin, fish, frog, lobster, manatee, muskrat,
newt, octopus, otter, oyster, penguin, platypus, salamander, sea lion, seal, shark, toad, turtle, whale
Beasts of burden: camel, donkey, horse, llama, ox
Animals used for their fur: beaver, chinchilla, fox, mink, rabbit
Pets: budgie, canary, cat, dog, gerbil, golden retriever, guinea pig, hamster, parrot, rabbit
Birds: budgie, condor, eagle, finch, kiwi, macaw, parrot, parakeet, pelican, penguin, robin, toucan,
woodpecker
Bovine: bison, buffalo, cow, musk ox, yak
Canine: coyote, dog, fox, hyena, jackal, wolf
Deers: antelope, caribou, eland, elk, gazelle, gnu, impala, moose, reindeer, wildebeest
Feline: bobcat, cat, cheetah, cougar, jaguar, leopard, lion, lynx, mountain lion, ocelot, panther,
puma, tiger
 378 ANGELA K. TROYER

Fish: bass, guppy, salmon, trout

Insects: ant, beetle, cockroach, flea, fly, praying mantis
Insectivores: aardvark, anteater, hedgehog, mole, shrew
Primates: ape, baboon, chimpanzee, gibbon, gorilla, human, lemur, marmoset, monkey, orangutan,
shrew
Rabbits: coney, hare, pika, rabbit
Reptiles/Amphibians: alligator, chameleon, crocodile, frog, gecko, iguana, lizard, newt, salaman-
der, snake, toad, tortoise, turtle
Rodents: beaver, chinchilla, chipmunk, gerbil, gopher, groundhog, guinea pig, hamster, hedgehog,
marmot, mole, mouse, muskrat, porcupine, rat, squirrel, woodchuck
Weasels: badger, ferret, marten, mink, mongoose, otter, polecat, skunk

Supermarket Items
Downloaded by [University of Western Ontario] at 11:37 11 May 2013

Fruits: applesauce, bananas, cranberries, juice, mango, nectarines, peaches, raisins

Vegetables: avocado, beans, carrots, eggplant, olives, pickles, tomatoes, zucchini
Dairy case items: cheese, cream, cream cheese, eggs, milk, sour cream, yogurt
Meats: bacon, chicken, fish, hamburger, hot dogs, pork, salmon, sausage, tuna
Beverages: coffee, juice, lemonade, milk, orange juice, pop, tea, water, wine
Condiments: jelly, ketchup, marmalade, mayonnaise, pickles, relish, salad dressing
Flavourings: chives, cinnamon, parsley, pepper, sage, salt, vanilla, vinegar
Sweets and snacks: candy, cake, crackers, donuts, gum, ice cream, pie, popcorn, pudding, torte
Grain products: barley, bread, cereal, corn meal, flour, macaroni, meal, muffins, oats, rice
Baking supplies: baking powder, cornstarch, eggs, flour, salt, shortening, spices, vanilla
Specific meals/dishes: coffee, eggs, syrup, waffles; spaghetti, tomato sauce; lettuce, onions, rad-
ishes, salad dressing; pork, beans
Household goods: ammonia, detergent, disinfectant, gift wrap, Kleenex, magazines, mop, pans,
paper bags, paper towels, stamps, tin foil, toilet paper, wax paper
Personal toiletries: aspirin, comb, deodorant, medicine, mouthwash, toothpaste, vitamins
Infrastructure: aisles, basket, butcher, cash register, cashier, grocery bags, pharmacy, price tags,
shelves, shopping cart

General Scoring Rules

In the case where two categories overlapped, with some items belonging to both categories, some
items belonging exclusively to the first category, and some items belonging exclusively to the second
category, the overlapping items were assigned to both categories. For example, for ‘‘dog, cat, tiger,
lion,’’ the first two items were scored as pets, and the last three items were scored as feline. ‘‘Cat’’
was included in both the pet category and the feline category.
In the case where smaller clusters were embedded within larger ones, or two categories over-
lapped, but all items could correctly be assigned to a single category, only the larger, common cate-
gory was used. For example, for ‘‘sly, slit, slim, slam’’ all begin with ‘‘sl,’’ but an additional cluster
was not scored for the last two words which differ only by a vowel sound.

Note. From ‘‘Clustering and switching as two components of verbal fluency: Evidence from younger
and older healthy adults,’’ by A. K. Troyer, M. Moscovitch, & G. Winocur, 1997, Neuropsychology,
11, p. 145-146. Copyright 1997 by the American Psychological Association. Adapted by permission
of the publisher.