When a Sunny Day Gives You Butterflies:
An Electrophysiological Investigation of Concreteness
and Context Effects in Semantic Word Processing

Laura Bechtold , Christian Bellebaum, and Marta Ghio

Abstract

■ Theories on controlled semantic cognition assume that
word concreteness and linguistic context interact during seman-
tic word processing. Methodological approaches and findings
on how this interaction manifests at the electrophysiological
and behavioral levels are heterogeneous. We measured ERPs
and RTs applying a validated cueing paradigm with 19 healthy
participants, who performed similarity judgments on concrete
or abstract words (e.g., “butterfly” or “tolerance”) after reading
contextual and irrelevant sentential cues. Data-driven analyses
showed that concreteness increased and context decreased
negative-going deflections in broadly distributed bilateral clus-
ters covering the N400 and N700/late positive component time

range, whereas both reduced RTs. Crucially, within a frontotem-
poral cluster in the N400 time range, contextual (vs. irrelevant)
information reduced negative-going amplitudes in response to
concrete but not abstract words, whereas a contextual cue
reduced RTs only in response to abstract but not concrete
words. The N400 amplitudes did not explain additional variance
in the RT data, which showed a stronger contextual facilitation
for abstract than concrete words. Our results support separate
but interacting effects of concreteness and context on auto-
matic and controlled stages of contextual semantic processing
and suggest that effects on the electrophysiological versus
behavioral level obtained with this paradigm are dissociated. ■

INTRODUCTION
When we process the meaning of a word such as “butter-
fly,” we access its conceptual representation stored in the
semantic memory. Semantic memory comprises our gen-
eral knowledge about the word’s referent, such as what a
butterfly looks like, how it moves, and that you usually
see it on a sunny day (Binder & Desai, 2011; Mahon &
Caramazza, 2011). Representational content, that is, the
conceptually integrated information about a word’s refer-
ent, and linguistic context are two factors that influence
semantic word processing (for a review, see Hoffman,
2016). One proxy to investigate the effects of representa-
tional content is the concrete versus abstract distinction.
Concrete words (e.g., “butterfly”) refer to physical entities
and their representations tap into richer, sensorimotor
information compared with abstract words (e.g., “toler-
ance”). Priming or cueing paradigms can be used to inves-
tigate how context affects word processing. Embedding
words in contextual information is thought to relieve
demands on semantic control mechanisms, which select
one context-relevant while inhibiting other context-
irrelevant (aspects of ) word meaning (Chiou, Humphreys,
Jung, & Lambon Ralph, 2018; Hoffman, McClelland, &
Lambon Ralph, 2018; Lambon Ralph, Jefferies, Patterson,
& Rogers, 2017).

Heinrich Heine University Düsseldorf

© 2022 Massachusetts Institute of Technology

Traditionally, competing theories tried to explain a pro-
cessing advantage of concrete over abstract words, the
so-called concreteness effect, exclusively with a richer
representational content (e.g., the dual coding theory;
Paivio, 1991) or higher context availability of concrete
than abstract words (e.g., the context availability model;
Schwanenflugel, Harnishfeger, & Stowe, 1988). Stand-
alone neither theoretical approach accounted for the
complex pattern of contextual semantic processing differ-
ences between concrete and abstract words: In a range of
tasks, contextual embedding either resulted in no more dif-
ferences between concrete and abstract word processing
(Schwanenflugel & Stowe, 1989; Schwanenflugel et al.,
1988; Schwanenflugel & Shoben, 1983) or in a residual
concreteness effect (Bechtold, Bellebaum, Hoffman, &
Ghio, 2021; Hoffman, Binney, & Lambon Ralph, 2015).
Neuroscientific evidence suggested that representational
content and context availability rely on interacting neural
mechanisms during semantic word processing (Hoffman
et al., 2015; Jessen et al., 2000; Holcomb, Kounios,
Anderson, & West, 1999).

The controlled semantic cognition framework
(Hoffman et al., 2018; Lambon Ralph et al., 2017)
combined these empirical observations and theoretical
considerations on differential semantic control effects on
concrete and abstract word processing (e.g., the hub-and-
spokes model; Patterson, Nestor, & Rogers, 2007) in a neu-
rocomputational model of semantic processing. In an

Journal of Cognitive Neuroscience 35:2, pp. 241–258
https://doi.org/10.1162/jocn_a_01942

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extensive line of research resulting in this framework, the
authors systematically applied a similarity judgment task
(SJT; originally called synonym judgment task in previous
studies, Bechtold et al., 2021; Hoffman, 2016), which
requires participants to choose the word most similar to
a concrete or abstract probe among three test words.
The line of research included investigations in neurological
samples (Hoffman, Jones, & Lambon Ralph, 2013;
Almaghyuli, Thompson, Lambon Ralph, & Jefferies, 2012;
Hoffman & Lambon Ralph, 2011; Jefferies, Patterson,
Jones, & Lambon Ralph, 2009) and in healthy participants,
where the SJT was embedded in a cueing paradigm with
contextual or irrelevant cue sentences (Hoffman et al.,
2015; Hoffman, Jefferies, & Lambon Ralph, 2010). Among
the research on healthy participants, an fMRI study
(Hoffman et al., 2015) identified distinct neural correlates
sensitive to word concreteness and demands on semantic
control, namely, the anterior temporal lobe and the infe-
rior frontal gyrus, respectively. RT data showed that espe-
cially abstract words profited from contextual embedding,
which we replicated in German in a recent behavioral
study (Bechtold et al., 2021). Altogether, this research,
in combination with computational simulations (Hoffman
et al., 2018), substantiated the evidence for the assumed
interplay of concreteness- and context-driven semantic
processes and especially its neural basis.

Even though the SJT cueing paradigm is a powerful tool
to investigate contextual semantic processing, based on
neurological and neuroimaging data alone it remains
unclear whether the effects of representational content
and context emerge at stages of (early) automatic versus
(late) strategic retrieval and/or integration. Therefore, it
is crucial to complement the extant functional neuroimag-
ing and neurological findings with measures of high tem-
poral resolution (Hauk, 2016). In this study, we thus
adopted the SJT cueing paradigm to measure ERPs with
excellent temporal resolution during the contextual
semantic processing of concrete and abstract words.

Previous ERP studies showed electrophysiological
concreteness effects in the form of higher amplitudes for
concrete than abstract words at distinct processing stages
reflected in the N400 and N700 ERP components
(Bechtold, Ghio, & Bellebaum, 2018; Barber, Otten,
Kousta, & Vigliocco, 2013; Adorni & Proverbio, 2012; West
& Holcomb, 2000; Kounios & Holcomb, 1994). The N400,
a negativity peaking at 300–500 msec poststimulus, is con-
sidered as a marker of semantic retrieval and integration
(for a review, see Kutas & Federmeier, 2011). It reflects a
stronger involvement of semantic activation or integration
processes driven by the relatively richer (multimodal sen-
sorimotor) information for concrete than abstract words,
which usually goes along with better behavioral perfor-
mance (but see Barber et al., 2013, for a dissociation of
N400 and RTs; Lau, Phillips, & Poeppel, 2008). The N700
is a late ERP component, starting around 500 msec and
lasting up to 1000 msec poststimulus. A higher N700
amplitude reflects (top–down) retrieval of information

(Adorni & Proverbio, 2012) or imagery processes when
the task demands it (Gullick, Mitra, & Coch, 2013). The
N700 concreteness effect possibly reflects the task-
dependent strategic retrieval of visual information at a
later stage of semantic processing driven by concrete
words’ higher imageability and tasks eliciting imagery pro-
cesses (Bechtold, Ghio, & Bellebaum, 2018; Barber et al.,
2013; West & Holcomb, 2000).

