Brain and Language 100 (2007) 150–162
www.elsevier.com/locate/b&l
Deriving meaning: Distinct neural mechanisms for
metaphoric, literal, and non-meaningful sentences
Argyris K. Stringaris a,*,1, Nicholas C. Medford a,1, Vincent Giampietro b,
Michael J. Brammer b, Anthony S. David a
a
Section of Cognitive Neuropsychiatry, Institute of Psychiatry, KingÕs College London, Denmark Hill, London SE5 8AF, UK
b
Brain Image Analysis Unit, Institute of Psychiatry, KingÕs College London, Denmark Hill, London SE5 8AF, UK
Accepted 1 August 2005
Available online 13 September 2005
Abstract
In this study, we used a novel cognitive paradigm and event-related functional magnetic resonance imaging (ER-fMRI) to investigate the neural substrates involved in processing three different types of sentences. Participants read either metaphoric (Some surgeons are butchers), literal (Some surgeons are fathers), or non-meaningful sentences (Some surgeons are shelves) and had to decide
whether they made sense or not. We demonstrate that processing of the different sentence types relied on distinct neural mechanisms.
Activation of the left inferior frontal gyrus (LIFG), BA 47, was shared by both non-meaningful and metaphoric sentences but not by
literal sentences. Furthermore, activation of the left thalamus appeared to be specifically involved in deriving meaning from metaphoric sentences despite lack of reaction times differences between literals and metaphors. We assign this to the ad hoc concept construction and open-endedness of metaphoric interpretation. In contrast to previous studies, our results do not support the view the
right hemispheric is specifically involved in metaphor comprehension.
Ó 2005 Elsevier Inc. All rights reserved.
Keywords: Metaphor; Figurative; Literal; Meaningfulness; Thalamus; Frontal cortex; Parietal cortex; Right hemisphere
1. Introduction
What does it take to decide whether or not a statement
makes sense? Are different neural mechanisms required
for understanding different types of sentences? Literal
sentences (such as ‘‘some men are young’’) may be
regarded as prototypical meaningful utterances. A standard view, dating back to antiquity, states that when
reading a sentence, the first attempt is at extracting a literal meaning and if this is found to be defective, the reader proceeds to consider alternative interpretations rooted
in metaphor, humour or irony (Aristotle, 1952; Grice,
*
Corresponding author. Fax: +44 020 7 848 0572.
E-mail address: [email protected] (A.K. Stringaris).
1
Both authors contributed equally to the accomplishment of this
work.
0093-934X/$ - see front matter Ó 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.bandl.2005.08.001
1975; Searle, 1993). According to this view, a sentence
of the type ‘‘Some men are lions’’ would first be recognized as literally false before its metaphoric meaning
was derived. This ‘‘primacy of the literal’’ doctrine implies an additional computational burden for the interpretation of figurative and ironic phrases. However,
metaphoric, idiomatic, and ironic utterances are abundant in every day discourse and are readily understood
by listeners. Indeed, empirical research on figurative language over the last few decades has found that literal and
figurative phrases follow the same time-course and that
readers cannot ignore figurative meaning in favour of a
literal interpretation (Glucksberg, Gildea, & Bookin,
1982; McElree & Nordlie, 1999). This is true even for relatively unfamiliar metaphors, and appears to be dependent on the aptness of the figurative expression and the
relative metaphoric salience of words involved (Blasko
A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
& Connine, 1993; Giora, 1999, 2003). These results have
led to the currently prevailing view that metaphorical
meanings are understood as automatically as their literal
counterparts (Glucksberg, 2003).
Equivalence at the behavioural level may conceal differences at the neural processing level and it is in such
circumstances that knowledge of functional neuroanatomy can make a significant contribution to cognitive science. There is considerable debate surrounding the brain
localization of figurative language processing. In early
lesion studies, Winner and Gardner (1977), using a picture matching task, showed that patients with right
hemispheric (RH) damage had more deficits in processing metaphors than those with left hemispheric lesions.
In addition, Brownell, Simpson, Bihrle, Potter, and
Gardner (1990) suggested a special role for the right
hemisphere in lexical semantic processes related to metaphor comprehension.
However, in Winner and GardnerÕs (1977) study,
when the same patients with right hemisphere damage
were asked to explicate the meanings of metaphors in a
verbal task they performed correctly. In keeping with
this finding, Rinaldi, Marangolo, and Baldassari (2005)
reported that right-hemisphere damaged patients tended
to inappropriately select literal over metaphoric meanings only in a picture matching task but not in a verbal
task. In addition, recent findings have challenged the notion that RH damage selectively impairs understanding
of verbal figurative language (Gagnon, Goulet, Giroux,
& Joanette, 2003; Giora, Zaidel, Soroker, Batori, &
Kasher, 2000; Tompkins, Boada, & McGarry, 1992). It
is possible that methodological issues related to matching of stimuli across experimental conditions and additional visuo-perceptual deficits of RH patients could
account for the differences observed. A study employing
lateralized presentation of metaphoric and literal targets
has also suggested an enhanced role of the RH in metaphor comprehension (Anaki, Faust, & Kravetz, 1998),
although a further study has found that hemispheric lateralization of metaphors is related to the type of task
used, e.g., whether the task involves the presentation of
single words, as opposed to whole sentences (Faust &
Weisper, 2000; see also Papagno and Caporali, this volume). In addition, recent findings from repetitive transcranial magnetic stimulation (rTMS) experiments
indicate that left, rather than right, temporal lobe stimulation affected comprehension of idioms (Papagno,
Oliveri, & Romero, 2002). Conversely, a PET study conducted with 6 subjects (Bottini et al., 1994) demonstrated
extensive right hemispheric involvement for the interpretation of figurative as opposed to literal sentences. However, this study utilized complex sentences, which were
not well matched across the two conditions. Subjects in
that study made significantly more mistakes interpreting metaphoric compared to literal sentences. A more
recent event-related functional magnetic resonance
151
(ER-fMRI) study indicated that reading metaphoric
sentences, in contrast to carefully matched literal sentences, lead to increased activation in left frontal and
temporal brain regions (Rapp, Leube, Erb, Grodd, &
Kircher, 2004). Similarly, a study by Lee and Dapretto
(manuscript submitted) using fMRI demonstrates that
processing of metaphoric semantic relationships mainly
lead to activation of left rather than right prefrontal
and temporoparietal regions. The authors argue that
previous findings suggesting a selective role of the right
hemisphere in comprehension of figurative language,
might be reflective of task complexity and not specific
to processing of metaphors.
