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Selective adaptation in sentence comprehension: Evidence from event-related brain potentials

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Abstract

The present study conducted two event-related potential experiments to investigate whether readers adapt their expectations to morphosyntactically (Experiment 1) or semantically (Experiment 2) anomalous sentences when they are repeatedly exposed to them. To address this issue, we experimentally manipulated the probability of occurrence of grammatical sentences and syntactically and semantically anomalous sentences through experiments. For the low probability block, anomalous sentences were presented less frequently than grammatical sentences (with a ratio of 1 to 4), while they were presented as frequently as grammatical sentences in the equal probability block. Experiment 1 revealed a smaller P600 effect for morphosyntactic violations in the equal probability block than in the low probability block. Linear mixed-effect models were used to examine how the size of the P600 effect changed as the experiment went along. The results showed that the smaller P600 effect of the equal probability block resulted from an amplitude's decline in morphosyntactically violated sentences over the course of the experiment, suggesting an adaptation to morphosyntactic violations. In Experiment 2, semantically anomalous sentences elicited a larger N400 effect than their semantically natural counterparts regardless of probability manipulation. Little evidence was found in favor of adaptation to semantic violations in that the processing cost associated with the N400 did not decrease over the course of the experiment. Therefore, these results demonstrated a dynamic aspect of language-processing system. We will discuss why the language-processing system shows a selective adaptation to morphosyntactic violations. 2
Final Draft
Quarterly Journal of Experimental Psychology
https://doi.org/10.1177/1747021820984623
Selective adaptation in sentence comprehension:
Evidence from event-related brain potentials
Masataka Yano 1, 2, 3, Shugo Suwazono 4, Hiroshi Arao 5, Daichi Yasunaga 6, and Hiroaki Oishi 7
1 Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
2 Faculty of Humanities, Kyushu University, Fukuoka, Japan
3 Department of Linguistics, Graduate School of Arts and Letters Tohoku University, Sendai, Japan
4 Department of Neurology and Centre for Clinical Neuroscience, National Hospital Organization
Okinawa National Hospital, Okinawa, Japan
5 Department of Human Sciences, Taisho University, Tokyo, Japan
6 Faculty of Letters, Institute of Human and Social Sciences, Kanazawa University, Japan
7 College of Comprehensive Psychology, Ritsumeikan University, Osaka, Japan
Abstract
The present study conducted two event-related potential experiments to investigate whether readers
adapt their expectations to morphosyntactically (Experiment 1) or semantically (Experiment 2) anomalous
sentences when they are repeatedly exposed to them. To ad d re s s t hi s i s su e , w e experimentally manipulated
the probability of occurrence of grammatical sentences and syntactically and semantically anomalous
sentences through experiments. For the low probability block, anomalous sentences were presented less
frequently than grammatical sentences (with a ratio of 1 to 4), while they were presented as frequently as
grammatical sentences in the equal probability block. Experiment 1 revealed a smaller P600 effect for
morphosyntactic violations in the equal probability block than in the low probability block. Linear mixed-effect
models were used to examine how the size of the P600 effect changed as the experiment went along. The
results showed that the smaller P600 effect of the equal probability block resulted from an amplitude’s decline
in morphosyntactically violated sentences over the course of the experiment, suggesting an adaptation to
morphosyntactic violations. In Experiment 2, semantically anomalous sentences elicited a larger N400 effect
than their semantically natural counterparts regardless of probability manipulation. Little evidence was found
in favor of adaptation to semantic violations in that the processing cost associated with the N400 did not
decrease over the course of the experiment. Therefore, these results demonstrated a dynamic aspect of
language-processing system. We will discuss why the language-processing system shows a selective
adaptation to morphosyntactic violations.
2
1. Introduction
Adaptation in response to physical properties of an environmental stimulus has been found in various
mechanisms, such as in perception. For example, it has been known in psychophysiology that an event-related
potential (ERP) called N1 attenuates in amplitude with repetitive exposure to the same auditory stimulus and
increases in response to a novel (‘deviant’) auditory stimulus (i.e., Mismatch Negativity (MMN), Fishman,
2014; May & Tiitinen, 2010; Näätänen, Jacobsen, & Winkler, 2005). There has been a great debate as to
whether such an ERP effect can be accounted for by the adaptation hypothesis, according to which people
become tuned to represent a frequent stimulus at relatively little neural cost and the input of an infrequent
stimulus recruits more neuronal populations, inducing greater neural cost (May & Tiitinen, 2010; May et al.,
1999), or by the predictive coding hypothesis, according to which people expect a probable stimulus and the
input of an improbable stimulus violates such an expectation and increases the neural cost (Friston, 2005;
Garrido, Kilner, Stephan, & Friston, 2009; Winkler, 2007). Although the debate continues, it is generally
assumed that people detect physical changes in the environment and adapt to them immediately.
Adaptive mechanisms in response to physical features of stimuli have also been found in human language
processing, namely, in categorical perception (Kleinschmidt & Florian Jaeger, 2015; Kleinschmidt & Jaeger,
2016). However, when it comes to human language processing, it is possible that adaptation is pervasive
beyond just physical features. In daily language use, people process sentences effortlessly in spite of the
prevalence of variable factors, such as a speaker’s syntactic preference, as well as lexical choice. That is, some
speakers prefer to use a certain construction where other speakers would use another construction to express
the same proposition, and thus, the distribution of syntactic constructions necessarily varies among speakers.
In this example, what people should adapt to is a generative rule that a speaker uses to produce a sentence,
rather than the physical properties of a sentence, such as sound pitch and intensity. This poses an important
question for cognitive science of language; Do people adapt to such sentence-level variations in
comprehension, and if so, how do they adapt?
Such a flexible aspect of sentence processing has not attracted attention until very recently. Tradi tion ally,
studies of human sentence processing have focused on fixed aspects of processing mechanisms, with an
(implicit) assumption that the processing is invariant. For example, a number of previous studies have
examined how people routinely resolve structural ambiguities, such as attachment ambiguity, assuming that
they should be consistent in responding to such ambiguities (e.g., Frazier, 1979; Frazier & Rayner, 1982;
Tanen hau s, Spi ve y-Knowlton, Eberhard, & Sedivy, 1995).
According to recent behavioral studies, however, people change their behavior according to probabilistic
occurrences of syntactic structures during an experiment (Farmer, Fine, Yan, Cheimariou, & Jaeger, 2014; Fine
& Jaeger, 2013, 2016; Kaan & Chun, 2018; Kamide, 2012; Wells, Christiansen, Race, Acheson, & MacDonald,
2009). For instance, while traditional studies showed that people experience difficulty in the processing of
garden-path sentences that require a revision of syntactic structures (e.g., The experienced soldiers warned
3
about the danger conducted the midnight raid), Fine et al. (2013) found that the difficulty of processing
garden-path sentences lessened as their participants were repeatedly presented with garden-path sentences
during an experiment (see Harrington-Stack, James, & Watson, 2018 for the failure to replicate Fine et al.’s
experiment). Previous accounts that explain this result and other similar results of temporal ambiguity
resolution can be categorized into two different positions. The expectation updating account proposes that
when encountering a less frequent structure (i.e., reduced relative clauses) many times, the language-
processing system updates its expectation about the probability of the structure occurrence and resolves a
structural ambiguity to the less frequent structure (Fine & Jaeger, 2013; Fine et al., 2013). The alternative
interpretation, in contrast, assumes that syntactic frames are stored in declarative memory. According to this
interpretation, when a less frequent syntactic frame (i.e., relative clause) is repeatedly used, its base-level
activation increases and therefore, its activation requires a less cost subsequently. Therefore, the processing
of sentences associated with such infrequent syntactic frames is facilitated. We refer to this account as the
representation-based account (cf. Reitter, Keller, & Moore, 2011). Since previous behavioral studies of
adaptation have mainly focused on the processing of structural ambiguity resolution, it is difficult to tease
these two accounts apart.
The present study disentangles these two accounts by examining whether people are able to adapt to
ungrammatical sentences (cf. Kaan & Chun, 2018). If people adapt to ungrammatical sentences, it supports
the expectation updating account, but not the representation-based account. The expectation updating
account predicts that native speakers can adapt to ungrammatical sentences if the distribution of
ungrammatical sentences enables people to expect them. In contrast, the representation-based account
predicts that people do not adapt to them because, by definition, native speakers have no licit representation
of ungrammatical sentences, and therefore, the activation level of its syntactic representation cannot increase.
Note that the absence of such adaptation provides support for neither the expectation updating account nor
representation-based account. To d a te , i t r em ai n s c on t ro ve r si a l a s t o w he t he r u n gr am m at i ca l s e nt en c es
trigger adaptation.
Yosh id a and Miyamoto (2017) show that native speakers of Japanese do not adapt to ungrammatical
sentences. In their self-paced reading experiment, Japanese speakers were presented with grammatical and
ungrammatical verbal nouns.
1
The results showed a longer reading time for the ungrammatical sentences but
1
Their study used a grammatical constraint on case marking in Japanese. In many languages including
Japanese, an unergative verb can assign an accusative case to its object, whereas an unaccusative verb cannot
(Burzio, 1986). Thus, a verbal noun can take an accusative verb in Japanese only if the combination of the
verbal noun and a light verb “suru (do)/sita (did)” is unergative, as shown in (i) and (ii) below (The asterisk *
indicates ungrammaticality). These four types of sentences were used in Yoshida & Miyamoto (2017).
(i) Unergative verb:
Matsumoto-san-ga sampo(-o) sita.
Matsumoto-Mr.-NOM walk-(ACC) do.PAST.
“Mr. Matsumoto walked.”
4
the difference between the grammatical and ungrammatical sentences did not decrease over the course of
their experiment. The authors propose that the repeated exposure to ungrammatical sequences does not
facilitate the processing of them and therefore, the adaption is limited to grammatically licit constructions.
According to the representation-based account, assuming that the structure of the ungrammatical sentences
used in their study does not have a licit syntactic representation, the absence of adaptation is attributable to
the possibility that the repeated exposure to ungrammatical sentences has not induced an activation that
could facilitate subsequent processing. However, the absence of adaptation is also compatible with the
expectation-based account, as mentioned above.
Several event-related potential (ERP) studies reported results consistent with the view that the behavior
of the language-processing system is affected by repeated exposure to ungrammatical sentences (Coulson,
King, & Kutas, 1998; Gunter & Friederici, 1999; Hahne & Friederici, 1999; but see also Osterhout, McKinnon,
Bersick, & Corey, 1996). For example, Gunter et al. (1997) manipulated the probability of grammatical and
ungrammatical sentence occurrence (verb inflection violations) in Dutch (High: grammatical: 25% vs.
ungrammatical: 75%, Low: grammatical: 75% vs. ungrammatical: 25%). They found a robust P600 effect for
verb inflection violations (“De vuile matten werden door de hulp kloppenThe dirty doormats were beat by
the housekeeper) emerging when ungrammatical sentences were presented less frequently than grammatical
sentences.
2
In contrast, the P600 effect was not observed when ungrammatical sentences more frequently
occurred than grammatical sentences (see also Coulson, King, & Kutas, 1998; Hahne & Friederici, 1999 for
similar results). Their results imply that the language-processing system is flexible enough to adapt to
ungrammatical sentences to avoid processing difficulties as much as possible in favor of the expectation-based
account of adaptation. However, decisive evidence for adaptation to ungrammatical sentences is lacking in
these studies because they have not examined how P600 changed during experiments, unlike previous self-
(ii) Unaccusative verb:
Nihonsya-no-yushutsu-ga zouka(*-o) sita.
