Running
Head: SPACE STRUCTURING
Metaphor
and the Space Structuring Model
Seana
Coulson
University
of California, San Diego
Teenie
Matlock
University
of California, Santa Cruz
Correspondence to:
Seana Coulson
Cognitive Science, 0515
9500 Gilman Drive
La Jolla, CA 92093-0515
phone: (858) 822-4037
fax: (858) 534-1128
ABSTRACT
We propose an account of metaphor
comprehension based on conceptual blending theory. We review data from on-line processing measures that support
predictions of conceptual blending theory, and report results of an off-line
feature listing study that assessed how different sorts of contexts alter the
information activated by a given word. Participants generated features for
words used in the null context, in sentences that promoted a literal reading of
the target word, sentences that promoted a metaphorical reading, and sentences
that required literal mapping. In literal mapping, the literal sense of the
word was used in a way that prompts the reader to blend it with structure from
a different domain. Results revealed
some overlap in the features generated in each of the four contexts, but that
some proportion of the features listed for words in literal, literal mapping,
and metaphoric sentence contexts were unique and context-specific.
Metaphor
and the Space Structuring Model
Characterizing
the precise relationship between literal and nonliteral meaning dominates
modern research on metaphor and figurative language. In traditional linguistic
theory, literal and nonliteral meanings are seen as two different beasts, only
one of which is well-behaved. On this view, “normal” language, that is to say,
literal language, involves recruiting word meanings from the mental lexicon and
combining them with grammar rules. Understanding normal language also demands
compliance to communicative maxims: Utterances must be truthful, relevant, and
maximally informative. In fact, on traditional accounts, conforming to these
maxims is what enables speakers to discern literal language, which is thought
to involve compositional parsing mechanisms, from nonliteral language, in which
world knowledge and general reasoning processes must be invoked to understand
the speaker’s intended meaning.
In
the current work, we begin with a review of two influential approaches to
metaphor processing, including the standard model of nonliteral language
comprehension, and a competing model based on conceptual metaphor theory. In section
2, we offer our own account of metaphor comprehension based on the space
structuring model (Coulson, 2001, in press), a theory of comprehension
motivated by mental space theory (Fauconnier, 1994) and conceptual integration,
or blending, theory (Fauconnier & Turner, 1998). In our model, metaphor
comprehension involves coordinating various conceptual domains in a blend,
a hybrid model that consists of structure from multiple input spaces, and that
often develops emergent structure of its own. In sections 3 and 4, we review
evidence consistent with our model, and discuss results of a feature listing
study designed to assess some of its claims.
In this study, people were asked to generate the features for a set of nouns
used in a null context and in three types of sentence contexts that promoted a
range of figurative readings.
Quantitative analysis of these data shows that features produced in each
sentential context differ from those for the same noun in the null context, and
qualitative analysis reveals blending operations, such as elaboration. In
section 5, we revisit the relationship between literal and nonliteral language
in light of our results, and argue that both metaphorical and non-metaphorical
meanings require the simultaneous activation of multiple cognitive models and
the mappings among them.
1.
Metaphor Processing
Classic
literal and nonliteral distinctions are incorporated into the standard
pragmatic model of metaphor processing (Grice, 1975; Searle, 1979), the
validity of which has been a major focus of research on this topic (see Gibbs,
1994 for review). On the standard model, metaphor comprehension begins when the
listener realizes that the speaker has intentionally violated the Gricean Maxim
of Quality, "Be truthful." Upon realizing the literal incongruity of
a metaphoric utterance, the listener must then derive a nonliteral
interpretation. Consequently, the standard model suggests that understanding
metaphoric language takes longer than non-metaphoric language, and involves
qualitatively different processes (see Gibbs, 1994, Gibbs & Matlock, in
press).
1.1
The Standard Model and Conceptual Metaphor Theory
Cognitive
linguists have challenged many of the traditional assumptions about literal and
nonliteral language. In particular, conceptual metaphor theory (CMT)
proponents have shown that metaphor is not merely a literary device, but an
integral part of everyday language and thought (Lakoff & Johnson, 1980;
Sweetser, 1990; Turner, 1991). Based on linguistic patterns that turn up in
language after language, CMT suggests a principled relationship between literal
and nonliteral language, with evidence that metaphoric meanings are
systematically related to literal ones. For instance, countless clusters of
expressions use the same kinds of words to talk about very different
experiential domains. This is seen in the way people describe love in terms of
travel, as with cruise and crash in, “Their relationship was cruising
along but suddenly crashed,” (see Gibbs, 1994; Gibbs & Nascimento,
1993), or in the way they use words referring to vision to express
understanding, as with see in “I see what you’re saying,” (see
Sweetser, 1990). This tendency is also apparent in the way verbal arguments are
described in terms of physical battles, as in “He attacked every weak
point in the argument,” (Lakoff & Johnson, 1980).
