Asynchronous behavioral and neurophysiological changes in word production in the adult lifespan

Abstract

Behavioral and brain-related changes in word production have been claimed to predominantly occur after 70 years of age. Most studies investigating age-related changes in adulthood only compared young to older adults, failing to determine whether neural processes underlying word production change at an earlier age than observed in behavior. This study aims to fill this gap by investigating whether changes in neurophysiological processes underlying word production are aligned with behavioral changes. Behavior and the electrophysiological event-related potential patterns of word production were assessed during a picture naming task in 95 participants across five adult lifespan age groups (ranging from 16 to 80 years old). While behavioral performance decreased starting from 70 years of age, significant neurophysiological changes were present at the age of 40 years old, in a time window (between 150 and 220 ms) likely associated with lexical-semantic processes underlying referential word production. These results show that neurophysiological modifications precede the behavioral changes in language production; they can be interpreted in line with the suggestion that the lexical-semantic reorganization in mid-adulthood influences the maintenance of language skills longer than for other cognitive functions.

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Received: December 15, 2023. Revised: April 5, 2024. Accepted: April 18, 2024
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Cerebral Cortex, 2024, 34, bhae187
https://doi.org/10.1093/cercor/bhae187
Advance access publication date 7 May 2024
Original Article
Asynchronous behavioral and neurophysiological
changes in word production in the adult lifespan
Giulia Krethlow1,*,Raphaël Fargier2,Tanja Atanasova1,Eric Ménétré1,Marina Laganaro1
1Faculty of Psychology and Educational Sciences, University of Geneva, Bd du Pont d’Arve 40, 1205, Geneva, Switzerland
2Université Côte d’Azur, CNRS, BCL, France
*Corresponding author: University of Geneva, Bd du Pont d’Arve 40, 1205 Geneva, Switzerland. Email: Giulia.Krethlow@unige.ch
Behavioral and brain-related changes in word production have been claimed to predominantly occur after 70 years of age. Most studies
investigating age-related changes in adulthood only compared young to older adults, failing to determine whether neural processes
underlying word production change at an earlier age than observed in behavior. This study aims to fill this gap by investigating
whether changes in neurophysiological processes underlying word production are aligned with behavioral changes. Behavior and the
electrophysiological event-related potential patterns of word production were assessed during a picture naming task in 95 participants
across five adult lifespan age groups (ranging from 16 to 80 years old). While behavioral performance decreased starting from 70 years
of age, significant neurophysiological changes were present at the age of 40 years old, in a time window (between 150 and 220 ms)
likely associated with lexical-semantic processes underlying referential word production. These results show that neurophysiological
modifications precede the behavioral changes in language production; they can be interpreted in line with the suggestion that the
lexical-semantic reorganization in mid-adulthood influences the maintenance of language skills longer than for other cognitive
functions.
Key words:aging; EEG; lexical-semantic processes; picture naming; topographic maps.
Introduction
Life expectancy (considered as the average age at the time of
death) in Europe has increased from approximately 44 years in
1900 to 74 years in 2008 (Mackenbach and Looman 2013), thus
altering how adulthood is defined (Deschavanne and Tavoillot
2007). Nowadays, adults are surrounded by an extended period
of youth, and an active third age still manifesting high cognitive
performance. A typical example of preserved cognitive abilities
with age is related to language production skills, which only
significantly decline after the age of 70 (e.g. Salthouse 2010).
This means that efficient utterance production is maintained
for at least 50 years of adulthood. Along with behavioral changes
it has been shown that older adults display differences in the
neurofunctional substrates of word production compared to
young adults (Baciu et al. 2021). Yet, few studies have investigated
whether behavioral and/or neurophysiological differences are
already present in intermediate adult groups. In this study, we
examined when and how neurophysiological changes underlying
word production occur over the course of adulthood and how they
relate to behavioral changes.
Changes in language production during
adulthood
Most cognitive functions start declining in early adulthood
(e.g. Salthouse 2004,2009;Spreng and Tur ne r 2019): skills like
episodic memory (e.g. Grady et al. 1999;Nyberg 2017) and working
memory (e.g. Mitchell et al. 2000), attention and inhibitory control
(e.g. Hasher et al. 1991), perceptual processing (e.g. Baltes and
Lindenberger 1997;Ansado et al. 2012), and general processing
speed (e.g. Salthouse 1996) show a progressive and linear decline
starting from 20 to 25 years, which becomes more pronounced
after 50 to 70 years (e.g. Anstey and Low 2004;Hedden and
Gabrieli 2004;Salthouse 2004,2009). In contrast, many language
skills (e.g. vocabulary, word retrieval) are among the most stable
cognitive domains throughout the lifespan, remaining better
preserved with aging (e.g. Wierenga et al. 2008;Kahlaoui et al.
2012;Hartshorne and Germine 2015). While verbal f luency shows
early age-related changes in performance, starting from the age
of 50 (e.g. Kavé and Knafo-Noam 2015), performance in other
language skills, notably word production, starts to decrease only
from about 60 to 70 years of age (e.g. Albert et al. 1988;Hedden
and Gabrieli 2004;Salthouse 2004;Verhaegen and Poncelet 2013;
Diaz et al. 2021) with the steepest decline occurring only after
70 to 75 years (e.g. Feyereisen 1997;Zec et al. 2005;Kavé et al.
2010). Word-finding difficulties are reported with longer latencies,
increased tip-of-the-tongue states (TOTs), and fewer accurate
responses in picture naming tasks in older adulthood relative
to young adults (e.g. Rabbitt et al. 1995;Mortensen et al. 2006;
Neumann et al. 2009;Kavé et al. 2010;Saryazdi et al. 2019).
In contrast to the decline observed in word retrieval, semantic
knowledge, vocabulary and the size of the mental lexicon increase
throughout adulthood, especially in older adults (e.g.Kemper and
Sumner 2001;Park et al. 2002;Verhaeghen 2003;Salthouse 2004;
Kavé and Halamish 2015;Keuleers et al. 2015;Brysbaert et al.
2016;Wul ff et al. 2016;Mokhber et al. 2019). Such changes in the

2|Cerebral Cortex, 2024, Vol. 34, No. 5
organization of the lexicon are at the core of recent interpretations
of the preserved language skills in aging, as further detailed
below. Note that in the studies taken into consideration, “older
adulthood” is generally considered starting from around 70 years,
while “young adults” generally involves participants aged around
20 to 30 years.
Different interpretations of the decline in word retrieval
abilities in older adults have been proposed (Newman and
German 2005;Burke and Shafto 2008;Lambon Ralph et al. 2017).
A first group of explanatory hypotheses attribute the decrease in
performance in word production to the same processes thought
to underpin the global decline in cognitive performance, namely
slowing down of processes by aging (i.e. the general slowing
theories, e.g. Myerson et al. 1990;Salthouse 1996,2000), a reduced
capacity for processing information (the resource theory; e.g.
