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Human Brain Mapping 00:00–00 (2017)
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Neural Correlates of Fine-Grained Meaning
Distinctions: An fMRI Investigation of Scalar
Quantifiers
Jiayu Zhan,1,2 Xiaoming Jiang,1,2 Stephen Politzer-Ahles
Xiaolin Zhou 1,2,4,5*
,3 and
1
School of Psychological and Cognitive Sciences, Peking University, Beijing, 100871,
China
2
Key Laboratory of Computational Linguistics (Ministry of Education), Peking University,
Beijing, 100871, China
3
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University,
Kowloon, Hong Kong, China
4
Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, 100871,
China
5
PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871,
China
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Abstract: Communication involves successfully deriving a speaker’s meaning beyond the literal expression.
Using fMRI, it was investigated how the listener’s brain realizes distinctions between enrichment-based meanings and literal semantic meanings. The neural patterns of the Mandarin scalar quantifier you-de (similar to
some in English) which implies the meanings not all and not most via scalar enrichment, with the specific quantifier shao-shu-de (similar to less than half in English) which lexico-semantically encodes the meanings not all
and not most, were compared. Listeners heard sentences using either quantifier, paired with pictures in which
either less than half, more than half, or all of the people depicted in the picture were doing the described activity; thus, the conditions included both implicature-based and semantics-based picture-sentence mismatches.
Imaging results showed bilateral ventral IFG was activated for both kinds of mismatch, whereas basal ganglia
and left dorsal IFG were activated uniquely for implicature-based mismatch. These findings suggest that
resolving conflicts involving inferential aspects of meaning employs different neural mechanisms than the
processing based on literal semantic meaning, and that the dorsal prefrontal/basal ganglia pathway makes a
contribution to implicature-based interpretation. Furthermore, within the implicature-based conditions, different neural generators were implicated in the processing of strong implicature mismatch (you-de in the context
of a picture in which “all” would have been true) and weak implicature mismatch (you-de in the context of a
Additional Supporting Information may be found in the online
version of this article.
Jiayu Zhan, Xiaoming Jiang, and Steven Politzer-Ahles contributed
equally to this study.
Contract grant sponsor: Natural Science Foundation of China;
Contract grant number: 31470976; Contract grant sponsor: Social
Science Foundation of China; Contract grant number: 12&ZD119;
Contract grant sponsor: Ministry of Science and Technology of
China; Contract grant number: 2015CB856400 (to Xiaolin Zhou)
C 2017 Wiley Periodicals, Inc.
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*Correspondence to: Xiaolin Zhou, School of Psychological and
Cognitive Sciences, Peking University, 5 Yiheyuan Road, Beijing
100871, China. E-mail: xz104@pku.edu.cn
Received for publication 13 July 2016; Revised 13 April 2017;
Accepted 17 April 2017.
DOI: 10.1002/hbm.23633
Published online 00 Month 2017 in Wiley Online Library
(wileyonlinelibrary.com).
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picture in which “most” would have been true), which may have important implications for theories of
C 2017 Wiley Periodicals, Inc.
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pragmatic comprehension. Hum Brain Mapp 00:000–000, 2017.
Key words: scalar implicature; pragmatics; semantics; picture–sentence verification; fMRI
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of the linguistic rules they violate—that is, it is not the
case that one violation is truly semantic and the other
truly pragmatic. Rather, both are violations of world
knowledge, and they differ only in level of granularity
[see, for example, Pylkk€anen, et al., 2011]: The Dutch trains
are white conflicts with the rather specific world knowledge
that Dutch trains are yellow in the world in which we live
(in 2004), and The Dutch trains are sour conflicts with the
broad world knowledge that trains are not edible in the
world in which we live. This study, therefore, does not
distinguish between the neural processing of semantic (literal, linguistically inherent) and pragmatic (socially
inferred) meaning. Adopting another approach, Shibata
et al. [2011] compared neural responses to indirect replies
(e.g., “What did you think of my presentation?”—“It’s hard to
give a good presentation”) and literal statements (e.g., “what
do you think of my oil painting?”—“Your painting is very
good”), and found that a frontotemporal network was activated in both conditions for contextual mismatch detection, whereas the medial frontal cortex was activated only
for indirect reply to generate the inference to make sense
of remarks. However, the scenario information, and the
following implied or stated meaning, of the indirect reply
and the literal statement are not matched across conditions. Hence this study did not compare pragmatic and
semantic aspects of meaning under maximally similar
contexts.
A linguistic phenomenon that offers a powerful means
for comparing pragmatic and semantic aspects of meaning
is scalar implicature, like the above example some of the children are riding bikes and its implicature that not all of the
children are riding bikes. Scalar implicatures introduce an
enriched, putatively pragmatic, aspect of meaning that is
distinct from semantic meaning in ways that are linguistically motivated and theoretically explicit. Moreover, its
pragmatic and semantic meaning are maximally similar in
structure and illocutionary force—for example, both the
enriched meaning “not all of the children are riding
bicycles” and the semantic meaning “more than zero of
the children are riding bikes” share roughly the same
structure and both are indicative statements, as opposed
to many classical examples of pragmatic meaning (e.g., the
statement It’s hot here and its implication “Turn on the air
conditioner!,” which differs substantially in both structure
and illocutionary force). While there is substantial debate
over whether scalar implicatures are actually derived
pragmatically as opposed to syntactically [e.g., Chierchia
et al., 2012], it is uncontroversial that the enriched meaning (e.g., “not all”) is derived in a qualitatively different
way than the lexico-semantic meaning (e.g., “more than
INTRODUCTION
The complexity of human communication is one of the hallmarks of our species. A striking demonstration of the sophisticated nature of our communication system is the distinction
between so-called “sentence meaning” (i.e., meaning that is
realized by retrieving the semantic meanings of lexical items
from long-term memory and combining them based on compositional constraints) and “speaker meaning” (i.e., meaning
that is realized by performing pragmatic inferences to recognize what a speaker intends to convey). The inference from
sentence meaning to speaker meaning is guided by the expectation that the speakers tailor their utterance to be optimally
relevant to the conversational situation, during which any
departures from this relevance drive the listener to infer additional meaning [Grice, 1975; Levinson, 2000; Sperber and Wilson, 1995; Wilson and Sperber, 2004]. For example, although
the statement “some of the children are riding bicycles” semantically conveys an inherent, existential meaning that is consistent with all of the children are riding bicycles (i.e., the fact a
nonzero number of children are riding bicycles does not rule
out the possibility that some and in fact all of the children are
riding bicycles), it still often drives the listener to infer that
only some, and not all, of the children are riding bicycles. This latter implicature that arises is the negation of a stronger alternative statement, all, that the cooperative speaker could have
been made but did not [Geurts, 2010; Grice, 1975; Horn,
1972]. As these different forms of meaning (literal and
inferred) have different linguistic and representational status
[for instance, inferences are defeasible and literal meaning is
not; see Geurts, 2010, among others], a topic of particular
interest in language processing is how the neural mechanisms
underlying the effective derivation of implicated pragmatic
meaning can be distinguished from those underlying the
interpretation of literal semantic meaning in sentence
comprehension.
