Neuropsychologia 43 (2005) 2084–2100
The role of the right hemisphere in processing nonsalient metaphorical
meanings: Application of Principal Components Analysis to fMRI data
N. Mashal a,d,∗ , M. Faust a,b , T. Hendler c,d
a
The Leslie and Susan Gonda (Goldschmied) Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
b Department of Psychology, Bar-Ilan University, Israel
c Sackler Faculty of Medicine, Tel Aviv University, Israel
d Functional Brain Imaging Unit, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Israel
Received 17 March 2005; accepted 18 March 2005
Available online 22 April 2005
Abstract
Some researches indicate that the right hemisphere (RH) has a unique role in comprehending the figurative meaning of metaphors whereas
the results of other studies do not support the notion of a selective role for the RH in accessing metaphorical meanings. The present research
used fMRI technology to test a theoretical explanation of the above conflicting findings. This theoretical account is derived from the Graded
Salience Hypothesis (GSH) [Giora, R. (1997). Understanding figurative and literal language: The Graded Salience Hypothesis. Cognitive
Linguistics, 7, 183–206; Giora, R. (2003). On our mind: Salience, context and figurative language. New York: Oxford University Press],
according to which the degree of meaning salience, rather than literality or nonliterality primarily affects differences between the LH and RH
in linguistic processing. Thus, the GSH predicts a selective RH involvement in comprehension of novel, nonsalient metaphoric meanings and
LH involvement in the comprehension of conventional, salient metaphoric meanings. Fifteen normal adults participated in a block designed
fMRI experiment that compared the patterns of brain activation induced by processing the meanings of literal, conventional metaphoric, novel
metaphoric and unrelated word pairs. The subjects performed a semantic judgment task. We applied the Principal Components Analysis
(PCA) technique in order to find different functional networks corresponding to the different stimuli. Our results, obtained from PCA of the
fMRI data indicate that the right homologue of Wernicke’s area has a special role in processing novel metaphors. We suggest that a unique
network, consisting of the right homologue of Wernicke’s area, right and left premotor areas, right and left insula and Broca’s area, is recruited
for the processing of novel metaphors but not for the processing of conventional metaphors.
© 2005 Elsevier Ltd. All rights reserved.
Keywords: Metaphors; Salience; Right hemisphere; fMRI; PCA
1. Introduction
Empirical evidence from neurologically intact as well as
from brain-injured participants indicates that both the left
(LH) and right hemisphere (RH) semantic systems are used
in the interpretation of linguistic material (e.g., Beeman,
1998; Brownell, Simpson, Potter, Bihrle, & Gardner, 1990;
Chiarello, 1991; Faust & Chiarello, 1998; Faust & Kahana,
2002; Van Lancker, 1997). However, each cerebral hemisphere is specialized to carry out distinct semantic processes.
∗
Corresponding author. Tel.: +972 506 509882; fax: +972 3 5352184.
E-mail address: m [email protected] (N. Mashal).
0028-3932/$ – see front matter © 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.neuropsychologia.2005.03.019
A major difference between these semantic systems concerns
the manner in which the LH and the RH process linguistic material that is associated with alternative, potentially
conflicting, meanings (e.g., Anaki, Faust, & Kravetz; 1998;
Burgess & Simpson, 1988; Faust & Gernsbacher, 1996). The
LH is thought to focus on a small set of highly related semantic associates while inhibiting the marginal and less salient
ones. In contrast, the RH activates and maintains a much
broader and less differentiated set of semantic associates, including also distantly related, unusual, and less salient meanings (for reviews, see Beeman, 1998; Chiarello, 1991; St.
George, Kutas, Martinez, & Sereno, 1999). This putative distinction between the two hemispheres may be crucial when
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
language comprehension requires the simultaneous consideration of more than one plausible meaning, as in understanding linguistic ambiguity (Burgess & Simpson, 1988; Faust &
Chiarello, 1998) and figurative metaphoric language (Anaki
et al., 1998; Brownell et al., 1990; Burgess & Chiarello, 1996;
Giora, Zaidel, Soroker, Batori, & Kasher, 2000). These linguistic forms are characterized by several, sometimes unrelated, meanings and may require revision of an initial interpretation (Chiarello, 1991; Giora et al., 2000). The aim of
the present study was to use neuroimaging technology with
a non-clinical population in order to test the prevalent claim
that the RH has a primary role in metaphor comprehension.
A metaphor has been traditionally defined as a denotative
violation since it conveys information by describing a thing
in terms of what it is not (Billow, 1975). The understanding
of metaphors (such as “this idea is a gem”) requires mental
linkage between the “topic” of the metaphor (idea) and the
“vehicle” of the metaphor (gem) since the idea is not really
a gem but rather it shares some properties with a gem (preciousness, uniqueness but not the color of the gem). Richards
(1936) refers to the shared properties between the topic and
the vehicle of the metaphor as the “ground” of the metaphor.
Thus, “understanding a metaphor can be seen as the process by which the metaphor’s ground becomes available and
salient” (Pynte, Besson, Robichon, & Poli, 1996, p. 294). According to this claim, the relation between the vehicle and the
topic of a metaphor can be characterized as unusual and distant. As noted above, there is much evidence that the semantic
system of the RH specializes in processing distant, unusual
semantic relations (Brownell et al., 1990; Faust & Kahana,
2002; Giora et al., 2000). Therefore, this view of RH language comprehension is consistent with a selective role for
the RH in processing such figurative language as metaphors
(see, e.g., Pynte et al., 1996, for review).
Indeed, the accumulated neuropsychological evidence obtained from brain-lesioned and brain-intact subjects suggests
that one domain where RH involvement is most pronounced
is figurative language processing. Right hemisphere damaged
(RHD) patients have been found to exhibit many difficulties
with this language. These include deficits in understanding indirect requests (e.g., Stemmer, Giroux, & Joanette 1994), difficulties in interpreting idioms (e.g., Van Lancker & Kempler,
1987), and poor comprehension of metaphors (e.g., Brownell
et al., 1990). McIntyre, Pritchard and Lombroso (1976) used
the Davits–Mattis Metaphor test to examine the metaphoric
capacities of right and left temporal epileptics. They found
that left temporal epileptics made more metaphoric interpretations than right temporal epileptics. Winner and Gardner
(1977) devised a test of metaphor competence and administered it to right (RHD) and left (LHD) brain-damaged patients. They found that the LHD patients more often selected
an appropriate picture depicting the metaphoric meaning than
did the RHD patients.
While the above study might be problematic because it
utilized pictorial stimuli and its results could be affected by
the visual-spatial abilities of the participants, other studies
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have extended this research and examined this issue from
a uniquely linguistic semantic perspective. In one study
(Brownell, Potter, Michelow, & Gardner, 1984), RHD and
LHD patients were presented with word triads and asked
to select the two words most similar in meaning. RHD
patients were more inclined to select the words sharing
the same literal relations while LHD patients preferred the
nonliteral metaphoric meaning. Brownell et al. (1990) got
similar results in another study that used the same paradigm.
Other more recent studies on adults with RH damage have
also suggested that the RH may play a unique role in
integrating alternate remotely related pieces of information
to appreciate metaphors and figurative meanings (Beeman,
1998; Brownell, Carroll, Rehak, & Wingfield, 1992; Burgess
& Chiarello, 1996; Kaplan, Brownell, Jacobs, & Gardner,
1990; McDonald, 2000; Rehak, Kaplan, & Gardner, 1992;
Van Lancker, 1997; Weylman, Brownell, Roman, & Gardner,
1989).