Contextual embedding and priming, irrespective of
word concreteness, have been found to reduce centropar-
ietal N400 amplitudes (Kotchoubey & El-Khoury, 2014;
Ortu, Allan, & Donaldson, 2013; Kutas & Federmeier,
2011; Lau et al., 2008; Holcomb et al., 1999). Contextually
reduced N400 amplitudes have been interpreted to reflect
automatic preactivation of conceptual information in the
sense of spreading activation (Pulvermuller, 1999; Collins
& Loftus, 1975), semantic retrieval facilitation through pre-
diction mechanisms (Lau, Weber, Gramfort, Hamalainen,
& Kuperberg, 2016), or facilitated post-lexical semantic
integration (Steinhauer, Royle, Drury, & Fromont, 2017).
A late positive component (LPC; sometimes referred to as
P600), emerging 600–1000 msec after stimulus onset,
showed larger centroparietal amplitudes in related versus
unrelated priming conditions (Meade & Coch, 2017;
Grieder et al., 2012; Bouaffre & Faita-Ainseba, 2007). The
LPC is thought to reflect controlled post-lexical integration
processes, which might be more substantiated when a
word is embedded in richer (related) contexts (Hill,
Strube, Roesch-Ely, & Weisbrod, 2002). In summary, the
effects of concreteness and context on electrophysiologi-
cal measures are partly dissociated from each other and
from behavioral measures.

So far, only few ERP studies directly investigated the
interplay of concreteness and context-driven processes.
Three studies compared concrete and abstract word pro-
cessing in single-word semantic priming with a visual
(Grieder et al., 2012; Wirth et al., 2008) or acoustic presen-
tation (Swaab, Baynes, & Knight, 2002), and one embed-
ded the words in sentences (Holcomb et al., 1999). Two of
these ERP studies focused on the N400 in fixed time
windows (300–500 msec in Holcomb et al., 1999; 350–
650 msec in Swaab et al., 2002), whereas two followed a
data-driven approach (Grieder et al., 2012; Wirth et al.,
2008). All four studies found main effects of concreteness
(concrete > abstract) and context (unrelated > related)
on the N400 in line with the literature reviewed above. Fur-
thermore, two of these studies found an interaction effect
of concreteness and context on ERP amplitudes. Holcomb
et al. (1999) reported that the anterior N400 concreteness
effect was cancelled out when words were embedded in a
congruent (vs. anomalous or neutral) sentence. The data-
driven approach applied by Wirth et al. (2008) revealed an
interaction on the N400 latency: An amplitude reduction
by congruent primes occurred later for concrete (512–
524 msec) than abstract (444–568 msec) words. Even
though the applied analysis allowed only an estimate of
the true onset of effects (Sassenhagen & Draschkow,

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2019), the finding suggests that previous studies with fixed
N400 time windows might have overlooked a potential
latency modulation. On the behavioral level, only
Grieder et al. (2012) reported independent beneficial
effects of concreteness and context on lexical decision
speed. In the other studies, RT differences following the
context manipulation were either confounded by the
tasks, which required different responses to the related
versus unrelated conditions (congruency judgment task
in Holcomb et al., 1999; semantic judgment in Swaab
et al., 2002) or not available (passive reading task in Wirth
et al., 2008). Even though the electrophysiological interac-
tion pattern suggests that context differentially affects
concrete and abstract word processing, no study has yet
directly assessed electrophysiological effects within the
controlled semantic cognition framework.

Given the heterogeneity of previous methods and find-
ings, the main aim of the current study was to investigate
the temporal dynamics of the effects of representational
content (in this case word concreteness) and contextual
information on semantic word processing via ERP mea-
sures. A secondary aim was to relate the electrophysiolog-
ical to behavioral effects. To achieve these aims, we used
the original SJT cueing paradigm (as described by
Hoffman et al., 2015), translated the original English
stimuli to German, optimized them (as in Bechtold
et al., 2021, Experiment 2), and adapted the procedure
to assess ERP data. ERP waveforms were examined with
data-driven, nonparametric cluster-based permutation
analyses, which allowed us to detect concreteness and
context effects without restrictive a priori assumptions
on time windows and electrodes (Maris & Oostenveld,
2007). As behavioral measure, we analyzed SJT RTs with
linear mixed-effects (LME) analyses as reported in our
previous behavioral investigation (Bechtold et al., 2021).
We expected an electrophysiological concreteness
effect, with higher negative-going ERP amplitudes for
concrete compared with abstract words in the N400
(approximately between 300 and 500 msec) and N700
time window (∼500–1000 msec). Concerning the context
effect, we expected ERP effects with lower negative-going
amplitudes in line with a reduced N400 (∼300–500 msec)
and enhanced LPC (∼500–1000 msec) after contextual
compared with irrelevant cues. Crucially, we also expected
an interaction, that is, a differential influence of contextual
information on concrete versus abstract word processing.
We refrained from a priori assumptions on the corre-
sponding spatiotemporal cluster for this effect, as con-
creteness and context have been shown to go along with
opposing N400 and late ERP modulations, namely,
enhanced (Barber et al., 2013; Lee & Federmeier, 2008;
Swaab et al., 2002; West & Holcomb, 2000) and reduced
(negative) amplitudes (Kotchoubey & El-Khoury, 2014;
Ortu et al., 2013; Kutas & Federmeier, 2011; Lau et al.,
2008; Holcomb et al., 1999), respectively. On the behav-
ioral level, we expected to replicate the concreteness
effect (with faster similarity judgments for concrete than

abstract words) and the context effect (with faster similar-
ity judgments for contextual than irrelevant cues). Contex-
tual facilitation (i.e., reduced RTs) should be stronger for
abstract than concrete words, replicating previous behav-
ioral findings (Bechtold et al., 2021; Hoffman et al., 2015).
Finally, we aimed to explore the relationship between
behavioral and ERP data by using ERP amplitudes in rele-
vant time windows as predictors for SJT RTs. Here, we
expected the respective ERP amplitudes to be significant
predictors of RTs.

METHODS

Participants

Twenty-two healthy young adults voluntarily participated
in this study. Criteria for participation were German as
mother language, no history of psychiatric or neurological
disease or dyslexia, a normal or corrected-to-normal visual
acuity, and right-handedness. The Edinburgh Handedness
Inventory (Oldfield, 1971) indicated right handedness for
all participants but one, who scored −0.55 (scores for the
other participants ranged from 0.57 to 1, M = 0.87, SD =
0.15). This participant was excluded from data analysis, as
well as two others, one due to technical artifacts in the EEG
data (less than 70% of artifact-free trials in the averaged
ERP waveforms) and one due to defective electrodes
during recording. The final sample for all analyses thus
consisted of 19 adults (5 men and 14 women) aged 18–
31 years (M = 22.84 years, SD = 3.75 years). Seventeen
participants were students with at least a university
entrance qualification; two had finished secondary school
and completed vocational training. The size of the final
sample was in line with sample sizes of 16–20 participants
in previous EEG studies, which found a significant interac-
tion of concreteness and context ( Wirth et al., 2008;
Holcomb et al., 1999). For the ERP analyses, we did not
conduct an a priori power analysis as the power of the
dependent-samples t tests per time-electrode sample
serving, as test statistic for the applied nonparametric
cluster-based permutation analyses (see below for details)
does not correspond to the power at the reported cluster
level (Maris, 2012). For RT analysis, we did not conduct a
priori power analysis as it aimed to replicate previous find-
ings obtained in a larger sample (Bechtold et al., 2021).
The study was in line with the ethical standards defined
in the Declaration of Helsinki and was approved by the
ethics committee of the Faculty of Mathematics and
Natural Sciences at Heinrich Heine University Düsseldorf.
We collected written informed consent from all volunteers
before participation, for which they received course credit
or a monetary compensation.

