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Efficacy of clustering and switching strategies in
verbal fluency tasks in a Finnish-English language
attrition population
Nana Lehtinen, Anna Kautto & Kati Renvall
To cite this article: Nana Lehtinen, Anna Kautto & Kati Renvall (19 Dec 2023): Efficacy of
clustering and switching strategies in verbal fluency tasks in a Finnish-English language
attrition population, The Language Learning Journal, DOI: 10.1080/09571736.2023.2294060
To link to this article:  https://doi.org/10.1080/09571736.2023.2294060
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Published online: 19 Dec 2023.
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Efficacy of clustering and switching strategies in verbal fluency
tasks in a Finnish-English language attrition population
Nana Lehtinen a,b, Anna Kautto a and Kati Renvall a,c
aDepartment of Psychology and Speech-Language Pathology, University of Turku, Turku, Finland; bDepartment of
Scandinavian, University of California, Berkeley, CA, USA; cSchool of Psychological Sciences, Macquarie University,
Sydney, Australia
ABSTRACT
Verbal Fluency (VF) task total scores are widely used in language attrition
studies, but they do not provide insight into the processes underlying
optimal performance. We analyse the efficacy of clustering
(subcategories within a category) and switching (shifting between these
subcategories) strategies in phonemic (PVF) and semantic (SVF) tasks.
First, we focus on L1 Finnish and L2 English performance among
attriters (N = 38). Our analyses suggest similar processes underlying
performance in both languages. These processes seem to remain
unaffected by immersion time in the L2 environment (LoR) and
frequency of L1 use, highlighting the importance of including L2 data
alongside comparisons to L1 monolingual populations to account for a
broad bilingual effect in language attrition studies.
Second, we compare attriters’ and monolinguals’ (N = 50) performance
in L1. Our findings suggest that attriters rely on clustering in PVF more
systematically than monolinguals, and they struggle to initiate a search
for a new subcategory or return to a previous category (switching) after
depleting a cluster in PVF and SVF tasks. Thus, our analysis
demonstrates a difference in processing strategies between the groups
that could potentially contribute to similar total scores in PVF and lower
total scores in the attriter group in SVF.
ARTICLE HISTORY
Received 27 November 2023
Accepted 7 December 2023
KEYWORDS
Verbal fluency task;
clustering; switching;
language attrition
Introduction
Language attrition is a complex phenomenon characterised by subtle alterations in the native
language of individuals who are immersed in another language, particularly late sequential bi- or mul-
tilinguals. Participants in language attrition studies are typically immigrants who share a history of
leaving their native country as young adults after achieving mature L1 proficiency and who have
spent a prolonged time in an immersive L2 environment with little exposure to their native language
(Schmid 2011b; Schmid 2019; Schmid and Jarvis 2014; Schmid and Köpke 2017). The participants in this
study align with the description above. In order to underscore the shared language background of the
participants, the terms ‘attrition’ and ‘attriter,’which are the conventional labels for this population, will
be used even if the presence of language attrition has not specifically been identified within this group
(Kasparian and Steinhauer 2017; Schmid and Köpke 2017).
Many language attrition studies focus on L1 performance of attriters, comparing it solely to that of
monolingual speakers of the same L1 (Schmid 2019; Schmid and Dusseldorp 2010; Yilmaz and
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this
article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
CONTACT Nana Lehtinen nanleh@utu.fi
THE LANGUAGE LEARNING JOURNAL
https://doi.org/10.1080/09571736.2023.2294060
Schmid 2018). However, languages constantly interact in a bidirectional process that influences the
cognitive and neural processes of both languages of a bilingual individual (Abutalebi and Green
2007; Grosjean 2013; Gurunandan, Garreiras, and Paz-Alonso 2022; Laine and Lehtonen 2018;
Linck and Kroll 2019; Treffers-Daller 2019). Thus, performances in both languages and individuals’
exposure to them should be taken into account. In this study, we explore performance in L1 and
L2 verbal fluency tasks and the impact of extralinguistic variables on both languages by a group
of Finnish immigrants who have resided in an English language environment for over 20 years.
Verbal fluency tasks
Verbal fluency (VF) tasks are widely used to investigate lexical processing in diverse bilingual popu-
lations, including those experiencing language attrition. These tasks are simple and quick to admin-
ister. During a VF task, the participant is prompted to generate as many items as possible following a
specific condition within a set time frame. Typically, the tasks are scored based on the total number
of words successfully produced, referred to as total scores. The two most typical VF task types are
phonemic verbal fluency (PVF, phonemic, or letter cue, e.g. /f/) and semantic verbal fluency (SVF,
semantic category cue, e.g. animals).
