ORIGINAL RESEARCH
published: 29 November 2019
doi: 10.3389/fpsyg.2019.02673

Merge-Generability as the Key
Concept of Human Language:
Evidence From Neuroscience
Kyohei Tanaka 1 , Isso Nakamura 2 , Shinri Ohta 3 , Naoki Fukui 4 , Mihoko Zushi 5 ,Hiroki Narita 6 and Kuniyoshi L. Sakai 1*
1
Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan, 2 Departmentof English Language and Literature, Faculty of Letters, The University of Tokyo, Tokyo, Japan, 3 Department of Linguistics,
Faculty of Humanities, Kyushu University, Fukuoka, Japan, 4 Graduate School of Languages and Linguistics, Sophia
University, Tokyo, Japan, 5 Faculty of Foreign Languages, Kanagawa University, Yokohama, Japan, 6 Department of English,
Faculty of Letters, Tokai University, Hiratsuka, Japan

Edited by:
Koji Fujita,
Kyoto University, Japan
Reviewed by:
Terje Lohndal,
Norwegian University of Scienceand Technology, Norway
Hiromu Sakai,
Waseda University, Japan*Correspondence:
Kuniyoshi L. Sakai
sakai@mind.c.u-tokyo.ac.jp
Specialty section:
This article was submitted toLanguage Sciences,a section of the journal
Frontiers in Psychology
Received: 06 August 2019
Accepted: 13 November 2019
Published: 29 November 2019
Citation:
Tanaka K, Nakamura I, Ohta S,
Fukui N, Zushi M, Narita H and
Sakai KL (2019) Merge-Generability
as the Key Concept of Human
Language: Evidence From
Neuroscience.
Front. Psychol. 10:2673.
doi: 10.3389/fpsyg.2019.02673

Ever since the inception of generative linguistics, various dependency patterns havebeen widely discussed in the literature, particularly as they pertain to the hierarchy basedon “weak generation” – the so-called Chomsky Hierarchy. However, humans can makeany possible dependency patterns by using artificial means on a sequence of symbols(e.g., computer programing). The differences between sentences in human language
and general symbol sequences have been routinely observed, but the question as towhy such differences exist has barely been raised. Here, we address this problem andpropose a theoretical explanation in terms of a new concept of “Merge-generability,” thatis, whether the structural basis for a given dependency is provided by the fundamental
operation Merge. In our functional magnetic resonance imaging (fMRI) study, we tested
the judgments of noun phrase (NP)-predicate (Pred) pairings in sentences of Japanese,
an SOV language that allows natural, unbounded nesting configurations. We further
introduced two pseudo-adverbs, which artificially force dependencies that do notconform to structures generated by Merge, i.e., non-Merge-generable; these adverbs
enable us to manipulate Merge-generability (Natural or Artificial). By employing this novel
paradigm, we obtained the following results. Firstly, the behavioral data clearly showedthat an NP-Pred matching task became more demanding under the Artificial conditions
than under the Natural conditions, reflecting cognitive loads that could be covariedwith the increased number of words. Secondly, localized activation in the left frontal
cortex, as well as in the left middle temporal gyrus and angular gyrus, was observed
for the [Natural – Artificial] contrast, indicating specialization of these left regions insyntactic processing. Any activation due to task difficulty was completely excluded from
activations in these regions, because the Natural conditions were always easier than the
Artificial ones. And finally, the [Artificial – Natural] contrast resulted in the dorsal portionof the left frontal cortex, together with wide-spread regions required for general cognitive

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Merge in the Human Braindemands. These results indicate that Merge-generable sentences are processed inthese specific regions in contrast to non-Merge-generable sentences, demonstratingthat Merge is indeed a fundamental operation, which comes into play especially under
the Natural conditions.
Keywords: syntax, Chomsky Hierarchy, Merge, Merge-generability, inferior frontal gyrus, lateral premotor cortex,
fMRI

INTRODUCTION

subject-predicate linking – typically instantiated by noun phrase(NP)-predicate (Pred) pairing – as observed in Japanese sentenceembedding (carried out by the so-called “External Merge;”
see below), as well as filler-gap dependency and operatorvariable relations in movement (created by the so-called “Internal
Merge”). Note that the latter type of dependency is the one
holding between more than one copy (occurrence) of the same,
single syntactic object (Chomsky et al., 2019) (see also the“Discussion” section), and is thus different in nature fromthe former type of dependency that holds between distinct
syntactic objects, NP and Pred for example. While much“processing/parsing” literature in psycholinguistics has been
focused on filler-gap dependencies, we do not directly deal
with this type of dependency between copies of the same
syntactic object in this study, simply pointing out that filler-gap
dependencies are totally Merge-generable.
The other subtype of Merge-generable dependency is theone such that although based on a structure generated byMerge (i.e., Merge-generable), the conditions for the relevant
dependency are not totally determined by Merge alone; rather,
it requires some other factors such as left-to-right precedence,
isomorphy, and pragmatic factors. This subtype of dependencies
is called partially Merge-generable, and it typically includes groupreading and cross-serial interpretation in coordinate structures.
Totally and partially Merge-generable dependencies are naturally
expected to be treated differently in the brain, but the thoroughand detailed experimental study of the different functioning ofthese dependencies falls outside of the scope of this article,
and we leave the investigation of this important topic for
future research, focusing on, in the present study, the crucial
and fundamental distinction between Merge-generable and nonMerge generable dependencies.
Regarding the neural basis of Merge, in our previous fMRI
study we demonstrated that Degree of Merger (DoM) accounted
for syntax-selective activations in the pars opercularis and pars
triangularis (L. F3op/F3t) of the left inferior frontal gyrus (L.
IFG) (Ohta et al., 2013b). The DoM is the maximum depthof merged subtrees usually within an entire sentence, and it
properly measures the complexity of tree structures. In contrast,
the number of applications of (External) Merge in a sentence
always becomes one less than the number of terminal nodes,
irrespective of sentence structures (see Appendix S2 of Ohta et al.,2013b). The DoM domain, i.e., the subtrees where the DoM iscalculated, is an entire sentence when there is no constraint,
but this changes dynamically in accord with syntactic operations
and/or task requirements (Ohta et al., 2013a). By comparing
short postpositional phrases/sentences with word lists, another
fMRI study also showed that Merge operations activated the L.

The present study aims to support the concept of humanlanguage, by putting forth a new theoretical hypothesis
and by providing novel experimental evidence drawn fromneuroscience. Our newly designed functional magnetic resonanceimaging (fMRI) experiment focused on the fundamental
operation of human language – Merge, with its ramified
functions in characterizing various formal dependencies andtheir computation in the brain. Merge is a simple andprimitive combinatory operation that takes n objects (usually
two, in the case of human language), say X and Y, andforms an unordered set of the objects (Chomsky, 1995, 2000).
The literature of theoretical linguistics has converged on the
hypothesis that human language at its core is a uniquely
human system of unbounded Merge, and this simple operationis the single generative engine underlying the infinity of
linguistic expressions.
If this hypothesis is correct, then it follows that natural
linguistic dependencies (such as those defined over embedding
and coordination) are possible only when phrase structuresthat lie behind the relevant dependencies are generable byunbounded Merge (Fukui, 2015). Capitalizing on the proposal
put forth in Fukui (2015), we make the distinction between“Merge-generable” dependencies and “non-Merge-generable”
dependencies. A dependency is Merge-generable if it is basedon a structure generated by Merge; otherwise, the dependency is
non-Merge-generable. The central role of Merge in characterizing
linguistic dependencies, as explicitly depicted by the notion ofMerge-generability just defined, leads to the following hypothesis:(1) Hypothesis: Only Merge-generable dependencies arenaturally computable as linguistic dependencies by thehuman language faculty.
This hypothesis makes sense under the “Merge-only” hypothesis
above, because if there is no structure generated by Merge fora given dependency, there will be no strictly linguistic way tocharacterize such a dependency. Thus, Merge-generability setsa necessary condition for “linguistically possible” dependencies.
Non-Merge-generable dependencies are, then, strictly speaking,“linguistically impossible.” This is a big – and crucial – line that
we would like to draw between various types of dependencies
defined over linguistic expressions.
Merge-generable dependencies (i.e., “linguistically possible”
dependencies) are further divided into two subtypes. One
subtype is a dependency that is based on a structure totally
determined by Merge. This type of totally Merge-generable
dependencies includes, among many other “core” dependencies,