RHD patients seem to have significant difficulties in appreciating jokes, metaphors, connotations, idioms, sarcasm
and indirect requests that echo the unique ability of the intact
RH to maintain the continued activation of multiple meanings
of words (Brownell & Martino, 1998; Burgess & Chiarello,
1996; Chiarello, 1991; Faust & Kahana, 2002). For example,
whereas subordinate meanings activated by an ambiguous
word tend to decay rapidly in the LH, the RH maintains activation of both meanings of the ambiguous word (Burgess &
Simpson, 1988). Divided visual field studies showed that semantic priming effects of remotely related words are obtained
in the RH, but not in the LH (e.g., Chiarello, 1991). According to the RH coarse semantic coding theory (Beeman, 1998),
immediately after encountering a word, the LH engages in
relatively fine semantic coding, strongly focusing activation
on a single interpretation of a word and a few close or contextually appropriate associates, whereas the RH engages in
coarse semantic coding, weakly and diffusely activating alternative meanings and more distant associates. Thus, one
of the factors that determine hemispheric differences in semantic access and retrieval is the nature of semantic relations
between words (Chiarello, 1991). While the LH strongly activates a relatively small semantic field, including only closely
related meanings and a single interpretation, the RH weakly
activates a much broader range of related meanings, including
peripheral and unusual meanings (e.g., metaphoric interpretations, multiple meanings of ambiguous words, see Anaki
et al., 1998; Burgess & Simpson, 1988).
One of the few studies with neurologically intact adults
(Anaki et al., 1998) investigated semantic priming for literal (stinging-mosquito) and metaphoric (stinging-insult) associates presented to either the left (LVF) or right (RVF)
visual fields across stimulus onset asynchronies (SOA) of
200 and 800 ms. For the short SOA condition, facilitation
was found for metaphorically related targets in both visual
fields while literally related targets were facilitated only in
the RVF/LH. For the long SOA condition, metaphorically related targets were facilitated in the LVF/RH, whereas literally
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N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
related targets were facilitated in the RVF/LH. These results
were interpreted as supporting an enhanced role of the RH
in metaphoric comprehension because of its unique ability to
maintain activation of distantly related meanings for a longer
period of time than the LH. This interpretation suggests that
while LH fine semantic coding (Beeman, 1998) has a clear
advantage for most linguistic processes, RH coarse semantic
coding is critical for mediating those aspects of comprehension, which require the simultaneous consideration of more
than one plausible meaning or the sustained activation of a
wide range of word meanings.
To sum up, data collected from brain damaged research
participants as well as the results of semantic priming studies
with neurological intact research participants suggest that the
RH contributes to language processing mainly by allowing
for widespread activation of word meanings, without subsequent selection. According to this claim, the undifferentiated
activation of alternative, and sometimes contradictory, interpretations for some indefinite period (Chiarello, 1991; Faust
& Kahana, 2002; Faust & Lavidor, 2003) may support the
view that the RH has a special role to play in the processing
of metaphors.
However, in spite of the parallels between the properties
of figurative language comprehension and the unique mode
of semantic processing in the RH, some of the research
findings are controversial. For example, Zaidel, Kasher,
Soroker, and Batori (2002) administered parts of the Gardner
and Brownell (1986) Right Hemisphere Communication
Battery to both LHD and RHD patients. The patients were
presented with sets of three words: an ambiguous word,
its metaphorical synonym, and a foil that was related to
its literal meaning. They were, then, instructed to choose
the two words that were most similar in meaning. The
results showed that the ability to access metaphorical word
meanings was negatively correlated with lesion extent in
the LHD patients, whereas no such relationship was found
for the RHD patients. Additional controversial results were
presented by Giora et al. (2000) that reported that LHD
patients made more errors in providing oral explanations
to conventional metaphors (“broken heart”, “warm heart”,
“a hard man”) with implausible literal meaning than RHD
patients. Similarly, in a split visual field study with non-brain
damaged research participants, no differences were found in
reaction times for RVF and LVF presentations when these
participants were required to process metaphorical word
meanings in a sentence context (Faust & Weisper, 2000).
Two different approaches have been proposed to account
for metaphor comprehension, hierarchical/sequential views
(i.e., the standard pragmatic model) and parallel views (i.e.,
direct access view). Linguists and cognitive psychologists
(e.g., Grice, 1975) who hold the hierarchical/sequential view,
contend that understanding nonliteral language requires a
special sequential process during which less complex literal
meaning comprehension is activated before more complex
nonliteral comprehension. According to this view access to
the metaphorical meaning is seen as a consequence of the fail-
ure to find an appropriate literal meaning for the utterance.
On the other hand, the parallel view of metaphor processing
claims that the nonliteral meaning can be accessed directly simultaneously with the literal meaning. According to this parallel view (Gibbs, 1994) a rich and supportive context would
affect comprehension to a significant extent. Consequently
in similarly strong contexts literal and nonliteral language
would involve equivalent initial processes (for a review, see
Giora, 2002).
Since research findings have not been consistent with either view (see Giora, 2002), Giora has proposed a more general view of language comprehension – the Graded Salience
Hypothesis (GSH) – which posits the priority of salient
(coded, context-independent, prominent) meanings rather
than the type of language processed. Giora (2002) reconciled
the above sequential view with the parallel view of metaphor
comprehension and suggests that meanings of linguistic expressions that are more salient, be they literal or figurative, are
processed before linguistic expressions that are less salient
whereas linguistic expressions that are equally salient are processed simultaneously (as shown by Giora & Fein, 1999a,b;
Williams, 1992). An expression’s degree of salience is determined by conventionality, frequency, familiarity and prototypicality. Nonsalient meanings are not coded in the mental
lexicon and rely on context for their derivation.
According to the GSH, the comprehension of novel
metaphors involves a sequential process, because it is the
literal, rather than the intended metaphoric meaning of their
components that is salient (i.e., conventional, frequent, familiar, prototypical). Consequently, the nonsalient metaphoric
meaning should be induced by contextual (inferential) mechanisms without obstructing access of the more salient, literal meaning, which would be probably retrieved earlier
(see Giora & Fein, 1999a). However, when conventional
metaphors are being processed, it is the figurative meaning
that is the most salient. Therefore, in the latter case, it is
the figurative meaning that should be processed first, without having to access the less salient (literal) meaning (Gibbs,
1980; Giora & Fein, 1999a; Turner & Katz, 1997). Actually, the literal meaning of conventional metaphors is less
salient than the figurative meaning (Gibbs, 1980; Giora &
Fein, 1999b).
In light of the GSH, evidence for RH specialization in the
comprehension of metaphors and linguistic reinterpretation
(e.g., Brownell, Pottet, Bihrle, & Gardner 1986; Chiarello,
1991) can be attributed to a greater sensitivity on the part
of the RH to less salient linguistic material (Burgess &
Simpson, 1988). Conventional metaphor processing may be
selectively associated with the LH because of LH sensitivity
to salient (e.g., frequent) meanings. Consequently, predictions concerning the comprehension of literal or metaphorical linguistic material by the RH or the LH should be based
on the degree of this material’s salience. Thus, the failure of
research to take the salience of linguistic material into consideration may account for the inconsistent findings regarding
comprehension of literal or metaphorical linguistic material
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
by the RH or the LH. The findings of a recent behavioral study
(Faust, Bar-Lev, & Chiarello, 2003) that examined sentence
processing by the two cerebral hemispheres in neurologically
intact individuals using the split visual field technique seem
to support this explanation. Syntactic priming sentences that
were very similar to novel metaphors and to poetry lines,
combining words in a syntactically correct, but semantically
unusual, often funny, manner, facilitated word recognition in
the RH but not in the LH.
The present experiment used a common neuroimaging
technique, the blood oxygenation level-dependent (BOLD)
contrast, to test a GSH reformulation of the claim that the RH
has a primary role in metaphor comprehension. This technique appears to be sensitive to psychological events that
correlate with acute changes in neural activity on order of
seconds (Le-Bihan & Karni, 1995) and thus enables localization of brain function by monitoring the hemodynamic
changes that are coupled with neural activity. Recent fMRI
data have focused on areas in the right temporal lobe and,
specifically, on the homologue of Wernicke’s area. Beeman
et al. (2004) found increased activity in the right anterior
superior temporal gyrus when subjects solved verbal problems with insight as compared to noninsight solutions. In
this study, participants were presented with three-word problems (pine, crab, sauce) and were asked to produce a word
(apple), which forms a familiar compound expression with
each of the three problem words (pineapple, crab apple,
applesauce). This finding is consistent with the coarse semantic coding of the RH since the solutions to these verbal
problems require the connection of distantly related words.
