Cerebral Cortex, July 2016;26: 3169–3182
doi: 10.1093/cercor/bhv148
Advance Access Publication Date: 24 July 2015
Original Article
ORIGINAL ARTICLE
Separate Brain Circuits Support Integrative and
Semantic Priming in the Human Language System
Gangyi Feng1, Qi Chen1,2, Zude Zhu1,3 and Suiping Wang1,2
1
Center for the Study of Applied Psychology and School of Psychology, South China Normal University,
Guangzhou 510631, China, 2Guangdong Provincial Key Laboratory of Mental Health and Cognitive Science, South
China Normal University, China and 3Collaborative Innovation Center for Language Competence, Jiangsu Normal
University, Xuzhou 221009, China
*Address correspondence to Suiping Wang. Email: [email protected]
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 2
concepts can be combined to form a unitary representation. 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 with an unrelated condition. However, a clearly dissociated brain response was observed between these 2 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 2 distinct types of priming, illuminating
the neural circuitry organization of semantic representation and integration.
Key words: diffusion tensor imaging, integrative priming, left anterior temporal lobe, left inferior frontal gyrus, semantic
priming
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
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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 with that of in associative priming
(Estes and Jones 2009). Moreover, integrative priming is not sensitive to the manipulation of relatedness proportion (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 2 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 semanticpriming operations. Specifically, we manipulated the relationship between prime and target words in a lexical decision task
(LDT). 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 2 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), 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 ThompsonSchill 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 (EC)
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
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 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,
standard deviation [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 lexicallevel 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
Brain Circuit of Integrative Priming
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 4 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 realword target was paired 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, 4 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 2 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 10
participants who did not participate in the fMRI experiment were
recruited for another 2 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 4 stimulus
assessments. The word pairs in the integrative condition
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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; freecombination: 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 integration or
similarity properties was further confirmed by directly comparing the integration ratings with 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; F2,168 = 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; F2,168 = 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 2 runs with 1 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 LDT, 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,
8 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
2 runs. The order of the 2 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).
Table 1 Experimental design and stimuli rating scores
Conditions
Samples
Integration
Similarity
Free-combination
Free-association
Integrative
樱桃 – 蛋糕
[Cherry – Cake]
面包 – 蛋糕
[Bread – Cake]
司机 – 蛋糕
[Driver – Cake]
4.77 (0.34)*
2.15 (0.58)
0.06 (0.14)*
0.02 (0.09)
2.19 (0.57)
4.04 (0.52)*
0.03 (0.07)
0.08 (0.15)*
1.47 (0.49)
1.23 (0.21)
0 (0)
0 (0)
Semantic
Unrelated
Nonword
– 蛋糕
[Kmbol – Cake]
–
–
–
–
The rating scores marked with asterisk in one condition is significantly higher than other conditions (Ps < 0.01). The number inside the parenthesis indicates standard
deviation of the mean.
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Stimulus presentation and data collection was controlled by
E-Prime (Psychology Software Tools, Pittsburgh, PA, USA; 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, of 1, 3, or 5 s (average 3 s). Therefore, each
trial lasted 6 s on average. We recorded the participants’ response
and reaction time (RT) in each trial during the fMRI experiment.
and DTI images. To minimize signal loss and distortion in bilateral
anterior temporal lobe (ATL) regions due to the magnetic susceptibility artifact, both low echo time (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 [repetition time (TR) = 2000 ms,
TE = 20 ms, flip angle = 90°, 38 slices, field of view = 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 sequence (176 slices, TR = 1900 ms, TE = 2.53 ms, flip
angle = 9°, 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/mm2, b0) were acquired. Each volume consisted of 85
slices in the intercommissural plane, 2-mm thickness with 2mm gap, with an in-plane resolution of 2 mm and field of
view = 256 mm × 256 mm.
Functional Localizers for Predefining Language System
Functional MRI Data Analysis
To predefine brain regions associated with language processing,
we conducted 2 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 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 sentencelevel processes, in which both semantic integrative and syntactic
processing would be implicated. Details of these 2 functional localizers were descripted in the Supplementary Material.
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 slicetime 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 full width at half maximum.