Japanese.car-GEN-export-NOM increase(*-ACC) do.PAST
Japanese car exports increased.
2
The P600 is a positive component with the peak latency of approximately 600 ms or later post-stimulus
onset. The P600 effect has been observed for syntactically manipulated sentences, including syntactically ill-
formed sentences, garden-path sentences, and sentences with filler-gap dependencies (Coulson et al., 1998;
Fiebach, Schlesewsky, & Friederici, 2002; Gouvea, Phillips, Kazanina, & Poeppel, 2010; Kaan, Harris, Gibson, &
Holcomb, 2000; Kaan & Swaab, 2003a, 2003b; Osterhout, 1997; Osterhout & Holcomb, 1992; Osterhout,
McLaughlin, & Bersick, 1997; Osterhout & Mobley, 1995; Phillips, Kazanina, & Abada, 2005). The peak latency
of P600 varies depending on various factors such as the difficulty of the task and the type of sentences (e.g.,
The P600 effect elicited by syntactic violations sometimes appears later than that elicited by GP sentences
and filler-gap integration). Molinaro et al. (2011) argued that the early P600 reflects difficulties in integrating
a word with previous sentence representations and the later P600 reflects repair processes. More recently,
the P600 is also reported for semantically anomalous sentences, such as “The hearty meal was devouring ...,
especially when the animacy is violated (Hoeks, Stowe, & Doedens, 2004; Kim & Osterhout, 2005; Kolk et al.,
2003; Kuperberg, 2007; Kuperberg et al., 2007, 2003; Oishi et al., 2011). The functional role of the P600 is a
matter of debate (see e.g., Bornkessel-Schlesewsky & Schlesewsky, 2008; Brouwer et al., 2012; Frenzel,
Schlesewsky, & Bornkessel-Schlesewsky, 2011; van de Meerendonk et al., 2009 for discussion).
5
paced reading experiments examining reading time changes (e.g., Fine et al., 2013; Yoshida & Miyamoto,
2017). It is possible that the smaller P600 effect in the high probability block relative to the low probability
block reflects the fact that ungrammatical sentences of the same condition were more likely to be presented
as an immediately preceding trial and facilitate the processing of the following ungrammatical sentence in the
high probability block.
3
If such trials were averaged together with trials with the grammatical sentences as an
immediately preceding trial, the averaged data should show a smaller P600 effect in the high probability block.
Therefore, the previous results are likely due to the result of a transient activation, but not a gradual
adaptation toward the ungrammatical sentences.
The present study, therefore, addresses this issue of whether a P600 effect elicited by syntactic violations
is modulated by the number of exposures to them, using mixed-effects modeling of single-trial data (i.e., trial
order effect on P600). In standard ERP studies, electroencephalograms (EEGs) are averaged across many trials
to obtain ERPs and compared between conditions of interest using analyses of variances (ANOVA) (Luck, 2005).
The averaging procedure assumes that the electrophysiological response to a particular stimulus is invariant
throughout an experiment, but such an assumption has been rarely verified (cf. Polich, 1989). Furthermore,
the averaging procedure loses important information as to a dynamic aspect of underlying cognitive processes.
The use of regression analyses, such as linear mixed-effects models, does not necessitate the averaging
procedure and allow for more flexible analyses. For example, regression analyses enable us to handle
continuous variables, such as trial order (i.e., nth trial in an experiment), as well as lexical properties of words
(e.g., frequency). Analyses of trial orders have been incorporated in recent psychological and
psychophysiological studies, including the studies of face and pain perception and cognitive controls, to
investigate cognitive processes underlying what has been referred to as habituation and learning (Volpert-
Esmond, Merkle, Levsen, Ito, & Bartholow, 2018; Von Gunten, Volpert-Esmond, & Bartholow, 2018; Vossen,
Van Breukel en, Her men s, Van Os, & Lousberg, 2011). In contrast, such an investigation has not been conducted
until recently in the literature of human language processing (see Delaney-Busch et al., 2019; Nieuwland et
al., 2018; Smith & Kutas, 2015a, 2015b; Yano, 2018). In the present study, we use mixed-effects modeling to
examine how the language-processing system dynamically changes the way it processes syntactic violations
and discuss an implication for the representation-based and expectation-based accounts.
The present study also examines whether the adaptation that occurs depends on the type of linguistic
violation, such as (morpho)syntactic violation and semantic violation. In the ERP literature of sentence
processing, the sensitivity to probabilistic manipulation has been discussed with respect to the P600 (in
relation to P3b), as explained above (Coulson et al., 1998; Gunter & Friederici, 1999; Hahne & Friederici, 1999;
3
There is evidence that the repetition of deviant stimuli attenuates an ERP in response to a deviant stimulus.
For example, Yano, Suwazono, Arao, Yasunaga, and Oishi (2019) examined the effect of a preceding trial on
P300 (P3b), which has been known to show characteristics similar to P600. Their analysis showed the
amplitude of P3b for the deviant stimulus was significantly smaller when the preceding trial was also the
deviant stimulus than when it was the standard trial.
6
Osterhout & Hagoort, 1999; Osterhout et al., 1996). Previous studies are informative as to syntactic adaptation
to different environments. However, few studies have examined whether non-syntactic processes also exhibit
a sensitivity to probabilistic manipulation, which should be informative as to the flexibility/limit of adaptation
in sentence processing. Specifically, the present study examines whether the language-processing system
adapts to semantic violations and, if so, how semantic adaptation differs from syntactic adaptation.
In sum, the present study examines whether the language-processing system adapts to anomalous
sentences, and if so, whether such an adaptation depends on the types of linguistic violations. By doing so,
the present study aims to contribute to an understanding of a dynamic aspect of the language-processing
system, which has not been examined extensively. In the next section, we report the results of two
experiments that examine how ERPs elicited by morphosyntactic and semantic violations change over the
course of experiments.
2. Experiment
The present study conducted two ERP experiments that investigated adaptation to morphosyntactically
anomalous sentences (Experiment 1) and semantically anomalous sentences (Experiment 2). Although
previous studies on syntactic adaptation have used self-paced reading methods, ERPs are more suitable for
the purpose of the present study. It has been known that different cognitive processes are associated with
different ERP components. The present study focused on P600 and N400. The P600 effect has been observed
for (morpho)syntactic violations (e.g., Coulson et al., 1998; Kaan & Swaab, 2003a, 2003b; Osterhout &
Holcomb, 1992; Osterhout & Mobley, 1995, see also footnote 2). In contrast, an N400 effect has been
observed for semantical violations and semantically unpredicted words (e.g., Kutas & Federmeier, 2000, 2011;
Kutas & Hillyard, 1980, 1984; Lau, Holcomb, & Kuperberg, 2013; Lau, Namyst, Fogel, & Delgado, 2016;
Nieuwland et al., 2020). Although the exact functional role of the N400 in sentence processing has been a
matter of debate, there is a consensus that it reflects lexico-semantic processing. Because the present study
aims to compare syntactic adaptation with semantic adaptation, the ERP recording provides an advantage
over other experimental methods in that it enables us to selectively track how processes of interest change
during the experiments.
However, the ERPs may change during experiments for several reasons other than adaptation, such as
fatigue and lack of attention (Volpert-Esmond et al., 2018). This means that if an ERP effect decreases in
amplitude during experiments, the difference is attributable to factors other than adaptation. To avoid such
interpretations, we manipulated the probability of grammatical and ungrammatical sentences, as in Gunter
et al. (1997), and assessed whether ERP differences between ungrammatical and grammatical sentences
decreased only when the participants were exposed to a large proportion of ungrammatical sentences.
7
Fifty-two pairs of the target sentences were created for Experiments 1 and 2. In both experiments, the
target sentences consisted of two phrases (NP with a case marker + verb with a period). The sentences given
in (1)–(3) show a sample set of the sentences. The sentence shown in (1a) is grammatical while the sentence
shown in (1b) involves a case-assignment violation, as unaccusative intransitive verb kareta(withered) must
mark a single argument with a nominative case (‘-ga) but not with an accusative case (‘-o’) regardless of
thematic roles of the argument (i.e., agentivity) in Japanese (a language with the nominative-accusative case
alignment system). This sentence is syntactically ungrammatical. Unaccusative verbs are, by definition, unable
to assign an accusative case to its single argument in Japanese, like in many other languages. Thus, the internal
argument must move to the specifier position of Te ns e P hr as e (TP) to receive a nominative case from the head,
Te ns e (T) (Kishimoto, 2001, 2010; Miyagawa, 1989). Given this widely held assumption, the ungrammatical
sentence in (1b) is analyzed such that the argument bara-ostays in the structurally unlicensed position within
the Verb Phrase and apparently receives a structural accusative case from the verb with no ability to assign
an accusative case. Note that starting a sentence with NP-ACC does not affect the grammaticality because
subjects often drop in Japanese.
The sentence given in (2a) is semantically natural while the sentence given in (2b) is semantically
anomalous because the intransitive verb naita(cried) takes an inanimate noun shikibo(baton) as its subject
in (2b). Note that (2b) is syntactically well-formed, as the single argument is nominative case-marked.
The ratio of the morphosyntactically/semantically natural and anomalous sentences was manipulated by
intermixing filler sentences exemplified in (3) to balance the number of trials of the target sentences. Twenty-
six sentences included two phrases and 52 sentences included three phrases. To prevent participants from
expecting (1b) to be ungrammatical when reading the accusative case-marked noun phrase, 26 grammatical
sentences with an accusative-marked noun phrase were included, as exemplified in (3a). The ungrammatical
fillers involve violations of some types different from the target sentences. The ungrammatical sentences in
(3c) and (3d) are syntactically and semantically anomalous. In Japanese, mono-transitive verbs take an
accusative or dative case for objects. Since case-assignment is structurally dependent, the ungrammaticality
in (3c) and (3d) is caused by shogakko-oand ‘mado-ni not occupying in the place where they should
(Matsuoka, 2003). Nevertheless, unlike the nominative case in intransitive verbs, the selection of dative and
accusative cases is also semantically dependent in mono-transitive verbs (Inoue, 1983). Verbs of acti on, s uch
as ‘kick’ and ‘punchtend to take an accusative case while non-action verbs, such as ‘call’ and ‘greettend to
take a dative case. In other words, an accusative-marked object is interpreted as an entity that is affected by
an action (e.g., patient) whereas a dative-marked object is not (e.g., goal, theme). Therefore, the anomalous
sentence in (3c), for example, is interpreted such that the resulting state of elementary school is somehow
affected by the event of the son going there.
8
(1) Experiment 1 (Morphosyntactic violation)
a. Grammatical sentence:
bara-ga kare-ta.
rose-NOM wither(intransitive)-PST
‘The rose withered.
b. Morphosyntactically anomalous sentence:
*bara-o kare-ta.
rose-ACC wither(intransitive)-PST
(2) Experiment 2 (Semantic violation)
a. Semantically natural sentence:
shinseiji-ga nai-ta.
baby-NOM cry(intransitive)-PST
‘The newborn baby cried.
b. Semantically anomalous sentence:
*shikibo-ga nai-ta.
baton-NOM cry(intransitive)-PST
‘The baton cried.
