HYPOTHESIS AND THEORY ARTICLE
published: 18 December 2013
doi: 10.3389/fnbeh.2013.00204

BEHAVIORAL NEUROSCIENCE

Computational principles of syntax in the regions
specialized for language: integrating theoretical linguistics
and functional neuroimaging
Shinri Ohta 1,2 , Naoki Fukui 3,4 and Kuniyoshi L. Sakai 1,4,5*
1

Department of Life Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
Japan Society for the Promotion of Science, Tokyo, Japan3
Department of Linguistics, Sophia University, Tokyo, Japan
4
CREST, Japan Science and Technology Agency, Tokyo, Japan
5
Department of Basic Science, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
2

Edited by:
Leonid Perlovsky, Harvard University
and Air Force Research Laboratory,
USA
Reviewed by:
Cedric A. Boeckx, Catalan Institutefor Research and Advanced Studies,
Spain
Noriaki Yusa, Miyagi Gakuin
Women’s University, Japan*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

The nature of computational principles of syntax remains to be elucidated. One promising
approach to this problem would be to construct formal and abstract linguistic models
that parametrically predict the activation modulations in the regions specialized for
linguistic processes. In this article, we review recent advances in theoretical linguistics
and functional neuroimaging in the following respects. First, we introduce the two
fundamental linguistic operations: Merge (which combines two words or phrases toform a larger structure) and Search (which searches and establishes a syntactic relationof two words or phrases). We also illustrate certain universal properties of humanlanguage, and present hypotheses regarding how sentence structures are processed in
the brain. Hypothesis I is that the Degree of Merger (DoM), i.e., the maximum depthof merged subtrees within a given domain, is a key computational concept to properly
measure the complexity of tree structures. Hypothesis II is that the basic frame of thesyntactic structure of a given linguistic expression is determined essentially by functional
elements, which trigger Merge and Search. We then present our recent functional
magnetic resonance imaging experiment, demonstrating that the DoM is indeed akey syntactic factor that accounts for syntax-selective activations in the left inferior
frontal gyrus and supramarginal gyrus. Hypothesis III is that the DoM domain changes
dynamically in accordance with iterative Merge applications, the Search distances, and/or
task requirements. We confirm that the DoM accounts for activations in various sentence
types. Hypothesis III successfully explains activation differences between object- andsubject-relative clauses, as well as activations during explicit syntactic judgment tasks.
A future research on the computational principles of syntax will further deepen our
understanding of uniquely human mental faculties.
Keywords: syntax, universal grammar, recursive computation, inferior frontal gyrus, supramarginal gyrus, fMRI

INTRODUCTION
Tree structures are one of the most ubiquitous structuresin nature, appearing in the branchings of rivers, lightning,snowflakes, trees, blood vessels, nervous systems, etc., and canbe simulated in part by fractal geometry (Mandelbrot, 1977). To
properly quantify the complexity of such tree structures, various
models have been proposed. The number of nodes would be oneof the simplest models; this approach consists of simply counting the total number of non-terminal nodes (branching points)
and terminal nodes of a tree structure (Figure 1A). This model
obviously cannot capture hierarchical levels within the tree (sister
relations in linguistic terms). To properly measure the hierarchicallevels of a tree structure, we have proposed the Degree of Merger(DoM) as a key computational concept (Figure 1B) (Ohta et al.,2013). The DoM is defined as the maximum depth of merged subtrees (called Mergers) within a given domain. With this model,
the same numbers are assigned to the nodes with an identical

Frontiers in Behavioral Neurosciencehierarchical level. The DoM corresponds to the number of iterations for generating fractal figures, when the tree structures are
self-similar.
In this article, we first explain certain universal propertiesof human language discovered in modern linguistics, and we
present hypotheses regarding how sentence structures are processed in the brain. We then introduce our recent functional
magnetic resonance imaging (fMRI) study, which demonstrated
that the DoM is indeed a key syntactic factor that accounts forsyntax-selective activations in the regions specialized for language (Ohta et al., 2013). We also show that the top-down
connectivity from the left inferior frontal gyrus to the left supramarginal gyrus is critical for the syntactic processing. Next, weclarify that the DoM can account for activation modulations in
the frontal region, depending on different sentence structures.
Finally, we hypothesize that the DoM domain changes dynamically in accordance with iterative Merge applications, the distancewww.frontiersin.org

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Computing syntax in the brain

To construct hierarchical tree structures, modern linguistics
has proposed the fundamental linguistic operation of Merge (capitalized in linguistics to indicate a formal operation). Merge is a
structure-building operation that combines two syntactic objects(words or phrases) to form a larger structure (Chomsky, 1995).
Merge would be theoretically “costless,” requiring no driving
force for its application (Saito and Fukui, 1998; Chomsky, 2004;
Fukui, 2011). Besides Merge, we have proposed Search operation of searching syntactic features, which applies to a syntactic
object already constructed by Merge, where Search couples andconnects two distinct parts of the same structure, thereby assigning relevant features from one to the other part (Fukui andSakai, 2003). Various other “miscellaneous” operations that havebeen employed in the linguistics literature, such as Agree, Scopedetermination, Copy, etc., are in fact different manifestations of
one and the same, i.e., more generalized, operation of Search(Fukui and Sakai, 2003). Human language, therefore, should minimally contain two universal operations, Merge and Search. The
total number of Merge and Search applications within an entire
sentence are here simply denoted as “number of Merge” and“number of Search,” respectively. The number of Merge in a sentence becomes always one less than the number of terminal nodes,
irrespective of sentence structures (see Appendix S2 of Ohta et al.,2013).
SYMBOL SEQUENCES AND FORMAL LANGUAGESFIGURE 1 | Two models for measuring the complexity of tree
structures. (A) “The number of nodes” counts the total number ofnonterminal nodes (branching points) and terminal nodes of a tree
structure. The number of nodes of the tree structure shown is 17. (B) “The
Degree of Merger (DoM)” quantifies the maximum depth of merged
subtrees, or the degree of branching. We increased the number one by one
for each node, starting from the trunk (zero) to terminal nodes. The DoM of
the tree structure shown is 5.

required for Search operations (or simply the “Search distance”),
and/or task requirements. This hypothesis accounts for activationdifferences between subject-relative and object-relative clauses,
as well as for activations during explicit syntactic judgmenttasks.