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,
Subject-Level Analysis
We performed the subject-level analysis by using general linear
modeling (GLM). Design matrices of the 3 tasks (2 localizers’
tasks and the LDT) were constructed and modeled separately.
In the 2 localizers, regressors of interest corresponding to word
and nonword as well as sentence and nounlist trials were convolved with canonical hemodynamic response function (HRF)
Figure 1. fMRI task procedures and behavioral results in the lexical decision task (LDT). (A) fMRI scanning schedule (top) and the LDT task schema in the priming
experiment (bottom). Red boxes indicate the 2 language localizers; dark gray box indicates the integrative run of the priming experiment, in which no semantically
related trial was included; blue box indicates semantic run, in which no integrative trial was included; light gray box indicates diffusion tensor imaging run. (B)
Behavioral performance of each condition in the priming experiment. Bar graphs represent reaction time, while the white circles in the bottom of each bar represent
error rate for each condition. Abbreviation of the 4 conditions: I, integrative; S, semantic; U, unrelated; N, nonword. (C) Priming effect size of both integrative and
semantic priming. Error bars indicate standard error of the mean. *P < 0.05; **P < 0.01.
Brain Circuit of Integrative Priming
to build GLMs for lexical-semantic and the sentence integration
effect, respectively. In the design matrix for the LDT task, 6 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 noninterest 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 s). 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 sentencelevel integration processes (sentence–nounlist) separately.
Voxel-level P < 0.001 and cluster-level corrected P < 0.05 using
family-wise correction were applied for multiple comparison
correction. The union of these 2 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 Analysis of variance (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 2
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 1996). The activation
coordinates were reported in MNI space.
Regions of Interest 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 (ROIs) 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 6 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 2 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 was 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
Feng et al.
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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 nonbrain 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 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 anterior inferior frontal gyrus [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 5000 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 = 0.05 after Bonferroni correction
(Fig. 1B). No significant main effect (integrative, unrelated,
and nonword) of accuracy in the integrative run was observed
(F2,25 = 1.74, P = 0.19). In contrast, a marginally significant main effect of accuracy in the semantic run (semantic, unrelated, and
nonword) was found (F2,25 = 2.91, P = 0.06). 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 = 0.02)
and nonword trials (t (25) = 2.11, P = 0.05). For the RT analysis, both
incorrect response trials and trials larger than 2.5 SD of the mean
were removed. In the integrative run, a significant main effect
of RT was found (F2,25 = 6.44, P = 0.003). Similarly, a marginally
significant main effect of RT in the semantic run was observed
(F2,25 = 2.93, P = 0.06). A post hoc planned comparison showed
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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 = 0.025). Similarly,
faster response time was also found in the pseudoword condition
compared with the unrelated condition (t (25) = 3.18, P = 0.004).
In the semantic run, participants responded significantly faster
in the semantic condition than in the unrelated condition
(20 ms; t (25) = 2.91, P = 0.007), indicating a semantic-priming effect. There was no significant difference between the unrelated
and nonword condition (t (25) = 0.49, P = 0.62). Finally, we did not
observe a significant difference between the integrative-priming
and the semantic-priming effect (Fig. 1C; t (25) = 0.58, P = 0.56).
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 signalto-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 Supplementary
Fig. 1) 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 2 localizers. Figure 2A
shows the distributed network activated during the word judgment task compared with the nonword matching task. These regions included left inferior frontal gyrus, a small portion of left
anterior superior temporal gyrus (aSTG), left posterior middle
temporal gyrus ( pMTG), left tempo-parietal junctions (TPJs), posterior cingulate gyrus, precuneus, and right pMTG (see Fig. 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 ATL, left pMTG,
left TPJ, and dorsal medial superior frontal gyrus (see Fig. 2A,
right panels: sentence > nounlist). Finally, a language mask was
created by unifying these 2 localization maps (i.e., all regions activated in both contrasts were included in the mask, see Fig. 2B).
Functional Dissociation of Integrative and Semantic
Priming Effects
Figure 3 illustrates the activation distributions in both the main
effect of integrative (Fig. 3A) and semantic runs (Fig. 3B) within
the language mask. The 2 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 2 main effects. Eight regions revealed a significant semantic or integrative effect, all in
the left hemisphere. Five regions showed a significant integrative
effect, including aIFG, aSTG, ATL, pMTG, and TPJs. In contrast, another 3 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).