Activation in the right superior temporal gyrus (BA 22/42)
was also found when participants were asked to generate
the best ending to a low Cloze probability sentence stem
(“These days the weather is rather . . .”) as compared to
reading a sentence stem (kircher et al., 2001). This task requires that subjects keep activation of wide semantic fields
while they process the meaning of the stem in order to fit
the best possible final word. The findings of both studies
suggest that the right superior temporal gyrus is involved in
the process of integrating distant lexical or semantic relations. In another fMRI study (Eviatar & Just, submitted for
publication), subjects were scanned while they read critical
statements that were preceded by two-sentence stories. These
statements were either literal, familiar metaphoric or novel
ironic sentences. The results showed significantly greater activation in the right superior and middle temporal gyri for the
novel ironic statements then for the literal statements. The
ironic meaning of the statements (“Great weather for a picnic”) following the context (“Tom and Mike planned to go
on a picnic. In the morning it was raining very hard”) in Eviatar’s study was somewhat similar to the novel metaphoric
meaning used in the present study in that the meanings of
both types of nonliteral stimuli were nonsalient. In addition, Eviatar’s study found greater activation for metaphoric
sentences then for literal sentences in left inferior frontal
gyrus.
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In the present study, predictions of the neurological realization of the GSH in the two cerebral hemispheres were evaluated through an experiment in which research participants
were presented with four kinds of word pairs: conventional
metaphoric expressions, novel metaphoric expressions, literal expressions and unrelated word pairs. In this experiment,
the novel metaphoric expressions constituted nonsalient linguistic material whereas the conventional metaphoric and literal expressions constituted salient linguistic material. Participants were asked to decide whether the relation between
the words of each word pair is literal or metaphoric. We
applied the Principal Components Analysis (PCA) method
to unfold different brain networks, which cooperate during
the processing of various types of stimuli. The aim of the
PCA method is to classify intercorrelated regions and, consequently, to detect structures of networks related to the different experimental conditions. This method, however, does
not provide information as to how two intercorrelated brain
regions are connected. Thus, the connection could be either direct (two areas that are anatomically and functionally
linked) or indirect, through a third region, which affects them
both.
The three theoretical accounts of metaphor comprehension predict different patterns of results: The standard pragmatic view would predict that the processing of novel and
conventional metaphors would be similar and distinct from
the processing of strictly literal expressions. The direct access view might predict no differences in the processing of
the three types of meaningful expressions whereas the GSH
would predict that processing of strictly literal expressions
and conventional metaphors would be similar and distinct
from processing of novel metaphors. As the functional connectivity technique identifies mutual engagement of different cortical areas, our hypothesis, based on the GSH, was
that different brain networks would be recruited for the processing of the nonsalient meanings as compared to the processing of the salient meanings. Specifically, we hypothesized that since the RH specializes in recognizing distant
semantic relations (Chiarello, Burgess, Richards, & Pollock
1990; Beeman, 1998; St. George et al., 1999; Kircher et al.,
2001; Beeman et al., 2004) the classical language regions in
the RH (the right homologues of Broca’s and/or Wernicke’s
area) will be associated with the processing of the novel
metaphoric expressions. However, these brain areas will not
be part of the neural network recruited for the processing
of salient meanings (i.e., literal expressions or conventional
metaphors).
2. Methods
2.1. Subjects and general comments
Fifteen healthy volunteers (ages 21–31 years; eight
males), native Hebrew speakers, were recruited for the study.
All participants were right handed, yielding a laterality quo-
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N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
tient of at least +90 on the Edinburgh Inventory (Oldfield,
1971). All signed an informed consent form that was approved by the ethical committees of the Tel Aviv Sourasky
Medical Center. The visual stimuli of this study were projected to subjects inside the magnet tube through a LCD video
projector (Epson MP 7200). Subjects viewed the stimuli via
a mirror located in front of their eyes. In addition to the 15
subjects participating in the fMRI experiment, 19 volunteers
who did not participate in the fMRI study, aged 23–29 years,
participated in a behavioral experiment outside the magnet.
All subjects were right handed, yielding a laterality quotient of at least +90 on the Edinburgh Inventory (Oldfield,
1971).
2.2. Stimuli
Stimuli included 96 pairs of words. Most of these word
pairs formed plausible expressions. The two words formed
four types of semantic relations: literal (broken vase), conventional metaphoric (bright student), novel metaphoric, taken
from poetry (crystal river) or unrelated (boot laundry). Stimuli were balanced between blocks according to word frequency, number of words, the number of noun versus verbs,
and concrete versus abstract words.
2.3. Behavioral judgment
The aim of the first pretest was to determine the type of
each two-word expression, (metaphoric, literal, or unrelated
word pairs). In order to do so, twenty judges were presented
with a list of two word expressions and asked to decide if
each expression is literally plausible, metaphorically plausible or unrelated. Expressions that were rated by at least 75%
of the judges as metaphorically/literally plausible or unrelated were selected as expressions with either a metaphoric
or a literal salient meaning or as unrelated word pairs,
respectively.
In order to distinguish between unfamiliar novel
metaphors and conventional metaphors (“dead metaphors”),
another group of 10 judges was presented with a list of
only the plausible metaphors from the first pretest. They
were asked to rate their degree of familiarity on a 5-point
familiarity scale ranging from 1 (highly nonfamiliar) to 5
(highly familiar). Metaphoric expressions scoring less than
3 on the familiarity scale were selected for the study as
novel metaphors (rating average 1.98), whereas those scoring more than 3 on this scale were selected as conventional
metaphors (rating average 4.67). In this way, we could distinguish between novel metaphors (i.e., metaphoric expressions
whose metaphoric meaning is not coded/ nonsalient) and
conventional metaphors (i.e., metaphoric expressions whose
metaphoric meaning is salient).
Since in Hebrew there is no extensive database for word
frequency, the third pretest tested the word frequency. Thirtyone additional judges were presented with the list of all the
words and asked to rate their degree of frequency on a 5point frequency scale ranging from 1 (highly nonfrequent)
to 5 (highly frequent). The average rates on the frequency
scale were 3.38, 3.45, 3.67 and 3.79 for the unrelated word
pairs, literal expressions, novel metaphors, and conventional
Fig. 1. Block design. (A) Four types of experimental blocks were randomly presented for 15 s with 15 s of fixation between blocks: conventional metaphors
(CM, i.e., sweet sleep); literal expressions (L, i.e., road crossing); novel metaphors (NM, i.e., wisdom dust) and unrelated two-words (UR, fuel rectangle). The
figure presents 5 blocks out of 16. (B) Each experimental block consisted of six stimuli. In order to apply the PCA technique, we concatenated blocks from
the same condition into one continuum. In this way, segments of time course activation corresponding to the presentation of each of the four experimental
conditions produced four time series. This procedure was applied for each subject in each region.
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
metaphors, respectively. No significant difference was found
between the four conditions (F < 1).
3. MRI experimental procedure and task
The stimuli were presented in a block design fashion. Each
condition block contained six pairs of words. These pairs
of words included familiar metaphoric expressions, conventional metaphors, (“sweet dreams”), novel metaphoric expressions (“wisdom dust”—avak tvuna in Hebrew), literal
expressions (“dog bite”) or unrelated word pairs (“fuel rectangle”), hence comprising four types of blocks (Fig. 1A).
Each stimulus was presented for 2100 ms followed by 400 ms
blank. The epochs were separated by 15 s during which participants viewed a fixation point on a gray background. These
fixation blocks (rest condition) provided the baseline for measuring the level of brain activation. Each experimental condition appeared four times (out of 16 blocks) in each scan
session in a way that balanced the order of conditions. The
first 18 s (six repetitions) of the scan were rejected to allow
for T2* equilibration effects. Consequently, the functional
part of this experimental session lasted 513 s.