Although at first sight, semantic decisions appear to
be binary, i.e., a given utterance can either make sense
or not make sense, it has been suggested that there are
significant qualitative differences in the extraction of
meaning from different types of sentences. For example,
despite the functional equivalence of metaphoric and
literal sentences, as witnessed by equal reaction times
between the two conditions, current cognitive models
suggest differences in the processing requirements
between literal and figurative language. One of the most
influential such models treats metaphors as attributive
assertions: in a sentence such as ‘‘My job is a jail,’’
‘‘job’’ and ‘‘jail’’ are members of a common attributive
category of unpleasant and confining situations
(Glucksberg & Keysar, 1993). It is claimed that metaphoric expressions differ from similes in that they have
more expressive force (Glucksberg, 2003). Furthermore,
it has been claimed that metaphors differ from literal
sentences in that they are ‘‘open ended’’ (Black, 1993;
Boyd, 1993), implying that their meaning is less well circumscribed and more flexible, and that they are not
readily paraphrasable into literal expressions (Searle,
1993). Indeed, in Gibbs (1992), idiomatic expressions,
while being understood more quickly than their literal
interpretations, involved a wider range of entailments.
We predicted that differences in the quality of meaning
of sentences would be reflected in their respective brain
representations and that fMRI would enable us to highlight this distinction, even when traditional behavioural
measures of performance were similar. For this purpose,
we compared two of the most commonly occurring sentence types, literal and metaphoric, and contrasted them
to non-meaningful sentences. Subjects were asked to decide whether sentences presented sequentially ‘‘made
sense or not’’ and indicate their decision by a button press
(for a similar task, see Gernsbacher, Keysar, Robertson,
& Werner, 2001). Reaction times and BOLD-signal
changes were measured.
In contrast to a recent imaging study on metaphors,
where subjects were asked to judge on the affective salience of figurative as opposed to literal sentences (Rapp
et al., 2004), our task was more explicit, focussing on the
attempt to extract meaning from sentences. Further-
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more, our task was complemented by the presence of
non-meaningful phrases. The advantage of this design
is twofold. First, it introduces more ambiguity to the
task, prompting subjects to distinguish between different
sentence types strictly on the basis of semantics. Second,
it allows for contrasts between successful and unsuccessful quests to comprehend a sentence. By using ERfMRI, we were able to present stimuli in random order
and thus rule out anticipation effects, and also exclude
subjectsÕ errors from our imaging analysis. Our main
hypothesis was that although processing of metaphoric
sentences compared to their literal counterparts would
not differ in terms of reaction times, it would nevertheless involve increased activation of brain areas implicated in processing of linguistic information and retrieval
of semantic knowledge in particular (Duncan & Owen,
2000; Thompson-Schill, DÕEsposito, Aguirre, & Farah,
1997). We therefore predicted that areas of the left inferior frontal gyrus (LIFG) would show increased activation for the processing of metaphoric, when compared
to literal, statements. Furthermore, we assumed that
judging a syntactically intact sentence to be non-meaningful would also require increased effort and access to
semantic stores, and this would also be reflected by activation in the LIFG when compared to literal statements.
In addition, we hypothesized that, since processing of
metaphoric statements is thought to require recognition
of common attributive categories, this would involve a
neural network allowing for the integration of multisensory input. Also, based on previous experimental evidence (Gagnon et al., 2003; Papagno et al., 2002;
Rapp et al., 2004), we expected that matching of stimuli
across conditions with respect to basic linguistic parameters would confirm that differential activation of the
right hemisphere is not involved in metaphor
comprehension.
2. Methods
2.1. Participants
For the fMRI study, the participants were 11 self-designated right-handed male volunteers with no history of
psychiatric or neurological illnesses who were native
English speakers.2 All provided written informed consent in accordance with procedures laid down by the local research ethics committee. Mean age was 33.3 years
(SD 8 years) and mean verbal IQ was 116 (SD 6), as
2
To avoid any potential confounding effects related to genderspecific language processing differences, this study was confined to
male subjects. While there is no compelling evidence from behavioural
studies to suggest that metaphor comprehension differs between male
and female subjects, future functional imaging studies would be useful
to address this issue.
assessed by using the National Adult Reading Test
(Nelson & Willison, 1982). For the outside the scanner
phase, additional 20 right-handed subjects (10 men, 10
women) were tested (mean age, 33.0 years (SD 8.5
years); mean IQ, 117, (SD 7)).
2.2. Experimental stimuli and design of the task
Triplets of sentences of the form ‘‘some X are Y’’
were constructed for the three experimental conditions:
literal (LIT), metaphoric (MET), and non-meaningful
(NONMEAN). The stem of the sentence ‘‘Some X
are. . .’’ was identical across conditions within a given
triplet, the last word varying; for example a sentence
stem such ‘‘Some surgeons are. . .’’ would be followed
by ‘‘fathers’’ for the LIT, ‘‘butchers’’ for the MET,
and ‘‘shelves’’ for the NONMEAN conditions, respectively. These last words were matched to within one letter for length and also to within one standard deviation,
for the following psycholinguistic norms using the MRC
Psycholinguistic database (http://www.psy.uwa.edu.au/
mrcdatabase/uwa_mrc.htm): imageability, familiarity,
Kucera-Francis written frequency, and concreteness.