When selecting the categories for VF tasks, language and, culture-specific factors should be con-
sidered (Abwender et al. 2001; Gollan, Sandoval, and Salmon 2011; Olabarrieta-Landa et al. 2017;
Roberts and Dorze 1997; Rosselli et al. 2002). In PVF, letter frequency in the target language can
impact the number of words generated, and localising letter prompts to high-frequency word-
initial letters in the target language has been shown to result in similar norms across languages
(Mardani et al. 2020; Oberg and Ramirez 2006; Schmid 2011a; Tombaugh, Kozak, and Rees 1999).
In English, a widely used letter compilation is FAS, selected initially due to the high yield of items
for these two consonants and one vowel (Borkowski et al. 1967; Ross 2003; Strauss et al. 2006).
In SVF, various demographic factors and cultural settings can influence semantic memory organ-
isation, semantic category size, and content (Abwender et al. 2001; Olabarrieta-Landa et al. 2017;
Roberts and Le Dorze 1997; Rosselli et al. 2002; Strauss et al. 2006; Troyer 2000). Selecting a culturally
and linguistically relatively neutral category, such as ‘animals’ (Pekkala et al. 2009), can stabilise these
effects between groups.
All VF tasks require rapid lexical retrieval and inhibition of unsuitable candidates, thus engaging
verbal knowledge and executive control skills. PVF tasks rely more on strategic cognitive organisa-
tion and maintenance of effort, requiring higher cognitive skills, while SVF tasks rely more on seman-
tic categorisation and hierarchical mental lexicon (Luo, Luk, and Bialystok 2010; Patra, Bose, and
Marinis 2020; Schmid 2011a; Strauss et al. 2006). VF task total scores can differentiate performance
among various populations and reflect verbal and executive processes underlying task performance.
In bilingual research, total scores have been used to infer vocabulary size and access, language dom-
inance patterns, and executive functions during task performance (Luo, Luk, and Bialystock 2010;
Marsh et al. 2019; Patra, Bose, and Marinis 2020; Roberts and Le Dorze 1997; Rosselli et al. 2002;
Schmid and Köpke 2009).
In PVF tasks, mono- and bilingual groups have shown either similar total scores (Rosselli et al.
2000; Rosselli et al. 2002; Soltani et al. 2021) or bilinguals have outperformed monolinguals
(Marsh et al. 2019). Higher scores in the bilingual groups have been associated with enhanced execu-
tive functions, potentially stemming from inhibiting language interference while switching between
languages (Ljungberg et al. 2013; Luo, Luk, and Bialystock 2010; Marsh et al. 2019; Patra, Bose, and
Marinis 2020; Sandoval et al. 2010). Studies that focus on language attrition populations rarely apply
PVF tasks (Jarvis 2019; Schmid and Köpke 2009). Some studies suggest a trend of language attriters
generating a smaller number of words compared to monolinguals in PVF tasks (Lazaridou-Chatzi-
goga and Karatsareas 2022; Opitz 2011) contradicting general bilingual findings.
In SVF, bilinguals, including language attriters, systematically generate lower total scores than
monolinguals. This bilingual disadvantage has been linked to language interference, weaker
2 N. LEHTINEN ET AL.
lexical connections, or smaller vocabularies (e.g. Badstübner 2011; Dostert 2009; Gollan, Montoya,
and Werner 2002; Opitz 2011; Rosselli et al. 2000; Sandoval et al. 2010; Schmid and Dusseldorp
2010; Schmid and Keijzer 2009; Schmid and Köpke 2009), but VF tasks have been shown to have
little effectiveness in distinguishing individuals as attriters or monolinguals (Schmid and Jarvis 2014).
VF task total scores reflect, but do not allow insight into the processes that support or hinder
optimal outcomes. Additional analysis methods focusing on temporal parameters, errors, and clus-
tering and switching strategies, have been proposed to further investigate VF data (Lehtinen, Luo-
tonen, and Kautto 2021; Thiele, Quinting, and Stenneken 2016). Following these suggestions,
Lehtinen, Kautto and Renvall (2023) analysed VF performance for total scores, temporal parameters
(15 second segments), and errors in three PVF tasks (letters: L1 /k/,/a/, /p/; L2 /f/, /a/, /s/) and one SVF
category (‘animals’) in a group of Finnish-English language attriters and Finnish monolinguals. The
current study further extends the analysis of this VF task performance via clustering and switching
analysis. In the following, we provide a brief overview of the previous literature on clustering and
switching strategies in the context of bilingual verbal fluency tasks before describing the current
study in detail.
Clustering and switching
In VF task analysis clustering refers to the ability to form sub-clusters in a category (e.g. in the category
’animals’: farm animals – pets – wild animals), and switching is the ability to transition between these
clusters (e.g. shifting from farm animals to pets to wild animals). Productive performance in a VF task
involves both clustering and switching strategies, typically measured respectively as the mean cluster
size and the number of switches. Various methods can be employed in determining clusters and
switches, and the technique used to compute clusters also influences the number of transitions
between those clusters. Thus, direct comparisons across studies employing different methods
should be made with careful consideration (Lehtinen, Kautto, and Luotonen 2021; Strauss et al.