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language has acquired and internalized a language in this sense –in current terminology, an “I-language” (Chomsky, 1986). The
theory of an I-language is its generative grammar, a grammar ofa particular language (henceforth in this text, “language” means“I-language”). A grammar is said to achieve descriptive adequacy(Chomsky, 1965), when it correctly describes the properties of
the target language, i.e., how it generates a digitally infinite arrayof hierarchically organized abstract structural descriptions forlinguistic expressions with the systematic interpretations at thetwo “interfaces” (sound and meaning) at which the language
interacts with other internal systems. The general theory oflanguages is called Universal Grammar (UG), which is the theoryof the genetic component of the language faculty that makes
it possible for humans to acquire a language under limited
conditions (Crain and Pietroski, 2001). UG determines the classof generative grammars that provide a set of correct structuraldescriptions for each language, thereby providing an explanation
for the well-known facts about language acquisition (Berwick
et al., 2011), in which case UG is said to achieve explanatory
adequacy. It is important to note that in this conception ofhuman language, there is virtually no room for the concept of“left-to-right” precedence or linear order, like how sounds orwords are arranged left-to-right, without referring to hierarchicalstructures. Rather, it is always an array of abstract structures
assigned to linguistic expressions – their structural descriptions –that plays a crucial role in the study of human language.
The relevant notion of weak and strong generation was
introduced by Chomsky (1963, p. 325, 1965, p. 60), and thestandard definition, adapted here from Chomsky (1965, p. 60),
is as follows: “Let us say that a grammar weakly generates a
set of sentences and that it strongly generates a set of structuraldescriptions. . .” Examples of weak generation are {aa, bb, aabb,. . .}, {John likes music, John ate an apple, . . .}, etc., dependingon the Vocabulary of a grammar; those of strong generationare {[aa], [bb], [[aa] [bb]], . . .}, {[S [NP John][VP [V likes] [NPmusic]]], [S [NP John] [VP [V ate] [NP [Det an][N apple]]]], . . .},etc. Structural descriptions assigned by a grammar are complex
objects and may be more than a single bracketing (or “tree”)
structure (or a “layered” set structure, in the case of Merge
systems), but rather, they can be a sequence of such abstract
hierarchical structures (Chomsky, 1957, 1965). It should be clear,though, from the definition (and the examples provided) abovethat weak generation is just an enumeration of sentences (stringsof elements), whereas strong generation has to do with more
abstract hierarchical structures (or sequences of hierarchicalstructures) assigned to sentences by a grammar. On strong
generation, an illuminating discussion is found in Kuroda (1976).
Theories of Merge aim to provide a minimal characterizationof strong generation, namely the generation of structuraldescriptions of linguistic expressions (Chomsky, 1965). Whiletheoretical linguistics has been developing increasingly betterand refined accounts of strong generation, attention has
been largely restricted in cognitive neuroscience to the studyof weak generation, i.e., the formation of terminal, left-toright strings of words (or word-like elements). There is apractical reason for the status of cognitive neuroscience inwhich a consistent focus has been placed on weak generation,

F3op/F3t, as well as a smaller region in the posterior superior
temporal sulcus (Zaccarella and Friederici, 2015). These fMRI
studies strongly suggest that the fundamental structure-building
operation, i.e., Merge, activates the L. F3op/F3t and the leftlateral premotor cortex (L. LPMC), which have been proposed as
grammar centers (Sakai, 2005). We are of course aware that there
is a general methodological challenge, not disproportionately
serious for the present study alone, but troublesome for any
attempt to connect linguistic computation and neural activation:
the problem as to how to substantively link cognition andneurobiology, as has been discussed in the literature (Chomsky,
2002; Embick and Poeppel, 2015). Our approach in this paper
can be taken as an “integrated” approach in the sense of
Embick and Poeppel (2015), with the goal of constructing an“explanatory” study in future work. We thus focus here on
the above-mentioned main Hypothesis (1), and we report thefindings revealed by our fMRI experiment that conforms to thisoverarching hypothesis.
We designed and conducted an fMRI experiment, the resultsof which provided a novel set of evidence supporting Hypothesis(1). As the target language, we chose Japanese because it
exhibits unbounded nesting at the core of its syntax – sentence
embedding. This is not the case in, say, English due to its
SVO order. Japanese, by contrast, straightforwardly provides
natural, unbounded nesting configurations, thanks to its SOVorder. Natural sentences with various Merge-generable structures(Figure 1A) were first tested with native speakers of Japanese [theNatural conditions, using four-word (4W) and six-word (6W)
sentences (excluding an adverb in the middle), i.e., Natural (4W)
and Natural (6W), respectively]. On a separate day, we tested
other conditions using two pseudo-adverbs (which do not exist in
the actual Japanese), in which these dependencies switched witheach other [the Artificial conditions, using 4W and 6W sentences,i.e., Artificial (4W) and Artificial (6W), respectively]. Morespecifically, these pseudowords were designed to require theparticipants to assign certain dependencies that do not conformto structures generated by Merge, i.e., non-Merge-generable.
We predicted that Merge-generable dependencies would induce
more specific activations in the grammar center and other syntaxrelated regions than non-Merge-generable dependencies would.
By our testing of Merge-generability, we speculated that thefundamental status of Merge would be clearly elucidated, further
highlighting the nature of the human language faculty.

Theoretical BackgroundIn this section, we explain the theoretical background of
our study that is minimally necessary for understanding thesignificance of the experimental results reported in this paper.
One of the remarkable – and fundamental – discoveries ofmodern theoretical linguistics is the recognition that humanlanguage involves abstract “structures,” and that its mechanisms
generate an infinite set of such structures. Linguistic expressions
are not merely sequences of sounds or words; rather, they
are associated with their “structural descriptions” – an arrayof abstract hierarchical structures – that determine theirinterpretations, both in terms of sound (pronunciation) andmeaning (semantic interpretation). A speaker of a particular

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FIGURE 1 | Basic types of Natural and Artificial sentences. We tested six sentence types each under Natural or Artificial conditions. Below each example inJapanese, phrases in Romaji and word-by-word translations in English are shown (NOM = a nominative case marker). Each type was presented as three visual
stimuli in the order of noun phrases (NPs), an adverb, and predicates (Preds). The same subscript letters stand for structurally bound correspondences between an
NP and a Pred in the sentence (S): e.g., NPi and Predi indicate that these two elements are paired (Predit denotes the predicate of an indefinite subject “it”). Curved
arrows also denote such NP-Pred pairings based on sentence structures. Each of tree structures represents unique structures for NPs and Preds. (A) There were six
types of Natural sentences with four words. Left: three types of sentences with sorezore: e.g., “Akiko and [her] friend are running and sitting, respectively” (s4-1), “Asfor Taro, [his] limbs are thick and warm, respectively” (s4-2), and “We meet with Taro and talk [with him], respectively” (s4-3). Right: three types of sentences withtokidoki: e.g., “Kenta says that Hanako sometimes ran” (t4-1), “Akiko and [her] teacher say that it was sometimes bright” (t4-2), and “Taro says with Kenta that it was
sometimes bright” (t4-3). (B) There were six types of Artificial sentences with four words, but only two of these are shown here. For the description of other four
sentence types (k4-2, k4-3, h4-2, and h4-3), see the “Stimuli” section. Left: artificial cross-serial dependencies (pairing relations between NPs and Preds). Right:
artificial nested dependencies. In these examples, pseudowords (“koregore” and “hokiboki”) artificially forced dependencies without conforming to Merge-generable
structures.

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In this structure, which is a typical sentence embedding
configuration in Japanese, the only possible linking pattern is toassociate a subject with its corresponding predicate in the mannerindicated by the subscript numbers (the same number for eachsubject-predicate pair), forming nested dependencies generated
by iterative applications of Merge. The other linking patterns,
including cross-serial dependencies, are simply impossible. For
example, sentence (2) can never mean that Ziro thinks that
Hanako said that Taro danced. Even though this interpretationis plausible either semantically or pragmatically, it is just nota possible interpretation provided by the grammar of Japanese.
By contrast, the nested dependencies as exemplified by sentence(2) are readily – and widely – available in other humanlanguages as well.
On the other hand, cross-serial dependency, which is generally
argued to be one of the characteristic properties that require
more powerful context-sensitivity, is only available in very
limited contexts, as has been widely acknowledged in the
linguistic literature (see above). One representative case is the“respectively” reading of coordination (Bar-Hillel and Shamir,1960). Consider the following example from Japanese.

virtually ignoring strong generation; it is much easier andmore straightforward to deal with weak generation than strong
generation, because you can literally see and readily construct
the word-string stimuli for online experiments, while abstract
hierarchical structures associated with the strings await indepth theoretical investigations. Further, the research trend
toward weak generation is boosted by a well-known result
from formal language theory/automata theory, namely that the
weak generative capacity of human language lies somewhere
above the context-free phrase-structure grammar in the so-called“Chomsky Hierarchy” (Chomsky, 1959, 1963; Joshi, 1985).
The formal properties that have been highlighted andwidely discussed in the literature are nested dependencies andcross-serial dependencies. Briefly put, nested dependencies are
dependencies that hold between xi and yi (i.e., x and y with thesame subscript) in the configuration x1 x2 . . . xn−1 xn . . . yn yn−1
. . . y2 y1 , forming a “nested” dependency structure, while crossserial dependencies are dependencies holding between xi andyi in the configuration x1 x2 . . . xn . . . y1 y2 . . . yn , forminga “crossing” dependency schema. And it has been observedthat human language exhibits nested dependencies in a great
number of instances, while it also shows, in very limited contexts,
cross-serial dependencies. Based on this difference, it has beenargued that the generative power of human language is beyond
the bounds of finite-state grammar and is beyond the scopeof context-free phrase-structure grammar, but perhaps stays, interms of its weak generation, within the bounds of a certain classof context-sensitive phrase-structure grammar (Joshi, 1985). This
claim makes sense only insofar as we restrict our attention to
weak generation, but recall that, as we pointed out above, thenature of human language has to do with strong generation –the generation of structural descriptions of linguistic expressions.
If this is true, then the whole discussion about weak generative
capacities of various generative systems (grammars) may in fact
be beside the point, as far as the empirical inquiry into the natureof human language is concerned. Questions of real empiricalinterest arise only when strong generation is at stake, or more
importantly, the problem of “explanatory adequacy” (see above)is in focus, a matter that goes beyond even strong generation(Chomsky, 1963, 1965).
In fact, if we shift our attention from dependencies definedon terminal strings to how abstract hierarchical structures behindthem are formed by linguistic computations, the well-known fact
mentioned in the preceding paragraph concerning mysterious
distribution of the two types of dependencies in human language
can be rightly addressed. Consider, for example, the subjectpredicate pairing in languages such as Japanese, as in the upper
row in sentence (2) below, resulting from the “SOV” (SubjectObject-Verb/Predicate) word order (see the topmost right panelof Figure 1A for a real stimulus of nested configurations).

