Dropping Beans or Spilling Secrets: How Idiomatic
Context Bias Affects Prediction
Manon Hendriks*, Wendy van Ginkel*, Ton Dijkstra, and Vitória Piai
Abstracto
■ Idioms can have both a literal interpretation and a figurative
interpretación (p.ej., to “kick the bucket”). Which interpretation
should be activated can be disambiguated by a preceding context
(p.ej., “The old man was sick. He kicked the bucket.”). We investi-
gated whether the idiomatic and literal uses of idioms have differ-
ent predictive properties when the idiom has been biased toward
a literal or figurative sentence interpretation. EEG was recorded as
participants performed a lexical decision task on idiom-final words
in biased idioms and literal (compositivo) oraciones. Targets in
idioms were identified faster in both figuratively and literally used
idioms than in compositional sentences. Time–frequency analysis
of a prestimulus interval revealed relatively more alpha–beta
power decreases in literally than figuratively used idiomatic
sequences and compositional sentences. We argue that lexico-
semantic retrieval plays a larger role in literally than figuratively
biased idioms, as retrieval of the word meaning is less relevant
in the latter and the word form has to be matched to a template.
The results are interpreted in terms of context integration and
word retrieval and have implications for models of language pro-
cessing and predictive processing in general. ■
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INTRODUCCIÓN
Predicting upcoming events in perception and in social
interactions plays an important role in establishing fluent
and appropriate behavior in the world (see Huettig, 2015,
for a review of studies). This insight motivates the ongoing
research interest in the phenomenon of prediction in the
perception and action domains of cognitive neuroscience
(p.ej., clark, 2013; Sebanz, Knoblich, & Príncipe, 2003).
Extraordinariamente, studies on predictive performance have been
more pessimistic with respect to language processing,
porque, as has been argued, the number of upcoming
words that could be predicted in sentences is staggering
(p.ej., Jackendoff, 2007).
Although recent psycholinguistic studies have collected
evidence in favor of predictive processing, there has been
a fierce debate as to its validity and consequences (p.ej.,
Lau, Holcomb, & Kuperberg, 2013; DeLong, Urbach, &
Kutas, 2005) and even how “prediction” should be
defined. For the purposes of the current article, we define
prediction as the (pre)activation of conceptual or word
information stored in long-term memory before the
appearance of that information in the linguistic input
stream. This will likely involve accumulation of evidence
over time until enough evidence is collected that a certain
conceptual item or word form will follow, when preactiva-
tion occurs.
Evidence for predictive processing in language is some-
times inconclusive. Por ejemplo, in an ERP study
Radboud University, Nimega, Los países bajos
*The first two authors contributed equally to this work.
© 2021 Instituto de Tecnología de Massachusetts
manipulating the expectancy of upcoming words,
DeLong et al. (2005) provided evidence in favor of proba-
bilistic preactivation of word forms in sentence context.
Desafortunadamente, in a large replication study across nine
labortories, Nieuwland et al. (2018) were unable to repli-
cate the prediction effect, weakening “the view that lis-
teners routinely pre-activate the phonological forms of
predictable words.” However, yan, Kuperberg, y
Jaeger (2017) argue that a reanalysis of Nieuwland et al.
using a surprisal measure rather than probabilities is in
favor of anticipatory “semantic” predictions. Martín,
Branzi, and Bar (2018) hold that prediction in language
comprensión, measured by ERPs and the N400 ERP
component in particular, might actually be the result of
production processes. Without taking such a strong theo-
retical position, we can at least describe such predictions
as involving retrieval of lexical–semantic information
from memory before the start of the upcoming event
( Jafarpour, Piai, lin, & Caballero, 2017; Piai, Roelofs,
Rommers, Dahlslätt, & Maris, 2015).
On the whole, these studies suggest that predictive pro-
cessing in sentence comprehension does indeed occur.
Sin embargo, they also indicate that predictive processing is
not an all-or-none process of a single kind. De hecho, predic-
tion in sentence comprehension could entail the availabil-
ity or preactivation of word form information, significado
información, o ambos, before a predicted word is actually
presented in the sentence. Además, prediction might
not always be equally strong; it might be strongest in case
there is only one, highly expected sentence continuation.
In many other cases, information activated on the basis of
Revista de neurociencia cognitiva 34:2, páginas. 209–223
https://doi.org/10.1162/jocn_a_01798
the preceding sentence context might arrive too late or be
too weak to directly affect activation and retrieval of the
upcoming word. We would refer to the later combination
of information sources of sentence and target word as inte-
gration rather than prediction.
This analysis further suggests that finding evidence for
predictive processing may also be dependent on the
experimental paradigm and stimulus materials used. En
este estudio, we argue that comparing the processing of
idioms versus literal sentences is an optimally suited alter-
native for investigating this issue. Idioms consist of rela-
tively fixed and therefore quite predictable sequences of
words that can be placed in rather natural discourse con-
textos. Considerar, por ejemplo, the following sentence pair:
“The farmer was old. He kicked the bucket” and “The
farmer was angry. He kicked the bucket.” Readers may
predict the word “bucket” in either sentence pair rather
than just integrate the word after it is recognized, depend-
ing on probabilistic continuation. Sin embargo, if predictive
processes differ for idiomatic expressions when they are
biased toward a figurative or literal sentence interpreta-
ción, their “fingerprint” in the associated brain waves
might well be different. In this article, we consider this
issue using the EEG, in particular oscillatory activity and
ERPs, and behavioral measures. Before zooming in on
our own study, we will briefly summarize recent key stud-
ies on idiomatic processing.
Rommers, Dijkstra, and Bastiaansen (2013) investigated
to what extent upcoming constituent word meanings are
activated during idiom comprehension. The meanings of
individual words in an idiom are theoretically unnecessary
to comprehend the figurative meaning of the expression.
De hecho, they might even be detrimental to processing, para
instancia, in opaque idioms where the figurative meaning
of the idiom as a whole is not easily or directly derived
from its constituent words. In two experiments combining
behavioral and electrophysiological measures, participar-
pants’ brain activity was measured in response to words
completing an idiomatic or literal sentence. Target words
were either the correct, expected completion of the sen-
tence; a semantic associate of the expected word; o un
unrelated word. In both RT and ERP results, a graded pat-
tern emerged for the literal sentences: The correct word
showed most semantic expectancy and fastest responses,
followed by the semantic associate and then the unrelated
objetivo. Sin embargo, for the idiom sentences, there was no
graded pattern: The correct target was processed fastest
and showed most semantic activation, but there was no
difference between the semantic associate and the unre-
lated target. These findings indicate that, at least in opaque
idioms in a biasing context, literal word processing is
suppressed by the presence of an idiomatic expression.