The initial corpus of sentences consisted of 100 triplets.
Following assessment of their comprehensibility, 30 sentences were selected for the outside-the-scanner version
of the task, of which 25 sentences were used for the
fMRI version. Construction of metaphoric sentences
was based on expressions commonly used in English.
Sentences were presented for a fixed duration of 1.5 s
each according to a ‘‘true’’ random sequence of numbers
generated from a random number service (www.
random.org). Intervals between stimuli were variable
following a Poisson distribution around an average
interstimulus interval of 7 s. This ‘‘jitter’’ was introduced
to increase trial variance and avoid concealment of signal information due to overlap of the haemodynamic response in ER-fMRI experiments (Donaldson &
Buckner, 2001; Surguladze et al., 2003). During the
interstimulus intervals, a fixation cross was present on
the screen, which served as a baseline condition for the
haemodynamic response. (Please refer to the Image
analysis section for details of percentage BOLD—blood
oxygenation
level
dependent—signal
change
calculations.)
2.3. Experimental procedure
Subjects were given instructions prior to performing
the test. They were asked to read each presented
sentence silently and decide as fast and as accurately
as possible whether this ‘‘made sense or not,’’ indicating
their decision by pressing one of two buttons. They were
advised that sentences may either be meaningful in a
formal or colloquial way, or non-meaningful. They were
given illustrative examples of sentences not included in
A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
the study proper. For the experiments outside the scanner, subjects were seated in front of a computer screen.
For experiments in the scanner, sentences were presented to the subjects through a standard mirror system of
presentation and legibility of the items was ascertained
prior to commencement of the task. For both the outside the scanner and fMRI versions, the button box
was placed in the subjectsÕ right hand.
2.4. Analysis of behavioural data
For both the fMRI and the outside the scanner versions of the task, only ‘‘correct’’ responses, i.e., the
‘‘makes sense’’ responses for metaphors and literal sentences, and the ‘‘doesnÕt make sense’’ response for the
non-meaningful, were included in the analysis. Behavioural data from each subject were averaged across each
condition following logarithmic transformation to deal
with reaction time outliers (Ratcliff, 1993). Results
across the three conditions were compared using a
repeated measures ANOVA followed by the Tukey–
Kramer test for multiple comparisons, and CohenÕs d
effect sizes values were calculated (Cohen, 1992; Rosnow
& Rosenthal, 1996; Rosnow, Rosenthal, & Rubin, 2000;
Thalheimer & Cook, 2002).
2.5. Image acquisition
Gradient echo echoplanar imaging (EPI) data were
acquired on a GE Signa 1.5 T system (General Electric,
Milwaukee WI, USA). A quadrature birdcage headcoil
was used for RF transmission and reception. Hundred
T2*-weighted images depicting blood oxygenation level
dependent (BOLD) contrast (Ogawa, Lee, Kay, & Tank,
1990) were acquired over the entire duration of the task
at each of 22 near-axial non-contiguous 5 mm thick
planes parallel to the intercommissural (AC-PC) line:
TE 40 ms, TR 2 s, in-plane resolution 5 mm, interslice
gap 0.5 mm. This EPI dataset provided almost complete
brain coverage. An inversion recovery EPI dataset was
also acquired. This was a 43 near-axial slice image; with
3 mm slices and 0.3 mm slice skip parallel to the AC-PC
(TE 80 ms, TI 180 ms, TR 16 s, in-plane resolution
1.5 mm). This high-resolution inversion recovery EPI
gives excellent soft tissue to CSF contrast for a template
image onto which the lower-resolution functional data
were mapped. The IR-EPI template has the same bandwidth as the low-resolution functional scans to avoid
any mismatching of functional to anatomical data as
they both have the same inherent geometric distortion.
2.6. Individual brain activation maps
Data were analyzed with software developed at the
Institute of Psychiatry, KingÕs College London, using
a non-parametric approach. Data were first processed
153
(Bullmore et al., 1999a) to minimize motion-related
artefacts. A 3D volume consisting of the average
intensity at each voxel over the whole experiment
was calculated and used as a template. The 3D image
volume at each time-point was then realigned to this
template by computing the combination of rotations
(around the x, y, and z axes) and translations (in x,
y, and z) that maximized the correlation between the
image intensities of the volume in question and the
template. Following realignment, data were then
smoothed using a Gaussian filter (FWHM 7.2 mm)
to improve the signal to noise characteristics of the
images.
Responses to the experimental paradigms were then
detected by first convolving each component of the
experimental design with each of two gamma variate
functions (peak responses at 4 and 8 s, respectively).
The best fit between the weighted sum of these convolutions and the time series at each voxel was computed
using the constrained BOLD effect model suggested by
Friman, Borga, Lundberg, and Knuttson (2003). This
reduces the possibility of the model fitting procedure
giving rise to mathematically plausible but physiologically implausible results. Following computation of
the model fit, a goodness of fit statistic was computed.
This consisted of the ratio of the sum of squares of
deviations from the mean image intensity (over the
whole time series) due to the model to the sum of
squares of deviations due to the residuals (SSQratio).
This statistic is used to overcome the problem inherent
in the use of the F (variance ratio) statistic that the
residual degrees of freedom are often unknown in
fMRI time series due to the presence of coloured noise
in the signal. Following computation of the observed
SSQratio at each voxel, the data are permuted by the
wavelet-based method described and extensively characterized in Bullmore et al. (2001). Repeated application of this method at each voxel followed by
recomputation of the SSQratio from the permuted data
allows (by combination of results over all intracerebral
voxels) the data-driven calculation of the null distribution of SSQratios under the assumption of no experimentally determined response. Using this distribution,
it is possible to calculate the critical value of SSQratio
needed to threshold the maps at any desired type I error rate. The detection of activated voxels is extended
from voxel to cluster level using the method described
in detail by Bullmore et al. (1999b). In addition to the
SSQratio, the size of the BOLD response to each
experimental condition is computed for each individual
at each voxel as a percentage of the mean resting image
intensity level. To calculate the BOLD effect size, the
difference between the maximum and minimum values
of the fitted model for each condition is expressed as
a percentage of the mean image intensity level over
the whole time series.