Since the experiment was aimed at understanding how
the two hemispheres cooperate during processing of familiar
versus novel metaphors and literal versus metaphoric expressions, participants were asked to silently decide whether the
two words in the word pair are metaphorically related, literally related or unrelated, while they were scanned. Subjects
were not informed that the different stimuli of each block
belonged to the same type. In addition, each block contained
one distracter, i.e., an unrelated word pair in the conventional metaphor, novel metaphor and literal conditions and
a metaphoric expression in the unrelated condition. The decision to include one distracter in each experimental block
was motivated by our intention to prevent subjects from automatically responding ‘metaphor related’, ‘literal related’, or
‘unrelated’. By asking subjects to directly decide what type
of semantic relatedness exists between the two words and not
only whether there is a relation between the two words, we
were better able to track the brain areas directly involved in
processing the basic type of semantic relation which exists
between the two words.
4. Behavioral experimental procedure and task
Participants performed a semantic judgment task outside
the magnet. They were asked to indicate whether the two
words presented simultaneously at the center of a computer screen are related literally, metaphorically or unrelated by pressing one of three keyboard buttons. The subjects
were informed that some of the word pairs represent novel
metaphoric expressions taken from poetry. The rate and order of presentation were identical for both the behavioral and
fMRI experiments.
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5. Data acquisition
Imaging measurements were performed on GE 1.5T Signa
Horizon LX 9.1 echo speed scanner (Milawaukee, W1) with
resonant gradient echoplanar imaging system, located at
the Wohl Institute for Advanced Imaging in the Tel Aviv
Sourasky Medical Center. All images used a standard quadrature head coil. The scanning sessions included anatomical and
functional protocols. Anatomical images were based on high
resolution sagital localizer acquired in the beginning of each
scanning. Twenty-three contiguous axial T1-weighted slices
of 4 mm thickness, 1 mm gap were prescribed, based on the
sagital localizer, covering the whole brain. In addition, a 3D
spoiled gradient echo (SPGR) sequence, with high resolution,
was acquired for each subject, in order to allow for a volume
statistical analysis of signal changes during the experiment
and to facilitate later coordinate determinations. The functional T2*-weighted protocols included gradient echo planar imaging pulse sequence (TR/TE/flip angle = 3000/55/90)
with FOV of 24 cm2 and matrix size of 80 × 80. No evidence
of structural abnormalities was found in any of the participants.
6. Data analysis
fMRI data was processed by applying BrainVoyager
software package (Version 4.9; Brain Innovation, Maastricht,
The Netherlands). Prior to statistical tests, raw data was
examined for motion and signal artifacts. We applied motion
correction, (scans with head movement >1.5 mm were rejected), high frequency temporal filtering (0.006 Hz) and drift
correction. In addition, slice acquisition times were corrected
by using sinusoid interpolation. Functional images were then
superimposed on 2D anatomical images, and incorporated
into 3D data sets through trilinear interpolation. The complete data set was transformed into Talaraich space (Talaraich
& Tournoux, 1988). In addition to allow for T2* equilibrium
effects, the first six images of each functional scan were
rejected.
7. Time course analysis and ROI’s
Three-dimensional statistical parametric maps were
calculated separately for each subject using a general linear
model (GLM, Friston et al., 1995) in which all stimuli
conditions were positive predictors and the blank as negative
predictor, with an expected lag of 6 s (to account for the
hemodynamic response delay). For each subject, task related
activity was identified by convolving the box-car function
with a hemodynamic response function (HRF). Then
time-courses of statistically significant voxels (threshold at
p < 0.0001, uncorrected) in the regions of interests (ROI) that
extended threshold of 130 voxels or more were acquired.
The exact number of significant voxels collected in each
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N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
Table 1
Regions of interest (ROI), number of significant voxels (p < 0.0001, uncorrected) collected in each ROI, Broadman areas (BA) and averaged Talaraich coordinates
(means ± S.D.s)
ROI
Wernicke’s area
Broca’s area
Left inferior frontal gyrus
Left anterior insula
Left premotor
Left precuneus
Left lingual gyrus
Left middle frontal gyrus
Left fusiform
Left parahippocampal
Homologue of Wernicke’s area
Right anterior insula
Right premotor
Right precuneus
Right lingual gyrus
Right middle frontal gyrus
Right fusiform
Homologue of Broca’s area
Right middle frontal gyrus
Voxels
200
300
150
200
200
130
350
250
180
200
250
200
200
130
350
250
180
200
150
ROI from all the subjects, its Broadman areas (BA) and the
mean Talaraich coordinates, are specified in Table 1.
The choice of the ROI’s was motivated by the aim to classify a broad range of brain areas, that showed a significant
level of activation by all subjects, into intercorrelated networks. In addition, previous neuropsychological and neuroimaging data has shown that these ROI’s are involved in linguistic processing (Bottini et al., 1994; Gabrieli, Poldrack, &
Desmond, 1998; Keller, Carpenter, & Just, 2001; ThompsonSchill, D’Esposito, Aguirre, & Farah, 1997; ThompsonSchill, D’Esposito, Aguirre, & Farah, 1998; Warrington &
Shallice, 1980). Our ROI’s were functionally defined by using multi-study GLM (statistical parametric maps of concatenated single design matrices of the 15 subjects) with the
experimental predictors as positive predictors (relative to a
silent block), and by using Talaraich coordinates. Our ROI’s
were also anatomically defined, by using anatomical markers (sulcus and gyri): in the inferior frontal gyrus (IFG) the
analysis focused on Broca’s area which lies on the pars opercularis (BA 44), and on pars triangularis (BA 45) of the IFG.
At the dorsolateral prefrontal cortex, in the middle frontal
gyrus, we focused on the premotor area (BA 6), and on BA
9, which lies anterior to the precentral sulcus. On the inferior
extrastriate we focused on the fusiform gyrus (BA 37) and
on the inferior occipital lobe, in the visual cortex below the
calcarine sulcus, we focused on the lingual gyrus (BA 18).
Wernicke’s area refers to the area near or at the posterior superior temporal sulcus between the superior temporal gyrus and
the middle temporal gyrus (BA 22/42). In the medial parietal
cortex, we focused on the precuneus, anterior to the parietooccipital sulcus. Right hemisphere homologues of all these
regions were also included. We also analyzed four additional
regions, which showed high significant level of activation by
all subjects: the anterior insula, medially adjacent to IFG (BA
BA
Talaraich coordinates
±S.D.
22/42
44
45
13
6
7
18
9
37
28
22/42
13
6
7
18
9
37
44
10
−46.2, −32.2, 3.9
−42.6, 15.6, 3
−39, 29.28, 14.85
−32.5, 15.9, 9.14
−41, −4.93, 47.8
−22.2, −59.6, 41.2
−8, −90, −11
−41.93, 10.2, 35.2
−34.9, −57, −17.87
−26.53, −39.13, −15.4
49.08, −28.9, 6.42
32.07, 23.07, 11.57
40.29, −0.64, 41
27.64, −52.43, 43.07
15, −83, −11
43, 13, 33
34.93, −57.1, −16.6
42, 21.8, 5.7
34, 45, 22
3.9, 8.7, 3.2
2.5, 3.5, 3.7
3.1, 4, 3.2
5.6, 5.1, 3.9
2.9, 4.5, 3.5
4, 5.7, 4.4
4, 3.2, 3.3
2, 4.6, 2.6
3.3, 7.6, 3.7
2.9, 5.2, 7.3
4.3, 6.3, 2.7
2.2, 3.1, 4.2
4.4, 3.7, 4.7
3.1, 12.5, 4.1
5, 3.4, 3.7
1, 8.7, 4.3
3.5, 5.4, 3
3.2, 4, 5.1
1, 3.2, 2
13), the right anterior area of dorsolateral prefrontal cortex at
the middle frontal gyrus (BA 10), and left parahippocampal
gyrus above the collateral sulcus.