(3) [[Taro1 -to Hanako2 -to Ziro3 ]-ga (sorezore) [odori1 ,hanasi2 , omotteiru3 ]][[Taro and Hanako and Ziro]-NOM (respectively)[dancing, saying, thinking]]“Taro1 , Hanako2 , and Ziro3 are dancing 1 , saying 2 , andthinking 3 , respectively”
The subject-predicate pairings in the Japanese sentence (3) (orits English counterpart for that matter) can be seen as exemplarsof cross-serial dependencies (see the topmost left panel of
Figure 1A). If the adverb sorezore “respectively” is absent, the socalled “group reading” is also possible, where the interpretation
is such that the group of people consisting of Taro, Hanako, andZiro are collectively dancing, saying, and thinking. However, otherdependency patterns are impossible to obtain here. Thus, thespecific question that should be addressed based on these facts
about linking patterns exemplified in sentence (2) and (3) is asfollows. Why is it that in a configuration such as sentence (2),only nested dependencies are allowed, whereas in sentence (3),cross-serial dependencies as well as group reading are allowed,
with nested dependencies being mysteriously excluded? Note
incidentally that context-sensitive phrase-structure grammar
easily generates all kinds of dependencies in these cases,
including non-existent cross-serial dependency for sentence(2), and also non-existent nested dependency for sentence (3).
Thus, it fails to distinguish “linguistically possible” dependencies
from “linguistically impossible” ones. As we will discuss indetail later on in the Discussion, the problem just mentioned
can be appropriately addressed and naturally resolved only
insofar as abstract structures generated by Merge are seriously
taken into account.
It is simply impossible to tackle the problem just mentioned
if we only look at terminal strings, because the examples (2)and (3) represent the same sequential patterns, with three NPs(2) [Taro-ga1 [Hanako-ga2 [Ziro-ga3 odotta3 -to] hanasita2 to] omotteiru1 ][Taro-NOM [Hanako-NOM [Ziro-NOM danced-that]said-that] thinks] (NOM, a nominative case marker)“Taro1 thinks1 that Hanako2 said2 that Ziro3 danced3 ”

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Merge in the Human Brainon the left and three Vs (or Preds) on the right (NP NP NP. . . V V V; see the Stimuli section for relevant discussion).
However, if we turn our attention to the structures of thesesentences, a clear picture emerges. To see this, let us consider
first the availability of the nested dependencies, which is available
in a sentence embedding structure such as that of sentence (2)but never possible in a coordinate structure such as that ofsentence (3). We argue that the nested dependencies between
NPs and Vs in sentence (2) are straightforwardly obtained as aconsequence of iterative applications of Merge, as it combines
an NP and a V, going on to embed a sentence within anothersentence, as illustrated roughly in (4). Note that we abstract awayfrom all the technical details of clausal architecture that are notdirectly relevant for our present discussion. In particular, in order
to avoid unnecessary complications in illustration, we refrainfrom depicting the “functional” portions of a clause structure.
Those “functional heads” such as T(ense) and C(omplementizer)
are – if they are indeed syntactic functional heads in Japanese,
not an innocent assumption – undoubtedly incorporated into
the central clausal structure by Merge. And to the extent thatthey are incorporated by Merge, their existence does not affectour discussion. Thus, for simplicity, we omitted them in our
exposition below. Also, for the gloss and translation, see (2).

Since, this process is just a normal mode of applying Merge
recursively (phase-by-phase, “phase” being a technical notion
indicating a restrictive domain for rule applications), we saythat such nested dependencies are totally Merge-generable. It
therefore comes as no surprise that nested dependencies as
exemplified in a sentence embedding structure such as that ofsentence (2) are widely available in human language. Noticeincidentally that the structures for the other linking patterns
pointed out above in connection with example (2) cannot be
generated by Merge in the way designated in (4), and thus are
non-Merge-generable, which accounts for the unavailability of
their associated interpretations.
Let us next consider the possibility of cross-serial
dependencies between NPs and Vs in sentence (3). The
crucial difference between sentences (2) and (3) in structure isthat in the former sentence, neither the sequence of the NPs(Taro, Hanako, and Ziro) nor that of the Vs (odotta, hanasita,
and omotteiru) form a constituent – a word or a group of
words that function(s) as a single syntactic unit (i.e., a set)
within a hierarchical structure, whereas in the latter sentence, thesequence of conjoined NPs or that of Vs each forms a constituent.
A step-by-step derivation for sentence (3) is illustrated in (5) [see(3) for the gloss and translation].

(4) a. Merge(Ziro-ga1 , odotta1 -(to))(5) a. Merge(Hanako-to2 , Ziro3 -(ga))= {Ziro-ga1 , odotta1 -(to)}= {Hanako-to2 , Ziro3 -(ga)}b. Merge(Taro-to1 , {Hanako2 -(to), Ziro3 -(ga)})– A verb phrase V(P)1 and NP1 are combined byMerge, forming a sentence S1= {Taro-to1 , {Hanako2 -(to), Ziro3 -(ga)}}b. Merge({Ziro-ga1 , odotta1 -(to)}, hanasita2 -(to))– NP1 , NP2 , and NP3
iterative Merge.

= {{Ziro-ga1 , odotta1 -(to)}, hanasita2 -(to)}are combined byc. Merge(hanasi2 , omotteiru3 )– S1 and V2 are combined by Merge, forming a V(P)2= {hanasi2 , omotteiru3 }c. Merge(Hanako-ga2 , {{Ziro-ga1 , odotta1 -(to)}, hanasita2 (to)})d. Merge(odori1 , {hanasi2 , omotteiru3 })= {Hanako-ga2 , {{Ziro-ga1 , odotta1 -(to)}, hanasita2 -(to)}}= {odori1 , {hanasi2 , omotteiru3 }}– V1 , V2 , and V3 are combined by iterative Merge.

– V(P)2 and NP2 are combined by Merge, forming an
S2

e. Merge({Taro-to1 , {Hanako2 -(to), Ziro3 -(ga)}}, {odori1 ,{hanasi2 , omotteiru3 }})d. Merge({Hanako-ga2 , {{Ziro-ga1 , odotta1 -(to)}, hanasita2 (to)}}, omotteiru3 )= {{Taro-to1 , {Hanako2 -(to), Ziro3 -(ga)}}, {odori1 , {hanasi2 ,omotteiru3 }}}= {{Hanako-ga2 , {{Ziro-ga1 , odotta1 -(to)}, hanasita2 -(to)}},omotteiru3 }As shown in (5), the sequence of conjoined NPs and that of Vs
in sentence (3) each forms a constituent (a set). This provides
the grammatical basis for the group reading, which requires
matching of the sequence of NPs and that of Vs as a whole.
Thus, such a reading becomes readily available. In addition
to this natural group reading, the cross-serial dependencies
are also possible here. Merge forms the two constituents –the conjoined NPs and complex of Vs – and the interpretive
mechanisms at the conceptual/thought interface apply in accord
with an “isomorphy” condition which incorporates an insightof the “copying transformation” of Chomsky (1957) that– S2 and V3 are combined by Merge, forming a V(P)3e. Merge(Taro-ga3 , {{Hanako-ga2 , {{Ziro-ga1 , odotta1 -(to)},hanasita2 -(to)}}, omotteiru3 })= {Taro-ga3 , {{Hanako-ga2 , {{Ziro-ga1 , odotta1 -(to)},hanasita2 -(to)}}, omotteiru3 }}– V(P)3 and NP3 are combined by Merge,
forming an S3 .