Miller 1956), a decline in inhibitory processes that regulate
attention and the contents of working memory (the inhibition
deficit theory; Hasher and Zacks 1988), reduced working memory
capacities (the working memory or resource theories; Kemper and
Kemtes 1999), or a decline in sensory and perceptual processes
(the degraded signal theory; e.g. Brown 2000;Schneider and
Pichora-Fuller 2000). Other proposals, like the transmission
deficit hypothesis (Burke et al. 2000), or the “semanticization
of cognition”/DECHA (Default–Executive Coupling Hypothesis of
Aging; Spreng and Tur n e r 2019),aremorespecifictothelanguage
domain. The transmission deficit hypothesis (Burke et al. 2000)
proposes a decrease in the strength of connections among
representational units in aging, affecting the transmission of
activation from the semantic to the phonological representations.
Single connections from lexical to phonological representations
make these connections more vulnerable to aging, compared
to the connections within the semantic system, which are
redundant. Semantic information is therefore thought to be more
preserved with aging, whereas phonological retrieval failures
would explain increased TOT states (Burke et al. 1991). Other
authors related the longer maintenance of language production
in older adults to enriched lexical-semantic networks over time.
Indeed, Spreng and Tur n e r (2019) hypothesized a shifting in the
neurocognitive architecture during the adult lifespan (from 20 to
80 years). They proposed the concept of the “semanticization of
cognition,” stating that, while cognitive control abilities decline
with age, semantic abilities continue to grow and are well
preserved during the adult’s lifetime. This means that in a
word production task, where the lexical-semantic component
is relevant, this neurocognitive reorganization may work in favor
of maintaining good performance in older adults. However, word
production tasks also rely on the processing speed and cognitive
control abilities. As a consequence, when decline of these latter
abilities becomes more pronounced in older adulthood, the
advantage due to greater semantic abilities hypothesized by
Spreng and Tur n e r (2019) might not be enough to compensate and
maintain high performance, leading to the behavioral changes
observed in many studies on the older adult groups.
Changes in neuroanatomy and in
neurophysiology of word production during
adulthood
Research sought to relate these age-related behavioral changes to
anatomical and functional neural changes. Age-related changes
in both brain structure (Raz 2005;Raz et al. 2005,2010;Peelle 2019)
and neurophysiological activity have been reported when com-
paring word production in young and older adults (e.g. Wierenga
et al. 2008;Vale nte and Laganaro 2015;Hoyau et al. 2017;Methqal
et al. 2019;Mohan and Webe r 2019). For example, the two studies
by Wierenga et al. (2008) and Hoyau et al. (2017) showed larger
activation of the frontal network in older adults (starting from
around 70 years old) during word retrieval. These findings suggest
that substrates involved in word retrieval decline with aging,
and must be compensated with additional neural recruitment. In
line with the integrity of semantic knowledge, these results may
be interpreted within the framework of the “semanticization of
cognition”.
Further evidence supporting this hypothesis is provided by
Guichet et al. (2024), who analyzed white matter changes related
to difficulties in lexical production during middle adulthood.
Their results revealed a discontinuity in brain structure within
distributed networks around the age of 50 years old, primarily
in dorsal, ventral, and anterior cortico-subcortical pathways. The
authors suggested that these findings support the idea of a decline
in certain general cognitive processes with age,such as multitask-
ing and fluid intelligence, which are associated with the onset of
difficulties in lexical production, resulting in increased naming
latencies. The authors also discuss how middle-aged adults may
initially rely on semantic abilities to compensate for initial diffi-
culties in lexical production; this strategy may be compromised
in later adulthood due to the loss of the ability to exert cognitive
control over semantic representations. However, what is the time-
course of behavioral changes with respect to neurophysiological
changes remains virtually unknown.
In addition, several fMRI studies reported significant dif-
ferences between the brain activations of young and older
adults in language production tasks, even when behavioral
performance was equivalent across age groups (e.g. Wierenga
et al. 2008;Meinzer et al. 2009;Diaz et al. 2014;Hoyau et al.
2017;Methqal et al. 2019). Generally, increased and/or more
widespread brain activations in older adults (around 70 years old),
compared to young adults (around 25 years old), are interpreted
as compensatory activity when it is associated with better
performance (Cabeza 2002;Reuter-Lorenz 2002)andsimply
as dedifferentiation when associated with worse performance
(Bernard and Seidler 2012). According to the literature, the age-
related neural dedifferentiation, characterized by less distinctive
neural representations of perceptual and conceptual information,
stems from reduced neural efficiency (e.g. Koen and Rugg 2019).
However, word production is underlaid by several encoding
processes that may be subject differently to compensatory
and dedifferentiation. A better insight into the different word
planning processes comes from studies with high-temporal-
resolution approaches such as electroencephalography/event-
related potentials (EEG/ERPs; Van Veen and Carter 2002). For
instance, Valen te and Laganaro (2015) reported ERP divergences
between young (18 to 30 years) and older adults (60 to 80 years)
in a picture naming task but not in a picture-word verification
task. The authors observed divergences across age groups in the
time window between 150 and 250 ms after the picture onset,
a timeframe that has traditionally been associated with lexical-
semantic processes (e.g. Dell’Acqua et al. 2010;Indefrey 2011).
This suggests that age-related changes affecting the processing
dynamics in the lexical-semantic system could determine word
production performance, compatible with the aforementioned
hypotheses of “semanticization of cognition” and related model
of the “default-executive coupling hypothesis of aging” (DECHA;
Spreng and Tur n e r 2019;Tu r n er and Spreng 2015).
The present study
So far, studies have focused on language production changes in
aging by comparing young adults (usually 20 to 30 years old) to
older adults (mostly over the age of 70 years old), thus lacking a

Krethlow et al. |3
Tab l e 1. Specifications of groups’ characteristics.
Age group NAge ranges meanage SDage Nb female participants meanschooling SDschooling
Adolescents 19 16 to 18 17.1 0.875 11 11.5 1.264
Young adults 19 20 to 30 24.6 3.006 12 16.4 2.090
Adults 19 40 to 50 45.7 3.331 12 17.3 4.382
Young-old adults 19 59 to 69 64.1 3.195 13 14.1 2.460
Older adults 19 70 to 80 73.2 3.184 13 13.5 2.970
proper description of the reorganization that likely occurs during
the years in the gap between these two extremes. Extending
the investigation to other groups of the adult lifespan than the
two adult lifespan extremes, may allow us to test whether the
appearance of behavioral changes related to word production over
the course of adult life is synchronous with the neurophysiolog-
ical changes, and whether this is the case for all word encoding
processes.
The present study thus aims to understand when and how word
production changes over the adult lifespan, combining behav-
ioral and neurophysiological investigations of picture naming. The
study fills the gap in knowledge by enrolling adolescents as well
as intermediate age groups between young adults and the elderly.