To
explain this systematicity, Lakoff and Johnson (1980) propose that metaphors
reflect the output of a cognitive process by which we understand one domain,
known as the target, by exploiting cognitive models from an analogically
related domain known as the source. The systematicity in the use of
source and target domain terminology derives from the fact that some of the
logic of the source domain has been imported into the target in a way that maintains
the mappings from one to the other. Thus construed, metaphoric language is the
manifestation of conceptual structure organized by a cross-domain mapping:
a systematic set of correspondences between the source and target that result
when cognitive models from a particular source domain are used to conceptualize
a given target domain.
These
observations point to the inadequacy of the standard model as a comprehensive
account of metaphor comprehension. The model fails to explain both the ubiquity
of systematic correspondences and the logic of metaphorical expressions. It also fails to explain current
metaphorical use and the development of well-documented cross-linguistic
patterns. While CMT proponents appeal to shared cognitive models to explain metaphor
comprehension, current vocabulary use, and semantic change (e.g., Lakoff,
1993), the standard model leaves these details unexplained.
1.2 Experimental Evidence
Besides
linguistic evidence against the literal/figurative dichotomy, the standard
pragmatic model is also undermined by a good deal of experimental
evidence. First, empirical work refutes
the assumption that literal processing is obligatory and necessarily prior to
metaphoric processing (e.g., Glucksberg, Gildea, & Bookin, 1982; Keysar,
1989). Psycholinguists have also
challenged the prediction that metaphoric meanings take longer to compute than
literal ones by contrasting reading times for both types of statements. While reading times for metaphors are
generally longer in minimal contexts, when the same stimuli are embedded in
longer passages that provide supporting context, literal and metaphorical
utterances are read and understood at the same rate (Inhoff, Lima, &
Carrol, 1984; Ortony, Schallert, Reynolds, & Antos, 1978).
Such
results go against the standard model, but are well-explained by one fairly
controversial model of metaphor processing, the direct access model (Gibbs,
1994). Motivated in part by conceptual
metaphor theory, this model holds that metaphor comprehension requires the same
processes as the comprehension of literal language. The direct access model holds that difficulty in processing
metaphoric language is a function of contextual support for the recruitment of
the cross-domain mapping or mappings needed to understand any given
metaphor. On this view, while literal
meanings may tend to predominate in the interpretation of decontextualized
utterances, metaphoric meanings require realistic social contexts. Controversially, the direct access model
maintains that context can even bias a metaphoric meaning over a literal one.
Although
the direct access model finds support in the finding that the nonliteral
meaning of familiar idioms is almost immediately available, it is undermined by
various reports that literal aspects of word meaning are primed even in
metaphorical contexts. For example,
using a word fragment completion task, Giora & Fein (1999) found that both
literal and metaphoric meanings were activated in the comprehension of familiar
metaphors. Similarly, using the
cross-modal priming technique, Blasko and Connine (1993) found priming for the
literal as well as the metaphoric meanings in familiar metaphors. For unfamiliar metaphors, they found priming
only for the literal meanings of their stimuli. Moreover, in the processing of unpredictable idioms, Cacciari and
Tabossi (1998) report priming for literal meanings immediately at the offset,
and for both literal and nonliteral meanings 300 ms later.
2. Conceptual Integration and Metaphor
Comprehension
Our
own model of metaphor comprehension, the space structuring model (SSM),
also acknowledges the prevalence of
metaphor in everyday language and thought, as well as commonalities
between the conceptual basis of poetic language and the conventional metaphors
described by cognitive linguists (e.g., Lakoff & Turner, 1989; Turner,
1996). Like many models of metaphor
comprehension, SSM also advocates commonalities in the construction of literal
and nonliteral meanings. However,
besides conceptual metaphor theory, SSM is directly motivated by conceptual
blending theory (Coulson, 2001, in press; Fauconnier & Turner, 1998). Blending
is a set of operations for combining cognitive models in a network of mental
spaces (Fauconnier, 1994; Fauconnier & Turner, 1998). In SSM, comprehension
involves the temporary construction of simple cognitive models along with the
establishment of mappings, or, systematic correspondences among objects and
relationships represented in various models. Mappings are based on pragmatic
functions such as identity, similarity, or analogy. Consequently, metaphoric
meanings – that use analogy to link objects in different spaces -- do not
fundamentally differ from meanings that employ other sorts of mappings.