En otras palabras, there is a lack of semantic expectancy in
idiomatic contexts, and prediction might instead be
oriented toward word form. Because Rommers et al.
measured the participants’ brain responses in time inter-
vals simultaneous with target word presentation, su
effects can be interpreted as evidence that expectations
affect EEG signals and RTs, but it is unwarranted to con-
clude that prediction already accrued before the target
appeared. En otras palabras, on the basis of their results,
no difference can be made between predictive and inte-
grative processes (note this was not the intention of
Rommers et al. anyway). en este estudio, we therefore per-
formed time–frequency analyses on a time window before
the target word appeared. Any effects arising in this inter-
val could be ascribed to prediction rather than integration.
Canal, Pesciarelli, Vespigniani, Molinaro, and Cacciari
(2016) considered how one and the same idiomatic
phrase was interpreted either figuratively or literally in
contexts that bias either meaning. Matched control sen-
tences were created where the idiom-final word was pre-
sented in isolation in a literal sentence. Using EEG, Canal
et al. found no N400 differences between literal and idio-
matic meanings at the last word in an idiomatic expression,
but they did observe amplitude differences in the post-
N400 positivity (PNP). The PNP has been associated with
sentence reanalysis mechanisms and is thought to be
modulated by prediction accuracy and context plausibility
of the upcoming word string (Brothers, Swaab, & Traxler,
2015). Words near the end of idioms embedded in a figu-
rative context were found to elicit a larger PNP than the
same words in the same idioms embedded in a literal con-
texto (es decir., when used literally). Interpreting these findings
in terms of sentence reanalysis, the authors concluded
that idiom processing may be more cognitively demand-
En g, especially when idioms can also be used as literal word
strings. It must be noted that all idioms used by Canal et al.
were highly plausible in their literal sense. This may have
hindered their figurative interpretation, especially if they
were highly transparent (van Ginkel & Dijkstra, 2019).
Sin embargo, the transparency of their idioms (and its interac-
tion with literal plausibility) was not controlled or tested
para. De este modo, it remains unclear whether their results general-
ize to different types of idioms.
Además, Canal et al. examined oscillations, demostración
differences in power specifically in the middle gamma fre-
quency band (50–70 Hz) between idiomatic and literal uses
of idioms. In literal context sentences, an increase in gamma
power was observed that may be reflective of successful
sentence processing or of a match between a predicted
word and the characteristics of the incoming word (Luis
& Bastiaansen, 2015; Monsalve, Pérez, & Molinaro, 2014;
Penolazzi, Angrilli, & Job, 2009). This increase of power in
the gamma band was absent in the idiomatic context com-
pared to the literal context, suggesting that processing of
the idiom string in the idiomatic context occurs at a lower
level than that of the same string used in a literal context.
This finding is also in line with Rommers et al. (2013),
who reported semantic unification to be less engaged in
idiom processing than in literal sentence processing.
Molinaro, Monsalve, and Lizarazu (2016) compared the
processing of words at the end of multiword units (p.ej.,
“on the other hand”) with processing of the same words
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at the end of literal, yet highly semantically constrained,
sequences. Measures based on the prestimulus interval
of the sentence (p.ej., before actual presentation of the
word completing the multiword-unit or literal sentence)
revealed relatively more anterior beta-band power decreases
in the multiword-unit condition (es decir., figurative condition)
than in the literal condition. The authors interpreted this
finding as possibly reflecting engagement of a more
detailed preparation process in the multiword-unit condi-
tion as compared to the compositional condition. En
otras palabras, prediction of the upcoming word is stronger
when the word completes a multiword unit rather than
a literal unit. The authors argue this is because, para el
word in the literal condition, the prediction is more likely
to be made for a “semantic field”: An array of possible tar-
gets is preactivated, although cloze probability is matched.
For the multiword unit, prediction is more deterministic
in that the unit can be retrieved from memory as a whole
with its associated continuation contained in the figurative
unit.
Curiosamente, the beta-band power decrease in the
multiword condition was followed by a larger alpha-
band power increase, which was absent in the composi-
tional condition. Previous research has suggested that
posterior alpha power increases reflect active functional
inhibition of task-irrelevant information (p.ej., Jensen,
Gips, Bergmann, & Bonnefond, 2014). Following this
propuesta, the presence of alpha power increases in the
multiword-unit condition could reflect the inhibition of
processing the target words in this condition as compared
to the compositional condition. The strength of the pre-
diction of the final word in the figurative multiword-unit
condition cancels the need for detailed processing or
encoding of the target word, whereas in the compositional
condición, such detailed encoding is still engaged. En
otras palabras, encoding of the word in the figurative condi-
tion may be shallow in comparison to the compositional
condición.
En resumen, it may be suggested that deriving figurative
and literal interpretations of an idiomatic expression
involves different processes that are sensitive to sentence
contexto. In a biasing context, such differences are reflected
in ERP (p.ej., N400) and oscillatory effects (p.ej., alpha–beta
band power decrease) in the figurative and literal condi-
ciones. en este estudio, we therefore examined both ERPs
and oscillations for evidence of biasing context on ease
and speed of figurative versus literal interpretation of
idioms.
This Study
To assess if prediction has different characteristics for
idioms and literally interpreted sentences in different bias-
ing discourse contexts, we used the following research
paradigma. Participants were presented with Dutch sen-
tence pairs like “The farmer was old. He kicked the
bucket” and “The farmer was angry. He kicked the
bucket.” The second sentence in these pairs consisted of
a phrase that could be interpreted either figuratively or lit-
erally. The first sentence biased either the figurative or lit-
eral interpretation of the second sentence. In half of the
oraciones, the last word of the second sentence was
replaced by a pseudoword. Participants had to decide as
quickly and accurately as possible if the last item of the sec-
ond sentence was an existing word or a pseudoword (lex-
ical decision). In a control condition, we substituted some
words of the idiomatic sequence by other words that were
matched in length and frequency, so the sequence was not
formulaic anymore. This sequence was then preceded by
an appropriate context sentence, as in the sentence pair
“The child was playing. He kicked the marble.” Hypothe-
ses were formulated to test if predictions differ for figura-
tive and literally interpreted sentences in a biasing context.
Our study focused on the time–frequency domain of the
EEG in the various conditions. If predictions take place
during idiomatic processing depending on the biasing
contexto, this might become visible in the brain waves of
the participants in the time window before the target actu-
ally appears. In terms of time–frequency analysis, we will
consider predictions for idiomatic effects in both the
gamma frequency band (50–70 Hz) and the alpha–beta
frequency bands (8–30 Hz). With respect to the gamma
frequency band, we hypothesized a relative power
decrease in the figurative compared to the literal context
condition in line with previous research. Gamma fre-
quency band power is thought to reflect semantic unifica-
tion processes (Rommers et al., 2013). Note that the
conclusions from Rommers et al. are based on measure-
ments from stimulus onset onward, whereas we are
measuring a prestimulus interval. Sin embargo, semantic
unification may still be less involved in the figurative
sentences if prediction of the final word is based on
template matching of the form rather than word seman-
tics: The idiom is already recognized as such and retrieved
from memory before presentation of the final word, entonces
semantic unification processes may already be less
involved in the prestimulus interval (Canal et al., 2016).