We start the adult lifespan with 16- to 18-year-old adolescents,
as it has been shown that the behavioral and neurophysiological
correlates of picture naming are very similar in adolescents and
young adults (e.g. Petanjek et al. 2008;Atanasova et al. 2020).
At the behavioral level, consistent with previous literature
summarized above, we expect changes only in the older group
(after the age of 70 years old), characterized by a decline in word
production performance (e.g. Feyere isen 1997;Zec et al. 2005;Kavé
et al. 2010). Concerning the neurophysiological data, our rationale
is as follows: If the differences in neural patterns across age
groups purely reflect the decline in word production abilities, they
should be aligned with the behavioral results, with differences
appearing only when a decrease in performance is observed; by
contrast, if compensatory mechanisms are at play in age groups
in which performance is still maintained, different neural pat-
terns are expected in age groups before the behavioral decline is
observed. In sum, this exploratory investigation examines when
different ERP patterns emerge in adulthood, determining whether
or not there is synchrony between behavioral changes and neural
changes, and if they are observed in specific time windows under-
lying word production.
Beyond ERP waveform analyses, we compute topographic
(microstate) analyses, i.e. an analysis of the quasi-stable periods
of synchronized neural activity that evolve dynamically over
time and that allow us to explore quantitative changes (same
microstates but different time distributions across groups) and
qualitative changes (different topographic patterns) reflecting the
involvement of similar or different underlying neural networks
across age groups (e.g. Michel et al. 2004;Laganaro 2014).
Performance in other cognitive tasks will also be assessed
in all age groups with the aim of verifying that participants’
performance fell within the normal range for their age group.
Materials and methods
Participants
Ninety-five participants took part in the study. They were sub-
divided into five groups: “adolescents,” “young adults,” “adults,”
“young-old adults” and “older adults” (all the details regarding the
groups are summarized in Table 1).
The choice to begin adulthood with a group of adolescents aged
16 to 18 years old stemmed from previous findings indicating
that they exhibit behavioral performance and neurophysiological
correlates very similar to those of young adults in picture naming
tasks (Atanasova et al. 2020). The next four groups of adults,
with a default age range of one decade, were composed of the
two groups often compared in the literature (20 to 30 years and
70 to 80 years) and two intermediate groups. Sample size was
based on previous studies using neurophysiological data, with
between 15 and 20 participants per group (e.g. Maess et al. 2002;
Wierenga et al. 2008;Strijkers et al. 2010;Baciu et al. 2016;
Fargier and Laganaro 2016;Atanasova et al. 2020). Participants
were all right-handed (according to the Edinburgh Handedness
Scale; Oldfield 1971) and native French speakers. However, con-
sidering the socio-geographical context of the recruitment region
(city of Geneva in Switzerland), the recruited population was
likely to be bilingual. No subjects had diagnosed neurological
diseases or speech disorders, and all had normal or corrected-
to-normal vision. Participants were recruited through announce-
ments posted at the University of Geneva and in surrounding
gyms, as well as through word of mouth. All subjects volunteered
and gave informed consent, in accordance with the Declaration
of Helsinki. Study procedures were approved by the local faculty
ethics committee of the University of Geneva (FPSE, University of
Geneva, Geneva, Switzerland). Additional parental consent was
collected for minors (under the age of 18 in Switzerland). All par-
ticipants received monetary compensation for their participation.
Tasks and materials
Note that the data collected through the picture naming task have
been made available and can be accessed via the link at the end
of the text.
Picture naming
For the picture naming task, we selected 120 monosyllabic (n=40),
bisyllabic (n= 60), and trisyllabic (n= 20) concrete French words
along with their corresponding pictures from two French datasets
(Alario and Ferrand 1999;Bonin et al. 2003). The selected stim-
uli were all nouns and had a name agreement of over 75%
(mean = 92.5%) to ensure consistent naming for the same picture
(Alario et al. 2004). Their lexical frequency varied from 0.37 to
255 occurrences per million words (mean= 25.96) in the French
database Lexique (New et al. 2004). The selected words had an
age of acquisition range of 1.19 to 3.55 on a five-point scale (1:
learned between 0 and 3 years; 5: learned after the age of 12; see
also Laganaro et al. 2015), meaning that all words were acquired
before 9 years of age. The images were black and white pictures
with homogenized dimensions of 280 ×280 pixels.
Other cognitive assessments
The participants underwent six additional cognitive assessments
in order to verify that all the groups followed the usual lifespan
trend in performance.

4|Cerebral Cortex, 2024, Vol. 34, No. 5
The other cognitive tasks included: a simple reaction times test
(which required pressing a specific key on the computer keyboard
when a cross appeared on the screen) including 120 trials; a choice
reaction times test (which required a choice in the response,
pressing two different keys on the computer keyboard,depending
on whether the longest of two bars appeared on the right or left
side of the screen) including 120 trials; a working memory test
(digit span test from the Wechsler Adult Intelligence Scale IV;
Wechsler 1997); a vocabulary test (from the Wechsler Adult Intelli-
gence Scale IV; Wechsler 1997); a Stroop Task (computerized serial
four colors Stroop task requiring oral responses and including
180 trials divided among the congruent, incongruent and neutral
conditions; for the complete procedure, see Ménétré and Laganaro
2019); and two verbal fluency tests (animal names and words
beginning with the letter P given in 2 min each; Cardebat et al.
1990).
Procedures
The participants underwent the picture naming task under con-
tinuous EEG recording in a sound-proof booth, and the other
cognitive assessment in a face-to-face setting. The order of pre-
sentation and administration of the different tasks, as well as
the transition between the two settings, was balanced by dividing
subjects into two groups (even and odd) to avoid an order effect
on the results.
Picture naming
Each participant sat in front of a computer screen at an approx-
imate distance of 60 cm (refreshment rate: 50 Hz), in a dimly
lit, sound-attenuated room. The stimuli were presented using the
E-Prime software (E-Studio). An experimental trial began with a
fixation cross presented for 500 ms on a gray background screen,
followed by the picture displayed for 2,000 ms. The participant
was instructed to overtly produce, as quickly and accurately as
possible, the word corresponding to the picture within 2,000 ms
before responses were classified as “no response.” At the end
of each trial, an inter-stimulus blank screen lasting 2000 ms
was shown. Stimuli were presented in two different pseudo-
randomized orders, with a self-managed break (after 60 items).
Pseudo-randomization was preferred over complete randomiza-
tion to avoid a succession of stimuli with phonological similarity
or within the same semantic category. After receiving instruc-
tions, and before the task’s trials, each participant completed
four training trials to familiarize themselves with the task. All
responses were recorded with a microphone and digitized for off-
line accuracy and latency check.