2.1 Mental Spaces
In
SSM, linguistic cues prompt speakers to set up elements in mental spaces,
a level of referential structure whose contents need not refer to objects in
the world (Fauconnier, 1994). A mental space can be thought of as a temporary
container for relevant information about a particular scenario as perceived,
imagined, remembered, or otherwise understood.
Initially devised to address indirect reference and referential opacity,
mental space theory has proven to be useful for semantic and pragmatic complexities
(see Fauconnier, 1997; Fauconnier & Sweetser, 1996). For instance, mental spaces can represent
examples in which Titanic refers to both the ship and the movie about
the ship, as in, “ Titanic is a movie about the voyage of the Titanic.”
By partitioning the information in this sentence into two linked spaces, mental
space theory captures the fact that though the ship and the movie differ, the
correspondence between them is not completely arbitrary.
Mental
space theory was initially designed to keep incompatible information about a single
object in discrete representations, for instance, a girl with green eyes in
reality could have blue eyes in a picture. But the more recent theory of
conceptual integration posits a particular kind of mental space, a blended
space, in which this sort of incompatible information is brought together to
generate inferences that can be projected to other spaces (Fauconnier &
Turner, 1998). For example, blended spaces can represent expressions using
structure from multiple spaces, as with the headline “Titanic: Unsinkable after
all.” In contrast to the previous
example, in which the film and the ship are clearly distinguished, the headline
exemplifies simultaneous reference to the ship, claimed by some to be unsinkable,
but which proved otherwise, and the movie about the ship, which proved to be
quite successful, both with the critics and the general populace.
2.2
Conceptual Integration Networks
A
computational- (though not algorithmic-) level account of blending appeals to a
conceptual integration network, an array of mental spaces (Fauconnier
& Turner, 1998). Blends have two or more input spaces structured by
information from discrete cognitive domains, a generic space that contains
abstract structure common to all spaces in the network, and a blended space that
contains selected aspects of structure from both input spaces, as well as
emergent structure of its own. For example, in the unsinkable Titanic blend,
one input space contains information about the historic ship (which sunk, and
therefore was not unsinkable), while the other input contains
information about the movie (which did well).
Though one does not usually talk about whether movies are good flotation
devices, the conceptual structure in these input spaces can nonetheless be
aligned via analogical mappings between the ship and the movie, the ship's
voyage and the movie's run, and between the ship's fate (sinking) and the
movie's fate (winning oscars). Blending theory
differs from CMT in that it explicitly allows for disanalogies in the
representation of metaphoric expressions.
Elements
in each of the four spaces in the integration network for the Titanic blend are
shown in Table 1. The generic space in this network contains a schematic
representation of the common event structure, that is, an unspecified agentive
object that undertakes a course with an unspecified purpose, and whose outcome
can be successful or unsuccessful. Conceptual structure in the two input
spaces, then, are analogically linked, while the mappings between the inputs
and the generic space involve category inclusion. The blended space, too,
shares the abstract event structure in the generic space, and is composed of a
combination of some structure from each of the input spaces. In this example, the blended space inherits
some structure from the scenario associated with the historic input, and some
structure from the movie input, in particular, the fate of the movie. The
mappings between the ship and the voyage in the blended space and the ship and
the voyage in the historic space are identity mappings. However, the successful
voyage of the Titanic in the blended space maps onto the success of the movie
via analogy mappings (see Turner & Fauconnier, 2000 for more Titanic
blends). Integrating a representation
of the Titanic's voyage with the fate of the movie yields a counterfactual
rendering of the Titanic's voyage in which the ship does not sink.