Sin embargo, as discussed above, the alpha and beta bands
are equally of interest when considering idiom process-
En g. Beta-band activity has been associated with predic-
tion mechanisms in multiple areas of human cognition
and action, such as the motor and visual domains and,
críticamente, the language domain (p.ej., Weiss & Mueller,
2012; Jenkinson & Marrón, 2011; ángel & Fries, 2010).
En particular, when comparing sentences that bias a final
target word (p.ej., “The farmer milked the …,” target:
cow) with neutral sentences (p.ej., “The child drew a
…”), relative alpha and beta power decreases have
been consistently found in the pretarget interval (Piai,
Rommers, & Caballero, 2018; Rommers, Dickson, norton,
Wlotko, & Federmeier, 2017; Piai, Roelofs, Rommers,
Dahlsätt, et al., 2015; Piai, Roelofs, & Maris, 2014). Follow-
ing an alternative (and not mutually exclusive) interpre-
tation, a relative power decrease in the alpha and beta
Hendriks et al.
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bands reflects the retrieval of complex conceptual repre-
sentaciones (p.ej., during prediction; Hanslmayr, Staresina,
& Bowman, 2016; Hanslmayr, Staudigl, & Fellner, 2012;
for lexical–semantic retrieval in the language domain, ver
Piai et al., 2018; Piai, Roelofs, Rommers, & Maris, 2015).
En resumen, if there is a lack of semantic expectancy in
the figurative context condition, participants may only be
processing the word form, eso es, template matching the
palabra. Prediction will then be limited to the word form, como
semantic information would not be retrieved in the pres-
timulus interval. Por lo tanto, we hypothesized less power
decrease in the alpha–beta frequency band in the figura-
tive than in the literal context condition.
Próximo, we analyzed the N400 effects in the ERPs for evi-
dence that sentence context influences the processing of
the correct target word. Available evidence suggests there
is an inverse relationship between N400 amplitude and
cloze probability (Lau et al., 2013; DeLong et al., 2005).
We define cloze probability, or the degree to which the
upcoming item is expected in offline questionnaires, as a
form of word predictability: Words that are highly
expected may show predictive processing before occur-
ring in the input stream, when preactivation of the word
may (parcialmente) occur before it appears. In our study, nosotros
therefore expect smaller N400 effects to arise for correct
targets in the figurative and literal conditions than in the
control condition, because cloze probability is high in
the first two conditions but lower in the control condition.
In line with previous literature (reviewed in Kutas, Van
Petten, & Kluender, 2006), we expect to find a larger
N400 amplitude for pseudoword than for word targets in
all context conditions. Note that the correct word should
inherently be more expected than any pseudoword.
Finalmente, as a check on the sensitivity of the paradigm we
aplicado, we tested the behavioral data for the presence of
basic effects of condition and lexical status. Primero, nosotros
expect shorter RTs to target words in the figurative and lit-
eral context conditions relative to the control sentence,
because in both conditions, the word continuation should
be strongly expected on the basis of a high cloze probabil-
idad. A diferencia de, no RT differences were expected for cor-
rect target words in the figurative and literal context
condiciones, precisely because target words in the two con-
ditions are high in cloze probability.
behavioral analyses because of poor performance in the
tarea, therefore reducing the sample size to 22 Participantes.
Materials and Design
Experimental materials consisted of 55 Dutch idioms that
could be used in both a figurative sentence and a literally
biasing sentence. Idiom selection was based on a
database of Dutch idioms developed by the Idiomatic
Second Language Acquisition Group of Radboud Univer-
sity Nijmegen (Hubers, van Ginkel, Cucchiarini, Strik, &
Dijkstra, 2018). Idioms were selected for pretesting if
ellos (1) contained no Dutch–English cognates; (2) estafa-
sisted of at least three words; (3) ended in a noun, adjec-
tivo, or preposition when put into a sentence; y (4)
could easily be interpreted both figuratively and literally.
This left us with a set of 203 idioms. These idioms were
provided with figurative and literal biasing context sen-
tences and extensively pretested (see Appendix A). Rating
studies led to the final selection of 55 idioms that were
highly familiar and frequent, where the target sentence
logically followed the context sentence and where both
sentences had a natural feel. Además, the control
sentences were matched to the literal target sentences
in terms of link and naturalness ratings (for link: pag =
.511, for naturalness: pag = .292; ver tabla 1 for values).
Participants performed a Dutch lexical decision task
(LDT) on the last word of each target sentence/idiom,
which required them to indicate as fast as possible
whether this word was an existing Dutch word or a pseu-
doword. Each of the 55 idioms was presented twice to
cada participante: in a literally biasing context and in a fig-
uratively biasing context. Además, matched control
sentences were created that were fully literal. Presentation
of targets was counterbalanced across participants; para
ejemplo, if a participant saw the existing target word in
the figurative context condition, they would see a pseudo-
word in the literal context condition. In total, the figurative
and literal context sentences combined with the matched
control sentences made for an even number of 166 ensayos
per participant.
MÉTODOS
Participantes
Twenty-four students from Radboud University Nijmegen
and the HAN University of Applied Sciences (edad media =
23.25 años, 18 women) gave informed consent and
received course credit or monetary reward for their partic-
ipation in the EEG experiment. All were right-handed,
native speakers of Dutch with normal or corrected-to-
normal vision and no history of neurological or language
disorders. Two participants were excluded from
Mesa 1. Significar (and Standard Deviation) of Cloze Probability
(0–1) and Link and Naturalness Ratings on a Scale from 1 (Very
Bajo) a 7 (Very High) for Figuratively Used Idioms (idiom-FIG),
Literally Used Idioms (idiom-LIT), and Literal Compositional
Sentences (lit-CON)
Context
Cloze Probability
Link
Naturalness
idiom-FIG
0.83 (0.20)
5.67 (0.56)
4.80 (0.68)
idiom-LIT
0.74 (0.30)
4.92 (0.64)
3.90 (0.83)
lit-CON
0.26 (0.32)
5.00 (0.78)
4.06 (0.87)
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In half of the trials (83 ensayos), the target word was
replaced by a nonexisting word. This pseudoword could
be either similar or dissimilar to the original existing
palabra. The pseudowords did not exist in the English or
Dutch language but were created by substituting roughly
a third of the letters of the existing word for similar
pseudowords and two thirds of the letters in dissimilar
pseudowords.
En el 166 experimental trials, Había 25 idiomatic
context sentences with existing words as targets, 25 literal
context sentences with words as targets, 33 control sen-
tences with existing words as targets, 30 idiomatic context
sentences with pseudowords as targets (half similar, half
dissimilar), 30 literal context sentences with pseudowords
as targets (half similar, half dissimilar), y 23 control
sentences with pseudowords as targets (half similar, half
dissimilar). In total, 50% of the items in each experimental
list were words and 50% were pseudowords. To illustrate
the conditions in the experiment, Mesa 2 provides the
example of the Dutch idiom op je tenen lopen (Inglés
traducción: “to walk on your toes”), which means “wanting
to achieve more than you can handle.”