Other cognitive assessments
The tasks were conducted in a paper-and-pencil setting and
audio-recorded, except for the two reaction time tests and the
Stroop task, which were computer-based, and whose stimuli were
presented using the E-Prime software (E-Studio).
EEG acquisition and preanalyses
EEG was recorded using the Active-Two Biosemi EEG system
(Biosemi V.O.F., Amsterdam, Netherlands) with 128 channels
at a 512 Hz sampling rate and filtered between 0.16 and
100 Hz. Data from all participants who completed all tasks were
considered, without excluding any participant. The preprocessing
was conducted with the Cartool 3.60 software (Brunet et al.
2011). Stimulus-aligned epochs (locked to the picture onset) of
approximately 600 ms (including around 100 ms prestimulus
signal) and response-aligned epochs (time-locked to around
100 ms before the acoustic onset of the vocal response, as
described in the behavioral analyses below) of 500 ms were
averaged across participants and age groups. Aligning epochs
to 100 ms before the vocal onset of each single trial is performed
to eliminate prearticulatory motor artifacts (see Fargier et al.
2018). Before the epoching, the data were high-pass-filtered at
0.2 Hz and low-pass-filtered at 30 Hz (using a second-order causal
Butterworth filter with −12 dB/octave roll-off), and then averaged
for each participant. In addition to an automated selection
criterion the rejected epochs with amplitudes reaching ±100 μV,
each trial was visually inspected. All epochs related to correct
productions were recalculated against the average reference,they
were manually inspected and only accepted if no artifacts were
present, such as eye blinks, movement-related artifacts, or large
amplitude variations. A minimum of 51 trials was averaged for
each participant. Bad electrodes (up to 22% of the 128 electrodes)
were interpolated using a 3D spline interpolation (Perrin et al.
1989).
Behavioral analyses
Picture naming
For the picture naming task, response latencies (RTs in millisec-
onds, i.e. the time separating the onset of the picture and the
acoustic onset) and accuracy were systematically checked using
speech analysis software (Check Vocal; Protopapas 2007), which
allowed the visualization of both waveforms and spectrograms
of each verbal response. Outlier RTs (shorter than 500 ms or
longer than 2,000 ms, corresponding to 0.06% and 0.73% of the
responses, respectively) and response errors (answers that did
not match the expected answer, addition of articles before the
word, or hesitation marks preceding the response, corresponding
to 1.04% of the responses) were excluded from the RTs analyses.
Mean RTs and accuracy data for each of the 120 items and each of
the 95 participants were analyzed using the R software (R-project,
R Development Core Team 2020) with the lme4 (Bates et al. 2007)
and lmerTest packages (Kuznetsova et al. 2015). Linear mixed
models were employed for RTs, and generalized mixed models
were used assuming a binomial distribution for accuracy. Age
group was considered as the independent variable, while partic-
ipants and items were included as random-effect variables. The
subdivision into age groups and thus the decision to consider age
as a factorial, rather than a continuous predictor was primarily
dictated by the choice to compare behavioral and ERP analyses,
relying on a microstate analysis. Therefore, in order to align the
two analyses, the same age groups were maintained. In addition,
this approach allows for a more homogeneous representation of
subjects in adulthood, making it possible to clearly observe differ-
ences between groups over such a wide age range in adulthood,
while also avoiding overrepresentation of certain age groups.
Post-hoc tests were conducted using the emmeans package
(Lenth et al. 2018), with Bonferroni correction for multiple
comparisons (Bonferroni 1936). Visualization of the behavioral
and neurophysiological data was performed using the ggplot2
(Wickham 2016) and NPL packages (Ménétré 2021).
Other cognitive assessments
For the other cognitive tasks, in order to evaluate whether
participants’ performance fell within the normal range for
their age group, comparisons were made to a normative corpus
when possible (i.e. for the tasks from the Wechsler Adult
Intelligence Scale IV). For the nonstandardized tasks, raw results
were reported. Additionally, to analyze changes across the
five age groups, separate mixed models were run for each

Krethlow et al. |5
task, considering age group as the independent variable. Raw
scores were used for tasks belonging to standardized tests in
these analyses. This approach allowed for the assessment of
performance evolution on the same task across the lifespan.
These results will only briefly be described below, as they are
beyond the scope of this study, and their purpose was to verify
that the participants’ performance in other cognitive tasks was
consistent with what is typically observed in their age group.
ERP analyses
All analyses were run on average-referenced data. The ERPs were
first subjected to a topographic analysis of variance (tANOVA) to
determine the time periods that showed differences in the global
distribution of the electric field between age groups. The tANOVA
employs a nonparametric randomization test on global dissim-
ilarity measures to identify periods of significant topographic
modulation between experimental conditions or groups (Murray
et al. 2008). Using this approach, it is possible to identify the
time periods during which distinct topographies were observed
between age groups, comparing time point by time point (with a
time period criterion of 20 consecutive milliseconds).
Then, a spatio-temporal, or microstate segmentation, was
applied on the group-averaged ERPs of each group to ascertain
topographic differences across groups, both for stimulus- and
response-locked ERPs. Topographic differences across groups
would suggest different underlying brain processes (Lehmann
and Skrandies 1984;Koukou and Lehmann 1987;Lehmann et al.
1998;Changeux and Michel 2004).
A temporal atomized and agglomerate hierarchical clustering
algorithm was applied to the group-averaged data to identify
the topographical maps (Murray et al. 2008). The Cartool meta-
criterion for selecting the best topography (Tom e s c u et al. 2018)
was initially followed, and then, a criterion of minimum duration
(20 ms) was added. However, the segmentation based on the opti-
mal number of microstates provided by the Cartool meta-criterion
showed inconsistent results (e.g. segregation of the P1 component
in two topographical maps), which is why a lower number of
microstates (5 maps, with special attention to preserving entirely
the P1 component, e.g. Laganaro 2017) was selected, which still
explains 95% of the variance.
Based on the results of the microstate segmentation, we
applied a “back-fitting procedure” that allows us to test how
much topographic maps observed in the grand-averaged signals
actually explain the ERP data of a single individual participant.
Other quantifiers, such as measures of map presence/absence
and duration in each individual ERP, were obtained and used to
test statistical differences among groups using nonparametric
statistical tests given the distribution of the data and the absence
of variance for map presence in some of the groups (Kruskal–
Wallis and Wilcoxon tests).
To better characterize the differences among the age groups,
a second, more restricted back-fitting (shorter time window) was
conducted on the time window exhibiting the biggest difference.
In this additional analysis the same rank-based statistical analy-
ses were applied to map presence and duration as well.
Brain electrical sources were also determined for this identified
time period of significant ERP changes. Two separate source local-
ization analyses were conducted based on the grand average ERPs
of the “young adults” and “older adults” age groups (the groups
with the largest difference). The procedure outlined by Michel
and Brunet (2019) was followed using the Cartool 3.91 (Brunet
et al. 2011) software. The head model for which the EEG forward
solution is calculated was constructed from the MNI average brain
Fig. 1. Mean RTs A) and mean accuracy B) for each age group in the
picture naming task with error bars representing standard errors between
subjects.