2006; Thiele, Quinting, and Stenneken 2016; Troyer 2000; Troyer, Moscovitch, and Winocur 1997).
Task congruent clusters in PVF, namely phonemic clusters, are based on phonemic characters,
such as the same onset-nucleus sequence as in ‘simple, simile, sieve’. Generating phonemic clusters
requires a cognitively effortful non-routine search based on phonemic attributes without the help of
semantic categorisation (Luo, Luk, and Bialystock 2010; Strauss et al. 2006; Troyer 2000; Troyer, Mos-
covitch, and Winocur 1997). In SVF, generating task-congruent clusters (semantic clusters) involves
verbal semantic memory and semantic categorisation, resembling a relatively spontaneous everyday
systematic semantic search, such as generating a shopping list. The transition between the clusters
(i.e. switching), involves higher executive functions, including cognitive flexibility (Thiele, Quintin,
and Stenneken 2016; Troyer, Moscovitch, and Winocur 1997; Troyer 2000). Consequently, Troyer,
Moscovitch, and Winocur (1997) showed that in a monolingual population, switching was more
crucial for optimal fluency than task-congruent clustering in PVF, while in SVF, both clustering
and switching strategies were more equally important in reaching a high total score.
In addition to task congruent clusters, task-discrepant clustering (semantic clusters in PVF and
vice versa) represents an additional effortful and often intentional strategy. It involves higher cogni-
tive processes than task-congruent clustering and can suggest the activation of semantic mechan-
isms in an effortful phonemic task (Abwender et al. 2001; Sung et al. 2013).
Within the field of bilingualism, clustering and switching strategies have been analysed to study
executive processes (Mardani et al. 2020; Marsh et al. 2019; Patra, Bose, and Marinis 2020) and cross-
linguistic fluency strategies (Roberts and Le Dorze 1997; Rosselli et al. 2002). It has also been
suggested that bilinguals, who regularly switch between languages in everyday life, may demon-
strate superior cognitive flexibility through more efficient switching strategies than monolinguals
(Gollan, Montoya, and Werner 2002).
However, many studies include only one of the participants’ languages, and the various methods
used to calculate clusters and switches can complicate the interpretation of the findings. For
THE LANGUAGE LEARNING JOURNAL 3
example, Patra, Bose, and Marinis (2020) studied the L2 performance of Bengali-English bilingual
immigrants (time spent in L2 environment M= 7.48 years) and showed that bilinguals generated
larger clusters than monolinguals in PVF, resulting in a higher total score, and groups performed
similarly in SVF. They suggested that the ability to maintain a more demanding strategy of PVF clus-
tering better than monolinguals could signal superior executive performance in the bilingual group.
In contrast, Mardani et al. (2020) reported a higher total score but no significant difference in L1 PVF
cluster size between monolinguals and self-reported Farsi-Balochi bilinguals with no sudden change
in their language environment. Thus, bilinguals relied more on switching to reach a higher total
score than monolinguals. In SVF, bilinguals generated smaller clusters than monolinguals in L1
with no significant difference in total scores, interpreted as a disadvantage in verbal-semantic
memory performance in the bilingual group.
As for studies investigating language attrition populations per se, we are not aware of a robust VF
task clustering and switching analysis. Ammerlaan (1996) notes that attriters rely heavily on effortful
strategies in VF but does not include an analysis of these strategies in his study.
When determining clusters and switches in bilingual populations, it is important to consider
varied language backgrounds. Roberts and Le Dorze (1997) studied L1 and L2 in a French-English
bilingual group and reported larger semantic clusters in L1 than in L2 in the category ‘animals’
but not ‘foods’. They suggested that as the names of animals were learned during childhood
when the participants were exposed primarily to L1, there may be a stronger semantic base in L1
for ‘animals’ than ‘foods’, since food-related words are typically learned later in life when the partici-
pants had more exposure to L2. Rosselli et al. (2002) also observed that differences in the semantic
clustering of two monolingual groups were less evident in English-Spanish bilinguals (specifically
that English monolinguals named more wild animals and Spanish monolinguals more birds and
insects). The authors interpreted this as indicating different semantic category structures between
bilingual and monolingual groups.
In clustering analysis, language-specific factors can be taken into consideration by analysing natu-
rally occurring clusters rather than using predetermined categories, especially in SVF (Abwender
et al. 2001; Gollan, Sandoval, and Salmon 2011; Lehtinen, Luotonen, and Kautto 2021; Olabarrieta-
Landa et al. 2017; Roberts and Dorze 1997; Rosselli et al. 2002). Naturally occurring semantic subca-
tegories can include taxonomic subcategories (e.g. wolf, dog), environmental (e.g. gazelle, lion), geo-
graphical (e.g. wombat, kangaroo), or visual semantics (e.g. snake, eel) in the semantic category. In
the phonemic category, these can include, e.g. two initial phonemes (e.g. fossil, foster) or rhyming
words (e.g. fight, flight). For a more detailed discussion on naturally occurring clusters, we refer
the reader to the VF task administration and analysis guide in Lehtinen, Luotonen, and Kautto (2021).
