(Not so) Great Expectations: Listening to Foreign-Accented Speech Reduces the Brain’s Anticipatory Processes

Abstract

This study examines the effect of foreign-accented speech on the predictive ability of our brain. Listeners actively anticipate upcoming linguistic information in the speech signal so as to facilitate and reduce processing load. However, it is unclear whether or not listeners also do this when they are exposed to speech from non-native speakers. In the present study, we exposed native Dutch listeners to sentences produced by native and non-native speakers while measuring their brain activity using electroencephalography. We found that listeners’ brain activity differed depending on whether they listened to native or non-native speech. However, participants’ overall performance as measured by word recall rate was unaffected. We discussed the results in relation to previous findings as well as the automaticity of anticipation.

Full text

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 1
ORIGINAL RESEARCH
published: 25 August 2020
doi: 10.3389/fpsyg.2020.02143
Edited by:
Cristina Baus,
Pompeu Fabra University, Spain
Reviewed by:
Kimmo Alho,
University of Helsinki, Finland
Janet Van Hell,
Pennsylvania State University (PSU),
United States
*Correspondence:
Niels O. Schiller
n.o.schiller@hum.leidenuniv.nl
†Present address:
Marianne L. S. De Heer Kloots,
Faculty of Science, University of
Amsterdam, Amsterdam, Netherlands
Marieke Meelen,
Theoretical and Applied Linguistics,
University of Cambridge, Cambridge,
United Kingdom
Specialty section:
This article was submitted to
Language Sciences,
a section of the journal
Frontiers in Psychology
Received: 25 April 2020
Accepted: 31 July 2020
Published: 25 August 2020
Citation:
Schiller NO, Boutonnet BP-A,
De Heer Kloots MLS, Meelen M,
Ruijgrok B and Cheng LL-S (2020)
(Not so) Great Expectations: Listening
to Foreign-Accented Speech
Reduces the Brain’s Anticipatory
Processes. Front. Psychol. 11:2143.
doi: 10.3389/fpsyg.2020.02143
(Not so) Great Expectations:
Listening to Foreign-Accented
Speech Reduces the Brain’s
Anticipatory Processes
Niels O. Schiller1,2*, Bastien P.-A. Boutonnet1, Marianne L. S. De Heer Kloots1†,
Marieke Meelen1†, Bobby Ruijgrok1,2 and Lisa L.-S. Cheng1,2
1Leiden University Centre for Linguistics, Leiden University, Leiden, Netherlands, 2Leiden Institute for Brain and Cognition,
Leiden, Netherlands
This study examines the effect of foreign-accented speech on the predictive ability of our
brain. Listeners actively anticipate upcoming linguistic information in the speech signal
so as to facilitate and reduce processing load. However, it is unclear whether or not
listeners also do this when they are exposed to speech from non-native speakers. In the
present study, we exposed native Dutch listeners to sentences produced d non-native
speakers while measuring their brain activity using electroencephalography. We found
that listeners’ brain activity differed depending on whether they listened to native or non-
native speech. However, participants’ overall performance as measured by word recall
rate was unaffected. We discussed the results in relation to previous findings as well as
the automaticity of anticipation.
Keywords: prediction, speech perception, sentence comprehension, foreign-accented speech, Dutch, native vs.
non-native speech processing
INTRODUCTION
Language comprehension involves many tasks such as word recognition including segmentation
(where are the boundaries between words?), lexical access (activating word forms and their
corresponding meanings stored in memory) and putting word meanings together to understand the
message. Our brains are able to fulfill these complex tasks without any problem. How? It has been
suggested that one factor contributing to the process of language comprehension and potentially
facilitating it is prediction. Our brains may pro-actively predict what is to come next in an utterance
and a conversation based on prior knowledge. Some researchers have claimed that the language
comprehension system makes use of the predictive power of the brain by predicting upcoming
information in the speech stream.
In language processing, but also in a wide array of other cognitive domains (Kok et al.,
2012b, 2014;Boutonnet and Lupyan, 2015;Samaha et al., 2016;Vandenbroucke et al., 2016),
comprehenders actively anticipate upcoming information thereby (pre-)activating specific features
of such linguistic information, ranging from basic acoustic features to high-level conceptual-
semantic ones (Federmeier and Kutas, 1999;DeLong et al., 2005;Van Berkum et al., 2005;Obleser
et al., 2007;Schiller et al., 2009;Vinck et al., 2011;McGettigan et al., 2012;Van Berkum, 2013;
Foucart et al., 2014, 2015;DeLong and Kutas, 2016), in order to facilitate and reduce the processing
load (Pickering and Garrod, 2007).
Frontiers in Psychology | www.frontiersin.org 1August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 2
Schiller et al. Listening to Foreign-Accented Speech
For instance, Schiller et al. (2009) present electrophysiological
data from two experiments demonstrating that listeners make
predictions for upcoming words using a speech-error detection
task. Their data strongly suggest that natural speech is processed
semantically even when speech is not task-relevant. Their results
further indicate that listeners attempt to predict upcoming words.
This process of prediction presumably facilitates comprehension
and communication in general. Nevertheless, more recently it
has been argued that pre-activation of the phonological form
of upcoming linguistic information may not be as stable as
suggested by previous research and that prediction may not be a
necessary condition for language comprehension (Ito et al., 2017;
Nieuwland et al., 2018).
However, the bulk of studies available to date has
demonstrated such anticipatory processes in the visual modality
in experiments where words are presented one-by-one on a
computer screen at a relatively slow and regular pace. Two
of the main limitations of such paradigms are that (a) the
auditory modality is not involved in such paradigms, and
(b) speech communication takes place at a much faster rate
(Riding and Vincent, 1980). If anything, visual presentation
rate is likely to enhance and entrench predictive mechanisms.
Investigations in the auditory modality have so far limited
themselves to measuring word anticipation in speech produced
and comprehended by native speakers (Foucart et al., 2015) with
the exception of studies by Lev-Ari (2015) and Romero-Rivas
et al. (2016). However, these studies do not directly investigate
anticipatory mechanisms proper in the brain.
In today’s society, daily interactions with non-native speakers
are becoming more and more frequent, if not the norm. Foreign-
accented speech differs from native speech in at least three ways:
the presence of non-canonical and unstable phonology (Nissen
et al., 2007;Wade et al., 2007;Wester et al., 2007;Hanulíková
et al., 2012) followed by unusual prosodic patterns (Gut, 2012)
and loose semantic word choice [e.g., the so-called “chocolate pie”
vs. “brownie” effect mentioned by Lev-Ari (2015)]. Given that
the reason for the brain to predict upcoming information is to
facilitate subsequent processing, it is not unreasonable to assume
that foreign-accented speech may strongly modulate the nature
and involvement of linguistic predictive mechanisms. However,
would our brain predict more, less, or just as much when listening
to a non-native speaker?
There are at least two ways in which predictive mechanisms
in the comprehension of foreign-accented speech might be
modulated. The first, and perhaps the most intuitive, is that
prediction may increase. Given unreliable and potentially
ambiguous input, generating stronger predictions could help
a comprehender normalize non-native deviance (Goslin et al.,
2012) so as to reduce processing load. The opposite could
also be true. Given that foreign-accented speech is more
variable (and essentially noisier) than speech produced by native
speakers, predictions may fall short of the signal (especially
in the case where not enough knowledge is present about
the specific ways in which an interlocutor deviates from the
native norm) thereby confronting the system with too many
prediction errors to be resolved – and consequently increasing
processing load.