(MNI 152, Montreal Neurological Institute, Montreal, Canada) with
the cerebellum removed. The LSMAC (Locally Spherical Model
with Anatomical Constraints) Lead Field was then calculated,
adapting the absolute conductivity of the skull to the mean age of
each group (0.0210 [S/m] for “young adults” and 0.0085 [S/m] for
“older adults”), providing the matrix from which the inverse prob-
lem was solved using the algorithm LAURA (Local AUtoRegressive
Average; De Peralta Menendez et al. 2004). After running these
analyses, the P100 time window was checked to verify that this
visual evoked potential (VEP) component was correctly localized
in the posterior regions.
Results
Behavioral results
Picture naming task
Slower production latencies (RTs) were observed for the groups
at the two extremities of the adult lifespan: “adolescents” versus
“older adults” (see Fig. 1A). The mixed model indicated a signif-
icant effect of age group on RTs [F(4,89.9) = 2.78, P= 0.032], with
only the “older adults” group being significantly slower than the
“young adult” group by approximately 109 ms [t(90.0) = −3.147,
P= 0.01]. Differences between the other groups did not reach
significance. Regarding accuracy, although a lower score was
observed in older adulthood (see Fig. 1B), the main effect of the
age group was not significant on accuracy (χ2(4) = 7.36, P= 0.118).
All details regarding these results are presented in Table IA and B
in the Supplementary Materials.

6|Cerebral Cortex, 2024, Vol. 34, No. 5
Fig. 2. A) Group-averaged stimulus- and response-aligned event-related potentials (ERPs) waveforms (128 electrodes) for five exemplar electrodes (Fpz,
Cz, Oz, A9 and B6),plotted in microvolts in function of time and B) results of the tANOVA show in red time windows where topographies are significantly
different between groups (P-value <0.05).
Other cognitive assessments
On the “Vocabulary” test and the “Working Memory” test, the
performance of all participants was within the population norms
for their age group. On the other cognitive assessments, none
of the participants obtained outlying results compared to other
participants in the same age group. As already mentioned, these
results will not be further elaborated here, but additional details
are presented in the Supplementary Materials (Fig. I and Tables II–
VIII)andintheworkofFargier and Laganaro (2023).
ERP results
Representative waveforms are presented in Fig. 2A. The visual
inspection revealed similar waveforms across age groups with
differences in amplitudes, especially in the stimulus-aligned ERPs
between 150 and 250 ms. The tANOVA results showed significant
differences across groups from about 120 to 250 ms, and from
300 to 500 ms for the signal aligned to the stimulus, while for
the signal aligned to the response, significant differences were
present from about 600 to 210 ms before vocal onset (see results
of tANOVA in Fig. 2B and contrasts between groups in Fig. II in
Supplementary Materials). Significant differences were found
for the “adolescents” and the “young adults” groups compared
with the three older age groups from about 120 to 250 ms and
from about 300 to 500 ms. Furthermore, the “adolescents” and the
“young adults” groups were significantly different from each other
between about 370 to 500 ms. Finally, the “adults” significantly
differed from the two older age groups from about 160 to 260 ms.
For the response-aligned signal significant differences were
highlighted only among the two younger compared with the three
older age groups in different fragmented time windows across the
signal, with the “adolescents” and “young adults” groups differing
from the “adults,” “young-old adults” and “older adults” groups
from about −600 to −200 ms.
Microstate analyses and fitting in the individual ERPs
The spatio-temporal segmentation applied on the five grand-
averages of groups revealed a best model explaining 95% of vari-
ance with 5 different topographic maps from the picture onset
to the first 500 ms (stimulus-aligned epochs) and 3 different
topographic maps on the time window starting 100 ms before
vocal onset and covering the preceding 500 ms (response-aligned
epochs; Fig. 3). A common pattern appears on the grand averaged
ERPs between the two younger groups of “adolescents”and “young
adults” and between the three older groups of “adults,” “young-
old adults” and “older adults,” characterized by an additional
microstate (Map 3 in Fig. 3) in the three older groups between
150 and 220 ms (Map3). Based on the distribution of the peri-
ods of topographic stability on the group averaged ERPs, one
back-fitting period from 150 to 500 ms (Maps 2, 3, 4, 5) was
applied to the individual stimulus-aligned signal in order to sta-
tistically assess the observed differences (the fitting in the indi-
vidual ERPs starts at the end of P100, at 150 ms, to prevent
the segmentation algorithm from confusing a relatively similar
map with that of P1). One single back-fitting period on all the
500 ms (Maps 6 to 8) of the individual response-aligned signal was
also applied.
Figure 4 displays the mean presence and duration of each
Map template in each age group. Presence of Map2 (and to a
lesser extent Map5) decreases across ages, while Map3 (and to

Krethlow et al. |7
Fig. 3. Results of the spatio-temporal segmentation on the grand-averaged ERPs from each age group from the stimulus onset to 100 ms before vocal
onset. The temporal distribution of the topographic maps is represented with color codes under the global field power. The 8 corresponding template
maps are displayed with positive values in red and negative values in blue. Crosses on the maps represent the maximum and minimum amplitude of
the electrical configuration.
Fig. 4. Bar plots representing the results of the fitting in the individual ERPs with standard error: percentage of maps presence per age group for
A) stimulus-aligned and C) response-aligned signal; mean map duration per age group for B) stimulus-aligned and D) response-aligned signal.
a lesser extent Map4) increases with ages. Duration of Map5
(and to a lesser extent Map2) progressively decreased over the
lifespan, while Map3 and Map4 were longer in the three older age
groups.