Procedimiento
All participants were tested individually in a soundproof,
electrically shielded room. The experiment was pro-
grammed in Psychopy (Peirce et al., 2011) and presented
on a computer screen. RTs were recorded via a dedicated
button box developed by the Donders Centre for Cogni-
ción (BitsiBox).
Participants received Dutch written instructions before
giving their informed consent. Participants were pre-
sented with printed sentences in rapid serial visual presen-
tation, preceded by context sentences. Their task was to
decide as fast as possible whether or not the last word of
the target sentence (presented in yellow on a black back-
ground) was an existing Dutch word by pressing one of
two designated buttons on the button box with their left
mano (see Piai et al., 2015). Half of the participants
Mesa 2. Overview of the Different Manipulations Used in This Experiment with Literal English Translations
Condition
Existing words
Ejemplo
Target Word
idiom-FIG
Wendy heeft het ontzettend druk.
Ze loopt op haar
Wendy is very busy.
She is walking on her
idiom-LIT
Wendy wil graag groter lijken dan ze is.
Ze loopt op haar
Wendy wants to look taller than she is.
She is walking on her
lit-CON
Mia is eigenaresse van een café.
Ze werkt in haar
Mia is the owner of a café.
She is working in her
Similar pseudowords
idiom-FIG
Wendy heeft het ontzettend druk.
Ze loopt op haar
Wendy is very busy.
She is walking on her
idiom-LIT
Wendy wil graag groter lijken dan ze is.
Ze loopt op haar
Wendy wants to look taller than she is.
She is walking on her
lit-CON
Mia is eigenaresse van een café.
Ze werkt in haar
Mia is the owner of a café.
She is working in her
Dissimilar pseudowords
idiom-FIG
Wendy heeft het ontzettend druk.
Ze loopt op haar
Wendy is very busy.
She is walking on her
idiom-LIT
Wendy wil graag groter lijken dan ze is.
Ze loopt op haar
Wendy wants to look taller than she is.
She is walking on her
lit-CON
Mia is eigenaresse van een café.
Ze werkt in haar
Mia is the owner of a café.
She is working in her
tenen.
toes.
tenen.
toes.
kroeg.
bar.
teben.
boes.
teben.
boes.
kroog.
bor.
paven
waas.
paven
waas.
spoog.
tir.
The meaning of the idiom “to walk on your toes” is “wanting to achieve more than you can handle.” Examples are given for figuratively used idioms
(idiom-FIG), literally used idioms (idiom-LIT), and literal compositional sentences (lit-CON).
Hendriks et al.
213
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responded to words by pressing the left button (with their
left middle finger); and the other half, by pressing the
right button (with their left index finger). This was
reversed for the pseudowords.
The experiment started with a practice block of 12
ensayos. Each trial began with a fixation cross (+) lasting
para 750 mseg. Entonces, the context sentence appeared in full
en la pantalla. All words were presented in a white color
on a black background in Arial font (font size: 28.5). Después
participants read the entire sentence, they pressed a ran-
dom button of their choice to continue to the target sen-
tence. The target sentence appeared on the screen word
by word. Each word remained on the screen for 350 mseg,
alternated with a blank screen of 300 msec between each
palabra. The final word was presented in a different color
(yellow), era 1.5 times bigger than the other words (font
tamaño: 42.5), and was indicated with a dot. These were cues
to indicate that the participant should perform the LDT on
this word.
The experiment was divided into three blocks with
breaks between blocks, each consisting of a mix of the
idiom context conditions and control sentences. For a par-
ticular idiom, each context was positioned in a different
block. Participants processed a pseudorandomized list of
items for which they never had to press the same button
more than three times in a row. Además, the order of
blocks was randomized across participants. After each
block, participants were allowed to take a break for as long
as they wanted. The session as a whole, including capping
for EEG, took approximately 120 mín.. Después, participar-
pants were presented with a multiple-choice question-
naire regarding the meaning of the idioms presented in
the LDT to ensure participants were familiar with the
respective idioms.
ERP Data Recording and Preprocessing
The EEG signals were recorded from 64 Ag–AgCl active
electrodes, of which 62 were mounted in a cap (ActiCAP
64Ch; Brain Products), and referenced online to the left
mastoid. Two separate electrodes were placed on the left
and right mastoids. The ground electrode was placed at
the AFz location. Four passive electrodes were placed
above and beneath the left eye, and at each outer canthus,
to measure eye blinks and horizontal eye movements,
respectivamente. The ground electrode for the passive elec-
trodes was placed on the tip of the nose. Electrode imped-
ance was kept below 15 kΩ. Participants were asked to
blink only during the presentation of the context sen-
tence, to keep the number of eye blinks to a minimum
in the time frame of interest.
Before EEG analyses were conducted, the data were rere-
ferenced offline to the average of the left and right mastoids.
The continuous EEG signal was segmented into epochs of
2250 mseg, lasting from 950 msec before the onset of the
target word until 1300 msec after word onset. el lineal
trend was removed from the data per trial. Before
statistical analysis, all trials were excluded where partici-
pants were unfamiliar with the meaning of the idiom
(as assessed through a multiple-choice test). Además,
all trials with incorrect responses on the LDT were
removed from both the EEG and behavioral analyses.
Preprocessing of the data was performed with the
Fieldtrip software package, an open-source MATLAB tool-
box for neuropsysiological data analysis (Oostenveld,
Fries, Maris, & Schoffelen, 2011). Primero, an independent
component analysis was performed to identify and
remove components related to eye blinks and muscle
actividad. Después, bad channels were identified, y
the signal was replaced with the interpolated activity from
the surrounding channels. Finalmente, trial outliers were
removed after visual inspection. Approximately 4% de
the trials were rejected on this basis, and the number of
rejected trials was comparable across conditions (F =
0.06, pag = .94).
RESULTADOS
Time–Frequency Analysis
Time-resolved spectra were computed using a Hanning
taper of length equals to 3 cycles of each frequency being
estimated (2–70 Hz). The taper was advanced in 1-Hz
frequency steps and 10-msec time steps. Power estimates
were averaged across trials for each context condition
(figurative, literal, control) separately for each participant.
We used cluster-based permutation tests (Maris &
Oostenveld, 2007) to assess the differences between con-
ditions in a way that naturally takes care of the multiple
comparisons problem by identifying clusters of significant
differences between conditions in the time, espacio, y fre-
quency dimensions. The statistical tests were performed
for the alpha–beta range (8–30 Hz; Piai et al., 2018; Piai,
Roelofs, Rommers, Dahlslätt, et al., 2015; Piai, Roelofs,
& Maris, 2014; Rommers et al., 2013) and gamma
range (50–70 Hz) separately. All available channels were
entered in the statistical analyses, but given that the
hypotheses were specific to the pretarget stimulus inter-
vale, the time window analyzed was −300 to 0 mseg, o
the blank screen between presentation of the penulti-
mate word and final word of the sentence. Primero, an F test
was performed to compare across the three context con-
ditions (es decir., control, literal, and figurative). If the F test
was significant, showing sensitivity to the experimental
manipulation as a whole, paired-samples t tests were
conducted to compare the three levels of the context con-
dition in a pairwise manner. Monte Carlo p values were
calculated on the basis of 1000 random permutations.