Two of the most relevant studies on foreign-accent processing
available to date point in these two opposing directions. In the
study by Lev-Ari (2015), participants, whose eye-movements
were being recorded, were presented with arrays of five pictorial
items, three of which shared two themes, and two of them
shared only one. On each trial, the participants were asked to
follow the auditory instructions to click on, e.g., “the witch
on a broom,” then on “the man on the magic carpet” and
then on “Santa riding a sleigh,” thereby setting up a semantic
theme of “imaginary creatures.” Instructions came either from
a native or a non-native speaker with a foreign accent. On
the next trial, two items would be left on the screen: a
mermaid and a ferry. The mermaid shares only the main theme
set up by the three items (“imaginary creatures”; i.e., witch,
Santa, etc.) and the ferry only shares the less dominant one
(“means of transportation”; i.e., broom, magic carpet, sleigh).
The participants were then instructed to click on the /fεri/,
which is interpretable as both “fairy” and “ferry.” The results
showed that upon word onset, the participants’ eyes were already
fixating more toward the mermaid and chose it more often
than the (target) picture of the ferry, especially when they were
instructed by a non-native compared to a native speaker and
when they had high as opposed to low working memory load.
This suggests that the participants were strongly relying on
the context (i.e., imaginary creatures) to anticipate upcoming
trials and less on the acoustic speech output of foreign-accented
speakers, and this somewhat tricked them into choosing the
“wrong” target.
In another relevant study, Romero-Rivas et al. (2016)
presented highly constrained high-cloze probability sentences
to two groups of native speakers of Spanish. The sentences
were produced either by a native or non-native speaker,
respectively. Participants’ brain activity was monitored using
electroencephalography (EEG). Each of these sentences ended
either with a highly probable lexical item (“best completion”),
an item semantically related to the best completion or an
unrelated item. Earlier research by Kutas and Hillyard (1980,
1984) has shown that such a manipulation results in what
has become known as the N400 effect. Processing of all
(content) words results in an event-related brain potential
(ERP) component with a negative polarity that peaks around
400 ms after word onset and is called the N400 component. In
their seminal study, Kutas and Hillyard (1980) demonstrated
that semantically anomalous words elicit a more negative
deflection in the ERP signal than semantically appropriate
words occurring in the same position in sentences, i.e., an
N400 effect (He spread the warm bread with butter vs. He
spread the warm bread with socks). The N400 effect has been
associated with lexical and post-lexical processing, e.g., lexical
access and semantic integration of words into context (Kutas
and Van Petten, 1994;Kutas and Federmeier, 2000;Van Petten
and Luka, 2006; for reviews; see also Holcomb, 1993). The
N400 effect is currently considered as indicating the difficulty
with which a word can be integrated in the utterance context
(Leckey and Federmeier, 2019).
By looking at the brain activity following the presentation
of the final word, Romero-Rivas et al. (2016) found that while
Frontiers in Psychology | www.frontiersin.org 2August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 3
Schiller et al. Listening to Foreign-Accented Speech
both groups of participants showed increased N400 activity
when presented with the semantically related but not expected
word, only the group which listened to native speakers showed
a difference between the semantically related and unrelated
conditions (which yielded further increased N400 activity). In
other words, regardless of whether the lexical item was related
to the best completion, it required just as much effort to be
integrated by the brain. From these results, Romero-Rivas et al.
(2016) concluded that anticipation is not affected by foreign
accent but that the activation of semantic relationships may not
be as “wide” when someone is listening to a non-native speaker –
a result consistent with some of the evidence from speech
comprehension in adverse conditions (Goslin et al., 2012;Strauß
et al., 2013). However, it is important to note that this study does
not measure anticipation per se, as Romero-Rivas et al. (2016)
monitored brain activity starting from the onset of the critical
word rather than before the critical word, as is commonly done.
At this stage, i.e., the onset of the critical word, brain activity
might reflect a mix between the pre-activation and integration
of the critical words rather than their pre-activation alone.
Finally, because the accent manipulation is applied between-
participants in their study, it may be the case that some degree
of accommodation to foreign-accented speech has occurred (but
see Witteman et al., 2013 who found rapid adaptation to foreign-
accented speech even in a within-participants design).
To disentangle these two potential explanations (i.e., whether
foreign-accented speech increases or decreases anticipation) and
to resolve some of the potential shortcomings from Romero-
Rivas et al. (2016) study, we designed an experiment that
enables us to measure word anticipation before the presentation
of the critical word and manipulates expectedness as well
as speaker’s accent within-participants. This design has the
advantage of suppressing potential accommodation effects, which
might happen in a blocked design, as well as reflecting a more
ecologically valid environment where a given person might be
communicating with a mix of native and non-native speakers
in a short span of time. To do so, we used a classic paradigm
including the creation of high-cloze probability sentences while
manipulating agreement between the highly expected lexical item
and a preceding article (DeLong et al., 2005;Martin et al., 2013) or
a preceding adjective (Van Berkum et al., 2005;Otten et al., 2007).
More specifically, we adapted the design of a study by Foucart
et al. (2015) which consisted of manipulating the grammatical
gender of the article (het or de, in Dutch) preceding the expected
or unexpected target while masking the critical noun so as to
record brain activity which only pertains to the processing of the
article and word anticipation rather than a mix of the two (see
section “Materials and Methods” for more detail).
We hypothesized that if anticipation processes take place
during speech processing, we should observe modulations of ERP
amplitudes depending on whether or not the article matches the
expected noun, with increased amplitudes for mismatches. Based
on Foucart et al. (2015), we expected to find modulations in
an early ERP time window (∼200–300 ms), i.e., a phonological
mismatch negativity (PMN), and a later N400 effect. The PMN
is a negative-going ERP component shown to be sensitive to
phonological properties of words and taken as an indicator
of early lexical processing, such as the initial (pre-lexical)
phonological processing stage of auditory speech perception
(Connolly and Phillips, 1994;Connolly et al., 1995;Schiller
et al., 2009). The PMN has been reported, for instance, during
the processing of a phonological mismatch in the onset of an
expected and an actually heard word. Temporally, the PMN
precedes the (auditory) N400 and has been shown to be largely
independent of the N400, since it occurred regardless of the
semantic appropriateness of the spoken words (Connolly and
Phillips, 1994;D’Arcy et al., 2004 for an overview). It is usually
identified in the ERP signal as the most negative peak between
150 and 350 ms after stimulus onset.
Furthermore, based on the studies mentioned earlier, we
hypothesized that the particular accent (i.e., non-native vs.
native) with which a trial was produced should interact with the
classic expectedness effect, especially in the earlier time window
which we believe is the most likely one to index the pre-activation
of stimulus-specific features (Foucart et al., 2015). However, we
did not have specific hypotheses regarding the direction of the
interaction since the evidence available to date suggests either
increased reliance on predictions (Lev-Ari, 2015) or a somewhat
more shallow effect of these predictions (Romero-Rivas et al.,
2015, 2016), suggesting a potentially less pronounced effect of
predictions during non-native accent processing.
Finally, we wanted to assess the impact of native vs. foreign
accent on the memory traces generated by word prediction
(Foucart et al., 2015). Therefore, we presented participants with a
list of words and asked them to indicate whether or not they had
heard them in the sentence listening part. The word list consisted
of an equal proportion of old expected and unexpected words
from the listening task as well as new and neutral (not dependent
on a predictive sentential context) ones (see section “Materials”
for details). As previously demonstrated (Foucart et al., 2015),
expected yet unheard, words seem to form stronger memory
traces than unexpected (and unheard) words. We expect that if
interactions between expectedness and accent are present at the
neural level, an impact on word recall may also be detected.