UNIVERSAL PROPERTIES OF HUMAN LANGUAGE
THEORETICAL BACKGROUND

Modern linguistics has clarified universal properties of humanlanguage, which, directly or indirectly, reflect the computational
power, or engine, of the human language faculty. A sentence is
not a mere string of words, but is made of phrase structure (called
constituent structure). Moreover, a single phrase contains the key
element (i.e., the “head”) that determines the basic properties ofthe phrase. Furthermore, a sentence can be recursively embedded within other sentences, as in, e.g., “I think that John believes
that Mary assumes that. . . ,” and there is in principle no upperbound for the length of sentences. These universal properties canbe adequately and minimally expressed by hierarchical tree structures with a set of relevant structural relations defined on such
structures (Chomsky, 1957, 1965).

Frontiers in Behavioral Neuroscience

In regard to formal symbol sequences beyond the bounds offinite state languages, three specific types of language have been
discussed in the linguistics literature: (i) “counter language,”(ii) “mirror-image language,” and (iii) “copying language” (cf.
Chomsky, 1957, p. 21).
(i) ab, aabb, aaabbb, . . . , and in general, all sentences consistingof n occurrences of a followed by n occurrences of b and only
these;(ii) aa, bb, abba, baab, aaaa, bbbb, aabbaa, abbbba, . . . , and ingeneral, all sentences consisting of a string X followed by the“mirror image” of X (i.e., X in reverse), and only these;(iii) aa, bb, abab, baba, aaaa, bbbb, aabaab, abbabb, . . . , and ingeneral, all sentences consisting of a string X of a’s and b’s
followed by the identical string X, and only these.
The counter language can be handled by a counting mechanism
to match the number of each symbol, whereas the mirror-image
language contains a mirror-image dependency, requiring morethan a mere counter. If the number of symbols is not fixed (i.e.,
infinite), both of these languages are beyond the bounds of finitestate grammars, and are to be generated by context-free (simple)
phrase structure grammars, while the copying language with across-serial dependency clearly goes beyond the bounds of evencontext-free phrase structure grammars, requiring a more powerful device, viz., context-sensitive phrase structure grammars
or transformational grammars (Chomsky, 1959; Hopcroft and
Ullman, 1979).
It remains a central issue in cognitive sciences whether or
not the faculty of language is also shared by animals. Animals
have thus been tested with regular symbol sequences such aswww.frontiersin.org

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An Bn (n ≥ 2; i.e., AABB, AAABBB, . . .) and (AB)n (n ≥ 2; i.e.,
ABAB, ABABAB, . . .), which differ in symbol order. In an animal study, songbirds were trained to discriminate patterns of
An Bn and (AB)n in more than ten thousand trials (Gentner
et al., 2006). However, this learning can be achieved by tracking
symbol repetition or counting strategy alone (Corballis, 2007).
There is also a recent report that songbirds seemed to discriminate strings with or without nesting (Abe and Watanabe, 2011),
but this learning can be achieved by simply remembering partial strings (Beckers et al., 2012). Along the lines of contrasting
An Bn and (AB)n , fMRI studies have tested participants with different symbol sequences, such as A2 A1 B1 B2 vs. A1 B1 A2 B2 (eachsubscript denotes a matching order), which also differ in matching order (Bahlmann et al., 2008). The difference in activation
patterns can be simply explained by differences in any factor
associated with matching orders and symbol orders, i.e., temporal order-related factors. It is thus necessary to completely
control these general factors when extracting any syntactic factor from a number of cognitive factors involved in actual symbol
processing.
Since the number of symbols is inevitably fixed (i.e., finite)in any actual experiment, it should be noted that any symbol
sequence can be expressed by a regular (finite state) grammar, i.e.,
the least powerful grammar in the so-called Chomsky hierarchy.
Therefore, one cannot, in principle, claim from the experimentsthat individual grammars (e.g., context-free phrase structure
grammars vs. regular grammars) are differentially represented in
the brain. Thus, the neural representation of individual grammars was not within the scope of Ohta et al. (2013). In addition to the various models examined, other non-structural andnon-symbolic models with simple recurrent networks have been
proposed to process some examples of even context-free andcontext-sensitive phrase structure languages, generalizing to somedegree to longer strings than the training set (Rodriguez, 2001).
However, these models do not account for any parametric modulation of the activations reported in Ohta et al. (2013), except thelength of sentences.
In the previous experiment, we introduced letter strings, which
had no lexical associations but had both symbol orders (e.g.,
AABB and ABAB) and matching orders (e.g., A2 A1 B1 B2 ). There
were two basic types of strings: reverse-order strings (Reverse)and same-order strings (Same). In the Reverse strings, the firstand second halves of a string were presented in the reverse order,
while in the Same strings the halves were presented in the sameorder (Figure 2). Under these conditions, there was actually nopath connecting the non-terminal nodes of symbol pairs (e.g.,
A1 B1 and A2 B2 ), as there was no Merge application to connect themultiple pairs. In regard to the symbol orders, both the Reverse
and Same strings took the above type (i) of An Bn . In regard to thematching orders, the Reverse string took the type (ii) of A2 A1 B1 B2
or A3 A2 A1 B1 B2 B3 , while the Same string took the type (iii) ofA1 A2 B1 B2 or A1 A2 A3 B1 B2 B3 .
HYPOTHESIS I

Given a tree structure with a formal property of Merge and iterativity (recursiveness) (Fukui, 2011), we propose the following
hypothesis (Hypothesis I):

Frontiers in Behavioral Neuroscience

FIGURE 2 | Two basic types of letter strings related to formal
languages. We tested two string conditions with short [(S) as a subscript]
stimuli: Reverse(S) and Same(S) . Each letter string was formed by jumbling
letters of either a pseudonoun or pseudoverb (see Figure 4). We also
tested the long stimuli with six items. Each curved arrow with anarrowhead denotes a Search operation, as in the following figures. Symbols
used: A, sample stimulus; B, comparison stimulus.

(1) The DoM, which can be defined as the maximum depth ofmerged subtrees within a given domain, is a key computational concept to properly measure the complexity of tree
structures.
The DoM can quantify and compare various syntactic phenomena, such as self-embedding, scrambling, wh-movement, etc.
Furthermore, when Search applies to each syntactic object with
its hierarchical structure, the calculation of the DoM plays a critical role. Indeed, from a nested sentence “[[The boy2 [we3 like3 ]2 ]1sings1 ]0 ” (subscripts denote the DoM for each node), two sentences “[The boy...]1 sings1 ” and “we3 like3 ” are obtained, where
relevant features (numbers and persons here) are searched andmatched between the nodes with the identical DoM. Since suchanalyses of hierarchical structures would produce specific loads
in syntactic computation, we expect that the DoM and associated “number of Search” would affect performances and corticalactivations.
Sentences with various constructions have been previously discussed in terms of the acceptability of sentences (cf. Chomsky,
1965, p. 12).
(i) nested constructions(ii) self-embedded constructions(iii) multiple-branching constructions(iv) left-branching constructions(v) right-branching constructions
The nested constructions are created by centrally embeddinga phrase within another phrase (with some non-null element
to its left and some non-null element to its right), and theself-embedded constructions are the special case of nested
constructions when nesting occurs within the same type of

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Ohta et al.