(3) filler sentences:
a. remon-o shibotta.
lemon-ACC squeeze (transitive)-PST
‘(someone) squeezed a lemon’).
b. *eda-ga otta.
branch-NOM break (transitive)-PST
‘The branch broke something.
c. musuko-ga shogakko-{ni/*o} it-ta.
son-NOM school-DAT/ACC go-PST
‘The son went to the elementary school.(gois a dative-taking verb)
d. seisoin-ga mado-{*ni/o} fui-ta.
cleaning.staff-NOM window-DAT/ACC wipe-PST.
‘The cleaning staff wiped the window’ (wipeis an accusative-taking verb)
In each experiment, 52 pairs of the target sentences, such as (1) and (2), were distributed into two lists
according to the Latin square design such that each participant read 26 sentences of each condition. Seventy-
eight filler sentences were added to each list such that the probability of grammatical and anomalous
sentences was 50% and 50% for the equal probability block and 80% to 20% for the low probability block (i.e.,
9
130 sentences in total). Concretely, the equal probability block included 39 grammatical and 39 ungrammatical
filler sentences in addition to 26 grammatical and 26 ungrammatical target sentences (i.e., 65 grammatical
and 65 ungrammatical sentences in total). The low probability block included 78 grammatical sentences in
addition to 26 grammatical and 26 ungrammatical target sentences (i.e.,104 grammatical and 26
ungrammatical sentences in total). The target sentences were identical between the low probability and equal
probability blocks (see Supplementary Materials for all sentences). Tab le 1 summarizes the number of target
and filler sentences in each list (i.e., for each participant)
Table 1. The summary of the number of target and filler sentences in each list
Low probability block
Equal probability block
Experient 1: NP-NOM intransitive (1a)
Experiment 2: NP-NOM intransitive (2a)
Experient 1: NP-NOM intransitive (1a)
Experiment 2: NP-NOM intransitive (2a)
Experient 1: *NP-ACC intransitive (1b)
Experiment 2: *NP-NOM intransitive (2b)
Experient 1: *NP-ACC intransitive (1b)
Experiment 2: *NP-NOM intransitive (2b)
NP-ACC transitive (3a)
NP-ACC transitive (3a)
* NP-NOM transitive (3b)
NP-NOM NP-DAT transitive (3d)
NP-NOM NP-DAT transitive (3c)
* NP-NOM NP-ACC transitive (3c)
NP-NOM NP-DAT transitive (3d)
NP-NOM NP-DAT transitive (3d)
* NP-NOM NP-ACC transitive (3d)
total
total
An anonymous reviewer pointed out that the ungrammatical sentences ‘bara-o karetain (1b) still have
a highly frequent, thematic representation such as withered the rosealthough the position of the thematic
role was violated. This is not true. We should point out that the Japanese language strictly distinguishes
intransitive verbs from transitive verbs and all target verbs are intransitive. There is no intransitive-transitive
alternation like ‘The rose withered’ and (Someone) withered the rose’ in Japanese. Therefore, in
ungrammatical sentences, there is no syntactic/thematic position in which a theme/patient can occupy as an
object. To put it simply, the sentence in (1b) cannot be interpreted such that someone withered a/the rose.
As noted in the Introduction, previous studies (Coulson et al., 1998; Gunter et al., 1997) have compared
high and low probability blocks. However, the present study compared high and equal probability blocks
because the equal probability is considered a deviant case to participants to some degree as grammatical
sentences occur far more frequently than ungrammatical sentences in typical language use. Furthermore,
these studies manipulated the probability of grammatical and ungrammatical sentences with target sentences.
In contrast, to avoid the interpretation of results in terms of simple repetition priming, the manipulation of
10
the probability was achieved by including filler sentences in the present study.
An offline acceptability judgment survey was conducted to check the acceptability of target and filler
sentences. Acceptability was measured from 24 native speakers of Japanese who were undergraduate
students at Tohoku University (11 females and 13 males, mean age = 20.5, standard deviation of age = 1.5,
age range: 18.523.2). They were assigned to the low or equal probability condition (i.e., between-participant
factor). They were presented with each sentence in the center of a screen and instructed to rate it from 1
(unacceptable) to 5 (acceptable) with no time restrictions. Target and filler sentences were randomized for
each participant using the Ibex Farm platform (Drummond, 2007).
Figure 1 shows the results of the acceptability judgment survey. The morphosyntactically/semantically
grammatical sentences were judged as acceptable, while the morphosyntactically/semantically anomalous
sentences were judged as unacceptable. The linear mixed-effects model was used to examine the acceptability
of the experimental sentences (fixed factors: EXPERIMENT, PROBABILITY, VIOLATION, and their interactions;
random factors: participants and items; covariant: item order). The model revealed a main effect of VIOLATION
(β = −3.48, t = −36.97, p < 0.01) and a main effect of EXPERIMENT with the sentences of Experiment 2 being
more acceptable (β = 0.22, t = 2.29, p < 0.05). Other effects were not significant (p > 0.10). Since the result of
the offline acceptability judgement showed a robust difference between morphosyntactically/semantically
grammatical and anomalous sentences, these materials were employed for subsequent ERP experiments.
Figure 1. Mean acceptability derived from the offline acceptability judgement survey. The y-axis indicates an
acceptability of each type of sentence shown in the x-axis. Error bars indicate standard errors.
A sentence completion task was also conducted to examine cloze and transitional probabilities of the
target verbs.
4
The participants were 20 native speakers of Japanese who were undergraduate or graduate
students at Kyushu University (16 females and 4 males, mean age = 21.6, standard deviation of age = 2.6, age
4
Cloze probability of a word refers to the proportion of participants who complete a sentence fragment with
that word. For example, if 90% of participants responded with kareta (withered)’ to the sentence ‘bara-ga
(the rose) ____’ the cloze probability of kareta’ would be 0.9. Transitional probability refers to the proportion
of a certain type of a continuation given a context. For example, if intransitive verbs follow a nominative noun
at 90% of the time, the transitional probability of intransitive verbs following a nominative noun is 0.9.
4.76
1.22
4.86
4.71
1.19
4.76
1.58
1
2
3
4
5
   

Low Probability
Equal Probability
4.90
1.42
4.90
4.92
1.53
4.89
1.90
1
2
3
4
5
       

Low Probability
Equal Probability
11
range: 18.11–24.10). The participants were presented with the first phrase of a sentence (e.g., bara-ga, rose-
NOM) in the center of the screen and asked to complete a sentence. The target sentences of Experiments 1
and 2 were used and distributed into two lists according to the Latin square design such that each participant
saw 26 sentence fragments of each condition (i.e., 104 trials for each participant). The experimental sentences
were randomized for each participant using the Ibex Farm platform (Drummond, 2007).
Table 2 shows the results of the sentence completion task.
5
The cloze probability of the target verbs of
Experiment 1 was 0.14 (74/520) for the grammatical sentences (i.e., a nominative noun + target verb) and
0.00 (0/520) for morphosyntactically violated sentences (i.e., an accusative noun + target verb). The
transitional probability of intransitive verbs following a nominative noun was 0.76 (395/520) while the
transitional probability of the intransitive verbs following an accusative noun was 0.00 (1/520). This means
that the accusative nouns trigger an expectation for transitive verbs and thus the input of an intransitive verb
should violate such an expectation.
In Experiment 2, the cloze probability of the semantically natural and anomalous verbs was 0.05 (28/519)
and 0.00 (0/519), respectively. This means that the semantically anomalous verbs are not predictable. The
transitional probability of intransitive verbs given a nominative case was 0.65 (341/519) for semantically
natural sentences and 0.56 (295/519) for semantically anomalous sentences.
Table 2. The responses in the sentence completion task.
5
Two responses for the materials of Experiment 2 were removed due to typing errors. Another response for
the materials of Experiment 2 included morphosyntactic violation (shashin-ga totta. picture-NOM took, (lit.)
‘A picture took.’), in which a transitive verb marks a theme argument with a nominative case, not with an
accusative case.
Others in
Table 2 include (i) a transitive verb with a volitional morpheme ‘-ta(i),’ (ii) a transitive verb with a potential
morpheme ‘-(r)e(ru), (iii) a transitive verb with a stative morpheme ‘-tea(ru)/-tei(ru),’ (iv) transitive verb with
a causative morpheme ‘-(s)ase(ru), (v) an object noun (marked with a dative case or accusative case), (vi)
copula sentences with -da.’ In the case of (i), (ii), and (iii), a theme/patient argument of a transitive verb
exceptionally takes a nominative case (e.g., keeki-ga tabe-tai/tabe-reru/oi-tearu, cake-NOM eat-tai/eat-
reru/put-tearu, ‘(someone) wants to eat a cake/(someone) can eat a cake/(someone) put a cake somewhere).’
Experiment 1 Intr ansiti ve Transitive Adj ec tive Transitive in passive voice Others
(1a) NP-NOM (grammatical) 75.96 0.00 14.42 3.08 6.54
(1b) NP-ACC (ungrammatical) 0.19 97.89 0.00 0.00 1.92
Experiment 2 Intr ansiti ve Transitive Adj ec tive Transitive in passive voice Others
(2a) NP-NOM (natural) 65.70 3.08 5.40 1.73 24.09
(2b) NP-NOM (anomalous) 56.84 0.19 23.89 8.48 10.60
12
Twenty native speakers of Japanese were recruited from Toh o ku U ni v er si t y and Kyushu University (10
females and 10 males, mean age = 21.5, standard deviation of age = 1.5, age range = 19.423.3). The sample
size of this experiment was based on our previous study that showed the sample size of 20 is sufficient to
observe a reliable P600 effect (Yano, Suwazono, Arao, Yasunaga, & Oishi, 2019). All participants were classified
as right-handed based on the Edinburgh handedness inventory (Oldfield, 1971). Three participants had at least
one left-handed family member. All participants had normal or corrected-to-normal vision and no history of
reading disabilities or neurological disorders. Written informed consent was obtained from all participants
prior to the experiments, and they were paid for their participation. The experiments were approved by the
ethics committee of Graduate School of Arts and Letters of Tohoku University and by the ethics committee of
the Department of Linguistics of Kyushu University.
For the ERP experiment, stimuli were presented in a word-by-word manner in the center of a monitor
using Presentation (Neurobehavioral Systems). The order of stimuli was randomized for each participant. Each
trial started with a fixation of 1000 ms followed by the presentation of a blank screen for 200 ms (Figure 2).
Each phrase of the sentences was presented for 800 ms with a 100 ms inter-stimulus interval. Upon being
presented with a response cue (a blue diamond shape) shown 500 ms after the offset of the final phrase,
participants were required to judge whether the sentence is acceptable and to press the YES (acceptable) or
NO (unacceptable) button of the response pad (Cedrus, RB-740). The participants pressed the YES and NO
buttons with thumbs of different hands. The response buttons (left vs. right) were counterbalanced among
the participants. The response cue was presented for 1500 ms. The sentences and response cue were
presented in Meiryo UI font with the size of 28.
Figure 2. Illustration of the sentence presentation.