8. Functional connectivity
We performed an analysis of brain functioning in terms of
networks in order to find temporal correlations between remote regions. The functional connectivity method suggests a
new approach that aims to determine the way in which different brain areas work together during the performance of a
specific cognitive task (see Horwitz, Tagamets, & McIntosh,
1999 for definition). This technique reveals the interactions
and the integration that bridge between the dynamics of specialized regions (Friston, 1998). This functional connectivity
method is based on the assumption that each experimental
condition, i.e., each cognitive or linguistic process, may be
mediated by a different functional network. Thus, if a specific
brain region belongs to the same functional network (i.e., factor) as other regions in one condition but to a different factor
in another condition this means that this area interacts differently with other brain areas in each condition and thus might
play a different role in processing the various linguistic stimuli. We expected that different networks would be recruited
for the processing of the salient versus the nonsalient condition and, specifically, for the processing of the conventional
versus the novel metaphor condition. We focused on the right
homologue of Wernicke’s area in order to identify different
networks associated with it. Our aim was to find logical and
plausible explanation for the resulted networks. A special
case of a network would be a single region network. If a network consists of a single brain region, this means that this
area does not “communicate”, cooperate, with other brain ar-
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
eas and thus might not be involved in the specific cognitive
task.
9. Constructing the data matrix
For each of the 15 subjects functional MRI time series
were extracted from each of the 19 ROI’s, such that each
individual produced a mean time series of all the activated
voxels as specified in Table 1. Then, the segments of each
regional mean time series corresponding to the four different experimental conditions were extracted. The segments of
time course activation corresponding to the presentation of
each of the experimental conditions were then concatenated.
In this way, each subject produced four time series, for each
of the four conditions, in each of the regions. Each time series of a particular condition consisted of T = 20 time points,
since each condition consists of four blocks and each block
lasts 15 s, i.e., five time points in each block (TR = 3 s). The
data matrix was constructed for each subject separately for
each of the four conditions. We averaged the 15 matrixes of
all the subjects. Thus, the resulted 20 × 19 matrix for each
condition consists of 19 columns, each one is the average
time course activation of the 15 subjects in one region. This
T × N matrix was then decomposed by PCA (Bullmore et al.,
1996; Fletcher et al., 1999).
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were in identifying both the novel metaphors (p < 0.001), and
the conventional metaphors (p < 0.05). The results also indicate that subjects were significantly more accurate in recognizing the literal expressions than they were in recognizing novel metaphors (p < 0.01). These results were consistent
with the results of the pretest. Novel metaphors were selected
if at least 75% of the judges considered them metaphorically
plausible and the subjects in the study identified 84.2% of the
novel metaphors as metaphorically plausible.
10.1.3. Types of errors
From among the incorrect responses, 90% of error responses to the novel metaphors were that they were unrelated;
whereas 10% of the error responses were that they were literal expressions. From among the incorrect responses to the
conventional metaphors, 78% of the errors were that they
were literal expressions and 22% were that they were unrelated word pairs. From among the incorrect responses to
the literal expressions, 72% of the errors were that they were
metaphors and 28% of the errors were that they were unrelated word pairs. From among the incorrect responses to the
unrelated word pairs, 58% of the errors were that they were
literal expressions and 42% that they were metaphors. These
results demonstrate a symmetrical pattern of errors between
the literal and the conventional metaphors, which comprise
the salient stimuli. Errors made for the novel metaphors indicate that misidentified stimuli were mostly understood, as
expected, as unrelated word pairs.
10. Results
10.2. fMRI data
10.1. Behavioral data
10.1.1. Reaction times (RTs)
As shown by (repeated measure) one-way ANOVA, the
differences in RTs among the four stimulus types (conventional metaphors, novel metaphoric expressions, literal expressions and unrelated two-word pairs) were statistically
significant (F(3,54) = 5.57, p < 0.01). Tukey’s honest significant difference (HSD) Post hoc comparisons revealed
that the mean RTs for the novel metaphors (M = 1385 ms,
S.D. = 205 ms) was longer than RTs for both the conventional
metaphors (M = 1275 ms, S.D. = 200 ms, p < 0.05) and the
literal expressions (M = 1261 ms, S.D. = 195 ms, p < 0.05).
In addition, RTs for unrelated word pairs (M = 1370 ms,
S.D. = 227 ms) were significantly longer than RTs for literal
expressions (p < 0.05) and marginally significantly longer
than the conventional metaphors (p = 0.07).
10.1.2. Accuracy
Subjects identified correctly 84.2, 87.2, 92 and 93.9%
of the novel metaphors, conventional metaphors, literal expressions and unrelated word pairs, respectively. A one-way
ANOVA reveled that the main effect of stimulus type was
significant (F(3,54) = 7.55, p < 0.001). Tukey’s HSD Post hoc
comparisons revealed that subjects were significantly more
accurate in identifying the unrelated word pairs than they
PCA was applied to the T × N matrix (T = 20, N = 19).
The columns of this matrix are the average time course of all
the subjects in each region at each condition. This method
produced two to four eigenvectors (the criteria for selecting
components was having eigenvalues greater than one which
means that the factor comprises the variation of at least one
single item), depending on the experimental condition: the
conventional metaphors, novel metaphors, literal expressions
and unrelated word pairs, produced 4, 3, 4, 2 eigenvectors
(factors), respectively. Table 2 presents the eigenvalues and
percent cumulative variance explained by each component
in each condition. The first eigenvalue of each condition explains half or more of the total variance (49.86, 65.72, 51.4,
71.19% for the conventional metaphor, novel metaphor, literal expression and unrelated word pair conditions, respectively). As can be seen in Table 2, the cumulative variance
explained by all the components of each of the four conditions
is high: 79.5, 82.3, 80.1 and 77.8 (in bold) for the conventional metaphor, novel metaphor, literal expression and unrelated word pair conditions, respectively. The components
obtained were then rotated using the Varimax normalized rotation, which leads to orthogonal eigenvectors, redistributing
the explained variance between the eigenvectors. As a result,
the loading on each component was obtained for each region.
We then found for each brain region the highest loading in
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
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Table 2
Eigenvalues and percent cumulative variance (in parentheses) explained by each component in each condition
Condition/components
First factor
Second factor
Third factor
Fourth factor
Conventional metaphors
Novel metaphors
Literal expressions
Unrelated two-words
9.47 (49.86)
12.48 (65.72)
9.71 (51.14)
13.53 (71.19)
2.93 (65.28)
1.66 (74.49)
2.71 (65.44)
1.27 (77.87)
1.49 (73.16)
1.48 (82.33)
1.49 (73.3)
–
1.22 (79.58)
–
1.29 (80.13)
–
each component. By doing this, the brain regions that comprise each network under each experimental condition were
defined (see Table 3). As our aim was to define brain regions that are correlated in time course of activation, i.e., that
function as a brain network, we treated the number of eigenvectors as reflecting the number of networks that comprise
each condition.
This technique revealed different networks for the different experimental conditions (Table 3). The conventional
metaphors and the literal expressions produced four components (i.e., four networks), the novel metaphors produced
three components and the unrelated word pairs produced two
components. Bold words denote regions that are common to
the first component of all the conditions and include extrastriatal and posterior regions: the left parahippocampal gyrus,
left and right lingual gyri and the left and right fusiform gyri.