Computing syntax in the brainphrases (e.g., noun phrases). The multiple-branching constructions are made by conjoining phrases at the same hierarchicallevel, and the left/right-branching constructions are yielded bymerging a phrase in the left-most or right-most phrase. The
degrees of nesting and self-embedding have already been proposed to model the understanding of sentences (Miller andChomsky, 1963). By generalizing this attractive idea in such a
way as to include any construction with merged phrases, we
introduced the DoM as a key computational concept.
Based on the nested (self-embedded), left/right-branching,and multiple-branching constructions, three basic types of sentences can be distinguished: the nested sentence (Nested), simple
sentence (Simple), and conjoined sentence (Conjoined), respectively. The sentences shown in Figure 3 are some examples inJapanese. Given syntactic structures like the ones shown, the correspondence of each subject-verb pair becomes fixed. Here N andV denote a noun phrase and a verb phrase, respectively. For thesentence shown in Figure 3A, an entire sentence is constructedby nesting sentences in the form of [N2 [N1 V1 ]V2 ], where [Ni Vi ]represents a subject-verb pair of a sentence. Since Japanese is ahead-last, and hence an SOV (verb-final) language, a main verb isplaced after a subordinate clause. Therefore, Japanese sentences
naturally yield nested structures without having to employ, as
in English, object-relative clauses (e.g., “The boy whoi we like tisings”), which require “movement” of an object (i.e., with more
Merge applications) and thus leave behind a “trace” (ti , subscripts
denote the same entity). For the sentence shown in Figure 3B,
a simple sentence is constructed by adding the same numberof left/right branches to both Ns and Vs. The last noun (i.e.,head) in the branches of Ns made a subject-verb pair with thelast verb (i.e., head) of a compound verb. Each simple sentencethus takes the form of [(NN1 )(VV1 )]. For the sentence shown in
Figure 3C, an entire sentence is constructed by conjoining sentences in the form of [N1 V1 ][N2 V2 ]. When considering longer
sentences like N3 N2 N1 V1 V2 V3 , these constructions have distinct
values for DoM.
HYPOTHESIS II

In any sentence, functional elements, such as inflections, auxiliary
verbs, and grammatical particles, serve an essentially grammatical
function without descriptive content. In regard to the fundamental role of these functional elements, we propose the following
hypothesis (Hypothesis II):
(2) The basic frame of the syntactic structure of a given linguistic expression (e.g., sentence) is determined essentiallyby functional elements, which trigger Merge and Search
operations.
In the non-sense poem “Jabberwocky” by Lewis Carroll, e.g.,“’Twas (‘It was’) brillig, and the slithy toves did . . . ,” the basic
frames of syntactic structures are indeed determined by thefunctional elements of “’Twas,” “and,” “the,” “-s,” and “did.” In
the Japanese language, grammatical particles and morphosyntactic inflections are functional elements. The sentences shownin Figure 3 actually contain only three kinds of grammaticalparticles, which represent canonical (i.e., in a prototypical use)case markings and syntactic information in Japanese: -ga, anominative case marker; -no, a genitive case marker; and -to, a
Frontiers in Behavioral Neuroscience

FIGURE 3 | Japanese sentences with three major constructions. The
figure shows three basic types of sentences in Japanese: the nested
sentence, simple sentence, and conjoined sentence. Based oncontemporary linguistics, each diagram represents a unique tree structureof each sentence constructed from nouns and verbs. Below each example,
word-by-word translations in English are shown. (A) A sentence (S) at thelowest hierarchical level was nested into an entire sentence (S’) (“Taro-ga
Hanako-ga utau-to omou,” “Taro thinks that Hanako sings”). (B) A simple
sentence was constructed by adding the same number of left/rightbranches to both nouns and verbs (“Taro-no ani-ga tabe hajimeru,” “Taro’s
brother starts eating”). (C) An entire sentence (S’) was constructed byconjoining two sentences (“Taro-ga utatte Hanako-ga odoru,” “Taro sings,
and Hanako dances”). Symbols used: S and S’, sentence; N, noun phrase;V, verb phrase; -ga, nominative case marker; -no, genitive case marker; -to,complementizer; -te, gerundive form; Nom, nominative case; Gen, genitivecase; Comp, complementizer.

complementizer. It should be noted that both the nested and simple sentences have the same symbol order of Nn Vn , but they havedifferent grammatical particles and syntactic structures. In contrast, both the simple and conjoined sentences have the same
tree structures as a result, but they have different symbol ordersof Nn Vn or (NV)n (n ≥ 2). It is the grammatical particles andmorphosyntactic inflections, but not symbol orders or matching orders themselves, that determine the basic frame of syntactic
structures of a sentence.
Following morphosyntactic and phonological features ofJapanese verbs (Tsujimura, 2007), Vs take a non-past-tense form(-ru), past-tense form (-ta), or gerundive form (-te); Vs ending with -to and -te introduce that-clauses and and-conjunctives,
respectively. The gerundive form can be used not only in andconjunctives, but in compound verbs (e.g., “tabete-simau,” “finish

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Ohta et al.

Computing syntax in the braina key computational concept to properly quantify the complexityof tree structures, and that the basic frame of the syntactic structure of a given linguistic expression is determined essentiallyby grammatical particles and morphosyntactic inflections, which
trigger Merge and Search operations.