Stimuli and Material

We used the modified stimuli of the SJT by Hoffman et al.
(2015) translated from English into German in our

Bechtold, Bellebaum, and Ghio

243

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previous behavioral study (Experiment 2 in Bechtold et al.,
2021). Modifications with respect to the original English
stimuli included substituting adjectives by nouns or verbs
to reduce variability in grammatical class of the probe and
target words, adjustments to balance concrete and
abstract probes for letter length and arousal, and reformu-
lation of the second cue sentence to avoid repetition of the
probe as had been the case in the original study (for more
details on modifications, see Bechtold et al., 2021). The
thereby carefully constructed German stimuli were divided
into the abstract versus concrete category based on a median
split applied to the preexperimental concreteness ratings
(see below). Two words that were originally categorized
as concrete and two words that were categorized as abstract
by Hoffman et al. (and in our replication study, Bechtold
et al., 2021) were now assigned to the other category,
respectively. Furthermore, the preexperimental ratings of
our German stimuli by native German speakers (for details,
see Bechtold et al., 2021) ensured that concrete probes
received significantly higher imageability, context availabil-
ity, and concreteness ratings than abstract probes (see
Table 1A). According to normative studies, these measures
are considered discriminatory dimensions of concrete ver-
sus abstract words across languages ( Yao, Wu, Zhang, &
Wang, 2017; Della Rosa, Catricala, Vigliocco, & Cappa,
2010; Altarriba, Bauer, & Benvenuto, 1999). Concrete and
abstract probes did not differ in length (number of letters),
frequency of occurrence in written and spoken language,
arousal, valence, and association with emotional experience
(see Table 1A). The full set of stimuli, including the psycho-
linguistic ratings, is available in the open access repository
(https://osf.io/nvufe/).

For each probe, we had three test words, from which
the participants had to choose the word that was most
similar to the probe. One test word was thus a semantically
similar target word, and two test words were semantically
unrelated foils. Because a differential reliance on
similarity-based and associative relations between con-
crete and abstract words has been postulated (Crutch &
Warrington, 2005), the relation of the probes and their
target words was quantified in additional ratings of their
similarity (i.e., “How similar are the two words? How well
can you put them in a common category?”) and association
strength (i.e., “How strongly associated are the two words?
How well do they form a common context?”). Concrete
probe–target pairs received higher ratings of association
and similarity-based relatedness than abstract probe–
target pairs (see Table 1B). We ruled out the potential
influence of these differences on RTs by conducting
covariate analyses (see below). Please note that electro-
physiological results were independent of the probe–
target relation, as we measured ERPs in response to the
probe, which appeared 1 sec before the target/foil presen-
tation (see the Design and Data Analysis section).

The contextual cues, each consisting of two short, pos-
itively formulated sentences, put each probe in a meaning-
ful context, without containing the probe or a direct

antonym/synonym. The cues often described situations,
in which the probe could occur (e.g., “It was a sunny
day. Many insects fluttered around outside.”—for the con-
crete probe “butterfly”) or paraphrased a definition of the
probe’s meaning (e.g., “I am against racism. I try to be
open-minded.”—for the abstract probe “tolerance”). Cues
for concrete and abstract probes did not differ in letter and
word count, arousal, and valence, but concrete cues
received significantly higher imageability and context
availability ratings (see Table 1C; for details on the rating
procedure, see Bechtold et al., 2021). Probes were divided
into two sets, A and B, with 50 concrete and 50 abstract
probes each. To create irrelevant cues, the contextual cues
were randomly reassigned to probes within each set. One
half of the participants saw probe set A with contextual
cues and set B with irrelevant cues, for the other half vice
versa (procedure as in Bechtold et al., 2021; Hoffman
et al., 2015).

We conducted an additional rating on how well the
probe fit into the context provided by the cue on Likert
scales from 1 (not at all ) to 7 (strongly fitting) with 30 Ger-
man native speakers (n = 15 for each of the two parallel
versions of the experimental stimuli). A 2 × 2 mixed
ANOVA on these context strength ratings with the factors
Cue (contextual, irrelevant) and Concreteness (concrete,
abstract) revealed a significant main effect of Cue, F(1,
198) = 6538.33, p < .001, ηp 2 = .971, with higher context strength ratings for contextual (M = 6.10, SE = 0.04) than irrelevant (M = 1.65, SE = 0.04) cues. The analysis revealed no significant main effect of Concreteness, F(1, 198) = 3.72, p < .055, ηp 2 = .018, but a significant Cue × Concreteness interaction effect, F(1, 198) = 17.73, p < .001, ηp 2 = .082 (for descriptive statistics, see Table 1C). Post hoc pairwise comparisons showed that there was no difference in context strength for contextual cues between concrete and abstract words, p = .171, whereas for irrelevant cues, concrete words received lower ratings than abstract words. The higher context strength of irrele- vant cues for abstract words can likely be driven by the abstract words’ higher semantic ambiguity (Hoffman et al., 2013). Notably, for both concrete and abstract words, contextual cues received a significantly higher rating than irrelevant cues, both p < .001, d > 5.04.

Procedure

Data acquisition took place in single-subject testing ses-
sions in an electrically shielded EEG laboratory at Heinrich
Heine University. After giving written, informed consent,
participants filled in a demographic questionnaire and
the Edinburgh Handedness Inventory. In the meantime,
EEG electrodes were attached (for details, see the EEG
Recording and Preprocessing section). Stimulus timing
and response recording during the EEG task were
controlled by the software Presentation ( Version 17.0,
Neurobehavioral Systems, Inc.) on a Windows 10 Dell Intel
Premium PC with a 22-in. LED Dell monitor with 1680 ×

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Table 1. Descriptive and Inferential Statistics of the Psycholinguistic Variables for (A) Probes, (B) Relation Strength between Probes
and Targets, and (C) Cue Sentences

Concrete

M (SE)

Abstract

M (SE)

Inferential Statistics

t

df

p

d

Psycholinguistic Variable

(A) Probes

Length (letters)

Frequency (written)a

Frequency (spoken)b

Arousal

Valence

Emotional experience

Imageability

Context availability

Concreteness

(B) Probe–target relation

Association

Similarity

(C) Cue sentences

Length (letters)

Length (words)

Arousal

Valence

Imageability

Context availability

7.70 (0.25)

8.06 (0.25)

48.04 (11.24)

47.59 (8.20)

38.51 (9.69)

22.13 (6.48)

2.54 (0.10)

0.36 (0.11)

2.96 (0.14)

6.07 (0.06)

5.74 (0.04)

6.18 (0.07)

2.59 (0.09)

0.28 (0.10)

2.59 (0.13)

2.98 (0.07)

4.29 (0.07)

2.52 (0.06)