Assessing the significance of differences in duration of the
periods of stable electrophysiological signal at scalp (map’s
number of time frames in the individual ERPs) across age groups
was not possible using mixed models analyses since some groups

8|Cerebral Cortex, 2024, Vol. 34, No. 5
had no or little variance (100% of presence within the group)
(Analysis with mixed model shows a significant interaction effect
between “age-groups” and “maps” on Maps Presence, with a
χ2(12) = 32.05, P<0.01. However, we will not take this data into
account as this type of model is not suitable in a situation
where one or more SD = 0.), (Analyses with mixed model show
a significant interaction effect between “age-groups” and “maps”
on Maps Duration, with an F(12, 260) = 2.698, P<0.01. However, we
will not take these data into account as this type of model is not
suitable also in this situation.). However,given the high variability
between the presence rate and duration among age groups, it
seemed optimal to proceed with a detailed analysis comparing
each group with the others for each map taken separately. This
was achieved using Kruskal–Wallis tests to evaluate the main
effect of groups and Wilcoxon tests to decompose the main effects
(see Fig. 4). In the stimulus-aligned signal, the analyses confirmed
that the microstate characterizing the P1 component (Map2)
was present after 150 ms in particular in the first two groups
(significant effect of Age-Groups for presence—χ2(4) = 10.071,
P= 0.04—with only “adolescents” differing significantly from
“older adults”—P= 0.034 on the Wilcoxon test). By contrast,
Map3 characterized the ERP signal in the last three groups, with
100% of presence in “older adults” group (significant effect of
Age-Groups for presence—χ2(4) = 40.959, P<0.01). For this map
“adolescents” and “young adults” significantly differed from
“adults” (P<0.01 and P= 0.032), “young-old adults” (both P<0.01)
and “older adults” (both P<0.01). Presence of the microstate
appearing in the following time window (Map4, starting around
210 ms) was higher in the three older groups, with 100% presence
in the individual ERPs of “young-old adults” (significant effect
of age groups—χ2(4) = 14.413, P<0.01—but only the group of
“adolescents” significantly differed from “young-old adults”—
P<0.01). Finally, even if the presence of Map5 (275 to 500 ms
in the two younger age groups, 450 to 500 ms in the three older
age groups) was higher in the first two groups, the effect of age
groups was not significant.
On duration in the stimulus-aligned signal, a significant effect
of age groups was observed only for Map5 (χ2(4)= 14.897, P<0.01)
which is progressively shorter across groups, with “adolescents”
differing significantly from “adults” (P= 0.019) and “young-old
adults” (P= 0.019). Moreover, only a tendency effect of age group
was observed on duration of Map3 (χ2(4) = 9.016, P= 0.061, with
“young adults” significantly differing from “young-old adults”—
P= 0.039—and “older adults”—P= 0.039).
On the three maps characterizing the response-locked signal
(Maps 6, 7, and 8) only minor differences appeared across groups
in Fig. 4, and both analysis on Presence (Map6: χ2(4)= 3.459,
P= 0.48; Map7: χ2(4) = 5.820, P= 0.21; Map8: χ2(4) = 3.459, P= 0.48)
and Duration (Map6: χ2(4) = 5.741, P= 0.22; Map7: χ2(4) = 2.779,
P= 0.60; Map8: χ2(4) = 6.329, P= 0.18) did not reveal any significant
effect of age groups.
To analyze whether the duration of specific microstates was
related to the performance of speakers, correlations between
the duration of each map and the average RTs of individual
subjects were calculated (on all participants, per group and on
the 3 oldest age groups) (Correlations were not analyzed for
Maps 5 and 6 (which are cut off by being at the borders of the
analyzed stimulus-locked and response-locked time windows,
respectively), neither for maps being present in less than 10
participants for a given age-group. Bonferroni corrections were
applied for multiple comparisons (P-value was set at <0.013
when all participants, or the 3 oldest age-groups were considered,
and at <0.003 for comparisons between each age-group).). The
results are presented in Table IX in the Supplementary Materials.
On all participants, significant positive correlations with RTs
are observed for Map2 and Map8 (respectively r(54)= 0.41,
P<0.01; and r(82) = 0.38, P<0.001) and only a marginal posi-
tive correlation was observed for Map3 (r(64) = 0.29, P= 0.016).
When grouping the 3 older groups the only correlation that
reaches significance is on Map8 (r(46) = 0.39, P<0.001). Finally,
on analyses carried out separately per age group only Map2
(r(14) = 0.77, P<0.001) consistently correlates with RTs and only a
marginal positive correlation was observed for Map8 (r(17) = 0.61,
P<0.01).
Further analyses on Map3—time period associated with
Map3
A further analysis on the time window showing qualitative
changes—Map3 progressively more present in the three older
adults groups—was conducted in two steps: First, we proceeded
to a second back-fitting analysis in the individual ERPs in the time
period from 152 to 212 ms (including Map3 and the adjacent Maps
2 and 4) and second, we performed a source localization on that
time period.
The presence of Map3 increased progressively over the lifespan,
until reaching 100% of presence in “older adults” (see Fig. 5). A
significant difference in the presence of this topographic pat-
tern was confirmed by a significant main effect of age groups
[χ2(4) = 49.734, P<0.01]. The Wilcoxon test showed that the groups
of “adolescents” and “young adults” (who did not significantly
differ from each other) significantly differed from “adults” (both
P<0.01), “young-old adults” (both P<0.01) and “older adults”
(both P<0.01). The latter two groups did not significantly differ
from each other. Analysis of duration was only applied to the
3 groups showing at least 50% of presence (“adults,” “young-
old adults” and “older adults”). We found a significant effect of
age groups (χ2(2) = 11.693, P<0.01) with longer duration in both
groups of “young-old adults” and “older adults” (who did not
significantly differ from each other) relative to “adults” (both
P<0.01; Fig. 5).
A source localization analysis was performed on this same time
period (from 152 to 212 ms), comparing the age group that showed
the maximum presence of Map3 (“older adults”) and the most
efficient group in terms of RTs (“young adults,” in which Map3
was very slightly represented).For “young adults,” results show an
activity mainly located in the occipital bilateral and left posterior
frontal regions. “Older adults” showed more extended activation
in the left hemisphere, especially in the left temporo-parietal and
frontal superior areas (see Fig. 6).
Exploratory analysis on Map3
Although the results of other cognitive tasks were anticipated
as a control to ensure that the performance of all participants
was in accordance with the expected outcomes for their age
group, we decided to exploit them for an exploratory analysis
to provide additional insights aimed to test, whether the duration
of the additional topographic pattern observed starting from
the age of 40 to 50 years old (Map3) was modulated by the
performance on the other cognitive assessments. Results to the
“Vocabulary,” “Verbal Fluency – Animals,” “Verbal Fluency – P
letter,” “Simple Reaction Times,” “Working Memory,” and “Stroop”
tasks were considered as independent variables in a linear
model in order to explore their effect on the duration of Map3.
Results showed that only performance on the “Verbal Fluency
– Animals” task predicted the duration of Map3 [F(1,82)= 5.055,
P= 0.027]. The effect indicated a longer duration of Map3 when
performance on the “Verbal Fluency – Animals” task was lower
(β=−0.907).

Krethlow et al. |9
Fig. 5. Bar plots representing on the left the percentage of presence and on the right the mean duration for Map3 (152 to 212 ms) in the individual ERPs
for each age group, with error bars representing standard errors.
Fig. 6. Source localization of activation in the 152 to 212 ms time window in the group of “young adults” on the left and the group of “older adults” on
the right displayed on two transversal sections.
Discussion
In this study, we aimed to investigate whether changes in the
neurophysiological correlates of word production are aligned
with behavioral changes occurring across the adult lifespan.