Extralinguistic variables
Sociolinguistic parameters, such as language exposure and use, can result in shifts in individual
language dominance patterns over time affecting language performance (Treffers-Daller 2019;
Yilmaz 2019). As language attrition populations share a history of prolonged immersion in an L2
environment with minimal exposure to their native L1 over an extended time (Schmid 2011b;
Schmid 2019; Schmid and Jarvis 2014; Schmid and Köpke 2017), in this study we focus on two vari-
ables that reflect the participants’ shared language history and may have a direct effect on language
attrition: length of residence in the L2 environment (LoR) and frequency of L1 use during that time.
Research shows that in general, LoR typically affects L1 language task performance during the initial
years of residence, while L2 is simultaneously rapidly acquired, with the effect typically levelling out
after the first decade (Linck and Kroll 2019; Opitz 2011; Schmid 2011b; Schmid 2019). The impact of
LoR may also become significant when combined with very little L1 use (de Bot and Clyne 1994).
The frequency of L1 use as a single measure has not been found to systematically impact L1 per-
formance (for an overview, see Schmid 2019). However, L1 use in professional settings has been
shown to positively impact lexical retrieval and lexical diversity in L1 (Schmid and Dusseldorp
4 N. LEHTINEN ET AL.
2010; Yilmaz and Schmid 2012). In addition, frequent L1 use in informal settings with peers has been
shown to contribute to increased variability in the phonemic domain, potentially suggesting
contact-induced language change in the community rather than at the individual level (De
Leeuw, Schmid, and Mennen 2010).
In previous studies on language attrition, VF total scores have been largely unrelated to the time
spent in an L2 environment (LoR) or frequency of L1 use (Schmid 2011a; Schmid and Jarvis 2014;
Schmid and Köpke 2009). In this study, we investigate the impact of LoR, frequency of L1 use,
and their interactions on the efficacy of clustering and switching in PVF and SVF among a group
of language attriters to better understand the impact extralinguistic variables have on the processes
underlying VF task performance.
Research objectives, questions, and hypotheses
The present study examines the effectiveness of clustering and switching strategies in phonemic
(PVF) and semantic (SVF) verbal fluency tasks among Finnish-English bilinguals who have experi-
enced prolonged immersion in an English-speaking environment, thus considered attriters. First,
we examine VF task performance in their first (L1, Finnish) and second language (L2, English) and
assess the influence of the length of residence (LoR) and frequency of L1 use on the use of clustering
and switching. Second, we compare L1 task performance between attriters and L1 monolinguals to
investigate differences in the efficacy of clustering and switching between the groups. Our research
questions and hypotheses are as follows:
Research question 1: L1 and L2 within the attriter group
(a) Do task-congruent mean cluster size and the number of switches predict total score similarly for
the attriters in L1 and in L2 in PVF and SVF tasks?
(b) Does LoR, the frequency of L1 use, and their interactions impact the efficacy of clustering and
switching strategies within the group?
Hypothesis: We expected attriters to demonstrate a robust semantic network in L1 by relying more
efficiently on clustering than switching in L1 compared to L2. We anticipated frequent L1 use to
support switching in both languages and task types, potentially reflecting cognitive flexibility stem-
ming from more frequent L1 use in an L2-dominant environment.
Research question 2: L1 in the attriter and monolingual groups
Do task-congruent mean cluster size and the number of switches predict total score similarly in the
attriter and monolingual groups for L1 PVF and SVF tasks?
Hypothesis: We expected attriters to demonstrate superior executive flexibility by relying more on
switching than monolinguals in PVF. In SVF, we anticipated attriters to display a weaker semantic
foundation and greater cognitive flexibility than monolinguals by relying less on clustering and
more on switching strategies.
Materials and methods
Participants
Two groups of healthy, neurotypical adults participated in this study: L1 attriters (N = 38, L1
Finnish, L2 English) and Finnish monolinguals (N = 50). Attriters were first-generation immi-
grants living in Northern California who had immigrated from Finland between 1948 and
THE LANGUAGE LEARNING JOURNAL 5
1998 [age M = 60.89, SD = 8.42, range 45–79; education: no academic degree (n = 16) and aca-
demic degree (n = 22), gender: female (n = 29) and male (n = 9)]. Attrition-specific data is
referenced in Table 1.