1.05

0.03

1.39

−0.42

0.5

1.94

32.76

17.33

38.04

198

186

194

198

198

198

198

154.38

198

.296

.974

.165

.675

.616

.054

< .001 < .001 < .001 5.84 (0.06) 5.53 (0.07) 5.61 (0.06) 5.18 (0.07) 2.77 3.49 198 198 .006 < .001 58.89 (0.90) 59.06 (1.07) 9.74 (0.17) 9.86 (0.17) 2.55 (0.13) 4.06 (0.10) 5.18 (0.12) 5.09 (0.09) 2.39 (0.11) 3.84 (0.09) 3.30 (0.15) 4.13 (0.11) 6.04 (0.07) 1.82 (0.06) −0.12 −0.49 0.93 1.58 10.09 6.81 1.37 −4.46 192.34 198 198 198 188.69 186.96 198 195 .903 .623 .352 .117 < .001 < .001 .171 < .001 0.15 0.00 0.20 0.06 0.07 0.27 4.63 2.45 5.38 0.39 0.49 0.02 0.07 0.13 0.22 1.43 0.96 0.19 0.63 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / / 3 5 2 2 4 1 2 0 6 5 8 7 8 / j o c n _ a _ 0 1 9 4 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Context strength (contextual cues) 6.16 (0.05) Context strength (irrelevant cues) 1.47 (0.05) Imageability, context availability, concreteness, emotional experience, arousal, context strength, and the strength of the probe–target relation based on association and similarity were rated on 1–7 Likert scales, and valence was rated on a scale from −3 to +3. Independent-samples t tests compared the psycholinguistic variables for concrete and abstract words. n = 100 per condition, except for frequency (written: nconcrete = 95, nabstract = 93; spoken: nconcrete = 100, nabstract = 96). a CELEX database (Baayen, Piepenbrock, & Gulikers, 1996) frequency of occurrence of Mannheim lemmas in 1 Mio. words. b SUBTLEX-DE database (Brysbaert et al., 2011) frequency of occurrence of case-insensitive lemmas. 1050 pixel resolution and a refresh rate of 60 Hz. All stimuli were presented in white letters (font: Arial, font size: 30 pt) on black background. The experimenter read the instructions of the experi- mental SJT presented on the computer screen to the par- ticipants. Figure 1 depicts the timing of the experimental trials. In the SJT, after a fixation cross centrally presented for 0.5 sec, either a contextual or an irrelevant cue appeared in the center of the screen for 5 sec. Then, the probe appeared alone on the screen for 1 sec, which included the 100–1000 msec time interval considered in the cluster analysis (see below). Please note that this period was introduced to avoid that eye movements and other motor artifacts that affected the ERP in the critical period after probe presentation (different from Bechtold et al., 2021; Hoffman et al., 2015, in which probe and target words appeared simultaneously). Finally, the target word and foils appeared below the probe until a response was given or for a maximum of 4 sec. Participants were instructed to read the cue sentences carefully and were informed that the cue sentences could or could not be semantically related to the probe. Participants were Bechtold, Bellebaum, and Ghio 245 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / / 3 5 2 2 4 1 2 0 6 5 8 7 8 / j o c n _ a _ 0 1 9 4 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 1. Timing and exemplary stimuli of an experimental trial of the SJT with (left) a concrete probe presented with a contextual cue and (right) an abstract probe presented with an irrelevant cue. further instructed to choose the word that was most similar to the probe as fast and accurately as possible via button press. The positions of the target word and the two foils were counterbalanced across the three possible positions and randomized over all trials. Participants used Buttons 3, 4, and 5 of an RB-740 Response Pad (Cedrus Corporation), corresponding to the three positions of the test words on screen. Partici- pants were instructed to position the index, middle, and ring fingers of their (dominant) right hand on the three buttons to reduce movement artifacts and RT variability. Furthermore, participants were asked to look at the fixa- tion cross as soon as it appeared on screen and to keep movements during trials to a minimum. Participants saw two practice trials and could ask questions before starting the experiment. The 200 experimental trials (50 for each condition: concrete contextual, concrete irrelevant, abstract contextual, and abstract irrelevant) were pre- sented in randomized order. Participants could take a self-paced break every 20 trials. Overall, the SJT took about 35 min to be completed. EEG Recording and Preprocessing We recorded EEG data with 28 Ag/AgCl passive ring elec- trodes mounted on a textile BrainCap (Brain Products GmbH) from positions according to the international 10–20 system (electrode sites: F7, F3, Fz, F4, F8, FC5, FC1, FC2, FC6, T7, C3, Cz, C4, T8, CP5, CP1, CP2, CP6, P7, P3, Pz, P4, P8, PO9, O1, Oz, O2, and PO10; Chatrian, Lettich, & Nelson, 1985). Four additional electrodes were placed at the outer canthi of the eyes and above and below the left eye to record the horizontal and vertical EOG, respectively. The ground electrode was positioned at AFz, and the reference electrodes were positioned on the right and left mastoid. We used a BrainAmp DC amplifier (Brain Products GmbH) with a sampling rate of 1000 Hz and an amplitude resolution of 0.1 μV with BrainVision Recorder software ( Version 1.20.0506) on a Windows 10 Dell Intel Premium PC. Careful scalp prepara- tion kept impedances below 5 kΩ. A standard EEG preprocessing procedure in Brain Vision Analyzer ( Version 2.1) was conducted. We applied Butterworth zero phase filters for frequencies below 0.1 Hz (time constant: 1.59) and above 30 Hz, with a slope of 24 dB/oct as well as a 50-Hz notch filter. To remove eye blink artifacts from the signal, a fast, restricted indepen- dent component analysis with classical sphering was applied to 120 sec of the continuous signal starting after 60 sec of the recording. Visual inspection identified one or two components with a frontally pronounced positivity temporally bound to eye blink artifacts recorded by the vertical EOG. These components were excluded before an inverse independent component analysis back transfor- mation. Subsequently, we segmented the continuous signal into epochs starting 200 msec before and ending 1000 msec after probe onset. The 200 msec before the probe onset were used for a baseline correction. Subse- quently, an automatic artifact detection marked rejected epochs based on the following criteria: voltage steps of more than 50 μV/ms, signal changes below 0.1 μV or above 100 μV within 100 msec, or absolute amplitudes above or below ±100 μV. Electrodes T7 and T8 were excluded from this artifact rejection and the subsequent cluster analyses due to excessive muscle artifacts. We finally averaged all artifact-free trials separately for the four experimental conditions with a minimum of 40 and a maximum of all 50 trials per participant per condition (concrete contextual, 246 Journal of Cognitive Neuroscience Volume 35, Number 2 concrete irrelevant, abstract contextual, and abstract irrel- evant) entering the averaged ERP waveforms (M = 48.9 trials, SD = 1.8 trials). We down-sampled the data to 100 Hz in order not to suggest millisecond precision regard- ing the onset and offset of effects, which the cluster-based permutation analysis cannot provide (Sassenhagen & Draschkow, 2019). Average ERP data are available in the open access repository (https://osf.io/nvufe/). Design and Data Analysis ERP Data A nonparametric cluster-based permutation analysis was applied to the averaged ERP waveforms as implemented in the FieldTrip toolbox ( Version 20210629; Oostenveld, Fries, Maris, & Schoffelen, 2011) in the MATLAB environ- ment ( Version 2020b; code and average ERP data are avail- able in the open access repository). We conducted three separate analyses for testing: (1) the Concreteness main effect (concrete vs. abstract), (2) the Cue main effect (con- textual vs. irrelevant), and (3) the Concreteness × Cue interaction effect (tested by comparing the difference contextual–irrelevant [i.e., the context effect] for concrete vs. abstract words; Bechtold, Ghio, Lange, & Bellebaum, 2018; Maris & Oostenveld, 2007). On the first level, to detect spatiotemporal clusters corresponding to signifi- cant differences between conditions, we calculated dependent-samples t tests separately for each time- electrode sample of interest, that is, the above-mentioned 26 electrodes in the time window from 100 to 1000 msec after the probe onset (resulting in 26 × 91 included sam- ples). The broad time window was chosen based upon visual inspection (see Figure 2) to not only cover the time intervals corresponding to the N400 and LPC/N700 ERP components, on which we based our hypotheses, but also earlier components, so that unexpected effects could have been detected as well (Mensen & Khatami, 2013). Based on spatiotemporal adjacency, we combined samples with at least two neighboring electrodes that reached a cluster- defining threshold of p < .05, to clusters. Successive time points were considered adjacent, as well as electrodes with a maximum distance of 0.225 artbitrary units (correspond- ing to approximately 22.5% of the nasion–inion distance according to the 10–20 system based on the customized acticap-64ch-standard2.mat 2D template layout). The weighted cluster mass (Hayasaka & Nichols, 2004) served as test distribution for the second-level