THE DoM AS A KEY SYNTACTIC FACTOR ELUCIDATED BY AN
fMRI EXPERIMENT
One possible way to elucidate the neural basis of computational properties of natural language is to examine how the brain
responds to the modulation of specified syntactic factors. We
should not be content with such a general cognitive factor as
so-called “syntactic complexity” or “syntactic working memory,”
which could involve both linguistic and non-linguistic factors. We
should instead identify minimal factors that sufficiently explain
any activation change obtained. In our recent study, we focusedon different sentence constructions, and found that the DoM and“number of Search” were the minimal syntactic factors associatedwith phrase structures, which parametrically modulate corticalresponses measured with event-related fMRI (Ohta et al., 2013).
In this section, we will present the basic paradigm and results ofthis work.
A PARADIGM TO TEST HYPOTHESES I AND IIFIGURE 4 | A paradigm for testing various sentence structures. We
tested three sentence conditions with short [(S) as a subscript]
jabberwocky sentences: Nested(S) , Simple(S) , and Conjoined(S) . Note thesyntactic structures of these jabberwocky sentences are same as those ofreal sentences in Figure 3. The digits shown in red and blue denote theDoM for each node and “number of Search,” respectively. We also tested
the long stimuli with six words.

eating”; actual Japanese words will be translated hereafter), muchas gerunds can in English. The -ga, -no, -to, and -te endings (green
letters in Figures 3, 4), together with the first verb of a compound verb in an adverbial form (e.g., “tabe”), are associated with
Merge applications to connect multiple nouns/verbs or sentences,amounting to “number of Merge.” The Japanese language lacks
the “agreement features” (i.e., number, person, gender, etc.), but
it is nevertheless equipped with the general Search procedure thatis employed in agreement phenomena in other languages. This
Search mechanism is in fact attested for various other phenomena in Japanese (see Fukui and Sakai, 2003 for further discussion).
For example, the Japanese language exhibits a phenomenon called“honorification,” where a noun phrase denoting an honored person and the form of honorifics in verbs are to be matched (Gunji,
1987; Ivana and Sakai, 2007).
In this section, we provided some theoretical discussions basedon modern linguistics, focusing on the two fundamental linguistic
operations of Merge and Search. We hypothesized that the DoM isFrontiers in Behavioral Neuroscience

We used jabberwocky sentences, which consist of pseudonoun
phrases (Ns) and pseudoverb phrases (Vs) that lack lexical
associations, but have grammatical particles and morphosyntactic inflections (Figure 4). According to Hypothesis II statedabove, these jabberwocky sentences had the same syntactic structures as normal sentences. Under the sentence conditions of
Nested, Simple, and Conjoined with the same structures shownin Figure 3, the jabberwocky sentences were visually presented
in a phrase-by-phrase manner to the participants. We made six
pseudonouns by repeating the same syllables with voiced consonants and any one of /a/, /u/, or /o/: rara, zaza, mumu, gugu,
yoyo, and dodo. We also made four pseudoverb roots by repeating the same syllables with voiceless consonants and either /i/ or/e/: kiki, hihi, sese, and tete. Here, vowel harmony was adopted
to change the last, i.e., the second, vowel of the verb root, sothat this vowel harmonized with the vowel (i.e., /a/, /u/, or /o/)of the corresponding subject (e.g., “rara-ga tetaru” from “teteru,”
underlined vowels within pseudowords). These features of vowels were only experimentally introduced, and these pseudoverbs
lacked grammatical features, as in the Japanese verbs. In all jabberwocky sentences, the distinction between Ns and Vs was clear
without memorizing pseudowords, because Ns, but not Vs, ended
with either -ga or -no, i.e., case markers in Japanese such as -ga
and -no can be generally attached only to nominal phrases.
To test whether participants actually paid attention to the correspondence of each subject-verb pair, we used a matching task,such that the vowel of a subject (Ni as a sample stimulus) wasmatched with the last vowel of the corresponding verb root (Vi as
a comparison stimulus), probing the goal with the same vowel as
explained above. It follows that the same syntactic structures wereconstructed from matching stimuli and non-matching stimuli(e.g., “rara-ga teturu”), which were both well-formed, i.e., grammatical, in Japanese. A matching strategy (counting, for example,

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the first and the fourth stimuli for matching) was useful in solving
the task, but performing the task was not prerequisite for constructing syntactic structures. Our matching task is different fromclassification tasks for symbol orders (e.g., AABB vs. ABAB, where
A and B are symbols representing certain sets of stimuli), which
can be solved by counting the maximum number of consecutively repeated symbols. The order of the Nested, Simple, andConjoined was pseudo-randomized without repetition. We further examined whether cortical activations were modulated by
the length of sentences: short (S as a subscript, e.g., Conjoined(S) ;four-word) and long (L as a subscript, e.g., Conjoined(L) ; sixword) sentences, where the DoM domain spanned four and six
relevant words, respectively.
We also used the same matching task under the string conditions of Reverse and Same (Figure 2), such that the first halfof a string (Ai as a sample stimulus) was matched with the corresponding second half (Bi as a comparison stimulus) in thereverse or same order. These string conditions also controlled
any involvement of the matching strategy stated above. Between
the Nested (N2 N1 V1 V2 ) and Reverse (A2 A1 B1 B2 ) conditions,the curved arrows shown in Figures 2, 4 represent the same
matching order of sequentially presented stimuli. The symbol
order was also identical among the Nested, Simple, Reverse, andSame conditions in the form of Nn Vn or An Bn . Combining these
multiple conditions, we were able to properly examine whether
different structures were actually constructed between sentences
and strings. The spatial and temporal resolution of fMRI, as
well as its sensitivity, has been proven to be high enough to

FIGURE 5 | Functional and anatomical evidence of a syntax-related
network. (A) Regions identified by the (Nested − Simple) > (Reverse− Same) contrast (see Figure 4). Activations were projected onto theleft (L) and right lateral surfaces of a standard brain. (B) Percent signal
changes for Nested − Conjoined and Simple − Conjoined in the L.
F3op/F3t and L. SMG. Overlaid red dots and lines denote the values
fitted with the estimates (digits in red) for the best models: DoM for
the L. F3op/F3t and “DoM + number of Search” for the L. SMG. (C)Frontiers in Behavioral Neuroscience

confirm various hypotheses about human cognitive functions
like ours.
SYNTAX-SELECTIVE ACTIVATIONS MODULATED BY THE DoM AND THE
NUMBER OF SEARCH

To control both matching orders and symbol orders, we
directly compared the Nested with the Reverse condition, using
the Simple and Same conditions as respective references, i.e.,(Nested − Simple) > (Reverse − Same), where we combined
the short and long stimuli. This contrast further controlled various linguistic and non-linguistic factors, such as the numberof Merge, number of case markers, number of nodes, memory span, and counting. This point is particularly important,because temporal order-related or memory-related factors haveoften been confused with differences in structure or grammar
type. Significant activation was elicited by this contrast in the pars
opercularis and pars triangularis of the left inferior frontal gyrus(L. F3op/F3t) [local maximum: (x, y, z) = (−51, 24, 24), Z =5.8], and the left supramarginal gyrus (L. SMG) [(−39, −45, 42),
Z = 5.7] (Figure 5A). Our results are best explained by the linguistic factors associated with the Nested condition, supporting
our second hypothesis that basic syntactic structures are constructed when well-formed sentences are given even without
lexical meanings.
For these two critical regions, we examined the percent signal changes under the Nested and Simple conditions by subtracting those under the Conjoined condition, which had thesimplest tree structures (Figure 4 and Table 1), separately for

The results of DCM, testing effective connectivity between the L.
F3op/F3t and L. SMG. The best model included a significant top-down
connection from the L. F3op/F3t to L. SMG (a thick line). (D)Anatomical connectivity between the L. F3op/F3t and L. SMG revealedby DTI. The population probability map is shown on the left lateral anddorsal surfaces of a standard brain with maximum intensity projection.
Blue spheres represent seed regions of the L. F3op/F3t and L. SMG.
Symbols used: L, long sentences; S, short sentences.

