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RUNNING TITLE: BRAIN CIRCUIT OF INTEGRATIVE PRIMING

Author preprint of a manuscript accepted for publication in Cerebral Cortex on June-11th, 2015

Separate Brain Circuits Support Integrative and Semantic Priming

in the Human Language System

Gangyi Feng1, Qi Chen

1,2, Zude Zhu

1,3, Suiping Wang

1,2*

1 Center for the Study of Applied Psychology and School of Psychology, South China Normal

University, Guangzhou 510631, China

2 Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science, South China

Normal University, China

3 Collaborative Innovation Center for Language Competence, Jiangsu Normal University, Xuzhou

221009, China

* Correspondence concerning this article should be addressed to:

Suiping Wang: Center for Studies of Psychological Application, South China Normal University,

Guangzhou 510631, China. Telephone: +86-020-85212193, E-mail: suiping@scnu.edu.cn.

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Abstract

Semantic priming is a crucial phenomenon to study the organization of semantic memory. A novel

type of priming effect, integrative priming, has been identified behaviorally, whereby a prime

word facilitates recognition of a target word when the two concepts can be combined to form a

unitary representation. Here, we used both functional and anatomical imaging approaches to

investigate the neural substrates supporting such integrative priming, and compare them with

those in semantic priming. Similar behavioral priming effects for both semantic (Bread – Cake)

and integrative conditions (Cherry – Cake) were observed when compared to an unrelated

condition. However, a clearly dissociated brain response was observed between these two types of

priming. The semantic priming effect was localized to the posterior superior temporal and middle

temporal gyrus. In contrast, the integrative priming effect localized to the left anterior inferior

frontal gyrus and left anterior temporal cortices. Furthermore, fiber tractography showed that the

integrative-priming regions were connected via uncinate fasciculus fiber bundle forming an

integrative circuit, whereas the semantic-priming regions connected to the posterior frontal cortex

via separated pathways. The results point to dissociable neural pathways underlying the two

distinct types of priming, illuminating the neural circuitry organization of semantic representation

and integration.

Keywords: Diffusion Tensor Imaging, Integrative Priming, Left Anterior Temporal Lobe, Left

Inferior Frontal Gyrus, Semantic Priming

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Introduction

Thousands of concepts are acquired in one’s lifetime. Concepts are bound together through

multiple relationships to form an interconnected conceptual network. The activation of one

concept within the network can facilitate access to another concept if they are semantically similar

(via feature overlapping, e.g., cookie – bread) or associated (generated by a free-association task,

e.g., salt – pepper)(Collins and Loftus 1975; Hutchison 2003). A robust behavioral phenomenon,

lexical-semantic priming, has been observed repeatedly, in which responding to a target word is

faster when preceded by a semantically-related prime word versus an unrelated one (Meyer and

Schvaneveldt 1971). In addition, concepts can prime one another even if they are not already

associated, but can be easily combined to form a unitary representation (e.g. Cherry – Cake), a

phenomenon termed integrative priming (Estes and Jones 2009; Mather et al. 2014). The aim of

the present study was to elaborate the potentially distinct neural underpinnings of semantic and

integrative priming.

Integrative priming has been argued to reflect distinct cognitive processes from that of

semantic priming (Estes and Jones 2009; Jones and Golonka 2012; Mather et al. 2014). In contrast

to semantic priming, complimentary role assignment and conceptual combinatorial processes are

implicated in integrative priming. According to the relational integration hypothesis (Estes and

Jones 2009), if there is an integrative relationship in a word pair, a process of role assignment is

automatically activated (Estes and Jones 2009; Mather et al. 2014) where the concepts are

assigned to complementary semantic roles. For example, when participants see the pair Cherry –

Cake with the words presented sequentially, the most common roles in which the Cherry is used to

modify other concepts are activated. If Cake has a compatible dimension to be modified by Cherry

(e.g., a cake has a particular type of flavor) this complementary semantic role can facilitate the

lexical processing of the target word Cake, which is also the process involved when integrating

individual concepts into a unique identity (Estes and Jones 2009). Behaviorally, it has been

demonstrated that the integrative priming effect tends to emerge in a short time interval between a

prime and a target, and diminish faster compared to that of in associative priming (Estes and Jones

2009). Moreover, integrative priming is not sensitive to the manipulation of relatedness proportion

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(the proportion of semantically-related trials in a stimuli context) while semantic priming is (Estes

and Jones 2009; Jones and Golonka 2012; Mather et al. 2014). These findings suggest that

integrative priming and semantic priming involve different cognitive operations.

In parallel to the behavioral differences between the two types of priming effects, recent

neuroimaging studies have demonstrated that integrative processes involved in both word and

sentence comprehension trigger distinct patterns of neural activation from those of classical

semantic priming. Specifically, a distributed frontal-temporal semantic network has been

implicated in semantic priming (Hickok and Poeppel, 2004; Lau et al., 2008; for meta-analysis,

see Binder et al. 2009 ) both in terms of neural response suppression (Devlin et al. 2000; Gold et

al. 2006; Lau et al. 2013) and enhancement (Kotz et al. 2002; Raposo et al. 2006; Sachs et al. 2011;

Lee et al. 2014) relative to unrelated word pairs. In contrast, when experimental tasks involve

conceptual combination, such as tasks focusing on basic integrative processing between adjectives

and nouns (e.g., “red – boat”) (Graves et al. 2010; Bemis and Pylkkanen 2011, 2013) or on

sentence-level comprehension (Lau et al. 2008; Rogalsky and Hickok 2009; Wilson et al. 2013),

different regions were implicated including left anterior inferior frontal cortices, left anterior

temporal cortices, bilateral tempo-parietal and medial prefrontal regions. Nevertheless, the exact

neuroanatomical bases of integrative priming, and whether integrative and semantic priming

effects are underpinned by different brain mechanisms, are still unknown.