cluster sta- tistic as it has been shown to deliver the best balance between precision and sensitivity based on a predefined cluster-forming threshold (Mensen & Khatami, 2013). On the second level, we created a reference distribution in form of 1000 random permutations of the data of the two conditions and applying the cluster detection method described above. By comparing the weighted cluster mass of the detected clusters in the ERP data with those in the random distributions, we estimated the p value of each cluster. This approach controls for multiple comparisons and is less conservative than Bonferroni correction (Maris & Oostenveld, 2007). Most importantly, it allows detecting effects without restrictive a priori assumptions on time windows and electrode sites and avoids compromising validity by reducing the data in a possibly biased way (Mensen & Khatami, 2013). Additionally, it allows exploring the spatiotemporal development of effects, even though the permutation-based cluster analysis does not allow strong inferences on the temporal onset and offset of effects (Sassenhagen & Draschkow, 2019). Please note that the reported cluster extents are descriptive in nature and only approximate the real extent of effects (Sassenhagen & Draschkow, 2019). Furthermore, descriptive statistics for the mean amplitudes in the significant clusters were calculated by extracting and averaging the amplitude values of the time-electrode samples constituting the clus- ter for the two conditions involved in the respective com- parison. Code and results of the ERP analyses are available in the open access repository (https://osf.io/nvufe/). Finally, to further explore the Concreteness × Cue interaction pattern, we extracted the single-trial ERP amplitudes, averaged across the time-electrode samples involved in the cluster that corresponded to a significant interaction effect in ERP Analysis 3 (see below). The overall 3720 single-trial ERP amplitude data points of all partici- pants were entered into an LME analysis (see Bechtold et al., 2021) conducted with the lme4 package ( Version 1.1-26; Bates, Mächler, Bolker, & Walker, 2015) in R ( Ver- sion 3.6.3). Following recommendations of Baayen and Milin (2010), we refitted the model after excluding trials, whose standardized residuals exceeded 2.5 SDs from the residual mean (<1.6% of the data). This resulted in 3663 data points, with a mean of 48.2 single-trial RT data points (SD = 2.2 data points, ranging from 39 to 50) per partici- pant per condition going into the following model. Fixed-effects factors included in the model were Con- creteness (concrete [+0.5], Abstract [−0.5]) and Cue (contextual [+0.5], irrelevant [−0.5]), as well as their interaction. We included random intercepts for the random-effects factors Participants and Items to control for interindividual variance (Baayen, Davidson, & Bates, 2008). As the inclusion of random slopes would have led to a singular fit, we did not include random slopes. We applied a restricted maximum likelihood approach (Luke, 2017) and estimated degrees of freedom and p values fol- lowing the Satterthwaite method with the lmerTest pack- age ( Version 3.1-3; Kuznetsova, Brockhoff, & Christensen, 2017). We applied simple slope analyses as implemented in the R package jtool ( Version 2.0.3) to resolve the inter- action. We calculated Cook’s distance with the package influence.ME ( Version 0.9-9) to verify that no participants exerted a disproportionately strong influence on the model. No participant reached the suggested cutoff of 0.24 (Bollen & Jackman, 1985) calculated as 4/n − p (n = sample size = 19; p = number of factors = 2), with values ranging from <0.01 to 0.13 (M = 0.04, SD = 0.03). Bechtold, Bellebaum, and Ghio 247 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / / 3 5 2 2 4 1 2 0 6 5 8 7 8 / j o c n _ a _ 0 1 9 4 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / / 3 5 2 2 4 1 2 0 6 5 8 7 8 / j o c n _ a _ 0 1 9 4 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 2. Grand average (n = 19) ERP curves from 200 msec before to 1000 msec after the probe onset (dashed vertical line) for the four experimental conditions (concrete contextual, concrete irrelevant, abstract contextual, abstract irrelevant) at nine exemplary electrodes (F3, Fz, F4, C3, Cz, C4, P3, Pz, P4; see upper right corner for electrode positions). To control the effects of the possibly confounding sig- nificant differences between concrete and abstract words in cue imageability, cue context availability, and context strength (please note that neither probe–target associa- tion nor similarity potentially affected ERP amplitudes as ERPs were measured before target onset), we included the respective rating measures as continuous, normalized predictors into separate LME analyses on the single-trial ERP amplitudes described above. We refitted these covar- iate models after excluding trials, whose absolute stan- dardized residuals exceeded 2.5 SDs from the residual mean (always <1.6% of the data). This resulted in 3663 data points included in each of the reported models. Code and results of the LME ERP analyses are available in the open access repository (https://osf.io/nvufe/). RT Data We measured RTs from the onset of the three test words to the button press. Raw RT data are available in the open access repository (https://osf.io/nvufe/). We refrained from a priori data trimming, as all RTs laid in a sensible range of 466–3940 msec (Baayen & Milin, 2010). We included only trials with correct responses. Responses were considered correct when the participant chose the similar target word among the three test words (partici- pants reached a mean of 94.7% accuracy, SD = 22.3%). The overall 3573 raw RT data points of all participants were entered into an LME analysis (see Bechtold et al., 2021) as described above. We refitted the model after excluding trials for which standardized residuals exceeded 2.5 SD from the residual mean (<2.5% of the data). This resulted in 3484 data points, with a mean of 45.8 single-trial RT data points (SD = 3.6 data points, ranging from 32 to 50) per participant per condition going into the following model. We included the same random effect factors as described above for the ERP amplitude model. For participants, we additionally included random slopes for Concreteness and Cue. No participants exerted a disproportionately strong influence on the model (Cook’s distance) with values ranging from <0.01 to 0.16 (M = 0.06, SD = 0.05). 248 Journal of Cognitive Neuroscience Volume 35, Number 2 We further used the single-trial ERP amplitudes from the cluster that corresponded to a significant interaction effect in ERP Analysis 3 as an additional normalized continuous pre- dictor into the original single-trial RT LME model described above. We excluded single-trial RT values from trials contain- ing ERP artifacts, as they did not have corresponding ampli- tude values, resulting in 3502 data points. We again refitted the model after excluding trials, whose absolute standard- ized residuals exceeded 2.5 SDs from the residual mean (<2.6% of the data). This resulted in 3412 data points, with a mean of 44.9 single-trial RT data points (SD = 4.3 data points, ranging from 25 to 50) per participant per condition. To investigate whether this additional predictor explained a significant amount of variance in the RT data, we compared the models with and without the ERP amplitude as predictor via a χ2 test conducted with the anova function imple- mented in the car package (Version 3.0-10). Please note that the model comparison was conducted upon the models without model-specific residual outlier exclusion, as it can only be conducted upon models fitted to the same amount of data points. To control the effects of the possibly confounding signif- icant differences between concrete and abstract words in cue imageability, cue context availability (see Table 1B), context strength, as well as probe–target association and similarity (see Table 1C), we included the respective rating measures as continuous, normalized predictors into LME analyses on the single-trial ERP amplitudes described above. We refitted these covariate models after excluding trials, whose absolute standardized residuals exceeded 2.5 SDs from the residual mean (always <2.6% of the data). This resulted in 3481 (probe–target association covariate), 3482 (probe–target similarity covariate), and 3483 (context strength, cue context availability, and cue imageability covariates) data points included in the reported models, respectively. Code and results of the LME RT anal- yses are available in the open access repository (https://osf .io/nvufe/). RESULTS ERP Data Figure 2 depicts the grand average ERP waveforms relative to probe onset at nine exemplary electrode sites. Ampli- tude differences between the experimental conditions were broadly distributed over the scalp and began around 400 msec after probe onset and lasted up to 1000 msec (probe offset). The N400 peaked at ∼400–500 msec and was followed by a sustained relative negativity visible in the irrelevant conditions and a less pronounced negativity or even relative positivity in the contextual conditions in the N700/LPC time range (>600 msec).