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Table 1 | Estimates of various factors to account for activations in Ohta et al. (2013).
FactorNested(L)Nested(S)Simple(L)Simple(S)Conjoined(L)Conjoined(S)Degree of Merger (DoM)

5

3

3

2

2

2

No. of Search

3

2

2

1

3

2

No. of nodes

11

7

11

7

10

7

Nested(L) − Conjoined(L)Nested(S) − Conjoined(S)Simple(L) − Conjoined(L)Simple(S) − Conjoined(S)

DoM

3

1

1

0

DoM + No. of Search

3

1

0

−1

No. of Search

0

0

−1

−1

No. of nodes

1

0

1

0

Estimates under the Conjoined condition were subtracted from those under the other Nested and Simple conditions [e.g., DoM for Nested(L) − Conjoined(L) ,5 − 2 = 3], separately for long and short sentences. We regarded “DoM + number of Search” (i.e., adding the estimates of two factors) as an additional factor.

long and short sentences. Since we used the Conjoined(L) andConjoined(S) as appropriate references, we examined whether
likewise subtracted estimates of each factor (e.g., DoM for
Nested(L) – Conjoined(L) ; see Table 1) directly explained theparametric modulation of activations in the four contrasts of
Nested(L) – Conjoined(L) , Nested(S) – Conjoined(S) , Simple(L) –Conjoined(L) , and Simple(S) – Conjoined(S) . The percent signal
changes in the L. F3op/F3t and L. SMG, averaged across significant voxels, indeed correlated exactly in a step-wise manner withthe parametric models of the DoM [3, 1, 1, 0] and “DoM +number of Search” [3, 1, 0, −1], respectively (Figure 5B). By generalizing the role of Search, we assumed that Search applied toa subject-verb pair, where the relevant features (vowels here) areexperimentally “inserted” (Ohta et al., 2013).
We further examined 19 models proposed in theoretical linguistics, psycholinguistics, and natural language processing toverify that the models of the DoM and “DoM + number ofSearch” best explained the cortical activations (Ohta et al., 2013).
All contrasts of Nested(L) – Conjoined(L) , etc. predicted that theactivations should be exactly zero when a factor produced noeffect or load relative to the Conjoined. We thus adopted a nointercept model, in which percent signal changes of each region
were fitted with a single (thus minimal) scale parameter to amodel of each factor using its subtracted estimates. For the four
contrasts, a least-squares method was used to minimize the residual sum of squares (RSS) for the four fitted values (i.e., four
estimates multiplied by a fitting scale) against the corresponding
signal changes averaged across participants (Table 2).
The model of the DoM for the L. F3op/F3t, as well as that of“DoM + number of Search” for the L. SMG, indeed producedby far the least RSS (≤0.0020) and largest coefficient of determination (r2 ) (≥ 0.97). Goodness of fit was further evaluatedfor each model by using a one-sample t-test (significance levelat α = 0.0125, Bonferroni corrected) between the fitted value foreach contrast and individual activations. The model of the DoM
for the L. F3op/F3t, as well as that of “DoM + number of Search”
for the L. SMG, produced no significant deviation for the four
contrasts (P ≥ 0.17). To further take account of interindividual
variability, we fitted “linear mixed-effects models” with individual activations, and found that the models of the DoM and “DoM

Frontiers in Behavioral Neuroscience+ number of Search” were by far more likely for the L. F3op/F3t
and L. SMG, respectively. Even if we took the Simple condition asa reference for subtracted estimates, we obtained the same resultsof best models. These results directly support Hypotheses I and II,such that the basic frame of syntactic structures are determined
essentially by functional elements, whereas the DoM, together
with the number of Search, is a key factor to properly quantify
the complexity of the syntactic structures.
THE SIGNIFICANCE OF THE CONNECTIVITY BETWEEN THE L. F3op /F3t
AND L. SMG

It has been reported that the L. F3op/F3t is specialized forsyntactic processing (Stromswold et al., 1996; Dapretto andBookheimer, 1999; Embick et al., 2000; Hashimoto and Sakai,
2002; Friederici et al., 2003; Musso et al., 2003; Suzuki andSakai, 2003; Kinno et al., 2008), suggesting that this region subserves a grammar center (Sakai, 2005). On the other hand, theleft angular gyrus and SMG (L. AG/SMG) have been suggested
to be important for vocabulary knowledge or lexical processing (Lee et al., 2007; Pattamadilok et al., 2010). To elucidate
the relationships between the L. F3op/F3t and L. SMG, we
modeled the effective connectivity between these two regionsby using dynamic causal modeling (DCM). Our interest was
to identify the direction of the connectivity modulated by the
Nested condition, which has the largest DoM of all conditions.
First, we assumed intrinsic, i.e., task-independent, bi-directional
connections, and the models were grouped into three “modulatory families”: families with modulation for the bottom-up
connection from the L. SMG to L. F3op/F3t, for the top-down
connection from the L. F3op/F3t to L. SMG, and for both connections. Each family was composed of three “input models” asregards the regions receiving driving inputs. We found that themodel with the modulation for the bottom-up connection, in
which the L. F3op/F3t received driving inputs, was the best andmost probable model (Figure 5C). We further confirmed that the
intrinsic top-down connectivity was significantly positive (+0.22;
P < 0.0002), while the bottom-up connectivity was negatively
modulated.
A recent DCM study with a picture-sentence matching task
has suggested that the L. F3op/F3t received driving inputs (denwww.frontiersin.org