MATERIALS AND METHODS
Participants
Twenty-four native speakers of Dutch (Mage = 23, SE = 0.82)
took part in the experiment. All participants were right-handed
and had normal or corrected-to-normal vision and reported no
neurological nor auditory disorder. Participants were students
at Leiden University. The study was carried out in accordance
with the recommendations of the local ethics committee at the
Faculty of Humanities of Leiden University. All participants
gave their written informed consent before taking part in the
experiment, in accordance with the Declaration of Helsinki.
The data of nine participants were removed due to low signal-
to-noise ratio and/or when ≥25% of the trials had to be
removed because of non-correctible artifacts. One of those
nine participants was also removed due to extremely low
accuracy (<40% correct) on the comprehension questions in
the listening task.
Frontiers in Psychology | www.frontiersin.org 3August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 4
Schiller et al. Listening to Foreign-Accented Speech
Materials
One-hundred-and-twenty highly predictive Dutch sentence
contexts were created such that each would generate a strong
expectation (≥70% cloze probability – see Supplementary
Material) for a particular lexical item. In the present experiment,
this lexical item was always a noun of a specific grammatical
gender in Dutch (e.g., the neuter noun boek ‘book’ preceded
by the neuter definite determiner het ‘theneu,’ or the non-neuter
noun tafel ‘table’ with the non-neuter definite determiner de
‘thenon−neu’). Sixty additional sentences were created as filler
sentences without strong expectations of any lexical item (see
Table 1).
Recordings
All 180 sentence contexts were recorded by a group of four
Dutch native speakers (two males, two females) and four non-
native Dutch speakers (two males, i.e., one native speaker of
German and one native speaker of English [Southern-Irish
variety], and two females, i.e., one native speaker of Macedonian,
the other of Polish). The decision to include a variety of
different accents was so that we would not observe an effect
of a particular accent but rather of accented speech in general.
Native speakers were asked to pronounce the sentences as
naturally as possible and with neutral prosody. Non-native
speakers were asked the same but listened to the native speakers’
recordings before recording their own production in order to
minimize differences in rate of speech and overall prosody. None
of the non-native recordings contained mispronunciations or
obvious errors, and the difference between native and non-native
recordings was that non-native speakers’ phonetic output was at
times non-canonical.
To alleviate potential systematic confounds between the length
of expected vs. unexpected sentences (see section “Acoustic
Processing”), the assignment of the expectedness conditions
was fully randomized for each participant. Each subgroup of
sentences was then further divided with one half coming from
native speakers and the other from non-native speakers, also
randomized for each participant. Thus, each participant was
presented only once with a given sentence context, either with an
expected or unexpected article and either produced with a native
or non-native accent.
It has been shown that strong foreign accents have a different
effect on listeners’ comprehension than slight accents (e.g.,
Witteman et al., 2013;Porretta et al., 2017). To obtain an
objective measure of the accents of the eight speakers, a separate
group of 40 native Dutch participants (Mage = 21.30, SE = 0.65)
took part in a rating study. These participants completed a
Qualtrics survey (Qualtrics, Provo, UT, United States) in which
they rated the sentences produced by each speaker on a slider
scale (from 1 = “no accent” to 7 = “very strong accent”). All
sentences including the fillers were presented auditorily, such
that participants were never presented with the same sentence
context twice. Participants were paid € 5 upon completion of
the task. The native Dutch speakers received a median rating
of 1.07 (M= 1.26, SE = 0.07) and the non-native speakers
received a median rating of 4.32 (M= 4.35, SE = 0.11). These
ratings differed significantly from each other, V= 0, p<0.001,
d= 1.11. Figure 1 illustrates the mean ratings obtained for each
individual speaker.
To test for differences between the eight speakers, pairwise
comparisons using a Wilcoxon signed rank test with Bonferroni
correction were calculated and summarized in Table 2.
Within the native Dutch speaker group, the mean ratings
of one male speaker (i.e., “m2”) differed significantly from
the three other speakers. Within the foreign-accented speaker
group, the mean ratings of all speakers differed significantly from
each other. Compared with the native Dutch speaker group,
the foreign-accented speaker group was less homogeneous.
TABLE 1 | Examples of the sentences (with simple glosses and translation between parentheses for the Control condition) used in the experiment.
Condition Sentence context Article +
deleted item
Sentence
closure
Expected Mijn gelezen boeken staan op de onderste plank in
my read books stand on the lowest shelf in
de kast
the (book)shelf
op mijn kamer.
in my room
Unexpected Mijn gelezen boeken staan op de onderste plank in
my read books stand on the lowest shelf in
het bureau
the desk
op mijn kamer.
in my room
Expected André is de nieuwe coach van
André is the new coach of
het team
the team
op zijn volleybalclub.
in his volleyball club
Unexpected André is de nieuwe coach van
André is the new coach of
de groep
the group
op zijn volleybalclub. in his
volleyball club
Control Lezen is niet saai, zolang je maar geen saaie boeken leest. reading
is not boring, as long you but no boring books read
(Reading is not boring as long as you do not read boring books.)
Lezen
reading
—
Control Mijn zoon woont al drie jaar in het buitenland, maar mijn
my son lives already 3 years in the foreign land, but my
(My son already lives abroad for 3 years, but my
dochter studeert en woont nog thuis.
daughter studies and lives still at home
daughter still studies and lives at home.)
zoon
son
—
Note that for the control sentences, the deletion was not always in a noun phrase or after an article. As explained in the Materials and Methods section (see section
“Acoustic Processing”), this was to disrupt the pattern in the experimental sentences.
Frontiers in Psychology | www.frontiersin.org 4August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 5
Schiller et al. Listening to Foreign-Accented Speech
FIGURE 1 | Error bar chart of the mean ratings per speaker.
TABLE 2 | Means and standard errors of the mean ratings and p-values of the Bonferroni-corrected multiple comparisons between the recorded speakers.
Comparison (p-value)
Speaker Mean SE m2 f1 f2 Irish German Polish Macedonian
m1 1.28 0.08 0.038 1.00 0.696 <0.001 <0.001 <0.001 <0.001
m2 1.39 0.08 – 0.037 0.001 <0.001 <0.001 <0.001 <0.001
f1 1.27 0.08 – – 0.095 <0.001 <0.001 <0.001 <0.001
f2 1.15 0.06 – – – <0.001 <0.001 <0.001 <0.001
Irish 2.39 0.10 – – – – <0.001 <0.001 <0.001
German 5.31 0.15 – – – – – <0.001 0.002
Polish 4.76 0.16 – – – – – – <0.001
Macedonian 5.67 0.15 – – – – – – –
However, since all participants received equally many sentence
contexts from each speaker, any potential advantage from a
particular speaker should be equal in all conditions and for
all participants.
Acoustic Processing
Recordings were processed and edited in Praat (Boersma, 2002).
To make each condition (expected vs. unexpected) comparable
as well as to reduce any bias due to cues such as overall prosody,
coarticulation, and so on, we used the same sentence context
for both versions of each sentence (either the one recorded
for the expected or unexpected condition) up to the word
preceding the article and spliced the rest of the sentence from
the other condition. Conditions were randomly selected with an
equal division between the two conditions. In each of the 120
experimental sentences, the (un)expected noun was completely
muted for 500 ms after the article offset and the sentence closure
followed this break. The average sentence context length was
of 3.05 s (SE = 0.03) and that of the article was of 0.16 s
(SE = 0.001).