10.3. Correlation matrix
The functional association between each pair of regions
can be estimated in terms of the correlations between the
fMRI time series of each condition. In order to do so, we an-
Table 3
Principal Components Analysis applied to all ROIs for each condition
Literal
Conventional metaphors
First factor
Second factor
Third factor
Fourth factor
First factor
Second factor
Third factor
Fourth factor
Lparahip
Llingualgyrus
Rlingualgyrus
Lfusiform
Rfusiform
LWernicke
Lprecuneus
Rprecuneus
Lpremotor
Rpremotor
RBA9
LBA9
LBA45
LBroca
RBroca
RBA10
Rinsula
Linsula
RWernicke
Lparahip
Llingualgyrus
Rlingualgyrus
Lfusiform
Rfusiform
LWernicke
LBroca
RBroca
RBA10
LBA9
Linsula
Rinsula
LBA45
Lpremotor
Rpremotor
RBA9
Rprecuneus
RWernicke
Novel metaphors
Unrelated
First factor
Second factor
Third factor
First factor
Second factor
Lparahip
Llingualgyrus
Rlingualgyrus
Lfusiform
Rfusiform
LWernicke
Lprecuneus
Rprecuneus
LBA9
RBA9
LBA45
LBroca
Rinsula
Linsula
Lpremotor
Rpremotor
RWernicke
RBA10
RBroca
Lparahip
Llingualgyrus
Rlingualgyrus
Lfusiform
Rfusiform
Rprecuneus
Rpremotor
LBA9
RBA10
LBroca
LBA45
Lprecuneus
Rinsula
Linsula
LWernicke
Lpremotor
RWernicke
RBA9
RBroca
The different networks (factors) corresponding to each stimulus type and the brain regions, which comprise each network. Bold regions are the brain areas
common to all the first factors of each stimulus type and represent the basic stage of words reading. We interpret the second factor as representing the process
of attributing meaning to the linguistic expression. Note that the right homologue of Wernicke’s area (bold and italic) is a member of the second factor of the
novel metaphoric expressions but appears alone in the last factor of the literal stimuli and the conventional metaphors. In the second factor of the literal and
the conventional metaphoric expressions a small network, consisting of Broca’s area and its right homologue, and right BA 10, is common to both of them
(italic font). Also, note that the left precuneus is absent in the conventional metaphors condition because it showed loading differences smaller then 0.1 at the
first and the third components. L before the name of the brain area represents the brain areas in the LH and R represents the brain areas in the RH. Lparahip,
left parahippocampal; LWernicke, Wernicke’s area; RWernicke, the homologue of Wernicke’s area; RBroca, homologue of Broca’s area; BA9, middle frontal
gyrus (BA 9); BA45, inferior frontal gyrus (BA 45); BA10, middle frontal gyrus (BA 10).
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
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Table 4
Explained variance (r2 ) of pairs of regions
r2
Lparahip
Llingualgyrus
Rlingualgyrus
Lfusiform
Rfusiform
Lprecuneus
LWernicke
1.00
0.91
0.80
0.71
0.72
0.55
1.00
0.72
0.75
0.67
0.50
1.00
0.83
0.68
0.71
1.00
0.53
0.67
1.00
0.53
1.00
B: conventional metaphor (CM) condition
Lparahip
1.00
Llingualgyrus
0.26
1.00
Rlingualgyrus
0.45
0.77
Lfusiform
0.5
0.65
Rfusiform
0.14
0.69
Lprecuneus
0.48
0.35
LWernicke
0.24
0.33
1.00
0.51
0.46
0.32
0.22
1.00
0.51
0.66
0.41
1.00
0.26
0.11
1.00
0.23
1.00
C: literal (L) condition
Lparahip
Llingualgyrus
Rlingualgyrus
Lfusiform
Rfusiform
Lprecuneus
LWernicke
A: novel metaphor (NM) condition
Lparahip
1.00
Llingualgyrus
0.78
Rlingualgyrus
0.83
Lfusiform
0.70
Rfusiform
0.61
Lprecuneus
0.79
LWernicke
0.50
1.00
0.76
0.61
0.78
0.64
0.52
0.60
1.00
0.87
0.74
0.70
0.49
0.53
1.00
0.69
0.55
0.53
0.36
1.00
0.59
0.67
0.66
1.00
0.62
0.68
1.00
0.70
1.00
D: unrelated (UR) condition
Lparahip
1.00
Llingualgyrus
0.4
Rlingualgyrus
0.41
Lfusiform
0.62
Rfusiform
0.34
Lprecuneus
0.33
LWernicke
0.27
1.00
0.9
0.74
0.5
0.71
0.62
1.00
0.76
0.49
0.7
0.51
1.00
0.63
0.63
0.53
1.00
0.45
0.27
1.00
0.7
1.00
r2
LBroca
RBroca
LBA9
RBA9
Lfusiform
Rfusiform
LWernicke
RWernicke
1.00
0.13
0.01
0.25
0.17
0.08
0.28
1.00
0.61
0.75
0.64
0.56
0.33
1.00
0.54
0.57
0.46
0.35
1.00
0.83
0.71
0.60
1.00
0.67
0.60
1.00
0.48
1.00
F: conventional metaphor (CM) condition
LBroca
1.00
RBroca
0.45
1.00
LBA9
0.44
0.39
RBA9
0.36
0.11
Lfusiform
0.14
0.07
Rfusiform
0.03
0.01
LWernicke
0.04
0.09
RWernicke
0.01
0.02
1.00
0.63
0.42
0.33
0.14
0.01
1.00
0.59
0.39
0.10
0.01
1.00
0.51
0.41
0.01
1.00
0.11
0.01
1.00
0.05
1.00
G: literal (L) condition
LBroca
1.00
RBroca
0.05
LBA9
0.19
RBA9
0.32
Lfusiform
0.17
Rfusiform
0.25
1.00
0.61
0.46
0.5
1.00
0.53
0.41
1.00
0.63
1.00
E: novel metaphor (NM) condition
LBroca
1.00
RBroca
0.22
LBA9
0.45
RBA9
0.25
Lfusiform
0.31
Rfusiform
0.28
LWernicke
0.24
RWernicke
0.30
1.00
0.03
0.00
0.04
0.00
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
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Table 4 (Continued )
r2
LWernicke
RWernicke
LBroca
RBroca
LBA9
RBA9
Lfusiform
Rfusiform
LWernicke
RWernicke
0.04
0.00
0.13
0.00
0.5
0.00
0.38
0.01
0.53
0.06
0.27
0.08
1.00
0.12
1.00
1.00
0.25
0.33
0.30
0.20
0.28
0.34
1.00
0.63
0.71
0.81
0.66
0.31
1.00
0.66
0.60
0.65
0.63
1.00
0.59
0.65
0.61
1.00
0.67
0.35
1.00
0.50
1.00
H: unrelated (UR) condition
LBroca
1.00
RBroca
0.53
LBA9
0.72
RBA9
0.6
Lfusiform
0.6
Rfusiform
0.65
LWernicke
0.74
RWernicke
0.39
For the: (A) novel metaphor (NM) condition, (B) conventional metaphor condition (CM), (C) literal (L) condition, (D) unrelated (UR) condition. More r2
values of other pairs of regions for the: (E) NM condition, (F) CM condition, (G) L condition, (H) UR condition. L before the name of the brain area represents
the brain areas in the LH and R represents the brain areas in the RH. Lparahip, left parahippocampal; LWernicke, Wernicke’s area; RWernicke, the homologue
of Wernicke’s area; LBroca, Broca’s area; RBroca, the homologue of Broca’s area; BA9, middle frontal gyrus (BA 9).
alyzed the T × N matrix, defined above, calculated Pearson
correlations (r’s) between each pair of regions and then converted these correlations into explained variance (r2 ). Large
positive correlations indicate that the time course activation of
two different areas tend to rise and fall simultaneously. Correlations close to zero indicate no linear relationship or connectivity between the two regions. Table 4 demonstrates the explained variance of most of the ROI’s and their homologues.
As expected, some homologue regions showed high r2 values. For example, the left and right lingual gyri showed 0.86
explained variance for the four conditions; the left and right
fusiform gyri showed an average of 0.64 explained variance;
the frontal areas showed lower values: the left and the right
premotor areas (BA 6) showed an average of 0.53 explained
variance; Broca’s area and its right homologue showed an
average of 0.31 explained variance; and the temporal pair
of regions, Wernicke’s area and its homologue, showed an
average of 0.26 explained variance.