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Table 2 | Fittings and likelihood of various models tested in Ohta et al. (2013).
FactorRSS

r2

P-values for four contrasts

Log-likelihood

Likelihood ratio

L. F3op/F3t*DoM0.00070.99

0.17, 0.92, 0.97, 0.99

65.0

1.0

DoM + No. of Search

0.00650.88

0.0035, 0.064, 0.63, 0.88

59.2

3.1 × 10−3

No. of Search

0.052

<0.1

<0.0001, 0.018, 0.019, 0.031

33.4

2.0 × 10−14

No. of nodes

0.015

0.72

0.0050, 0.0082, 0.018, 0.17

53.7

1.2 × 10−5DoM

0.00630.92

0.013, 0.083, 0.44, 0.49

58.8

0.079

*DoM + No. of Search

0.00200.97

0.22, 0.30, 0.42, 0.62

61.4

1.0

No. of Search

0.075

<0.1

<0.0001, 0.0061, 0.045, 0.090

23.6

3.8 × 10−17

No. of nodes

0.033

0.56

0.0004, 0.0005, 0.0061, 0.013

40.1

6.0 × 10−10

L. SMG

Percent signal changes in the L. F3op/F3t and L. SMG were fitted with a single scale parameter to a model of each factor using its subtracted estimates (Table 1)for the four contrasts of Nested(L) − Conjoined(L) , Nested(S) − Conjoined(S) , Simple(L) − Conjoined(L) , and Simple(S) − Conjoined(S) . The P-values for the t-tests
are shown in ascending order. The models with an asterisk resulted in the best fit of 19 models tested (four models are shown here) for explaining activations in the
L. F3op/F3t or L. SMG, i.e., with the least residual sum of squares (RSS), largest coefficient of determination (r2 ), and larger P-values. The likelihood ratio was taken
as the ratio of each model’s likelihood to the best model’s likelihood. The best models were by far more likely than the other models.

Ouden et al., 2012), which was consistent with our DCM results.
Moreover, our previous studies revealed that the functional connectivity between the L. F3t/F3O (pars orbitalis) and L. AG/SMG
was selectively enhanced during sentence processing (Homae
et al., 2003), and that the L. AG/SMG was also activated during
the identification of correct past-tense forms of verbs, probably
reflecting an integration of syntactic and vocabulary knowledge (Tatsuno and Sakai, 2005). Considering the role of the L.
AG/SMG in lexical processing, the Search operation based on
the DoM would be essential in assigning relevant features to thesyntactic objects derived from lexical items.
To further confirm the anatomical plausibility of the network
between the L. F3op/F3t and L. SMG revealed by DCM, we useddiffusion tensor imaging (DTI) with a probabilistic tractography.
We observed that a single continuous cluster of the left superior longitudinal and arcuate fasciculi (SLF/AF) connected theseregions (cluster size, 3189 mm3 ), together with much smallerclusters or islands (Figure 5D). Moreover, the left SLF/AF was
consistently observed in all participants.
The findings of recent DTI studies have been controversial
regarding the functional roles of two different pathways in language processes: the dorsal tracts of the SLF/AF, and the ventral tracts of the middle longitudinal fasciculus (MdLF) andextreme capsule (EmC). Both pathways connect the inferior
frontal and superior/middle temporal areas (Saur et al., 2008;
Wilson et al., 2011; Wong et al., 2011; Griffiths et al., 2013).
Our DCM and DTI results indicate that the L. SMG activations reflecting the DoM mirrored a top-down influence fromthe L. F3op/F3t through the left dorsal pathway of the SLF/AF,
revealing the most crucial network and pathway for syntactic
computation.

FURTHER CONFIRMATION OF HYPOTHESES I AND IIA PICTURE-SENTENCE MATCHING PARADIGM

We further examined whether our hypotheses hold for various
cases discussed in previous studies. In our fMRI study (Kinno
et al., 2008), we used a picture-sentence matching task with three

Frontiers in Behavioral Neuroscience

sentence types in Japanese: active, passive, and scrambled sentences (Figure 6A). In the picture-sentence matching task, theparticipants read a sentence covertly and judged whether or not
the action depicted in a picture matched the meaning of the sentence. Each sentence had two noun phrases called arguments,
each of which assumes a different grammatical relation (“subject, direct object, or indirect object” in linguistic terms) and asemantic role (“agent, experiencer, or patient” in linguistic terms,
i.e., an agent who performs the action, and an experiencer/patient
who is affected by it); these three conditions were thus called Twoargument conditions. More specifically, the active, passive, andscrambled sentences corresponded to “agent and patient” (subject
and direct object), “experiencer and agent” (subject and indirect
object), and “patient and agent” (direct object and subject) types,
respectively. Pictures consisted of two stick figures, each of which
was distinguished by a “head” symbol: a circle (◦), square (), or
triangle (). These sentences excluded the involvement of pragmatic information about word use (e.g., “An officer chases a thief ”
is more acceptable than “A thief chases an officer”). To minimize
the effect of general memory demands, a whole sentence of aminimal length was visually presented for a longer time than wasneeded to respond.
In Japanese syntax, the grammatical relations are first marked
by grammatical particles (nominative, dative, or accusative),
which in turn allow the assignment of semantic roles. In theactive sentences we used, a noun phrase with the nominative
case marker -ga (green letters in Figure 6B) is associated with anagent, and the one with the accusative case marker -o is associated with a patient. For the passive sentences we used, however,a noun phrase with the nominative case marker -ga is associatedwith an experiencer (a person experiencing a situation), whereas
a passive bound verb “-(r)areru” marks passiveness, making a
subject-verb pair with the experiencer. In contrast, a noun phrasewith the dative marker -ni is associated with an agent, whereas
an action verb (e.g., “hik(u),” “pull”) makes a subject-verb pairwith the agent, forming a subordinate clause within the mainclause “◦-ga... -(r)areru.” Note that there exist similar causative

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FIGURE 6 | A picture-sentence matching paradigm in Kinno et al.
(2008). (A) A picture-sentence matching task under either Two-argument
conditions or a One-argument condition. Each stimulus consisted of one
picture (top) and one sentence (bottom). Below each example,
word-by-word and full translations in English are shown. An identical
picture set was used under the Two-argument conditions, where wetested three sentence types: active sentences (“-ga ◦-o hiiteru”),
passive sentences (“◦-ga -ni hik-areru”), and scrambled sentences(“◦-o -ga hiiteru”). Under the One-argument condition, we presented
syntactically simpler active sentences (“-to -ga aruiteru”). (B) The
syntactic structures of three sentence types. The digits shown in red
and blue denote the DoM for each node and “number of Search,”
respectively. Symbols used: S and S’, sentence; N, noun phrase; V, verb
phrase; Nom, nominative case; Acc, accusative case; Dat, dative case;-ga, nominative case marker; -o, accusative case marker; -ni, dative casemarker; -to, coordinator; ti , trace (subscripts denote the same entity).

structures in both Japanese and English: “Hanako-ga kare-ni hikaseta,” “Hanako made him pull.” Actually, there are two typesof passivization in Japanese: ni passive (e.g., “Hanako-ga Taroni hik-areru,” “Hanako is affected by Taro’s pulling her”) and ni
yotte passive (e.g., “Hanako-ga Taro-ni yotte hik-areru,” “Hanako
is pulled by Taro”). According to Kuroda (1992), the ni passive
involves no noun-phrase movement, while the ni yotte passive
involves a movement similar to the case in English. For thescrambled sentences, an object moves from its canonical position
to higher nodes by undergoing another Merge operation. This
type of constructions is perfectly normal, not only in Japanese
but in German, Finnish, and other languages. We also tested
the One-argument condition, under which each sentence waspresented with an intransitive verb and double agents. This condition did not involve two-argument relationships, and was thus
syntactically simpler than any of the Two-argument conditions.