To ensure that the purpose of the experiment remained
unclear to the participants, we included silences (between 200 and
300 ms long) in the filler sentences at random positions. Finally,
to justify the presence of silences to the participants, sentences
were band-pass filtered in the range 300–3,000 Hz to make them
sound like they were extracted from a telephone conversation.
Crucially, for the present study (see the sections “Hypothesis
Testing” and “Results”), while sentence-context length and article
length were significantly longer in the non-native recordings than
in the native recordings (b= 0.52, t= 3.861, p<0.0002; b= 0.03,
t= 5.37, p<0.0001, respectively), these effects never interacted
with the article’s expectedness.
PROCEDURES
Listening Task
Participants were seated in a sound-proof testing room and
sat approximately one meter from a computer screen and two
loud speakers placed on either side of the screen. Participants
were told to listen and pay attention to the sentences that were
going to be played to them and that they would be asked
questions about those sentences during and after the listening
phase. Thirty percent of the sentences were randomly followed
Frontiers in Psychology | www.frontiersin.org 5August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 6
Schiller et al. Listening to Foreign-Accented Speech
by a written question on the screen regarding the preceding
spoken sentence and requiring a “yes” or “no” response via
button presses, the sole purpose of which was to ensure that
participants paid attention to the sentences. After a practice of
five sentences, participants were auditorily presented with 180
sentences (60 expected sentences [30 native/30 non-native], 60
unexpected sentences [30 native/30 non-native], and 60 fillers
[30 native/30 non-natives]). Each sentence was preceded by a
black fixation cross on the white screen in front of them for a
duration of 1 s after which it turned red for a period of 500 ms to
announce to the participant that the sentence was about to start,
and then it subsequently turned blue when the sentence started
and remained on screen until the sentence ended. A blank screen
of 200 ms preceded the next trial. When a question followed
the sentence, it appeared on screen following the blank screen
and remained visible until participants responded. Another blank
screen of 200 ms followed participants’ answers before the next
trial started. Participants were invited to take a break every
30 sentences. Their electroencephalogram (EEG) was recorded
throughout the listening task of the experiment which lasted 30–
40 min.
Lexical Recall Task
Upon completion of the listening task, participants were
presented with a series of words presented one-by-one on the
screen. For each word, they were asked to decide whether
or not they had heard it in the listening task. These words
included the 60 expected nouns, the 60 unexpected nouns
(both unheard/muted), and 60 words (heard/presented) from the
filler sentences. Word-presentation order was randomized for
each participant.
DATA COLLECTION, EEG
PRE-PROCESSING AND ANALYSES
The EEG was recorded from 64 Ag/AgCl electrodes placed
on the participants’ scalp according to the extended 10–20
convention (American Electroencephalographic Society, 1994)
at a rate of 1 kHz in reference to the common mode sense
(CMS) and driven right leg (DRL) using a BioSemi (Active
Two) system. Data were filtered offline with a high-pass 0.1 Hz
filter and a low-pass 20 Hz filter and re-referenced to the
common average of all scalp electrodes. Epochs ranging from
−100 to 600 ms relative to article onset were extracted from the
continuous recording. Epochs with activity exceeding ±75 µV
at any electrode site were automatically discarded. The Gratton
and Coles algorithm (Gratton et al., 1983) was used to correct
for vertical and horizontal eye movement artifacts. Baseline
correction was applied in relation to the 100 ms EEG signal of
pre-stimulus activity. After these steps, all remaining epochs were
averaged by condition and for each participant. Pre-processing
steps up to and including eye movement artifact correction
were performed in BrainVision Analyzer (Brain Products GmbH)
and the remaining steps were performed in the MATLAB
environment (v. 2013a – The MathWorks) using a combination
of in-house scripts and routines implemented in EEGLAB (v.
13.2.3) and ERPLAB (v. 4.0.3.1).
Hypothesis Testing
Statistical hypothesis testing on all analyses were performed in
the R environment (v. 3.2.3). Linear mixed-effects modeling
was performed using the lme4, R package (v. 1.1.10; Bates
et al., 2014) and p-values from those models were obtained
using the Satterthwaite approximation for degrees of freedom
implemented in the lmerTest, R package (Tests in Linear Mixed
Effects Models [R package lmerTest version 2.0-32], 2014).
Behavioral Data
The only behavioral data we analyzed in the present study
was the proportion of words recalled by participants. To assess
the potential effects of accent and/or expectedness, we used
a generalized linear mixed-effect model to predict from the
interaction between accent and expectedness by participant
whether or not a given word was remembered. We restricted
our analysis to the comparison between words which were never
heard (due to muting) in the experimental sentences (expected or
unexpected) and words which were heard in the control sentences
(neutral in terms of expectations).
ERP Analyses
Two ERP modulations were identified from grand-averaged data
and congruent with previous investigations of word anticipation
in the auditory modality (Van Berkum et al., 2005;Foucart et al.,
2015). First, we observed an early negative differential in the 120–
300 ms time window and, second, a classic N400 effect in the
400–600 ms time window. Both modulations were maximal on
electrode Cz and measured over (fronto-)central sites (FC3, FC1,
FCz, FC2, FC4, C3, C1, Cz, C2, C4, CP3, CP1, CPz, CP2, CP4).
Since the effect was uniformly distributed, and since we were
not interested in its spatial distribution, mean ERP amplitudes
for the two time periods were measured over the whole central
region of interest. For both time windows, mean ERP amplitudes
were subjected to a linear mixed-effect model, where mean
amplitudes were predicted by the interaction between accent and
expectedness, with random slopes by participant.
RESULTS
Listening Task (EEG)
120–300 ms Period
The linear mixed-effect model carried out on the first ERP
modulation revealed that ERP mean amplitudes could be reliably
predicted by an interaction between accent and expectedness
(b= 0.71, t= 3.41, p= 0.0007), whereby ERP amplitudes were
more negative upon hearing an unexpected article than when
hearing an expected article when participants were listening to
native speech but this was not the case when they were listening
to foreign-accented speech (Figure 2).
400–600 ms Period
In this time window, article expectedness was the only reliable
predictor of ERP mean amplitudes (b= 0.43, t= 2.46, p= 0.014),
Frontiers in Psychology | www.frontiersin.org 6August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 7
Schiller et al. Listening to Foreign-Accented Speech
FIGURE 2 | Grand-average waveforms of event-related potentials (ERPs) from the expected and unexpected articles across the central region of interest (FC3, FC1,
FCz, FC2, FC4, C3, C1, Cz, C2, C4, CP3, CP1, CPz, CP2, CP4) and across all participants. In blue are ERPs from the expected articles and in red are the ERPs
from the unexpected articles. (A) ERPs from the articles in sentences presented in a native accent. (B) ERPs from the articles in sentences presented in a non-native
accent.
whereby ERP amplitudes were more negative upon hearing an
unexpected article compared to an expected one, regardless of
the accent (i.e., native vs. non-native) with which the speech was
produced (Figure 2).