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participants, who showed larger head movements (see below)in ≥75% of runs under one or more of the four conditions[Natural (4W), Natural (6W), Artificial (4W), and Artificial(6W)]. We excluded three more participants, whose accuracy
on one or more sentence types (see Figure 1) was ≤60% (thechance level was at most 34% as shown below). The remaining
16 participants [six females; mean ± standard deviation (SD) age:
20.1 ± 1.1 years] showed right-handedness (laterality quotients:
81 ± 10) as determined by the Edinburgh inventory (Oldfield,
1971). None had a history of neurological or psychiatric diseases.
Prior to participation in the study, written informed consent
was obtained from each participant after the nature and possible
consequences of the study were explained. Approval for theexperiments was obtained from the Institutional Review Boardof The University of Tokyo, Komaba Campus.

requires parallel (isomorphic) hierarchical structures for the twoconstituents at hand, yielding the cross-serial interpretation.
Thus, details of interpretive processes aside, it is clear that Merge
sets out a necessary structural basis for cross-serial dependencies.
Needless to say, nested dependencies and other linking
patterns are impossible in sentence (3), simply because Merge, as
it applies to generate the structure of sentence (3), does not yield
the structural basis for such dependencies and there is no otherway to obtain these linking patterns. By contrast, in sentenceembedding constructions like sentence (2), neither the sequenceof NPs nor that of Vs forms a constituent, and thus the groupreading is impossible. Nor is the cross-serial dependency allowed
in sentence (2), since there is no structural basis, i.e., constituencyof the relevant elements, for such a dependency.
Thus, the generalization we can draw from these facts is thatcross-serial dependencies in human language become possible
only when the relevant terminal elements form a constituent. As
we demonstrated above, Merge sets out the necessary structural
condition, forming the relevant constituents in the coordinate
structures such as that of sentence (3). However, Merge does notin and of itself provide a direct structural basis for cross-serial
dependencies. This is probably why the interpretation requires a
special device such as “sorezore” or “respectively” that effectively
forces this interpretation, rather than the more natural (andapparently default) group reading, which is available only byvirtue of Merge.
Summing up the discussion in this section, we have re-iterated
the fundamental discovery of modern theoretical linguistics
according to which the nature of human language criticallydepends on its mechanisms, particularly Merge – the basic andfundamental operation of (unordered) set-formation in syntax.
Correspondingly, well-known results in formal language theory
concerning the generation of dependencies defined over terminal
strings (e.g., context-free vs. context-sensitive phrase-structure
grammars) and the related discussion should be reconsidered
and re-evaluated from the new theoretical point of view basedon Merge. We have looked at two typical examples fromJapanese, and we have suggested that these simple examples
demonstrate important points about the nature of formal
dependencies in human language. These points strongly suggestthat dependencies are possible in human language only to theextent that they result from abstract structures generated byMerge, leading to the conclusion that it is Merge-generability
that determines the availability of various dependencies inhuman language [Hypothesis (1)]. We will thus argue that inhuman language, the apparent generation of various “types”
of dependency defined on terminal strings is rather illusory,
emerging only as an epiphenomenon of linguistic computation.

Stimuli
As visual stimuli, we first prepared sentences under the Natural
conditions, which were all grammatical and meaningful inJapanese. Under the Natural (4W) condition, there were 30
sentences in each of six sentence types (s4-1, s4-2, s4-3, t4-1,
t4-2, and t4-3; see Figure 1A). Every sentence with four words(excluding an adverb) had two noun phrases (NPs, subjects),
an adverb, and two predicates (Preds) in the form of NP-NPAdverb-Pred-Pred. Under the Natural (6W) condition, there
were 30 sentences in each of six sentence types (s6-1, s62, s6-3, t6-1, t6-2, and t6-3; see Supplementary Figure 1).
Every sentence with six words (excluding an adverb) was in
the form of NP-NP-NP-Adverb-Pred-Pred-Pred. Since Japaneselacks overt, semantics-free subject-predicate formal agreement,
we chose another phenomenon in the language, namely, thesubject-predicate linking, which in fact has been often utilized in
the formal language/automata theory literature, and which, likemost other linguistic dependencies, may not be immune fromsemantic, pragmatic, and other factors. We carefully examined
the relevant phenomena to see if the nature of linking patterns is
actually independent from those factors [cf. our discussion about
example (2) above], and, as we will present below, we paid close
attention to controlling non-syntactic factors as much as possible
in our experiments.
For the nouns, we used common names of persons (e.g.,“Taro”), (singular) animate nouns [e.g., “sensei” (teacher)], theirplural forms [e.g., “sensei-gata” (teachers)], and part(s) of body[e.g., “teasi” (limbs)]. For the predicates, we used transitive
verbs [e.g., “kangae-ru” (think)] (all of these select a clausal
complement), intransitive verbs [e.g., “odor-u” (dance)], andadjectives [e.g., “akaru-i” (bright)]. Adjectives in Japanese act as
Preds without copula verbs, and they have their own present
and past forms {e.g., “akaru-i” [(is) bright]; “akaru-k-atta” [(was)bright]}. To avoid the undesirable possibility of default groupreading (which collectively relates all NPs to all Preds as a group)for the s4-1 (see Figure 1A) and s6-1 types (see Supplementary
Figure 1), we selected at least two verbs indicating actions thatcannot be collectively performed at the same time [e.g., “hasiri”(running) and “suwatteiru” (sitting)]. For these types, we also put
a last predicate in progressive form, which was the case for all
sentences with tokidoki (see below).

MATERIALS AND METHODS
Participants
We recruited 25 native speakers of Japanese. They were
undergraduate students who had not majored in linguistics or
language sciences. Two participants were dropped from ouranalyses due to their health conditions. We also dropped four

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Merge in the Human Braindependency (see h4-1 which is made from s4-2), as illustrated byexample (7) below. Again, the linking pattern is prohibited as an
actual Japanese sentence. Both examples (6) and (7) thus deviate
from Merge-generable structures.

The adverbs under the Natural conditions were either“sorezore” (respectively; denoted here as “s” for such types) or“tokidoki” (sometimes; denoted here as “t” for such types), whichwere presented in hiragana (the basic Japanese syllabary that
represents each mora in the Japanese language). While nested
dependencies are created in sentence embedding constructions
with or without an adverb, cross-serial dependencies are created
only with the help of sorezore in coordinate configurations, ina way similar to English sentences with respectively: e.g., Taroand Hanako sang and danced, respectively. Note that the Mergegenerable syntactic structures are naturally generated underthese conditions.
We used three types of grammatical particles, which represent
canonical case markings and syntactic information in Japanese:
the nominative case marker -ga (which is realized as -wa when
the subject represents the topic of the sentence; thus, we used wa for s6-2, t6-1, and t6-2), a postposition -to (with/and), anda complementizer -to (that). In the sentences with tokidoki, thecomplementizer was placed at the end of the first Pred under
4W (Figure 1A), and of the first and second Preds under 6W.
Each subject-predicate pair could not be made correctly, if rather
rare and non-canonical usages of -ga – such as object marking
and an external possessor – were employed. To correctly makeeach subject-predicate pair, the participants had to use -ga asa canonical nominative subject case marker. Since a Pred in
past-tense form with a complementizer -to cannot be interpreted
as a conditional clause like -suru-to containing a Pred in apresent-tense form, we used, in an attempt to avoid the unwanted
conditional clause interpretation, a past-tense form for all thePreds except for the last one in the sentences with tokidoki.
In the sentences with sorezore, all the Preds except for thelast one took an adverbial form, forming conjunctives for thePreds. In order to prevent participants from anticipating certain
dependencies from particle patterns alone, we used NPs with thesame particle patterns in two sentence types (with either sorezore
or tokidoki) under 4W (e.g., an NP-to-NP-ga-Adverb-Pred-Predpattern is used in s4-1 and t4-2). Due to syntactic characteristics
of Japanese, this procedure was not possible under 6W.
Under the Artificial conditions, we used the same set ofphrases as with the Natural conditions except that the adverbs
sorezore and tokidoki were replaced with pseudo-adverbs, orphonotactically legal pseudowords, koregore (denoted here as“k”) and hokiboki (denoted here as “h”). There were 30 different
sentences for each of six types of Artificial sentences (for two
representative types, see Figure 1B; for six types with six words,
see Supplementary Figure 2). Using six examples for eachcondition, we instructed the participants to pay attention to
the fact that each pseudo-adverb determined correspondence
among the other four or six words (see the Appendix of
Supplementary Material). As shown in Figure 1B, nested or
cross-serial dependency was enforced depending on which ofthese pseudo-adverbs was used. More specifically, the pseudoadverb koregore artificially imposed cross-serial dependency (seek4-1 which is made from t4-2), as shown in example (6) below, inwhich brackets and indices denote artificial reading. This linking
pattern is impossible as a real Japanese sentence. The pseudoadverb hokiboki, on the other hand, artificially imposed nested

Frontiers in Psychology | www.frontiersin.org(6) [[Taro-gai inu-gaj Hanako-gak ] (koregore) [aruita-toi
kizuita-toj hanasiteiruk ]][[Taro-NOM dog-NOM Hanako-NOM] (–) [walked-that
noticed-that says]]“Taroi walksi , the dog j noticesj , and Hanakok saysk ”(7) [Taro-toi [inu-toj [Hanako-gak (hokiboki) kizukik ,] arukij ,]hanasiteirui ][Taro and [dog and [Hanako-NOM (–) noticing,]
walking,] saying]“Taroi saysi , the dog j walksj , and Hanakok noticesk ”
Here, the same indexed letters indicate each NP-Pred pairing.
These examples became thus ungrammatical, due to theillegitimate linking patterns imposed by the artificial adverbs.
Under the Artificial (4W) condition, we prepared six sentence
types (k4-1, k4-2, k4-3, h4-1, h4-2, and h4-3, which were madefrom four sentence types under the Natural (4W) condition: [k4-1and h4-2 from t4-2], [h4-1 and k4-2 from s4-2], [k4-3 from s4-3],and [h4-3 from t4-3]. Note that the original sentences with crossserial and nested dependencies (i.e., s4-1 and t4-1, respectively)
were not used under the Artificial (4W) condition, because they
were conflicting with each other. Thus, the Artificial condition
included two types of cross-serial and nested dependencies forthe task, as well as four types derived from the original sentences(i.e., s4-2, s4-3, t4-2, and t4-3). The examples (6) and (7), in which
nested and cross-serial dependencies were switched with eachother, are presented above for the purpose of explanation only.
The same procedures were used for the Artificial (6W) condition
as well (see Supplementary Figure 2).
The resultant artificial NP-Pred pairings were all meaningfulin terms of selectional restrictions on the words we used. We
assessed the plausibility of the NP-Pred pairs (typical 20 pairs
each for those used under the Natural conditions alone, Artificial
conditions alone, or both), by asking their likelihood (Likert
or five-point scale) to participants (n = 9), and the likelihood
was not significantly different from the highest point (“definitelyso”) under both Natural and Artificial conditions (one sample
t-test, corrected p > 0.05). The NP-Pred pairs were hence
equally plausible; non-syntactic factors such as semantic andpragmatic knowledge, as well as frequencies of constructions,were strictly controlled between the conditions. Merge-generable
and non-Merge-generable dependencies were thus expected to berealized under the Natural and Artificial conditions, respectively.
Accordingly, we tested the participants first under the Natural
conditions, and then under the Artificial conditions to compare
Merge-generability (Natural vs. Artificial).
Each sentence was serially presented in three groups of NPs,
adverb, and Preds. Each of these phrases was shown with two
to six yellow characters in hiragana and kanji (the adopted
logographic Chinese characters used in written Japanese) for