Several studies have directly compared the neural processing of pragmatic versus semantic meaning, although
many did not fully separate various cognitive demands
from the pragmatic/semantic manipulation. In a classic
study, Hagoort et al. [2004] compared neural responses to
so-called semantic violations (The Dutch trains are sour) and
so-called pragmatic violations (The Dutch trains are white—
in reality, Dutch trains were yellow at the time the experiment was conducted), and found both types of sentences
activated left inferior frontal gyrus (IFG), reflecting the
greater cost of unifying the unexpected or mismatched
input into the sentential context. In this study, however,
such violations do not actually qualitatively differ in terms
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Neural Correlates of Fine-Grained Meaning Distinctions
penguins are on the bus) and some-based scalar implicature mismatch, and found two conditions activated similar
brain regions (i.e., middle frontal gyrus and medial frontal
gyrus). However, it should be noted that the extent to
which number processing differs from quantifier processing is still under debate [e.g., Breheny, 2008; Carston, 1988;
Geurts, 2006; Spector, 2013].
The present study aimed to compare the processing of
the implicature-based and semantic-based aspects of a scalar quantifier against that of a maximally similar quantifier
whose upper bound (i.e., “not all” interpretation) is based
on the literal semantic meaning. To accomplish this, we
focused on the Mandarin quantifiers you-de (approximately
“some of”) and shao-shu-de (approximately “less than half
of”). While the “not all” interpretation of you-de is based
on a scalar implicature, the “not all” interpretation of shaoshu-de is explicitly semantically encoded. One critical piece
of evidence for this distinction is that the “not all” interpretation implied by you-de or some can be revised or cancelled without resulting in a nonsensical sentence [e.g.,
Some of the students passed this exam. In fact, all of them did;
Doran et al., 2012; Rullman and You, 2006], while the “not
all” meaning that is semantically encoded by shao-shu-de
or less is uncancellable (e.g., *Less than half of the students
passed this exam. In fact, all of them did).
Based on the studies reviewed above, we expect that
implicature mismatch may recruit the left middle frontal
gyrus and the medial frontal gyrus indexing successful
implementation of meaning enrichment [Shetreet et al.,
2014a,b]. Conflict between scalar quantifier and contextual
quantity may also activate regions related to inhibition
and executive control, such as the right IFG [Li et al., 2014;
Nieuwland, 2012; Ye and Zhou, 2009a,b] and the basal
ganglia (BG) [Mestres-Misse et al., 2014], as well as the left
LIFG that supports the meaning unification [Hagoort,
2005]. For the semantic meaning mismatch, previous studies showed that numeric mismatch (e.g., listening to three
penguins are on buses while seeing a picture in which all of
the penguins are on buses) engendered brain regions similar to the scalar mismatch [e.g., listening to some of the penguins are on buses while seeing a picture in which all of the
penguins are on buses; Shetreet et al., 2014b]. Hence, we
expect that bilateral IFG will also be activated in dealing
with semantic mismatch [Hagoort et al., 2004; Ni et al.,
2000; Tesink et al., 2009] for conflict resolution and semantic unification. However, since no context-appropriate
meaning is available in the mental lexicon for the specific
quantifier you-de and no successful switching could take
place in the semantic mismatch condition, no activation of
the BG was predicted for this condition.
An additional goal of the present study was to test for
more fine-grained distinctions between implicature-based
interpretations than what has been done in previous neurolinguistic experiments. Specifically, in addition to examining neural responses to the “not all” interpretation of
some, we also examined responses to the potential “not
zero”). Furthermore, it is likely that both pragmatic and
syntactic operations are involved in the derivation of scalar implicatures [Chemla and Singh, 2014]. In the following, we will refer to the “not all” interpretation of a
quantifier like some as “implicature-based” to avoid making a commitment to pragmatic versus semantic accounts
of scalar implicature realization (while it is possible under
a semantic account that this meaning is not based on
implicatures and inferences per se, but rather on semantic
operations, here we use it as a catch-all term to refer to
the enriched aspect of meaning that is putatively not the
core lexical meaning).
Several recent studies have examined the processing of
implicature versus semantic aspects of scalar implicatures
using neurolinguistic methods. Using a picture–sentence
verification paradigm and event-related potentials (ERP)
technique, Politzer-Ahles et al. [2013] compared auditory
sentences beginning with a Mandarin scalar quantifier
(you-de, roughly equivalent to English some of) which were
preceded by a picture describing an action that was performed by not all versus all characters (three out of six
girls were sitting on a blanket, or six out of six girls were
sitting on a blanket), creating conditions that either were
matched or mismatched because of the scalar implicature.
At the onset of the scalar quantifier, different neural
responses were elicited depending on whether
picture–sentence mismatch was implicature-based or
semantic-based. When the implicature-based interpretation
of the quantifier was inconsistent with the context, a sustained broadly distributed negative component was elicited, which suggested a pragmatic reanalysis: inhibiting
the enriched interpretation of some and strengthening the
core lexical reading. Using similar constructions in
English, Shetreet et al. [2014a] conducted an fMRI study
and found that the left middle frontal gyrus and the
medial frontal gyrus were activated by implicature mismatch (i.e., some-related mismatch). In these two studies,
however, the semantic control mismatch, against which
the some-related mismatch were compared, were every sentences—for example, a picture in which some children are
riding bikes and some are not, paired with a sentence
every child is riding a bike. While such a sentence is unambiguously a semantic mismatch, it differs from the somerelated mismatch in ways other than scalar implicature;
every and some have different denotations and possibly different verification strategies [see Politzer-Ahles and Gwilliams, 2015 for discussion]. It would be preferable to
compare the processing of the implicature-based “not all”
interpretation of some to that of a word which also
expresses “not all” but does so without scalar implicatures, for instance, the comparison between processing of
some and that of only some [Bott et al., 2012; Hartshorne
et al., 2014; Marty and Chemla, 2013; Politzer-Ahles and
Gwilliams, 2015]. Recently, Shetreet et al. [2014b] directly
compared number-based semantic mismatch (e.g., three
penguins are on the bus, paired with a picture in which five
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TABLE I. Experimental design
Conditions
Strong implicature
mismatch
Weak implicature
mismatch
Scalar quantifier
match
Strong semantic
mismatch
Weak semantic
mismatch
Specific quantifier
match
Picture examples
All children are
riding bicycles
Six of seven children
are riding bicycles
Two of seven children
are riding bicycles
All children are
riding bicycles
Six of seven children
are riding bicycles
Two of seven children
are riding bicycles
Sentence examples
Picture in has seven children, you-de children are riding bicycle
There are six children in the picture, some children are riding bicycles
Picture in has seven children, shao-shu-de children are riding bicycle
There are six children in the picture, less than half of the children are riding bicycles
written informed consent, and the study was approved by
the Ethics Committee of the Department of Psychology at
Peking University.
most” interpretation. While some previous studies failed
to show online processing consequences of this interpretation in Mandarin [Politzer-Ahles, 2012, 2014], there is both
a strong intuition and strong empirical evidence that some
elicits such an interpretation. For example, in a naturalness
rating task in English [Degen and Tanenhaus, 2015; see
also Newstead, 1988], some was considered most natural
when describing subsets of slightly less than half of a
superset (e.g., five or six out of thirteen), less natural when
describing subsets close to the full superset (e.g., twelve
out of thirteen), and least natural when describing the
whole set (thirteen out of thirteen). Moreover, under a traditional alternatives-based account of how scalar inferences are derived [e.g., Levinson, 2000], it would be
predicted that such an interpretation would be realized, as
long as most is a relevant alternative to some in the same
way that all is. Such an account would, furthermore, predict that the not most and not all inferences are realized in
a qualitatively similar way, as they would be derived by
the same mechanism, even though they could show quantitative dissimilarities based on differences in the salience
or relevance of the alternatives all and most. In the present
study, therefore, we test both types of inferences, by creating “weak” and “strong” mismatch conditions in the
experimental design, to see if they are processed in similar
ways. The comparison between the conditions would also
help address the question of whether the some-as-“not all”
inference is representative of pragmatic processing in general, or different inferences are processed differently.