Lexical Recall Task
Results for this task are presented in terms of proportions
of words reported as “heard” by the participants resulting in
aggregated values between 0 and 1. However, as explained in
the “Hypothesis Testing” section above, statistical analyses were
performed using logistic regression with the aim to predict a
binary label (0 or 1) for each item. The analysis was performed
on the unheard expected and unexpected items as well as the
heard items from the control sentences. In this task, expectedness
reliably predicted whether or not a participant would report
a word as heard. In the case where words were actually
unheard, participants reported hearing words they had expected
significantly more often than words they had not expected
(b= 1.12, z= 7.5, p<0.00001) and, unsurprisingly, participants
reported hearing significantly more words that they had actually
heard (albeit not strongly expected) compared to words they
had not expected (b= 1.1, z= 7.4, p<0.00001). Importantly,
words which were actually heard were recalled just as often
as unheard expected words as indicated by the absence of a
difference between control and expected sentences (Figure 3).
DISCUSSION
It is hard to deny that our brain predicts upcoming information.
Indeed, prediction and anticipation have been demonstrated in
a number of domains ranging across a wide array of cognitive
functions such as visual perception, attention and consciousness
(Kok et al., 2014;Summerfield and de Lange, 2014) as well as
auditory perception (Groppe et al., 2010). Linguistic information
has been shown to bias systems as early and as basic as vision
(Boutonnet et al., 2013;Lupyan and Ward, 2013;Boutonnet
and Lupyan, 2015;Samaha et al., 2016), as well as processes
at the interface of these systems (Francken et al., 2014, 2015),
and finally, the language system itself (DeLong et al., 2005;Van
Berkum et al., 2005;Martin et al., 2013;Van Berkum, 2013;
Foucart et al., 2014, 2015).
A key driver of brain predictions is likely to be contextual
information. Context influences the content of such predictions
(Bar, 2004) as well as their effects on brain activity itself (up-
/down-regulation) as is often the case in attention/prediction
trade-offs (Kok et al., 2012a,b). While the content of linguistic
predictions is known to be influenced by the linguistic context
(see “Introduction” section for arguments), we have yet to know
whether a given, more general, context can affect how the
brain predicts. That is, whether it always predicts or whether
its predictions always have the same weight on upcoming
information. One of the most common communicative contexts
in which people interact nowadays involves people who are
communicating in a non-native language. Undeniably, the
speech of a non-native speaker is likely to be less accurate
in terms of syntax, word choice (e.g., “the chocolate pie” vs.
“the brownie” effect; Lev-Ari, 2015), phonological and acoustic
realization and so on. If the anticipation mechanism of the
brain is set off so that it does not have to fully process
upcoming stimuli and it only dedicates the full and costly
extent of its processing pipeline when predictions do not match
the signal, it is likely that a non-native speaker’s production,
which matches canonical predictions less, will affect how
our brain predicts.
As reviewed earlier, work by Lev-Ari (2015) demonstrated
that participants listening to non-native speech were more likely
to rely on contextual information, suggesting that listeners
may predict more in such a condition. Conversely, in a study
on word integration, Romero-Rivas et al. (2016) showed that
integrating the best completion of a sentence (i.e., the most likely
lexical candidate given a sentence context) leads to identical
brain activity in both native and non-native accent conditions.
However, the depth of semantic activation seemed shallower
when participants listened to sentences uttered in a foreign
accent. While this result cannot speak directly to the issue
Frontiers in Psychology | www.frontiersin.org 7August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 8
Schiller et al. Listening to Foreign-Accented Speech
FIGURE 3 | Bar plot showing the proportion of items reported as heard in the sentence listening task. In green are heard words from the control sentences, in blue
are unheard but expected words from the experimental sentences, and in red are unheard and unexpected words from the experimental sentences. Error bars
depict standard errors of the mean. The red horizontal bar depicts the 50% chance level. *Indicates statistically significant differences (p<0.05).
of anticipation per se, it certainly suggests differences in the
processing of native versus non-native speech.
To tease apart these preliminary hypotheses, as well as
to gain further insights in the anticipatory processes in a
manner that is more ecologically valid and at the same time
overcoming limitations from between-subject designs, we tested
word anticipation in a within-subject design while recording
participants’ brain activity using EEG. We expected both early
(PMN) and late (N400) ERP components to be modulated by
expectedness such that an unexpected article would lead to
increased negativities in both time windows (Foucart et al., 2015).
We further hypothesized that the expectedness effect should be
modulated by the accent with which the sentence was uttered, but
we had no strong expectation with regards to its direction due to
the mixed results available to date (Lev-Ari, 2015;Romero-Rivas
et al., 2015, 2016).
Our results provide support for the fact that the brain does not
passively integrate information but, rather, anticipates upcoming
words in continuous speech (DeLong et al., 2005;Van Berkum
et al., 2005;Otten et al., 2007;Martin et al., 2013;Van Berkum,
2013;Foucart et al., 2014, 2015). Furthermore, we show that
context may influence the brain’s predictions as, depending on
the reliability of the stimuli, such processes can be up- or down-
regulated. Indeed, the simple fact of listening to a non-native
speaker reduced the brain’s anticipatory processes as shown
by the lack of modulation between expected and unexpected
articles in the early ERP time window (PMN). Importantly,
reduced anticipation does not mean complete system breakdown
as indicated by a similar modulation of the classic N400 (Kutas
and Federmeier, 2011;Leckey and Federmeier, 2019) component
in both accent conditions. Together with the results from the
lexical recall task, which showed that expected items form a more
robust memory trace than unexpected items so that participants
were just as likely to report a word as “previously heard”
when it was expected (but not heard) as compared to when
they actually heard the word in either accent conditions, it is
clear that successful performance does not depend solely on
early anticipation.
The results presented in this study complement earlier
findings pointing toward an effect of foreign accent on sentence
comprehension (Romero-Rivas et al., 2015) and reinforces the
need to measure anticipation proper (as done in the present
study) since our results are not fully compatible with the
supposedly unaffected “anticipation” presented by Romero-
Rivas et al. (2016). The interpretation of what processes
and mechanisms lead to the early negative deflection are
still unclear. However, under an account of the brain as a
prediction machine which pre-activates stimulus templates of
the sensory input it has predicted (Kok et al., 2014), we
believe the early ERP modulation to index feature mismatch
at the phonological level given the auditory nature of the
experiment as well as the timing, and the topographic distribution
of the effect (see also Schiller et al., 2009). This ERP
component is often referred to as phonological mismatch
negativity (PMN) (Connolly and Phillips, 1994;Hagoort and
Brown, 2000;Diaz and Swaab, 2007). This interpretation has
also been advanced by Foucart et al. (2015) although they
remain quite agnostic as to whether the ERP modulation is
an early manifestation of a classic N400 effect or whether it
is another component. We believe that our results can help
settle that debate, given the fact that while the late ERP
modulation – a classic N400 effect – is obtained in both
accent conditions, the early modulation is not. This suggests
that these two time windows reflect somewhat independent
processes: an early feature (phonological) matching mechanism
driven by predictions and a late “lexical” recovery/integration
process, respectively.
Interestingly, the results from the lexical recall task, showing
no interaction between noun expectedness and accent, suggest
that the brain is highly flexible and can recover and re-create
Frontiers in Psychology | www.frontiersin.org 8August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 9
Schiller et al. Listening to Foreign-Accented Speech
similar end-of-process effects in behavior. Prediction alone,
understood here as the pre-emptive activation of specific stimulus
templates, may not be necessary for successful performance
in language comprehension – a point recently advocated by
Huettig and Mani (2015). That being said, it is important to note
that this point does not undermine the fact that prediction is
known to take place in several perceptual and cognitive tasks
and therefore should not be underestimated. Indeed, further
research on linguistic prediction and prediction in the brain in
general, preferably including data from more participants, should
focus on determining the complex dynamics between predictive
and integrative processes to understand the degree of overlap
and separation between these processes and their neural and
behavioral consequences.