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2.5 s (4W) or 3.5 s (6W) with an interval of 0.2 s after eachgroup. The participants were instructed to read each sentence
including particles like -wa, -ga, or -to. After the presentationof the sentence, a “question-set” was presented, which contained
one of the Preds in its upper row, as well as three (4W) or
four (6W) NPs in its lower row. The NPs in a question-set
were chosen from those contained in the sentence, together
with a conjunction of two NPs with -to, or an NP which was
not contained in the sentence; these possibilities were informed
to the participants. In the question-set, the NPs were always
presented without any particle, and the Pred in the present
form in order to avoid the use of particles or verb forms ascues. Question-sets were presented for 2.0 s (4W) or 3.0 s (6W)with a post-trial interval of 1.9 s; each trial lasted for 12.0 s(4W) or 16.0 s (6W).
The stimuli were presented against a dark background at thecenter of an eyeglass-like MRI-compatible display (resolution,
800 × 600; VisuaStim XGA, Resonance Technology Inc.,
Northridge, CA, United States), and the participants wore
earplugs. For fixation, a red cross was always displayed at thecenter of the display, and the participants were instructed to keep
their eyes on this position. The stimulus presentation and thecollection of behavioral data [accuracy and reaction times (RTs)]
were controlled using Presentation software (Neurobehavioral
Systems, Albany, CA, United States).

MRI Data Acquisition

Tasks

The fMRI data were analyzed by using SPM12 statisticalparametric mapping software (Wellcome Trust Centre
for Neuroimaging)1 (Friston et al., 1995) implemented on
MATLAB software (MathWorks, Natick, MA, United States).
We confirmed that all available fMRI data were free fromlarge head movements, with a translation of <3 mm in thethree directions and with a rotation of <2◦ around the threeaxes. The acquisition timing of each slice was correctedusing the middle slice (the 15th slice chronologically) as areference for the fMRI data. The time-series data in multiple
runs were then realigned to the first volume in all runs,
and resliced using seventh-degree B-spline interpolation, sothat each voxel of each functional image matched that of
the first volume.
The T1-weighted structural image of each participant wasaligned to the AC-PC line, and coregistered to the meanfunctional image generated during the realignment of the fMRI
data. Each T1-weighted image was bias-corrected with light
regularization, and segmented to the gray matter, white matter,
cerebrospinal fluid, bone, other soft tissues, and air by using
default tissue probability maps and a standard tool in the
SPM12 that uses an affine regularization to warp images to theInternational Consortium for Brain Mapping East Asian brain
template (Ashburner and Friston, 2005). The realigned functional
images were also spatially normalized to the standard brain
space as defined by the Montreal Neurological Institute (MNI),
which converted voxel sizes to 3 × 3 × 3 mm3 and smoothed
images with an isotropic Gaussian kernel of 9-mm full-widthat half maximum.

For the MRI data acquisition, a participant was in a supine
position, and his or her head was immobilized inside the radiofrequency coil. The MRI scans were conducted on a 3.0 T MRI
system equipped with a bird-cage head coil (GE Signa HDxt 3.0T;
GE Healthcare, Milwaukee, WI, United States). During the fMRI
session, we scanned 30 axial 3-mm thick slices with a 0.5-mm
gap, covering the volume range of –38.5 to 66 mm from theanterior to posterior commissure (AC-PC) line in the verticaldirection, using a gradient-echo echo-planar imaging (EPI)
sequence [repetition time (TR) = 2 s, echo time (TE) = 30 ms,
flip angle (FA) = 90◦ , field of view (FOV) = 192 × 192 mm2 ,resolution = 3 × 3 mm2 ). In a single scan, we obtained 114[Natural (4W) and Artificial (4W)] or 104 [Natural (6W) and
Artificial (6W)] volumes, in which the first six or eight images(the first dummy trial in each scan), for the rise of the MR
signals were discarded. High-resolution T1-weighted images of
the whole brain (192 axial slices, 1.0 × 1.0 × 1.0 mm3 ) wereacquired from all participants with a three-dimensional fast
spoiled gradient recalled acquisition in the steady state (3D
FSPGR) sequence (TR = 8.4 ms, TE = 2.6 ms, FA = 25◦ ,FOV = 256 × 256 mm2 ). These structural images were used fornormalizing the fMRI data.

fMRI Data Analyses

Under both Natural and Artificial conditions, we used a task of
NP-Pred matching, in which the participants were instructed tonote all of the two (4W) or three (6W) NP-Pred pairs based on
dependencies. NP-Pred matching under the Natural conditions
imposed building syntactic structures of a given sentence (seeFigure 1), rather than “word-to-word correspondence,” and thus
required syntactic judgment at the sentence level; the task cannot
be solved correctly by semantic or pragmatic judgment (seeabove). The participants then chose one of the three (4W) or four(6W) NPs corresponding to the Pred on the question-set (seeabove), by pressing a button on a handheld controller (see the
Appendix of Supplementary Material). After these instructions
were given to the participants, the participants were trainedon each condition (4W or 6W) outside the scanner, until they
confidently performed the task for two consecutive runs (at least
four correct trials out of six trials per run). The condition of 6W
was always tested after that of 4W, with a short break. During the
MR scanning, no feedback on each trial’s performance was given
to any participant.
The sentences of six types were presented randomly in thesame frequency. A single run of MR scans contained 19 (4W)or 13 (6W) trials of either task. For every participant, the taskwith eight runs for 4W and nine for 6W under the Natural
conditions were first conducted, and then the task under the
Artificial conditions were tested in another day. By separating
the task trials under the Natural and Artificial conditions in thisorder, we imposed the participants to read sentences in a natural
way while performing the task under the Natural conditions. On
the other hand, the participants might perform the task with astrategy like puzzle-solving under the Artificial conditions.

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In a first-level analysis (i.e., the fixed-effects analysis) for eachparticipant, hemodynamic responses induced by the correctresponse trials for each session were modeled with a boxcar
function with a duration of 7.9 s (4W) or 10.9 s (6W) from theonset of each visual stimulus, i.e., the length of the time for the
five/seven words, as well as with a duration of 2 s (4W) or 3 s(6W) from the onset of a question. The boxcar function wasthen convolved with a hemodynamic response function, and lowfrequency noises were removed by high-pass filtering at 1/128 Hz.
To minimize the effects of head movement, the six realignment
parameters obtained from preprocessing were included as a
nuisance factor in a general linear model. The images of the six
conditions were then generated in the general linear model foreach participant and used for our intersubject comparison in asecond-level analysis (i.e., the random-effects analysis).
In the second-level analysis, we performed a repeatedmeasures analysis of variance (rANOVA) with a t-test, the resultsof which were thresholded at uncorrected p < 0.001 for thevoxel level, and at corrected p < 0.05 for the cluster level,with topological false discovery rate (FDR) correction across thewhole brain (Chumbley and Friston, 2009). For the anatomical
identification of activated regions, we used the Anatomical
Automatic Labeling method2 (Tzourio-Mazoyer et al., 2002) andthe labeled data as provided by Neuromorphometrics Inc.3 , underacademic subscription.

FIGURE 2 | Behavioral data. (A) Accuracy for an NP-Pred matching task.
Filled and open bars represent Natural and Artificial conditions, respectively,
each with either 4W (two NPs and two predicates) or 6W (three NPs and three
predicates) conditions. Error bars denote the standard error of the mean (SEM)
for the participants. ∗ Corrected p < 0.01. (B) Reaction times (RTs) from theonset of a question-set for judgment. Only correct trials were included for RTs.