The participants were presented with two lists, one in the equal probability block and the other in the
low probability block. The order of the blocks was counterbalanced among the participants (i.e., 10
participants for each order). The participants took a break between these two blocks. Prior to each block, 10
practice trials were administered with the same grammatical/ungrammatical probability as for the subsequent
block. The participants were informed that they completed two experiments but not of how they differed (i.e.,
the manipulation of probability).
→ → → →
200 ms 100 ms 100 ms 500 ms
went
1500 ms
800 ms
800 ms
Phrase 2
Response
fixation
Phrase 1
Phrase 3
+ + +
1000 ms
800 ms
son-NOM
shool-DAT
13
EEGs were recorded from 19 Ag electrodes (QuickAmp, Brain Products and NE-113A, Nihon Kohden)
located at Fp1/2, F3/4, C3/4, P3/4, O1/2, F7/8, T7/8, P7/8, Fz, Cz, and Pz according to the international 10–10
system (Acharya, Hani, Cheek, Thirumala, & Tsuchida, 2016). Additional electrodes were placed below and to
the left of the left eye to monitor horizontal and vertical eye movements. The online reference was set to the
average of all electrodes and EEGs were re-referenced offline to the average value of the earlobes. The
impedances of all electrodes were maintained at less than 10 kthroughout the experiment. The EEGs were
amplified with a bandpass of DC to 200 Hz, digitized at 1000 Hz.
EEGs were filtered offline with a 30 Hz low-pass filter and were time-locked to the onset of the second
phrase of the target sentences (i.e., verbs), in which morphosyntactic violations can be detected. The time-
window of −100 to 900 ms relative to the onset of the second phrase was epoched. The 10 Hz low-pass filter
was applied only for plotting grand average ERPs. EEGs were normalized for each trial using a 100 ms
prestimulus baseline. Tri als wit h lar ge ar tefact s ( excee ding ±80 µV ) wer e aut omat ically removed from the
analysis. The number of rejected trials was not statistically significant between conditions in Experiment 1
(χ2(3) = 5.10, n.s., the mean number of rejected trials was 3.43, standard deviation was 3.68) or in Experiment
2 (χ2(3) = 3.97, n.s., the mean number of rejected trials was 3.83, standard deviation was 3.59).
Statistical analyses were conducted using linear mixed-effects (LME) models fitted with the lmer function
of the lme4 package in R (Bates, Mächler, Bolker, & Walker, 2015) to examine how the ERP effect changed
throughout the experiment. The dependent variables included mean amplitudes calculated for each
participant, trial, and electrode.
6
Although P600 effects were expected to appear in response to
morphosyntactic violations in the time windows of 700900 ms on the basis of previous studies in Japanese
(Mueller, Hirotani, & Friederici, 2007; Nashiwa, Nakao, & Miyatani, 2007; Yano, 2018; Yano & Sakamoto, 2016;
Yan o, Su zu ki, & K oiz um i, 20 18 ), the mean amplitudes of 300500 ms and 500700 ms were also analyzed to
examine at which time-windows the ERPs started to change over the course of the experiment. The division
of a P600 time-window into early and late time-windows (i.e., early P600 time-window of 500700 ms and
late P600 time-window of 700900 ms) is motivated by previous studies showing that different types of
sentences show a P600 effect with different peak latencies and topographical distributions (Barber & Carreiras,
2005; Carreiras, Salillas, & Barber, 2004; Hagoort & Brown, 2000; Kaan & Swaab, 2003b, 2003a; Molinaro,
Barber, & Carreiras, 2011, see also footenote 2).
6
Although trials with incorrect responses were included (i.e., YES response for ungrammatical sentences and
NO response for grammatical sentences), the significance of statistical analyses did not change after removing
them.
14
One might wonder why the time-window of P600 is late in Japanese. This issue may be related to verb-
finality of Japanese because the argument-verb relation is formed at the verb position. Although it is often
said that a critical word should not be placed at the sentence-final word, it cannot be avoided in Japanese, a
strict verb-final language. However, Stowe, Kaan, Sabourin, and Taylor (2018) argue that there is little evidence
to believe that sentence-final ERPs reflect sentence wrap-up. Furthermore, to preview our results, the
sentence-final verb did not show any prolonged negativity trend that has been interpreted as a wrap-up effect
in the literature (see e.g., Friederici & Frisch, 2000; Ueno & Kluender, 2003) because our experimental
sentences were short and did not induce an excessive working memory load. The late peak latency of P600
may also be related to the task. In the present experiment, the participants were asked to not execute the
button press quickly in order to avoid the movement-related artifact contaminating the ERPs of the second
phrase. Since the peak latency of P600 is response-aligned (Sassenhagen & Bornkessel-Schlesewsky, 2015;
Sassenhagen, Schlesewsky, & Bornkessel-Schlesewsky, 2014), it is plausible to expect a late P600 effect rather
than an early P600 effect. Furthermore, previous experiments using similar materials and the same instruction
have observed a P600 at the late time-window (Yano, 2018; Yano & Sakamoto, 2016; Yano et al., 2019, 2018).
The models included independent variables of interest, namely, PROBABILITY (low vs. equal), VIOLATION
(morphosyntactically grammatical vs. anomalous sentences), and ITEM ORDER of each block (1130) with
their interactions as fixed factors. Note that ITEM ORDER refers to the order of sentences that the participants
have read in each block, not the order of the two blocks. Because the ordering of the block did not have a
significant impact on the P600 effect, the following results section reports results of the models without the
ordering as a factor of interest. The morphosyntactically grammatical and anomalous sentences were coded
as0.5 and 0.5, respectively. The deviation coding of ±0.5 was used because a positive estimated coefficient
indicates a P600 effect for ungrammatical sentences relative to grammatical sentences and an intercept
indicate a grand mean P600 amplitude across conditions. Note that we use the term P600 to refer to a
positivity observed in a single condition, while we refer to the P600 difference between two conditions as a
P600 effect. The coding choice is only for the ease of interpreting results of coefficients and does not affect a
significance of statistical analyses reported below. The low and equal probability blocks were assigned values
of 0.5 and 0.5, respectively. The participant, item, and position of the electrode were treated as random
factors (Payne, Lee, & Federmeier, 2015).
7
Including random intercept and slope of participants and items for
the Item Order term caused a convergence problem due to the complexity of models. Thus, the random
intercept and slope of participants and items were assumed for PROBABILITY and VIOLATION, but not for ITEM
7
According to Delaney-Busch (2015), mixed-effect models are more likely to induce a type I error as the
number of electrodes increase and it is recommended to average the P600 amplitudes across the electrodes
of interest. We conducted statistical analyses following this recommendation and confirmed that the
statistical significance of the results did not differ crucially for the purpose of our discussion (see appendix).
We included the location of channels as a random factor (i.e., (1|ch), following Payne et al. (2015). Again, this
did not significantly change the results reported in the main text.
15
ORDER. As the topographical distribution of P600 is well known, the statistical analyses used EEG data for the
centro-parietal regions (Cz, Pz, C3/4, P3/4, P7/8, and O1/2).
8
The maximal model was built and then
compared with more parsimonious models with simpler random effects in the backward stepwise method
using the anova function (cf. Matuschek, Kliegl, Vasishth, Baayen, & Bates, 2017). The final models were
reported for each analysis below. P-values were calculated based on the Satterthwaite’s method by submitting
the final model to the lmer function of the lmerTest package (Kuznetsova, Brockhoff, & Christensen, 2017).
The effects of interest included the VIOLATION effect, the interaction of PROBABILITY × VIOLATION, the
interaction of VIOLATION × ITEM ORDER, and the interaction of PROBABILITY × VIOLATION × ITEM ORDER. The
interaction of PROBABILITY × VIOLATION was resolved by conducting separate analyses at each level of
PROBABILITY. When the interaction of VIOLATION × ITEM ORDER (continuous variable) was significant, simple
slopes of VIOLATION were calculated on values of +1 SD and −1 SD from the mean of ITEM ORDER, following
Cohen and Cohen (1983) on the website of Preacher, Curran, & Bauer (2006). Only for the sake of simplicity,
we refer to the trials of +1 SD and −1 SD from the mean as the first and second halves of the experiments.
When multiple comparisons were conducted, an α level of 0.05 divided by the number of comparisons are
considered to be a significant level (i.e., the Bonferroni correction).
The morphosyntactic violations were expected to elicit a P600 effect, according to similar experiments in
Japanese (Yano, 2018; Yano & Sakamoto, 2016; Yano et al., 2018) and the literature of other languages. A left
anterior negativity (LAN) effect has also been observed for morphosyntactic violations in Indo-European
languages with verb agreement system (see Molinaro et al., 2011 for review), but less often in East Asian
languages including as Japanese, except for a special case such as a verb is distant from its subject, when a
reader makes a strong prediction for a verb morphology (Yano, 2018). Thus, we did not expect the LAN to be
elicited in the present experiment.
The focus of the present study was whether the magnitude of the P600 effect changed over the course
of each block, depending on the probability of anomalous sentences. If the language-processing system does
not adapt to anomalous sentences, the P600 effects were expected to appear throughout both low and equal
probability blocks. Alternatively, if the language-processing system is adaptive in nature, the P600 effect
should decrease in amplitude during the equal probability block, in which the participants were exposed to a
large number of anomalous sentences. Statistically speaking, this should expect the interaction of VIOLATION
× ITEM ORDER to be significant in the equal probability block, due to the attenuation of the P600 effect. In the
8
Although ERP studies typically treat topographical factors as independent factors of ANOVA, the present
study did not include the electrode’s position (ANTERIORITY and HEMISPHERE) as a fixed factor due to the
problems of model convergence. However, as the topographical distribution of ERPs was not of interest for
the present study, this is not an issue here.
16
low probability block, the interaction of VIOLATION × ITEM ORDER reflecting the attenuation of the P600 effect
is not expected even if the language processing system is adaptive because the number of anomalous
sentences is small in this block. Accordingly, the interaction of PROBABILITY × VIOLATION × ITEM ORDER
should be expected if the language-processing system adapts to anomalous sentences.
The result of the acceptability judgment task was analyzed with a linear mixed-effects model that
included PROBABILITY, VIOLATION, and ITEM ORDER as fixed effects and participants and items as random
factors. The main effect of VIOLATION was significant, showing that the acceptability of morphosyntactically
grammatical sentences was higher than anomalous sentences (β = −7.95, t = −20.63, p < 0.01) (Ta bl e 3). The
interaction of PROBABILITY × VIOLATION × ITEM ORDER was also found to be significant (β = 1.80, t = 2.67, p
< 0.01). To e xa m in e w hi c h c on d it i on ITEM ORDER affected, post hoc analyses were conducted. The result
showed a significant ITEM ORDER effect for the morphosyntactically violated sentences for the equal
probability block (β = 1.04, t = 2.58, p < 0.01), showing that the participants were more likely to accept them
with increasing exposure. The ITEM ORDER effect was found to be significant for morphosyntactically
grammatical sentences of the low probability block, showing an increase in their acceptability (β = 0.57, t =
2.01, p < 0.05).