2.3 Conceptual Blending and Metaphor
Comprehension
Following Fauconnier and Turner’s
conceptual integration theory (1998), we argue that metaphor is more than a set
of mappings between a source domain and a target domain. On our view, metaphor
involves a complex of mappings with multiple spaces in conceptual integration
networks. SSM differs from a number of other models of metaphor comprehension
in that it does not posit the existence of a discrete metaphorical
meaning. Rather, metaphorical meaning
arises out of the information represented in the integration network. For instance, understanding the metaphor in
“All the nurses at the hospital say that surgeon is a butcher,” requires
coordinating conceptual structure associated with surgery, butchery, and a
blend of the two (Grady, Oakley, & Coulson, 1999).
As in CMT, comprehension of the butcher
metaphor requires one to apprehend the mappings between surgeon and butcher,
patient and dead animal (e.g. cow), as well as scalpel and cleaver. However, it
also involves construction of a blended space in which structure from each of
these inputs can be integrated. In this example, the blended space inherits
goals of the surgeon, and the means and manner of the butcher. The inference
that the surgeon is incompetent arises when these structures are integrated to
create a hypothetical agent with both characteristics. Behavior that is perfectly appropriate for a
butcher whose goal is to cut up a dead cow is indeed appalling for the surgeon
operating on a live human being.
Integration
in the blended space involves three related processes, composition, completion,
and elaboration, each of which provides for the possibility of emergent
structure. Composition involves
attributing a relation from one space to an element or elements from the other
input spaces. Composition can be as simple as integrating an element (such as dinner)
with a frame (such as four-course), or can involve more creative
blending, as in the integration of frames for Irish, and four-course,
with dinner (three pints of Guinness and a bag of crisps). In either case,
emergent structure arises from the contextual accommodation of a concept from
one domain to apply to elements in a different domain. Completion is
pattern completion which occurs when structure in the blend matches information
in long-term memory. For instance, if a friend told you that he had gone to
Baskin Robbins for ice cream, you might infer that he had eaten a cone there as
well. Elaboration, related to
completion, involves mental simulation of the event represented in the
blend. For example, we suggest that the
following excerpt from a performance report is funny because the reader
mentally imagines the scene, “Since my last report, this employee has reached
rock bottom and has started to dig.”
We
suggest that speakers exploit explicit grammatical cues to construct a blended
space with conceptual structure from both input domains. Metaphor comprehension thus involves the
activation of conceptual structure needed to construct the model in the blended
space, the activation of conceptual structure in the input and generic spaces,
and the establishment of mappings between spaces in the network. Emergent
structure is activated in order to produce a relatively coherent juxtaposition
of disparate aspects of conceptual structure from the input domains. Moreover, particular inferences that issue
from the use of a given metaphoric expression reflect the fact that metaphoric
projections recruit processes of conceptual blending to produce emergent
structure that can be mapped back onto the inputs.
3. Processing Metaphoric Language
The
SSM makes a number of predictions for on-line meaning construction. For instance, because it is based on a
general theory of conceptual integration, SSM suggests the same conceptual
operations are involved in the comprehension of literal and nonliteral language. For example, understanding butcher in
“During the war, that surgeon had to work as a butcher,” requires the
comprehender to set up simple cognitive models in mental spaces, and establish
mappings based on shared relational structure.
As in metaphoric uses of butcher discussed in the previous section,
inferences are generated in the blended space, where information about a
surgeon’s training and skill is integrated with general information about
butchers, or other aspects of the context.
One might, for instance, infer that the surgeon in question was
overqualified for his job, or that he was forced to work as a butcher in a
labor camp.
Like
many modern models of metaphor processing (see Giora, 1997 for review), the SSM
suggests that qualitatively similar processing operations underlie the comprehension
of literal and nonliteral meanings.
Consequently, the model is supported by evidence that metaphoric
meanings are understood in approximately the same amount of time as literal
control statements. Moreover, findings
from a small set of on-line studies demonstrate that variables pertaining to
difficulty of processing metaphoric items also pertain to the difficulty of
processing literal items. For instance,
familiarity, one such variable, is a determinant of processing difficulty for
literal and non-literal language alike (Gernsbacher, 1984). In a cross-modal priming study, Blasko and
Connine (1993) show that the familiarity of a metaphor affected reaction times
for words related to its metaphorical meaning.
In an eye-tracking study, Blasko and Briihl (1997) found that gaze
durations for metaphorical expressions decreased both as a function of
familiarity and as a function of contextual support. Similarly, Frisson and
Pickering (1999) found equivalent gaze durations for sentences containing interpretable
metonymies and sentences containing literal interpretations of the same words
(see also Frisson & Pickering, this volume).