Power in the alpha–beta frequency range was sensitive
to the manipulation of sentence context (F test, Monte
carlo: pag = .02; ver figura 1). Pairwise comparisons
showed less alpha–beta power in the literally used idiom
condición (idiom-LIT) than in the compositional control
( lit-CON ) condición (Monte Carlo p = .01) en el
214
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individual contrasts were not examined. For visualization
of the F test in a 50- to 70-Hz range, ver figura 3. A
exclude the possibility that gamma power was found in
lower gamma frequency ranges, we conducted an F test
en el 30- to 50-Hz range as well. This Monte Carlo F test
did not show a significant effect either ( pag = .11).
ERP Analysis
Cifra 1. Visualization of the F test in the alpha–beta frequency range
(8–30 Hz).
prestimulus interval examined. The comparison between
the figuratively used idiom condition (idiom-FIG) y el
lit-CON condition did not yield a significant effect (Monte
Carlo p = .27), despite vast differences in cloze probability
scores between these conditions. Comparing literally and
figuratively used idioms, we found more alpha–beta
power decreases for the idiom-LIT condition as compared
to the idiom-FIG condition (Monte Carlo p = .04). Para
visualization of the pairwise comparisons, ver figura 2.
When examining gamma power, the Monte Carlo F test
did not show a significant effect ( pag = .13) in the hypoth-
esized frequency range between 50 y 70 Hz. Por lo tanto,
The single-trial epochs were averaged per condition and
partícipe. No baseline correction or filtering was further
aplicado. As the inclusion of a baseline correction is quite
common and the validity of excluding such a correction
may be subject of debate, we ran additional analyses on
the ERPs after baseline correcting the signal using the
segment of −950 to 0 mseg. Appendix Figure A1 presents
the comparison between the ERPs with and without base-
line correction, which were virtually identical to each other.
As expected, the inferential statistics was virtually identical
between the ERPs with and without baseline correction, como
also reported in the Appendix A. The hypotheses regarding
the ERPs were tested using cluster-based permutation tests
(Maris & Oostenveld, 2007). All available channels were
entered in the statistical analyses, but given that the
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Cifra 2. Comparison between time–frequency representations of the power changes for figuratively biased idioms (idiom-FIG), literally biased
idioms (idiom-LIT), and literal control condition (lit-CON), with their associated topographies. The time–frequency representations are shown for
the average over all channels associated with the significant cluster. For the nonsignificant contrast, all channels were used for the average. El
topographies show the distribution of the differences across the scalp and indicate frequencies between 8 y 30 Hz and in a time range from −0.30
a 0 sec before target onset for the idiom-FIG – lit-CON, idiom-LIT – lit-CON, and idiom-LIT – idiom-FIG contrasts.
Hendriks et al.
215
condiciones (both Monte Carlo ps = .002), but there was no
statistically significant difference between the idiom-FIG
and idiom-LIT conditions (Monte Carlo p = 1). En esto
latency range, the difference between the idiom-LIT and
idiom-FIG conditions relative to the lit-CON condition
was most pronounced over centro-posterior channels
(averaged activity over 250–490 msec), como se muestra en el
topographical maps in Figure 4A.
Figure 4B shows the ERPs time-locked to the onset of the
target stimulus for real words and pseudowords, collapsed
over context type. Testing for an ERP effect in the latency
range from 250 a 650 msec poststimulus, the cluster-based
permutation test revealed a significant positive cluster (es decir.,
larger amplitude for real words vs. pseudowords; Monte
Carlo p = .002) and a significant negative cluster (es decir., más grande
amplitude for pseudowords vs. real words; Monte Carlo p =
.040). The positive cluster was most pronounced in the
time window of 250–590 msec; and the negative cluster,
in the time window of 460–650 msec. la diferencia
between the real words and the pseudowords was most
pronounced over central channels, as shown in the topo-
graphical map in Figure 4B, for the activity averaged over
250–590 msec.
Behavioral Results
Behavioral analyses were conducted only on trials with
correct responses to the LDT. If a participant incorrectly
answered the multiple-choice question on the meaning
of an idiom, that idiom’s trials were removed from analysis
for that participant as he or she was unfamiliar with the
idiom’s meaning. In total, 5% of all trials were rejected fol-
lowing these criteria. The number of rejected trials was
comparable across conditions (F = 1.961, pag = .141), con
1083 trials in the idiom-FIG condition, 1074 trials in the
idiom-LIT condition, y 1050 trials in the CON condition.
Two participants were then excluded for having average
RTs more than 2.5 SDs from the mean of all participants.
Three idioms were then excluded from analysis because
they led to a large number of errors (18%–21%: “een held
op sokken zijn,” “iemand blij maken met een dode mus,"
and “de mussen vallen van het dak”). Finalmente, individual
trials above 2.5 SDs of the mean per participant were
Cifra 3. Visualization of the (nonsignificant) F test in the gamma
frequency range (50–70 Hz).
hypotheses were specific to the N400 component, el tiempo
window analyzed was 250–650 msec. Primero, an F test was
performed on the real words to compare across the three
context conditions (es decir., control, literal, and figurative).
Paired-samples t tests were then used to compare the levels
of the context condition pairwise. To examine the lexical
status effect, the ERPs were averaged across the three con-
text conditions for words and pseudowords separately, y
paired-samples t tests were used to compare them.
Post hoc, an ERP analysis was conducted over the time
point of −950 to 0 mseg (presentation of the target word),
to ensure that no relevant ERP differences were caused by
aspects of the sentence (such as determiners, possessives,
etc.) before presentation of the target. The F test indicated
no significant differences across the three conditions in this
período (Monte Carlo p = .574). Although the F test was not
significativo, we ran paired tests as an additional check. None
of these tests came back significant (all Monte Carlo ps >
.613). Por lo tanto, we are confident that no aspects of the
sentences before the target word have confounded the
ERP analyses of the critical target word summarized below.
Figure 4A shows the ERPs time-locked to the onset of the
target words (only existing words) for each context condi-
ción. Testing for an ERP effect in the latency range from 250
a 650 msec poststimulus, the cluster-based permutation
test revealed a significant difference on the real target words
across the three context conditions idiom-FIG, idiom-LIT,
and lit-CON (F test, Monte Carlo p < .001). In the pairwise
tests, a difference was observed between the idiom-LIT and
lit-CON conditions and between the idiom-FIG and lit-CON
Figure 4. ERPs for the literally biased idioms (idiom-LIT), figuratively biased idioms (idiom-FIG), and literal control sentences (lit-CON). On the left,
A depicts the ERPs for the contrasts between experimental conditions. The −650-msec mark corresponds to the onset of the penultimate word of the
sentence. On the right, B depicts the ERP comparison between existing words and pseudowords across all experimental conditions.
