ORIGINAL RESEARCH ARTICLE
published: 03 November 2014
doi: 10.3389/fnsys.2014.00217

SYSTEMS NEUROSCIENCE

Subliminal enhancement of predictive effects during
syntactic processing in the left inferior frontal gyrus: an
MEG study
Kazuki Iijima 1,2,3† and Kuniyoshi L. Sakai 1,2*
1

Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Meguro-ku, Japan
CREST, Japan Science and Technology Agency, Chiyoda-ku, Japan
3
Japan Society for the Promotion of Science, Chiyoda-ku, Japan
2

Edited by:
Mikhail Lebedev, Duke University,
USA
Reviewed by:
Xing Tian, New York University, USA
Masha Westerlund, New York
University, USA*Correspondence:
Kuniyoshi L. Sakai, Department of
Basic Science, Graduate School ofArts and Sciences, The University ofTokyo, 3-8-1 Komaba, Meguro-ku,
Tokyo 153-8902, Japane-mail: sakai@mind.c.u-tokyo.ac.jp† Present address:
Kazuki Iijima, Tamagawa University
Brain Science Institute, Machida-shi,
Japan

Predictive syntactic processing plays an essential role in language comprehension. In
our previous study using Japanese object-verb (OV) sentences, we showed that the left
inferior frontal gyrus (IFG) responses to a verb increased at 120–140 ms after the verb
onset, indicating predictive effects caused by a preceding object. To further elucidate
the automaticity of the predictive effects in the present magnetoencephalography study,
we examined whether a subliminally presented verb (“subliminal verb”) enhanced thepredictive effects on the sentence-final verb (“target verb”) unconsciously, i.e., withoutawareness. By presenting a subliminal verb after the object, enhanced predictive effects
on the target verb would be detected in the OV sentences when the transitivity of thetarget verb matched with that of the subliminal verb (“congruent condition”), because thesubliminal verb just after the object could determine the grammaticality of the sentence.
For the OV sentences under the congruent condition, we observed significantly increased
left IFG responses at 140–160 ms after the target verb onset. In contrast, responses
in the precuneus and midcingulate cortex (MCC) were significantly reduced for the
OV sentences under the congruent condition at 110–140 and 280–300 ms, respectively.
By using partial Granger causality analyses for the OV sentences under the congruent
condition, we revealed a bidirectional interaction between the left IFG and MCC at
60–160 ms, as well as a significant influence from the MCC to the precuneus. These
results indicate that a top-down influence from the left IFG to the MCC, and then to
the precuneus, is critical in syntactic decisions, whereas the MCC shares its task-set
information with the left IFG to achieve automatic and predictive processes of syntax.
Keywords: MEG, sentence processing, syntax, frontal cortex, prediction, consciousness

INTRODUCTION
Human language consists of more than linear strings of words:
hierarchical syntactic structures of a sentence are constructed byrecursively merging a pair of syntactic objects (Chomsky, 1995).
Understanding the processes by which syntactic structures areconstructed is thus crucial for elucidating the neural mechanisms
underlying the human language faculty. Recently, computational
parsing theories with incremental predictions based on syntactic structures have been developed (Levy, 2008; Hale, 2011).
According to these theories, the difficulty of processing a given
phrase can be quantitatively explained by deviations from a prediction about the syntactic features of upcoming words in a sentence; such a prediction is based on the incrementally constructedsyntactic structures. Assuming that a preceding noun phrase (NP)with a case marker (dative or accusative) in a Japanese sentence provides information about the argument structures of asentence-final verb, we have shown that predictable canonicalsentences increased the left inferior frontal gyrus (IFG) responses
to the verb in our recent magnetoencephalography (MEG) study

Frontiers in Systems Neuroscience(Inubushi et al., 2012). In another MEG study with visually presented object-verb (OV) sentences, we showed that the left IFG
responses to a verb significantly increased in a syntactic decision task, at 120–140 ms after the verb onset (Iijima et al., 2009).
We interpreted this component as “predictive effects” caused bya preceding object with an accusative case marker (Acc, “-o”),
such that a transitive verb (vt) was the only grammatical verb
type for the final verb. Subject-verb (SV) sentences may lacksuch strong predictive effects, because the NP with a nominative case marker (Nom, “-ga”) cannot fully specify the following
verb types, such as an intransitive verb (vi), vt, and copular verb(“desu, da, etc.,” which are similar to “be, etc.” in English) associated with a nominal/adjectival predicate. In that previous study(Iijima et al., 2009), we observed such predictive effects only for
OV sentences in the syntactic decision task compared with othertasks (e.g., a semantic decision task). By presenting a verb subliminally (“subliminal verb” hereafter), enhanced predictive effects on
the sentence-final verb (“target verb” hereafter) in OV sentences
would be detected even in a single task of syntactic decision.

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Subliminal syntactic processing in brain

Moreover, such enhancement is expected when the transitivity (vtor vi) of the target verb matched with that of the subliminal verb,
because the subliminal verb just after the object could determine
the grammaticality of the sentence.
We further hypothesize that the predictive effects caused by
the preceding object actually represent early syntactic processesof determining the transitivity of the final verb. To further elucidate the automaticity of the predictive effects, we examined
whether a subliminal verb after the object enhanced the predictive
effects on the target verb unconsciously, i.e., without awareness(Figure 1A). We presented a subliminal verb for a limited time
between two masks after the NP (Figure 1B). Participants were
not notified of even the existence of subliminal stimuli during
the experiment, although they were fully aware that a target verb
would appear after the NP while paying attention to a sequenceof stimuli. Therefore, they could not have expected to receive
any information about the upcoming verb from the subliminalverb. As shown in Figure 1A, the target verb was either congruent (Cong) or incongruent (Incong) with the subliminal verb interms of their transitivity, leading to four stimulus conditions:
OV-Cong, SV-Cong, OV-Incong, and SV-Incong. The lexicosemantic relationships between the noun and subliminal verb, as
well as between the noun and target verb, were always normal
and equivalent among these four conditions. This strict semantic
control is one of the merits of the present study.
We defined syntactically “normal OV” and “normal SV” sentences as object-vt and subject-vi combinations, respectively.
From each of the normal OV and SV sentences, we made a syntactically anomalous sentence by simply exchanging the verb withthe rest of a verb pair, which consisted of a morphologically andsemantically related vt and vi (Table 1). Here we defined “anomalous OV” and “anomalous SV” sentences as those with an object(vi with “-o”) and subject (vt with “-ga”), respectively. From anormal OV sentence [e.g., “yuki-o tok-as-u(= vt)”: “(someone)
melts snow”], we made an anomalous OV sentence [e.g., “yukio tok-e-ru (=vi)”], which is ungrammatical, since a vi cannot
take an object. From a normal SV sentence [e.g., “yuki-ga toke-ru (=vi)”: “snow melts”], we made an anomalous SV sentence[e.g., “yuki-ga tok-as-u (= vt)”], which is ungrammatical, since
its error can be immediately corrected by the grammatical counterpart: either “yuki-ga tok-e-ru” (the verb type counterpart) or“yuki-o tok-as-u” (the case marker counterpart) in this example. By presenting both normal OV and normal SV sentences, a
judgment on the grammaticality would surpass a judgment on
selectional restrictions, if any.
A recent MEG study has shown that phonologically predictable words (e.g., spoken sounds from written words)
increased the left IFG responses at around 150 ms after theword onset (Sohoglu et al., 2012), while semantically or syntactically unpredictable words increased neural responses in posterior
regions (DeLong et al., 2005; Dikker et al., 2009). Moreover,previous neuroimaging studies have reported an increase of theleft IFG responses reflecting syntactic predictive effects (Iijima
et al., 2009; Inubushi et al., 2012; Santi and Grodzinsky, 2012).
These previous studies have indicated that syntactic prediction
generated by a preceding NP remains effective for as long as
300–900 ms. We thus expect to observe the increased left IFG