RESULTS
Behavioral Data

orbital part of the inferior frontal gyrus (L. F3O), together withthe left middle temporal gyrus (L. MTG). In addition to theselanguage-related regions, additional activation was observed in
the right hemisphere, such as the right LPMC (R. LPMC)
and parietal cortex, together with medial regions including the
pre-supplementary motor area (pre-SMA), anterior cingulate
cortex (ACC), cuneus, caudate nucleus, and tegmentum/tectum.
Under the Artificial (4W) condition, in contrast, the left frontal
activation was greatly reduced to the dorsal portion (Figure 3B),
and the left temporal activation was also decreased. The otherright and medial regions were mostly consistent with those under
the Natural (4W) condition. Regarding the 6W conditions, theoverall activation patterns were similar to those under the 4W
conditions, and left frontal activations were not enhanced as
expected. Therefore, we combined the 4W and 6W conditions for
subsequent analyses.
We then examined the [Natural (4W and 6W) – Artificial(4W and 6W)] contrast (Figure 3C), and found clearly localized
activation in the ventral portion of the left frontal cortex,
including the L. F3op, L. F3t, and L. F3O, as well as the L.
MTG and the left angular gyrus (L. AG) (Table 1). On the other
hand, the [Artificial (4W and 6W) – Natural (4W and 6W)]
contrast resulted in a completely different pattern of activation(Figure 3D). As mentioned above, the left frontal activation was
greatly reduced to the dorsal portion of the L. LPMC and L.
F3op. Moreover, activated regions were more wide-spread insuch regions as the R. LPMC, pre-SMA/ACC, left supramarginal
gyrus (L. SMG), bilateral inferior parietal lobule (IPL), bilateral

The accuracy and RTs are shown in Figure 2. For the accuracy,
an rANOVA with two factors [Merge-generability (Natural,
Artificial) × word numbers (4W, 6W)] showed that both main
effects of Merge-generability [F(1, 15) = 10, p = 0.006] andword numbers [F(1, 15) = 6.4, p = 0.02] were significant,without the interaction between them [F(1, 15) = 1.7, p = 0.2]
(Figure 2A). The main effect of Merge-generability was due tolower accuracy under the Artificial conditions, while the main
effect of word numbers may be simply caused by processing loads(see Supplementary Figures 1, 2).
Regarding RTs, there were significant main effects of Mergegenerability [F(1, 15) = 26, p = 0.001] and word numbers [F(1,15) = 119, p < 0.0001], as well as the significant interaction
between them [F(1, 15) = 18, p < 0.001] (Figure 2B). In
addition to consistent results with the accuracy, the significant
interaction suggests that general cognitive loads under the
Artificial conditions became more demanding for the increased
number of words.

Modulation of the Cortical Activation by
Natural and Artificial Conditions
As shown in Figure 3A, the most prominent activation under the
Natural (4W) condition was mostly localized in the left frontal
cortex, spanning most of the L. LPMC, L. F3op/F3t, and the23

http://www.gin.cnrs.fr/en/tools/aal/
http://www.neuromorphometrics.com/

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FIGURE 3 | Modulation of the cortical activation by Natural and Artificial conditions. Regions identified by (A) Natural (4W), (B) Artificial (4W), (C) [Natural (4W and
6W) – Artificial (4W and 6W)], and (D) [Artificial (4W and 6W) – Natural (4W and 6W)]. Exclusive masks of [– Artificial (i.e., negative activation)] and [– Natural](uncorrected p < 0.001) were applied to the comparisons of C and D, respectively. Activations were projected onto the left (L) and right lateral surfaces, and medial
section (x = –9) of a standard brain (FDR-corrected p < 0.05). Each yellow dot indicates the local maxima of activated regions. See Table 1 for the stereotactic
coordinates of activation foci.

middle occipital gyrus (MOG), left superior/inferior occipitalgyrus (L. SOG/IOG), and bilateral fusiform gyrus (FG). The preSMA/ACC activation was much stronger under the Artificial(6W) condition than under the Natural (6W) condition, although
this tendency was reversed under the Natural (4W) and Artificial

Frontiers in Psychology | www.frontiersin.org(4W) conditions. These results indicate that the ventral portionof the grammar center was critically activated under the Natural
conditions, providing clear evidence that the Natural (Mergegenerable) and Artificial (non-Merge-generable) sentences weredifferentially processed in the brain.

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TABLE 1 | Regions with enhanced activations under the Natural or Artificial condition.
Brain regions

BA

Side

x

y

z

Z

VoxelsNatural (4W and 6W) – Artificial (4W and 6W)
F3op

44

L

−60

11

8

4.9

653

F3op/F3t

44/45

L

−54

14

5

4.8

∗

F3t/F3O

45/47

L

−42

8

−1

4.7

∗

MTG

21

L

−66

−34

2

4.6

∗

AG

39

L

−51

−52

26

5.8

∗

Artificial (4W and 6W) – Natural (4W and 6W)
LPMC
pre-SMA/ACC

6/8
6/8/32

LPMC

6/8

F3op

44

IPL

7/40

L

−33

−7

47

4.9

785

R

21

−7

53

4.6

∗

M

−9

11

47

5.9

∗

M

12

14

41

6.1

∗

M

12

5

59

4.4

∗

L

−54

8

35

4.3

165
∗

L

−45

8

23

5.7

L

−33

14

8

3.9

∗

L

−27

−55

50

4.6

176

L

−18

−67

47

4.1

∗

−36

−40

41

4.1

∗

SMG

40

L

IPL

7/40

R

27

−52

44

7.2

274

SOG

7/19

L

−27

−73

26

5.3

592

MOG

18/19

L

−27

−82

14

4.9

∗

IOG

18/19

L

−39

−79

−13

7.2

∗

FG

19

L

−42

−70

−16

6.8

∗

MOG

18/19

R

30

−85

14

4.9

417

FG

19

R

30

−85

−4

Inf

∗

R

39

−67

−10

6.2

∗

Stereotactic coordinates (x, y, z) in the MNI space (mm) are shown for each activation peak of Z values. The threshold was set at FDR-corrected p < 0.05 for the cluster
level. BA, Brodmann’s area; L, left; R, right; M, medial; F3op/F3t/F3O = opercular/triangular/orbital part of the inferior frontal gyrus; LPMC, lateral premotor cortex; preSMA, pre-supplementary motor area; ACC, anterior cingulate cortex; AG, angular gyrus; SMG, supramarginal gyrus; IPL, inferior parietal lobule; MTG, middle temporalgyrus; FG, fusiform gyrus; SOG/MOG/IOG, superior/middle/inferior occipital gyrus. The region with an asterisk is included within the same cluster shown one row above.

specific regions in contrast to non-Merge-generable sentences,demonstrating that Merge is indeed a fundamental operation,
which comes into play especially under the Natural conditions.
As explained in the Introduction, Merge is the fundamental
local structure-building operation proposed by modern
linguistics (Chomsky, 1995), which reflects a formal propertyof the competence system. Merge itself would be theoretically“costless,” requiring no driving force for its application (Saito
and Fukui, 1998; Chomsky, 2004; Fukui, 2011; Chomsky et al.,2019). In addition to Merge, an indispensable operation in any
language-like symbolic system, the DoM also seems to play a rolein accounting for enhanced activation under the Merge-generable
Natural conditions (Figure 3C); note that the DoM remained at
a minimum (one) for artificially forced dependencies without
conforming to Merge-generable structures (see Figure 1B). As
we noted in the Introduction, further experimental studies are
required to clarify whether totally Merge-generable (e.g., nested)and partially Merge-generable (e.g., cross-serial) dependencies
are analyzed differently in the brain, i.e., in terms of differential
sub-regions and/or activation levels.
Neuroimaging studies have established that syntactic
processing selectively activates the L. F3op/F3t and L. LPMC
(Stromswold et al., 1996; Dapretto and Bookheimer, 1999;

DISCUSSION
By employing a novel paradigm to manipulate Mergegenerability (Natural or Artificial), we obtained the following
three striking results. Firstly, the behavioral data clearly showedthat the NP-Pred matching task became more demanding underthe Artificial conditions than under the Natural conditions(Figure 2), reflecting cognitive loads that could be covaried with
the increased number of words. Secondly, localized activationin the L. F3op, F3t, and F3O, as well as in the L. MTG and L.
AG, was observed for the [Natural (4W and 6W) – Artificial(4W and 6W)] contrast (Figure 3C), indicating specialization ofthese left regions in syntactic processing. Any activation due totask difficulty was completely excluded from activations in theseregions, because the Natural conditions were always easier thanthe Artificial ones (see Figure 2). And finally, the [Artificial (4W
and 6W) – Natural (4W and 6W)] contrast resulted in the dorsal
portion of the L. LPMC and L. F3op (Figure 3D), together withwide-spread regions required for general cognitive demands,
such as visual attention (in the bilateral MOG and L. SOG/IOG),
error detection (in the pre-SMA/ACC), and cognitive conflict(just as during a Stroop task) (Bush et al., 2000). These results
indicate that Merge-generable sentences are processed in these