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A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
2.7. Group maps
The observed and permuted SSQratio maps for each
individual, as well as the BOLD effect size maps are
transformed into the standard space of Talairach and
Tournoux (1988) using the two stage warping procedure
described in detail in Brammer et al. (1997). This involves first computing the average image intensity map
for each individual over the course of the experiment.
The transformations required to map this image to the
structural scan for each individual in the first instance
and then from structural space to the Talairach template
are subsequently computed by maximizing the correlation between the images at each stage. The SSQratio
and BOLD effect size maps are then transformed into
Talairach space, using these transformations. Group
activation maps are then computed by determining the
median SSQratio at each voxel (over all individuals) in
the observed and permuted data maps (medians are used
to minimize outlier effects). The distribution of median
SSQratios over all intracerebral voxels from the permuted data is then used to derive the null distribution of
SSQratios and this can be thresholded to produce group
activation maps at any desired voxel or cluster-level type
I error rate. Cluster level maps are thresholded at <1
expected type I error cluster per brain. The computation
of a standardized measure of effect SSQratio at the individual level, followed by analysis of the median SSQratio maps over all individuals treats intra and inter
subject variations in effect separately, constituting a
mixed effect approach to the analysis, which is deemed
desirable in fMRI.
2.8. Sensitivity of detection of fMRI responses
To assess the ability of the above analysis software to
detect activations, an extension of the technique previously described by Desco, Hernandez, Santos, and
Brammer (2001) was used. This involved embedding
artificial activations in resting state fMRI data. Artificial
fMRI responses were produced using the Balloon model
described by Buxton, Wong, and Frank (1998) in the region of the hippocampus (bilateral), extrastriate visual
cortex (bilateral), left inferior frontal cortex, and anterior cingulate gyrus. The decision to embed activations
using a physiological model and analyze using a pair
of gamma variate functions was taken to bias detection
excessively by using the same method for embedding
and analysis. Combinations of gamma functions are
commonly used to model BOLD effects in fMRI analysis. The activation sizes simulated (spatial extents) were
comparable with those commonly detected in these regions in fMRI experiments on encoding recall, motion
perception and verbal fluency (500–1000 mm3). BOLD
effect sizes of up to 1% were simulated with block designs (10 alternating on/off blocks of 10 images each)
and randomized event-related designs with 10 or 50
events per experiment.
Activations were embedded in the raw fMRI data for
6 subjects with reference to the available anatomy of the
images, and data were then processed through the individual and group analysis steps described above. The
threshold for detection of responses for all designs
occurred with a BOLD effect of 0.1–0.15%. With a 1%
effect size, approximately 90% of the embedded network
was detected with the block design, 70% with 50 trials
and 50% with 10 trials in the event-related simulations.
At an effect size of 0.5%, these figures fell to 65%
(block), 40% (50 events), and 20% (10 events).
2.9. Group comparisons
Comparisons of responses between experimental conditions are performed by fitting the data at each intracerebral voxel at which all subjects have non-zero data
using a linear model of the type
Y ¼ a þ bX þ e;
where Y is the vector of BOLD effect sizes for each individual, X is the contrast matrix for the particular intercondition contrasts required, a is the mean effect across
all individuals in the various conditions, b is the computed condition difference, and e is a vector of residual
errors. The model is fitted by minimizing the sum of
absolute deviations rather than the sums of squares to
reduce outlier effects. The null distribution of b is computed by permuting data between conditions/groups
(assuming the null hypothesis of no effect of experimental condition) and refitting the above model. Group difference maps are computed as described above at voxel
or cluster level by appropriate thresholding of the null
distribution of b. In this paper, BOLD effect maps were
used to compute significant condition differences rather
than standardized measures, such as SSQratio, F or t, as
these contain explicit noise components (error SSQ or
error variance), raising the possibility that group differences resulting from F, SSQratio or t comparisons could
reflect differences in noise rather then signal.
3. Results
3.1. Behavioural data
Behavioural results from the outside the scanner and
in the scanner experiments are summarized in Table 1
and highlighted in Fig. 1. In brief, reaction times for
metaphoric and literal sentences did not show a statistically significant difference (p > .05). Conversely, reaction
times for comprehension of non-meaningful sentences
differed significantly compared to both literal and metaphoric sentences (p < .001 and p < .05, respectively).
A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
155
Table 1
Mean age, mean verbal IQ, reaction times (RT) with respective standard deviations (SD) in milliseconds and logarithmically transformed RT (log
RT) and accuracy data of subjects
Offline
Age
VIQ
n = 20
33
117
Online
Age
VIQ
n = 11
32
116
Mean
RT
SD
log Mean
RT
log SD
Accuracy
(%)
ANOVA
F(2/19) = 12.069
p = .001
MET
LIT
NONMEAN
1229.9
1149.7
1308.8
338.7
324.4
379.5
3.0605
3.0329
3.0903
0.1084
0.1082
0.1173
74.2
92
90
MET vs LIT
MET vs NONMEAN
LIT vs NONMEAN
d = 0.25
d = 0.2639
d = 0.5
p > .05
p < .05
p < .001
SD
log Mean
RT
3.1387
3.1259
3.1783
log SD
Accuracy
(%)
78
98
89
ANOVA
F(2/20) = 4.262
p = .0395
MET
LIT
NONMEAN
Mean
RT
1460.2
1392.3
1576.7
MET vs LIT
MET vs NONMEAN
LIT vs NONMEAN
d = 0.25
d = 0.34
d = .625
p > .05
p > .05
p < .05
237
265
358
0.075
0.079
0.092
d Stands for effect size, where values of 0.20 are considered small; 0.50 medium; and 0.80 large (Cohen, 1992); statistical significance is reached when
p < .05.