Frontiers in Systems Neuroscience

FIGURE 1 | A paradigm with subliminal stimuli. We presented two-word
sentences such as an object-verb (OV) sentence [e.g., “yuki-o tokasu”:“(someone) melts snow”] and a subject-verb (SV) sentence (e.g., “yuki-ga
tokeru”: “snow melts”). The transitive verb (vt) and intransitive verb (vi)were both morphologically and semantically related (see Table 1), but
always different words, just as in the “raise/rise” distinction in English. (A)Examples of visually presented stimuli of an OV sentence. In a syntactic decision task, participants decided whether a presented sentence was(Continued)

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Subliminal syntactic processing in brain

FIGURE 1 | Continued
syntactically normal or anomalous. A supraliminally presented verb (“targetverb”) appeared at the end of each trial for the participants to respond to. A
subliminally presented verb (“subliminal verb”) was inserted between anoun phrase (NP) and the target verb. The target verb was either congruent(Cong) or incongruent (Incong) with the subliminal verb in terms of theirverb transitivity (vt or vi). Red arrows indicate a prediction about the verb,
provided by an object with an accusative case marker (Acc), such that thefollowing vt is normal, and the following vi is anomalous. (B) A single trial in
the syntactic decision task. We sequentially presented an NP, a subliminalverb or NP, and a target verb, together with a forward mask and a backward
mask before and after the subliminal verb, respectively. We focused on
cortical responses to target verbs, and we presented the masks withrandom intervals between 100 and 200 ms, so that cortical responses totarget verbs were not confounded with those to the other stimuli. (C) A
single trial in a forced-choice recognition task to assess the visibility of amasked first verb. At the end of this task, two stimuli were presented, andparticipants simply chose which stimulus had actually appeared as the firstverb (interval, 14–50 ms). We made the stimulus presentation of each trial
identical to that in the syntactic decision task, except that two verbs were
presented as a choice stimulus.

responses within this time frame under the OV-Cong condition(Figure 1B). Our focus is neither on simple priming effects oftransitivity (i.e., congruency) nor on the generation of a prediction itself (i.e., NP-type effects), but on the enhancement ofpredictive effects due to a subliminal verb. We infer that the left
IFG shows enhanced responses under the OV-Cong condition
when compared with the combined conditions of SV-Cong and
OV-Incong, i.e., (SV-Cong + OV-Incong), in which the congruency and NP-type effects are separately controlled. We assume
that the OV-Incong condition is ineffective and neutral regarding
the enhancement; the use of this condition is thus a better control
than that of the OV condition without a subliminal verb, because
the stimulus presentation (including the presence of a subliminal verb) is physically controlled among the compared conditions.
Moreover, our prediction is more focused than an interaction ofsentence structure by congruency, i.e., (OV-Cong + SV-Incong)vs. (SV-Cong + OV-Incong), because we expect no enhancement
under the SV-Incong condition. To examine whether the left IFG
responses were robust enough across both spatial and temporal domains, we applied whole-brain analyses of MEG responses
in an unbiased manner, where neither particular regions nor
temporal bins were selected a priori, which was equivalent to performing all possible functional region of interest (ROI) analyses(Friston and Henson, 2006).
In an SV sentence, the verb type cannot be uniquely specified,
and thus the bottom-up determination of the transitivity from apresented stimulus had to be done for both subliminal and target verbs. This possible interference would lead to longer reaction
times (RTs) for the SV sentences than for the OV sentences, irrespective of the Cong and Incong conditions. As a control for theinterference from a subliminal verb, we compared behavioral data
for the SV and OV sentences when a subliminal NP was presented
instead of a subliminal verb. Because a subliminal NP was always
the same as the preceding NP in each trial, the use of subliminal
NPs introduced no confounding effects.
Previous functional magnetic resonance imaging (fMRI) studies of normal participants have established that the left IFG and

Frontiers in Systems Neurosciencethe left lateral premotor cortex play a crucial role in syntactic
processes (Stromswold et al., 1996; Dapretto and Bookheimer,
1999; Embick et al., 2000; Hashimoto and Sakai, 2002; Friederici
et al., 2003; Musso et al., 2003; Suzuki and Sakai, 2003; Kinno
et al., 2008); these regions have been proposed as putativegrammar centers (Sakai, 2005). Moreover, our recent fMRI
study has shown that the left IFG responses were parametrically modulated by “the Degree of Merger (DoM),” which
was defined as the maximum depth of merged subtrees (i.e.,
Mergers) within an entire sentence (Ohta et al., 2013). “Merge”
is a simple local structure-building operation proposed by modern linguistics; Merge would be theoretically “costless,” requiring no driving force for its application (Saito and Fukui, 1998;
Chomsky, 2004; Fukui, 2011). We suggest that structure-building
involves automatic Merge processes, which would be facilitated by syntactic prediction from a preceding phrase. It has
been suggested that a simple type of information integration isfacilitated without awareness (Mudrik et al., 2014). There has
been recent supporting evidence that sentence processing actually occurs in the absence of awareness (Batterink and Neville,
2013; Axelrod et al., 2014), while some subliminal priming studies have targeted lower levels of phonology, morphology, andlexico-semantics (Kouider and Dehaene, 2007; Nakamura et al.,2007; Lehtonen et al., 2011; Wilson et al., 2011). We hypothesize that further subliminal processes at the lexical level extend
to hierarchically higher syntactic processes without awareness;the subliminal syntactic process is a critical assumption in thishypothesis.
Another candidate region for response modulation under the
OV-Cong condition is the midcingulate cortex (MCC), which is
involved in task-set formation (Dosenbach et al., 2006; Hyafil
et al., 2009). We also tried to elucidate causal influences among
these regions by using partial Granger causality analyses (Guoet al., 2008; Barrett et al., 2010). Under the OV-Cong condition,
we expect that causal interactions between the left IFG and otherregions were enhanced. Our present study should help to clarify
the neural basis of syntactic processes that are both automatic andpredictive.

MATERIALS AND METHODSPARTICIPANTS

The participants in the MEG experiments were 16 native Japanese
speakers. One participant, who reported that he was able todetect the subliminal verbs during the MEG experiment, was
excluded from the behavioral and MEG data analyses, leaving a total of 15 participants (19–43 years; four females). Allof them showed right-handedness (laterality quotients: 87–100)
as determined by the Edinburgh inventory (Oldfield, 1971).
In the pilot study for determining an appropriate interval of
subliminal stimuli, 10 other native Japanese speakers (22–35
years; one female) participated. All participants were neurologically normal without any psychiatric symptoms. Written
informed consent was obtained from each participant after
the nature and possible consequences of the studies wereexplained. Approval for these experiments was obtained fromthe institutional review board of the University of Tokyo,
Komaba.