The monolingual participants identified themselves as monolinguals who had always lived in
Finland and used only Finnish in their everyday life [age M = 62.58, SD = 7.59, range 49–79, edu-
cation: no academic degree (n = 27) and academic degree (n = 23), gender: female (n = 35) and
male (n = 15)]. The groups were matched for age, education (no academic degree/academic
degree), and gender with no significant differences detected for age (attriters M = 60.90, monolin-
guals M = 62.60) z =−0.97, p = .332, education (χ(1) = 0.49, p = .27), or gender (χ(1) = 0.43, p = .51)
between the groups.
Administration and analyses of data
Task administration, scoring, and analyses were conducted following guidelines in Lehtinen, Luoto-
nen, and Kautto (2021). For clarity, we briefly describe the methods below.
During administration, participants were asked to generate as many words as possible in a 60-
second time frame following the given criteria. The only exclusion was proper names and instruc-
tions included the phrase ‘individual words’ to discourage participants from generating inflections
of the same word in Finnish [e.g. kirja (book), kirjassani (in my book)].
L1 language-appropriate phonemic prompts /k, /a/, /p/ were selected following a high-frequency
dictionary approach for two consonants and one vowel to correspond to L2 letter selection (Lehti-
nen, Luotonen, and Kautto 2021; Mardani et al. 2020; Oberg and Ramirez 2006; Schmid 2011a). For L2
PVF, letters /f/, /a/, /s/ were selected based on their frequent use in the literature (Borkowski et al.
1967; Ross 2003; Strauss et al. 2006). For the SVF, we selected the culturally and linguistically rela-
tively neutral category ‘animals’ (Pekkala et al. 2009).
We calculated the mean cluster size for naturally occurring semantic clusters in SVF and the mean
cluster size for naturally occurring phonemic clusters in PVF. The minimum cluster size was two
words. The number of switches was based on the task- congruent clusters. Our preliminary analysis
showed that task-discrepant clusters (semantic clusters in PVF and vice versa) occurred only rarely in
the data. Due to the small sample size, analysis for task-discrepant clusters was omitted, but the data
is included in the data repository of this study.
Extralinguistic factors were extracted from a sociolinguistic questionnaire and included self-
reported length of residence in an L2 environment in years (LoR) and overall frequency of L1 use
during that time [How often do you speak Finnish? 5-point Likert scale: rarely (n = 1), few times a
year (n = 1), monthly (n = 7), weekly (n = 22), daily (n = 7)].
R software (R Core Team 2019) with packages dplyr (Wickham et al. 2019), tidyr (Wickham 2020),
lme4 (Bates et al. 2015), and lmerTest (Kuznetsova, Brockhoff, and Christensen 2017) were used in
data clean-up and analyses. Packages sjPlot (Lüdecke 2018), jtools (Long 2020), ggeffects
(Lüdecke 2018), and ggplot2 (Wickham 2016) were used in tables and figures. Scripts and data
used for the analyses are available at https://osf.io/95q3j/.
Table 1. Age at emigration, length of residence in L2 environment, frequency of L1 use, and self-reported language proficiency in
L1 and L2 in the attriter group (N = 38).
M Range SD
Age at emigration 26.68 9–48 7.38
Length of residence in L2 environment 34.24 20–50 10.83
Very good Good Good enough to get by
Self-report on L1 before emigration 23 15 0
Self-report on L1 at the time of interview 6 26 6
Self-report on L2 at the time of interview 21 16 1
6 N. LEHTINEN ET AL.
Statistical analyses
For comparison between the efficacy of clustering and switching strategies in L1 and L2 within the
attriter group, we used linear mixed-effects models to predict total scores as a function of task-con-
gruent cluster size, the number of switches and language (L1/L2) separately for PVF and SVF with LoR
and L1 use as predictors. Cluster size and the number of switches were centred to the sample mean.
Participant ID was used as a random factor to account for individual variation in performance. Com-
parisons of Bayesian Information Criterion (BIC) values in models with and without LoR as a predictor
suggested the models without LoR to be the most parsimonious fits to the data.
To compare the L1 performance in the attriter and monolingual groups, we modelled total scores as
a function of task-congruent cluster size, the number of switches, and the participant group separately
for PVF and SVF. As our PVF task data included three observations for each participant (/k/, /a/, and /p/),
participant ID was used as a random factor in that model.
Results
Descriptive statistics for total scores in L1 and L2 in the attriter group and L1 in the monolingual
group are shown in Table 2.
Research question 1: L1 and L2 within the attriter group
The model summaries, including estimates, t-values, and p-values for PVF are presented in Table 3,
and for SVF in Table 4. For both tasks, we found significant main effects for mean cluster size and the
number of switches with higher total scores linked to efficient use of clustering and switching strat-
egies. We found no significant interactions for task language or frequency of L1 use. The relationship
between clustering and switching was consistent in L1 and L2 for both task types, and the frequency
of L1 use did not modify the use of clustering and switching within the attriter group.