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Although we cannot go into the details in this paper, and thedefinitive analyses of those phenomena surely remain to beproperly formulated, it seems clear to us that those “cross-serial
phenomena” can – and should – be naturally treated in termsof externalization mechanisms [see Huybregts (1984), Haegeman
and Riemsdijk (1986), and subsequent works for much relevant
discussion]. The generation of cross-serial dependencies, which
requires the specification of linear (left-to-right) order, cannot
be directly carried out by the core component of humanlanguage (Merge). Thus, those cross-serial phenomena, as wellas, perhaps, the famous crossing case of “Affix Hopping”
discussed in Chomsky (1957), ought to be handled in theexternalization process.
Mainstream cognitive or neuroscientific investigations into
human language have been centering around the Chomsky
Hierarchy of weak generation. In their discussion, nested
dependencies are treated as a hallmark of context-freegrammars as distinguished from finite-state grammars, andcross-serial dependencies are used as testing grounds for contextsensitive grammars. However, these dependencies are usuallycharacterized on the basis of terminal strings, and if we adopt
the contemporary theory of Merge, we are instead led to an
entirely different conception of the relevant dependencies. As wediscussed above, nested dependencies naturally arise as a resultof unbounded Merge, whereas cross-serial dependencies may
appear in human language only insofar as the relevant structure
is generated by Merge, and some other mechanisms/conditions
are also fulfilled.
This conclusion may lead to an entirely new interpretation of
the question of why nested dependencies abound, whereas crossserial dependencies are severely limited in natural languages(see the discussion in section “Theoretical Background”). This
is not because human language requires a characterization
at the complexity of context-sensitive grammars or Turingmachines (type-0 grammars), but because human language isso simple that it only avails itself of a minimal apparatus forstrong generation, i.e., Merge. Merge-based phrase structures
provide a direct basis for various nested dependencies, andalso a rather partial (but necessary) means for characterizing
limited kinds of cross-serial dependencies. In contrast, the humanlanguage faculty becomes rather extraneous whenever the task
goes beyond the narrow channel of Merge-generability, suchas when dealing with artificial nested/cross-serial dependences
within terminal strings. Thus, dependencies defined on terminal
strings are processed even if they are artificially imposed, but
those processes are significantly enhanced when dependencies
are Merge-generable. Ultimately, then, a weak generation ofterminal strings is reduced to just a secondary effect ofMerge-based strong generation. Consequently, our results also
shed fresh light on another long-standing question, namely
why the classical Chomsky Hierarchy does not constitutean entirely adequate scale along which human language is
to be characterized and evaluated (cf., the notion of “mild”
context-sensitivity). The Chomsky Hierarchy is typically ameasure of weak generative capacity, and it is thus more orless orthogonal to the empirical study of human language(Chomsky, 1963, 1965).

Embick et al., 2000; Hashimoto and Sakai, 2002; Friederici
et al., 2003; Musso et al., 2003), indicating that these regions
have a critical role as grammar centers (Sakai, 2005). We
also observed activations in the L. F3op/F3t and L. LPMC in
our studies using sentences with non-canonical word orders,which contained filler-gap dependency and operator-variable
relations in movement (created by the “Internal Merge”)(Kinno et al., 2008; Ohta et al., 2017; Tanaka et al., 2017).
Moreover, our magnetoencephalography studies revealed asignificant increase in the responses in the L. IFG, whichreflected predictive effects on a verb caused by a preceding
object in a short sentence (Iijima et al., 2009; Inubushi
et al., 2012; Iijima and Sakai, 2014). In the present study, weobserved selective activation in the L. F3op, L. F3t, and L.
F3O in the [Natural (4W and 6W) – Artificial (4W and 6W)]
contrast, which is consistent with these previous findings. Ourpresent findings provide further and significant experimental
evidence to support the hypothesis that the concept of Mergegenerability plays a critical role in the processes subserved by thegrammar centers.
Compared with the ventral portion of the grammar centers(i.e., the L. F3op, L. F3t, and L. F3O), the dorsal portion(the L. LPMC, or the left dorsal prefrontal cortex) has
been shown to be involved in more automatic or implicit
aspects of syntactic processing (Hashimoto and Sakai, 2002),
while the R. LPMC was supportively required for syntactic
processing (Kinno et al., 2014) or for memorizing mere strings(requiring memory span) (Ohta et al., 2013b). Moreover,
the L. LPMC activations were particularly enhanced forscrambled, i.e., object-initial sentences (Kinno et al., 2008),
which were also confirmed by lesion studies (Kinno et al.,2009, 2014). In the present study, L. LPMC activations wereenhanced under the Artificial conditions (see Figure 3D),
which required more pattern-based or procedural strategy –just as in the case of puzzle-solving – for artificially matchingan NP-Pred pair. The left frontal activations were notenhanced as expected under the Natural (6W) condition in our
experiment. This is probably because the task became more“mechanical,” requiring less conscious efforts and thus inducing
less activations to process, as the number of words in thesentences increases.
It is instructive to note in this connection that while there
has been much discussion on nested constructions/structures in
the literature, there has been virtually no reference, as far as weare aware, to cross-serial constructions/structures; rather, only
cross-serial dependencies defined on terminal strings have been
discussed. Treating nested and cross-serial dependencies on apar may in fact mean that we are mixing apples and oranges,
because nested dependency (as well as filler-gap/movement
dependency and operator-variable dependency) is, as we haveseen, a direct consequence of Merge (totally Merge-generable),
whereas cross-serial dependency is a result of some interpretive
mechanisms, with Merge only providing the necessary structural
basis for the process (partially Merge-generable). We are ofcourse aware that there are cross-serial dependency phenomena,
typically in West Germanic languages [see Wurmbrand (2006)
for an extensive review], reported and discussed in the literature.

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Our conclusion should not be underestimated, since there are
numerous studies that center on the relation between humanlanguage and the Chomsky Hierarchy. For instance, it has
been reported that the computation of “regular-grammatical”
dependencies of the form (AB)n (n = 2, 4) and “contextfree” dependencies of the form An Bn (n = 2, 4) selectively
activated different brain regions (the left frontal operculum and
L. IFG, respectively) (Friederici et al., 2006). However, finite
sequences of artificial symbols are a matter of weak generation
at best, and there is little evidence that their participants were
truly computing finite An Bn (where n = 2, 4) sequences interms of hierarchical structures, i.e., phrase-structure grammars,
let alone Merge-based human syntax. The literature on nonhuman animals’ capacities for computing finite (AB)n versus
An Bn patterns are equally misguided (Gentner et al., 2006;
Abe and Watanabe, 2011), if not only due to the unresolvable
finiteness limitations.
We emphasize that the real novelty of our present experiments
lies in its focus on Merge-generability, not merely phrase
structures. Of course, there are quite a few neuroscientificstudies that do try to discuss the relevance of phrase structures,but few have spoken to the Merge-generable versus nonMerge-generable distinctions [but see Ohta et al. (2013a,b)
for notable exceptions]. For example, it has been reported
that a selective cortical activation of the L. IFG for two-wordphrase formation is enhanced compared to an unstructured
list of two words (such as this ship vs. stone, ship) (Zaccarella
and Friederici, 2015). This is a finding of importance, also
consistent with our results, but the relevant dependencies
between two adjacent words are so elementary that they may
be characterized by any theory of strong generation. Anothernotable study provided an interesting set of data that support
the primacy of structure-dependent computations in humanlanguage (Musso et al., 2003). Those authors asked German
native speakers to learn two sets of transformational or pseudorules of Italian and Japanese (passive, negative construction,etc.). The first set of learned rules are real rules of therespective languages and thus consistent with the structuredependence principle of human language, which holds thatthe applicability of transformational rules must be definedin terms of abstract phrase structures, not terminal strings.
The second set of learned rules are unreal or artificiallymanipulated pseudo-rules that use the same lexicon as therespective languages but systematically violate the principleof structure-dependence, defined just on terminal strings (forexample, putting the negation after the third word counting
from the left). The results obtained in that study indicate thatan increase of cortical activation in L. IFG was observed only
for the acquisition of real structure-dependent rules, irrespectiveof the types of language. This work is significant in thatit points to the primacy of phrase structures over terminal
strings in the acquisition of transformational rules. It canthus be interpreted as constituting another empirical support
for our broader hypothesis that abstract hierarchical structuresgenerated by Merge are critical, not just for the formulation oftransformational rules, but for possible dependencies in humanlanguage in general.

Frontiers in Psychology | www.frontiersin.org

CONCLUSION
In sum, our discussion points to the broad conclusion that all
natural dependencies admissible in human language are Mergegenerable, including certain types of nested, cross-serial, andtransformational (such as filler-gap/movement) dependencies,
and that non-Merge-generable dependencies of any type are
extraneous to the human language faculty. There are only abstract
hierarchical phrase structures in human language, generated all
the way through via Merge. Here, we provided a novel setof neuroimaging data that confirm this general picture, thus
corroborating the overarching hypothesis that human language
at its core is a surprisingly simple system of unbounded Merge,
and that Merge is the single generative engine underlying every
aspect of linguistic computations.

DATA AVAILABILITY STATEMENT
The datasets generated for this study are available on request to
the corresponding author.

ETHICS STATEMENT
The studies involving human participants were reviewed andapproved by the Institutional Review Board of The Universityof Tokyo, Komaba Campus. The patients/participants provided
their written informed consent to participate in this study.

AUTHOR CONTRIBUTIONS
All authors contributed to the designing of the study and writingof the manuscript. KT, IN, SO, and KS conducted and analyzed
the experiments.

FUNDING
This research was supported by the Grants-in-Aid for Scientific
Research (B) (Nos. JP17H02347 and JP19H01256), a Grantin-Aid for Scientific Research (C) (No. JP16K02779), and a
Grant-in-Aid for Scientific Research on Innovative Areas #4903(Evolinguistics), JP17H06379, from the Ministry of Education,
Culture, Sports, Science, and Technology of Japan.

ACKNOWLEDGMENTS
We thank Keita Umejima, Yuma Tsuta, and Naoko Komoro
for their technical assistance, and Hiromi Matsuda for her
administrative assistance.

SUPPLEMENTARY MATERIALThe Supplementary Material for this article can be found online
at:https://www.frontiersin.org/articles/10.3389/fpsyg.2019.
02673/full#supplementary-material

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Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.
Copyright © 2019 Tanaka, Nakamura, Ohta, Fukui, Zushi, Narita and Sakai. Thisis an open-access article distributed under the terms of the Creative Commons
Attribution License (CC BY). The use, distribution or reproduction in other forums
is permitted, provided the original author(s) and the copyright owner(s) are credited
and that the original publication in this journal is cited, in accordance with accepted
academic practice. No use, distribution or reproduction is permitted which does notcomply with these terms.