Taken together, our results show that the brain is highly
flexible and proactive in language comprehension as well as
highly sensitive and responsive to contextual task demands,
thereby fine-tuning the influence of higher-level knowledge on
lower-level sensory experience, providing strong challenges to
models of language comprehension, but also of more general
cognition, such as “passive resonance” or other models assuming
an almost strictly bottom-up information flow (Biederman,
1987;Serre et al., 2007;Kuperberg et al., 2011;Paczynski and
Kuperberg, 2012).
What are the consequences of the discrepancy between the
differences detected in the electrophysiological data and their
apparent lack of impact on behavioral performance? Consider
the following point: if brain predictions are the processes by
which we think the brain activates specific stimulus templates
(Kok et al., 2014), i.e., phonological features in the present
case, how can it be that expected but unheard words were
recalled to the same extent in sentences in which prediction
supposedly occurred as well as when it did not? It is important
to note that although the early stages of brain activity (potentially
related to the activation of phonological features) differed
between native and non-native sentences, expectancy effects
were obtained in both conditions in the later time window.
It must be noted that while the classic N400 effect is often
associated with “integration,” given that our stimuli never
contained the critical stimuli, the expectancy effect detected
on this component cannot come from integration since there
is nothing to integrate. Rather, we believe that higher-level
conceptual features are likely to have been activated from the
highly predictive sentential context at a later stage, yielding
a “false” memory trace. Furthermore, we do not exclude the
possibility that the activation of higher-level conceptual features
can lead to the activation of lower-level perceptual feature
as demonstrated in cuing/priming paradigms (Edmiston and
Lupyan, 2013, 2017;Boutonnet and Lupyan, 2015;Samaha
et al., 2016). Our results are therefore in line with previous
reports of such effects on memory by Foucart et al. (2015)
and compatible with the evidence showing that degraded
speech or phonemes can be restored to the extent that their
perceptual experience might not differ from that in optimal
conditions (Warren, 1970;Groppe et al., 2010;Sohoglu et al.,
2012;Bendixen et al., 2014) and fit very well into theories
of perception which allow for higher-level representation to
feedback (top-down) information to lower levels. In other words,
expecting a specific lexical item, leads to the activation of specific
stimulus features across the whole processing stream, which, in
case information is absent, resembles that of the actual input
(SanMiguel et al., 2013).
CONCLUSION
The human brain anticipates upcoming words in on-going
conversation. Such anticipation is likely to be supported by
predictive mechanisms already identified in various aspects of
human cognition and believed to be a key driver of brain
function as a whole (Bar, 2003;Friston, 2005;Clark, 2013).
However, such predictions may be down-regulated depending
on the general context such as stimulus reliability, e.g., whether
a sentence is produced by a native or a non-native speaker in
the current study. We found that when native speakers of Dutch
listened to non-native speakers producing Dutch sentences
containing a highly predictable lexical item, their early brain
activity did not reveal word anticipation. While brain activity
differed depending on whether participants listened to native
or non-native accents, their overall performance, measured by
word recall, was unaffected, and both accent conditions led to
higher recall rates of expected compared to unexpected words,
independently of the accent in which the sentences were heard. In
other words, listening to a non-native speaker of one’s own native
language reduces our brain’s chances to deal with conflicting
information only at the levels where the input might be most
misaligned with one’s predicted features such as acoustic or
phonological features.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by Ethics Committee of the Faculty of Humanities,
Leiden University. The patients/participants provided their
written informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
NS and LC contributed to the design of this study and the
writing including the revision. BB contributed to the design
of the study, carried the study out, analyzed the data, and
wrote the first draft. MD carried out the study. MM created
the stimuli and carried out part of the study. BR carried out
the control measurements between the native and non-native
speakers. All authors contributed to the article and approved the
submitted version.
Frontiers in Psychology | www.frontiersin.org 9August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 10
Schiller et al. Listening to Foreign-Accented Speech
FUNDING
BB was supported by the European Union’s Seventh Framework
Programme for research, technological development and
demonstration under grant agreement no. 613465.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpsyg.
2020.02143/full#supplementary-material
REFERENCES
American Electroencephalographic Society (1994). Guideline thirteen:
guidelines for standard electrode position nomenclature. American
Electroencephalographic Society. J. Clin. Neurophysiol. 11, 111–113.
doi: 10.1097/00004691-199401000- 00014
Bar, M. (2003). A cortical mechanism for triggering top-down facilitation in
visual object recognition. J. Cogn. Neurosci. 15, 600–609. doi: 10.1162/
089892903321662976
Bar, M. (2004). Visual objects in context. Nat. Rev. Neurosci. 5, 617–629. doi:
10.1038/nrn1476
Bates, D., Maechler, M., Bolker, B., and Walker, S. (2014). lme4: Linear Mixed-
Effects Models Using Eigen and S4, 1st Edn. Available online at: http://CRAN.
R-project.org/package=lme4 (accessed August, 2016).
Bendixen, A., Scharinger, M., Strauß, A., and Obleser, J. (2014). Prediction in the
service of comprehension: modulated early brain responses to omitted speech
segments. Cortex 53, 9–26. doi: 10.1016/j.cortex.2014.01.001
Biederman, I. (1987). Recognition-by-components: a theory of human image
understanding. Psychol. Rev. 94, 115–147. doi: 10.1037/0033-295x.94.2.115
Boersma, P. (2002). Praat, a system for doing phonetics by computer. Glot Int. 5,
341–345.
Boutonnet, B., Dering, B., Viñas-Guasch, N., and Thierry, G. (2013). Seeing objects
through the language glass. J. Cogn. Neurosci. 25, 1702–1710. doi: 10.1162/
jocn_a_00415
Boutonnet, B., and Lupyan, G. (2015). Words jump-start vision: a label advantage
in object recognition. J. Neurosci. 35, 9329–9335. doi: 10.1523/jneurosci.5111-
14.2015
Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the
future of cognitive science. Behav. Brain Sci. 36, 181–204. doi: 10.1017/
s0140525x12000477
Connolly, J. F., Byrne, J. M., and Dywan, C. A. (1995). Assessing adult receptive
vocabulary with event-related potentials: an investigation of cross-modal and
cross-form priming. J. Clin. Exp. Neuropsychol. 17, 548–565. doi: 10.1080/
01688639508405145
Connolly, J. F., and Phillips, N. A. (1994). Event-related potential components
reflect phonological and semantic processing of the terminal word of spoken
sentences. J. Cogn. Neurosci. 6, 256–266. doi: 10.1162/jocn.1994.6.3.256
D’Arcy, R. C. N., Connolly, J. F., Service, E., Hawco, C. S., and Houlihan, M. E.
(2004). Separating phonological and semantic processing in auditory sentence
processing: a high-resolution event-related brain potential study. Hum. Brain
Mapp. 22, 40–51. doi: 10.1002/hbm.20008
DeLong, K. A., and Kutas, M. (2016). Hemispheric differences and similarities
in comprehending more and less predictable sentences. Neuropsychologia 91,
380–393. doi: 10.1016/j.neuropsychologia.2016.09.004
DeLong, K. A., Urbach, T. P., and Kutas, M. (2005). Probabilistic word pre-
activation during language comprehension inferred from electrical brain
activity. Nat. Neurosci. 8, 1117–1121. doi: 10.1038/nn1504
Diaz, M. T., and Swaab, T. Y. (2007). Electrophysiological differentiation of
phonological and semantic integration in word and sentence contexts. Brain
Res. 1146, 85–100. doi: 10.1016/j.brainres.2006.07.034
Edmiston, P., and Lupyan, G. (2013). “Verbal and nonverbal cues activate concepts
differently, at different times,” in Proceedings of the 35th Annual Conference
of the Cognitive Science Society, eds M. Knauff, M. Pauen, N. Sebanz, and I.