Design and Materials
A total of 108 critical sentences were created. Sentences
began with a scalar quantifier (you-de) or a specific quantifier (shao-shu-de) stated a general description of the quantities of characters or objects in the corresponding pictures.
Sentences were recorded by a female native Chinese
speaker, and intensity was normalized using CoolEdit
(Syntrillium Software) to 70 dB. Pictures were used to
establish the context for the sentences.
Each sentence was preceded by a simple picture containing six or seven characters or objects. There were three
types of pictures, based on the proportion of characters or
objects that met the description in the following sentence.
In less-than-half pictures, two of six/seven individuals had
the same property stated in the sentence, and thus these
were congruent with a following you-de sentence (the
Match condition); in most pictures, five of six, or six of
seven individuals had the same property stated in the sentence, making the picture weakly mismatched with a following you-de sentence (the Weakly Mismatch condition);
and in all pictures, all individuals had the same property
stated in the sentence, making the picture strongly mismatched with a following you-de sentence (the Strongly
Mismatch condition). Note, while we did not consider
these mismatch conditions were fundamentally different
in the case of shao-shu-de sentences, we named them this
way in accordance with the extent of mismatch on the
pragmatic scale. The difference between the implicature of
you-de and the meaning of “most” is considered to be
smaller or weaker than the difference between the implicature of you-de and the meaning of “all.” Factorially crossing sentence type and picture type yielded six conditions
(Table I). Crucially, we expect effects related to scalar
implicature processing to appear in the you-de sentences
(in which the upper bound of the quantifier must be
METHOD
Participants
Thirty-six university students (19 women, ages: 18–25,
mean: 22.1) participated in the experiment. They were
native Chinese speakers without neurological or psychiatric disorders. None of them suffered from any hearing or
language disorders. All were right-handed with normal or
corrected-to-normal vision. All participants provided
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Figure 1.
(A) demonstrates the experimental procedure. (B) shows the behavioral results for each critical
condition in the online picture–sentence consistence rating. Error bars represent 61 standard
error of the mean.
inferred via pragmatics or enrichment) and not the shaoshu-de sentences (in which the upper bound of the quantifier is semantically explicit).
The critical stimuli were assigned into six test versions
using a Latin square design. Six conditions created from the
same scenario (i.e., children are riding bicycles) were split
into different versions. Eighteen scenarios of utterancepicture pairs were generated. The all/most pictures always
mismatched quantity information inferred or stated in the
sentences, and less-than-half pictures were always matched.
As fillers, we additionally created 84 picture–sentence pairs
to prevent participants from predicting their response by
judging from the quantity information in the pictures. Among
these, 20 were all pictures paired with suo-you-de (similar to
all in English) sentences, 20 were most pictures that paired
with duo-shu-de (similar to most in English) sentences, and 20
were less-than-half pictures that paired with suo-you-de or duoshu-de sentences. Another 24 picture–sentence pairs were
included that had matched quantifiers but with an object that
did not match any of the objects in the picture, or a verb that
did not match the activity shown. This manipulation encouraged the participants to deploy their attention evenly to each
part of the sentence. The same fillers were used in all
versions. Altogether there were 192 picture–sentence pairs
in each version, and the order of the pairs was pseudorandomized, with the restriction that no more than three consecutive trials were from the same condition. Participants
were randomly assigned to one of the versions and gender
was counterbalanced across versions.
its duration from 2,800 to 4,000 ms. Following a varied
period of time interval of 1,000–2,000 ms after the disappearance of the fixation point, a 1–7 scale was shown on
the screen and lasted for 5,000 ms. Participants were asked
to rate the extent to which the sentence was matched with
the preceding picture and the end of the scale indicating
matched or not was counterbalanced across participants
(1 5 very matched, 7 5 very mismatched). The interval
between the disappearance of the rating screen and the
beginning of the next trial was randomized between 3,000
and 5,000 ms, with a fixation point presented on the
screen.
The fMRI scan was divided into two sessions, lasting
about 30 min per session. During the break between the
two sessions, participants were asked to close their eyes
and to keep their head still. At the beginning of each session, a fixation cross was displayed for 10 s to allow the
scanner to reach stability. Before entering the scanner, all
the participants completed a practice session following the
same procedure as the formal test.
Data Acquisition
Functional images were acquired on a 3-T Siemens Trio
system, using a T2*-weighted echo planar imaging (EPI)
sequence, with 2,200 ms repetition time, 30 ms echo time,
and 908 flip angle. Each image consisted of 32 axial slices
covering the whole brain. Slice thickness was 3 mm and
inter-slice gap was 0.75 mm, with a 220 mm field of view
(FOV), 64 3 64 matrix, and 3.4 3 3.4 3 3.4 mm3 voxel
size. Head motion was minimized using pillows and cushions around the head and a forehead strap.
PROCEDURE
Each trial began with a fixation point presented at the
center of the screen for 500 ms, followed by the picture
display, which was presented at the center of the screen
for 4,000 ms (Fig. 1A). When the picture disappeared, a
fixation point appeared on the screen for another 500 ms
and then the auditory sentence was presented while the
fixation point remained on screen. The sentence varied in
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Data Analysis
Data were pre-processed with Statistical Parametric
Mapping (SPM) software SPM8 (Wellcome Department of
Imaging Neuroscience, London, http://www.fil.ion.ucl.ac.
uk). The first five volumes were discarded to allow
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Zhan et al.
mismatch > implicature match), (3) (weak implicature
mismatch > implicature match) > (weak semantic mismatch > semantic match), and (4) (weak semantic
mismatch > semantic match) > (weak implicature mismatch > implicature match). The overlap between the brain
activation of implicature and semantic mismatch were also
investigated by conducting two conjunction analyses:
(1) (strong implicature mismatch > implicature match) \
(strong semantic mismatch > semantic match), and (2) weak
implicature mismatch > implicature match) \ (weak semantic mismatch > semantic match). Brain regions survived
with voxel-level threshold of P < 0.001 uncorrected and a
cluster-level threshold of P < 0.05, FWE (family-wise error)
corrected for multiple comparisons. The corresponding
contrasts for above comparisons survived with voxel-level
threshold of P < 0.001 uncorrected and a cluster-level threshold of P < 0.05, FWE corrected for multiple comparisons.
To examine how specific activations are associated with
the behavioral rating, we extracted the beta value in four
regions of interest (ROIs, i.e., ventral LIFG, dorsal LIFG,
RIFG, and BG) based on the contrast in above GLM. The
ventral LIFG and RIFG were activated in all mismatch
condition. The BG and dorsal LIFG were specifically activated in implicature-based mismatch conditions where the
former one was for both implicature-based mismatches
and the latter one was only for weak implicature-based
mismatch. Pearson correlation was conducted to examine
the association between brain activation and the behavioral response in the same contrast.