To this end, the goal of the present study was to delineate the distinct neural substrates

between integrative and semantic priming operations. Specifically, we manipulated the

relationship between prime and target words in a lexical decision task. Integrative word pairs were

constructed that displayed high integrative potential but had a low level of prior association and

were semantically dissimilar. In contrast, the semantically related word pairs were both highly

similar and associated but had low integrative potential. These two types of word pairs were

contrasted with a matched unrelated word pairs to define the priming effects. Such design

permitted us to determine the neural substrate for integrative priming while minimizing

confounding factors from semantic similarity and association, and vice versa. In addition, to help

isolate the neural underpinnings of the semantic or integrative priming, separate from processing

involved in word recognition and access to the meanings of individual words (Badre et al. 2005),

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we constructed another condition in which the prime word was a meaningless nonword and the

target word was the same as in the other conditions. Finally, we expect an increased activity

pattern to integrative versus unrelated word pairs for the semantic integration regions mentioned

above, reflecting distinct neural mechanism associated with the process of forming a new

representation (Henson 2003; Lee et al. 2014).

Beyond the activation-based functional localization, we also investigated the brain circuitry

underlying semantic and integrative priming. Researchers in both psycholinguistic and neurology

of language have shown increasing interest in devising explicit models of the brain circuitry

underpinning language functions. Indeed, the language network has been proposed as a highly

interactive system (Dick and Tremblay 2012). Both activation of language-related regions and

effective communication among them by fiber bundles are proposed to be associated with the

implementation of language functions (Fedorenko and Thompson-Schill 2014). Using diffusion

tensor imaging (DTI) technology, researchers can track the language-related fiber pathways in

vivo (Dick and Tremblay 2012; Friederici 2012; Thiebaut de Schotten et al. 2012; Friederici and

Gierhan 2013). Previous studies have found that several fiber bundles, such as the extreme capsule

fiber system (ECFS), superior longitudinal fasciculus /arcuate fasciculus (SLF/AF) and uncinate

fasciculus (UF), connecting the tempo-parietal cortices to the frontal cortices (Saur et al. 2008;

Wong et al. 2011), largely contribute to human language processing (Friederici and Gierhan 2013).

In the present study, we investigated the anatomical pathways associated with each type of

priming by using a probabilistic fiber-tracking method to track the most likely fiber bundles

directly connecting those priming-related regions. We hypothesized that the integrative-priming

regions may connect with each other via specific fiber bundle(s) forming a circuit, while the

semantic-priming regions may connect with each other by separated fiber bundle(s) forming

another circuit.

Materials and Methods

Participants

A total of 28 native Chinese speakers participated in the experiment, and were paid for

participation. All participants were right-handed, with normal or corrected to normal vision, and

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no prior history of neuropsychiatric disorders. All participants signed a written consent form

approved by the local ethical review board in South China Normal University. One participant was

excluded due to poor task performance (accuracy < 70%) in the lexical decision task (LDT) and

another was excluded due to excessive head movements (> 1 mm). Thus, the analyses were

conducted on data from 26 participants (11 males; 18 – 28 years old, Mean age = 21.2 years, SD =

2.2).

Experimental design and stimulus construction

To isolate neural substrates associated with integrative and semantic priming, our fMRI study

adopted a priming paradigm in combination with a LDT. Two-character nouns in Chinese were

used as primes and targets. Four types of prime-target relationships were constructed, including

Integrative (e.g., 樱桃 /Cherry – 蛋糕 /Cake), Semantic (e.g., 面包 /Bread – 蛋糕 /Cake),

Unrelated (e.g., 司机/Driver – 蛋糕/Cake) and Nonword (e.g., /Kmbol – 蛋糕/Cake).

Here the integrative (or semantic) priming effect was defined as the contrast of the Integrative (or

Semantic) and Unrelated condition. While the Unrelated condition served as a control for

assessing the impact of semantic or integrative relations, the Nonword condition served as a

control for dissociating lexical-level processes/effects (e.g., lexical-semantic retrieval of the

meanings of the constituent words) from those beyond the single word level (e.g., priming

processes). Because there was only one real word (i.e. the target, Cake in the example) in the

Nonword condition, the amount of meaning retrieval in the Nonword condition was assumed to be

less than other conditions. Thus, we defined the effect of word retrieval as the contrast of the

Unrelated and Nonword conditions.

The materials included a total of 168 sets of words. Each set included a unique target word

paired with four types of prime words. The integrative word pairs were constructed to have a high

degree of integration potential (easy to combine prime and target into a meaningful phrase).

Constituents of integrative pairs were selected to be semantically dissimilar and to have no prior

association. In contrast, the semantically related word pairs were constructed to be both highly

similar and associated but with minimal degree of integrative potential (Estes and Jones 2009;

Mather et al. 2014). In the Unrelated condition, the constituents were nonsensical if combined,

semantically dissimilar and unassociated. In the Nonword condition, a real-word target was paired

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with a nonword prime that did not convey any semantic information. These nonword primes were

unpronounceable nonwords created by randomly assembling Chinese radicals (see Table 1 for

more details).

To ensure that stimulus materials met the above requirements, four normative assessments

were collected: integration rating, similarity rating, free-combination and free-association

generation assessments. Sixty young adults who did not participate in the fMRI experiment

provided responses to the first two ratings. The participants were asked to rate both the degree of

integration and semantic similarity for all 504 word pairs (Nonword condition was excluded). For

the similarity rating, the participants were asked to judge how similar the words were in their

perceptual or functional properties (from 1 for very different to 5 for very similar). For the

integration rating the participants were asked to judge how well the first word and the second

word could be linked together to form a meaningful phrase (from 1 for totally unable to be linked

to 5 for tightly linked). In addition, another ten participants who did not participate in the fMRI

experiment were recruited for another two stimulus assessments. In the free-combination

generation task, the participants were required to write down a two-character noun that could be

easily combined with a given noun to form a meaningful phrase (e.g., were shown Cherry and

wrote Cake). In the free-association generation task, the participants were asked to write down a

two-character noun that was semantically associated with a given noun but was not integrative

(e.g., were shown Bread and wrote Cake). Finally, we calculated the proportion of the appearing

word that is the same as the target word in all self-generated responses for both free-combination

and free-association generation tasks.