The response times were analyzed in the same manner. The ITEM ORDER effect was significant, showing
that faster response times were achieved over the course of the experiment (β = −9.58, t = −2.82, p < 0.01). A
marginally significant interaction of PROBABILITY × VIOLATION × ITEM ORDER was also observed (β = 25.65, t
= 1.88, p = 0.05). Post hoc analyses showed a marginally significant interaction of VIOLATION × ITEM ORDER
was found for the low probability block but not for the equal probability block (Low: β = −16.84, t = −1.79, p =
0.07; Equal: β = 8.23, t = 0.83, p > 0.10). The interaction of VIOLATION × ITEM ORDER means that the tendency
for the slower response times at the grammatical sentences relative to the morphosyntactic violation
increased during the low probability block.
Table 3. Mean and SD (in parentheses) of acceptability of reaction times (RT) in Experiment 1.
Low/Grammatical
Low/Morphosyntactic violation
Equal/Grammatical
Equal/Morphosyntactic violation
Acceptability (%)
96.2 (3.9)
1.8 (3.0)
98.4 (2.4)
3 (5.4)
RT (ms)
450.8 (172.9)
413.9 (144.1)
443.9 (206.9)
435.7 (163.5)
Figure 3 shows grand average ERPs of the second phrases (i.e., verbs) in Experiment 1. A visual inspection
suggests that morphosyntactically ill-formed sentences elicited a larger P600 than grammatical counterparts
17
in Experiment 1. Furthermore, the P600 effects were greater for the low probability block than for the equal
probability block.
Figure 3. Grand average ERPs of the second phrase in Experiment 1.
The x-axis represents the time duration and each hash mark represents 100 ms. The Y-axis represents the
voltage. Negativity is plotted upward. The isovoltage maps represent violation effects calculated as
morphosyntactic violations minus grammatical conditions at 300500 ms, 500700 ms, and 700900 ms.
At the 300500 ms time-window, a significant interaction of VIOLATION × ITEM ORDER and PROBABILITY
× VIOLATION × ITEM ORDER was observed (Tab le 4). Planned comparisons made at each level of PROBABILITY
revealed a significant interaction of VIOLATION × ITEM ORDER only for the equal probability block (β = −0.91,
t = −4.21, p < 0.01), showing that ungrammatical sentences elicited a small negativity compared to
Grammatical Morphosyntactic Violation
-5
15
F3
-5
15
Fz
-5
15
F4
-5
15
C3
-5
15
Cz
-5
15
C4
-5
15
P3
-5
15
Pz
-5
15
P4
Equal Probability Block
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
-5
15
F3
-5
15
Fz
-5
15
F4
-5
15
C3
-5
15
Cz
-5
15
C4
-5
15
P3
-5
15
Pz
-5
15
P4
Grammatical Morphosyntactic Violation
Low Probability Block
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
18
grammatical sentences for the second half of the experiment but not the first half of the experiment (β = 0.45,
t = 0.60, p > 0.10; β = −1.34, t = −1.78, p = 0.08).
9
For 500700 ms the main effect of VIOLATION was found to be marginally significant, showing a greater
positivity found for the ungrammatical sentences (Ta bl e 5). Furthermore, interactions of VIOLATION × ITEM
ORDER and of PROBABILITY × VIOLATION × ITEM ORDER were significant. The planned comparison showed a
marginally significant effect of VIOLATION for the low probability block but no significant effect for the equal
probability block (β = 1.80, t = 1.65, p = 0.10; β = 0.60, t = 0.71, p > 0.10).
From 700900 ms the VIOLATION effect and the interaction of PROBABILITY × VIOLATION × ITEM ORDER
reached a significant level (Ta ble 6). The VIOLATION effect was found to be significant for the low probability
block but not for the equal probability block (β = 3.85, t = 3.08, p < 0.01; β = 0.88, t = 0.77, p > 0.10). The
interaction of VIOLATION × ITEM ORDER was significant in both low and equal probability blocks (β = 1.27, t =
4.56, p < 0.01; β = −1.57, t = −5.72, p < 0.01). As indicated by their coefficients in the opposing direction, ITEM
ORDER had a different effect on the P600 effect for the low and equal probability blocks. For the low
probability blocks, ungrammatical sentences exhibited an increasing P600 effect over the course of the
experiment consistent with the marginally significant effect of VIOLATION observed for the first half and with
a significant effect observed for the second half of the experiment (β = 2.58, t = 2.01, p = 0.05; β = 5.13, t =
4.00, p < 0.01). For the equal probability block, on the other hand, the P600 effect was observed for the first
half and it then declined throughout the experiment (β = 2.45, t = 2.07, p < 0.05; β = −0.68, t = −0.57, p > 0.10).
Table 4. The linear mixed-effect model for the 300500 ms time-window for Experiment 1.
*p < .05, **p < .01
Table 5. The linear mixed-effect model for the 500700 ms time-window for Experiment 1.
9
An anonymous reviewer pointed out that since the participants read similar sentences, the repetition
priming effect should be expected. This effect is shown by the main effect of Item Order (see the bottom row
of Ta bl e 4).
.94 8 2.  
<868 * * ) 
1545991  )  *)
3949<3 * ()  **
13 ()  ( (
10  ( * 
30 )(  
130 )  
80780 *  
19
+p < .10, **p < .01, *** p < .001
Table 6. The linear mixed-effect model for the 700900 ms time-window for Experiment 1.
+p < .10, **p < .01, *** p < .001
To e xa mi n e which condition contributed to P600 changes during the experiment, additional linear mixed-
effect analyses were conducted for each condition (i.e., EEG ~ IO + (1|Participant) + (1|Set) + (1|ch)). The
model included ITEM ORDER as a sole independent factor and a mean amplitude of 700900 ms as a
dependent variable. The results showed a significant effect of ITEM ORDER for all four conditions
(Low/Grammatical: β = −0.68, t = −3.46, p < 0.01; Low/Ungrammatical: β = 0.58, t = 2.95, p < 0.01;
Equal/Grammatical: β = 0.54, t = 2.78, p < 0.01; Equal/Ungrammatical: β = −1.03, t = −5.27, p < 0.01; positive
coefficients denote an increase in the P600’s amplitude).
10
As is shown by the positive/negative coefficients
of ITEM ORDER, the P600 decreased in amplitude under the grammatical condition while it increased under
the ungrammatical condition for the low probability block (Figure 4., left). On the other hand, the P600
increased under the grammatical condition and decreased under the ungrammatical condition in the equal
probability block (Figure 4., right).
10
Note that throughout the manuscript, we refer to a positivity as a P600 and a difference in the positivity
between grammatical and ungrammatical sentences as a P600 effect.
<59 3  
0979 *+*   .
26562 )  ) )*
454  * + 
2E4 ) 
2E01 ) + ) )
4E01 + * .
2E4E01 )+ ) + .
09<18901   (*  
<59 3  
0979 ( + *) 
26562   + *
454   +  
2E4 * ( +
2E01   
4E01 )  +) ()
2E4E01 )  ++ . 
09<18901 )  )
20
Figure 4. The P600 change in the low (left) and equal (right) probability blocks of Experiment 1.
The x-axis represents item order (i.e., 1–130) and the y-axis represents the amplitude of the P600 in the time-
window of 700900 ms. Positivity is plotted upward. Each line represents the P600 changes estimated by the
linear mixed-effects models and each dot represents an estimated P600 amplitude of every 20 trials. The gray
areas represent the 95% confidence interval.
We also examined the effect of ITEM ORDER on the P600’s peak latency for the grammatical and
ungrammatical sentences.
11
The results showed a significant interaction of VIOLATION × ITEM ORDER
reflecting the increasing peak latency of the P600 throughout the experiment (β = 10.16, t = 5.70, p < 0.01).
The three-way interaction of PROBABILITY × VIOLATION × ITEM ORDER was significant (β = 8.68, t = 2.43, p <
0.01), as the peak latency of the ungrammatical sentences slowed relative to that of the grammatical
sentences as the participants were repeatedly exposed to them during both low and equal probability blocks
(Low: β = 5.84, t = 2.29, p < 0.05; Equal: β = 14.59, t = 5.84, p < 0.01).
To summarize the result of Experiment 1, a P600 effect for the morphosyntactically anomalous sentences
decreased when the probability of ungrammatical sentences was the same as that of grammatical sentences
while it increased when their probability was low relative to that of grammatical sentences.
In the equal probability block, the P600 effect was smaller because of a decline in the P600’s amplitude
11
Peak latency was obtained for each participant, trial, and channel by finding a time in which the greatest
positivity was observed for the verb after 500900 ms.
Morphosyntactic violation
Grammatical
Morphosyntactic violation
Grammatical
21
under the ungrammatical condition and an increase under the grammatical condition. If the P600 reflects a
syntactic repair (Kaan & Swaab, 2003), the attenuation of the P600 suggests that the participants experienced
less syntactic processing difficulty when encountering a mismatch between the accusative-marked noun and
the intransitive verb as they increased their expectation for them. Alternatively, the result can be interpreted
in terms of other functional interpretations of P600. For example, there have been a number of reports that
semantic violations, such as “The hearty meal was devouring ...,” elicit a P600 effect, suggesting that the P600
indexes more general integration difficulty when the syntactic information conflicts semantic information
(Bornkessel-Schlesewsky & Schlesewsky, 2008; Brouwer, Fitz, & Hoeks, 2012; Chow & Phillips, 2013; Kim &
Osterhout, 2005; Kolk, Chwilla, Van Herten, & Oor, 2003; Kuperberg, Caplan, Sitnikova, Eddy, & Holcomb,
2006; Kuperberg, Kreher, Sitnikova, Caplan, & Holcomb, 2007; Kuperberg, Sitnikova, Caplan, & Holcomb, 2003;
Oishi, Jincho, & Mazuka, 2011; Van Herten, Kolk, & Chwilla, 2005). In the case of our sentences (i.e., *bara-o
kareta, ‘rose-ACC withered’), the morphosyntactic information signals an argument as a patient of the event
whereas the semantic information indicates it is a theme. If the P600 effect reflects a resolution of such
conflict, the decline of the P600 effect suggests that the language processing system became familiarized with
the conflict resolution during the experiment. More recently, Fitz and Chang (2019) propose that a P600
reflects a cost of a learning process to develop an accurate probabilistic model. According to their
computational model, when the language processing system faces a processing error, it propagates the error
back to the lower-level units to enable learning probable syntactic representations. If their interpretation of
the P600 is correct, the P600 change reflects a successful learning process that attempt to minimalize the
prediction error.
Although the exact functional contribution of P600 to language comprehension is still debated, the result
of the equal probability block suggests that the language-processing system can adapt to morphosyntactically
violated sentences and supports the expectation-based account. This finding might lead one to expect that
the language-processing system is generally adaptive and attempts to minimize a prediction error. We will
return to this issue in General discussion in relation to the result of Experiment 2.
In contrast, the present result is not compatible with the hypothesis that accounts for syntactic
adaptation in terms of (cumulative) syntactic priming, which is assumed to involve more passive processing
(cf. Reitter et al., 2011; Traxler & Tooley, 2008). According to this interpretation, increased base-level activation
(or residual activation) of syntactic frames facilitates the processing of subsequent sentences with the same
syntactic frames. We, however, consider this interpretation unlikely in the present case. As noted in
Introduction, ungrammatical sentences, such as case-assignment violations, do not have a licit syntactic
representation that Japanese speakers can have. Thus, it is impossible to increase an activation level of such
syntactic representation. Furthermore, the number of ungrammatical target sentences was kept constant
between the low and equal probability blocks. Therefore, the repeated presentation of case-assignment
violations in target sentences should prime their processing to the same degree for the low and equal
22
probability blocks if this interpretation is correct. P600 attenuation was observed for the ungrammatical
sentences only for the equal probability block, which is at odds with the representation-based account.