In addition to above factorial design, we also performed a
parametric analysis to further reveal the brain regions manipulated by the mismatch level (strong > weak > match, and
vice versa), independently for implicature-based condition
and semantic-based condition. This analysis would reveal the
neural activity underlying the fine-grained distinctions (in
terms of the extent of mismatch on the semantic or pragmatic
scale) between semantic-based interpretations or between
implicature-based interpretations. In the GLM model for this
parametric analysis, we included four regressors for the picture presentation (implicature condition, semantic condition,
filler, and outlier and nonresponse trial). Then we included
four regressors for the sentence presentation (implicature
condition, semantic condition, filler, and outlier and nonresponse trial). Importantly, the sentence regressors of implicature and semantic conditions were additionally accompanied
by parametric regressors containing the design manipulated
mismatch level. Rating regressor was also included and
accompanied by the number of button-press in the trial. Brain
regions survived in parametric analysis with voxel-level
threshold of P < 0.001 uncorrected and a cluster-level threshold of P < 0.05, FWE corrected for multiple comparisons.
stabilization of magnetization. The remaining images were
time sliced and realigned to the sixth volume of the first
session for head movement. A temporal high-pass filter
with a cutoff frequency of 1/128 Hz was used to remove
low-frequency drifts in the fMRI time series, and the mean
functional image for each subject was coregistered to the
EPI template provided by SPM8. Images were anatomically normalized to the MNI space (resampled to 2 3 2 3
2 mm3 isotropic voxel) by matching gray matter [Ashburner and Friston, 2005], and smoothed with a Gaussian
kernel of 6 mm full-width half-maximum (FWHM). No
participants’ head movement exceeded 3 mm.
Statistical analysis was based on the general linear model
(GLM). The hemodynamic response to each event was
modeled with a canonical hemodynamic response function.
We defined seventeen regressors: eight for the picture presentation, eight for the sentence presentation, and one for
the rating. The rating-related regressors were additionally
accompanied by parametric regressors containing the
number of button-press in a trial. For both the picture and
sentence presentation regressors, six were defined as the
conditions of interest (i.e., strong implicature mismatch,
weak implicature mismatch, implicature match; strong
semantic mismatch, weak semantic mismatch, semantic
match), one as the filler condition, and one as the nonresponse trials and outliers (which fell outside the range of
mean 6 2.5 * SD). The six sentence regressors were defined
as regressors of interest. Six rigid body parameters were also
included to correct for the head motion artifact. The onset of
the regressors of interest was set to the onset of each auditory sentence. To pinpoint regions significantly activated for
the conditions of interest, we first calculated the simple
effects in each condition. The first-level individual images of
six conditions of interest were then fed to a flexible factorial
repeated measures analysis of variance in the second-level
design matrix.
Firstly, we are interested in the brain activity for strong
and weak implicature mismatches and the two corresponding semantic mismatches independently. To this
end, we defined two contrasts between each individual
level of mismatch for the you-de condition (i.e., strong
implicature mismatch vs. implicature match, weak implicature mismatch vs. implicature match) and the shao-shude condition (strong semantic mismatch vs. match, weak
semantic mismatch vs. match). We also defined three
main contrasts of mismatch and two main contrasts of
mismatch type by collapsing across quantifier (i.e., strong
and weak mismatch vs. match, strong mismatch vs. match,
weak-mismatch vs. match, strong vs. weak mismatch, weak
vs. strong mismatch).
Then we directly examined the difference between
the neural representation of implicature and semantic
mismatch, by defining four interaction contrasts: (1) (strong
implicature
mismatch > implicature
match) > (strong
semantic mismatch > semantic match), (2) (strong
semantic mismatch > semantic match) > (strong implicature
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Psychophysiological Interaction Analysis
Psychophysiological interaction (PPI) analysis is used to
investigate the physiological connectivity between two
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Neural Correlates of Fine-Grained Meaning Distinctions
type affected compatibility rating more for implicature
mismatch than for semantic mismatch.
Previous studies have classified the participants into
pragmatic and semantic responders [Bott and Noveck,
2004; Noveck and Posada, 2003; Tavano, 2010]. Response
distribution across participants in terms of subjective mismatch rating (mean across trials) under the strong and the
weak implicature mismatch condition are shown in Supporting Information Figure S1A (see Supporting Information Materials), suggesting no clear distinction of logical
and pragmatic responder in our participants group [Spotorno et al., 2015]. Performance data showing the rating
distribution across trials based on all participants in six
critical conditions can be found in Supporting Information
Figure S1B (see Supporting Information Materials).
brain regions that is varied with the psychological context
[Friston et al., 1997]. Here we were interested in the connectivity that is modulated by implicature and semantic
mismatch. Firstly, we chose the regions shared by the
implicature and semantic mismatch (i.e., ventral LIFG,
BA47; RIFG, BA45/47) as the seed regions and searched in
the whole brain for regions whose functional connectivity
with these seed regions was modulated by implicature
and semantic mismatch, respectively. Secondly, we chose
brain regions which were only involved in implicature
mismatch contrasts (i.e., dorsal LIFG for weak mismatch,
BG for both strong and weak mismatch) as seed regions
and investigated the physiological connectivity that was
varied only within the context of implicature mismatch.
For semantic mismatch, no region was activated specifically. Moreover, we were interested in identifying target
regions whose change in connectivity with the seed
regions were modulated by behavioral performance. Here,
the difference in mismatch rating between four types of
mismatch and their respective matched sentences in a
certain sentence type over all trials was computed as
an index to reflect the severity of the mismatch the
participants perceived.
fMRI Data
General linear model
For the main effect of mismatch type (by collapsing the
strong and weak mismatch over quantifiers), the mismatch
conditions, compared with the match conditions, evoked
greater activity in the left ventral IFG (BA47), right IFG
(BA45/47), bilateral BG (caudate), and left lingual (BA18;
Table II).
As we were interested in the differential activations
associated with different types of mismatch, the mismatchtype-specific pattern was revealed by contrasting strong
and weak mismatch conditions with the corresponding
matched controls in both quantifier types respectively
(Table II). Compared with the matched controls, the strong
implicature mismatch activated left ventral IFG (BA47),
right IFG (from ventral to dorsal part, BA45/47), bilateral
BG (putamen/caudate), left lingual (BA18), and right
visual regions (inferior/middle occipital gyrus, fusiform;
BA19); the weak implicature mismatch activated a similar
network including left ventral IFG (BA47), right IFG
(BA44/45), and bilateral BG (putamen/caudate), and in
addition, the dorsal LIFG (BA45). As for the implicature
mismatch, both the strong and weak semantic mismatches
activated left and right ventral IFG (BA47) compared with
the matched controls, and the strong mismatch additionally activated right Angular gyrus (BA39). Direct comparison between strong and weak implicature mismatch
showed that the right occipital gyrus (BA19) was activated
under strong over weak mismatch; and the left dorsal IFG
(BA45), left inferior temporal gyrus (BA37), and left middle occipital gyrus were activated by weak over strong.
For semantic mismatch, we found more activation in right
middle frontal gyrus (BA9), supramarginal gyrus (BA40),
angular gyrus (BA39), precuneus (BA23), and occipital
gyrus (BA19) for strong than weak mismatch, and more
activation in left IFG/precentral gyrus (BA44) and middle
occipital gyrus (BA7) for weak than strong mismatch.
Further interaction analysis in four ROIs (i.e., left ventral
IFG, left dorsal IFG, RIFG, and BG) showed the activation
RESULTS
Behavioral Data
Figure 1B shows the average consistency rating between
the visual context and the sentence over the 36 participants. A repeated measure ANOVA was conducted with
both quantifier type (implicature-based you-de vs.
semantic-based shao-shu-de) and mismatch type (strong
mismatch vs. weak mismatch vs. match) as withinparticipant factors. The main effect of quantifier type was
significant, F (1, 35) 5 95.79, P < 0.001, with the compatibility in the implicature based (you-de) (mean 5 5.15,
SE 5 0.30) significantly higher than the rating in the
semantic-based (shao-shu-de) group (3.71 6 0.30). There was
also a significant main effect of mismatch type, F (2,
34) 5 264.06, P < 0.001, suggesting that the compatibility
increased from the strong mismatch to the weak mismatch
to the match conditions (3.17 6 0.25 vs. 3.65 6 0.17 vs.