Merge applies iteratively to an existing phrase or sentence, theDoM domain becomes thus larger in accordance with the number of Merge applications. The Search distance is the structural
distance between two distinct parts to which the Search operation applies, regardless of the nodes that are irrelevant to the
Search operation. As observed from Figure 4, the DoM domainchanges in accordance with the Search distance. On the other
hand, for every sentence stimulus in the study of Ohta et al.
(2013), the construction of syntactic structures was ensured bytask requirements, in which three sentence types had to be distinguished while they were completely mixed. Task requirements
include not only certain constraints required by experimental
tasks, but detailed parsing naturally required to understand apart of phrases or sentences (e.g., subject-verb relationships andnoun-pronoun (coreference) relationships).
In the above mentioned paradigm (Kinno et al., 2008), the fourtask conditions (three sentence types under the Two-argument
conditions, as well as one type under the One-argument condition) were completely mixed (see Figure 6A). With such task
requirements, the DoM domain spanned three relevant words
for all sentence types under the Two-argument conditions. Under
the One-argument condition, the action of two stick figures wasalways identical, and thus a subject (a triangle just below N in
Figure 6B) is regarded as a unit. Under these four task conditions, participants were required to check at least one of theargument-verb relationships, demanding Search at least once. For

HYPOTHESIS III

Here we present the following hypothesis (Hypothesis III):(3) The DoM domain changes dynamically in accordance with
iterative Merge applications, the Search distances, and/or task
requirements.
Since Merge combines two syntactic objects to form a larger
structure, Merge always produces a one-level higher node. When

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Computing syntax in the brain

the scrambled sentences alone, an additional Search operation
should match the identical indices of the moved object and its
trace. For the active, passive, and scrambled sentences, the estimates of DoM were 2, 3, and 3, respectively, while those of the
DoM was 1 under the One-argument condition.

DoM actually showed a comparable or better goodness of fit for
activations in the L. F3t, when compared with their log-fitting
models.

FURTHER CONFIRMATION OF HYPOTHESIS III
THE EFFECT OF THE SEARCH DISTANCES ON THE DoM

APPLYING THE DoM TO VARIOUS SENTENCE TYPESIn the study of Kinno et al. (2008), we directly contrasted passive and active sentence conditions to identify a cortical region
that is activated by purely syntactic processes. This stringent contrast resulted in significant activation in the left dorsal F3t (L.
dF3t) alone [(−48, 24, 21), Z = 3.8] (Figure 7A), which wasvery close to the L. F3op/F3t activation in the study of Ohta
et al. (2013). The L. dF3t activation was significantly enhanced
under both the passive and scrambled sentence conditions compared to that under the active sentence condition (P ≤ 0.033)(Figure 7B), whereas there was no significant difference between
the passive and scrambled sentence conditions (P = 0.15). Taking
the One-argument condition as a reference for subtracted estimates, the signal changes in the L. dF3t were precisely correlated
in a step-wise manner with the parametric model of the DoM [1,2, 2], producing the RSS of 0.0001 and r2 of 0.99, without significant deviation for the three contrasts (P ≥ 0.87). The modelof the DoM thus sufficiently explains the L. dF3t activations. It
should be noted that the parametric model of “the number ofnodes” [2, 4, 4] also yielded the same fitting results in this case.
The design of experimental paradigms limits the separation ofmultiple factors.
In a recent fMRI study, only right-branching constructions
were examined, and activations in the L. F3t were modulatedby the size of constituents (i.e., number of terminal nodes)(Pallier et al., 2011). Since the estimates of the DoM were identical to those of “the number of Merge” or “the number of
non-terminal nodes” in this case, it was not possible to separate these factors. Taking their simplest condition (lists ofunrelated words) as an appropriate reference, the model of the

FIGURE 7 | Activations in the L. dF3t modulated by the DoM.
(A) A region identified by the Passive – Active contrast (see Figure 6).
Activations were projected onto the left (L) lateral surface of a standard
brain. (B) Percent signal changes for the active, passive, and scrambled
sentence conditions in the L. dF3t, taking the One-argument condition as areference. Overlaid red dots and lines denote the values fitted with theestimates (digits in red) for the model of the DoM.

Frontiers in Behavioral Neuroscience

Neuroimaging and psycholinguistic studies have reported that
English sentences with object-relative clauses have higher processing loads than those with subject-relative clauses (Just et al.,
1996; Stromswold et al., 1996; Gibson, 2000). To properly parsethe relative clauses, the relative pronoun and its antecedent arecoindexed; “whoi ” and “the boyi ,” respectively, in the example
shown in Figure 8. In a subject-relative clause, a relative pronoun “whoi ” was displaced from the subject position denoted bya trace ti (originally, “the boyi likes the girl”), while in an objectrelative clause, a relative pronoun was displaced from the object
position (originally, “the girl likes the boyi ”). Following the proposal by Hawkins (1999), we assume that the relative pronoun
searches the corresponding trace within tree structures of a sentence (see curved arrows in Figure 8). In a subject-relative clause,
Search ends at the initiation of the verb phrase, while in an objectrelative clause, Search ends after a verb appears within a subordinate clause. In accordance with the Search distances for theseexamples, the DoM would become one unit larger for the objectrelative clause than the subject-relative one. Higher processing

FIGURE 8 | The DoM domains varied with the Search distances.
(A) A sentence with a subject-relative clause. (B) A sentence with anobject-relative clause. In these relative clauses, a relative pronoun whoi isdisplaced from its subject or object position denoted by a trace ti . A set of
red straight arrows corresponds to the DoM domain. The digits shown in
red denote the DoM for each node within the domain. Symbols used: S andS’, sentence; N, noun phrase; V, verb phrase; ti , trace (subscripts denotethe same entity).

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Ohta et al.