A decrease in behavioral performance was observed only in
the group aged over 70 years old (“older adults”). However, the
neurophysiological results were not aligned with the behavioral
results, as inter-group differences in ERPs began to be present
at the age of 40 (“adults group”) and continued through the two
older age groups. The main differences in the ERP signal were
consistent and limited to a specific time window, between 150
and 220 ms after the onset of the stimulus (Map3 in Fig. 3), and
were characterized by an additional increasingly longer period of
stable electrophysiological activity on the scalp.These qualitative
changes were first observed at the age of 40 when behavioral
performance was still maintained.
We will briefly discuss the behavioral results before presenting
an in-depth interpretation of these ERP results and the misalign-
ment between behavioral and ERP differences across age groups.
Behavioral differences across age groups
Behavioral results in our target picture naming task are consistent
with the literature, as only the group of “older adults” differed
from the group of “young adults” in terms of RTs, while on the
other nonverbal cognitive assessments, the expected progressive
decrease in performance is also observed. In addition, the results

10 |Cerebral Cortex, 2024, Vol. 34, No. 5
across all other cognitive tasks confirmed that the performance
of all subjects was consistent with what is expected for their age
group.
No significant differences across age groups were observed
for accuracy, which also aligns with several previous studies (e.g.
Wierenga et al. 2008;Kavé et al. 2010;Kahlaoui et al. 2012). Note
that changes in performance in terms of production latencies
versus accuracy are debated in the literature (e.g. Goulet et al.
1994;Verhaegen and Poncelet 2013;Val ent e and Laganaro 2015).
For example, Kavé et al. (2010) found a “bow-shaped” pattern
for accuracy in response to a picture naming task, but only the
groups of children up to 8 years old significantly differed from
the other age groups. This was interpreted as a combined effect
of increased vocabulary skills and changes in retrieval processes.
Wierenga et al. (2008) also argued that the differences observed
in both RTs and accuracy suggest that decreased performance in
word retrieval is likely due to difficulty accessing lexical-semantic
information rather than progressive decline in knowledge or in
the neural substrates underlying semantic knowledge.Additional
analyses performed on our data to explore this hypothesis con-
firmed that performance on the “Simple Reaction Times” task
predicted picture naming RTs, while performance on the “Vocab-
ulary” task predicted accuracy. In contrast, no significant effect of
“Vocabulary” was found on RTs, and no significant effect of “Sim-
ple Reaction Times”was found on accuracy (Results to the “Simple
Reaction Times” task and to the “Vocabulary” task were considered
as independent variables in linear models in order to verify the
hypothesis that RTs are more related to general processing speed
while accuracy mainly reflects word retrieval capabilities. Results
showed that performance on the “Simple Reaction Times” task
predicted picture naming RTs [F(1) = 4.041, P= 0.048], while perfor-
mance on the “Vocabulary” task predicted accuracy [F(1) = 4.918,
P= 0.029]. The effects indicated faster production speed when
“Simple Reaction Times” were shorter (β= 3.912e−4)andmore
accurate responses when results on “Vocabulary” task were higher
(β= 1.929e−3). No significant effect of age-group was found.). Note
that the absence of significant differences in accuracy across
age groups may also be related to high accuracy due to material
selection with relatively high-frequency nouns (e.g. “train”, “dog”,
“sun”) compared to other studies (Gertel et al. 2020).
Crucially for our purpose here, these behavioral changes were
not aligned with the neurophysiological changes across adult-
hood.
ERP changes are not aligned to behavioral
changes
Our results showed similar ERP patterns associated with picture
naming for the two youngest groups and ERP differences across
groups starting from the age of 40 to 50 years old, in the absence
of any behavioral changes until the oldest age group. The main
inter-group difference was reflected by the increasingly longer
period of stable electrophysiological activity on the scalp (Map3
in Fig. 3) in the three older age groups. The presence and dura-
tion of this topographic pattern significantly increased for the
latter groups and was virtually absent in the signal of the two
youngest groups. The progressive increase in the duration of this
microstate in the neural signal thus suggests a gradual change that
is related to detrimental performance only after 70 years old. Note
that qualitative differences in topographic patterns (microstates)
are assumed to reflect the involvement of different underlying
neural networks (Michel et al. 2004;Michel and Murray 2012).
As support of this idea, source localization applied in this time
window of interest revealed different activations in young and
older adults, with mainly occipital activation in “young adults”
and more extended activations in left temporal and bilateral
frontal areas in older adults. In the following, we interpret the loci
and nature of these neural changes and tackle the issue of the
discrepancy between neurophysiological and behavioral results.
Qualitative age-related changes and functional
reorganization
The major ERP differences consisted of qualitative changes
between 150 and 220 ms: distinct topographic patterns were
observed for younger and older individuals, as evidenced by the
presence of Map3 only for adults above 40 years old. This time
window has been associated to lexical-semantic processes, i.e. the
activation of semantic features guiding lexical selection (Indefrey
and Levelt 2004;Indefrey 2011 see also Python et al. 2018;
Schendan and Kutas 2003;Simon et al. 2004). This interpretation
is also supported by the exploratory analyses conducted on the
other cognitive tasks, which highlighted that only performance
on the “Verbal Fluency – Animals” task predicted the duration
of Map3. In fact, the semantic (here animal) fluency task is the
only one among those tested that could be more representative
of lexical-semantic selection effort.
The changes across age groups observed with this additional
microstate in the 150 to 220 ms time window may therefore be
related to previous observations suggesting lifespan changes in
the lexical-semantic network (see Wul ff et al. 2019,2022;Krethlow
et al. 2020;Cosgrove et al. 2023;Guichet et al. 2024). Indeed,
age-specific characteristics of the lexical-semantic network have
been demonstrated to influence performance in word production
(Krethlow et al. 2020), resulting in faster word production speed
when the semantic network was richer and more prototypical.
Further evidence supporting this interpretation is derived from a
deeper look at the neural changes. In “older adults” where Map3
was highly present, we found bilateral frontal activation, which
is coherent with a functional reorganization of the underlying
brain networks with aging (Cabeza 2002;Ansado et al. 2013;Shafto
and Tyler 2014). The activation of a larger network, including
the left temporo-parietal lobe in the older age group, is compat-
ible with the “semanticization of cognition”/DECHA hypothesis.
Within this framework, there are two commonly cited patterns
of change in brain function (Tur n e r and Spreng 2015;Spreng and
Tur n e r 2019). The first one involves an increased recruitment of
lateral prefrontal brain regions underlying cognitive control,with
a reduced suppression of the default network. The second one
involves the recruitment of functionally connected brain regions
associated with the storage and retrieval of prior-knowledge rep-
resentations. Based on these functional brain changes, the DECHA
model (Tur n e r and Spreng 2015;Spreng and Tu r n e r 2019)proposes
that as efficiency in executive skills declines, the engaged default
network becomes increasingly dependent on the lateral prefrontal
cortex. Considering the patterns observed in young and old adults,
our results are also compatible with the posterior-anterior shift in
aging (PASA) phenomenon of intra-hemispheric reorganization of
activation patterns, from the occipitotemporal to frontal cortex
(Davis et al. 2008;Dennis and Cabeza 2008). These assumptions
of functional reorganization are thus coherent with the timing of
the observed changes (150 to 220 ms), tapping into processes that
deal with the temporal coactivation of multiple lexical represen-
tations driving the selection of the correct word to be produced
(Nozari and Pinet 2020).