6.47 6 0.36), with uncorrected P < 0.001 between each pair
of conditions. Importantly, the interaction between quantifier type and mismatch type was significant, F (2,
34) 5 103.01, P < 0.001. For you-de sentences (implicature
mismatch), compatibility monotonically increased from the
strong mismatch to the weak mismatch to the match condition (all Ps < 0.001). For the shao-shu-de (semantic mismatch) sentences, on the other hand, strong and weak
mismatch were both rated as less consistent than congruent sentences (Ps < 0.001) but were not quite significantly
different from one another (uncorrected P 5 0.017, Bonferroni a 5 0.016). These results indicate that the mismatch
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TABLE II. Brain regions revealed by all contrasts of simple effect
ALL
PRAGMATIC
SEMANTIC
Size
BA
Coordinate
Region
Size
BA
Coordinate
Region
Size
BA
Coordinate
Match
L IFG
235
11/47
224, 23, 223
L IFG*
11/47
–
18
45/47
–
11/47
–
215, 288, 217
45, 41, 22
12, 17, 22
251, 32, 22
L caudate/putamen
–
R IFG
R caudate/putamen
L IFG
48
44/45
–
47
51, 41, 13
15, 17, 1
251, 32, 22
–
–
R IFG
–
L IFG*
L caudate/
putamen*
L lingual
R SFG
R IFG
–
R angular
R IOG/MOG/
lingual
L IFG*
84
–
221, 14, 28
L caudate/putamen*
116
–
224, 14, 28
–
12
33
–
–
55
–
76
20
–
11/47
–
58
247
82
294
239, 41, 217
245, 38, 22
215, 8, 28
L IFG*
–
L lingual
R IFG
R caudate
L IFG
119
45
74
–
232
96
111
–
242, 41, 211
224, 20, 226
–
–
45, 41, 211
–
227, 29, 214
242,41, 211
–
135
117
507
–
153
196
18
8/9
45/47
–
39
18/19
221, 2100, 217
9, 38, 55
45, 41, 22
–
48, 267, 34
218, 294, 28
L lingual
–
R IFG
R caudate/putamen*
R angular
R IOG/MOG/fusiform
146
–
352
85
–
168
18
–
45/47
–
–
18/19
212, 285, 217
–
42, 41, 22
12, 17, 22
–
33, 288, 211
–
–
R IFG
–
R angular
–
–
–
170
–
106
–
–
–
47
–
39
–
–
–
42, 38, 211
–
48, 267, 34
–
21
31
–
43
11/47
L IFG*
6
47
242, 38, 214
45
–
239, 41, 217
221, 26, 223
245, 35, 16
218, 214, 28
L IFG*
L IFG
L caudate/putamen*
15
20
57
64
11/47
–
–
239, 41, 217
224, 36, 223
–
221, 17, 28
–
–
–
–
–
–
–
–
19
168
251
187
106
107
203
–
261
392
47
–
10/47
9
7
40
39
–
19
44
48, 38, 28
15, 14, 7
51, 32, 25
30, 29, 43
30, 246, 67
66, 228, 43
45, 270, 34
–
33, 285, 211
242, 20, 28
R IFG
R caudate/putamen
–
–
–
–
–
–
R MOG/IOG
L IFG
62
143
–
–
–
–
–
–
66
289
45/48
–
–
–
–
–
–
–
19
45
51, 14, 13
12, 5, 4
–
–
–
–
–
–
33, 282, 28
248, 29, 31
R IFG*
–
–
R MFG
–
R SMG
R angular
R Precuneus
R MOG/IOG
L IFG/Precentral
15
–
–
68
–
170
215
65
144
152
47
–
–
9
–
40
39
23
19
44
45, 38, 211
–
–
27, 32, 40
–
63, 231, 43
45, 270, 31
18, 258, 25
33, 282, 4
242, 8, 31
130
533
37
7
251, 255, 217
227, 255, 37
L ITG
L MOG
139
517
37
7
245, 261, 28
222, 255, 37
–
L MOG
–
186
–
7
–
227, 255, 37
Strong >
Match
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Strong >
Weak
Weak >
Strong
–
L caudate/
putamen*
R IFG*
R caudate
R IFG
R MFG
R postcentral
R SMG
R angular
–
R MOG/IOG
L IFG/
Precentral
L ITG
L MOG
Note: L, left; R, right; BA, Broadman area. * These regions were activated under ROI masks analysis.
r
Weak >
Match
–
–
47
–
47
Zhan et al.
Region
r
Mismatch >
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Neural Correlates of Fine-Grained Meaning Distinctions
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TABLE III. Brain regions revealed in interaction and conjunction analysis
CONTRAST
Interaction
Conjunction
Region
Size
BA
Implicature Strong > Implicature Match
vs.
Semantic Strong > Semantic Match
L IFG*
SMA
L putamen*
R putamen*
23
197
46
39
45/48
6
–
–
242, 26, 22
0, 24,67
218, 5, 10
18, 14, 10
Implicature Weak > Implicature Match
vs.
Semantic Weak > Semantic Match
L IFG*
L putamen*
R Thalamus
R SMG
R IPL
R ITG
R Lingual
35
12
59
104
149
58
130
45/48
48
27
40
40
20
18
242, 26, 22
233, 213, 25
21, 228, 1
54, 231, 46
30, 249, 49
51, 237, 223
18, 276, 22
Semantic Strong > Semantic Match
vs.
Implicature Strong > Implicature Match
–
–
–
–
Semantic Weak > Semantic Match
vs.
Implicature Weak > Implicature Match
–
–
–
–
60
118
11
47
224, 23, 223
48, 38, 28
–
–
–
Implicature Strong > Implicature Match
\
Semantic Strong > Semantic Match
L IFG
R IFG
Implicature Weak > Implicature Match
\
Semantic Strong > Semantic Match
–
Coordinate
Note: L, left; R, right; BA, Broadman area. * These regions were activated under ROI masks analysis.
IPL (BA7) and the mismatch level. The activation of bilateral IFGs was stronger as the mismatch was stronger
(strong implicature mismatch > weak implicature mismatch > implicature matched), and the activation of left
IPL was stronger as the mismatch was weaker (implicature
matched > weak implicature mismatch > strong implicature mismatch). For the specific quantifier shao-shu-de, we
only found a negative correlation between IPL (bilateral,
BA 7) and mismatch level. When we lowered the threshold (P < 0.005 at voxel level), there was a negative correlation between right ventral IFG (BA 7) and mismatch level.
The behavioral results showed that the weak implicature
mismatch was given higher ratings than the strong implicature mismatch (Figure 1B); this difference might be the
behavioral consequence of the neural response in the left
dorsal IFG (BA45), uniquely present in the weak mismatch
sentences (see “Discussion”). We performed a subsequent
analysis to further delineate the brain regions which were
especially activated by the weak implicature mismatch.