A
B
Fig. 1. Behavioural results from experiments performed outside the scanner (A, n = 20) and during fMRI (B, n = 12). Logarithmically transformed
reaction times (log RT) and respective standard deviations are depicted.
3.2. fMRI results
3.4. Literal sentences
An overview of all activations obtained in the contrast between conditions is provided in Table 2 and
highlighted in Fig. 2.
Comprehension of literal sentences showed strong
activation of the right precentral gyrus (BA 4) when contrasted to either MET or NONMEAN sentences. There
was also activation of the medial prefrontal cortex (BA
11) and the right inferior frontal gyrus (RIFG; BA 44/
45) when comprehension of literal sentences was contrasted to metaphors and non-meaningful sentences,
respectively. There were also activations of the left cerebellum, right precuneus (BA 7), and the right inferior
temporal gyrus next to the middle occipital gyrus (BA
37/19) when LIT were contrasted to MET. Contrasting
LIT sentences with NONMEAN phrases also activated
BA 21 in the left temporal lobe. Furthermore, there was
activation in the occipital cortex in the LIT > NONMEAN condition.
3.3. Metaphoric sentences
Comprehension of MET sentences contrasted to
both LIT and NONMEAN sentences revealed activation of the left thalamus. In addition, there was activation in the left inferior frontal gyrus (LIFG) at
BA 47 and in the right middle temporal gyrus
(MTG) next to the middle occipital gyrus (MOG) at
BA 39/19 in the MET > LIT condition. Furthermore,
there was widespread activation in the supplementary
motor cortex (BA 6) and the right cerebellum as well
as in the primary visual cortex bilaterally for the MET > LIT comparison. Also, there was activation in the
inferior parietal lobule close to the occipital cortex
(BA 40/19) when metaphors were contrasted to literal
sentences.
3.5. Non-meaningful sentences
Contrasting the non-meaningful sentences with metaphoric and literal sentences reveals very extensive activa-
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A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
Table 2
3D cluster-based activations observed from contrasts of the three experimental conditions with respective number of voxels and probabilities
(p values)
Number of voxels
Tal (x)
Tal (y)
Tal (z)
Probability
BA
Side
Cerebral region
MET > LIT
12
17
10
8
10
9
6
41
7
29
14
7
43
32
21
25
51
29
59
81
15
29
67
81
66
11
59
29
13
4
2
15
20
25
36
42
0.001309
0.001047
0.002618
0.002618
0.005497
0.004974
0.008377
0.000262
0.007592
71
71
67
47
39/19
18
18
6
40/19
L
R
L
L
R
L
R
L
L
Cerebellum
Cerebellum
Thalamus
Inferior frontal gyrus
Middle temporal gyrus/middle occipital gyrus
Primary visual (peristriate) cortex (V2,V3)
Primary visual (peristriate) cortex (V2,V3)
Precentral gyrus
Inferior parietal lobe
7
15
4
0.007790
67
L
Thalamus
18
18
L
L
R
L
R
L
R
L
L
R
R
L
R
L
R
Fusiform gyrus
Fusiform gyrus
Cerebellum
Inferior frontal gyrus (BrocaÕs)
Posterior cingulate gyrus
Cuneus
Inferior frontal gyrus (BrocaÕs)
Inferior frontal gyrus (BrocaÕs)
Cuneus
Inferior temporal gyrus/middle occipital gyrus
Precuneus
Precuneus
Inferior parietal lobule
Precuneus
Lobus paracentralis
Medial prefrontal cortex GFd
Inferior parietal lobule
MET > NONMEAN
13
NONMEAN > MET
10
10
26
26
9
28
13
5
10
45
16
7
30
50
13
55
99
25
40
43
32
14
11
47
43
14
47
11
26
32
4
11
0.00
40
85
70
59
26
52
77
4
18
74
63
63
44
52
56
33
7
26
13
13
18
7
9
9
20
20
31
7
20
37
42
37
48
48
42
0.001778
0.002890
0.000222
0.000222
0.002445
0.000222
0.003112
0.005335
0.004001
0.000222
0.000222
0.007335
0.000222
0.000222
0.001556
0.000222
0.000222
47
29
17
44
44
19
37/19
31
31
40
23
5
6
40
LIT > MET
10
17
6
10
12
17
24
32
22
0
47
29
7
40
63
78
56
63
78
41
11
18
12
13
7
15
42
42
0.005159
0.002150
0.009243
0.004944
0.004084
0.001935
0.001290
71
71
11
37/19
71
7
4
R
L
R
R
Cerebellum
Cerebellum
Medial prefrontal cortex GFd
Inferior temporal gyrus/middle occipital gyrus
Cerebellum
Precuneus
Precentral gyrus
LIT > NONMEAN
7
16
7
17
51
22
47
36
7
74
15
7
7
15
20
42
0.004070
0.002713
0.009722
0.004296
21
18
44/45
4
L
L
R
R
Middle temporal gyrus
Cuneus
Inferior frontal gyrus (BrocaÕs)
Precentral gyrus
NONMEAN > LIT
14
15
15
8
7
18
41
9
10
22
30
11
133
18
40
32
32
47
22
14
54
29
29
4
0
40
56
59
18
48
52
78
67
4
56
59
7
56
30
40
18
2
2
4
9
20
20
37
42
48
53
48
0.002665
0.000485
0.000242
0.004846
0.006058
0.000485
0.000242
0.000727
0.001212
0.000727
0.000242
0.004604
0.000242
71
71
47
10
37
17
31
4
39/19
40/19
6
18
40
L
R
L
R
L
L
R
R
L
R
L
Cerebellum
Cerebellum
Inferior frontal gyrus
Middle frontal gyrus
Middle temporal gyrus
Cuneus
Precuneus
Precentral gyrus
Angular gyrus
Inferior parietal lobe
Middle frontal gyrus
Precuneus
Lobus parietalis inferior
BA stands for Broadman area.