Table 1 shows the statistical results of these four stimulus assessments. The word pairs in the

Integrative condition were more amenable to integration and had a higher free-combination

proportion than in the Unrelated (integration rating: t(167) = 80.7, P < 0.01; free-combination: t(167)

= 6.8, P < 0.01) and Semantic condition (integration rating: t(167) = 59.1, p < 0.01;

free-combination: t(167) = 3.8, p < 0.01). In contrast, word pairs were both more similar and more

frequently associated in the Semantic condition than in the Unrelated (similarity rating: t(167) =

74.2, P < 0.01; free-association: t(167) = 7.4, P < 0.01) and Integrative condition (similarity rating:

t(167) = 34.9, P < 0.01; free-association: t(167) = 4.4, P < 0.01). The condition specified for

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integration or similarity properties was further confirmed by directly comparing the integration

ratings to the similarity ratings in the same condition. Specifically, there were significantly higher

integration ratings than similarity ratings in the Integrative condition (t(167) = 55.3, P < 0.01), while

the reverse pattern was observed in the Semantic condition (t(167) = 38.1, P < 0.01). Moreover, we

strictly controlled and matched low-level linguistic variables of prime words across conditions:

word frequency (Mean ± SD, Integrative = 36.99 ± 79.70; Semantic = 36.63 ± 115.88; Unrelated =

35.20 ± 76.90 per million; F(2,168) = 0.10, P = 0.89) and total number of strokes (Integrative =

15.84 ± 4.76; Semantic = 15.94 ± 4.79; Unrelated = 15.84 ± 4.49; F(2,168) = 0.03, P = 0.97).

Procedure

To maximize the integrative and semantic priming effects and to avoid interference from

preceding priming trials (Sachs et al. 2011), experimental trials were divided into two runs with

one run devoted to the integrative stimuli and respective controls (Integrative, Unrelated, and

Nonwords), and the other to the semantic stimuli and respective controls (Semantic, Unrelated and

Nonwords).

For the lexical decision task, we added 112 word pair fillers where the target words were

two-character pseudowords. To prevent participants from adopting a strategy during the

experiment (e.g., they might tend to judge a target word as a real word when they read a nonword

prime first), nonwords were used as the prime in half of the filler trials. Therefore, the number of

filler trials with nonword primes was the same as the Nonword trials.

To counterbalance stimuli across conditions and participants, eight lists of the stimulus

materials were constructed. No target word appeared twice in the same list, but the same target

appeared in all conditions across participants. Given this scheme, the differences between

conditions are not attributable to lexical properties of the targets. During the fMRI experiment,

participants were required to complete one list of stimuli which consisted of two runs. The order

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of the two runs was also counterbalanced across participants to avoid any confounding from the

presentation order. Therefore, each list contained a total of 280 trials (168 experimental trials and

112 filler trials).

Stimulus presentation and data collection was controlled by E-Prime (Psychology Software

Tools, Pittsburgh, PA; version 2.0). The stimuli were presented on a screen within the MRI-cabin

by a MRI-compatible LCD projector. The stimulus presentation schema is showed in the Figure

1A. Each trial constituted a white-color central fixation with a black background for 400 ms, a

blank screen for 200 ms, a white-color prime word for 400 ms, a blank screen for 200 ms, and

finally a yellow-color target word for 1500 ms followed by a 300-ms blank screen. This resulted in

a stimulus-onset-asynchrony (SOA) of 600 ms. The participants were required to judge whether

the yellow-color target words were real words or not by pressing a “yes” or “no” button with

either their right index or middle finger, counterbalanced across participants. The target words

disappeared once the participant made a response. In addition, to better estimate the hemodynamic

response related to the onset of target words, we used jittered intertrial intervals (ITI), of 1, 3, or 5

sec (average 3 sec). Therefore, each trial lasted 6 seconds on average. We recorded the

participants’ response and reaction time (RT) in each trial during the fMRI experiment.

Functional localizers for predefining language system

To predefine brain regions associated with language processing, we conducted two functional

localization experiments before the LDT experiment. One was the word judgment task and

another was a sentence reading task. The word judgment task was used to localize brain regions

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associated with lexical-semantic processing (Badre et al. 2005; Visser et al. 2012). The sentence

reading task was used to localize brain regions associated with sentence-level processes, in which

both semantic integrative and syntactic processing would be implicated. Details of these two

functional localizers were descripted in the Supplementary Materials.

MRI data acquisition

MRI data were acquired using a Siemens Trio 3T MRI system with a 32-channel head coil at the

Shenzhen Institutes of Advanced Technology, Chinese Academic of Science. Three modalities of

imaging data were collected, including functional, structural and DTI images. To minimize signal

loss and distortion in bilateral anterior temporal lobe (ATL) regions due to the magnetic

susceptibility artifact, both low TE (20 ms) and coronal slice orientation scanning parameters were

applied (Axelrod and Yovel 2013) for the functional imaging runs. Specifically, the functional

imaging were recorded by a T2*-weighted gradient echo-planar imaging (EPI) pulse sequence (TR

= 2000 ms, TE = 20 ms, flip angle = 90º, 38 slices, FOV = 224 mm × 224 mm, in-plane resolution

= 3.5 × 3.5, slice thickness = 3.5 mm with 1.1 mm gap). T1-weighted high resolution structural

images were acquired using a magnetization-prepared rapid acquisition gradient echo (MP-RAGE)

sequence (176 slices, TR = 1900ms, TE = 2.53 ms, flip angle = 9º, voxel size = 1×1×1 mm3).

Finally, DTI data with 30 diffusion encoding directions (TR = 10.8 s, TE = 87 ms, flip angle = 90°,

b = 1000 s/mm2) and one image without diffusion weighting (b value = 0 s/mm

2, b0) were

acquired. Each volume consisted of 85 slices in the intercommissural plane, 2-mm thickness with

2-mm gap, with an in-plane resolution of 2 mm and field of view = 256 mm × 256 mm.

Functional MRI data analysis

Preprocessing

All functional imaging data were preprocessed using SPM8 (Wellcome Department of Imaging

Neuroscience, London, UK; www.fil.ion.ucl.ac.uk/spm/). The preprocessing procedure included

slice-time correction, head-movement correction, coregistration between EPI and structural

images, normalization to a standard T1 template in the Montreal Neurological Institute (MNI)

space (resampling into 2 × 2 × 2 mm3

voxel size) and smoothing with a Gaussian kernel of 8-mm

FWHM.