McElree
and Nordlie (1999), however, argue that the presence or absence of differences
in reading times can result from a number of different factors, not all of
which reflect true differences in processing time. One way to tease apart
stimulus-related processing from decision-related processing is to measure the
speed accuracy tradeoff (SAT) curves as participants perform a judgment task at
varying amounts of processing time.
With adequate sampling, it is possible to observe the full timecourse of
processing by establishing the point in time when performance exceeds that of
chance, the point at which performance reaches an asymptotic level, and the
slope of the curve between the former and the latter. Using SAT to investigate the timecourse of meaning activation in
literal and metaphorical statements, McElree and Nordlie (1999) found no
evidence of literal meanings being available earlier than figurative meanings.
Moreover,
event-related brain potential (ERP) data support the claim in the direct access
model that difficulty in the comprehension of metaphoric utterances is largely
a function of contextual support (Pynte, Besson, Robichon, & Poli,
1996). This latter finding is
especially important because the ERP methodology can address some limitations
of chronometric studies. As Gibbs
(1994) notes, parity in reading times for literal and metaphorical expressions
need not entail parity in the underlying comprehension processes. It is possible, for example, that literal
and metaphorical meaning might take the same amount of time to comprehend, but
that the latter required more effort or processing resources (Coulson & Van
Petten, 2000 submitted). Alternatively,
comprehension processes for literal versus metaphoric utterances might take the
same amount of time to complete, and yet involve quite different computations
(Gibbs & Gerrig, 1989).
3.1 ERPs
Because
they involve a direct and continuous measure of brain activity, event-related
potentials (ERPs) can potentially distinguish between qualitatively different
sorts of processing, even if their corresponding behavioral manifestations
require the same amount of time (see Coulson, King, & Kutas, 1998 for
review). ERPs are small voltage
fluctuations in the EEG that are timelocked to sensory, motor, or cognitive
events collected by recording EEG while participants perform a cognitive task such
as reading (Rugg & Coles, 1995). By
averaging the EEG timelocked to multiple tokens of a given type (e.g. the onset
of a word used metaphorically), it is possible to isolate aspects of the
electrical signal that are temporally associated with the processing of that
type of event (such as understanding a metaphoric meaning). The result of averaging is a waveform with a
series of positive and negative peaks, known as components labeled by
reference to their polarity ('P' for positive-going and 'N' for
negative-going), and when they occur relative to the onset of the stimulus
event, or relative to other ERP components.
One
ERP component of particular interest to researchers interested in meaning is
the N400, so-called because it is a negative-going wave that peaks
approximately 400 ms after the presentation of a meaningful stimulus. The N400 was first noted in experiments
contrasting sentences that ended sensibly and predictably with others that
ended with an incongruous word.
Congruous words elicited a late positive wave, while incongruous endings
elicited a negative wave beginning about 200 ms after the stimulus was
presented and peaking at 400 ms post-stimulus (Kutas & Hillyard,
1980). Subsequent research indicates
that N400 is elicited by all words, written, spoken, or signed, and that N400
amplitude indexes the difficulty of integrating a word into the established
context (see Kutas, Federmeier, Coulson, King, & Muente, 2000 for
review). The greater the processing
difficulty associated with a word, the larger the N400 component it elicits (see
Figure 1, and note that negativity is plotted up).
Taking
advantage of this well-known interpretive feature of the N400, Pynte and
colleagues contrasted ERPs to familiar and unfamiliar metaphors in relevant
versus irrelevant contexts. They found that regardless of the familiarity of
the metaphors, N400 amplitude was a function of the relevance of the
context. Moreover, by using ERPs Pynte
and colleagues employed a measure which is in principle capable of revealing
the qualitative processing differences predicted by the standard model. In fact, they observed no evidence of a
qualitative difference in brain activity associated with the comprehension of
literal and metaphoric language.