Analysis 1: Concreteness Main Effect

The first cluster-based permutation analysis indicated a
significant Concreteness effect, p = .003. Descriptively,
the effect corresponded to a large cluster with more
negative (i.e., less positive) amplitudes for concrete
(M = 1.09 μV, SD = 1.70 μV) than abstract probes (M =
2.20 μV, SD = 1.68 μV), extending from approximately
370–1000 msec after probe onset. Within this time
interval, a centroparietally pronounced cluster roughly
covering the N400, transitioned into a frontocentrally
pronounced cluster covering the N700/LPC time range
(for the spatiotemporal development, see Figure 3).

Figure 3. Spatiotemporal development (in 50-msec steps) of the cluster corresponding to a significant amplitude difference between concrete and
abstract probes. Black circles mark time-electrode samples for which the concrete versus abstract comparison reached the cluster-defining threshold
at the first level ( p < .05). The color grading represents the distribution of t values. Positive t values indicate a more negative amplitude for concrete than abstract probes. Bechtold, Bellebaum, and Ghio 249 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / / 3 5 2 2 4 1 2 0 6 5 8 7 8 / j o c n _ a _ 0 1 9 4 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 4. Spatiotemporal development (in 50-msec steps) of the cluster corresponding to a significant amplitude difference between probes after contextual and irrelevant cues. Black circles mark time- electrode samples for which the contextual versus irrelevant comparison reached the cluster- defining threshold at the first level ( p < .05). The color grading represents the distribution of t values. Positive t values indicate a more negative amplitude for irrelevant than contextual cues. Analysis 2: Cue Main Effect The second analysis indicated a significant context effect, p < .001. Descriptively, the effect corresponded to a cluster with more negative (less positive) amplitudes for irrelevant (M = 1.10 μV, SD = 1.99 μV) than contextual cues (M = 2.44 μV, SD = 1.79 μV), extending from approx- imately 470–770 msec after probe onset. Within this time interval, the cluster covering the late N400 and in the N700/ LPC time range was broadly distributed over frontocentroparietal electrode sites (for the spatiotempo- ral development, see Figure 4). Analysis 3: Concreteness × Cue Interaction Effect The third analysis indicated a significant interaction effect of Concreteness and Cue, that is, a significant difference in the context effects on concrete versus abstract word processing, p = .031. Descriptively, the effect corresponded to a cluster Figure 5. Left: Spatiotemporal development (in ∼50 msec steps) of the cluster corresponding to a significant interaction, that is, a significant difference between the context effects for concrete versus abstract probes. Black circles mark time-electrode samples for which the comparison of the context effect for concrete and abstract probes reached the cluster-defining threshold at the first level ( p < .05). The color grading represents the distribution of t values. Positive t values indicate a larger amplitude difference between irrelevant and contextual cues for concrete than abstract probes. Right: Mean amplitude of the negative-going deflection in the four experimental conditions (concrete contextual, concrete irrelevant, abstract contextual, and abstract irrelevant) in the detected cluster. Error bars depict ±1 standard error. 250 Journal of Cognitive Neuroscience Volume 35, Number 2 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / / 3 5 2 2 4 1 2 0 6 5 8 7 8 / j o c n _ a _ 0 1 9 4 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 with a stronger context effect (i.e., the amplitude difference of contextual minus irrelevant cues) for concrete than abstract probes in a time window extending from approxi- mately 400–540 msec after probe onset and thus consistent with the N400. The cluster was broadly distributed over the scalp in the early phase of the time window, whereas in the later phase, it was most pronounced over frontotem- poral electrode sites, mostly over the left hemisphere (for the spatiotemporal development, see Figure 5, left). The descriptive pattern of the mean amplitudes in this cluster (Figure 5, right) suggests that a contextual cue reduced the negative-going amplitudes in response to concrete probes but not in response to abstract probes. The post hoc LME analysis (for inferential statistics, see Table 2A) confirmed that participants’ mean amplitudes in this cluster differed significantly between the contextual versus irrelevant condition for concrete, β = 1.24 (SE = 0.33), p < .001, but not abstract probe processing, β = −0.24 (SE = 0.33), p = .476. Additional covariate analyses showed that the reported inferential pattern of the interaction effect on the ERP amplitude in the detected cluster was robust (all ps ≤ .006) against potential confounds by the cues’ imageabil- ity, context availability ratings, and the context strength (see Table 2B, C, D, respectively). Notably, only in the con- text strength covariate analysis, the main effect of cue was not significant, p = .333, which was expected, as the con- text strength differentiates between contextual and irrele- vant cues. All other main effects remained significant throughout the covariate analyses, all ps ≤ .040. Table 2. Inferential Statistics of the Single-trial ERP Amplitude LME Analyses Predictor β SE df t p (A) Post hoc analysis Cue 0.50 0.24 3466.56 2.14 .033 Concreteness −0.79 0.26 197.00 −3.04 .003 Concreteness × 1.48 0.47 3466.50 3.14 .002 Cue (B) Cue imageability covariate analysis Cue imageability −0.13 0.15 965.00 −0.91 .364 Cue 0.48 0.24 3465.86 2.05 .040 Concreteness −0.64 0.31 280.74 −2.09 .037 Concreteness × 1.49 0.47 3469.51 3.16 .002 Cue (C) Cue context availability covariate analysis Cue context availability −0.04 0.13 1163.42 −0.29 .775 Cue 0.50 0.24 3466.28 2.13 .033 Concreteness −0.76 0.28 234.33 −2.72 .007 Concreteness × 1.48 0.47 3467.70 3.15 .002 Cue (D) Context strength covariate analysis Context strength 0.76 0.50 743.96 1.52 .129 Cue Concreteness −0.96 −0.76 0.99 0.26 824.99 −0.97 198.91 −2.89 .333 .004 Concreteness × 1.33 0.48 3585.28 2.76 .006 Cue The ERP amplitude was extracted from the cluster detected to reflect a significant interaction effect of Concreteness and Cue in Analysis 3. RT Data Figure 6 shows the descriptive RT data, whereas Table 3A summarizes the inferential pattern of the LME RT analysis. We found significant main effects of Concreteness, p < .001, and Cue, p = .004. RTs for concrete words (M = 1291 msec, SD = 417 msec) were faster than those for abstract words (M = 1543 msec, SD = 530 msec), and contextual cues (M = 1386 msec, SD = 476 msec) led to faster RTs than irrelevant cues (M = 1437 msec, SD = 504 msec). The Concreteness × Cue interaction effect was significant, p = .001. Simple slope analyses showed that a contextual cue significantly reduced the RTs for abstract probes, β = −104.18 (SE = 23.18), p < .001, but not for concrete probes, β = −24.53 (SE = 22.54), p = .285. Additional covariate analyses showed that the reported inferential pattern the Concreteness × Cue inter- action effects was robust (all ps ≤ .006) against potential confounds by the association- and similarity-based probe–target relation as well as the cues’ imageability and context availability ratings and context strength (see Table 3B, C, D, E, F, respectively). As for the post hoc ERP analysis, the main effect of cue was not signifi- cant in the context strength covariate analysis, p = .057, whereas other main effects remained significant through- out the covariate analyses, all ps ≤ .005. To explore the relationship between behavioral and ERP data, we included the single-trial ERP amplitude averaged across the time-electrode samples comprised in the clus- ter detected in ERP Data Analysis 3 as an additional predic- tor into the single-trial RT LME analysis reported above. The inferential pattern is shown in Table 3G. The main effects of Cue and Concreteness and their interaction remained significant, all ps ≤ .005, whereas neither the main effect of ERP amplitude nor any of its interactions were significant, all ps ≥ .175. Including the ERP amplitude did not explain any additional variance (Bayesian informa- tion criterion = 52,966) compared with the original model (Bayesian information criterion = 52,936), χ2 = 2.96, p = .565. Bechtold, Bellebaum, and Ghio 251 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / / 3 5 2 2 4 1 2 0 6 5 8 7 8 / j o c n _ a _ 0 1 9 4 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Figure 6. Mean RTs in the four experimental conditions; error bars depict the 90% confidence interval. Table 3. Inferential Statistics of the Single-trial RT LME Analyses Predictor (A) General analysis Cue Concreteness Concreteness × Cue β −64.35 −272.05 79.65 (B) Probe–target similarity covariate analysis Probe–target similarity Cue Concreteness Concreteness × Cue −129.20 −64.22 −214.26 72.59 (C) Probe–target association covariate analysis Probe–target association Cue Concreteness Concreteness × Cue (D) Cue imageability covariate analysis Cue imageability Cue Concreteness Concreteness × Cue −117.15 −63.13 −232.36 70.88 −24.78 −63.25 −244.78 79.33 SE df t p 19.33 35.10 24.40 13.07 18.58 30.63 24.32 13.39 18.38 30.93 24.30 9.65 19.18 36.93 24.36 17.03 107.76 3238.47 196.25 17.57 79.21 3241.48 197.04 17.62 87.36 3239.84 3133.52 16.93 127.62 3236.09 −3.33 −7.75 3.26 −9.89 −3.46 −7.00 2.99 −8.75 −3.44 −7.51 2.92 −2.57 −3.30 −6.63 3.26 .004 < .001 .001 < .001 .003 < .001 .003 < .001 .003 < .001 .004 .010 .004 < .001 .001 252 Journal of Cognitive Neuroscience Volume 35, Number 2 l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . e d u / j / o c n a r t i c e - p d l f / / / / 3 5 2 2 4 1 2 0 6 5 8 7 8 / j o c n _ a _ 0 1 9 4 2 p d . f b y g u e s t t o n 0 7 S e p e m b e r 2 0 2 3 Table 3. (continued ) Predictor (E) Cue context availability covariate analysis β SE df t p Cue context availability Cue Concreteness Concreteness × Cue (F) Context strength covariate analysis Context strength Cue Concreteness Concreteness × Cue (G) ERP predictor Cue Concreteness ERP Concreteness × Cue Cue × ERP Concreteness × ERP Concreteness × Cue × ERP −10.68 −63.91 −264.30 79.26 34.51 −128.93 −269.86 68.97 −62.43 −275.52 1.33 73.53 10.10 −6.04 34.38 8.42 19.14 35.97 24.38 33.59 67.56 35.40 25.20 19.12 35.03 6.58 24.74 12.87 12.86 25.34 3257.64 17.01 114.88 3236.15 2915.32 1482.61 109.85 3302.51 17.12 108.56 3249.33 3165.70 2697.03 2361.84 3230.65 −1.27 −3.34 −7.35 3.25 1.03 −1.91 −7.62 2.73 −3.26 −7.87 0.20 2.97 0.79 −0.47 1.36 .205 .004 < .001 .001 .304 .057 < .001 .006 .005 < .001 .839 .003 .433 .639 .175 DISCUSSION In this electrophysiological study, we investigated the interplay of concreteness and context effects on semantic word processing assumed by theoretical approaches on contextual semantic processing (Hoffman et al., 2018; Lambon Ralph et al., 2017; Holcomb et al., 1999). We explored its spatiotemporal dynamics by means of non- parametric cluster-based permutation analyses of ERP data acquired in a well-validated cueing paradigm with an SJT (see Bechtold et al., 2021; Hoffman et al., 2010) to complement insights from previous functional neuro- imaging data (Hoffman et al., 2015). Electrophysiologi- cally, we found a concreteness effect with higher negative-going amplitudes for concrete than abstract words covering the N400 and N700/LPC time range, as expected. Further in line with our hypotheses, contextual compared with irrelevant cues reduced negative-going amplitudes also in the N400 and N700/LPC time range. Crucially, we also found an interaction of concreteness and cue, with contextual cues reducing negative-going amplitudes in response to concrete words, erasing the (concrete > abstract) concreteness effect in the N400
time range. Unexpectedly, however, no cluster showed
a significantly stronger modulation of abstract than