216
Journal of Cognitive Neuroscience
Volume 34, Number 2
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Table 3. Mean RTs in Milliseconds and Error Rate per Context
Condition (Standard Deviation between Parentheses)
Words
Pseudowords
Context
RT
Error Rate
RT
Error Rate
idiom-
FIG
536 (161)
.04 (.20)
634 (163)
.05 (.22)
idiom-LIT
549 (155)
.04 (.18)
645 (143)
.05 (.22)
lit-CON
642 (143)
.07 (.26)
692 (136)
.06 (.20)
removed for each participant. The number of rejected tri-
als was comparable between context conditions (F =
0.046, p = .955). In total, 19.02% of the data was removed
before analysis, of which 5.5% was because of the exclu-
sion of the three idioms with higher error rates.
Linear mixed effects model regression analyses were
conducted in Rstudio (lmerTest package in R, Version
3.4.1; R Project for Statistical Computing, Vienna, Austira).
Mean RTs and error rates are summarized in Table 3 for
both existing words and pseudowords. For the behavioral
analysis, we included responses to existing words only as
Table 4. Results for the Releveled Linear Mixed Effects Regression Analysis Subdivided for the Three Levels of the Model for Simple
Effects
Estimate
SE
df
t Value
p Value
Overall context effects
idiom-FIG vs. lit-CON
idiom-LIT vs. lit-CON
idiom-FIG vs. idiom-LIT
Simple effects in the lit-CON context
Cloze probability
Word Frequency
Cloze × Word Frequency
Simple effects in the idiom-FIG context
Cloze probability
Word Frequency
Cloze × Word Frequency
Simple effects in the idiom-LIT context
Cloze probability
Word Frequency
Cloze × Word Frequency
Context × Cloze Probability
(idiom-FIG)lit-CON × Cloze
(idiom-LIT)lit-CON × Cloze
(idiom-FIG)idiom-LIT × Cloze
Context × Word Frequency
(idiom-FIG)lit-CON × Word Frequency
(idiom-LIT)lit-CON × Word Frequency
(idiom-FIG)idiom-LIT × Word Frequency
.07312
.09130
−.01818
−.1441
−.008071
.03484
−.3484
−.02333
−.04362
−.2390
.01583
.03349
.2043
.09495
.1094
.01526
−.02390
.03916
.02453
.02221
.02003
.03911
.02377
.04546
.05652
.02044
.06482
.04201
.01586
.04283
.06828
.05810
.06880
.02903
.02664
.02308
1088
1103
1452
7614
3569
6761
9609
3660
7876
8407
2349
7207
9369
7314
1119
1080
9540
1410
2.981
4.111
−0.908
−3.684
−0.340
0.766
−6.164
−1.142
−0.673
−5.690
0.998
0.782
2.992
1.634
1.590
0.526
−0.897
1.697
.003
.000
.364
.000
.734
.444
.000
.254
.501
.000
.319
.435
.003
.103
.112
.599
.370
.090
Interaction effects for context conditions with cloze probability or word frequency are listed under the respective headers.
Hendriks et al.
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we were interested in how the correct target word was
processed across different context conditions. Further-
more, we were interested in the effect of cloze probability,
and the cloze probability of pseudowords is inherently
zero. Model selection began with a theoretically maximal
model including predictors at the level of the target and
the idiom. At the word level, these were word frequency,
word length, and cloze probability. At the idiom level, sev-
eral predictors were taken into account for the idiom-
bearing sentences (Hubers et al., 2018): usage rates,
subjective frequency measures, imageability, transpar-
ency, and familiarity scores. Insignificant interaction terms
and predictors were removed from the model in an itera-
tive manner, with each model tested against its predeces-
sor in an ANOVA and the most explanatory model being
selected to proceed with. The final model took the log-
transformed RTs as the dependent variable and included
a random slope for participant over trial number to take
into account trial order effects as well as a random slope
for item at the level of the idiom. Fixed effects consisted
of a three-way interaction between Condition (idiom-
FIG, idiom-LIT, lit-CON), Cloze probability (centered),
and Word frequency as well as a fixed effect for Trial order.
All p values reported are given by the lmerTest statistics
package. Comparisons between conditions were exam-
ined by releveling the Context conditions factor to change
the condition on the intercept and to allow for compari-
sons within the same linear mixed effects regression
model. The results of this analysis are summarized in
Table 4 for all the levels of the model. As there were no
significant interaction effects with Word frequency
between any of the context conditions (all ps > .2), estos
comparisons are not listed in the table for brevity.
Targets in the FIG context condition were identified as
words faster than targets in the lit-CON condition ( pag =
.003), but there was no difference between the idiom-
FIG and idiom-LIT conditions. RTs to targets in the LIT
condition were faster than those in the lit-CON condition
( pag < .001).
In all three context conditions, RTs to targets were faster
if their cloze probability was higher (all ps < .001). There
was an interaction effect of cloze probability and context
condition that revealed that the effect of cloze probability
differed between the idiom-FIG and lit-CON contexts ( p <
.01). There was no difference between the idiom-FIG and
idiom-LIT conditions or between the idiom-LIT and lit-
CON conditions. This effect showed that the facilitation
of RTs because of higher cloze probability in the idiom-
FIG condition was significantly larger than that in the lit-
CON condition.
Error Analysis
Table 3 reports means and standard deviations for the
error analysis. A binary logistic regression run on correct-
ness of judgments did not yield differences in error rates
between any of the experimental conditions overall. There
was also no difference in accuracy for pseudowords and
existing words in the idiom-FIG and idiom-LIT contexts,
but in the lit-CON context, pseudowords were rejected
slightly more reliably than existing words were accepted
as words (estimate = −.7769, SE = .3081, Z = −2.521,
p = .012).
DISCUSSION
In this study, we hypothesized that prediction processes
for idiomatic and literal sentences differ from each other.
Prediction was considered to be the (pre)activation of con-
ceptual or word information stored in long-term memory
before the appearance of that information in the linguistic
input stream. We tested our hypothesis by examining
whether behavioral and electrophysiological manifesta-
tions of prediction would differ between figuratively and
literally biased idioms and literal (compositional)
sentences. We examined the EEG signal in terms of both
ERPs and time–frequency modulations as these two
measures are known to capture different aspects of
brain activity and have been shown to dissociate under cer-
tain circumstances (see Piai, Roelofs, Jensen, Schoffelen,
& Bonnefond, 2014; Laaksonen, Kujala, Hultén, Liljeström,
& Salmelin, 2012; Davidson & Indefrey, 2007).