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 (F2,48 = 6.92; P = 0.009),
aSTG (F2,48 = 10.87; P = 0.001), ATL (F2,48 = 9.48; P = 0.003), mMTG
(F2,48 = 4.14; P = 0.043), and pSTG (F2,48 = 6.56; P = 0.012) showed
significant priming-by-condition interactions.
To further examine whether those 8 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 (Fig. 4B,C) and
further performed paired t-tests to directly compare the 2 types
Figure 2. Brain activation maps of the 2 language localizers. (A) The left panel showed the activation maps of contrast between the word judgment task and nonword
matching task (word > nonword); the right panel showed the activation map of the sentence versus nounlist condition (sentence > nounlist). R, right hemisphere; L,
left hemisphere. (B) Brain regions activated in both localizers were mapped onto a rendered brain surface. All these regions were used to construct an inclusive mask
for the subsequent analyses in the priming experiment. Green areas indicate brain regions activated in the word judgment task versus nonword matching task, while
red areas indicate brain regions activated in the sentence reading versus nounlist condition; yellow areas were the overlapping regions activated in both contrasts.
Brain Circuit of Integrative Priming
Feng et al.
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Figure 3. Brain activations associated with the integrative and semantic effects. (A) Unthresholded activation maps associates with the integrative effect (the main effect
of the 3 conditions in the integrative run). (B) Unthresholded activation maps associates with the semantic effect (the main effect of the 3 conditions in the semantic run).
The activation patterns within the language mask were projected onto rendered brain surfaces. Regions survived after the multiple comparison correction (cluster-level
P < 0.05 corrected) were labeled with black lines. Left rendered brain were displayed here. Regions abbreviation: see Table 2 for details.
Table 2 Brain regions showed significant main effects in the semantic
and integrative runs
Regions
Semantic
pIFG
mMTG
pSTG
Integrative
aIFG
aSTG
pMTG
ATL
TPJ
BA
MNI
z
Peak
F-value
Number
of voxels
x
y
44
22
22/39
−54
−64
−48
20
−26
−42
4
0
16
6.98
10.88
10.18
36
44
145
47
38
21
38
39
−46
−52
−64
−38
−42
26
12
−58
12
−70
−8
−18
0
−34
26
8.21
11.32
8.07
11.03
9.92
304
–
33
60
180
aIFG, anterior inferior frontal gyrus; pIFG, posterior inferior frontal gyrus; aSTG,
anterior superior temporal gyrus; pSTG, posterior superior temporal gyrus;
mMTG, middle part of middle temporal gyrus; pMTG, posterior middle temporal
gyrus; ATL, anterior temporal lobe; TPJ, temporal–parietal junctions. All these
regions were located in left hemisphere.
of priming effect (i.e., integrative–unrelated vs. semantic–unrelated, or unrelated–integrative vs. unrelated–semantic). We
found 3 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 3 regions for the integrative
compared with 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 with the unrelated trials
in these 2 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 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 (Supplementary Table 1). Specifically, the fMRI priming
effect in both the aIFG and ATL were exclusively associated
with the behavioral integrative-priming effect, while both the
pSTG and mMTG were exclusively associated with semanticpriming 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 (F2,25 = 4.49, corrected P = 0.03), pMTG
(F2,25 = 4.43, corrected P = 0.04), and TPJ (F2,25 = 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 with
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 with the
pIFG and pMTG. Significant increased response in the related
condition compared with the unrelated one (t (25) = 4.34, corrected
P < 0.001) was found. Similarly, increased response was found in
the nonword condition compared with 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,
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Figure 4. Results of the ROI analysis. (A) regions in red showed the dissociated integrative priming effect, while regions in green showed the dissociated semantic-priming
effect; regions in light gray only showed main effect of relatedness regardless whether the prime–target relationship is integrative or semantic. Each region labeled an
Arabic number corresponding to the number at the top of each bar graph. (B) ROI analysis for each labeled region. The beta estimates of each condition in the
integrative run were displayed in the bar graphs in dark gray, while the beta estimates in the semantic run were displayed in the bar graphs in blue. Red bold lines
under the bar graphs indicated regions showed the dissociated integrative-priming effect; green bold lines indicated regions showed the dissociated semanticpriming effect. The red asterisk represents the integrative (or the semantic) condition is significantly different from the unrelated condition. The black asterisk
represents the nonword condition is significantly different from the unrelated conditions. (C) Response patterns of the 3 regions showed significant main effect of
relatedness. Here, we combined the integrative and semantic condition as the related condition (I+S) and combined the unrelated and nonword condition from the 2
runs, respectively. The black asterisks represent the marked condition was significantly different from another 2 conditions. I, integrative; S, semantic; U, unrelated;
N, nonword condition. *P < 0.05; **P < 0.01 corrected for multiple comparisons.