Research question 2: L1 in the attriter and monolingual groups
The model summary for switching and clustering in the PVF task is presented in Table 5. A significant
interaction between group and cluster size was observed, but no significant interaction was
detected for the number of switches and group. The stronger relationship between cluster size
and the total score in the attriter group compared to the monolingual group is shown in Figure 1.
The Model summary for switching and clustering in the SVF is presented in Table 6. We discov-
ered a significant interaction between group, cluster size, and the number of switches. Figure 2
shows the relationship between cluster size and the total score becoming weaker as the number
of switches increases in the monolingual group, while in the attriter group, the connection
between cluster size and the total score remains consistent regardless of the number of switches.
Table 2. Descriptive data for total scores in L1 and L2 in the attriter Group and L1 in the monolingual group.
L1 L2
k a p animals f a s animals
Attriters
Mean 18.92 14.90 16.45 21.90 14.05 10.60 15.32 20.60
SD 4.94 5.03 4.54 4.31 4.23 5.13 5.21 4.85
Range 11–33 6–24 7–27 13–33 7–27 1–20 6–28 9–32
Monolinguals
Mean 19.38 14.68 17.44 25.96
SD 6.12 5.65 5.50 5.90
Range 5–35 5–31 4–30 12–43
Note. L1 attriters N = 38; L2 attriters N = 37; L1 monolinguals N = 50. Overall VF performance and performance changes during
the tasks were previously reported in Lehtinen, Kautto, and Renvall (forthcoming) for attriters and in Lehtinen, Luotonen, and
Kautto (2021: 1–13) for the monolingual group.
THE LANGUAGE LEARNING JOURNAL 7
Discussion
Verbal fluency tasks are one of the most utilised tasks in language attrition studies (Schmid 2019).
However, their analyses are typically limited to total scores, offering little insight into the processes
underlying task performance. In the current study, we extended the analyses of VF task performance
Table 3. Phonemic Verbal Fluency task total scores as a function of task-congruent cluster size, the number of switches, and
language (L1/L2) in the attriter group.
Predictors
Total score
Estimates 95% CI t p
(Intercept) 14.66 13.95–15.38 40.29 <0.001
Number of switches 3.92 3.28–4.56 12.04 <0.001
Mean cluster size 2.17 1.40–2.94 5.56 <0.001
Task language 0.72 −0.08–1.51 1.78 0.08
Frequency of L1 use 0.57 −0.23–1.36 1.41 0.16
Number of switches* Mean cluster size 0.02 −0.62–0.67 0.06 0.95
Number of switches* Task language −0.56 −1.39–0.28 −1.31 0.19
Mean cluster size* Task language 0.51 −0.49–1.50 1.01 0.32
Number of switches* Frequency of L1 use 0.16 −0.59–0.91 0.43 0.67
Mean cluster size* Frequency of L1 use 0.43 −0.25–1.11 1.24 0.22
Task language* Frequency of L1 use 0.33 −0.54–1.2 0.75 0.46
Number of switches* Mean cluster size* Task language 0.09 −0.80–0.99 0.21 0.84
Number of switches* Mean cluster size* Frequency of L1 use 0.01 −0.65–0.68 0.03 0.97
Number of switches* Task language* Frequency of L1 use 0.03 −0.89–0.96 0.07 0.94
Mean cluster size* Task language* Frequency of L1 use 0.49 −0.43–1.40 1.05 0.30
Number of switches* Mean cluster size* Task language* Frequency of L1 use 0.29 −0.63–1.22 0.63 0.53
Random effects
σ2 6.77
τ00 ID 1.81
ICC 0.21
N ID 38
Observations 225
Marginal R2 / Conditional R2 0.697 / 0.761
Table 4. Semantic Verbal Fluency task total scores as a function of task-congruent cluster size, the number of switches, and
language (L1/L2) in the attriter group.
Predictors
Total score
Estimates 95% CI t p
(Intercept) 21.08 20.09–22.07 42.64 <0.001
Number of switches 3.56 2.49–4.64 6.64 <0.001
Mean cluster size 2.43 1.44–3.43 4.90 <0.001
Task language 0.87 −0.42–2.16 1.36 0.18
Frequency of L1 use 1.09 −0.06–2.23 1.90 0.06
Number of switches* Mean cluster size 0.33 −0.88–1.53 0.55 0.59
Number of switches* Task language −0.64 −2.10–0.82 −0.87 0.39
Mean cluster size* Task language −0.89 −2.43–1.66 −1.15 0.26
Number of switches* Frequency of L1 use −0.13 −1.34–1.08 −0.22 0.83
Mean cluster size* Frequency of L1 use −0.57 −1.85–0.72 −1.88 0.38
Task language* Frequency of L1 use 0.55 −1.08–2.18 0.68 0.50
Number of switches* Mean cluster size* Task language 0.65 −0.92–2.21 0.83 0.41
Number of switches* Mean cluster size* Frequency of L1 use −0.37 −1.84–1.10 −0.51 0.62
Number of switches* Task language* Frequency of L1 use 0.27 −1.41–1.96 0.33 0.75
Mean cluster size* Task language* Frequency of L1 use 1.18 −0.90–3.27 1.14 0.26
Number of switches* Mean cluster size* Task language* Frequency of L1 use 1.88 −0.44–4.21 1.62 0.11