L
L
L
L
A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
157
Fig. 2. (A) Activation of the left thalamus for metaphors. Top panel shows coronal (left) and transversal (right) section images observed in the
MET > LIT contrast (z coordinate = 4). Lower panel shows left thalamic activation in the MET > NONMEAN contrast (z coordinate = 4). Note
that activation of the left IFG is only observed when metaphors are contrasted with literal sentences but not with the non-meaningful (top panel). (B)
Activations for non-meaningful sentences contrasted to literal sentences. Axial images (left) show BOLD signal increases in the anterior cingulate,
paracentral lobule, left precuneus, and regions of the inferior parietal lobule bilaterally (z coordinate = 42).
tion in areas of the parietal cortex and precuneus bilaterally, although activation at the inferior parietal lobule
was clearly more pronounced in the left hemisphere. In
addition, activation of BA 47 in the LIFG was observed
for NONMEAN sentences when contrasted to either
LIT or MET phrases. A further activation of BA 6 in
the left middle prefrontal gyrus was observed for NONMEAN sentences contrasted to either MET or LIT sentences. Processing of NONMEAN sentences led to
activation of the RIFG (BA 44/45) when contrasted to
MET and to activation of BA 10 in the right middle
frontal gyrus when contrasted to LIT sentences. Activations common to both the NONMEAN > LIT and
NONMEAN > MET conditions included the right and
left cerebellum and parts of the occipital cortex. Also,
there was extensive activation in the right inferior
temporal gyrus next to the middle occipital gyrus
(BA 37/19) for the comparison between NONMEAN
and MET sentences. Activation of the left middle temporal cortex (BA 37) was seen when NONMEAN were
contrasted to LIT sentences.
4. Discussion
The results presented here support our prediction
that grasping the meaning of utterances engages different brain areas depending on the type of sentence involved. Contrasting two of the most commonly
occurring sentence types, LIT and MET, shows that,
in addition to increased recruitment from areas classically involved in semantic processing, i.e., BA 47 in the
LIFG, the left thalamus is involved in the processing
of metaphors. Furthermore, judging a sentence to be
non-meaningful requires significantly more processing
time, and involves an extensive parietal cortical network
in addition to the activation of areas implicated in conflict monitoring and decision-making. Activation in BA
47 of the LIFG was observed in both MET and NONMEAN sentences when contrasted to LIT sentences, but
was also apparent in the NONMEAN > MET contrast.
This finding is in accordance with previous findings suggesting a relative specificity of BA 47 for semantic tasks
(Costafreda, Fu, Lee, Brammer, & David, 2003; McDermott, Petersen, Watson, & Ojemann, 2003). Our finding
of an increased demand from the LIFG in MET and
NONMEAN phrases as compared to LIT sentences
may be taken to reflect a more extensive search for
semantic information, in keeping with the hypothesis
that the LIFG mediates retrieval and/or selection of
semantic knowledge (Duncan & Owen, 2000; Fiez, Petersen, Cheney, & Raichle, 1992; Kapur et al., 1994;
Thompson-Schill et al., 1997). Alternatively, this finding
might be attributable to an increased demand for control during retrieval as suggested by Wagner, Pare-Blagoev, Clark, and Poldrack (2001). Either way, the
finding of increased activation in the LIFG for both
MET and NONMEAN as opposed to LIT sentences
158
A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
suggests that additional semantic processing capacities
were required in the former. This is analogous to experimental tasks involving semantically incongruent versus
congruent word presentation following a priming clause;
indeed in an ER-fMRI study, Kiehl, Laurens, and Liddle (2002) have shown that, in a semantic task known to
elicit an ERP-response at 400 ms post-stimulus (N400),
activation of bilateral inferior frontal gyrus was significantly stronger for incongruous sentence-terminating
words. Interestingly, metaphoric sentences have also
been shown to elicit larger N400 responses when compared to literal sentences (Coulson & Van Petten,
2002; Pynte, Besson, Robichon, & Poli, 1996) Therefore,
it is conceivable that the observed activation in the
LIFG reflects an attempt to resolve semantically ‘‘unexpected’’ sentences by controlled retrieval from semantic
stores. The gradation of the LIFG response, being
stronger for NONMEAN than meaningful MET sentences would then reflect the degree of conflict and increased demand for retrieval for NONMEAN phrases.
This is supported by our finding that anterior cingulate
and middle prefrontal cortex, known to be associated
with conflict monitoring (Bush, Luu, & Posner, 2000;
Kerns et al., 2004), were significantly active only in the
NONMEAN > MET
and
NONMEAN > LIT
comparisons.
Our data demonstrate that processing of NONMEAN sentences compared to both MET and LIT
leads to significantly stronger activation in the inferior
parietal lobule and the precuneus bilaterally. We propose that activation in these brain areas reflects the attempt to extract potential meaning from the
NONMEAN sentences by deploying an extensive search
mechanism. Parietal areas, in particular the precuneus,
have been consistently implicated in memory-related
imagery (Bottini et al., 1994; Kosslyn, 1994; Fletcher
et al., 1995; Fletcher, Shallice, Frith, Frackowiak, &
Dolan, 1996). In addition, recent findings indicate that
evocation of imagery is an important component of sentence understanding and deductive reasoning (Just,
Newman, Keller, McEleney, & Carpenter, 2004; Knauff,
Mulack, Kassubek, Salih, & Greenlee, 2002), and that
parietal lobe activation is observed during transitive
inference (Acuna, Eliassen, Donoghue, & Sanes, 2002).