Reports
that literal and nonliteral language comprehension both display a similar
timecourse and recruit a similar set of neural generators are consistent with
predictions of the SSM. Moreover, the
SSM also makes predictions for comprehension difficulty, predicting a gradient
of processing difficulty related to the extent to which the integration
requires the comprehender to elaborate the scenario set up in the blended
space. This prediction was tested by
Coulson and Van Petten (2000, submitted) when they compared ERPs elicited by
words in three different contexts on a continuum from literal to figurative, as
suggested by blending theory. For the
literal end of the continuum, Coulson and Van Petten used sentences that
promoted a literal reading of the last term, as in, “He knows that whiskey is a
strong intoxicant.” At the metaphoric
end of the continuum, they used sentences which promoted a metaphoric reading
of the last term, as in, “He knows that power is a strong intoxicant.” Coulson and Van Petten also posited a literal
mapping condition, hypothesized to fall somewhere between the literal and
the metaphoric uses, such as, “He has used cough syrup as an intoxicant.”
Literal
mapping stimuli employed fully literal uses of words in ways that were
hypothesized to include some of the same conceptual operations as in metaphor comprehension. These sentences described cases where one
object was substituted for another, one object was mistaken for another, or one
object was used to represent another – all contexts that require the comprehender
to set up mappings between the two objects in question, and the domains in
which they typically occur. In line
with many models of metaphor comprehension (e.g., Gibbs, 1994; Giora, 1997;
Glucksberg, 1998), the space structuring model predicts qualitatively similar
brain responses to literally and metaphorically used words, suggesting the same
processes are used in literal and nonliteral language comprehension. Further, in positing a continuum from
literal to metaphorical based on the difficulty of the conceptual integration
needed to comprehend the statement, blending theory predicts a graded
difference in N400 amplitude for the three sorts of stimuli.
Overall,
data reported by Coulson and Van Petten (2000, submitted) were largely
consistent with the predictions of the SSM.
In the early time window, 300-500 ms post-onset and before, ERPs in all
three conditions were qualitatively similar, displaying similar waveshape and
scalp topography. This suggests that
during the initial stages, processing was similar for all three sorts of
contexts. Moreover, as predicted, N400
amplitude differed as a function of metaphoricity, with literals eliciting the
least N400, literal mappings the next-most, and metaphors eliciting the most
N400, suggesting a concomitant gradient of processing difficulty. The graded N400 difference argues against
the literal/figurative dichotomy inherent in the standard model, and suggests
processing difficulty associated with figurative language is related to the
complexity of mapping and conceptual integration.
4. Feature Study
In
their ERP study, Coulson and Van Petten (2000, submitted) show a processing
gradient, which they attribute to the complexity of blending operations needed
to understand words in the literal, literal mapping, and metaphorical contexts. However, aside from the authors' native
speaker intuitions, there was no evidence to show that placing these words in
different sentential contexts would promote the retrieval of different sorts of
conceptual structure, as hypothesized in the space structuring model. Indeed, a general characteristic of research
that addresses the issue of continuity between processes underlying literal and
metaphoric language comprehension is that it fails to address the details of
metaphor comprehension. However,
another way of addressing the relationship between both sorts of meaning
construction is to examine the information that people activate when they
understand literal versus nonliteral language.
This
is the approach taken by Tourangeau and Rips (1991), in a study that compared the
sorts of features people listed for metaphoric language with those listed for
the contributing source and target domain concepts. Tourangeau and Rips found that many of the features listed for
the metaphoric meanings were emergent, that is, they were not
established parts of either of the domains in the metaphor. For instance, respected was listed as a feature of the eagle in “The eagle
is a lion among birds,” but was not listed as characterizing either eagles or
lions when considered independently (Tourangeau & Rips, 1991). Further, their participants rated the
emergent features as being more crucial to the meaning of the metaphor than,
for example, features that people listed for both eagles and lions. Tourangeau and Rips (1991) suggest that this
pattern of data argues against models, such as Gentner’s (1983; Gentner &
Wolff, 1997, Wolff & Gentner, 2000) structure mapping engine and Glucksberg
and Keysar’s (1990) property attribution model, that posit the computation of
shared features as the basis of metaphor comprehension.
Like
Tourangeau and Rips (1991), we suggest that metaphor comprehension requires the
transformation rather than pure transfer of properties from one domain to
another. Moreover, the transformation
occurs via blending processes such as completion and elaboration. In positing continuity between literal and
nonliteral meaning construction, the SSM predicts that “emergent” features
should arise in the course of conceptual integration across the continuum from
literal to figurative meanings.
Consequently, we conducted an off-line study that compared the sorts of
features participants generated to words in a null context with the features
they listed for the same words in literal, literal mapping, and metaphoric
contexts of the sort employed by Coulson and Van Petten (2000, submitted).