Another possible interpretation of the result is that, as the participants read ungrammatical sentences
many times, they were reluctant to repair anomalous input to avoid wasting cognitive resources, resulting in
a decrease in the P600 amplitude. If the interpretation is correct we would expect the acceptability of
ungrammatical sentences to not change and for the response times of the acceptability judgment and the
peak latency of the P600 to shorten during the experiment because the participants should respond “NO”
without repairing a morphosyntactic violation after detecting it. However, neither of these outcomes resulted.
The acceptability of the ungrammatical sentence improved during the equal probability block. Furthermore,
response times for ungrammatical sentences did not shorten relative to those of the grammatical sentences
although a general pattern of acceleration was observed as often reported from behavioral experiments. The
peak latency of the P600 in response to ungrammatical sentences slowed throughout the experiment.
Therefore, we interpreted the decline in the P600 observed for ungrammatical sentences of the equal
probability block as evidence for adaptation to morphosyntactic violations. One might wonder whether the
participants were aware of the probability manipulation and took some strategies to process the
ungrammatical sentences. After the experiment, the participants were asked if they noticed any difference
between the first and second experiments (i.e., a difference between the low and equal probability blocks).
Those who noticed our probability manipulation were not representative (i.e., only three of 20). Thus, the
adaptation to morphosyntactic violation seems unconscious rather than conscious and strategic.
The grammatical sentences, on the other hand, exhibited an increase in P600 for the equal block. This
may be related to the cue validity of case markers in predictive processing. As the half of the sentences were
ungrammatical in the equal block, the participants recognized that the pre-verbal phrases were not
informative as to syntactic structure of a sentence as the experiment continued and thus did not incorporate
a potentially expected verb into the syntactic representation prior to its appearance. Consequently, the
processing cost of a verb increased throughout the processing of grammatical sentences. This type of
increased processing load is reminiscent of those described in Fine et al. (2013), who reported a trade-off of
processing costs between a priori frequent and infrequent structures. According to their self-paced reading
experiment, adjusting expectations to infrequent structures increased the processing costs of frequent
structures. However, adaptive behavior was limited in that the processing of a less frequent structure did not
become easier than that of a more frequent structure. This holds true for the present case, as the P600 effect
was not reversed for the equal probability block of Experiment 1 (i.e., P600 effect for grammatical sentences).
Other studies also show no differences in P600 between grammatical and ungrammatical sentences for the
high probability block (Hahne & Friederici, 1999). This implies that morphosyntactic rules (e.g., case
assignment and verb inflection) are robust to the extent to which they cannot be replaced by a priori
ungrammatical morphosyntactic rules, like an intransitive verb marks an argument with an accusative case.
23
The observation that the opposite pattern was found for the low probability block supports our
interpretation of the results of the equal probability block. For the low probability block, the P600 amplitude
decreased for the grammatical sentences and increased for the ungrammatical sentences. As the pre-verbal
phrase provided useful information on the syntactic structure of a sentence in this block, the participants
incorporated this information into their predictive computations. Consequently, processing was facilitated at
the verb, attenuating the P600 amplitude of grammatical sentences. On the other hand, such an expectation
should lead to a severe processing cost at the verb of the ungrammatical sentences.
12
Thus, the participants
needed to repair the syntactic structure of the sentence upon encountering the verb. The increase in the P600
amplitude observed for the ungrammatical sentences can be considered a consequence of such processing
errors, as the participants expected a grammatical sentence more over the course of the experiment.
An anonymous reviewer pointed out that the nominative-initial sentences are always grammatical and
the accusative-initial sentences are either grammatical or ungrammatical in the low probability block. In
contrast, a great deal of the nominative-initial fillers is ungrammatical as well in the equal probability block,
so the response mapping is not as straightforward as in the low probability block. Thus, adaptation patterns
found may be reflecting adaptation of response strategies rather than adaptation to the anomalies. However,
this possibility seems less likely. If the participants strategically judged the acceptability of a sentence based
on the first phrase, we should expect the P600 effect to decrease over the course of the low probability block,
in which the participants were more certain about the acceptability of a sentence upon reading the first phrase.
In a similar vein, if the participants strategically judged the acceptability of a sentence in the equal probability
block as well, they were more uncertain about an acceptability at the timing of the first phrase. Accordingly,
they should have needed to perform more effortful processes in the second phrase and thus the
ungrammatical sentences should elicit a robust P600 effect. These predictions were not borne out by the
present results because the P600 effect increased in the low probability block and it decreased in the equal
probability block.
Experiment 1 showed evidence for syntactic adaptation to ungrammatical sentences. In Experiment 2,
we aim to extend an understanding of the nature of linguistic adaptation by examining whether adaptive
behavior depends on the type of linguistic violations, using semantic violations.
Some previous studies examined the probability effect on semantic processing associated with an N400,
12
According to a corpus analysis, inanimate accusative phrase are followed by transitive verbs most of the
time (97%), implying that an intransitive verb following an accusative phrase violates a syntactic expectation
(Yano, 2018).
24
although there are few studies that directly examined adaptation to semantic violations. Zhang et al. (2019)
examined the effect of the ratio of predictable and unpredictable words in sentences on the N400 effect. The
result showed an N400 effect for unpredictable words regardless of the ratio, suggesting that the probability
manipulation does not affect the way the language-processing system processes semantic information.
However, another study reported a different pattern. Lau, Holcomb, and Kuperberg (2013) manipulated the
probability of semantically related and unrelated word pairs (Related: shove push vs. Unrelated: blaze - push).
In their priming experiment, a robust N400 effect (i.e., the difference between the semantically primed and
unprimed word) was observed in the high relatedness block (50% of primes are related to targets), unlike in
the low relatedness block (10% of primes are related).
13
Nevertheless, it is difficult from the observation of
these studies to discern the limit of adaptive behavior.
Experiment 2 focuses on adaptation to semantic violations to clarify this issue. Given the result of
Experiment 1, the language-processing system seems to track the probability distribution of anomalous
sentences and adapt to it by updating its expectation. However, it is conceivable that the language-processing
system does not always adapt to anomalous input considering that there should be a trade-off between
flexible and fixed behavior. A flexible processing system can reduce expectation error, allowing for more
efficient processing in a given situation. However, it consumes the cost of keep estimating the probability
distribution of the input. In contrast, a more fixed processing system is more likely to experience expectation
error, but does not need to change the way it processing the input constantly. Thus, it is hypothesized that
there is a mediating factor, such as typicality of error, which encourages or discourages the language-
processing system to adapt to unexpected input. Given that semantic anomaly is more atypical than
(morpho)syntactic error (e.g., non-native utterances, agreement illusion, syntactic blends, cf. Frazier, 2014), it
may not be optimal for the language-processing system to adapt to semantic errors. This issue will be
investigated in Experiment 2.
Twenty undergraduate and graduate students were recruited from Kyushu University (15 females and 5
males, mean age = 20.6, standard deviation of age = 1.6, age range = 19.423.11). None of them took part in
13
There is another relevant study conducted by Nieuwland and Van Berkum (2006). The authors used
cartoon-like short story in which an inanimate entity behaves as if it was animate (‘A woman saw a dancing
peanut who had a big smile on his face’). The result showed that an N400 amplitude was smaller for typically
implausible but contextually plausible words (‘The peanut was in love’) than typically plausible but
contextually implausible words (‘The peanut was salted’). However, it is unclear whether this observation
suggests that the language-processing system can use semantic context to direct expectation to a typically
implausible event when necessary. Semantically related words were used in prior contexts (e.g., the peanut
was totally crazy about her), which semantic priming effect may also have played some role in attenuating
the N400 amplitude for the typically implausible word (i.e., in love). Furthermore, because all experimental
sentences were cartoon-like stories and the probability of plausible/implausible stories was not manipulated,
it is not clear whether the N400 effect was due to an N400 attenuation for the contextually plausible word
(i.e., semantic adaptation) rather than an N400 enhancement for the contextually implausible word.
25
Experiment 1. Four participants had at least one left-handed family member. All participants had normal or
corrected-to-normal vision and no history of reading disabilities or neurological disorders. Written informed
consent was obtained from all participants prior to the experiments, and they were paid for their participation.
The experiments were approved by the ethics committee of the Department of Linguistics of Kyushu University.
Because Experiments 1 and 2 were conducted for different participants, their individual cognitive traits
were obtained to assess the extent to which the participants were different between experiments, using Paced
Auditory Serial Addition Test (PASAT) (Gronwall, 1977), Symbol Digit Modalities Test (SDMT) (A. Smith, 1968,
1973), autistic-spectrum quotient (AQ) (Wakabayashi, Baron-Cohen, Wheelwright, & Tojo, 2006), and P3b.
14
Two sample t-tests were conducted for each score. The results showed no significant difference in any of these
measures, suggesting that the participants of Experiments 1 and 2 were not different in terms of cognitive
abilities (Ta bl e 7).
Table 7. Mean and SD of individual cognitive traits in Experiments 1 and 2.
Experiment 1
Experiment 2
PASAT
54.9 (SD = 5.15)
53.25 (SD = 4.93)
t (38) = 1.00, p > 0.10
SDMT
69.70 (SD = 12.25)
70.55 (SD = 11.57)
t (38) = −0.21, p > 0.10
AQ
24.60 (SD = 6.35)
22.35 (SD = 4.41)
t (38) = 1.26, p > 0.10
P3b
8.42 (SD = 6.00)
5.92 (SD = 5.50)
t (38) = 1.33, p > 0.10
The target sentence of Experiment 2 is reproduced in (4). The sentence in (4a) is semantically natural,
whereas (4b) is semantically anomalous due to animacy violation.
(4) Experiment 2 (Semantic Violation)
14
PASAT and SDMT are standardised tests of Clinical Assessment for Attention (CAT) that assess how rapidly
and efficiently one can control cognitive resources and assign resources to multiple information. In PASAT, a
series of one-digit number was presented auditorily at the pace of 2 seconds and the participants were
required to answer the sum of a new number and the number immediately prior to it (For example, when
hearing 7, 2, 3, and 4, they should answer 9, 5, and 7). The maximum score is 60. In SDMT, the participants
are shown a sheet on which nine number-symbol pairs (e.g. “1-(“) and a series of symbols were written. They
answer numbers matched with the symbols while looking at the number-symbol pairs. The score is the
number of correct responses out of 110 within 90 seconds.
AQ is a self-reporting questionnaire with respect to autistic traits, which consist of five components, namely,
social skill, attention switching, attention to details, communication skill, imagination. High AQ scores mean
that a person has an autistic trait. The maximum score is 50.
In the P3b experiment, the standard (triangle: ) and target (inverted triangle: ) stimuli were presented
in the ratio of 4 to 1 (120 trials for the standard and 30 trials for the target). The order of stimuli was
randomized for each participant. The participants were asked to press a button when they saw a target as
accurate and quickly as possible (no response for the standard stimuli). The SOA (stimulus onset asynchrony)
was 1100 ms and the DOS (duration of stimulus) was 100 ms. The P3b effect (µV) was quantified by calculating
amplitude differences between the target minus the standard at the time-window of −30 to 30 ms centred
on the individual peak latency at Pz, where the difference was largest.