Dedifferentiation in the context of maintained or reduced
performance
Generally, aging is studied through the prism of cognitive aging
and decline, with many studies using fMRI approaches. When

Krethlow et al. |11
more widespread and frequently bilateral brain activations are
observed in older adults, and when performance is maintained
or even improved relative to young adults, this pattern is seen as
compensatory (Cabeza 2002;Reuter-Lorenz 2002;Reuter-Lorenz
and Lustig 2005;Cabeza et al. 2018). When different neural pat-
terns are associated with worse performance or are unrelated to
the task, this is interpreted as dedifferentiation of brain networks
(Riecker et al. 2006;Heuninckx et al. 2008;Bernard and Seidler
2012;Koen and Rugg 2019;Cassady et al. 2020). The crucial aspect
of the present work is that the same pattern could be seen as com-
pensatory—in adults and young-old adults—or as dedifferentia-
tion in older adults, if interpreted in isolation. So far, whether ded-
ifferentiation is compensatory is not clearly understood, probably
because studies have been confined to comparisons of the two
extremes of adulthood. Our results are thus in line with accumu-
lating evidence that neural dedifferentiation does not exclusively
reflect detrimental aging (Koen and Rugg 2019), but it is likely a
stage of progressive qualitative change undergone by the brain
during healthy aging, which cannot indefinitely support com-
pensation. Actually, the misalignment between neural changes
and behavioral changes is not a new phenomenon. For example,
McLaughlin et al. (2004) also found discrepancies between the
results of the two approaches in a longitudinal study of word
learning in young adults. This allowed the authors to conclude
that these different methodologies highlight slightly different
processes, both contributing to a more complete understanding of
the cognitive processes underlying the examined language skills.
Focusing on aging, previous fMRI studies have found differences in
neural activation between young and older adults, in the absence
of differences at the behavioral level (Grady et al. 2003;Aine
et al. 2006;Ansado et al. 2013), but to our knowledge, none have
investigated intermediate age groups. The discrepancy between
behavioral performance and neurophysiological correlates on ver-
bal tasks may be due to a compensatory mechanism related either
to an increase in lexical-semantic abilities throughout adulthood
or/and to the reorganization of brain activation underlying the
preserved function (Wierenga et al. 2008;Baciu et al. 2021), or a
combination of both. More work is needed to clarify this specific
issue.
Other age-related changes in the ERP signal
Differences across age groups were also present in other time win-
dows beyond the time period between 150 and 220 ms, but they
were related to a different distribution of the same microstates
rather than different microstates. For instance, Map2 was more
prominent in “adolescents” and “young adults” due to the ana-
lyzed time window (Map3 time window corresponds to the end
of Map2 for the two youngest groups). This map corresponds
to the P100 component related to visual processing and object
recognition, and it had an extended duration in the two youngest
groups. After 300 ms, the duration of patterns was distributed
differently between the youngest and the older groups (Map4
and Map5). Map4 was observed in all groups in the P2 time win-
dow. This component has been associated with lexical selection
and phonological code retrieval (Indefrey and Levelt 2004;Costa
et al. 2009;Indefrey 2011;Cai et al. 2020). Here, the same brain
process seems to underpin lexical selection and phonological
code retrieval in all groups, possibly with shorter duration in the
two youngest groups. Part of these differences is possibly due
to the additional Map3 in the older groups, as discussed above.
Considering the absence of Map3 and the shorter duration of
Map4 in the youngest group despite similar production latencies
with the next two adult groups, there might be faster processing
in early time windows in the younger adults, which would then
be slowed down in the later time window (after 300 ms). Hence,
along with the different brain processes discussed above for Map3,
the results of the following microstates suggest different time
distributions of similar processes, which even out in terms of the
behavioral final outcome up to the oldest group. The lengthening
of Map4 after 300 ms for the three older age groups in a time
window likely associated with lexical selection and phonological
code retrieval (Indefrey 2011) is compatible with the “transmission
deficit hypothesis” (Burke et al. 1991,2000), where the priming
from the semantic to the phonological representations is thought
to decrease with aging. Note that in the response-aligned signal,
significant differences were only observed in a time window rela-
tively far from the response, which may overlap with the results
observed on the stimulus-aligned data.
Conclusion
The present study clearly shows a misalignment of behavioral
and neurophysiological changes in word production, revealing
significant differences at the neurophysiological level that appear
as early as the age of 40, while a decrease in performance in
word production was only observed from the age of 70. The main
ERP changes were qualitative differences characterized by an
additional activated brain network, which progressively increased
in presence and duration in the three older age groups.Converging
evidence from the corresponding source localization and the
time window of effects suggests dedifferentiation of the neu-
ral networks supporting lexical-semantic processes. The results
indicate compensatory mechanisms that allow the maintenance
of language skills longer than other cognitive functions up to
70 years, which, however, are no longer sufficient to compen-
sate for the decrease in performance afterwards. The lifespan
approach adopted here, especially the inclusion of middle-aged
adults, allowed us to demonstrate that the same qualitative neu-
ral change—likely restricted to lexical-semantic processes—can
be seen in two ways: as compensation or dedifferentiation. The
present study sheds new light on whether dedifferentiation is
compensatory (Koen and Rugg 2019), illustrating that it is actually
a progressive phenomenon, and contributes to the emerging trend
of refining brain–behavior relationships through neural degener-
acy (Westlin et al. 2023).
Author contributions
Giulia Krethlow (Conceptualization, Data curation, Formal
analysis, Investigation, Methodology, Resources, Writing—original
draft), Raphaël Fargier (Data curation, Writing—review & editing),
Tanja Atanasova (Data curation, Writing—review & editing),
Eric Ménétré (Data curation, Formal analysis, Writing—review
& editing), Marina Laganaro (Conceptualization, Formal analysis,
Funding acquisition, Supervision, Validation, Writing—review &
editing).
Supplementary material
Supplementary material is available at Cerebral Cortex online.
Funding
This work was supported by the Swiss National Science Founda-
tion (SNSF) under Grant no. 100014_165647.
Conflict of interest statement: None declared.

12 |Cerebral Cortex, 2024, Vol. 34, No. 5
Data availability
Behavioral and ERP data are available at the following link: https://
doi.org/10.26037/yareta:ves7bzbscjebhaurlfs7rniscy.
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