It is possible that evocation of imagery and spatial
representations are an important part of the attempt
to arrive at the meaning of sentences that are not readily
understood, such as our NONMEAN set of sentences in
this experiment. Alternatively, the activations observed
in NONMEAN could be interpreted as the result of
the very inability to arrive at meaning rather than the
search for it. In both cases, it appears that attempting
to comprehend semantically anomalous sentences involves a strategy which probably entails comparing sentence clues against existing information that includes
spatial and image-based representations. However, we
also observed activation of the right precuneus in the
LIT > MET condition, which is not readily accounted
for by imageability.
The most striking finding is the differentially increased
activation observed in the thalamus during comprehension of MET sentences. A role for the thalamus in language is suggested by data from patients with thalamic
lesions and from electrophysiological studies (Johnson
& Ojemann, 2000; Lhermitte, 1984; Ojemann & Ward,
1971; Ojemann, Fedio, & Van Buren, 1968). Vascular
lesions of the thalamus in the dominant hemisphere have
been shown consistently to produce language deficits,
most notably anomia, a finding that has been interpreted
as reflecting damage to networks involved in attentional
gating and working memory (Nadeau & Crosson, 1997;
Schmahmann, 2003). While one might suggest that reading metaphoric as opposed to literal sentences requires
increased recruitment of attention, this would fail to
explain why there is also increased thalamic activation
in MET compared to NONMEAN sentences, particularly given the behavioural data obtained here suggest
NONMEAN sentences to have been the most difficult
to process. We propose that instead of reflecting increased linguistic demand, thalamic activation observed
in our study, is a specific feature of the processes involved
in identifying attributive categories in the semantic network. Interpretation of a sentence such as ‘‘some men
are lions,’’ involves the identification of an emerging object in the semantic system, probably in terms of the class
inclusion assertions proposed by Glucksberg and Keysar
(1993), which accommodates features of both the words
‘‘men’’ and ‘‘lions,’’ e.g., ‘‘courageous person.’’ This implies that the association of the two words is made as a
holistic conjunction of the constituent terms, thus yielding a newly constructed, ad hoc representation (see also
Asch & Ebenholtz, 1962; Gernsbacher et al., 2001;
Kahana, 2002). In contrast, the interpretation of a corresponding literal sentence, e.g., ‘‘Some men are soldiers’’
does not necessitate resorting to a newly constructed,
ad hoc concept, but can be resolved compositionally by
simply juxtaposing the two words ‘‘men’’ and ‘‘soldiers.’’
In this sense, the type of association made during the
interpretation of metaphors is close to the concept of
non-compositional representations as suggested by
Fodor and Pylyshyn (1988).
A prototypical example of this model is the association made between words such as ‘‘computer’’ and
‘‘virus,’’ which would fuse to produce ‘‘computer virus,’’
a novel semantic object. In contrast, association by
juxtaposition is performed in the case of word pairs such
as ‘‘salt’’ and ‘‘pepper,’’ which usually requires no fusion
of the constituent terms. Recent empirical data demonstrate that the brain representations of compositional
and non-compositional associations differ (Kounios,
Smith, Yang, Bachman, & DÕEsposito, 2001). In an
fMRI study addressing this issue, Kraut et al. (2002a)
A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
have shown that activation of the left thalamus is specific for a task requiring non-compositional fusion, but not
for tasks that involve compositional association or the
identification of superordinate categories. The authors
have also shown thalamic activation to be present in a
similar task of non-compositional fusion where multimodal feature stimuli (picture–word associations) were
used (Kraut et al., 2002b). In addition, simultaneous
recordings from thalamic and scalp electrodes in a human, during a task requiring non-compositional associations, demonstrated functional interactions between
thalamic and cortical rhythms, suggesting a neural network underlying semantic recall (Slotnick, Moo, Kraut,
Lesser, & Hart, 2002). Taken together these data imply a
significant role of the thalamus during non-compositional associations, although the precise mechanism by
which this is accomplished remains unclear. It is obvious
that the neuroanatomical location of the thalamus and
the extensive thalamocortical, interthalamic, and corticothalamic connections plays an important part in this
process. The thalamus, as a relay station, probably acts
not only to coordinate but also to modulate cortical processing (Guillery & Sherman, 2002). It has also been argued that fast activities in thalamocortical/
corticothalamic connections allow for the interaction
between remote cortical regions (Lumer, Edelman, &
Tononi, 1997a, 1997b; Steriade, 2000). Such processes
would allow for the integration of activities in multiple
cortical areas during semantic recall in the MET
condition.
Activations were observed for all three conditions in
the right temporal cortex. BOLD signal increase in the
middle temporal gyrus close to the middle occipital lobe
(BA 39/19) was observed in the MET > LIT contrast,
whereas an activation in the right temporo-occipital
junction (BA 37/19) was observed in the NONMEAN > MET and LIT > MET contrasts. It is possible
that activations in this area reflect attempts for integration of audiovisual information during comprehension
(Calvert, Campbell, & Brammer, 2000; Giraud & Truy,
2002), although it remains unclear why the corresponding left hemispheric areas do not show significant activity in our study. However, there was significant
activation of the left middle temporal gyrus (BA 21),
confined only to the comprehension of LIT sentences
compared to NONMEAN. A number of studies suggest
that this region is important for speech comprehension
(Giraud et al., 2004; Narain et al., 2003).