26
a. Grammatical sentence:
shinseiji-ga nai-ta.
baby-NOM cry-PAST
‘The newborn baby cried.’
b. Semantically anomalous sentence:
*shikibo-ga nai-ta.
baton-NOM cry-PAST
‘The baton cried.’
Fifty-two pairs of the target sentences were distributed into two lists according to the Latin square design
such that each participant read 26 sentences of each condition. The filler sentences used in Experiment 1 were
added to create the equal and low probability blocks (i.e., 130 sentences in total). The participants saw two
lists, one in the equal probability block and the other in the low probability block. The order of the blocks was
counterbalanced among the participants. EEG recordings and analyses were conducted in the same way as
those used in Experiment 1 (see Section 2.2.2.).
The recording and data analyses were conducted in the same way as in Experiment 1. As in Experiment
1, the models included independent variables of interest, namely, PROBABILITY (low vs. equal), VIOLATION
(semantically natural vs. anomalous sentences), and ITEM ORDER (1130) with their interactions as fixed
factors. As it has been well-known that an N400 effect appears over the centro-parietal regions at the time-
window of 300500 ms post-stimulus onset (Kutas & Federmeier, 2000, 2011), the statistical analyses used
EEG data of this time-window at the centro-parietal regions (Cz, Pz, C3/4, P3/4, P7/8, and O1/2) as dependent
variables. The semantically natural and anomalous sentences were coded as −0.5 and 0.5, respectively, such
that a negative estimated coefficient of Violation indicates an N400 effect for anomalous sentences relative
to natural sentences.
Semantic violation was expected to elicit N400 effects according to previous literature (e.g. Kutas &
Hillyard, 1980). The focus of Experiment 2 was whether the magnitude of the N400 effects changed over the
course of experiments. If language processing is susceptible to semantically anomalous sentences, the N400
effect should decrease in amplitude during experiments. However, it is also conceivable that the type of
linguistic violations affects such an adaptation. This hypothesis predicts that the N400 effect should appear
throughout the experiments.
27
For acceptabilities of the sentences, the result showed a significant VIOLATION effect, indicating that the
semantically anomalous sentences were judged less acceptable than the semantically natural sentences (β =
9.34, t = −15.91, p < 0.01) (Ta bl e 8). The other effects were not significant.
As for response times, only the effect of ITEM ORDER was significant because the participants responded
faster over the course of the experiment (β = −15.40, t = −4.28, p < 0.01).
Table 8. Mean and SD (in parentheses) of acceptability of reaction times (RT) in Experiment 2.
Low/Natural
Low/Semantic violation
Equal/Natural
Equal/Semantic violation
Acceptability (%)
99.4 (1.9)
1.8 (3.6)
99.2 (2.0)
3.1 (5.6)
RT (ms)
434.2 (150.3)
427.7 (168.7)
411.4 (119.1)
426.9 (125.3)
Figure 5 shows grand average ERPs of the second phrases in Experiment 2. Unlike what was observed for
Experiment 1, semantically anomalous sentences exhibited a negativity irrespective of probability
manipulation. A small P600 effect appeared in response to semantically anomalous sentences only for the low
probability block.
Natural Semantic Violation
Low Probability Block
-5
15
P3
-5
15
F3
-5
15
Pz
-5
15
Fz
-5
15
F4
-5
15
C3
-5
15
Cz
-5
15
C4
-5
15
P4
400 ms 600 ms
400 ms 600 ms
400 ms 600 ms 400 ms 600 ms
400 ms 600 ms
400 ms 600 ms 400 ms 600 ms
400 ms 600 ms
400 ms 600 ms
-5
15
P3
-5
15
Pz
-5
15
F3
-5
15
Fz
-5
15
F4
-5
15
C3
-5
15
Cz
-5
15
C4
-5
15
P4
Natural Semantic Violation
Equal Probability Block
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
400 ms
600 ms
28
Figure 5. Grand average ERPs of the second phrase in Experiment 2.
The x-axis represents the time duration and each hash mark represents 100 ms. The Y-axis represents the
voltage. Negativity is plotted upward. The isovoltage maps represent violation effects calculated as semantic
violations minus natural conditions at 300500 ms, 500700 ms, and 700900 ms.
In the 300500 ms time-window, the main effect of Violation was marginally significant (p = 0.053),
showing that the semantically anomalous sentences elicited an N400 effect compared to the semantically
natural sentences (Tab le 9). Although Figure 6 suggests that the N400 effect increased over the course of the
experiment, the interaction of Violation and Item Order was not significant. Importantly, the three-way
interaction of Probability, Violation, and Item Order was not significant, indicating that there is no evidence
that the N400 effect decreases during the experiment as the participants read more anomalous sentences.
Table 9. The linear mixed-effect model for the 300500 ms time-window for Experiment 2
+p < .10, **p < .01
Estimate SE t p
(Intercept ) 3.06 0.97 3.13 < 0.01
Probability (P) 0.83 0.72 1.14 0.25
Violation (V) -1.55 0.78 -1.99 0.05 +
P × V 0.03 1.44 0.02 0.98
P × IO 1.00 0.20 4.80 < 0.01 **
V × IO H0.31 0.20 H1.51 0.12
P × V × IO H0.90 0.60 H1.49 0.13
Item Order (IO) H0.17 0.10 H1.70 0.08
29
Figure 6. The N400 change in the low (left) and equal (right) probability blocks of Experiment 2.
The x-axis represents item order (i.e., 1130) and the y-axis represents the amplitude of the N400 in the time-
window of 300500 ms. Positivity is plotted upward. Each line represents the N400 changes estimated by the
linear mixed-effects models and each dot represents an estimated N400 amplitude of every 20 trials. The gray
areas represent the 95% confidence interval.
For 500700 ms, the interaction of VIOLATION × ITEM ORDER was significant (Tabl e 10). However, post-
hoc analyses revealed no significant effect of Violation for the first or second half of the experiment. For 700
900 ms, interactions of VIOLATION × ITEM ORDER and of PROBABILITY × VIOLATION × ITEM ORDER were
significant (Ta bl e 11). A planned comparison conducted at each level of PROBABILITY revealed a significant
interaction of VIOLATION × ITEM ORDER for the low probability block (β = −1.13, t = −4.14, p < 0.01), showing
the greater positivity for the semantically anomalous sentences for the first half of the experiment but not for
the second half of the experiment (β = 2.39, t = 1.75, p = 0.09; β = 0.14, t = 0.10, p > 0.10). We again examined
the effect of ITEM ORDER for each condition. The results showed that the P600 increased in natural sentences
and decreased in semantically anomalous sentences of the low probability block, suggesting that P600
amplitude changes observed in both semantically natural and anomalous sentences contributed to a reduced
P600 effect during the experiment (β = 0.59, t = 3.10, p < 0.01; β = −0.53, t = −2.72, p < 0.01). The equal
probability block did not have an ITEM ORDER effect on the semantically natural or anomalous sentences.
Table 10. The linear mixed-effect model for the 500700 ms time-window for Experiment 2
anomalous
natural
natural
anomalous
30
**p < .01
Table 11. The linear mixed-effect model for the 700900 ms time-window for Experiment 2.
**p < .01
We directly compared the N400 effect elicited by semantic violations with the P600 effect elicited by
morphosyntactic violations to check whether they were differently affected by ITEM ORDER and PROBABILITY.
If the decrease of the ERP effect due to the repeated exposure to violations is specific to the P600 effect in
the equal probability block of Experiment 1, the interaction of EXPERIMENT × PROBABILITY × VIOLATION ×
ITEM ORDER should be significant. To this end, we created a larger dataset by merging P600 data (700900
ms) of Experiment 1 and N400 data (300500 ms) of Experiment 2. The result of the LME analysis showed a
significant four-way interaction, supporting the results reported above (β = 2.47, t = 5.03, p < 0.01)(Figure 7).
.948 2.  
<868 (  )( 
1545991  ) *
3949<3 ) *  *
13 * ( 
10 (* *  
30 * ( 
130  ( 
80780   ( 
.948 2.  
<868  * ( 
1545991 ) *) ) 
3949<3 * ( * )
13   
10 (   *(
30 ((   
130 )   
80780 (  ) (
31
Figure 7. The grand average of the P600 effect in Experiment 1 (left) and the N400 effect in Experiment 2
(right)
We also compared the P600 effects in Experiments 1 and 2 to examine whether the reduction in the P600
to grammatical violations unfolded differently in Experiments 1 and 2. We created a larger dataset by merging
P600 data (700900 ms) of both experiments. Again, the results showed a significant interaction of
EXPERIMENT × PROBABILITY × VIOLATION × ITEM ORDER, suggesting that the P600 effect decreased drastically
in the equal probability block of Experiment 1, unlike in the other blocks of Experiments 1 and 2 (β = 3.68, t =
6.79, p < 0.01).
To summarize, the semantically anomalous sentences elicited a negativity in the N400 time-window,
regardless of the manipulation of probabilities. There was no evidence that the N400 effect changed over the
course of the experiment.
In Experiment 2 semantically anomalous sentences elicited a P600 effect in the first half of the low
probability block, but this effect diminished as the experiment proceeded. In the 300500 ms time-window,
they elicited a greater N400 effect.
In the low probability block, a P600 was observed for semantically anomalous sentences. A strong
semantic anomaly, such as animacy violation, has been known to elicit a P600 effect, often referred to as
semantic P600 (Bornkessel-Schlesewsky & Schlesewsky, 2008; Brouwer et al., 2012; DeLong, Quante, & Kutas,
2014; Kuperberg, 2007). The P600 effect observed in the present study could reflect similar underlying
processes as the semantic P600, such as repairing processes of semantic anomalies. The reduction of the P600
effect can be attributed to a reduced effort for repairing processes or to adaptation to anomalies. Nevertheless,
the latter interpretation is less plausible in the case of Experiment 2, as the participants did not accept
semantically anomalous sentences consistently. Furthermore, the P600 attenuation was not observed for the
equal probability block, for which the participants were more heavily encouraged to adapt their expectations
32
to anomalous input. Therefore, the reduction of the P600 suggests that the participants became more likely
to be reluctant to engage in severe semantic anomalies.
The N400 result suggests that the processing difficulty of semantic violations did not decrease during the
experiment. In the 300500 ms time-window, the three-way interaction of Probability, Violation, Item Order
was not significant. Although the interaction of Violation and Item Order was not significant, the negative
coefficient of the interaction suggests that there was a trend for the increased N400 effect during the
experiment. Therefore, the effect was opposite if people were expected to adapt to semantic violations. The
reason that the N400 effect is small is probably because the semantic anomaly is less robust for eliciting an
N400 effect compared to other factors such as cloze probabilities (Lau et al., 2016).