We first examined whether the core network that was contained in the first component of each condition after applying
PCA, and that includes the left parahippocampal gyrus, left
and right Lingual gyri, left and right fusiform gyri, and Wernicke’s area, have indeed large positive correlation in all of
the four conditions (Table 4A–D). As can clearly be seen, all
the areas, which are part of the core neural network involved
in the basic processing of word recognition and comprehension are highly correlated. In fact, only a few pairs of regions
did not exceed the threshold of 0.3 (r2 ). Note that Wernicke’s
area showed values under 0.3 to the conventional metaphor
condition (Table 4B).
As compared to Wernicke’s area, the right homologue
of Wernicke’s area revealed a different pattern of interconnections with other regions for the different experimental
conditions. In the salient stimulus conditions (conventional
metaphors and literal expressions), the right homologue of
Wernicke’s area did not show r2 greater than 0.4 with any of
the regions. In contrast, the novel metaphors and the unrelated word pairs recruited widespread regions. In the novel
metaphor condition, the right homologue of Wernicke’s area
seemed to cooperate with its homologue region, Wernicke’s
area, and it was also highly correlated with the left and right
precuneus, the left and right premotor areas (BA 6), and the
right insula. These results support our PCA results as these
regions are members of the first and the second component
of the novel metaphor condition. Actually, the right homologue of Wernicke’s area demonstrated high explained variance with other regions in the novel metaphor (last row of
Table 4F) and the unrelated word pair conditions (last row of
Table 4F), i.e., expressions with nonsalient meanings, but not
in the literal word pair (last row of Table 4G) and the conventional metaphor conditions (last row of Table 4H), i.e.,
familiar, salient meanings. This can be seen by comparing
the last rows.
11. Group activation maps
Two-color activation maps for the novel (yellow color)
versus conventional (blue color) metaphor conditions are
presented in Fig. 2A and B. Novel metaphors show pronounced activation in the right homologue of Broca’s area
while the conventional metaphors show activation in Broca’s
area (Fig. 2A). The overall greater contribution of novel
metaphors relative to conventional metaphors in the homologue of Wernicke’s area is demonstrated in Fig. 2B. The
color-coded map at Fig. 2C and D shows greater activation
for unrelated word pairs and literal expressions, respectively,
than for blank. Literal expressions show stronger and more
extensive activation than the unrelated word pairs in the classical languages areas and in the prefrontal areas but not in the
visual cortex.
12. Discussion
The aim of the present study was to examine hemispheric
differences in processing literal, conventional metaphoric and
novel metaphoric linguistic expressions. Our expectations,
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
based on the graded salient hypothesis (GSH, Giora, 1997,
2002, 2003) were that processing nonsalient linguistic meanings, such as novel, unfamiliar metaphors, would recruit RH
regions whereas processing salient meanings, i.e., accessing
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the lexicalized meanings of either conventional metaphors
or of literal expressions, will mainly activate the LH, where
most of our linguistic knowledge is stored. Thus, according
to the GSH, our predictions concerning the comprehension of
Fig. 2. Group analysis map of 15 subjects for sagittal (left picture) and axial (right picture) sections of the brain group activation map obtained by contrasting
novel metaphors (blue) with conventional metaphors (yellow) in a GLM analysis presenting the relative contribution of novel metaphors (NM; blue–green) or
conventional metaphors (CM; yellow–red). (A) Broca’s area (red-cross position); right homologue of Broca’s area (white circle position). (B) Red-cross at the
right homologue of Wernicke’s area; white circle at Wernicke’s area. Group activation map obtained by contrasting (C) unrelated word pairs and (D) literal
expressions vs. blank in a GLM analysis (pcorrected < 0.05). Broca’s area (red-cross position); Wernicke’s area (white circle); visual cortex (dashed circle).
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N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
Fig. 3. The special network recruited for the processing of the novel metaphoric expressions, obtained from the second component of the novel metaphors
condition from PCA. (A) Group analysis map of averaged signal obtained from 15 subjects on axial view. The color-coded map presents more activation for
novel metaphors than for blank (threshold: t(1636) > 2.4; p < 0.05). The circled regions comprise the second component of the novel metaphors condition of
PCA. (B) The table represents the explained variance (r2 ) values for the brain regions of the network. L before the name of the brain area represents the brain
areas in the LH and R represents the brain areas in the RH; RWernicke, homologue of Wernicke’s area; RBroca, homologue of Broca’s area.
literal and metaphoric linguistic material by the RH or the LH
were based solely on the degree of the salience of the material.
In order to examine processing differences between the two
cerebral hemispheres we applied PCA and a factor analysis
to identify areas of the brain that are networked and co-active
while processing information. This method does not reflect
the chronological or temporal order of events in the brain, so
that Factor 1 does not necessarily precede Factor 2, but rather
identifies the brain network associations co-active during the
processing of each of the four types of linguistic expressions
tested in the present study. This technique was applied by using PCA of the 20 × 19 matrix, with columns that represent
the averaged activation of all subjects in a specific region. We
also calculated all the correlations (r) and the explained variance (r2 ) between each pair of brain regions (Table 4A–H).
Our PCA results provide evidence for the unique role of
the right homologue of Wernicke’s area in processing novel
metaphors. We decomposed the 20 × 19 matrix by the PCA
method in order to find the regions (i.e., items) constituting the different networks (i.e., components) for each of the
four experimental conditions. It seems that the processing of
novel metaphoric expressions that have nonsalient linguistic
meanings requires the recruitment of a special neural network
consisting of the right homologue of Wernicke’s area, Broca’s
area (BA 44), left and right insula, and left and right premotor
areas (BA 6) (Table 3, second factor in the novel metaphor
condition). Thus, in order to turn a novel metaphoric word
pair into a meaningful linguistic expression, a special network that includes the right homologue of Wernicke’s area
might be recruited (Fig. 3). Fig. 3B shows the contribution
of the intercorrelated brain regions of this special network
and their contribution to the total variance (r2 values) (Table
below Fig. 3B). As can be seen, the smallest r2 value is obtained for the connection between Broca’s area and the right
homologue of Wernicke’s area (r2 = 0.30) whereas the highest r2 value is obtained for the connection between the left
insula and Broca’s area (=0.76). This result is consistent with
the claim that the right middle temporal gyrus, which is adjacent to the right homologue of Wernicke’s area, is specialized
for integrating linguistic material into a coherent content (St.
George et al., 1999), a process that might be crucial for deciding that the two words of the novel metaphors constitute a
coherent linguistic expression. The findings are also consistent with results obtained by Bottini et al. (1994), that used
the PET brain imaging technique to study the processing of
metaphoric and literal sentences. They reported more RH involvement when subjects had to make plausibility judgments
for new metaphoric sentences (“The investigators were squirrels collecting nuts”) as compared to literal sentences (“The
boy used stones as paperweights”). Significant greater activation for new metaphoric sentences was found in the right
inferior temporal gyrus, right premotor cortex, and the right
homologue of Wernicke’s area. According to the authors,
their results reflect the recruitment of episodic memories or
the generation of visual imagery, which are both required for
facilitating metaphoric judgment. The fact that the right homologue of Wernicke’s area appeared as a separate factor (last
factor) when processing conventional metaphors and literal
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
expressions suggests that this area plays a different role, if
any at all, in the processing of familiar linguistic expressions
that convey salient meanings.
The results of the correlation matrix obtained from the
above mentioned 20 × 19 matrix support those obtained with
PCA. As can be seen by the large r2 values (last row in
Table 4E and H), the right homologue of Wernicke’s area
showed similar patterns of correlations when processing the
novel metaphors as when processing the unrelated word pairs,
i.e., when processing nonsalient linguistic meanings. In contrast, low r2 values were obtained when processing the conventional metaphors and the literal expressions (see last row
in Table 4F and G). These results further suggest that the right
homologue of Wernicke’s area is involved in the processing
of nonsalient meanings.