We first performed correlations between brain activations
of left dorsal IFG (BA45) and BG specific to implicature
mismatch. We did not find any regions activated for
semantic mismatch effect over the implicature mismatch
effect. Conjunction analysis showed the activation of bilateral ventral IFG (left BA11, right BA47) in both implicature
and semantic strong mismatches (Table III). For weak mismatch, though both implicature and semantic mismatches
activated bilateral IFG (BA47) compared with the match
conditions (Table III), we did not find any significant
regions under conjunction analysis (P < 0.001 at voxel
level). When we lowered the threshold (P < 0.005 at voxel
level), we found bilateral IFG (BA47, left voxel size 5 4,
right voxel size 5 1) activation under both weak mismatches. The type-specific effects in the regions of interest
are shown in Figure 2A. Full results based on the whole
brain and ROIs are shown in Table III.
The parametric analysis, which aimed to examine the
brain regions modulated by the mismatch level, revealed a
positive correlation between bilateral IFG (BA47) and the
mismatch level, and a negative correlation between left
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Figure 2.
(A) shows different type of mismatch effect in bilateral IFG and Basal Ganglia, and beta values in
these regions under six conditions. S, strong; W, weak; M, match. (B) reveals the correlation
between the behavioral rating difference and the beta value difference in the dorsal LIFG, under
the weak-implicature versus strong-implicature contrast.
other regions (left ventral IFG, RIFG, and BG) and behavioral ratings.
and behavioral ratings across individual participants. We
found that the difference in beta values in the left dorsal
IFG (BA45) between the weak and strong implicature mismatches was significantly and positively correlated with
the difference in ratings between these two conditions
(r 5 0.3, P < 0.05; Fig. 2B), suggesting that this region plays
a specific role in processing the weak implicature mismatch. No significant correlations were observed between
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PPI Analysis
Using RIFG as a seed region, the PPI analysis revealed
an increased functional connectivity with bilateral superior
temporal gyrus (STG) under the contrast “strong
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Neural Correlates of Fine-Grained Meaning Distinctions
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Figure 3.
PPI results under different contrast. (A) shows increased connectivity between ventral RIFG/LBG and bilateral STG, under the
strong-implicature mismatch versus implicature match contrast.
(B) shows the decreased connectivity between dorsal LIFG and
right SFG, and response-modulated increased connectivity
between dorsal LIFG and left IPL, under the weak-implicature
mismatch versus implicature match contrast. (C) shows the
increased connectivity between ventral RIFG and left STG, under
the strong-semantic mismatch versus semantic match contrast.
[Color figure can be viewed at wileyonlinelibrary.com]
implicature mismatch versus matched scalar quantifier”
(Fig. 3A, 1st row), and an increased functional connectivity
with left STG under the contrast “strong semantic mismatch versus matched specific quantifier” (Fig. 3C). Using
left BG as a seed region, the PPI analysis revealed an
increased functional connectivity with bilateral STG under
the contrast “strong implicature mismatch versus matched
scalar quantifier” (Fig. 3A, 2nd row). With left dorsal IFG
as the seed region (BA45), PPI comparing the weak implicature mismatch and matched scalar quantifier revealed a
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TABLE IV. Regions showing functional connectivity with seed regions for the contrast “Implicature Strong Mismatch vs. Implicature Match,” “Implicature Weak Mismatch vs. Implicature Match,” “Semantic Strong Mismatch
vs. Semantic Match”
Contrast
Seed region
Connectivity
Target region
Size
BA
Coordinate
Implicature Strong vs.
Implicature Match
R IFG
Increased
L STG
83
22
260, 231, 10
167
269
86
828
471
445
22
47
22/48
22/48
7/40
257, 27, 25
54, 210, 28
236, 32, 211
54, 27, 25
54, 216, 22
230, 240,43
97
200
8
22
24,20,49
266, 219, 7
L BG
Increased
Increased
Implicature Weak vs.
Implicature Match
L dorsal IFG
Increased
R STG
L ventral IFG
L STG
R STG
L IPL
Semantic Strong vs.
Semantic Match
R IFG
Decreased
Increased
R SFG
L STG
Notably, the connectivity between dorsal LIFG and left IPL were modulated by the rating difference between “Implicature Weak Mismatch
vs. Implicature Match.”
Note: L, left; R, right; BA, Broadman area. “Implicature Strong” means implicature strong mismatch, and “Implicature Weak” means
implicature weak mismatch. Coordinates a displayed in MNI system.
neural level, we found that compared with the implicature
match, the implicature mismatch, regardless of the strength,
was associated with activity in left ventral IFG (BA47), right
IFG (BA 45/47), and bilateral BG, and showed an increased
connectivity between right IFG/left BG and bilateral STG.
Moreover, left dorsal IFG (BA45) was additionally activated
in the weak implicature mismatch; this region had a
decreased connectivity with right SFG and a behaviorally
modulated increased connectivity with left IPL. For the
semantic conditions, compared with the matched control,
both strong and weak mismatch also evoked left ventral
IFG (BA47) and right IFG (BA47) activity; an increased connectivity between right IFG and left STG was also found for
strong mismatch. These findings suggest that reinterpretation of meaning in implicature and semantic failures have
both common and distinct neural substrates. Moreover, consistent with theoretical models associating sub-regions in
left IFG with different linguistic processes [Badre et al.,
2005; Friederici, 2012; Hagoort, 2005; Jung-Beeman, 2005;
Lau et al., 2008; Zhu et al., 2013; see Bookheimer, 2002;
Price, 2012; Rogalsky and Hickok, 2011 for reviews], we
revealed a division of labor between left ventral (BA47) and
dorsal (BA45) IFG. Whereas the ventral IFG was activated
for mismatch conditions regardless of the type of meaning
and may have played a domain-general role for meaning
unification, the dorsal IFG (BA45) was specific to the weak
implicature mismatch in the current study and may have
played a pragmatic-specific role for scalar implicature
realization.
Below we explore three issues related to: (1) brain areas
commonly involved in both the implicature-based and
semantic-based meaning failures and the brain network
involved in dealing with the domain-general mismatch;
(2) brain areas uniquely involved in the implicature-based
decreased functional connectivity with right superior frontal gyrus (SFG, BA8); and more importantly, the amount
of rating difference across participants between weak
implicature mismatch and matched scalar quantifier
strongly modulated the change in connectivity between
left dorsal IFG (BA45) and left inferior parietal lobe (IPL,
BA40): individuals who perceived the weak implicature
mismatch condition as more mismatched showed a more
positive change than those who perceived this condition
as more congruent (Fig. 3B, see also the PPI results in
Table IV).
DISCUSSION
The main goal of the present study was to characterize
the difference between scalar-implicature-based and
semantic-based meaning processing, and to isolate the neural correlates of these two processes in a picture–sentence
verification task. To do this, we manipulated the level of
mismatch between the quantitative information displayed
in pictures and the information either implied by a quantifier (you-de) or stated by a quantifier (shao-shu-de). Behaviorally, we found that sentences were considered less
mismatched with their corresponding pictures when the
mismatch was based on a scalar implicature rather than on
inherent semantics; more importantly, the implicature mismatch conditions showed a difference between weak and
strong mismatch that was not observed as strongly in the
semantic-based mismatch conditions. These results suggest
that implicature-inducing quantifier you-de and specific
quantifier shao-shu-de are processed differently, even though
both encoded (either via implicature or via lexical semantics) the same interpretations, not most and not all. At the
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Neural Correlates of Fine-Grained Meaning Distinctions
implicature and semantic failure in a multi-modal presentation of the context and sentence information. In face of
an infelicitous scalar inference, the frontal-temporal system
recruits right IFG to suppress a context-inappropriate
interpretation (e.g., some but not all) and strengthen the
context-appropriate information (e.g., some [at least one, up
to and including all]). The subsequent representation may
or may not be integrated with the pictorial context through
the audio-visual integration in STG, which cross-modal
integration may ease the access of the logic meaning of
some, allowing for the number information (some and possibly all) in the sentence to be re-integrated into the picture
context that was dependent on the activation of BG (see
detailed discussion later). In face of the semantic mismatch
between quantifier information and the proportion of entities shown in the visual context, the right IFG suppresses
the quantity expression of shao-shu-de, also inducing large
efforts in the cross-modal integration in STG. This effort
results in a final replacement of this violated quantifiers
with the appropriate ones that match the picture [Li et al.,
2014], or reinterpret the meaning of specific quantifier
based on the contextual quantity.