concrete probe processing. Analyses on the behavioral
level revealed concreteness and context effects, with fas-
ter RTs for concrete (vs. abstract) probes and contextual
(vs. irrelevant) cues, in line with our hypotheses. We also
replicated the finding of an interaction of concreteness
and context, with stronger contextual facilitation for
abstract (than concrete) probes, namely, a reduced con-
creteness effect in the contextual versus irrelevant cue
condition (see Bechtold et al., 2021; Hoffman et al.,
2015). Unexpectedly, including the ERP amplitudes
obtained from the cluster corresponding to the inter-
action effect as predictor did not explain additional
variance in the RT data.

Concreteness Effects

The expected pronounced electrophysiological concrete-
ness effect in the N400 and N700/LPC time range is in
line with previous findings showing higher negative
amplitudes for concrete than abstract words in both time
windows (Bechtold, Ghio, & Bellebaum, 2018; Barber
et al., 2013; Gullick et al., 2013; West & Holcomb, 2000;
Holcomb et al., 1999; Kounios & Holcomb, 1994). In
these studies, the N400 concreteness effect has been

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interpreted to reflect a stronger involvement of semantic
activation or integration processes driven by the relatively
richer (multimodal sensorimotor) information for con-
crete than abstract words. The visual inspection of the
extent of the detected cluster does not suggest a laterali-
zation of the observed N400 (nor N700) concreteness
effect, which would have supported the (context-
extended) dual coding theory (Welcome, Paivio, McRae,
& Joanisse, 2011; Kounios & Holcomb, 1994; Paivio,
1986). Furthermore, in previous studies, which focused
on single-word processing, the N400 concreteness effect
was most pronounced over frontal areas (Barber et al.,
2013; Lee & Federmeier, 2008; Swaab et al., 2002; West
& Holcomb, 2000), whereas for our cued probes, the
cluster covering the N400 time window was centropar-
ietally pronounced. Because the N400 as marker for
semantic integration often shows a rather bilateral parietal
topography (Kutas & Federmeier, 2011; Lau et al., 2008),
the topographical discrepancy between the concreteness
effect of the present study and previous single-word pro-
cessing studies might be due to the high task relevance of
integrating contextual information with the probe’s repre-
sentational content in the cued SJT.

Taken together with visual inspection of the ERP wave-
forms (see Figure 2), the effect corresponding to a fron-
tally pronounced cluster in the N700/LPC time range can
be interpreted as an N700 concreteness effect, which has
previously been suggested to reflect strategic mental
imagery processes (Bechtold, Ghio, & Bellebaum, 2018;
Malhi & Buchanan, 2018; Gullick et al., 2013). The N700
effect can also be interpreted in the sense of more general
controlled memory retrieval processes (i.e., not restricted
to visual information; Adorni & Proverbio, 2012).

To sum up, the electrophysiological concreteness
effects possibly reflect a stronger involvement of multi-
modal integration (in the N400; Barber et al., 2013) and
mental imagery processes (in the N700; Bechtold, Ghio,
& Bellebaum, 2018; Gullick et al., 2013) of the richer rep-
resentational content of concrete than abstract words. In
line with this interpretation, concrete probes received
higher context availability, imageability, and concreteness
ratings, all of which are indicators of semantic richness
(Muraki, Sidhu, & Pexman, 2019; Hoffman, 2016). Taken
together with the behavioral processing advantage for
concrete over abstract words, our results suggest that
the assumed richer representational content facilitated
their processing (Hoffman, 2016; Grieder et al., 2012).

Context Effects

The significant main effect of context in our findings
corresponded to a spatially broadly distributed cluster
covering the N400 and N700/LPC time range, in which
contextual versus irrelevant cues reduced relatively nega-
tive and enhanced relatively positive ERP deflections.
Reduced N400 amplitudes in priming paradigms have
been interpreted to reflect the facilitation of semantic

processing through preactivation of semantic informa-
tion (Brouwer, Fitz, & Hoeks, 2012; Lau et al., 2008;
Pulvermuller, 1999) or through facilitated prediction of
upcoming words (Lau, Holcomb, & Kuperberg, 2013;
Lau et al., 2008). After visual inspection of the grand aver-
ages in Figure 2, we interpret the later context effect in
terms of an enhanced LPC amplitude after semantic prim-
ing, which has been frequently reported in the literature
(Meade & Coch, 2017; Bakker, Takashima, van Hell,
Janzen, & McQueen, 2015; Yao & Wang, 2014; Grieder
et al., 2012; Bouaffre & Faita-Ainseba, 2007; Hill et al.,
2002). The LPC priming effect has been interpreted to
reflect post-lexical integration and memory retrieval in
the service of strategical prime target-matching processes
(Brouwer et al., 2012). Our electrophysiological context
effects went along with reduced RTs in the SJT, supporting
the interpretation that the reduced N400 and enhanced
LPC amplitudes reflect mechanisms in favor of semantic
processing. Based on our stimulus design, in which the
probes were not syntactically embedded in the cue sen-
tences, we assume to have minimized any lexicosyntactic
influence on the processing of the probe, thereby maxi-
mizing the influence of semantic priming effects.

An important limitation of the interpretation of the late
electrophysiological concreteness and context effects is
the temporal overlap of the N700/LPC components in
the late time window. Even though based on the reviewed
literature and visual inspection of the grand averages we
proposed an interpretation in the sense of an N700 con-
creteness effect and an LPC context effect, the two ERP
components with opposing polarity might have affected
each other. Future research could apply principal compo-
nent analyses based on high-density EEG (Pourtois,
Delplanque, Michel, & Vuilleumier, 2008) and time–
frequency information (Bernat, Nelson, Holroyd, Gehring,
& Patrick, 2008) to disentangle the underlying compo-
nents and thus cognitive processes in the late ERP time
window.