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Table 1 | A list of 72 normal sentences.
Verb subgroup

I

Object-Verb (OV) sentence
Noun-Acc

vt

Noun-Nomtama-oat-e-rutama-gaIII

sor-as-u
huku-oIIIshiru-onuno-otam-e-ru

iki-o

tom-e-rukabe-osam-as-u

yuki-otok-as-u

III
II
II
III
III

ashi-oIIIIIIwaza-ohuta-oIIIItabi-oIII

kom-or-u

the fever pervades
snow melts

tabi-gao(w)-e-ru
boya-oI

ok-os-uthe rice gets steamed

tok-e-rumizu-ga

nob-as-u

the wall gets fixed

mur-e-ruyuki-gatam-e-ruI

the wall gets burnt

nao-r-uthe rice remains

huta-ga

mit-as-u

kog-e-ru

the fever wanes

shim-e-ru
mizu-othe rice withers

sam-e-ruwaza-ga

maw-as-u

the rice is planted

kar-e-ru

netsu-gaashi-gaik-as-uuw-ar-unok-or-u

kak-e-ruIIII

the breath gets out

mag-e-ruIthe cloth gets dyed

mor-e-rumado-ga

hit-as-u

som-ar-u

hot water cools

mi-se-ruIsauce drips off

the breath ceases

kome-ga

yur-as-u

sauce mixes

tar-e-ru

hi-e-ru

ot-os-umado-oclothes get wet

maz-ar-u

tom-ar-u

kom-e-ruIIIclothes get selectediki-ganok-os-u
netsu-okim-ar-u
nur-e-ru

the cloth gets burnt

kabe-ga

mur-as-u

the bullet hits (someone)
the bullet misses

hot water pools

nao-s-ukome-oat-ar-usor-e-rutam-ar-u

ine-gakog-as-uvi

oyu-gakar-as-uIIIIIu(w)-e-ru

Translation of SV sentencemo(y)-e-ru

mor-as-u
ine-o

III
II

nuno-gahiy-as-uIIIIshiru-gasom-e-ru

oyu-o

II
I

maz-e-rumoy-as-uIIIhuku-gatar-as-u

III

kim-e-ru
nur-as-u

II
I

Subject-Verb (SV) sentenceboya-ga

tom-e-ru

ot-i-rusnow drops

yur-e-ru

the window shakes

mi-e-ru

the window can be seen

hit-ar-u

the legs soakmag-ar-u

the legs bend

ik-i-ru

techniques get utilized

kak-ar-u

techniques succeed

maw-ar-u

the lid gets screwed

shim-ar-u

the lid gets closed

mit-i-ruwater brims in (something)tam-ar-u

water pools

nob-i-ru

the travel gets extended

ow-ar-uthe travel ends

ok-i-ru

small fire occurs

tom-ar-u

small fire stops

In every two rows with the same noun, two pairs of a transitive verb (vt) and an intransitive verb (vi) are shown, where each pair in a row is morphologically related
and shares the same meanings. For a single trial, a subliminal verb and a target verb were chosen from each of the two vt-vi pairs (see Figure 1A). According
to Shibatani (1990), verb pairs of vt and vi can be divided into three verb subgroups in terms of their morphological/phonological regularity: I (vt/vi: -e-ru/-ar-u), II(-as-u/-e-ru), and III (others). Verbs from two different subgroups were selected for each noun.

STIMULIIn most languages, there are two types of intransitive verbs: unaccusative verbs and unergative verbs. The subjects of unaccusative
verbs, as well as the objects of transitive verbs, have the semanticrole of “theme” (the entity undergoing the effect of some action).
In this study, in order to equate semantic factors among theconditions, we used unaccusative verbs alone for the intransitive
verbs, so that the NPs of both OV and SV sentences had the same
semantic role. Moreover, we used the same set of nouns for both

Frontiers in Systems Neuroscience

sentence structures. Note that in Japanese a null nominative-casepronoun is allowed as a subject, as well as in Spanish and Italian,
and we omitted from the SOV sentences a subject whose semanticrole is “agent” (the entity instigating some action). The following
examples clarify the distinction between SVO and SV sentences inEnglish, which is similar to the OV and SV distinction:(a) The coach (= agent) substituted (= vt) John (= theme) for
Dave, and I (= agent) would have done so,

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Subliminal syntactic processing in brain(b) John (= theme) substituted (= vi) for Dave, and I (= theme)
would have done so,
as “done so” substitutes for the verb phrase “substituted John forDave” or “substituted for Dave.”
The distinction between vt and vi, i.e., verb transitivity, is
one of the universal aspects of syntactic features among natural languages. In the Japanese language, there are a numberof verb pairs, each of which consists of a morphologically andsemantically related vt and vi (e.g., “at-e-ru” and “at-ar-u”;
Table 1). The vt-vi pair relationships are determined by complex rules of morphosyntax (Shibatani, 1990), similar to the
distinction of “raise/rise, fell/fall, lay/lie, set/sit” in English. There
are some Japanese verbs which lack such morphological distinction [e.g., “hirak-u” (“open”) for both vt and vi], but we
did not use them in the present study. Two vt-vi pairs werechosen for each noun, which was always inanimate and semantically related with the four verbs. For every trial, a subliminalverb and a target verb were chosen from each of the two vtvi pairs (e.g., “at-e-ru” and “sor-as-u”; see Table 1), so thatthe subliminal and target verbs had neither direct semantic normorphological/phonological relationships that may have affectedcongruency.
Each word stimulus was either an NP (a noun and a casemarker) or verb (Figure 1B), and always consisted of three letters(three moras or syllables) spelled only in kana (Japanese phonograms) to ensure a consistent reading time. In each trial startingfrom an NP for 300 ms, a mask was presented with a random
interval of 100, 117, 134, 150, 167, 184, or 200 ms. This mask
served as a forward mask for the next-coming subliminal verb,
which was presented for 34 ms. A backward mask followed thissubliminal stimulus with the same random intervals. A target
verb was then presented for 300 ms. By randomizing the intervals of backward and forward masks, we separated the effects on
the target verb from any responses to an NP, subliminal verb, ormask stimuli (Figure 1B). This procedure enabled us to minimize
the interference of preceding stimuli with the baseline activity ofthe target verbs. Moreover, overlapping between responses to thebackward mask and initial responses to the target verb could not
explain any response differences among the conditions, because
the mask stimulus presentation was common across all tested
conditions. We also confirmed that the larger baseline noises (seeFigure 2D) were restricted to posterior sensors. The inter-trial
interval was randomly varied within the range of 5 ± 0.5 s to
reduce any periodical noises.
Mask stimuli, which should be unreadable while retainingsome features of the kana stimuli, were made in the following procedures. Three verb stimuli were selected, and for each stimulus
three kana letters were rotated randomly at three different angles(±90◦ , 180◦ ). We made 27 different mask stimuli (see Figure 1B)
by superimposing one of these three resultant stimuli with two
new stimuli consisting of three pseudoletters. In a pilot study inwhich each mask stimulus were presented alone for 200 ms, wetested whether any of the “letters” could be identified as any kanaletter. In the 243 trials tested, only two answers matched with theoriginal letters, and this result was not significantly different fromchance (P > 0.6, t-test).