Subject-level analysis

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We performed the subject-level analysis using general linear modeling (GLM). Design matrices of

the three tasks (two localizers’ tasks and the LDT) were constructed and modeled separately. In

the two localizers, regressors of interest corresponding to word and nonword as well as sentence

and nounlist trials were convolved with canonical hemodynamic response function (HRF) to build

GLMs for lexical-semantic and the sentence integration effect, respectively. In the design matrix

for the LDT task, six regressors of interest were included: Integrative, Unrelated and Nonword

trials from the integrative run, and Semantic, Unrelated and Nonword trials from the semantic run.

Both the filler trials and the incorrect response trials in each run were also modeled as non-interest

regressors. The hemodynamic response at target onset was modeled for each of the 8 event types

with the canonical HRF. In addition, low-frequency drifts were removed using a temporal

high-pass filter (cutoff at 128 sec). Six head movement parameters were included in all design

matrices as nuisance regressors to regress out motion-related artifacts. The standard gray-matter

volume created from the segmentation for each subject was used an inclusive mask to restrict

voxels of interest.

Group-level analyses

A random effect model was used for the group-level analyses. For functional localizers, we used a

one-sample t-test to define brain regions associated with word-level semantic processes (word

judgment task – nonword matching task) and sentence-level integration processes (sentence –

nounlist) separately. Voxel-level P < .001 and cluster-level corrected P < .05 using family-wise

correction (FWE) were applied for multiple comparison correction. The union of these two

contrasts was defined as a language mask. This mask was further used as an inclusive mask in the

LDT task.

To calculate semantic and integrative effects, we constructed within-subject one-way

ANOVA to define brain regions showing a main effect either (or both) in the semantic and

integrative runs in the LDT. The language mask created from the localizers was used as an

inclusive mask in these two group-level ANOVAs to increase the statistical power. Monte Carlo

simulations with the AlphaSim program were used to determine the activation threshold, taking

into account both the number of voxel within the language mask and the smoothness of the

preprocessed data (voxel-level P < 0.005, corrected to P < 0.05 with a 30-voxels cluster size, Cox

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1996). The activation coordinates were reported in MNI space.

Regions of interest (ROI) analyses and parametric modulation analyses

To further investigate the priming effect in regions showing significant main effects in the

ANOVAs, we conducted regions of interest (ROI) analyses. Separate spherical ROIs with a 6-mm

radius were created based on the peak coordinates of the brain regions revealed by the main

effects in the ANOVAs. Such ROI construction approach is widely used (Poldrack 2007) and it

can be used to ensure each ROI has the same number of voxel. The beta estimates of all the six

conditions in the LDT were then extracted for each participant within each ROI using MarsBaR

toolbox. The main aims of the ROI analyses were to detect which regions displayed significant

priming effect (Integrative vs. Unrelated or Semantic vs. Unrelated) in each run and whether they

showed dissociated priming effect (the contrast of the two types of priming effects).

In addition, we examined whether the regions associated with the integration or semantic

effects were also related to the degree of integration or semantic association strength of word pairs

by performing parametric modulation analyses. Two subject-level design matrices were

constructed. The integration rating (or the semantic association) score for each trial from the

stimulus assessments were used as a parametric modulation weight in the design matrix. The

Nonword trials, filler trials, error response trials, and motion parameters were modeled separately

as nuisance regressors. The parametric modulation analysis was performed at the subject-level

first. Subsequently, we used the regions showing significant integrative or semantic effect as ROIs

to extract beta estimates for each participant and conducted a one-sample t-test at group level.

Moreover, we also conducted voxel-wise parametric modulation analyses to confirm the ROI

results. Thus, the result would reveal which regions would show a monotonic modulation in

activity as a function of integration or association strength.

DTI data analysis and probabilistic tractography

Diffusion-weighted imaging (DWI) data were analyzed using the FSL toolbox (Behrens et al.

2007) and functions from the AFNI package (Cox 1996). The DWI data were preprocessed for

eddy currents and head motion using an affine registration model. Subsequently, the non-brain

tissues were removed using FSL’s automated brain extraction tool (BET). In the tractography

analysis, ROIs were selected from the regions showing significant semantic or integrative effects

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in the LDT experiment, and translated into white matter template. Here we were interested in how

the tempo-parietal regions anatomically connect to the frontal regions. Thus, a multiple-ROI

approach was used (e.g., from ATL to aIFG). The algorithm implemented in FSL (BedpostX) was

first used to calculate the diffusion parameters for each voxel. After that, probabilistic tracking

was performed by repeating 5,000 random samples from the first ROI voxels to the second ROI

voxels. These streamline samples started at the first ROI voxels and propagated through the local

probability density functions of the estimated diffusion parameters. When a 2-ROI approach was

used, only those streamlines initiated from the first ROI that reach a voxel in the second ROI (or

vice versa) are retained.

Because we focused on within-hemisphere left fronto-temporal fiber connections, the right

hemisphere was used as an exclusive mask, which has an effect of rejecting streamlines from the

right hemisphere. All voxels within the left hemisphere will have a value representing the

connectivity value between the first ROI voxels and the second ROI voxels (i.e., the number of

samples that pass through that voxel). Probability maps were then normalized to the total number

of fibers (Upadhyay et al. 2007). Therefore, the probability maps represent the fiber density in the

bundle between the 2 ROIs and are an indication of the most likely pathway between two gray

matter regions.

Results

Behavioral performance in LDT

A one-way repeated-measures ANOVA was used for testing the condition differences in both

accuracy and RT, with planned comparison threshold set at P = .05 after Bonferroni correction

(Figure 1B). No significant main effect (Integrative, Unrelated, and Nonword) of accuracy in the

integrative run was observed (F(2,25) = 1.74, P = .19). In contrast, a marginally significant main

effect of accuracy in the semantic run (Semantic, Unrelated, and Nonword) was found (F(2,25) =

2.91, P = .06). Further, post-hoc comparisons found that the participants made less errors in the

Semantic trials than in both the Unrelated (t(25) = 2.48, P = .02) and Nonword trials (t(25) = 2.11, P

= .05). For the RT analysis, both incorrect response trials and trials larger than 2.5 standard

deviation of the mean were removed. In the integrative run, a significant main effect of RT was

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found (F(2,25) = 6.44, P = .003). Similarly, a marginally significant main effect of RT in the

semantic run was observed (F(2,25) = 2.93, P = .06). A post-hoc planned comparison showed that

the participants responded significantly faster in the Integrative than in the Unrelated condition,

indicating an integrative priming effect (26 ms; t(25) = 2.38, P = .025). Similarly, faster response

time was also found in the Pseudoword condition compared to the Unrelated condition (t(25) = 3.18,

P = .004). In the semantic run, participants responded significantly faster in the Semantic

condition than in the Unrelated condition (20 ms; t(25) = 2.91, P = .007), indicating a semantic

priming effect. There was no significant difference between the Unrelated and Nonword condition

(t(25) = 0.49, P = .62). Finally, we did not observe a significant difference between the integrative

priming and the semantic priming effect (Figure 1C; t(25) = 0.58, P = .56).