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SUPPLEMENTARY FIGURE 1

Six types of Natural sentences with six words. Left: Threetypes of sentences with sorezore: e.g., “Kenta, Akiko, and [their] friend are swimming,
sitting, and crying, respectively” (s6-1), “As for Akiko, [her] hands and ears are big,
beautiful, and warm, respectively” (s6-2), and “Taro and Hanako talk with Akiko, play [withher], and dance [with her], respectively” (s6-3). Right: Three types of sentences withtokidoki: e.g., “Taro thinks that Hanako said that [their] mother sometimes walked” (t6-1),“Taro and [his] friend think that [their] teacher said that it was sometimes bright” (t6-2), and“Kenta feels that [his] mother said with [his] teacher that it was sometimes bright” (t6-3).
1

SUPPLEMENTARY FIGURE 2

Six types of Artificial sentences with six words. Left:artificial cross-serial dependencies (pairing relations between NPs and Preds). Right:
artificial nested dependencies. In these examples, pseudowords (“koregore” or “hokiboki”)
artificially forced the dependencies that are not Merge-generable.

2

APPENDIX

Task instructions

Day 1, first half
You will perform a task of judging the correspondences between subjects and predicates during MRI
scanning.
While the stimuli are being shown, a small red cross will be presented at the center of the monitor.
Please look at the red cross, and do not move your eyes if possible.
During performing the task, please do not speak or read the sentences aloud.
・During the task, you will respond by pressing buttons.
・Please hold the controller with the button 1 on your left, and with the button 4 on your right.
・Please put your left thumb between the button 1 and 2, and put your right thumb between the
button 3 and 4.
・Please press buttons with your thumbs as fast as you can.
・During scanning, you will wear goggles, and you will not be able to see the buttons.
・Please be careful not to overlook the sentences, and try to answer correctly as much as possible.
・If a sentence disappears before you respond, please do not try to press buttons anymore, andconcentrate on the next sentence.
---------------------------------------------・Japanese sentences are shown on the monitor, in the order of subjects, an adverb, and predicates.
・Please read the sentences carefully, and do not overlook particles such as -wa, -ga and -to.

3

・After a sentence disappears, one predicate will be presented in the upper row, and three subjects
will be presented in the lower row. Some words which are not contained in the sentence may appear
in the alternatives.
・From the three alternatives, choose in the original sentence the most appropriate subject, whichcorresponds to that predicate.
・The alternatives of subjects from left to right correspond to the buttons 1, 2, and 3, respectively.

♦ Sorezore (4W)

taro-tohanako-ga

sorezore[Taro and Hanako-NOM respectively

hasiri,suwatteiru

running

sitting](Taro and Hanako are running and sitting, respectively)[NB: Square brackets denote additional information to the reader.][A predicate and alternatives: ]hasirutaro

hanakotaro-to-hanako

1

2

3

○

×

×

(Use only leftmost three buttons)

taro-gateasi-gasorezorehosoku,siroi[Taro-NOM

limbs-NOM

respectively

skinnywhite](Limbs are skinny and white, respectively)

4

oyako-ga

taro-tosorezore[parent-and-child-NOM Taro-with respectively

odori,

asobu

dance

play]

(Parent-and-child dance and play, respectively)

Oyako [parent-and-child] will not be presented as separate alternatives, i.e., oya (parent) and ko(child), but it will be always presented as one alternative; the same holds for ryosin [parents],
senseigata [teachers], watasitachi [we], simai [sisters], sohubo [grandparents], and husai [husbandand wife]. [NB: The second NP for this sentence type was not used as an alternative.]♦ Tokidoki (4W)

taro-gahanako-ga

tokidoki[Taro-NOM

Hanako-NOM sometimes

aruita-tohanasiteiru

walked-thatsays](Hanako walked, and Taro says)

taro-tohanako-ga

tokidoki[Taro and Hanako-NOM sometimes

akarukattato hanasiteiru

bright-that(Taro and Hanako say)

5

say]

taro-gahanako-to

tokidoki[Taro-NOM

Hanako-with sometimes

akarukatta-to hanasiteiru
bright-thatsays](Taro says)[NB: The second NP for this sentence type was not used as an alternative.]

Practice: You will practice the task until you can perform two serial sets of trials, each with at least
four correct answers in six trials.
In the MRI scanner, you will have 8 runs, and each run contains 19 trials.

Day 1, second half・Please read the sentences carefully, and do not overlook particles such as -wa, -ga and -to.
・After a sentence disappears, one predicate will be presented in the upper row, and four subjects
will be presented in the lower row. Some words which are not contained in the sentence may appear
in the alternatives.
・From the four alternatives, choose in the original sentence the most appropriate subject, whichcorresponds to that predicate.

♦ Sorezore (6W)

taro-tohanako-to[Taro and Hanako andtitioya-ga

sorezorefather-NOM respectively

6

hasiri,suwari,waratteiru

running

sittinglaughing][A predicate and alternatives: ]
warau
taro

hanakotitioya taro-to-hanako

1

2

3

4

×

×

○

×

taro-wate-to

ashi-ga[Taro-NOM

sorezorehosoku,siroku,atatakaihands and legs-NOM

respectively

skinny

white

warm]

titioya-to

hahaoya-ga

sorezoreai,hanasi,odoru[father andmother-NOM Taro-with respectively

meet

talk

dance]

taro-wahanako-ga

aruita-to[Taro-NOM

Hanako-NOM father-NOM sometimes walked-thattaro-to♦ Tokidoki (6W)

titioya-ga

tokidoki7

omotta-tohanasiteiru

though-thatsays]taro-totitioya-wa

hahaoya-ga

tokidokiakarukatta-to hanasita-to[Taro and father-NOM mother-NOM sometimes bright-thattaro-gahahaoya-ga

titioya-to[Taro-NOM

Hanako-NOM father-withtokidokisaid-that

kangaeteiru

think]

akarukatta-to hanasita-to kangaeteiru

sometimes bright-that

said-thatthink]

Practice: You will practice the task until you can perform two serial sets of trials, each with at least
four correct answers in six trials.
In the MRI scanner, you will have 9 runs, and each run contains 13 trials.

Day 2, first half
A task of judging the correspondences between words
・In this task, novel words of koregore and hokiboki will be used.
・According to these novel words, correspondences between other four words are determined asshown by the diagrams below. These novel words work completely differently from sorezore andtokidoki used on Day 1.
・Under any sentence types, please remember the correspondences between words, not the meaningof a sentence.
・From the three alternatives, choose one corresponding to that word in the upper row. Some words
which are not contained in the sentence may appear in the alternatives.
8♦ Koregore (4W)

taro-tohanako-ga

koregore akarukatta-to hanasiteiru[Taro and Hanako-NOM—

bright-thatsay][A predicate and alternatives: ]hanasutaro

hanako

kenta

1

2

3

○

×

×

(Use only leftmost three buttons)

taro-gateasi-ga[Taro-NOM

limbs-NOM

oyako-gakoregore hosoku,—

taro-toskinnysiroi
white]

koregore odori,[parent-and-child-NOM Taro-with

—

dance

asobu
play]

♦ Hokiboki (4W)

taro-tohanako-ga

hokibokiakarukatta-to hanasiteiru[Taro and Hanako-NOM—

brigh-thattaro-gahanako-to

hokibokikurakatta-tokanjiteiru[Taro-NOM

Hanako-with—dark-thatfeels]

9say]taro-gateasi-ga[Taro-NOM

limbs-NOM

hokibokihosoku,siroi—

skinnywhite]

Practice: You will practice the task until you can perform two serial sets of trials, each with at least
four correct answers in six trials.
In the MRI scanner, you will have 8 runs, and each run contains 19 trials.

Day 2, second half
Just like the first half, you will perform the task of judging the correspondences between six words inthe second half.

♦ Koregore (6W)

titioya-to

hahaoya-ga

taro-to[father andmother-NOM Taro-withkoregore ai,—[A predicate and alternatives: ]autitioyahahaoya

taro

kenta

1

2

3

4

○

×

×

×

10

meet

hanasi,

odoru

talk

dance]

taro-wate-to[Taro-NOM

hands and legs-NOM

taro-totitioya-wa

asi-gakoregore hosoku,—

hahaoya-ga

skinnysiroku,atatakai

white

warm]

koregore akarukatta-to hanasita-to[Taro and father-NOM mother-NOM—

bright-that

said-that

kangaeteiru
think]♦ Hokiboki (6W)

taro-totitioya-wa

hahaoya-ga

hokibokiakarukatta-to hanasita-tokangaeteiru—

bright-that

said-thatthink]

hokibokikurakatta-to

itta-tokanjiteiru—

dark- thatsaid thatfeel]

hokibokihosoku,siroku,atatakai—skinny

white

warm]

[Taro and father-NOM mother-NOM

taro-totitioya-wa

hahaoya-ga[Taro and father-NOM mother-NOM

taro-wate-to

asi-ga[Taro-NOM

hands and legs-NOM

Practice: You will practice the task until you can perform two serial sets of trials, each with at least
four correct answers in six trials.
In the MRI scanner, you will have 8 runs, and each run contains 19 trials.

11