Frontiers in Systems Neuroscience

FIGURE 2 | Behavioral results and MEG signals. (A) Results of theforced-choice recognition task. The discriminability of stimuli (d  ) is shown
against various intervals of the first verb. The SEMs and 95% confidence
intervals (Bonferroni-corrected) are shown in the lighter and lightest
shades, respectively (n = 10). The results showed that subliminal verbs of
34 ms were too short to be seen. (B) Interference from a subliminal verb
for the SV sentences in the syntactic decision task. The histograms show
the differences in RTs obtained by subtracting RTs for the OV sentencesfrom those for the SV sentences (mean ± s.e.m., n = 15), averaged underboth Cong and Incong conditions. A significantly increased difference in(Continued)

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FIGURE 2 | Continued
RTs was observed for a subliminal verb, but not for a subliminal NP. An
asterisk denotes a significant difference (P = 0.05, paired t-test). (C) MEG
signals for the NPs from artifact-free and correct trials, averaged for all of
the four conditions and across 15 participants, are shown for each sensor
before normalization. (D) MEG signals for the target verbs are shown foreach sensor. The black bars (a: 110–140; b: 140–160; c: 280–300 ms) denoteall of the temporal bins that showed any significant differences between
the tested conditions (Figures 3A–C). (E) MEG topographies on the scalp
averaged under the OV-Cong condition, at each temporal bin of a-c (D). The
upper and lateral scalp surfaces are shown.

We prepared 36 verb pairs of vt and vi, and made 72 normal
sentences (Table 1), each of which consisted of an NP and one ofthese verbs as a target verb (36 each for OV and SV sentences).
Using Google (http://www.google.co.jp/), we calculated a transitional probability from an NP to a verb within a sentence, andthere was no significant difference between the normal OV and
SV sentences [T(35) = −0.053, P > 0.9 (paired t-test)]. We made72 anomalous sentences from these normal sentences, exchanging the vt and vi for the corresponding NPs (36 each for the OV
and SV sentences). For each of the normal and anomalous sentences, we tested two different subliminal verbs, corresponding
to either the Cong or Incong condition (see Figure 1A). For eachof four conditions (i.e., OV-Cong, SV-Cong, OV-Incong, and SVIncong), there were thus 72 combinations for the set of an NP,
a subliminal verb, and a target verb. In addition, we prepared
144 possible combinations for the set of an NP, a subliminal NP(34 ms), and a target verb (72 each for normal and anomalous
sentences); we randomly chose 72 combinations (on average, 36
each for OV and SV sentences) for each participant. Each of thesedifferent combinations with subliminal stimuli (verb or NP) wastested only once for each participant.
Stimulus presentation and behavioral data collection were
controlled using Presentation software (Neurobehavioral Systems
Inc., Albany, CA) and an NI-DAQ interface board (National
Instruments, Austin, TX). Visual stimuli in gray against a dark
background were projected with a refresh rate of 60 Hz (i.e.,
16.67 ms for one video frame) from outside of the shield room
onto the translucent screen within a visual angle of 5.7◦ , using a
Digital Light Processing projector (TDP-EX20J; Toshiba, Tokyo,
Japan) equipped with a projection lens (modified by NewOpto,
Tokyo, Japan). For fixation to minimize eye movements, a red
cross was always shown at the center of the screen, and the participants were instructed to stare at it and refrain from blinking
before the response.
TASKS

Native Japanese speakers judged the grammaticality of two-word
sentences, i.e., an NP with a case marker and a target verb
(Figure 1B). The participants were instructed to respond to thetarget verb by pressing one of two buttons (right or left) as quickly
as possible by using the right hand alone. Assignments of the two
buttons for the judgment of sentences as normal or anomalous
were counterbalanced across participants. This syntactic decisiontask, per se, was designed in the same way as in our previous
study (Iijima et al., 2009). The syntactic decision task could not
be solved on the basis of the lexico-semantic relationship between

Frontiers in Systems Neurosciencea noun and a target verb, as it was always correct as explained
above.
Each of the four MEG runs performed on a single day forany given participant included 90 trials mixed randomly with
subliminal verb and subliminal NP stimuli. Each of the four conditions (i.e., OV-Cong, SV-Cong, OV-Incong, and SV-Incong)
consisted of 72 trials for each of the 15 participants, resulting
in 1080 observations per condition for an entire experiment.
For all participants, the orders of sentence structures (OV orSV), congruency, and grammaticality were fully randomized andcounterbalanced. Only trials with participants’ correct responses
were used for analyzing RTs and MEG data.
PILOT STUDY FOR DETERMINING AN APPROPRIATE INTERVAL OF
SUBLIMINAL STIMULI

In order to test whether the participants were actually unawareof a subliminal verb for 34 ms, we performed another pilot study
with a forced-choice recognition task, thereby varying the intervalof a masked verb (first verb) (Figure 1C). We made the stimulus
presentation of each trial identical to that in the syntactic decision task, using the same set of 288 combinations for the set of
an NP, a masked first verb, and a second verb, except that two
verbs were presented as a choice stimulus, which remained on
the screen until the participant responded. In each trial, participants chose which of the two verbs had actually appeared as thefirst verb, simply neglecting the NP and second verb. The participants were explicitly informed of the presence of a first verb evenwhen it was too short to recognize. For each choice stimulus, a
distractor was taken from the particular vt-vi pair of the first verb
(Table 1). There were two runs, in which we used a fixed refresh
rate of the Digital Light Processing projector (one with 60 Hz, andthe other with 75 Hz). For the refresh rate at 60 Hz (i.e., 16.67 ms
for one video frame), we randomly tested three intervals of thefirst verb (17, 34, or 50 ms set with the Presentation software); forthe refresh rate at 75 Hz (i.e., 13.33 ms for one video frame), we
also randomly tested three intervals of the first verb (14, 27, or
40 ms). We calculated d , i.e., the discriminability of stimuli, fromeach participant’s hit and false-alarm rates.
MEG AND MRI DATA ACQUISITION

The MEG data were acquired with a 160-channel whole-headsystem (MEGvision; Yokogawa Electric Corporation, Kanazawacity, Japan), and they were digitized with an on-line bandwidthof 0.3–1000 Hz and a sampling rate of 2000 Hz. This bandwidth was set according to the Nyquist sampling theorem. At
the time of setting up the MEG system, there was no salient
noise just below 2000 Hz that might have caused aliasing in ourtarget frequency of 2–30 Hz. We basically followed the same procedures described in our previous studies (Iijima et al., 2009;
Inubushi et al., 2012). The MEG signals within the period of −100
to +400 ms from the target verb onset were analyzed using the
BESA software, version 5.2 (BESA, Gräfelfing, Germany). Undereach condition for a single participant, only artifact-free trials(peak-to-peak amplitude <2500 fT) with correct responses wereaveraged without filtering. The signals from −100 to 0 ms at
the target verb onset were used as a baseline, which was within
the period of presenting the backward mask (see Figure 1B).