Wachsmuth (Austin, TX: Cognitive Science Society), 2243–2248.
Edmiston, P., and Lupyan, G. (2017). Visual interference disrupts visual
knowledge. J. Mem. Lang. 92, 281–292. doi: 10.1016/j.jml.2016.07.002
Federmeier, K. D., and Kutas, M. (1999). A rose by any other name: long-term
memory structure and sentence processing. J. Mem. Lang. 41, 469–495. doi:
10.1006/jmla.1999.2660
Foucart, A., Martin, C. D., Moreno, E. M., and Costa, A. (2014). Can bilinguals
see it coming? Word anticipation in L2 sentence reading. J. Exp. Psychol. Learn.
Mem. Cogn. 40, 1461–1469. doi: 10.1037/a0036756
Foucart, A., Ruiz-Tada, E., and Costa, A. (2015). How do you know I was about
to say “book?” Anticipation processes affect speech processing and lexical
recognition. Lang. Cogn. Neurosci. 30, 768–780. doi: 10.1080/23273798.2015.
1016047
Francken, J. C., Kok, P., Hagoort, P., and de Lange, F. P. (2014). The behavioral and
neural effects of language on motion perception. J. Cogn. Neurosci. 27, 175–184.
doi: 10.1162/jocn_a_00682
Francken, J. C., Meijs, E. L., Ridderinkhof, O. M., Hagoort, P., De Lange, F. P.,
and Van Gaal, S. (2015). Manipulating word awareness dissociates feed-
forward from feedback models of language-perception interactions. Neurosci.
Consciousness 2015:niv003. doi: 10.1093/nc/niv003
Friston, K. (2005). A theory of cortical responses. Philos. Trans. R. Soc. B Biol. Sci.
360, 815–836.
Goslin, J., Duffy, H., and Floccia, C. (2012). An ERP investigation of regional and
foreign accent processing. Brain Lang. 122, 92–102. doi: 10.1016/j.bandl.2012.
04.017
Gratton, G., Coles, M. G. H., and Donchin, E. (1983). A new method for off-line
removal of ocular artifact. Electroencephalogr. Clin. Neurophysiol. 55, 468–484.
doi: 10.1016/0013-4694(83)90135- 9
Groppe, D. M., Choi, M., Huang, T., Schilz, J., Topkins, B., Urbach, T. P., et al.
(2010). The phonemic restoration effect reveals pre-N400 effect of supportive
sentence context in speech perception. Brain Res. 1361, 54–66. doi: 10.1016/j.
brainres.2010.09.003
Gut, U. (2012). “The LeaP corpus. A multilingual corpus of spoken learner German
and learner English,” in Multilingual Corpora and Multilingual Corpus Analysis,
eds T. Schmidt and K. Wörner (Amsterdam/Philadelphia, PA: John Benjamins
Publishing Company), 3–24.
Hagoort, P., and Brown, C. M. (2000). ERP effects of listening to speech: semantic
ERP effects. Neuropsychologia 38, 1518–1530. doi: 10.1016/s0028-3932(00)
00052-x
Hanulíková, A., Van Alphen, P. M., Van Goch, M. M., and Weber, A. (2012). When
one person’s mistake is another’s standard usage: the effect of foreign accent
on syntactic processing. J. Cogn. Neurosci. 24, 878–887. doi: 10.1162/jocn_a_
00103
Holcomb, P. J. (1993). Semantic priming and stimulus degradation: implications
for the role of the N400 in language processing. Psychophysiology 30, 47–61.
doi: 10.1111/j.1469-8986.1993.tb03204.x
Huettig, F., and Mani, N. (2015). Is prediction necessary to understand language?
Probably not. Lang. Cogn. Neurosci. 31, 19–31. doi: 10.1080/23273798.2015.
1072223
Ito, A., Martin, A. E., and Nieuwland, M. S. (2017). How robust are prediction
effects in language comprehension? Failure to replicate article-elicited N400
effects. Lang. Cogn. Neurosci. 32, 954–965. doi: 10.1080/23273798.2016.
1242761
Kok, P., Failing, M. F., and De Lange, F. P. (2014). Prior expectations evoke
stimulus templates in the primary visual cortex. J. Cogn. Neurosci. 26, 1546–
1554. doi: 10.1162/jocn_a_00562
Kok, P., Jehee, J. F. M., and De Lange, F. P. (2012a). Less is more: expectation
sharpens representations in the primary visual cortex. Neuron 75, 265–270.
doi: 10.1016/j.neuron.2012.04.034
Kok, P., Rahnev, D., Jehee, J. F. M., Lau, H. C., and De Lange, F. P. (2012b).
Attention reverses the effect of prediction in silencing sensory signals. Cereb.
Cortex 22, 2197–2206. doi: 10.1093/cercor/bhr310
Kuperberg, G. R., Paczynski, M., and Ditman, T. (2011). Establishing causal
coherence across sentences: an ERP study. J. Cogn. Neurosci. 23, 1230–1246.
doi: 10.1162/jocn.2010.21452
Frontiers in Psychology | www.frontiersin.org 10 August 2020 | Volume 11 | Article 2143

fpsyg-11-02143 August 21, 2020 Time: 15:50 # 11
Schiller et al. Listening to Foreign-Accented Speech
Kutas, M., and Federmeier, K. D. (2000). Electrophysiology reveals semantic
memory use in language comprehension. Trends Cogn. Sci. 4, 463–470. doi:
10.1016/s1364-6613(00)01560- 6
Kutas, M., and Federmeier, K. D. (2011). Thirty years and counting: finding
meaning in the N400 component of the event-related brain potential (ERP).
Annu. Rev. Psychol. 62, 621–647. doi: 10.1146/annurev.psych.093008.131123
Kutas, M., and Hillyard, S. A. (1980). Reading senseless sentences: brain potentials
reflect semantic incongruity. Science 207, 203–205. doi: 10.1126/science.
7350657
Kutas, M., and Hillyard, S. A. (1984). Brain potentials during reading reflect
word expectancy and semantic association. Nature 307, 161–163. doi: 10.1038/
307161a0
Kutas, M., and Van Petten, C. K. (1994). “Psycholinguistics electrified:
event-related brain potential investigations,” in Handbook of
Psycholinguistics, Ed. M. A. Gernsbacher (San Diego, CA: Academic Press),
83–143.
Leckey, M., and Federmeier, K. D. (2019). “Electrophysiological methods in the
study of language processing,” in The Oxford Handbook of Neurolinguistics, eds
G. I. de Zubicaray and N. O. Schiller (Oxford, NY: Oxford University Press),
42–71.
Lev-Ari, S. (2015). Comprehending non-native speakers: theory and evidence for
adjustment in manner of processing. Front. Psychol. 5:1546. doi: 10.3389/fpsyg.
2014.01546
Lupyan, G., and Ward, E. J. (2013). Language can boost otherwise unseen objects
into visual awareness. Proc. Natl. Acad. Sci. U.S.A. 110, 14196–14201. doi:
10.1073/pnas.1303312110
Martin, C. D., Thierry, G., Kuipers, J.-R., Boutonnet, B., Foucart, A., and Costa,
A. (2013). Bilinguals reading in their second language do not predict upcoming
words as native readers do. J. Mem. Lang. 69, 574–588. doi: 10.1016/j.jml.2013.