Brain activations of the functional localizers

To ensure a high signal quality of the functional images, especially in the bilateral ATL regions,

we calculated the temporal signal-to-noise ratio (tSNR, the ratio of the average signal intensity to

the signal standard deviation across time points) for each voxel within the brain (Murphy et al.

2007). The results revealed that there was a good tSNR in the bilateral ATL (see Figure S1 in

Supplementary Materials) such that most of the ATL regions were significantly higher than 40 (a

minimal tSNR required for detecting condition differences).

Figure 2 presents the results of the two localizers. Figure 2A shows the distributed network

activated during the word judgment task compared to the nonword matching task. These regions

included left inferior frontal gyrus, a small portion of left anterior superior temporal gyrus, left

posterior middle temporal gyrus, left tempo-parietal junctions, posterior cingulate gyrus,

precuneus and right posterior middle temporal gyrus (see Figure 2A, left panels: Word >

Nonword). In addition, the comparison between the Sentence and Nounlist condition showed more

distributed activations, including the left superior and middle frontal gyrus, bilateral inferior

frontal gyrus, bilateral anterior temporal lobe, left posterior middle temporal gyrus, left

tempo-parietal junction and dorsal medial superior frontal gyrus (see Figure 2A, right panels:

Sentence > Nounlist). Finally, a language mask was created by unifying these two localization

maps (i.e., all regions activated in both contrasts were included in the mask, see Figure 2B).

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Functional dissociation of integrative and semantic priming effects

Figure 3 illustrates the activation distributions in both the main effect of integrative (Figure 3A)

and semantic runs (Figure 3B) within the language mask. The two brain maps were not

thresholded to enable a visual comparison between the integrative and semantic effects. Figure 4A

shows significant activations after applying a multiple comparisons correction in the two main

effects. Eight regions revealed a significant semantic or integrative effect, all in the left

hemisphere. Five regions showed a significant integrative effect, including anterior inferior frontal

gyrus (aIFG), anterior superior temporal gyrus (aSTG), anterior temporal lobe (ATL), posterior

middle temporal gyrus (pMTG), and tempo-parietal junctions (TPJ). In contrast, another three

regions showed a significant semantic effect, including posterior inferior frontal gyrus (pIFG),

middle portion of middle temporal gyrus (mMTG) and posterior superior temporal gyrus (pSTG).

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We performed a priming (semantic vs. integrative) by condition (priming, unrelated vs.

nonword) ANOVA for each ROI first, followed by planed comparisons. These ANOVA analysis

results showed that only the aIFG (F(2,48) = 6.92; P = 0.009), aSTG (F(2,48) = 10.87; P = 0.001),

ATL (F(2,48) = 9.48; P = 0.003), mMTG (F(2,48) = 4.14; P = 0.043) and pSTG (F(2,48) = 6.56; P =

0.012) showed significant priming-by-condition interactions.

To further examine whether those eight regions showed a significant dissociated priming

effect (i.e., more activation associated with one type of priming effect versus the other), we plotted

the parameter estimates for each of the conditions (Figure 4 B&C) and further performed paired

t-tests to directly compare the two types of priming effect (i.e., Integrative – Unrelated vs.

Semantic – Unrelated, or Unrelated – Integrative vs. Unrelated – Semantic). We found three sets

of regions. First, we found that only the aIFG (t(25) = 2.78, corrected P = 0.02), aSTG (t(25) = 3.16,

corrected P = 0.008) and ATL (t(25) = 3.48, corrected P = 0.003) showed a significant dissociated

integrative priming effect. There was more activation (response enhancement) in the same three

regions for the Integrative compared to the Unrelated condition.

Second, both the pSTG (t(25) = 2.55, corrected P = 0.05) and mMTG (t(25) = 2.78, corrected P

= 0.04) showed a significant dissociated semantic priming effect. The semantically related trials

induced increased activity compared to the Unrelated trials in these two regions as well.

Furthermore, to test whether the semantic priming with response suppression in the aSTG was

dissociated from its response enhancement in the integrative run, another contrast was performed:

(Unrelated – Semantic) – (Unrelated – Integrative). The result showed a significant dissociated

17 / 34

semantic priming effect in the aSTG (t(25) = 2.95, corrected P = 0.01).

In addition, we further investigated whether the size of each behavioral priming effect was

correlated with the level of each fMRI suppression or enhancement in these priming regions

across subjects. We treated the behavioral priming effect (unrelated minus integrative or

semantically-related trials for each subject) as an independent variable and the fMRI priming as a

dependent variable to build a regression model. The results showed that the magnitude of each

behavioral priming effect was associated with the level of each fMRI priming for those priming

regions (Table S1, Supplementary Materials). Specifically, the fMRI priming effect in both the

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aIFG and ATL were exclusively associated with the behavioral integrative priming effect, while

both the pSTG and mMTG were exclusively associated with semantic priming effect.