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The baseline-corrected MEG signals were then band-pass filtered from 2 to 30 Hz to eliminate large eye movement noises,
which may shift the baseline level from zero. While this bandpass filtering removed information of the gamma band (above
30 Hz), some recent studies revealed the important role of thebeta band (13–30 Hz) in language processing (Weiss and Mueller,2012). Artifact-free trials with participants’ correct responses
accounted for approximately 85% of observations, and thispercentage did not differ significantly across the different conditions (P > 0.9).
For mapping with the individual brain, high resolution T1weighted MR images (repetition time, 8.4 ms; echo time, 2.6 ms;
flip angle, 25◦ ; field of view, 256 × 256 mm2 ; resolution, 1 × 1 ×
1 mm3 ) were acquired using a 3.0-T Scanner (Signa HDxt; GE
Healthcare, Milwaukee, WI). The sensor positions for each of fourruns were realigned with five fiducial markers (small coils) on
the head surface, and coregistered with a least-squares fit algorithm to the MR images (MEG Laboratory; Yokogawa Electric
Corporation, Kanazawa-city, Japan); we attached MR markers(alfacalcidol beads; diameter: 3 mm) at the same positions as
the fiducial markers. Using BrainVoyager QX 1.8 software (Brain
Innovation, Maastricht, Netherlands), each individual brain was
normalized to the image of the Montreal Neurological Institutestandard brain, which was already transformed into the Talairach
space (Talairach and Tournoux, 1988). The gray and white matter of the transformed standard brain was segmented, and theirboundary was then partitioned into 3445 cortical patches with amean distance of 5.6 mm (Kriegeskorte and Goebel, 2001). We
confirmed that the cortical patches were appropriately created inboth the lateral and medial regions. Using the transformation
matrix for normalization, the cortical patches on the standard
brain were inversely transformed into each participant’s space,
and were used for cortex-based data analyses.
MEG DATA ANALYSES

An overview of the MEG data analyses is as follows. We firstestimated current dipoles in each participant’s space. For eachcortical patch’s current density in a temporal bin, we compared
cortical currents between specified conditions across the participants (n = 15). For the clusters, each of which was the groupof selectively responsive patches, we used a cluster permutation
test to calculate each cluster’s corrected P-value across the wholebrain. We further corrected each cluster’s P-value across temporal bins by using the false discovery rate. Through these two steps,
we corrected the P-values across both the spatial and temporal
domains (Pcorr = 0.05); a similar correction method in temporaland then spatial domains was adopted in a previous MEG study(Brennan and Pylkkänen, 2012). The details of our procedures are
as follows.
Using the minimum norm estimates of currents computedwith BESA 5.2, we modeled the distribution of cortical activation underlying the MEG signals, which were averaged among all
correct trials under each condition. A current dipole was perpendicularly placed at each center of the 3445 transformed corticalpatches; the multiple dipoles approximated any spatial distributions of currents on the cortex, without assuming the number or positions of responsive dipoles (Dale and Sereno, 1993;

Frontiers in Systems Neuroscience

Hämäläinen et al., 1993). Using in-house programs on MATLAB(http://www.mathworks.com/products/matlab), the current density at each cortical patch was obtained by dividing the strengthof each current dipole by the mean area of the cortical patches.
The current density at each cortical patch was averaged for a binof 20 ms; the temporal bin was slid in 10 ms steps over the 100–
400 ms period after the target verb onset, resulting in 29 temporal
bins. We have adopted the same procedures for temporal bins in
our previous studies (Iijima et al., 2009; Inubushi et al., 2012).
We first reduced the search spaces by selecting potentially
responsive cortical patches, in which the current density averaged across temporal bins of 0–400 ms was larger than the meanbaseline responses (−100–0 ms) under all of the four conditions (paired t-tests among the participants; uncorrected P <0.001). For each cortical patch’s current density in a temporalbin, we then compared cortical currents between specified conditions across the participants (paired t-tests). We chose responsive
patches whose absolute t-values were larger than the threshold ofT(14) = 3.8 (selection criteria: Z = 3.3; uncorrected P = 0.001).
If the distance between two of those patches in the Talairach space
was within 7 mm, we paired them and connected the adjacentpairs of patches as a cluster. An isolated responsive patch was also
regarded as a cluster. Within each cluster, the t-values (absolutevalues) from responsive patches were summed up to represent
the cluster. The statistical significance of observing those clusters
was then evaluated and corrected for multiple comparisons acrossthe whole brain by using the following cluster permutation test
(Maris and Oostenveld, 2007). For all cortical patches of the brain,
the current density was exchanged between specified conditions
in some of the 15 participants, and the t-values were recalculated,followed by the generation of new clusters. The largest sum of thet-values was then determined among the imaginary clusters foreach permutation. There were 215 = 32, 768 permutations, which
produced a reference distribution of the sum of t-values for determining the corrected P-values of observed clusters. Next, eachcluster’s P-value was further corrected for multiple comparisonsacross temporal bins by using the false discovery rate based on
the Benjamini-Hochberg procedure (Benjamini and Hochberg,
1995). To visualize the resulting significant cluster, color spheres(7 mm in diameter) were placed on cortical patches. Using SPM8
(http://www.fil.ion.ucl.ac.uk/spm/software/spm8) on MATLAB,
these spheres were spatially filtered with a Gaussian (full widthat half maximum, 7 mm) and superimposed onto the Talairachtransformed standard brain with MRIcron (http://www.cabiatl.
com/mricro/mricron/index.html).
PARTIAL GRANGER CAUSALITY ANALYSES

By using Granger causality analyses (Granger, 1969; Geweke,1982), we further examined which pairs of two clusters had significant causality for a specified period. Among the three clusters
that we selected, there were six possible causal influences, e.g.,
from a cluster X to a cluster Y. According to the standard Granger
causality, a variable x (a time series of the cluster X) “Grangercauses” a variable y (a time series of the cluster Y), if informationin the past of x (with specified time-lags) helps predict the futureof y with better accuracy than is possible when considering only
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Subliminal syntactic processing in braina superior extension of the standard Granger causality, in thatit takes into account causal influences of any exogenous inputs
and latent variables (Guo et al., 2008; Barrett et al., 2010). Thismethod is suitable for our present study, because it can adequately
examine multiple clusters that may receive exogenous common
inputs under all conditions. Under each condition, the time series
data of the current density without binning were averaged withineach cluster for every participant. For this averaging, we considered only the magnitude of the current density at each corticalpatch, since the orientation of a dipole was fixed perpendicularly in a similar direction for adjacent patches in a cluster. Basedon the results of cortical responses, the averaged time series data
were divided into three periods of 100 ms relative to a referencetime of 160 ms (determined by the left IFG responses): 60–160,
160–260, and 260–360 ms. Such 100-ms periods have been usedin Granger causality analyses of cortico-cortical interactions invarious human systems (Ploner et al., 2009; Lou et al., 2011).
Using a MATLAB Toolbox called GCCA (Granger Causality
Connectivity Analysis) (Seth, 2010), we removed the linear trends
from the time series data with the function cca_detrend. With
the function cca_rm_ensemblemean, non-stationarities during asingle period were further removed by subtracting the ensemble mean across participants, and each participant’s standard
deviation was divided by the ensemble standard deviation.
The non-stationarities of the resultant data were not statistically significant (P > 0.05) according to the previously proposed
test (Kwiatkowski et al., 1992), implemented as the function
cca_kpss_mtrial.
Using the time series data of 15 participants regarded as 15 repetitions, partial Granger causalities for the six causal influenceswere calculated with the function cca_partialgc_doi_permute.
A model order, i.e., the number of time-lags used in a multivariate autoregressive model, was specified by the function
cca_find_model_order_mtrial, using Akaike information criterion (Akaike, 1974). The range of a model order was first set
between 10 and 20 ms as used previously (Gow et al., 2008;
Gaillard et al., 2009), and the resultant optimal model order
was between 10 and 16.5 ms. This time range is consistent withthe latency of cortico-cortical evoked potentials from the parietal regions to the frontal regions (Matsumoto et al., 2012). Any
spatial spread of the MEG field might produce spurious causal
influences among multiple regions. Based on simulated data, it
has been recommended that causality analyses be performed on
estimated cortical currents, but not on signals of MEG sensors,
while contrasting specified conditions to cancel out the general effects of field spread (Schoffelen and Gross, 2009; Grosset al., 2013). Following this recommendation, we examined thedifferences in causalities between specified conditions. The statistical significance of the observed differences was evaluated byusing the following permutation test for each condition. The
time series data were divided into bins of 20 ms, which should
be longer than the optimal model order, and these bins frommultiple participants were permutated randomly and independently for each cluster. For each pair of i-th permutations (i =1, 2, . . . , 1000) for specified conditions, a difference in partial
Granger causalities was recalculated to produce a reference distribution for determining P-values of observed differences. In