The results of PCA applied for each of the four experimental conditions reveal a core neural network, common to
all of these conditions. This core network (regions in bold,
Table 3) is found in the first factor when processing all four
types of word pairs and consists of the left parahippocampal
gyrus, the left and right lingual gyri, and the left and right
fusiform gyri. This core network includes regions which (apparently) are involved in the processing of written words and
in creating internal image. The left fusiform is known to be
involved in active image generation (Farah, 1995; ThompsonSchill, Aguirre, D’Esposito, & Farah, 1998) and is also associated with the retrieval of visual knowledge (ThompsonSchill et al., 1998) that may be necessary for the understanding of written linguistic expressions. The left fusiform and
the lingual gyri might be selectively involved in the processing of written words (Polk & Farah, 1998) since damage to
the left fusiform area can cause pure alexia (Warrington &
Shallice, 1980). Both the lingual gyri and the fusiform gyri are
activated while reading written words (as opposed to hearing
words) but these areas are also activated in a picture naming
task (for a review, see Price, 2000). In a PET study (Murtha,
Chertkow, Beauregard, & Evans, 1999) subjects were asked
to name animal pictures and to perform a semantic judgment
task (i.e., decide whether the animal had hooves, claws or
neither). As compared to the naming task, the semantic judgment task might require an additional cognitive stage, i.e.,
retrieving semantic information. The judgment task minus
the naming picture task elicited an increase in blood flow in
the left fusiform (BA 19) as well as in other brain areas. According to the authors, this pattern of results suggests that the
fusiform area is involved in visual perceptual semantics.
Wernicke’s area is also part of the first factor core network, but only in three of the four experimental conditions,
i.e., excluding the unrelated word pair condition. The latter does not require the integration of the meanings of two
words into a meaningful linguistic expression. This finding
suggests that Wernicke’s area might play a significant role in
integrating the meanings of two words that create either familiar meaningful expressions (such as conventional metaphors
and literal expressions) or unfamiliar, but yet plausible, expressions (i.e., novel metaphors) but not when processing
2097
unrelated word pairs. Thus, the attribution of meaning performed by this area might be relatively basic, leading only to
a decision as to whether a linguistic expression is meaningful
or meaningless, but not as to whether this meaning is literal
or metaphoric. In contrast, Broca’s area is the brain region
constituting part of the second factor of each condition but
does not constitute a part of the first factor. The role of Broca’s
area is well established. According to recent studies, this area
is involved in the selection of relevant features of semantic
knowledge among competing alternatives (Thompson-Schill,
DEsposito, Aguirre, & Farah, 1997; Gabrieli, Poldrack, &
Desmond, 1998; Keller, Carpenter, & Just, 2001). This finding could indicate that Broca’s area plays a higher-level role
in constructing the meanings of linguistic expressions. Thus,
understanding metaphors seems to require a process during
which subjects select salient, dominant, attributes of the vehicle and compare them with features of the topic of the
metaphor.
The right precuneus was found to play an important role
in the main network involved in the processing of novel
metaphors (member of the first factor) but not in the processing of conventional metaphors. The involvement of the
right precuneus in processing novel metaphors could reflect
the retrieval of information from long-term episodic memory (Grasby et al., 1993; Shallice et al., 1994). According to
Lakoff and Johnson (1980) comprehending metaphoric language may depend on conceptualizations of personal experiences retrieved from episodic memory. Retrieving information from episodic memory and using mental imagery may
both be required for the understanding of unfamiliar novel
metaphoric expressions, much more than for understanding
conventional, familiar metaphors. This is because the meanings of the conventional metaphors are lexicalized through
frequent usage while the meanings of the novel metaphors
are not part of the lexicon and hence might require additional
processing. According to Bottini et al. (1994), additional cognitive processes, such as the retrieval of information from
episodic memory, are required in order to overcome the denotative violation characterizing metaphorical language. However, it is not completely clear why the right precuneus is a
member of the first factor when processing literal meanings
but not when processing conventional metaphors.
The findings of the present study suggest that the understanding of salient, familiar expressions requires the recruitment of a small network that includes bilateral frontal areas,
Broca’s area and its right homologue (BA 44) and right BA
10 (note Factor 2 in the conventional metaphors and the literal condition of Table 3). This pattern of activation is not
found for either the novel metaphor or for the unrelated word
pair conditions. These findings are consistent with models
proposing that a common mechanism is recruited for the understanding both literal and nonliteral language (Cacciari &
Glucksberg, 1994).
Our PCA results, as well as the RTs obtained from the behavioral data, reveal different networks for processing salient
versus nonsalient meanings and thus support the GSH’s pre-
2098
N. Mashal et al. / Neuropsychologia 43 (2005) 2084–2100
diction of similar networks that would be involved in the
processing of literal expressions and conventional metaphors
but not in the processing of novel metaphors. However, our
results do not support the predictions of either the standard
pragmatic model or the direct access view. The similarity between the networks involved in the processing of the conventional and the literal stimuli is noteworthy. Both produce four
networks and both share common brain areas especially in the
first, second and the forth factor (Table 3), and differ mainly in
the third factor. The novel metaphors and the unrelated word
pairs (the nonsalient stimuli) are also very similar as can be
seen by the commonality of the brain areas involved in the
first and the second factors. However there do exist some differences between the networks with regard to the processing
of novel metaphors versus trying to make sense of unrelated
words. Wernicke’s area which is a member of the first factor
of the novel metaphors (as well as for the conventional and
the literal expressions) is a member of the second factor in the
unrelated condition. This fact might indicate, as discussed before, that Wernicke’s area is highly involved in the processing
of meaningful expressions and may play a different role in
the processing of meaningless expressions. The second difference is that the novel metaphors elicited, in addition to the
two factors of the unrelated condition, a third factor consisting
of the homologue of Broca’s area and right BA10. This third
factor explains (82.33 − 74.49 =) 7.84% of the variance over
the second factor which is approximately the same amount
of variance explained by the contribution of the second factor
in the unrelated condition (77.87 − 71.19 = 6.68%, Table 2).
Furthermore, the findings that the right BA10 is a member of
the first factor of the unrelated condition, and the right homologue of Broca’s area is highly loaded on the second factor of
the unrelated condition, might exclude the possibility that this
additional network is an arbitrary result of the PCA. Thus,
it seems that understanding novel metaphors requires more
than just making sense of unrelated word pairs.
To sum up, the application of PCA technique suggests
that during the performance of a relatively complex semantic
judgment task, multiple different brain areas are cooperating through distributed patterns of co-activity that are highly
correlated. Although each experimental condition seems to
activate a different functional network, it could be argued
that they all share a core neural network which is involved
in the basic processes of reading and understanding coherent linguistic expressions (conventional metaphors, novel
metaphors and literal expressions). This basic network includes bilateral non-frontal regions: left parahippocampal
gyrus, left and right lingual gyri, left and right fusiform gyri,
and Wernicke’s area. The present findings also indicate that
the right homologue of Wernicke’s area plays a special role
in the processing of novel metaphors, which is related to the
unique sensitivity of this brain area for nonsalient linguistic material and not for metaphoric language per se. Thus,
when processing nonfamiliar, yet meaningful linguistic expressions, activation in the right homologue of Wernicke’s
area seems to be highly correlated with activation in frontal
areas: Broca’s area, left and right insula and left and right premotor areas. Finally, Broca’s area seems to play a more important role than Wernicke’s area in attributing either metaphoric
or literal meanings to two-word expressions. Both the behavioral and fMRI data support the claim, derived from the GSH,
that the degree of salience, rather than the literal/metaphoric
distinction, is the critical factor affecting linguistic processing by the two cerebral hemispheres.
Acknowledgments
We thank Ricardo Tarrasch and Maya Bleich for their assistance in analyzing and presenting the data.
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