In the time–frequency domain, we examined idiomatic
effects in both the alpha–beta (8–30 Hz) and gamma (30–
70 Hz) frequency bands in the pretarget interval. With
respect to the alpha–beta band, an analysis of the EEG data
indicated differences in predictive processes between con-
ditions. More power decreases were found in the literally
used idiom condition than in the figuratively used idiom
and control conditions. There was no difference in power
between the figuratively used idioms and the control sen-
tences. Under the hypothesis that power decrease in the
alpha and beta bands is sensitive to prediction, our finding
suggests that prediction played a bigger role during the lit-
erally interpreted idioms in the interval immediately pre-
ceding the target word. In particular, assuming that
alpha–beta power decrease reflects lexical–semantic
retrieval (Piai, Roelofs, Rommers, Dahlslätt, et al., 2015;
Piai, Roelofs, Rommers, & Maris, 2015), there might be less
semantic and/or lexical activation in the interval immedi-
ately preceding the target word during the figurative use
of idiom sentences compared to the literal use of these
sentences, as well as compared with weakly biasing con-
texts. We hypothesize that these differences may arise
because a literally biased idiom is processed in two ways:
both literally and figuratively, as the phrase may still be rec-
ognized as an idiom. Therefore, more predictive processes
are at play here than in figuratively biased idioms or fully
literal sentences. In contrast, fully literal sentences are only
processed one way (literally), and figuratively biased
idioms are primarily being processed in a figurative way
(however, some superficial literal word processing may
still be necessary). In terms of lexical–semantic retrieval,
this means that a literally biased idiom affords more
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information to be retrieved than in the case in the other
two conditions. Future research should examine these
potential differences in underlying processing. As of yet,
a mechanistic theory linking alpha–beta power decreases
to lexical–semantic retrieval is lacking (for developments
in this direction, refer to Piai & Zheng, 2019; Meyer,
2018). We note that the current study used strongly bias-
ing contexts to bias the interpretation of a sentence that
has a potential idiomatic interpretation as either a literal
or figurative sentence. We controlled for subjective fre-
quency of these idioms (how often participants in a large
survey reported encountering the idiom themselves). In
the current study, we did not consider objective frequency
such as frequency counts of the idioms in corpora, as these
counts differ vastly between the different types of corpora
examined (e.g., newspaper corpora, spoken language cor-
pora, corpora of Internet text, provided largely differing
measures). Especially when considering idiomatic
sequences presented without a strongly biasing context,
how often the sentence is used figuratively and literally
in corpora can potentially be a relevant factor to consider,
along with how often the sequences are used in their
figurative and literal forms in these corpora.
Next, we analyzed gamma band frequency effects
between 50–70 and 30–50 Hz. On the basis of previous
research (e.g., Canal et al., 2016; Rommers et al., 2013),
we hypothesized a power decrease in the figuratively com-
pared to the literally used idioms, reflecting increased
semantic unification in literal versus figurative language.
Whereas Rommers et al. reported this pattern for literal
versus figurative sentences in a time window after the
onset of a stimulus, Canal et al. showed similar effects
for literally used idioms versus figuratively used idioms
in a prestimulus interval, suggesting that semantic unifica-
tion is less involved in figuratively used idioms before pre-
sentation of the idiom-final word. However, in our study,
we found no effects in the gamma frequency bands (50–70
and 30–50 Hz). The extent to which gamma band effects
are informative about processing of idiomatic expressions
should be examined in future studies.
With respect to the EEG, we also analyzed if sentence
context influences the processing of the correct target
word in terms of the N400. Earlier studies suggest there
is an inverse relationship between N400 amplitude and
cloze probability (Lau et al., 2013; DeLong et al., 2005).
Because cloze probability in our study was high in both
the figurative and literal conditions and was lower in the
control condition (see above), we expected smaller
N400 effects for correct targets in the figurative and literal
conditions than in the control condition. Indeed, we
observed amplitude differences for these comparisons.
We further hypothesized that an N400 difference would
arise between the figurative and literal conditions if
encoding of the word in the figurative condition would
be relatively shallow in comparison to the literal condition
(in line with Molinaro et al., 2016). However, there was no
significant ERP difference between the two conditions. It
is possible that any effect was too subtle in light of differ-
ences in the cloze probability of individual sentences, but
we cannot be sure as we found no difference. We also
found amplitude differences between pseudowords and
real-word targets in all context conditions, in line with
previous literature (reviewed in Kutas et al., 2006). We
note that our ERP results were measured on the last word
of the sentence, which may induce wrap-up effects as the
sentence is fully processed. However, the time–frequency
effects observed in the alpha–beta band were measured
on a pretarget interval and showed differences between
the conditions. In addition, our N400 results followed
the expected pattern given the N400 literature, indicat-
ing that any wrap-up effects were not confounded in
our study.
Behavioral analyses confirmed the sensitivity of our
paradigm, revealing basic effects of condition and lexical
status. As expected, in all conditions, RTs were shorter for
words than for pseudowords. Finally, we observed shorter
RTs when cloze probability was high. RTs were shorter to
target words in both the figurative and literal idiom context
conditions than in the control condition, likely because
cloze probability was lower in the latter condition. This
facilitation effect is in line with previous studies in which
faster responses were found to formulaic sequences com-
pared to compositional sequences (Siyanova-Chanturia,
Conklin, & Schmitt, 2011; Conklin & Schmitt, 2008). There
were no significant RT differences between target words
in the figurative and literal context conditions.
In summary, the results of the behavioral and ERP anal-
yses attest to the sensitivity of our manipulations, provid-
ing a solid ground for interpreting the time–frequency
effects in the time window just before the target appears.
Measures time-locked to target word onset (RTs and ERPs)
follow the pattern of cloze probability, where targets in
conditions with a higher cloze probability are processed
faster and show more semantic expectancy than targets
in the lower cloze probability control condition. Crucially,
time–frequency results measured before target-word
onset revealed a different pattern, discordant with cloze
probability, suggesting that predictions differ as a function
of the type of sentence context. In particular, we found
evidence for stronger lexical–semantic retrieval in the
interval preceding target onset in the literally biased idiom
condition than in a weakly biasing literal sentence context
or a strongly biasing figurative context. We note that the
trials used in the analysis for the behavioral and ERP results
were not always the same: Because of our stringent selec-
tion criteria on the quality of the trials coupled with a rel-
atively low number of trials per condition, removing trials
based on the criteria of both types of analyses left too few
trials for adequate power.
Interestingly, in our study, the figurative interpretation
of the idiomatic expressions did not differ from the control
condition in terms of alpha–beta oscillations. One explana-
tion for this finding is that the idiom might already be
recognized earlier, necessitating only a very superficial
Hendriks et al.
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processing of the (word form of the) target word. This
interpretation needs to be tested in future research by
considering the temporal aspects of activation in figura-
tively and literally interpreted sentences in more detail.
Experimental manipulations could try to slow down or
speed up the relative availability of information on the
figurative or literal interpretation of the idioms. For
instance, a stronger context manipulation leading to
higher cloze probabilities might affect the temporal
availability of form and meaning information.