Frontiers in Systems Neuroscience(SV-Cong + OV-Incong), we calculated partial Granger causalities separately for the SV-Cong and OV-Incong conditions,and averaged the results. These P-values were further corrected
for multiple comparisons across six causal influences using thefalse discovery rate based on the Benjamini-Hochberg procedure(Pcorr = 0.05).
For each condition, the P-value of a partial Granger causality
was also determined with a permutation test as explained above.
Because the statistical thresholds of partial Granger causalities
were different for different periods and conditions, we presented
the partial Granger causality normalized with its own threshold. For each cluster pair with a significant influence, we further
examined the difference of two directed influences, which was
determined by the permutation test (Roebroeck et al., 2005).

RESULTSASSESSMENT OF THE VISIBILITY OF MASKED STIMULI

In the pilot study with the forced-choice recognition task, we
assessed the visibility of a first verb by varying the intervalof this masked stimulus itself (Figure 1C). Among the intervals of 50, 40, 34, 27, 17, and 14 ms, the mean d data for the
50 and 40 ms intervals were significantly different from zero
(Bonferroni-corrected) [50 ms: d (mean ± s.e.m.) = 0.78 ±
0.12, T(9) = 6.4, Pcorr = 0.0008; 40 ms: d = 0.40 ± 0.10, T(9) =4.0, Pcorr = 0.02] (Figure 2A), indicating that the first verb
was clearly visible to the participants. In contrast, the meand data for the other intervals were not significantly different[34 ms: d = 0.020 ± 0.083, T(9) = 0.24, Pcorr > 0.9; 27 ms: d =0.016 ± 0.12, T(9) = 0.13, Pcorr > 0.9; 17 ms: d = 0.16 ± 0.062,
T(9) = 2.6, Pcorr = 0.2; 14 ms: d = −0.28 ± 0.12, T(9) = 2.4,
Pcorr = 0.3]. For the MEG experiments, we thus chose the longest
interval of 34 ms for subliminal stimuli (verb or NP) of which theparticipants were unaware, so that the presence of a subliminalverb was long enough to affect syntactic decisions.
In order to confirm that the participants in the MEG experiments were indeed unaware of the subliminal verbs, two additional examinations were performed after the MEG recordings.
First, the participants were notified for the first time that a subliminal verb actually appeared between an NP and a target verb,
and asked if they were aware of any subliminal verbs or not. Only
one participant reported that he was aware of the existence of
subliminal verbs during the MEG experiment; this participant
was thus excluded from the behavioral and MEG data analyses. Secondly, we carefully assessed the visibility of the first verb
by using a forced-choice recognition task with a fixed intervalof 34 ms (100 trials for each participant). Consistent with theresults of the pilot study, the mean d for the first verb was not
significantly different from zero [d = 0.20 ± 0.12, T(14) = 1.6,
P = 0.1]. These results confirmed that the participants remained
unconscious to subliminal verbs even after repeated exposures tothe stimuli during the MEG experiment.
BEHAVIORAL RESULTS

The behavioral data of the syntactic decision task performed during the MEG experiments are shown in Table 2. From the trials
with a subliminal NP, we analyzed behavioral data, but not MEG
signals, as those trials were half of the trials with a subliminal

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Table 2 | Behavioral data of the syntactic decision task performed
during the MEG experiments.
Subliminal stimuli
Verb
Cong
OV

Accuracy (%)
RTs (ms)SV

Accuracy (%)
RTs (ms)

NP
Incong89 ± 1.4

90 ± 2.3

87 ± 2.1

1044 ± 37

1037 ± 39

1052 ± 35

90 ± 1.8

90 ± 2.0

91 ± 2.1

1082 ± 441083 ± 51

1063 ± 35

Behavioral data (mean ± s.e.m.) of the accuracy and reaction times (RTs) are
shown for each condition performed by the 15 participants. Only correct trials
were included for RTs, which were measured after the target verb onset.

verb. As regards the accuracy, there were neither significant main
effects nor an interaction in a two-way repeated measures analysis
of variance (rANOVA) [sentence structure (OV, SV) × subliminal stimulus (verb, NP)] (P > 0.09). As regards RTs, an rANOVA
showed a significant main effect of sentence structure [F(1, 14) =5.2, P = 0.04] with neither a main effect of subliminal stimulus [F(1, 14) = 0.073, P = 0.8] nor an interaction [F(1, 14) = 2.7,
P = 0.1]. A post-hoc t-test revealed that the RTs under the conditions with subliminal verbs were significantly greater for the SV
sentences than for the OV sentences [mean difference ± s.e.m.:
41 ± 15 ms; T(14) = 2.7, P = 0.02] (Figure 2B). As regards theRTs under the conditions with subliminal NPs, there was no suchdifference [11 ± 14 ms; T(14) = 0.76, P = 0.5].
We further examined the behavioral data under the conditions with subliminal verbs separately for the Cong and Incong
conditions. As regards the accuracy, an rANOVA [sentence structure (OV, SV) × congruency (Cong, Incong)] showed neither
significant main effects nor an interaction (P > 0.09). As regards
the RTs, an rANOVA showed a significant main effect of sentence structure [F(1, 14) = 7.2, P = 0.02] with neither a main
effect of congruency [F(1, 14) = 0.25, P = 0.6] nor an interaction[F(1, 14) = 0.19, P = 0.7]. The increased RTs for the SV sentences irrespective of the Cong and Incong conditions suggest that
subliminal verbs interfered only with the SV sentences (see theIntroduction). The significant differences of RTs under the conditions with subliminal verbs, but not under the conditions with
subliminal NPs, confirmed the effect of subliminal verbs irrespective of their relevance to task demands. Another possibility
is that the shorter RTs under the OV sentence conditions reflectfacilitatory effects of the subliminal verbs.
SELECTIVE CHANGES IN CORTICAL RESPONSES UNDER THE OV-CONG
CONDITION

We first checked the presence of MEG signals reflecting earlyvisual responses to the stimuli. Such M100 and M200 components
were detected both after the NP onset (Figure 2C) and after thetarget verb onset (Figure 2D). Figure 2E shows MEG topographies observed after the target verb onset under the OV-Congcondition. These specific temporal bins are those when significant
responses were observed in the results presented below; note thatthe three components were outside the M200 components (seeFrontiers in Systems Neuroscience