It is worth noting that, compared with the strong
semantic mismatch, there is a decreasing activation of left
ventral IFG and right IFG for weak semantic mismatch,
and no frontal–temporal connectivity was engaged in comparing weakly-semantic mismatch with matched sentences. This was surprising, as no significant difference
between the participants mismatch rating under weak and
strong semantic condition, for which we should expect for
both mismatches the same activation of left ventral IFG
and right IFG, and similar conflict resolution strategies:
the inhibition of inappropriate meaning and the replacement/reinterpretation with the appropriate ones. Indeed, a
direct comparison between strong and weak semantic mismatch shows different network involved for different mismatch (e.g., right SMG, AG for strong mismatch, left IFG/
precentral gyrus for weak mismatch). Further research is
need in order to determine what guided the behavioral
and neural processing difference between strong and weak
semantic mismatch in quantity processing.
meaning failure and the brain network involved specifically in resolving such mismatch; and (3) neural differences between strong and weak implicature mismatch
processing and their implications for theories of pragmatic
comprehension.
Domain-General Mismatch Processing: left IFG
(BA47), Right IFG (BA45/47), and Bilateral STG
In both implicature (you-de) and semantic (shao-shu-de)
conditions, the strong and weak mismatch between the
quantity information displayed in pictures and the information implied by the scalar quantifier activated left ventral IFG (BA47) and right IFG (BA45/47). Left ventral IFG
has been found to be activated in contexts that require
unifying sentence meaning based on pragmatic inference
into the sentential context [Chan et al., 2012; Tesink et al.,
2009; see also Shetreet et al., 2014a for scalar implicature],
and also implicated in sentences containing mismatch of
counterfactual [Nieuwland, 2012] or event possibility [Li
et al., 2014]. Therefore, the ventral IFG may function as a
general meaning unification: on one hand, it works for
combinatorial processing of the semantic representations
of the individual words to form a meaningful and coherent representation in face of semantic mismatch [Hagoort,
2005; see, however, Bemis and Pylkk€anen, 2011], and on
the other hand, it utilizes background knowledge and discourse context to bridge successive utterances in face of
pragmatic mismatch [Li et al., 2014]. When a conflict is
detected, the executive control region right IFG (BA 47) is
activated for conflict inhibition [Badre and Wagner, 2007;
Badre et al., 2005; Li et al., 2014; Miller and Cohen, 2001;
Ye and Zhou, 2009a,b]. For implicature mismatch, right
IFG allows the individuals to suppress the inappropriate
implicatures using the contextual information and to
implement retrospective reevaluation in search of the origin of conflict. For semantic mismatch sentences, since no
alternative interpretation is available, the RIFG functions
to inhibit or replace the inappropriate access of the lexical
meaning [Li et al., 2014; Nieuwland et al., 2007]. The general role of left ventral IFG for meaning unification and
right IFG for conflict meaning inhibition is also supported
by the results of parametric analysis for implicature mismatch. We found a monotonic increasing activation in
ventral LIFG and RIFG under three implicature conditions:
implicature match, weak implicature mismatch, strong
implicature mismatch, in which the perceived mismatch
rating is monotonic increasing and therefore more cognitive resources are required to resolve the mismatch.
For strong mismatch in both “you-de” and “shao-shu-de”
conditions, we additionally found an increased connectivity between right IFG and STG. The STG is a multimodal
association area [Beauchamp et al., 2004; Calvert et al.,
2001; Hein et al., 2007; Taylor et al., 2006; Van Atteveldt
et al., 2004], and this connectivity may reflect how the
executive control system was involved in resolving both
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Implicature-Specific Mismatch Processing:
Basal Ganglia
In addition to frontal-temporal network, we found BG
which is specifically activated to resolve implicature mismatch. Different from the quantifier shao-shu-de, coherence
between the picture and sentence in the you-de condition
could be achieved by accessing the logical meaning of youde (some [at least one, up to and including all]). This processing is likely to be operated in BG, which has been implicated in determining which of several possible behaviors
is to execute at a certain time [Cameron et al., 2010; Van
Schouwenburg et al., 2010]. In particular, the activation of
BG facilitates the processing of meaning switching [e.g.,
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Zhan et al.
difference would have interacted with the context manipulation to produce the effects we have observed, which are
consistent with effects related to realizing scalar inferences.
These effects might also be consistent with an effect
related to entailment or some similar semantic property,
but we are not currently aware of a theory that would
make this prediction.
reinterpreting the input from the nonliteral and mismatched meaning into a literal but matched meaning;
Mestres-Misse et al., 2014], and through an visual-auditory
integration operation in STG (for strong implicature mismatch) implements the comprehension of sentences with
infelicitous but technically true quantifiers like you-de. For
semantic mismatch, no “switching” process is available as
there is no alternative interpretation of shao-shu-de, and
therefore no activation occurred in BG.
In a neuropsychological study [McNamara et al., 2010],
the author found that Parkinson’s patients suffering from
basal ganglia dysfunction have difficulty in comprehending indirect replies. Understanding an indirect requires the
comprehension system to reorganize the input information
into a plausible, nonliteral interpretation of sentence. This
result, together with our findings, points to a role of BG in
contextual-based “frame-shifting” [Coulson and Williams,
2005; Coulson and Wu, 2005], which means that, if an activated meaning does not fit the contextual expectancy, an
alternative one is derived/retrieved. Future studies should
be carried out to investigate the functions of BG in pragmatic
meaning processing, based on the current findings about the
role of such subcortical activity in both non-literal meaning
generation but also in such meaning failure.
The activation of BG, together with the bilateral IFG,
allows us to hypothesize that executive function resources
are involved in the processing of scalar implicature mismatch; this hypothesis remains to be confirmed in future
research [see Ye and Zhou, 2009b]. It is worth noting that,
while various individual difference measures other than the
executive function (e.g., social skill and working memory)
have been implicated in scalar implicature processing in
other studies, most of these have used paradigms very different from the present study [e.g., downstream processing,
Nieuwland et al., 2010; explicit implicature cancellation,
Husband, 2014; implicature generation, Politzer-Ahles et al.,
2014]. In paradigms that do explicitly test the comprehension of infelicitous scalar implicatures, however, correlations
with individual working memory [De Neys and Schaeken,
2007; Dieussaert et al., 2011] and logical ability [PolitzerAhles, 2013, Experiment 3] have been observed more often
than correlations with executive function.
We note that a potential alternative explanation of the
BG activation may have to do with semantic processing.
While we have interpreted the difference between the
pattern observed in response to the quantifier you-de and
the quantifier shao-shu-de as being due to the fact that youde has an implicature-based upper bound and shao-shu-de
has a semantically explicit upper bound, there are other
potentially relevant differences between these quantifiers.