In a third set of regions, we observed a main effect of relatedness (integrative or

semantically-related, Unrelated and Nonword) in the left pIFG (F(2,25) = 4.49, corrected P = 0.03),

pMTG (F(2,25) = 4.43, corrected P = 0.04) and TPJ (F(2,25) = 9.52, corrected P < 0.001) independent

of the type of relationship between primes and targets. Both the pIFG and pMTG showed similar

decreased response in the Nonword condition compared to the Related conditions (pIFG: t(25) =

2.99, corrected P = 0.005; pMTG: t(25) = 2.93, corrected P = 0.006) or Unrelated conditions (pIFG:

t(25) = 2.37, corrected P = 0.03; pMTG: t(25) = 2.53, corrected P = 0.02). However, there was no

significant difference between the Related and Unrelated condition (pIFG: t(25) = 0.30, corrected P

= 0.76; pMTG: t(25) = 0.01, corrected P = 0.88). In contrast, the TPJ showed different response

patterns compared to the pIFG and pMTG. Significant increased response in the Related condition

compared to the Unrelated one (t(25) = 4.34, corrected P < 0.001) was found. Similarly, increased

response was found in the Nonword condition compared to the Unrelated condition (t(25) = 2.85,

corrected P = 0.02).

Parametric modulation of integration ratings and semantic association strength

To test whether activity of those regions were also modulated by integration strength or semantic

association, we extracted the beta estimates from the first-level parametric modulation analysis.

The results showed that only the left ATL (t(25) = 4.29, corrected P = 0.001), TPJ (t(25) = 3.95,

corrected P = 0.002), aSTG (t(25) = 3.51, corrected P = 0.008) and aIFG (t(25) = 2.94, corrected P =

0.03) showed a significant modulation effect of integration strength. That is, increasing integration

rating was associated with increased activation in these regions. In contrast, the increased

activation of the left pSTG (t(25) = 2.47, corrected P = 0.06), mMTG (t(25) = 2.67, corrected P =

0.04) and TPJ (t(25) = 2.60, corrected P = 0.05) showed significant modulation as a function of

increased semantic association strength. There were no regions showing decreases of activation as

a function of increases in either the integration ratings or semantic association strength.

Voxel-wise parametric modulation analysis within the language mask further confirmed these ROI

results (Figure S2).

To further verify whether the free-combination ratings could also account for the observed

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brain activations, we performed another parametric modulation analysis by using the

free-combination ratings as the only regressor in the general linear model. We observed that only

the TPJ showed significant parametric modulation effect (t(25) = 3.18, corrected P = 0.015). In

addition, we conducted another parametric modulation analysis using integrative ratings as

regressor of interest while controlling for the free-combination ratings (noninterest regressor). We

could still observe the three integrative-priming regions showing significant parametric

modulation effect on integrative strength (aIFG: t(25) = 2.96, P = 0.026; aSTG: t(25) = 3.21, P =

0.015; ATL: t(25) = 4.30, P = 0.0009; TPJ: t(25) = 3.92, P = 0.002; pMTG: t(25) = 1.09, P = 0.71;

pIFG: t(25) = 1.71, P = 0.34; mMTG: t(25) = 1.74, P = 0.32; pSTG: t(25) = 0.85, P = 0.83; All p-value

were corrected for multiple comparisons).

Fiber tractography

Figure 5 summarizes all the results from probabilistic tractography, where mean normalized

connectivity was thresholded at 3%, corresponding to the 95th percentile of the observed

distribution (Wong et al. 2011). The tractography results showed that the brain regions associated

with the integrative effect exhibited distinct fiber connection patterns from the pattern in the

regions associated with the semantic effect (Figure 5A). Specifically, the integrative-priming

regions in temporal lobe (aSTG and ATL) connected to the aIFG through uncinate fasciculus (UF)

via the extreme capsule (EC) (Figure 5A, left panel). In contrast, the semantic-priming regions in

temporal lobe (mMTG and pSTG) connected to the pIFG by both the extreme capsule fiber

system (ECFS) and superior longitudinal fasciculus /arcuate fasciculus (SLF/AF) (Figure 5A, right

panel). Figure 5B summarizes two distinct fiber bundles associated with the integrative priming

and semantic priming effects respectively.

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Discussion

We found increased activation in the left aSTG, aIFG and ATL when two concepts can be

integrated into a unitary representation relative to those that cannot. This suggests that these

regions contribute to integrative priming. These integrative-priming regions were different from

the regions associated with semantic priming in both their localization and response patterns.

Tractography analyses further indicated that fiber connections within the integrative-priming

regions and those within the semantic-priming regions were separated. These findings offer the

first empirical evidence in healthy humans that both different regions and potential brain circuits

support integrative versus semantic priming.

Although lexical-semantic priming effects have been consistently observed behaviorally, the

neural mechanisms of this phenomenon are still unclear. The primary reason is that it is not

straightforward to associate the neural (or fMRI) activity to the behavioral priming. Theoretically,

21 / 34

similar behavioral priming might be a consequence of different neural processes or associated with

different neural mechanisms (Henson 2003; Hutchison 2003; Estes and Jones 2009). Here, we

used custom fMRI scanning parameters to overcome signal distortion in anterior temporal cortices,

and demonstrated such possibility by separating the neural substrates associated with the two

behavioral priming effects, and further uncovered that the integrative strength and semantic

association might be the underlying factors driven both behavioral and neural priming effects.

These findings are inline with the theoretical predictions and also provide us an opportunity to

understand the neural formation (functional segregation and anatomical interaction across regions)

of the semantic system, and how these circuits associated with the representation and integration

of semantic information.

Anterior fronto-temporal regions associated with integrative priming effect

RTs were faster in the Integrative condition compared to the Unrelated condition. The effect

size of this behavioral integrative priming is comparable with previous findings (Estes and Jones

2009; Mather et al. 2014), suggesting that we have successfully manipulated the prime-target

relationship and replicated the integrative priming effect. Enhanced activity in the Integrative

condition relative to the Unrelated condition were found in the left aSTG, aIFG and ATL. The

involvement of these three regions has been associated with semantic integrative processes in

previous studies on sentence comprehension. For example, they have been frequently observed

activated when contrasting sentences versus lexical-level baseline (Humphries et al. 2006;

Rogalsky and Hickok 2009; Pallier et al. 2011) and incongruent sentences versus congruent ones

(Tesink et al. 2009; Zhu et al. 2012; Zhu et al. 2013). In our localization experiment, we also

replicated the findings using the contrast of sentences versus noun lists (Figure 2).