Conclusion
We examined predictive and integrative processing of
words contained within idioms that were biased toward
a literal or figurative interpretation by a preceding con-
text sentence. Measures after presentation of the
idiom-final word (ERP and RT), when this word was avail-
able for integration, showed patterns following cloze
probability. Measures before presentation of the idiom-
final word deviated from cloze probability patterns. Dif-
ferences in alpha–beta band power showed evidence
for stronger semantic word retrieval in the literally used
idioms compared to the figuratively used idioms and fully
literal (compositional) sentences. In contrast, no such
differences were found between figuratively biased
idioms and compositional sentences, despite vast differ-
ences in cloze probability. We interpret these findings as
reflecting the type of prediction (preactivation of lexical
information) that is made in figurative versus literal lan-
guage. A word completing a literally used idiom is subject
to semantic retrieval before it is presented, but the same
word completing a figuratively used idiom may only be
subject to a process of “template matching” where the
word form is matched to the expected word form once
the word is encountered.
APPENDIX A
This appendix contains the pretests and piloting of stimu-
lus materials.
Pretest 1: Rating Study
Ratings on subjective frequency (“how often have you
seen or heard this expression,” scale: 1–5, with 1 being
the least often) and familiarity (“how familiar are you
with the meaning of this expression,” scale: 1–5, with 1
being the least familiar) were available for 30 of the
selected idioms. For another 55 of our selected idioms,
30 participants filled out a rating study consisting of
one of two randomly distributed lists. Idioms with ratings
of at least 3.5 of 5 on both frequency and familiarity were
selected for the next phase. This left us with 83 idioms for
the next validation round.
Pretest 2: Sentence Selection and Validation
For each of the 83 idioms, we created a literal sentence and
an idiomatic context sentence. This sentence was followed
by a target sentence containing the idiom (see Table 2 in
the main text). The target sentence was kept identical for
both conditions. Furthermore, each idiom was converted
into a control target sentence. The structure of the literal
target sentence was kept, but some words (matched to the
original words in length and frequency as presented in the
Subtlex-NL database [Keuleers, Brysbaert, & New, 2010])
were replaced to produce an unrelated but plausible, lit-
eral alternative. Furthermore, we created a control context
sentence to precede each control target sentence. Next, all
context and target sentence pairs were pseudorandomly
distributed across three different lists, and participants
viewed one of three lists. Each idiom appeared only once
in each list.
The items were then rated by 66 independent partici-
pants in an online survey. First, we estimated the cloze
probability of each item as an operationalization of pre-
dictability. Participants processed the context and target
sentences with the final word of the target sentence
omitted. They then completed the sentence with the first
word that they could think of, without trying to be original.
In a second task, participants provided two judgments on
a 7-point Likert scale: the semantic link between the con-
text sentence and the target sentence (“How well does the
target sentence relate to the context sentence?”) and the
naturalness of the items (“Would you ever use or encounter
this sentence in your daily life?”). Items were selected only if
they received a rating of at least 3.8 on the semantic link
between context and target sentence, as we wanted the
target sentence to be a logical continuation of the context.
Furthermore, as the control items were based on the literal
condition, we made sure the ratings on the link and natural-
ness did not differ significantly between the literal and
control items in a dependent t test (for link: p = .511, for
naturalness: p = .292). The cloze probability was allowed
to differ between conditions ( p < .01), as this is an inherent
feature of our selected context conditions. For this same
reason, ratings between figurative and literal sentences
were allowed to be different, as long as they surpassed
the minimal threshold rating (for link and naturalness:
p < .01, cloze probability: p = .037). For the means of
the dimensions tested in the pretests, see Table A1.
Table A1. Mean of Cloze Probability (0–1) and Link and
Naturalness Ratings on a Scale from 1 (Very Low) to 7 (Very
High)
Context
Cloze Probability
Link
Naturalness
idiom-FIG
0.83 (0.20)
5.67 (0.56)
4.80 (0.68)
idiom-LIT
0.74 (0.30)
4.92 (0.64)
3.90 (0.83)
lit-CON
0.26 (0.32)
5.00 (0.78)
4.06 (0.87)
Standard deviation is listed in parentheses.
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Pilot
Acknowledgments
The remaining 56 idioms (168 items) were subjected to a
pilot study in which 25 participants took part. On the basis
of the results, we decided to discard the idiom “een klein
hartje hebben” (literal translation: “to have a small heart”)
as too few people gave the correct meaning of this idiom.
Thus, we conducted the actual experiment with 55 idioms.
APPENDIX B: ERP ANALYSIS WITH
BASELINE CORRECTION
Testing for an ERP effect in the latency range from 250 to
650 msec poststimulus, the cluster-based permutation test
revealed a significant difference on the real target words
across the three context conditions idiom-FIG, idiom-LIT,
and lit-CON (F test Monte Carlo p < .010). In the pairwise
tests, a difference was observed between the idiom-LIT and
lit-CON conditions, and between the idiom-FIG and the
lit-CON conditions (both Monte Carlo ps = .002), but no
statistically significant difference between the idiom-FIG
and idiom-LIT conditions (Monte Carlo p = 1).
For real words and pseudowords, collapsed over context
type, testing for an ERP effect in the latency range from 250
to 650 msec poststimulus, the cluster-based permutation
test revealed a significant positive cluster (i.e., larger ampli-
tude for real words vs pseudowords, Monte Carlo p = .002).
This research was funded as part of the research program Free
Competition in the Humanities with Project Number 23000349
NWO ISLA FdL, financed by the Netherlands Organisation for
Scientific Research (NWO) and approved by the Ethical Com-
mittee of the Faculty of Social Sciences at Radboud University
Nijmegen (ECG2012-2711-059). The authors gratefully acknowl-
edge the open access funding provided by the Donders Centre
for Cognition of the Donders Institute for Brain, Cognition and
Behaviour, Radboud University
Reprint requests should be sent to Wendy van Ginkel, Kapittelweg
29, Room B.00.026, 6525 EN Nijmegen, Radboud University,
Nijmegen, The Netherlands, or via e-mail: W.vanGinkel
@donders.ru.nl.
Funding Information
Research funded as part of the research program Free
Competition in the Humanities with project number
23000349, financed by the Netherlands Organisation for
Scientific Research (NWO).
Diversity in Citation Practices
A retrospective analysis of the citations in every article
published in this journal from 2010 to 2020 has revealed
a persistent pattern of gender imbalance: Although the
proportions of authorship teams (categorized by estimated
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Figure A1. Demonstration of the EEG data from the original analysis without baseline correction (A and B) and with baseline corrections applied
(C and D).
Hendriks et al.
221
gender identification of first author/last author) publishing
in the Journal of Cognitive Neuroscience ( JoCN) during
this period were M(an)/M = .408, W(oman)/M = .335,
M/ W = .108, and W/ W = .149, the comparable propor-
tions for the articles that these authorship teams cited
were M/M = .579, W/M = .243, M/ W = .102, and W/ W =
.076 (Fulvio et al., JoCN, 33:1, pp. 3–7). Consequently,
JoCN encourages all authors to consider gender balance
explicitly when selecting which articles to cite and gives
them the opportunity to report their article’s gender cita-
tion balance.
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