For example, “less than half” is downward entailing
whereas “some” is not [Deschamps et al., 2015], and their
Mandarin equivalents shao-shu-de and you-de appear to
also have these properties (we thank an anonymous
reviewer for pointing out this possibility). However, we
are not aware of any a priori reason to predict that this
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Weak Versus Strong Implicature-Specific
Mismatch Processing: Left Dorsal IFG
While the weak implicature activated mostly similar
regions as the strong implicature mismatch [i.e., left ventral IFG (BA47), right IFG (BA45/47) and BG], it also activated more dorsal regions of the IFG (BA45) than the
strong mismatch did, and also showed different patterns
of connectivity. It could be the case that comprehension of
the weak mismatch involved widening the interpretation
while still keeping some scalar-inference-based interpretations active (e.g., eschewing the “not most” interpretation
while retaining the “not all”), in which case the left dorsal
IFG activation may be related to nearby activation previously shown in inferior or middle prefrontal cortex for
scalar inference realization [Politzer-Ahles and Gwilliams,
2015; Shetreet et al., 2014a]. Indeed, the rating difference
between the weak and strong implicature mismatch was
positively correlated with the activation in dorsal LIFG.
This suggests that individuals who recruited more cognitive resources to infer the contextually appropriate, alternative scalar implicature also were more willing to accept
the weakly mismatching sentence. Moreover, the activation
of left dorsal IFG further inhibited the activation of right
SFG, seeing the decreased connectivity between left dorsal
IFG and right SFG for the weak implicature mismatch relative to the matched one, and increased the activation of
left IPL in which the connectivity increased in proportion
to how mismatched the participants found the weak mismatch sentences. As the SFG has been found to be activated for conflict detection [Braver and Barch, 2006; Nee
et al., 2007; Ye and Zhou, 2009a], the inference process can
effectively help individuals to call off the mismatch they
originally perceived. As the left IPL has been found to be
activated for quantity processing [Heim et al., 2012; McMillan et al., 2005; Sandrini et al., 2004; Wei et al., 2014], it is
possible that the re-realizing of the alternative pragmatic
interpretation recruited cognitive resources for quantitative
re-analysis, especially for individuals who perceived the
weakly mismatch sentences as more mismatched. These
individuals might have difficulty in realizing the not all
interpretation effectively, and accordingly to make efforts to
identify the alternative set for the sentence you-de.
A traditional account of scalar inference processing may
not straightforwardly predict such differences between the
weak and strong implicature mismatches. From a linguistic
standpoint, both the “not all” and “not most” interpretations
are assumed to be realized in qualitatively similar ways (by
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Neural Correlates of Fine-Grained Meaning Distinctions
negating an alternative, all or most, which is informationally
stronger than some). Realizing that some can be consistent
with “most” and that some can be consistent with “all”
would also be assumed to work in qualitatively similar
ways: both would just involve re-allowing a stronger alternative term. Under such an account, there is no clear reason
why the endpoint of a scale (e.g., all) should have a privileged status relative to a middle point (e.g., most) that would
make inferences associated with it be processed differently.
The present results, however, suggest that it did [see similar
arguments about scalar adjectives, Kennedy and McNally,
2005, among others]. Another possibility is that it is not the
endpoint or ordering of the scale that has special relevance,
but that certain alternatives on the scale differ in their
salience and relevance to a given discourse context [see, e.g.,
Geurts, 2010]. On the <some, most, all> scale, it is possible
that most and all differ in terms of their prototypicality or
default relevance on this scale (with most being less prototypical or less relevant) in which case their processing may
also differ. If this were the case, changing the context to
make one or the other alternative more relevant or more
salient could also change the pattern of brain activity
between them; this is an open question for future research.
If the present results can be taken as evidence that strong
and weak implicature were processed in a qualitatively different way, this would pose a strong challenge to the notion
that research on the some-as-“not all” inference (which constitutes the vast majority of studies in the field of experimental pragmatics) represents well the phenomenon of
pragmatic processing in general. If two inferences as similar
as some-as-“not all” and some-as-“not most” are processed in
different ways, then even more different implicatures [e.g.,
argument saturation: the interpretation of Rachel picked up a
hammer and smashed a vase as meaning that Rachel used the
hammer to smash the vase; see, e.g., Doran et al., 2012, for
other examples] are likely to be processed in even more different ways. However, while this is a reasonable expectation
(indeed, inference is not a monolithic phenomenon and it is
highly unlikely that all inferences are processed in the same
way) we believe such an interpretation of the results may be
premature. First of all, even though weak mismatch did
elicit activity in a different region than strong mismatch, this
was nonetheless a very nearby region (a more dorsal portion
of the same gyrus); without more specific hypotheses about
the functional significance of each of these regions and
effects, it is difficult to quantify how different the BOLD activation patterns must be to really represent qualitatively different processing mechanisms. Secondly, while different
brain activation patterns were observed, this occurred in an
exploratory, effect-nonspecific test, and thus these differences should be confirmed through replication in targeted
experiments before concluding that weak and strong implicature mismatch absolutely do have different neural
substrates.
According to the classic linguistic approach to scalar
implicatures, mentalizing is assumed to be involved in
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generating implicatures as other types of pragmatic meaning [Wilson and Wharton, 2006], since the listener speculates
about the intentions of the speaker [Flobbe et al., 2008; Grice,
1975; Pijnacker et al., 2009]. However, neither strong nor
weak implicature mismatch in our study reveals activation
in the mentalizing (i.e., Theory of Mind) network. It is
possible that different networks are involved in realizing the
scalar inference versus revising the interpretation when the
inference is infelicitous [see, e.g., Shetreet et al., 2014a;
although neither this study nor Politzer-Ahles and
Gwilliams, 2015, observed activation in the mentalizing
network even for inference realization, let alone inference
failure]. Another possibility is that scalar implicatures are
generated only by a grammatical-semantic component [for
detailed accounts, see Chierchia, 2004; Chierchia et al., 2012],
resulting in activation in IFG rather than in regions related
to mentalizing [Shetreet et al., 2014a]. Our result seems consistent with such an argument, in that it revealed an additional involvement of the ventral IFG in processing weak
pragmatic incongruence that has high demand of inference,
as well as dorsal IFG. Of course, finding that semantic mechanisms are involved in scalar implicature does not rule out
the possibility that pragmatic mechanisms are also crucially
involved [Chemla and Singh, 2014], and might be revealed
in future studies with different methods or manipulations.
Likewise, potential differences between the neural representation of scalar implicatures and other, arguably more
“pragmatic” implicatures (such as irony, indirect replies,
bridging inferences, manner implicatures, etc.), should be
investigated in future study, as evidence for involvement of
the semantic network in scalar implicatures does not necessarily entail that this network will be involved in other types
of pragmatic meaning
CONCLUSION
By manipulating the consistency between quantitative
information of referents in a picture and the quantifier
used in a sentence, we investigated the brain activity
underlying the processing of inference-based meaning and
explicit lexical meaning. Behaviorally, we found that sentences that mismatched the context because of a scalar
implicature were more acceptable than sentences that mismatched the context because of their semantic meaning.
Neurally, implicature mismatch elicited activity in several
regions that semantic mismatch did not, including the
basal ganglia and dorsal IFG. Interestingly, somewhat different regions were activated for strong implicature mismatch, in which the scalar quantifier you-de (“some”) was
used in a context where all was expected, versus for weak
implicature mismatch, in which you-de was used in a context where most was expected. These results both point to
unique roles played by the basal ganglia and dorsal IFG in
the realization of meaning enrichment in quantity implicatures, and raise important questions about the nature of
pragmatic processing in general and its neural substrates.
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