The neural response enhancement in these regions might be associated with the relational

integration processes that could facilitate the lexical decision on the target words. Such response

enhancement has been proposed to be associated with additional processes linked to the formation

of new representations (Henson, 2003; Sachs et al., 2011; Lee, et al., 2014). Indeed, cognitive

processes of integrative word pairs have been assumed to involve additional components

compared with the unrelated pairs. According to the relational integration hypothesis (Estes and

Jones 2009; Mather et al. 2014), two critical components, the complementary role activation and

22 / 34

combinatorial processing, are involved during integrative priming (Estes and Jones 2009; Mather

et al. 2014). In line with this hypothesis, we found similar enhanced response patterns in both left

aSTG and aIFG, which indicate they are both associated with integrative processing. In addition,

studies on local phrase composition align with our interpretation, showing that the aSTG is

involved in the process of building local phrase structures (Friederici et al. 2000; Friederici et al.

2003; Grodzinsky and Friederici 2006; Friederici 2011). Similarly, the semantic integration role of

the left IFG (particularly its anterior portion locating in the BA 47), are supported by previous

findings on sentence comprehension, in which the aIFG was activated when participants were

required to integrate semantic information from different information sources (e.g., speaker

identities and world knowledge) (Hagoort et al. 2004; Tesink et al. 2009). Furthermore, activation

in left aIFG has been parametrically modulated by semantic integration load in sentence

comprehension, which was independent of task manipulations (Zhu et al. 2012) and general

executive control processes (Zhu et al. 2013). Here, we replicated these findings and further

revealed activities of both aSTG and aIFG increasing as a function of increasing integration

strength between primes and targets. Altogether, these results suggested that both the left aSTG

and aIFG play important roles in integrating semantic information to create a unitary

representation.

It worth to note that previous studies have found stronger activation in the left aIFG for

higher semantic integration load (Hagoort et al. 2004; Tesink et al. 2009; Zhu et al. 2012; Zhu et al.

2013), while here we found increased activation with increased prime-target integration strength.

This seemingly opposite effect may reflect how the integration component is involved in different

task contexts. Here we used a priming paradigm with relatively short SOA, in which participants

were not required to explicitly process the prime-target relationships. Participants were not

required to explicitly detect prime-target relationships, or to try to integrate word pairs when they

encountered unrelated pairs. Thus, the integration component is less likely implicated for the

unrelated than the integrative pairs. In contrast, most of previous studies used sentence

comprehension task and explicitly asked participants to judge the congruency of sentences. In this

task context the integration load could be increased for the incongruent conditions that were

similar with the unrelated condition in our study. Therefore, the integration component is more

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likely implicated for the incongruent than the congruent sentence.

In contrast, left ATL showed both a robust dissociated integrative priming effect and a

lexical-semantic effect (increased activation in the Nonword condition compared to the Unrelated

condition), which were different from that found in the left aSTG and aIFG. Consistent with our

findings, ATL activation was frequently observed during sentence comprehension (Humphries et

al. 2006; Rogalsky and Hickok 2009; Pallier et al. 2011). Lesions in these regions are associated

with difficulty in sentence-level language comprehension (Dronkers et al. 2004). In addition,

Bemis et al. (2011, 2013) have found increased activation in the ATL during the two-word

composition compared to the one-word composition task in early time windows (150 – 250 ms) by

using magnetoencephalography (MEG). The evidence together with our findings suggests that the

left ATL activation is associated with basic elementary integrative processing.

Another possible explanation is that the ATL may encode integrative relationships stored in

long-term memory. It has been proposed that ATL plays a role as a core semantic hub (Rogers et al.

2004; Patterson et al. 2007; Jefferies 2013). This semantic hub would function as a convergence

zone for distributed features of concepts, responsible for integrating feature information from

modality-specific regions (e.g., regions response specifically to colors, shapes and movements etc.)

(Patterson et al. 2007; Pereira et al. 2009; Correia et al. 2014; Coutanche and Thompson-Schill

2014; Lambon Ralph 2014). This “convergent representation” hypothesis implies that ATL might

play a role in representing semantic relationships between concepts and their features. Indeed,

recently researchers have found that multivoxel patterns in the ATL encode semantic relationships

between features (such as “green” and “round”) and object identity (such as “lime”)

(feature-to-identity links) (Clarke and Tyler 2014; Coutanche and Thompson-Schill 2014).

Similarly, the integrative priming effect observed in ATL might rely on the same mechanism. In

the Integrative condition, the prime word acts as one of a feature property of the target word

(Cherry – Cake) while the target word acts as the main concept. Retrieval of this

“feature-to-identity” relational information resulted in increased activation in the integrative word

pairs relative to the unrelated condition. Future studies are required to disentangle the relationship

between the basis semantic integrative processing and the semantic representation in the ATL

(Westerlund and Pylkkänen 2014).

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Posterior temporal regions associated with semantic priming effect

We also found the classic semantic priming effect in terms of both reduced error rate and

facilitated RT in the semantically related word pairs relative to the unrelated ones. Importantly, we

also found both neural response suppression and enhancement in the Semantic condition relative

to the Unrelated condition in temporal cortex.

Semantic priming has been associated with spreading activation of semantic information

across concepts via conceptual similarity or association relationship (Lucas 2000; Hutchison

2003). In our study, the left aSTG showed neural response suppression (i.e., Unrelated >

Semantic), in accordance with the pattern of behavioral facilitation. In addition, such neural

response suppression in the aSTG is also consistent with previous findings using the masked

priming paradigm (Lau et al. 2013; Ulrich et al. 2013) and semantic priming with short SOA

(Rissman et al., 2003; Sass K et al. 2009; see review in Lau et al., 2008). Researchers have

proposed that response suppression during semantic priming might be associated with spreading

activation via feature overlap across concepts. When the prime and target share semantic features

on functional or perceptual dimensions, activation of these features in the primes could ease the

activation of the target concepts, resulting in neural response decreases (Henson 2003; Sachs et al.

2011).

In contrast, neural response enhancement was observed in both the left pSTG and mMTG

(i.e., Semantic > Unrelated), suggesting they might play distinct roles during semantic priming

compared to the aSTG. There is increasing evidence that semantic priming not only leads to

response suppression but also to response enhancement (Kotz et al. 2002; Raposo et al. 2006; Sass,

Krach, et al. 2009; Sass, Sachs, et al. 2009; Sachs et al. 2011; Lee et al. 2014), especially in the

left temporal regions observed here (Kotz et al. 2002; Raposo et al. 2006; Sass, Krach, et al. 2009).