In
the current study, we are primarily concerned with the role of sentential
context in the construction of meaning, especially how manipulating the context
in which a word appears can influence the interpretation of that word, as
determined by the features participants produce. One possibility is that participants would generate the same
features for a word, regardless of the context in which it appeared. Such a result would suggest the construction
of word meaning is removed from contextual integration, being identical from
context to context. Alternatively,
people might generate features relevant to and reflective of the particular
sentential context in which they occur.
This pattern of responses would indicate that people integrate
contextual factors in such a way as to alter their understanding of individual
words. Further, in a qualitative
analysis of features participants generate, we should expect to see evidence of
blending processes such as completion and elaboration in all three sorts of
contexts.
Method
Design, Stimuli, and Participants. The
study was a within-subjects design with four conditions, including a null
context and three sentential contexts. In the null context, the target word appeared
in isolation. In the sentential contexts, the target word appeared at the end
of a sentence context. In the literal condition, the target word appeared in
its literal sense, as with anchor in, “Last time he went sailing he
almost forgot about the anchor.” In the metaphoric context, the target
word appeared in its metaphorical sense, as with anchor in, “Amidst all
the trappings of success, his wife was his anchor.” The literal mapping condition is served as
an in-between condition, whereby the target word was used in its literal
sense, but appeared in a context requiring the reader to perform some of the
same integration operations hypothesized to underlie metaphor comprehension.
For example, the literal mapping stimulus for anchor was, “We were able to
use a barbell for an anchor,” in which a barbell has been projected into
the sailing scenario to fulfill the function of an anchor.
The
35 words in this study were embedded in a larger feature listing study which
included 12 lists seen by 120 UCSC undergraduates, all fluent English speakers.
In the null context, each word was seen and rated by 20 participants. In the sentential contexts, each word was
seen by 10 participants in each of the three types of sentences. Stimuli were distributed across lists such
that no participant saw the same item in more than one context.
Procedure. Participants were given a booklet with
two sections: Part A, a list of words (null context condition), and Part B, a
list of sentences (randomly ordered items from three sentential context
conditions). In Part A, participants read each item and jotted down two to
three features or characteristics of that item. In Part B, they read each
sentence and quickly listed two or three features for the underlined word.
Participants were told that they were not being timed, but encouraged not to
dwell on any one item. When unsure about the meaning of a word, they were to
leave a blank.
Results. For each of the 35 stimuli, participants’
responses were compiled into a file that contained a list of features generated
for that word in the null context, and in each of the three sentence
conditions. Data were quantified in two ways, one a measure of the proportion
of unique features in each condition, and one a measure of the similarity of the
features for words in different sentential contexts. First, for each of the three sentence types, we calculated the
proportion of features that were unique to that condition, viz. not produced
for any of the other conditions. When
words were presented in literal contexts, 41.77% of the features were not
generated in either of the other sentential contexts or for the same words in
the null context. When words were presented in literal mapping contexts, 39.66%
of the features were unique to that context.
Finally, when words were presented in the metaphorical context, 46% of
the features were unique.
As
is evident in Figure 2, metaphors elicited reliably more unique features than
the other two (literal) sentence types.
Nonetheless, placement of the stimuli in all three sorts of sentences
resulted in the elicitation of a substantial proportion of unique
features. The high proportion of unique
features in each of the sentence contexts (ranging from 40% to 46%) suggests a
remarkable degree of context-sensitivity in the conceptual structure
participants retrieved for these materials. Although the off-line feature
listing task cannot assess whether participants actually use this information
during comprehension, the generation of unique features indicates a systematic
difference across conditions in the availability of the information that the
participants considered relevant. These
differences suggest a word's appearance in any sentential context can modulate
which aspects of conceptual structure participants are likely to exploit in
meaning construction. This was
especially the case for sentential contexts that promoted a term's metaphorical
meaning.
However,
it is potentially misleading to focus on the percentage of unique
features. For example, it is possible that participants listed different words
to express characteristics of the stimuli in each of the sentential contexts,
but that the conceptual differences denoted by those words were minimal. For this reason, we assessed the similarity
of the feature sets elicited by stimuli in each sentence type by using the latent
semantic analysis (LSA) method, a method for creating statistical profiles
of linguistic items, via the representation of words in a high dimensional
semantic space derived from statistical analysis of large text corpora (see
Landauer, Foltz, & Laham, 1998). By
extracting multivariate correlation contingencies between a word and its
context, LSA produces representations whose relative proximity in semantic
space can be shown to closely mimic human judgments of semantic similarity
(Landauer & Dumais, 1997).