Random effects
σ2 5.71
τ00 ID 1.25
ICC 0.18
N ID 38
Observations 75
Marginal R2 / Conditional R2 0.689 / 0.745
8 N. LEHTINEN ET AL.
to the efficacy of clustering and switching strategies in three phonemic (PVF) and one semantic (SVF)
verbal fluency tasks. Our first objective was to explore processes that support optimal performance
in L1 and L2 in a group of Finnish-English language attriters and to investigate the influence LoR and
frequent L1 use has on these processes. We then set out to determine if the processes of optimal VF
task performance differ between attriters and L1 monolinguals.
We expected attriters to rely more efficiently on clustering in L1 than L2 as a marker of a more
robust semantic network in L1. Contrary to our expectations, attriters utilised clustering and switch-
ing strategies equally efficiently to reach a high score across languages and tasks. Thus, the perform-
ance in L1 and L2 SVF and PVF tasks mirrors typical, monolingual SVF performance where both
Table 5. Phonemic Verbal Fluency task total scores as a function of task-congruent cluster size, the number of switches, and
participant group in the attriter and monolingual groups.
Predictors
Total score
Estimates 95% CI t p
(Intercept) 16.93 16.06–17.80 38.39 <0.001
Group [monolingual] −0.01 −1.16–1.13 −0.02 0.98
Number of switches 3.93 3.18–4.67 10.38 <0.001
Mean cluster size 3.51 2.42–4.60 6.34 <0.001
Group [monolingual]* Number of switches −0.26 −1.16 - 0.63 −0.58 0.56
Group [monolingual]* Mean cluster size −1.53 −2.74 – −0.33 −2.51 0.013
Number of Switches* Mean cluster size 0.55 −0.26–1.37 1.33 0.18
Group [monolingual]* Number of switches* Mean cluster size −0.72 −1.61–0.17 −1.59 0.11
Random effects
σ2 5.88
τ00 ID 5.17
ICC 0.47
N ID 88
Observations 264
Marginal R2 / Conditional R2 0.603 / 0.789
Phonemic Verbal Fluency Task Total Scores as a Function of Task-Congruent Cluster Size, the Number of Switches, and Participant
Group in the Attriter and Monolingual Group.
Figure 1. Total scores as a function of task-congruent cluster size (centred), the number of switches (centred), and participant
group in the Phonological Verbal Fluency task.
THE LANGUAGE LEARNING JOURNAL 9
strategies contribute equally to a high score as reported by Troyer, Moscovitch, and Winocur (1997).
This finding is discussed below in relation to the performance of the monolingual group. Our
findings of similar performance in L1 and L2 point to the direction of a general bilingual effect in
VF task performance across languages rather than L1 language attrition among attriters. Conse-
quently, our results highlight the importance of investigating performance in both languages in
language attrition studies to account for the broad impact bidirectional cognitive and neural pro-
cesses of bilingualism can have on the linguistic performance of a bilingual individual as a whole
(Abutalebi and Green 2007; Gurunandan, Carreiras, and Paz-Alonso 2022; Laine and Lehtonen
2018; Schmid and Köpke 2017).
Our first research question also addressed the impact of LoR and frequency of L1 use on strategies
that underlie task performance. These extralinguistic variables did not appear to contribute to the
efficacy of either strategy. In future research, analysing task-discrepant clusters and extralinguistic
factors’ influence on their occurrence in L1 and L2 can offer insight into the impact of extralinguistic
factors on processing strategies between languages.
Table 6. Semantic Verbal Fluency task total scores as a function of task-congruent cluster size, the number of switches, and
participant group in the attriter and monolingual groups.
Predictors
Total score
Estimates 95% CI t p
(Intercept) 23.44 22.21–24.66 38.17 <0.001
Group [monolingual] 1.39 −0.18–2.95 1.76 0.08
Number of switches 3.59 2.31–4.88 5.58 <0.001
Mean cluster size 3.08 1.64–4.52 4.27 <0.001
Group [monolingual]* Number of switches 0.79 −0.80 - 2.39 0.99 0.33
Group [monolingual]* Mean cluster size −1.36 −3.05–0.33 −1.61 0.11
Number of switches* Mean cluster size 0.72 −0.69–2.13 1.02 0.31
Group [monolingual]* Number of switches* Mean cluster size −2.12 −3.85 – −0.40 −2.45 0.017
Observations 264
Marginal R2 / Conditional R2 0.603 / 0.789
Note: Descriptive data for total scores in both languages and groups is shown in Table 2.