08.001
McGettigan, C., Faulkner, A., Altarelli, I., Obleser, J., Baverstock, H., and
Scott, S. K. (2012). Speech comprehension aided by multiple modalities:
behavioural and neural interactions. Neuropsychologia 50, 762–776. doi: 10.
1016/j.neuropsychologia.2012.01.010
Nieuwland, M. S., Politzer-Ahles, S., Heyselaar,E., Segaert, K., D arley, E., Kazanina,
N., et al. (2018). Large-scale replication study reveals a limit on probabilistic
prediction in language comprehension. eLife 7:e33468.
Nissen, S. L., Dromey, C., and Wheeler, C. (2007). First and second language tongue
movements in Spanish and Korean bilingual speakers. Phonetica 64, 201–216.
doi: 10.1159/000121373
Obleser, J., Wise, R. J. S., Dresner, M. A., and Scott, S. K. (2007). Functional
integration across brain regions improves speech perception under adverse
listening conditions. J. Neurosci. 27, 2283–2289. doi: 10.1523/jneurosci.4663-
06.2007
Otten, M., Nieuwland, M. S., and Van Berkum, J. J. (2007). Great expectations:
specific lexical anticipation influences the processing of spoken language. BMC
Neurosci. 8:89. doi: 10.1186/1471-2202- 8-89
Paczynski, M., and Kuperberg, G. R. (2012). Multiple influences of semantic
memory on sentence processing: distinct effects of semantic relatedness
on violations of real-world event/state knowledge and animacy selection
restrictions. J. Mem. Lang. 67, 426–448. doi: 10.1016/j.jml.2012.
07.003
Pickering, M. J., and Garrod, S. (2007). Do people use language production to
make predictions during comprehension? Trends Cogn. Sci. 11, 105–110. doi:
10.1016/j.tics.2006.12.002
Porretta, V., Tremblay, A., and Bolger, P. (2017). Got experience? PMN amplitudes
to foreign-accented speech modulated by listener experience. J. Neurolinguistics
44, 54–67. doi: 10.1016/j.jneuroling.2017.03.002
Riding, R. J., and Vincent, D. (1980). Listening comprehension: the effects of sex,
age, passage structure and speech rate. Educ. Rev. 32, 259–266. doi: 10.1080/
0013191800320303
Romero-Rivas, C., Martin, C. D., and Costa, A. (2015). Processing changes when
listening to foreign-accented speech. Front. Hum. Neurosci. 9:167. doi: 10.3389/
fnhum.2015.00167
Romero-Rivas, C., Martin, C. D., and Costa, A. (2016). Foreign-accented speech
modulates linguistic anticipatory processes. Neuropsychologia 85, 245–255. doi:
10.1016/j.neuropsychologia.2016.03.022
Samaha, J., Boutonnet, B., and Lupyan, G. (2016). How prior knowledge prepares
perception: prestimulus oscillations carry perceptual expectations and influence
early visual responses. bioRxiv [Preprint] doi: 10.1101/076687v5
SanMiguel, I., Widmann, A., Bendixen, A., Trujillo-Barreto, N., and Schroger, E.
(2013). Hearing silences: human auditory processing relies on preactivation of
sound-specific brain activity patterns. J. Neurosci. 33, 8633–8639. doi: 10.1523/
jneurosci.5821-12.2013
Schiller, N. O., Horemans, I., Ganushchak, L., and Koester, D. (2009). Event-
related brain potentials during the monitoring of speech errors. Neuroimage
44, 520–530. doi: 10.1016/j.neuroimage.2008.09.019
Serre, T., Oliva, A., and Poggio, T. (2007). A feedforward architecture accounts
for rapid categorization. Proc. Natl. Acad. Sci. U.S.A. 104, 6424–6429. doi:
10.1073/pnas.0700622104
Sohoglu, E., Peelle, J. E., Carlyon, R. P., and Davis, M. H. (2012). Predictive top-
down integration of prior knowledge during speech perception. J. Neurosci. 32,
8443–8453. doi: 10.1523/jneurosci.5069-11.2012
Strauß, A., Kotz, S. A., and Obleser, J. (2013). Narrowed expectancies under
degraded speech: revisiting the N400. J. Cogn. Neurosci. 25, 1383–1395. doi:
10.1162/jocn_a_00389
Summerfield, C., and de Lange, F. P. (2014). Expectation in perceptual decision
making: neural and computational mechanisms. Nat. Rev. Neurosci. 15, 745–
756. doi: 10.1038/nrn3838
Tests in Linear Mixed Effects Models [R package lmerTest version 2.0-32] (2014).
Tests in Linear Mixed Effects Models [R package lmerTest version 2.0-32].
Available online at: http://CRAN.R- project.org/package=lmerTest (accessed
August, 2016).
Van Berkum, J. J. A. (2013). Anticipating communication. Theor. Linguist. 39,
1–12.
Van Berkum, J. J. A., Brown, C. M., Zwitserlood, P., Kooijman, V., and Hagoort,
P. (2005). Anticipating upcoming words in discourse: evidence from ERPs and
reading times. J. Exp. Psychol. Learn. Mem. Cogn. 31, 443–467. doi: 10.1037/
0278-7393.31.3.443
Van Petten, C., and Luka, B. J. (2006). Neural localization of semantic context
effects in electromagnetic and hemodynamic studies. Brain Lang. 97, 279–293.
doi: 10.1016/j.bandl.2005.11.003
Vandenbroucke, A. R. E., Fahrenfort, J. J., Meuwese, J. D. I., Scholte, H. S., and
Lamme, V. A. F. (2016). Prior knowledge about objects determines neural
color representation in human visual cortex. Cereb. Cortex 26, 1401–1408.
doi: 10.1093/cercor/bhu224
Vinck, M., Oostenveld, R., Van Wingerden, M., Battaglia, F., and Pennartz, C. M. A.
(2011). An improved index of phase-synchronization for electrophysiological
data in the presence of volume-conduction, noise and sample-size bias.
Neuroimage 55, 1548–1565. doi: 10.1016/j.neuroimage.2011.01.055
Wade, T., Jongman, A., and Sereno, J. (2007). Effects of acoustic variability in
the perceptual learning of non-native-accented speech sounds. Phonetica 64,
122–144. doi: 10.1159/000107913
Warren, R. M. (1970). Perceptual restoration of missing speech sounds. Science 167,
392–393. doi: 10.1126/science.167.3917.392
Wester, F., Gilbers, D., and Lowie, W. (2007). Substitution of dental fricatives in
English by Dutch L2 speakers. Lang. Sci. 29, 477–491. doi: 10.1016/j.langsci.
2006.12.029
Witteman, M. J., Weber, A., and McQueen, J. M. (2013). Foreign accent strength
and listener familiarity with an accent codetermine speed of perceptual
adaptation. Atten. Percept. Psychophys. 75, 537–556. doi: 10.3758/s13414-012-
0404-y
Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2020 Schiller, Boutonnet, De Heer Kloots, Meelen, Ruijgrok and Cheng.
This is an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). The use, distribution or reproduction in other forums
is permitted, provided the original author(s) and the copyright owner(s) are credited
and that the original publication in this journal is cited, in accordance with accepted
academic practice. No use, distribution or reproduction is permitted which does not
comply with these terms.
Frontiers in Psychology | www.frontiersin.org 11 August 2020 | Volume 11 | Article 2143