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 (Supplementary Fig. 2).
To further verify whether the free-combination ratings could
also account for the observed brain activations, we performed
Brain Circuit of Integrative Priming
another parametric modulation analysis by using the free-combination ratings as the only regressor in the GLM. 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 3 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 (Fig. 5A,B). Specifically, the
integrative-priming regions in temporal lobe (aSTG and ATL)
connected to the aIFG through UF via the EC (Fig. 5A). In contrast,
the semantic-priming regions in temporal lobe (mMTG and
pSTG) connected to the pIFG by both the ECFS and the SLF/AF
(Fig. 5B). Figure 5C summarizes 2 distinct fiber bundles associated
with the integrative-priming and semantic-priming effects,
respectively.
Discussion
We found increased activation in the left aSTG, aIFG, and ATL
when 2 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
Feng et al.
| 3177
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 semanticpriming regions were separated (Fig. 6). 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 BOLD) activity to the
behavioral priming. Theoretically, 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 2 behavioral
priming effects, and further uncovered that the integrative
strength and semantic association might be 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 with 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
Figure 5. Probabilistic tractography results. (A) Fiber connections from the left tempo-parietal integrative-effect regions ( pMTG, TPJ, ATL, and aSTG) to the aIFG were
identified separately. These fiber bundles included the uncinate fasciculus (UF) and extreme capsule fiber system (ECFS). (B) Fiber bundles from the pMTG, pSTG,
mMTG, and aSTG to the pIFG were identified separately. These fiber bundles included the ventral (ECFS) and dorsal (arcuate fascile, AF) pathways. (C) Three regions
(ATL, aSTG, and aIFG) associated with the dissociated integrative-priming effect were connected with each other only via UF (red areas), while the 2 regions ( pSTG
and mMTG) associated with the dissociated semantic-priming effect connected to the pIFG via both ECFS and AF (blue areas).
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Figure 6. Functional descriptions of the 2 brain circuits based on the results of the functional brain activations and DTI tractography in the present study.
condition relative to the unrelated condition was found in the left
aSTG, aIFG, and ATL. The involvement of these 3 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, 2013). In our
localization experiment, we also replicated the findings using
the contrast of sentences versus nounlists (Fig. 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), 2 critical components, the complementary role activation and 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, 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, 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 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 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 with 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 with
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 ThompsonSchill 2015; Lambon Ralph 2014). This “convergent representation”
hypothesis implies that ATL might play a role in representing semantic relationships between concepts and their features. Indeed,
Brain Circuit of Integrative Priming
Feng et al.
| 3179
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 2015). 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).
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).
Posterior Temporal Regions Associated With
Semantic-Priming Effect
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 (2 words > 1 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 postlexical controlled semantic
process during integrative and 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.
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, Krach, 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 with 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 associationbased semantic activation. Consistent with this interpretation,
we observed 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
Common Effect During the Semantic and
Integrative-Priming
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.
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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 with 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
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 sentencelevel 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.
Supplementary Material
Supplementary material can be found at: http://www.cercor.
oxfordjournals.org/.
Funding
This work was supported by grants from the Natural Science
Foundation of China (31271086 and 31300834), and key project
from the Natural Science Foundation of Guangdong Province,
China.
Notes
We thank Shaowei Guan for her assistance with stimuli preparation 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. Conflict
of Interest: None declared.
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