Interaction of Concreteness and Context

The core finding of this study is the interaction of con-
creteness and context, which, however, became evident
in a descriptively slightly left-lateralized N400 cluster with
a significantly stronger contextual modulation for concrete
than abstract probes and, unexpectedly, no cluster with
the opposite effect. Specifically, post hoc comparisons of
the mean amplitude in the detected cluster showed that
contextual cues reduced the N400 in response to concrete
words in a way that cancelled out the (concrete > abstract)
concreteness effect, which has been reported previously
(Holcomb et al., 1999). One explanation for this interac-
tion pattern might be that the N400 reflects a modulation
of anterior temporal semantic integration processes
(Matsumoto, Iidaka, Haneda, Okada, & Sadato, 2005;
Rossell, Price, & Nobre, 2003) rather than context-driven
inferior frontal semantic control processes ( Van Petten

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& Luka, 2006). Context might have especially preactivated
the (richer) representational content (Lau et al., 2008) of
concrete words, thereby leading to the N400 interaction pat-
tern. Picking up the example used before, we assume that
much of the representational content of the “butterfly”
had already been retrieved and integrated when preceded
by in a “meadow full of insects on a sunny day.”

In the grand averages (see Figure 2; e.g., electrodes C3
and Cz), it seems like the amplitude reduction by the con-
textual versus irrelevant cues occurred later for abstract
compared with concrete words, yielding the interaction
time window of reduced amplitudes for concrete but not
abstract words. Explorative cluster analyses (one-sample
t tests of the contextual–irrelevant amplitude difference
for abstract and concrete words against zero, investigating
the context effect separately for concrete and abstract
words) showed that the context effect for abstract words
involved fewer frontal electrodes, started later and ended
earlier (580–670 msec) than for concrete words (440–
750 msec). Our findings regarding the interaction effect
thus directly oppose findings by Wirth et al. (2008), who
found a stronger contextual N400 reduction for abstract
than concrete words in single-word priming (abstract
words: 444–568 msec and additionally 492–568 msec, con-
crete words: 512–524 msec). In their reasoning, Wirth
et al. pointed out the possibility that attention directed
to semantics in active tasks (referring to Holcomb et al.,
1999) might have led to the discrepant findings based in
their passive task, which might have also affected the
onset latency. Please note that the applied analyses only
allow an estimate of the true onset of effects (Sassenhagen
& Draschkow, 2019). In how far such attentional mecha-
nisms differentially modulate the strength as well as the
latency of N400 context effects for concrete and abstract
words has thus to be investigated in future research.

It was unexpected that no cluster showed a significantly
stronger modulation of abstract than concrete word pro-
cessing, which would have been in line with empirical
findings of a stronger contextual modulation of behavioral
responses to abstract than concrete words (Bechtold et al.,
2021; Hoffman, 2016; Hoffman et al., 2015; Schwanenflugel
& Shoben, 1983) linked to semantic control processes
involving the inferior frontal cortex (Hoffman et al., 2015;
Hoffman et al., 2010). Wirth et al. (2008) found such a sig-
nificantly stronger contextual modulation for abstract than
concrete words not only in the N400 time range as
described above but also in the early N1–P1 complex
(116–140 msec). They traced this early effect back to activity
in the left inferior pFC; thus, their results might hint at an
early involvement of semantic control mechanisms. In our
study, an early (190–230 msec) bilateral centroparietal clus-
ter showed the expected stronger modulation for abstract
words, which, however, failed to reach significance ( p =
.069). Therefore, we can neither support nor rule out the
possibility of early semantic control effects in our paradigm,
especially when acknowledging the risk of false negatives
in cluster-based permutation analyses (Sassenhagen &

Draschkow, 2019). From this pattern of results, we would
like to hypothesize that context might lead to semantic con-
trol effects in early processing stages (e.g., Wirth et al.,
2008) as well as task demands-specific effects on semantic
retrieval and integration in later stages, which has to be
tested in future research.

On the behavioral level, the interaction of concrete-
ness and context was dissociated from the ERP pattern
and showed a stronger processing facilitation by contex-
tual information for abstract words. The behavioral pat-
tern was in line with previous studies and has been
interpreted to reflect the higher semantic diversity and
thus more context-dependent processing of abstract
words (Bechtold et al., 2021; Hoffman et al., 2015;
Schwanenflugel & Shoben, 1983). Notably, we found
the same interaction pattern in our smaller sample in
this study (n = 19) as in two previous behavioral exper-
iments with larger sample sizes (n = 55 and n = 83; see
Bechtold et al., 2021), and in both studies, this behavioral
effect persisted in additional covariate analyses, underlin-
ing its robustness. The finding that the amplitudes
extracted from the detected N400 interaction cluster
did not explain a significant amount of variance in SJT
RTs further substantiates the assumption of a dissocia-
tion of behavioral from electrophysiological modulations.
We can, however, not rule out that our design might
have been too underpowered to detect a potential effect
of the ERP amplitude predictor in interaction with the
other fixed effect factors. For concreteness effects, a dis-
sociation of RTs and N400 amplitudes has previously
been highly dependent on the task, timing, and stimulus
material (Barber et al., 2013; Grieder et al., 2012) so that
we cannot assume our findings to generalize across other
paradigms.

Conclusion

Our findings support separate as well as interacting effects
of representational content (i.e., concreteness) and lin-
guistic context as postulated, for example, in the con-
trolled semantic cognition framework, with a dissociation
of electrophysiological and behavioral results. By adopting
the well-validated SJT cueing paradigm for this electro-
physiological investigation, our findings complement pre-
vious insights from neuropsychology (Hoffman et al.,
2010) and neuroimaging (Hoffman et al., 2015). Crucially,
the temporally highly resolved ERP measures allowed us to
disentangle effects of representational content and con-
text on processing stages of (earlier) automatic and (later)
controlled semantic retrieval and integration, reflected in
the N400 and N700/LPC, respectively.

Reprint requests should be sent to Laura Bechtold, Department
of Biological Psychology, Institute for Experimental Psychology,
Heinrich Heine University Düsseldorf, Building 23.03, Uni-
versitätsstraße 1, Düsseldorf 40225 Germany, or via e-mail:
Laura.Bechtold@hhu.de.

Bechtold, Bellebaum, and Ghio

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Data Availability Statement

Stimuli, data, ratings, and code and results are available
here: https://osf.io/nvufe/.

Author Contributions

Laura Bechtold: Conceptualization; Data curation; Formal
analysis; Investigation; Methodology; Visualization;
Writing—Original draft. Christian Bellebaum: Conceptual-
ization; Project administration; Resources; Validation;
Writing—Review & editing. Marta Ghio: Conceptualiza-
tion; Methodology; Validation; Writing—Review & editing.

Diversity in Citation Practices

Retrospective analysis of the citations in every article pub-
lished in this journal from 2010 to 2021 reveals a persistent
pattern of gender imbalance: Although the proportions of
authorship teams (categorized by estimated gender
identification of first author/last author) publishing in
the Journal of Cognitive Neuroscience ( JoCN ) during this
period were M(an)/M = .407, W(oman)/M = .32, M/ W =
.115, and W/ W = .159, the comparable proportions for the
articles that these authorship teams cited were M/M =
.549, W/M = .257, M/ W = .109, and W/ W = .085 (Postle
and Fulvio, JoCN, 34:1, pp. 1–3). Consequently, JoCN
encourages all authors to consider gender balance explic-
itly when selecting which articles to cite and gives them
the opportunity to report their article’s gender citation
balance. The authors of this article report its proportions
of citations by gender category to be as follows: M/M =
.448; W/M = .259; M/ W = .121; W/ W = .172.

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Journal of Cognitive Neuroscience

Volume 35, Number 2

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