Some lesion studies (Winner & Gardner, 1977) and a
previous neuroimaging study (Bottini et al., 1994) have
suggested that comprehension of figurative language,
as opposed to literal language, is dependent on the right
hemisphere. However, studies on processing of idioms
(Papagno et al., 2002), lesion studies (Gagnon et al.,
2003; Giora et al., 2000), and two recent imaging study
(Lee and Dapretto, manuscript submitted; Rapp et al.,
159
2004) on metaphors have disputed this claim. Our study
also failed to find evidence supporting a predominant
role of right hemispheric structures for the comprehension of figurative language. In keeping with recent studies, we also show that the LIFG is significantly more
active during processing of metaphors. Recent findings
support the view that rather than metaphoric language
being processed in the right hemisphere, it is non-salient
meanings that place an increased demand on the RH
(Giora et al., 2000; Mashal, Faulst, Hendler & JungBeeman, 2007). It is, therefore, possible that the hemispheric asymmetries found in previous studies of metaphors, reflect insufficient matching of stimuli salience
across conditions rather than a basic difference between
metaphoric and literal sentence processing. In our study,
we observed activation of the right inferior frontal gyrus
(BA 44/45) in the LIT > NONMEAN contrast, but not
in the LIT > MET contrast. We hypothesize that this
finding may be due to the need to compare a sentence
to immediate context (Caplan & Dapretto, 2001). Lack
of differential activation of the RIFG in the LIT > MET
comparison also argues against its specificity for either
the MET or LIT condition. We also found activation
in the right precentral/premotor cortex (BA 4/6) for
the LIT > MET and LIT > NONMEAN comparisons.
This may be seen as surprising, given that our subjects
used their right hand to indicate their response, however
we note that similar activations of the ipsilateral premotor areas have been previously reported during sentence
processing (Friederici, Ruschemeyer, Hahne, & Fiebach,
2003).
Taken together, our data indicate that grasping the
meaning of a sentence, far from being a unitary process,
depends on the type of sentence involved. In addition,
our data raise a further, more fundamental issue, namely the dissociation between behavioural and imaging
results. In this study, sentences were carefully matched
for the MET and LIT condition, and as we had predicted and as was to be expected from the relevant literature, reaction times between these two conditions were
not significantly different. However, the imaging results
indicate that the neural substrates underlying metaphoric and literal sentence processing do differ. How can this
apparent antinomy be resolved? Clearly, behavioural
experiments and fMRI scanning measure different
things. It could be argued that reaction times from
behavioural experiments represent a very robust, albeit
crude measure of cognitive activity, whereas fMRI provides insight into the neuroanatomical correlates of such
activity. In other words, the finding that two cognitive
tasks have similar reaction times does not necessarily
imply that they are served by common brain pathways.
However, equivalence at the behavioural level is useful
to minimize the confounding effects of relative difficulty
for each given task. Of particular importance for the
present study is that reaction times can be seen as a mea-
160
A.K. Stringaris et al. / Brain and Language 100 (2007) 150–162
sure of relative salience, as has been suggested in the
past (Giora, 2003). Therefore, it is unlikely that the differences in brain activations observed between metaphors and literal sentences in this study are effects of
salience. Our study was not designed to address this issue, however, as noted above, recent imaging studies
suggest that right hemispheric involvement is more
extensive when processing low-salience linguistic items
(Mashal et al., 2005). An important aspect of the current
study is that it was specifically designed to examine
brain mechanisms involved during judgements of meaningfulness, as opposed to implicit processing of metaphoric sentences. Also, by excluding erroneous
responses, we were able to concentrate on differences between successfully arriving at meaning as opposed to
deeming a sentence as meaningful or non-meaningful.
Our finding of extensive parietal activation in the
NONMEAN condition supports the view that distinct
prefrontal regions may dynamically and selectively
interact with domain specific posterior regions (Miller,
2000). This might be seen as a representation of an effort to extract the meaning out of sentences with
incongruous elements. This result is also interesting
in light of recent findings from in vivo diffusion tensor
tractography suggesting that frontal and temporal language areas may be interconnected through an indirect
pathway involving areas of the inferior parietal cortex
(Catani, Jones, & Ffytche, 2005). Importantly, present
data support the view that understanding metaphors
involve construction of an attributive category, a concept that resembles object activation in the semantic
system.
Our results are also in line with previous findings
pointing to an important role of thalamic activation as
an integrative and modulatory device (see Kraut et al.,
2002a, 2002b). We propose that this transthalamic processing mechanism may be sufficient to explain why, despite an increased demand for semantic processing as
reflected by larger N400 amplitudes and increased LIFG
activation, metaphoric and literal sentences are eventually computed in equal time. Fast reciprocal connections
between cortical and thalamic regions may be responsible for this (Steriade, 2000). We also assume that such a
mode of processing for metaphors, depending on the
fast interplay of multiple cortical regions for recruitment
of information, would lead to a rich, but less circumscribed representation of meaning. Clearly this is a tentative explanation and would require further
experimental work to be corroborated. Given that our
finding of LIFG activation for processing of metaphors
appears to be a stable result across a number of well designed studies (Ahrens et al., this volume; Lee and Dapretto, in press; Rapp et al., 2004), the question is
whether the thalamus is actually required for this type
of cognitive processing. Ours is the only one of these
studies where metaphor processing is associated with
both LIFG and thalamus activation. We suggest that
this may be an effect of methodology, as our study is
the only event-related fMRI study which also involves
an explicit judgement of meaningfulness; the study by
Ahrens et al. (this volume) was block designed, whereas
Rapp et al. (2004) employed a paradigm based on
implicit linguistic processing involving judgement of
emotional salience. Our finding of left thalamic activation was robust and specific to metaphors, and thus
we propose that the thalamus may be part of the neural
circuitry underlying the repeatedly described phenomenon of open-endedness in metaphoric meanings (Black,
1993; Boyd, 1993; Gibbs, 1992).
Acknowledgments
The authors are indebted to Professor Sam
Glucksberg from Princeton University for his helpful
comments on an earlier draft of this manuscript.
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