Figure 2D). A strong signal at the left frontal region was evident
from the MEG topography at as early as 110–140 and 140–160 ms(Figures 2Ea,b, lateral scalp surface; the midpoint between thestrongest source-sink pair). Some signals were also observed at
the medial regions (Figure 2E, upper scalp surface).
We estimated the current density of every cortical patch undereach of the four conditions, and compared OV-Cong vs. (SVCong + OV-Incong). Significantly increased responses to the
OV-Cong condition were found in the left IFG [Talairach coordinates of the peak patch, (x, y, z) = (−50, 5, 29), Brodmann’sareas (BAs) 44/45/6, Pcorr = 0.04] at 140–160 ms (Figure 3A, left
panel). According to the temporal changes in the Z-values of thiscomparison [positive for OV-Cong > (SV-Cong + OV-Incong)],
the difference started to appear as early as 120 ms (Figure 3A,
middle panel), which matched with the latency reported in ourprevious study (Iijima et al., 2009). An rANOVA [sentence structure (OV, SV) × congruency (Cong, Incong)] on the current
density at 140–160 ms confirmed that there was a significant
interaction of sentence structure by congruency [F(1, 14) = 15,
P = 0.002], with no significant main effects of sentence structure[F(1, 14) = 2.1, P = 0.2] or congruency [F(1, 14) = 0.64, P = 0.4]
(Figure 3A, right panel). In addition, there was no significant
difference in the current density between the OV-Incong condition and the OV condition with subliminal NPs [T(14) = −1.3,
P = 0.2], consistent with our assumption that the OV-Incong
condition is ineffective and neutral regarding the enhancement.
In contrast, we observed significantly reduced responses under
the OV-Cong condition [i.e., OV-Cong < (SV-Cong + OVIncong)] in the precuneus [(−10, −46, 54), BA 7/31, Pcorr =0.01] at 110–130 ms (Figure 3B, left panel), extending to thesuperior parietal region [(−15, −54, 62), BA 7, Pcorr = 0.04].
At 120–140 ms, we also observed similar responses in the superior parietal region [(−16, −37, 63), BA 7, Pcorr = 0.04]. In theseclusters, an immediately earlier bin (100–120 ms) also satisfied
the selection criteria of patches (Z < −3.3) (Figure 3B, middle
panel). An rANOVA on the current density at 110–140 ms showeda significant main effect of sentence structure [F(1, 14) = 7.3,
P = 0.02] and an interaction of sentence structure by congruency [F(1, 14) = 11, P = 0.005] with no main effect of congruency[F(1, 14) = 0.44, P > 0.9] (Figure 3B, right panel). This sentence
structure effect is consistent with longer RTs, i.e., a larger load, for
the SV sentences.
We also observed significantly reduced responses to the OV
sentences in the MCC [(−6, −7, 36), BA 24, Pcorr = 0.05] at 280–
300 ms (Figure 3C, left panel). This temporal bin and an immediately earlier bin (270–290 ms) satisfied the selection criteria ofpatches (Z < −3.3) (Figure 3C, middle panel). An rANOVA on
the current density at 280–300 ms showed a significant interaction of sentence structure by congruency [F(1, 14) = 4.4, P =0.05], with no significant main effects of sentence structure[F(1, 14) = 2.2, P = 0.2] or congruency [F(1, 14) = 3.3, P = 0.09](Figure 3C, right panel). The precuneus and MCC responses were
optimal solutions located in the medial wall of the brain. Basedon simulated data with minimum norm estimates, the peak of
estimated currents was shown to be the true deep source in the
medial plane, even when the deep sources tended to be estimatedin widespread regions (Hauk, 2004).

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FIGURE 3 | Selective changes in cortical responses under the OV-Congcondition. The left panels show t-maps on the transformed standard brain(Pcorr = 0.05, false discovery rate). The middle panels show temporal changesof the current density, averaged within each significant cluster. The red andblue line graphs show the current density for the OV-Cong and (SV-Cong +OV-Incong) conditions, respectively (mean ± s.e.m., n = 15). The black line
graphs plotted for each temporal bin show temporal changes in the Z valuesof these comparisons [positive for OV-Cong > (SV-Cong + OV-Incong)]. The
horizontal black lines at Z = ±3.3 denote the selection criteria of patches(uncorrected P = 0.001, paired t-test), and the vertical black lines denote

temporal bins, when significant responses were observed (e.g., 150 ms for abin of 140–160 ms). The right panels show histograms for the current density
under each of four conditions (i.e., OV-Cong, SV-Cong, OV-Incong, and
SV-Incong). (A) Significantly increased responses under the OV-Congcondition observed in the left inferior frontal gyrus (L. IFG) at 140–160 ms.
The left lateral side is shown. (B) Significantly reduced responses under the
OV-Cong condition observed in the precuneus at 110–140 ms. A para-sagittal
section at x = −10 is shown. (C) Significantly reduced responses under the
OV-Cong condition observed in the midcingulate cortex (MCC) at
280–300 ms. A para-sagittal section at x = −6 is shown.

SELECTIVE CHANGES IN PARTIAL GRANGER CAUSALITIES UNDER THE
OV-CONG CONDITION

we found a significant difference of OV-Cong > (SV-Cong +OV-Incong) in the causalities at 60–160 ms (left IFG → MCC:
Pcorr < 0.002; left IFG ← MCC: Pcorr < 0.002) (Figure 4A).
Under the SV-Cong condition, the normalized partial Granger
causality was significant, but much weaker, from the left IFG
to the MCC (Figure 4B). Between the left IFG and MCC, theBy using the partial Granger causality analyses, we examinedcausal influences among the left IFG, MCC, and precuneus
that showed significant responses under the OV-Cong conditionin the whole-brain analyses. Between the left IFG and MCC,

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FIGURE 4 | Selective changes in partial Granger causalities under the
OV-Cong condition. Significant differences in causalities (Pcorr = 0.05, false
discovery rate) are schematically shown with arrows between clusters on thetransformed standard brain. (A) A significant difference of OV-Cong >(SV-Cong + OV-Incong) in causalities at 60–160 ms, observed between the L.

IFG and MCC, as well as from the MCC to the precuneus. A thick arrowdenotes the strongest causality among these regions. (B) Histograms for thenormalized partial Granger causalities shown in (A). Horizontal lines at 1.0 in
the histograms denote the significance of differences from zero in thenormalized partial Granger causalities (Pcorr = 0.05, false discovery rate).

difference of directed influences, i.e., (left IFG → MCC) vs. (left
IFG ← MCC), was also significant under the OV-Cong condition(Pcorr < 0.002). From the MCC to the precuneus, we observed
a significant difference of OV-Cong > (SV-Cong + OV-Incong)(Pcorr = 0.003). Between the MCC and precuneus, the differenceof directed influences was also significant under the OV-Congcondition (Pcorr < 0.002). At 160–260 and 260–360 ms, there wasno such significant causality (Pcorr > 0.05).