Such response enhancement has been attributed to different or additional processes linked to the

formation of new associations or representations (Henson 2003). Given that these two temporal

regions have been also associated with strategic semantic priming processes (Badre et al. 2005;

Gold et al. 2006), such as semantic relationship (association or similarity) detection (Badre et al.

2005; Raposo et al. 2006), we interpret such response enhancements in both mMTG and pSTG as

related to association-based semantic activation. Consistent with this interpretation, we observed

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that parametrically increased association strength between primes and targets was related to

enhanced activation in these two regions. Here, such association-based activation is considered to

reflect controlled component of semantic priming processing but we did not exclude the

possibility of involving automatic spreading activation for these regions. Automatic priming

mainly occurs at short stimulus onset asynchronies (SOAs) and have been associated with the

automatic spreading activation across semantic memory (Collins and Loftus 1975). In contrast,

controlled semantic priming (at a long SOA) have been considered to reflect the postlexical

strategic semantic processes (Gold et al. 2006). Nevertheless, the automatic spreading activation

can still occur at a long SOA, but the controlled processes will dominate. Altogether, different

response patterns in these temporal regions suggest a functional segregation in the semantic

processing system (Binder et al. 2009; Price 2012).

Common effect during the semantic and integrative priming

In contrast to the dissociated priming effects, there was a main effect of condition in the left pIFG,

pMTG and TPJ independent of the type of prime-target relationships. Both the pIFG and pMTG

showed similar response patterns, in which we found decreased activations in the Nonword

condition relative to the other two conditions. While the semantic priming effect with both neural

enhancement (Sachs et al. 2011; Whitney et al. 2011; Lau et al. 2013; Lee et al. 2014) and

suppression (Gold et al. 2006; Liu et al. 2010) has been observed, these two regions have also

been associated with controlled semantic processes (Badre et al. 2005; Gold et al. 2006; Ye and

Zhou 2009; Whitney et al. 2011). Specifically, increased activations have been found when more

competitors are involved. Indeed, it has been suggested that the left pIFG contributes to

maintaining or inhibiting irrelevant internal representations while the pMTG has been associated

with controlled semantic retrieval (Whitney et al. 2011; Zhu et al. 2013). Such an explanation was

further confirmed in the present study. Our results showed that the two regions are sensitive to the

manipulation of the number of presented words (two words > one word condition) rather than

conceptual relationships. Therefore, more conceptual meanings would be retrieved and maintained

in the two-word conditions, whereby further selection processes might be involve to manipulate

the representation based on the task goals. Taken together, these findings suggest pIFG and pMTG

might be related to a common post-lexical controlled semantic process during integrative and

26 / 34

semantic priming.

The left TPJ was frequently found to be activated in varieties of semantic tasks such as

lexical-semantic and sentence comprehension (Binder et al. 2009; Price 2012). We replicated such

findings in our two localizers. In addition, previous studies have found that this region is sensitive

to the manipulation of constituting individual meanings (Pallier et al. 2011) and building semantic

association (Seghier et al. 2010). Consistent with these observation, we found that the activation

of left TPJ was sensitive to both the number of words and amount of relatedness (Related >

Unrelated), suggesting that the left TPJ may play a general role in maintaining semantic

information and be involved in semantic associations regardless of an integrative or semantic

relationship.

Separated brain circuits associated with the two types of priming effects

One novelty of our results is that we showed possible pathways associated with integrative and

semantic priming. The regions associated with the two types of priming effects were not only

partially separated in localization, but also showed different brain circuitry patterns.

Probabilistic fiber tracking showed that the left temporal regions associated with dissociated

integrative priming effect (ATL, aSTG and aIFG) were connected with each other via only the UF

running through the EC. Such fiber connection was consistent with observations from both

neurotypical adults and semantic dementia (SD) patients. In neurotypical adults, the temporal pole

regions, especially Brodmann Area (BA) 38, connects to frontal operculum (BA 47) via UF

(Friederici et al. 2006; Thiebaut de Schotten et al. 2012). Moreover, SD patients not only suffered

from ATL atrophy but also showed decreased white matter integrity of the UF that connected the

ATL and the anterior frontal regions (Agosta et al. 2009). Furthermore, most of the previous DTI

studies did not precisely define the functions of the activated region used for fiber tractography

(but see Griffiths et al., 2012 for defining syntactic processing regions). For example, Saur et al.

(2008) only used normal sentences compared to meaningless sentences to define regions of

interest that were associated with semantic processing. In contrast, here we precisely isolated

regions associated with integrative priming and characterized the functional roles of these regions

based on their neural response patterns. Our findings further extend the knowledge concerning the

UF pathway where it connects three regions (ATL – aSTG – aIFG) associated with integrative

27 / 34

priming.

In contrast, pSTG, mMTG and aSTG connect to pIFG via two language-related fiber bundles,

ventral (ECFS) and dorsal pathways (SLF/AF). Consistent with this observation, previous studies

have found that the language-related temporal cortices connect to the frontal cortices via both

ventral and dorsal pathways (Saur et al., 2008; Rolheiser et al., 2011; Dick and Tremblay, 2012;

see review in Friederici, 2012). These two fiber bundles are not limited to semantic priming but

also support other language functions. For instance, the SLF/AF fiber bundle has been associated

with speech repetition (Saur et al. 2008), complex syntactic processing (Wilson et al. 2011),

lexical-semantic processing (Glasser and Rilling 2008) and reading ability (Zhang et al. 2014).

Similarly, the ECFS has been associated with sentence-level semantic processing (Saur et al. 2008)

and ‘sound-to-meaning’ association learning (Wong et al. 2011). Here, it is possible that the left

pSTG, mMTG, aSTG and pIFG connect with each other via both the SLF/AF and ECFS forming

an interconnected language circuit supporting spreading activation of semantic information.

28 / 34

Funding

This work was supported by grants from the Natural Science Foundation of China (NSF

31271086) , and key project from the Natural Science Foundation of Guangdong Province, China..

Acknowledgments

We thank Shaowei Guan for her assistance with stimuli construction and fMRI data collection.

We also thank Erica Middleton and Tom Verguts for their constructive comments and helpful

language editing of our earlier version of the manuscript.

29 / 34

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