Figure 2. Total scores as a function of task-congruent cluster size (centred), the number of switches (centred), and participant
group in the Semantic Verbal Fluency task.
10 N. LEHTINEN ET AL.
To answer our second research question, we investigated differences in the efficacy of clustering
and switching strategies in L1 task performance between attriters and L1 monolinguals. Based on the
literature, we anticipated that attriters would demonstrate a cognitive advantage by relying more on
switching than monolinguals in PVF. However, we detected no significant differences in the effec-
tiveness of switching strategies between the groups. On the contrary, in PVF attriters seemed to
be able to maintain the effortful cognitive strategy of generating task-congruent phonemic clusters
more efficiently than monolinguals. Our results align with Patra, Bose, and Marinis (2020), who inter-
preted their finding of larger task-congruent clusters in the bilingual group as superior executive
functions compared to a monolingual group. In the current study, we similarly infer that rather
than relying on switching (i.e. returning to a previous subcategory or initiating a new subcategory
when a subcategory has been depleted), attriters generated larger clusters to reach a similar total
score as monolinguals in PVF. In line with this finding, a comparison between L1 and L2 performance
among the attriters showed that attriters rely more equally on clustering and switching in PVF than
anticipated in both languages.
The phenomenon of attriters relying on clustering for optimal performance was also apparent in
SVF, where the connection between cluster size and the total score remained consistent in the attri-
ter group regardless of the number of switches. In contrast, monolinguals used clustering and
switching strategies more equally to achieve higher scores, in line with Troyer, Moscovitch, and
Winocur (1997). Thus, our findings underscore that while monolinguals were able to support their
performance by switching between categories and clustering, support from switching between
the categories was less efficient for optimal performance in the attriters group and these tendencies
were reflected in L2 performance.
Our results for PVF align partly with Patra, Bose, and Marinis (2020), where bilinguals generated
higher total scores in their L2 by relying on larger clusters than monolinguals. While their study
did not address language attrition per se, their participants were immigrants who had spent a sig-
nificant time in an immersive L2 environment (M = 7.48 years). In contrast, Mardani et al. (2020)
found that bilinguals with no sudden change in their language environment relied more on
switching than clustering, generating a higher total score than monolinguals in L1 PVF. They
also report that bilinguals generated smaller clusters in L1 SVF, resulting in similar total scores
with monolinguals. When interpreting these findings, it is important to note that there are
various methods for determining clusters and, consequently, the number of switches between
these clusters. Both Patra, Bose, and Marinis (2020) and Mardani et al. (2020) used predetermined
cluster categories in their analysis, but scarce details were given of their specific analysis. We cal-
culated the size of naturally occurring task- congruent clusters to include all unique clustering
strategies and natural switches generated by participants without the constraints of predeter-
mined semantic categories (Lehtinen, Luotonen, and Kautto 2021; Roberts and Dorze 1997; Ros-
selli et al. 2002).
Thus, comparison across these studies requires careful consideration. In addition, the relatively
small study samples and different language pairs call for further consideration when generalising
our results. However, we remain confident that our analysis based on well-defined guidelines on ana-
lysing naturally occurring clusters and switches offers a solid foundation and direction for future
studies involving larger data sets, diverse language pairs, and various language contact situations.
Future comparative studies between attriters and bilinguals with no change in their language
environment could also determine the role of extralinguistic circumstances in the use of clustering
and switching in VF tasks across bilingual populations.
We set out to investigate the processes that support or hinder optimal outcomes within a Finnish-
English language attrition population L1 and L2 VF task performance and between attritters and L1
monolinguals. Our results demonstrate that an extended VF task analysis has the potential to yield
insight into the subtle language processes underlying VF task performance and provide a solid start-
ing point for future studies to further investigate the similarities in language attrition populations in
contrast to monolinguals and bilinguals with varied backgrounds.
THE LANGUAGE LEARNING JOURNAL 11
Acknowledgments
We thank Ida Luotonen for her contributions to data analysis and overall support, Professor Marja-Liisa Helasvuo for
guidance and insightful discussions on Finnish language characteristics, and Professor Monika Schmid for encourage-
ment during the formulation of this study. We also want to thank Lotta Weckström for her help in recruiting participants
for the study in the US. Finally, we express our gratitude to the participants for the generous donation of their time and
enthusiasm in participating in this study.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Funding
This work was supported by Finlandia Foundation National; The Alfred Kordelin Foundation; The Migration Institute of
Finland; Turun Yliopistosäätiö; Turun yliopiston tutkijakoulu.
Data availability statement
The data supporting this study’s findings are available in [Verbal Fluency Switching and Clustering in Language Attri-
tion] at [https://osf.io/95q3j].ote.
ORCID
Nana Lehtinen http://orcid.org/0000-0002-3606-1302
Anna Kautto http://orcid.org/0000-0003-4885-9167
Kati Renvall http://orcid.org/0000-0002-3329-4602
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