bottom-up prediction errors (Rao and Ballard, 1999; Friston,
2005; Feldman and Friston, 2010). According to these theories,
two distinct layers of neurons are proposed: representational
neurons and error neurons. Violation of prediction would lead toincreased responses of the error neurons, while prediction causes
their suppression; this relationship resembles classical dishabituation and habituation. If these two layers are assumed in all
regions that are functionally equivalent in an entire brain, thisassumption may not be compatible with our results, since theleft IFG showed increased responses whereas the precuneus/MCC
showed reduced responses. However, such regional differences
could be explained by any changes in relative contribution of thetwo layers, depending on the hierarchical levels of each region.
Moreover, “prediction” or prior information would increase
responses of representational neurons by definition. Indeed, it
has been proposed that the higher regions provide top-down
prediction, while error neurons in the primary cortex receive
bottom-up sensory inputs, separating the relative functional rolesof anterior and posterior regions (Summerfield and Egner, 2009).
A previous fMRI study has demonstrated that the coincidence
between predicted and observed stimuli increased responses in
the orbital prefrontal regions, suggesting the reinforcement ofprior expectations (Summerfield and Koechlin, 2008). A recent
MEG study has also reported that predicted words increased
left IFG responses at around 150 ms (Sohoglu et al., 2012).
These two studies have also indicated that prediction generated by a preceding supraliminal stimulus, just like our NP-type
effects, remains effective for more than 1 s. Our results of the

DISCUSSION
We obtained the following results in this study. In the comparisonof OV-Cong vs. (SV-Cong + OV-Incong), in which the stimulus presentation was physically controlled, we found significantly
increased left IFG responses at 140–160 ms after the target verb
onset (Figure 3A), confirming the existence of subliminal syntactic processes. In contrast, the precuneus and MCC responses weresignificantly reduced under the OV-Cong condition at 110–140
and 280–300 ms, respectively (Figures 3B,C). Finally, by means of
the partial Granger causality analyses under the OV-Cong condition, we revealed a bidirectional interaction between the left IFG
and MCC at 60–160 ms, as well as a significant influence from the
MCC to the precuneus (Figure 4). These results indicate that a
top-down influence from the left IFG to the MCC, and then to
the precuneus, is critical in syntactic decisions.
Under the OV-Cong condition, we observed increased
responses in the left IFG, rather than reduced responses. Recent
computational theories, called “predictive coding,” have proposedthat perceptual processes involve both top-down predictions andFrontiers in Systems Neuroscience

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enhanced left IFG responses to congruent target verbs are consistent with the reinforcement of prior expectations in these predictive coding theories and previous neuroimaging studies, andfurther indicate that the left IFG subserves predictive syntactic
processing.
Our previous MEG study showed increased left IFG responses
at 120–140 ms after the verb onset of the OV sentences, indicating predictive effects during syntactic processing (Iijima et al.,2009). In the present study, we observed the left IFG responses at
140–160 ms after the target verb onset, indicating more enhanced
predictive effects in OV sentences. The predictive effects thus
occurred very fast as soon as a target verb appeared. The current
results further showed that subliminal verbs under the OV-Congcondition indeed enhanced the left IFG responses, indicating thatthe predictive effects were unconscious. The predictive effects
were also obligatory, since they were elicited by a preceding object,i.e., in a stimulus-driven manner; note also that the effects wereelicited independently of both the transitivity of target verbs andthe grammaticality of sentences (normal or anomalous); that is,they were elicited in a goal-independent manner. In general, fast,
unconscious, and obligatory features support the automaticity ofcertain processes (Moors and De Houwer, 2006), which can thus
be applied to the predictive effects.
Our previous transcranial magnetic stimulation (TMS) study
showed that event-related TMS pulses facilitated syntactic decisions for OV sentences in a selective manner—i.e., only when
the TMS pulses to the left IFG were administrated at 150 ms
after the verb onset; this timing was also at 150 ms after the offset of the preceding NP (Sakai et al., 2002). It is possible that the
TMS pulses temporarily raised the overall excitability of neurons,
thereby creating a “stand-by” state in the left IFG, which leads tomore effective activation when specific responses of those cells are
required for syntactic decisions (Sakai et al., 2003). This timing
is consistent with that of our present study, in which subliminalverbs were presented at 100–200 ms after the offset of the preceding NP (see Figure 1B). These results suggest that the automatic
predictive effects in the left IFG were closely related to the priorstate of this region.
In the precuneus, we observed significantly reduced responses
at 110–140 ms under the OV-Cong condition, together with the
top-down influence from the MCC at 60–160 ms. It has beenreported that subliminal phonological priming reduced the precuneus responses during visual word recognition (Wilson et al.,2011), and it was also suggested that the precuneus was activatedfor correct responses to a target stimulus, which was incongruent
with a prior stimulus (Fassbender et al., 2006). These previous
results were consistent with the present ones under the conditions other than OV-Cong, suggesting that unexpected stimuli
were detected at this early time range in the precuneus. The
reduced precuneus responses under the OV-Cong condition arethus consistent with the suppression of error-neuron responses
in posterior regions. The results of the partial Granger causality analyses indicate that the prior syntactic prediction would betransmitted from the left IFG to the precuneus through the MCC
in a top-down manner, providing predictions about stimulusspecific information, such as the transitivity of a verb. Afterthe suppression of error-neuron responses at 110–140 ms, theFrontiers in Systems Neuroscienceenhanced responses of the left IFG at 140–160 ms may reflect thereinforcement of prior expectations.
Under the OV-Cong condition, we also observed reduced
MCC responses at 280–300 ms, together with a bidirectional
interaction between the left IFG and MCC at 60–160 ms. Previous
studies have suggested that the medial prefrontal regions, including the MCC and adjacent dorsal anterior cingulate cortex,
are involved in task-set formation (Dosenbach et al., 2006;
Hyafil et al., 2009). In these studies, task sets were definedas “context-appropriate stimulus-response relationships.” In thepresent study, the task sets for syntactic decisions were the relationships between the transitivity of the target verb and thegrammaticality of the sentence (see the Introduction). Under
the OV-Cong condition, task-set formation would be facilitated,since the transitivity of subliminal verbs could already specify thetask sets for final responses. Such facilitation would be realized
by the bidirectional interaction between the left IFG and MCC.
This period includes that of increased left IFG responses at 140–
160 ms, which is consistent with the involvement of the left IFG at
this early time range. As a result of the established task-set formation for syntactic decisions, the contribution of the MCC would
thus later be reduced at 280–300 ms under the OV-Cong condition. Under the SV-Cong condition, in contrast, the normalized
partial Granger causality was significant from the left IFG to the
MCC at 60–160 ms, suggesting task-set formation even under thisless specified condition.
To conclude, the present study indicates that the subliminal
enhancement of predictive effects is related to the generation oftask sets for syntactic decisions. The elucidation of this processhighlights the dynamic interactions among the identified regions,such that the MCC shares its task-set information with the left
IFG to achieve automatic and predictive processes of syntax.

ACKNOWLEDGMENTS
We thank H. Narita, R. Kinno, H. Miyashita, T. Inubushi, and
S. Ohta for their helpful discussions, N. Komoro for her technical assistance, and H. Matsuda for her administrative assistance.
This work was supported by a Core Research for Evolutional
Science and Technology (CREST) grant from the Japan Scienceand Technology Agency (JST), by a Grant-in-Aid for Scientific
Research (S) (No. 20220005) from the Ministry of Education,
Culture, Sports, Science, and Technology of Japan, and by Grantsin-Aid for Japan Society for the Promotion of Science (JSPS)
Fellows (Nos. 2210126 and 2611465).

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Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could beconstrued as a potential conflict of interest.
Received: 03 July 2014; accepted: 13 October 2014; published online: 03 November
2014.
Citation: Iijima K and Sakai KL (2014) Subliminal enhancement of predictive effects
during syntactic processing in the left inferior frontal gyrus: an MEG study. Front. Syst.
Neurosci. 8:217. doi: 10.3389/fnsys.2014.00217
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