The finding is consistent with the result of a recent ERP study in Chinese (Zhang et al., 2019). They found
that an N400 effect for unpredictable words was not modulated by the ratio of predictable and unpredictable
sentences in the experiment. However, there is another study by Lau, Holcomb, and Kuperberg, (2013) that
showed that the experimental setting modulated the magnitude of the N400 effect. In their priming
experiment, a robust N400 effect (i.e., the difference between the semantically primed and unprimed word)
was observed in the high relatedness block (50% of primes are related to targets), unlike in the low relatedness
block (10% of primes are related). The priming experiment differs from the experiments by Zhang et al. (2019)
and ours because people are supposed to routinely generate a semantic prediction for subsequent input
during sentence comprehension (e.g., Altmann & Kamide, 1999), but probably not during the processing of
word pairs. In Lau et al. ’s (2013) experiment, the highly probable occurrence of semantically related words
likely encouraged participants to process a target word predictively. This interpretation is supported by the
finding of Delaney-Busch et al. (2019) that reanalyzed Lau et al.’s (2013) data to examine the effect of item
order on the N400 magnitude. According to their analysis, the semantically primed and unprimed target words
showed a similar N400 amplitude at the beginning of the experiment, which diverged as their participants
read more semantically related pairs (see also Nieuwland, m.s. for a counterargument against semantic
adaption in Lau's et al. (2013) experiment).
15
The same anonymous reviewer who raised a concern over the response strategy in Experiment 1 noted
that an inanimate nominative is always unacceptable and served as a cue that the sentence is unacceptable.
Thus, the results of Experiment 2 may reflect a response strategy. Again, this possibility seems incompatible
with our results. If the participants have noticed an inanimate nominative-marked phrase is a useful cue for
the NO response as they saw more sentences, as the reviewer suggested, they would take a strategy to simply
ignore the second phrase (i.e., verb) while performing the task correctly. Accordingly, an N400 effect should
be expected to decrease as the experiment went along. This prediction is not consistent with the results of
Experiment 2.
15
Obviously, even If our speculation is correct, it does not undermine the importance of Lau et al.’s (2013)
findings that semantic prediction contributes to an N400 activity independently from semantic associations.
33
3. General discussion
The present study explored the adaptive nature of the language-processing system by examining how
ERPs change over the course of experiments. Experiment 1 tested two hypotheses about linguistic adaptation,
namely, the expectation updating account and the representation-based account. According to the
expectation updating account, people have a probabilistic belief about the occurrence of syntactic structures
and adapt to a new linguistic environment by updating it. In contrast, the representation-based account
explains a linguistic adaptation in terms of increased base-level activations. When a sentence associated with
a less frequent syntactic frame is repeatedly encountered, the base level activation of the syntactic frame
increases and therefore, its subsequent activation requires a less cost. The former account expects people to
be able to adapt to ungrammatical sentences if they can update its probabilistic belief, whereas the latter
account does not expect people to be able to adapt to ungrammatical sentences because the ungrammatical
sentences do not have a licit representation. The results of Experiment 1 showed that the P600 amplitude
decreased when the participants were repeatedly exposed to morphosyntactic violations, suggesting that the
participants adapted to morphosyntactic violations. Therefore, the results provided support for the
expectation updating account and against the representation-based account.
The finding of Experiment 1 raised a question of whether the language-processing system is generally
adaptive such that it attempts to minimize any types of errors. Thus, we further explored whether Japanese
speakers can also adapt to semantic violations in Experiment 2 because the limit of linguistic adaptation is yet
unknown. The results of Experiment 2 showed no interaction of Violation and Item Order, showing that the
N400 effects did not change over the course of the experiment. Therefore, we found little evidence that
Japanese speakers adapt to semantic violations to alleviate a difficulty in the processing of semantic violations
as long as the result of the N400 is concerned. The plausible interpretation of the P600 effect in Experiment 2
requires further experiments to be conducted.
Overall, Experiments 1 and 2 present ERP evidence for a rapid adaptation to morphosyntactic violations
but do not show solid evidence for adaptation to semantic violations. This suggests that people take into
consideration not only the probability of violations but also types of violations in determining whether to
adapt to deviant linguistic input. This brings us a new question of why it is the case that people adapt to
morphosyntactic violations, but not to semantic violations.
This selective adaptation might involve a frequency/typicality of morphosyntactic and semantic
violations. In spontaneous speech, native Japanese speakers as well as Japanese-speaking children and non-
native speakers sometimes produce morphosyntactic errors. In contrast, semantic errors are infrequent.
Therefore, the difference in adaptive behavior suggests that the language-processing system is easily
34
familiarized with morphosyntactic violations whereas semantic knowledge is so fixed that that the language-
processing system is immune to the repeated exposure to semantic anomalies.
Consistent with this possibility, recent studies have found that the error typicality affects ERPs, although
they focused on the processing of non-native utterances. Hanulíková et al. (2012) observed that native
speakers of Dutch exhibited a P600 effect for gender disagreement between a determiner and a noun when
a sentence was produced by a native speaker but did not show any effect when a sentence was produced by
a non-native speaker (whose native language is Turkish). In contrast, semantic violations elicited an N400
effect irrespective of the speaker’s manipulation (see also Grey & Hell, 2017; Romero-Rivas et al., 2015, 2016).
Similarly, Caffarra & Martin (2018) showed that native Spanish speakers exhibited a P600 effect for subject-
verb number errors (infrequent errors) but not for subject-verb gender errors (frequent errors) in English
accented speech. However, there are some differences between their studies and ours. Hanulíková et al.
(2012) interpreted the absence of the P600 effect as a result of participants’ prior experiences with error
likelihood in accented speech and not of a gradual adaptation during the experiment because the P600 did
not appear in accented speech even in the first half of the experiment. This difference between Hanulíková et
al. (2012) and the present study in the time-course of the P600 decline may be related to the difference in the
way that participants adapted to deviant linguistic input. According to Qian, Jaeger, and Aslin (2012),
adaptation can be executed in several different ways, namely, resetting parameters of an existing model and
switching models learned in the past (see also Kuperberg & Jaeger, 2016). Since Hanulíková et al. (2012)
reported that their participants were familiar with Turkish accented speech, the participants knew how likely
Tur kish speakers produce morphosyntactic errors and therefore, they switched their model that best
represent their probability distribution. On the other hand, the present result is more likely to be attributable
to the update of an existing model about how likely morphosyntactic violations occur.
From a broader perspective, the present findings suggest the importance of examining how physiological
responses change according to a preceding trial. In traditional studies, EEGs are averaged across many trials
to obtain ERPs. However, the assumption that physiological response to a stimulus is invariant throughout an
experiment has been rarely verified. Using single-trial analyses, such as linear mixed-effects models, offers a
new insight into the dynamic aspect of cognitive processes, which remains unexplored especially in the
domain of language processing.
Finally, we mention four remaining issues. The first issue pertains to the involvement of domain-general
ability to the linguistic adaptation. The present finding revealed an adaptive nature of the language-processing
system. Although the linguistic adaptation must employ language-specific mechanisms to some extent
because the linguistic knowledge of case makers, verb subcategorization (intransitive/transitive), and lexical
information (e.g., animacy) plays an integral part of how the processing system changes the way it processes
35
linguistic information, it is unknown whether the linguistic adaptation involves some domain-general
mechanisms. Although we conducted an exploratory analysis for individual cognitive traits (i.e., PASAT, SDMT,
AQ, and P3b), no correlation was found between individual cognitive traits and the change of the N400/P600
amplitudes. Obviously, it does not preclude the possibility that other cognitive traits involve linguistic
adaptation. It is premature to draw a conclusion about how the language-processing system adapts to
linguistic violations in relation to non-language-specific mechanisms.
The second issue involves the effect of a secondary task. Previous studies have shown that the
acceptability judgment task affected the way the participants processed linguistic violations, reflected by P600
effects (Gunter & Friederici, 1999; Kolk, Chwilla, Van Herten, & Oor, 2003; Münte, Matzke, & Johannes, 1997,
see Kuperberg, 2007 for review). This issue is applied to the present study, although it cannot explain the
difference between the low and equal probability blocks or the difference between Experiment 1 and 2
because the participants performed the task after every trial.
The third issue is the effect of the type of filler sentences on adaptation. The participants could have
perceived the filler sentences as syntactically anomalous but not semantically anomalous. If so, the non-
adaptive behavior in Experiment 2 may be attributable to the possibility that the amount of semantic
violations is not enough to trigger an adaptation to semantic violations in the equal probability block. Future
study needs to examine semantic adaption using unambiguously semantically violated sentences.
The fourth issue is the difference of the probability of ungrammatical sentences between the present
study and previous studies (Coulson et al., 1998; Gunter et al., 1997). As noted in Section 2.1., the present
study employed the equal probability block in contrast to the previous studies that used the high probability
block (grammatical: 25% vs. ungrammatical: 75%) because the equal probability is considered a deviant case
to participants to some degree as grammatical sentences occur far more frequently than ungrammatical
sentences in typical language use. Since the result showed adaptation to syntactic violations, the one-to-one
ratio of grammatical and ungrammatical sentences is sufficient to trigger it.
An anonymous reviewer pointed out that the two-word sentences used in the present study are almost
flat with little hierarchical syntactic structure and questioned whether the present results can generalize to
longer sentences. We do not believe the two-word sentences used in the present study are hierarchically flat
given that a VP-internal subject moves to the specifier of TP and forms a structural case-assignment relation
with T and that a verb moves to T to be conjugated with it. Furthermore, the two-word paradigm is useful to
avoid possible confounding factors (Lau et al., 2013, 2016; Pylkkänen, 2019, 2020 and therein). They can avoid
the effect of a wrap-up process that elicits a robust negativity at the sentence-final position (Friederici & Frisch,
2000; Yano & Koizumi, 2018). This concern is important given that Japanese is strictly verb-final and thus, the
region of interest is often at the sentence-final position. The research question of the present study is whether
the language-processing system is capable of adapting to (morpho)syntactic/semantic violations. Since the
issue of whether people routinely adapt to such anomalies in longer sentences is another issue, which we
36
could not cover, further investigation is necessary to answer the question of the generalizability.
4. Conclusion
The present study investigated the dynamic nature of the language-processing system using mixed-
effects model of single-trial EEG data. Our results suggest that native speakers of Japanese adapt their
expectations to morphosyntactically ill-formed sentences, as evidenced by a P600 attenuation pattern
observed from our experiments. On the other hand, we found no evidence of adaptation to semantic
anomalies. We argue that differential adaptive behavior results when participants consider how likely certain
types of errors are to occur.
Acknowledgements
We thank anonymous reviewers and the editor for their insightful comments and suggestions. This study
was supported by JSPS KAKENHI (JP17K02755, PI: Hiroaki Oishi, JP19K13182, PI: Masataka Yano, 20K00539,
PI: Daichi Yasunaga).
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... For example, the P600but not the N400is modulated by task relevance, with a reduced or absent P600 when the instructions do not require the participant to process the anomaly (Gunter & Friederici, 1999;Hahne & Friederici, 1999;Molinaro et al., 2011;Schacht et al., 2014;Vissers et al., 2007) or participants do not notice the anomaly (Batterink & Neville, 2013;Osterhout & Mobley, 1995;Xu et al., 2019). The two components are also differentially affected by error probability, with increasing number of violations within an experimental stimulus set diminishing the P600, but not affecting the N400 (Coulson et al., 1998;Hahne & Friederici, 1999;Yano et al., 2021). Relatedly, the P600, but not N400, is sensitive to the attentional blink and other manipulations testing conscious vs unconscious processing of linguistics stimuli (Batterink & Neville, 2013;Kiefer, 2002;Luck et al., 1996;Rohaut & Naccache, 2017;Service et al., 2007;van Gaal et al., 2014). ...
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