Computing syntax in the brainloads observed with object-relative clauses are consistent with thisinference about the DoM domain.
THE EFFECT OF TASK REQUIREMENTS ON THE DoM

If Hypothesis III is correct, then the L.F3op/F3t activations canbe different in accordance with task requirements, even when thesame sentences are presented. In our previous fMRI study, we
compared three explicit linguistic tasks with the same set of normal two-word sentences: syntactic decision, semantic decision,and phonological decision tasks (Suzuki and Sakai, 2003). In thesyntactic decision task, the participants judged whether or not
the presented sentence was syntactically correct, and this judgment required syntactic knowledge about the distinction between
transitive and intransitive verbs (e.g., normal sentence, “yuki-ga
tumoru,” “snow lies (on the ground)”; anomalous sentence, “yukio tumoru,” “(something) lies snow”). In the semantic decisiontask, lexico-semantic knowledge about selectional restrictions
was indispensable. In the phonological decision task, phonological knowledge about accent patterns was required. Neither thesemantic decision task nor the phonological decision task, bothwith implicit syntactic processing, elicited significant activationsin the L. F3op/F3t (−57, 9, 6), which was significantly activatedduring explicit syntactic processing, even by a direct comparison
between the syntactic decision task and the other tasks. These
results suggest the presence of the DoM domain in accordancewith the task requirements of explicit syntactic processing.
THE MIXED EFFECTS OF THE SEARCH DISTANCES AND TASKREQUIREMENTS ON THE DoM

In another fMRI study, we directly compared syntactic decisionand short-term memory tasks (Hashimoto and Sakai, 2002). In
this unique paradigm, we visually presented nested sentencesthat included two proper nouns, two verbs, and one pronoun,in which either verb or pronoun was underlined. After presenting one complete sentence in a phrase-by-phrase manner,
paired phrases including an underlined phrase were shown. In
one syntactic decision task (SYN-1), participants were required
to judge whether the subject of an underlined verb corresponded
to the person in paired phrases (Figure 9A). In this case, theSearch distance was the structural distance between the subject
and verb of the same clause. In the other syntactic decision task
(SYN-2), the participants were required to judge whether anunderlined pronoun was able to refer to the person in paired
phrases (Figure 9B). In this case, the Search distance was the
structural distance between the coindexed noun and pronoun.
In these syntactic decision tasks, the Search distance, and consequently the DoM domain, changed dynamically in accordancewith the different task requirements, even when the same sentences were presented. The estimate of the resultant DoM was
2 for both cases. In a short-term memory task with a sentence,the participants memorized the linear order of the phrases, andjudged whether the left-hand phrase preceded the right-hand one
in the original sequence (Figure 9C). With such a task requirement, the factor of DoM would become less effective. Indeed, we
found that activations in the L. F3op/F3t were equally enhanced inboth syntactic decision tasks when compared with the short-term
memory task.

Frontiers in Behavioral Neuroscience

FIGURE 9 | The DoM domains varied with the Search distances andtask requirements. In this task, participants read Japanese nested
sentences (“Taro-wa Saburo-ga jibunjishin-o homeru-to omou,” “Taro thinksthat Saburo praises himself ”), in which phrases were sequentiallypresented. (A) A syntactic decision task 1, in which participants judged
subject-verb relationships. A set of red straight arrows corresponds to theDoM domain. The digits shown in red denote the DoM for each node within
the domain. (B) A syntactic decision task 2, in which participants judged
noun-pronoun (coreference) relationships (subscripts denote the same
entity). (C) A short-term memory task with a sentence, in which
participants judged the temporal order of the phrases. Symbols used: S andS’, sentence; N, noun phrase; V, verb phrase; Top, topic; Nom, nominative
case; Comp, complementizer.

CONCLUSIONSIn this article, we reviewed recent advances in theoretical linguistics and functional neuroimaging in the following respects.
First, we provided theoretical discussions about the hierarchicaltree structures of sentences, and introduced the two fundamental linguistic operations of Merge and Search. We also presented
our hypotheses that the DoM is a key computational concept toproperly measure the complexity of tree structures (HypothesisI), and that the basic frame of the syntactic structure of a given
linguistic expression is determined essentially by functional elements, which trigger Merge and Search operations (Hypothesis

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Computing syntax in the brain

II). Second, we presented our recent fMRI studies, which havedemonstrated that the DoM, together with the number of Search,is indeed a key syntactic factor that accounts for syntax-selective
activations in the L. F3op/F3t and L. SMG (Ohta et al., 2013).
Moreover, based on the DCM and DTI results, we revealed the significance of the top-down connection from the L. F3op/F3t to L.
SMG, suggesting that information about the DoM is transmitted
through this specific dorsal pathway. Third, we further hypothesized that the DoM domain changes dynamically in accordancewith iterative Merge applications, the Search distances, and/or
task requirements (Hypothesis III). We showed that the DoM sufficiently explains activation modulations due to different structures reported in previous fMRI studies (Kinno et al., 2008; Pallier
et al., 2011). Finally, we confirmed that Hypothesis III accounts
for higher processing loads observed with object-relative clauses,
as well as activations in the L. F3op/F3t during explicit syntactic
decision tasks, reported in the previous neuroimaging and psycholinguistic studies (Just et al., 1996; Stromswold et al., 1996;
Gibson, 2000; Hashimoto and Sakai, 2002; Suzuki and Sakai,2003). It is likely that the DoM serves as a key computational
principle for other human-specific cognitive capacities, such as
mathematics and music, both of which can be expressed by hierarchical tree structures. A future investigation into the computational principles of syntax will further deepen our understandingof uniquely human mental faculties.

ACKNOWLEDGMENTS
We would like to thank R. Kinno, H. Miyashita, K. Iijima, and T.
Inubushi for their helpful discussions, N. Komoro for her technical assistance, and H. Matsuda for her administrative assistance.
This research was supported by a Core Research for Evolutional
Science and Technology (CREST) grant from the Japan Scienceand Technology Agency (JST), by Grants-in-Aid for Scientific
Research (S) (Nos. 20220005) from the Ministry of Education,
Culture, Sports, Science and Technology, and by a Grant-in-Aid
for Japan Society for the Promotion of Science (JSPS) Fellows(No. 24·8931).

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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: 30 September 2013; paper pending published: 17 October 2013; accepted: 01
December 2013; published online: 18 December 2013.
Citation: Ohta S, Fukui N and Sakai KL (2013) Computational principles of syntax inthe regions specialized for language: integrating theoretical linguistics and functional
neuroimaging. Front. Behav. Neurosci. 7:204. doi: 10.3389/fnbeh.2013.00204
This article was submitted to the journal Frontiers in Behavioral Neuroscience.
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