To
assess the semantic similarity of the feature sets elicited in our study, we
transformed each feature set into a vector in a high dimensional semantic space
(300 dimensions) derived from latent semantic analysis (LSA) of a large corpus
(119,627 paragraphs) of machine readable texts, including novels, newspaper
articles, and educational texts. This
yielded four vectors for each word, one that represented the null context
features, and one for each of the literal, literal mapping, and metaphorical
feature sets. Semantic similarity was
assessed by measuring the cosine of the angle between the vectors in each
sentence condition to the vector representing the null context feature
set. The cosine thus functions as a
measure of proximity in semantic space, where 1 is identity, and 0 represents
orthogonal vectors.
The
average similarity score was 0.84 between the null context and the literal
feature sets, 0.81 for the null context and the literal mapping feature sets,
and 0.78 for the null context and the metaphorical feature sets. These scores indicate that the features
listed for words in the metaphorical contexts were the least similar to those
listed in the null context, words in the literal contexts were the most
similar, and words in the literal mapping contexts fell somewhere in
between. Repeated measures analysis of
variance on cosine measures revealed a main effect of sentence context, F(2,68)=5.48,
p<0.01, but post-hoc comparisons suggested that while the literal and
metaphorical measures differed reliably from each other, t(1,34)=3.26, p<0.01,
the literal mappings did not differ from either the literal, or the
metaphorical measures. This result is consistent with the assumption that the
literal mapping stimuli were intermediate with respect to literal and
metaphorical stimuli.
Our
analysis also included examination of unique features generated for a few words
in the three sorts of sentence conditions.
Although participants were specifically instructed to focus on the word
at the end of the sentence, many features listed were apparently influenced by
previous context. For example, with “Unfortunately, what started as a mere
flirtation with the stock market has become an orgy,” participants
generated unique responses such as EXCESSIVE, CROWDED, INDULGENT, that might be
classified as low-salient properties of orgies. However, they also listed CONFUSING, COMPLICATED, and EXPENSIVE. These negative properties are clearly
influenced by context, such as the word unfortunately, and the
integration of concepts related to orgies with concepts related to the stock
market.
Moreover,
evidence of integration was not limited to contexts that promoted a
metaphorical reading. It was also observed in the literal mapping and literal
contexts for orgy. For the
literal mapping context, “He saw some hippies headed for the river and assumed
it was an orgy,” participants listed unique features such as 70's,
DRUGGIES, SMOKING, WOODS, and SKINNY DIP, which clearly reflect concepts
related to context, including hippies and river. It is reasonable
to assume that such responses reflect the process of elaboration, or
imaginative simulation of what the hippies might do or how they might behave.
Similarly, features generated in the literal context, “They ended the year with
a huge party that everyone remembered as the orgy,” also show the
influence of context. For instance,
unique responses for orgy in the literal context include FOOD and DRINK,
items not normally associated with the canonical meaning of orgy, but
which emerge through completion of the party scenario.
In
the metaphor “The coach said he'd miss his seniors because they were the backbone,”
responses included RELIABLE, SECURE, and RIGID, as well as BEST and FASTEST,
which were clearly influenced by integration of information about the role of
backbones in vertebrates and the role of the seniors on the coach's team. Examples such as this underline the
importance of the relational structure shared between the input domains, as
emphasized in Gentner and colleagues’ model of metaphor comprehension (Gentner
& Wolff, 1997, Wolff & Gentner, 2000).
While the SSM also maintains an important role for analogical mapping in
metaphor comprehension, the presence of shared relational structure is not as
essential for our model as for Gentner and colleagues. In fact, the SSM predicts that people can
comprehend metaphorical meanings that involve explicit disanalogies between the
input domains.
Responses
for the literal mapping context, “The paleontologists quickly discovered that
the foot bones were actually fragments of backbone,” included BREAK,
BROKEN ARMS, DELICATE, and INJURY, features that have little or nothing to do
with backbones per se. Once again it is
apparent that context influenced the features participants produced. We suggest that fragments drove the
choice of responses in these examples, and that the people who listed these
features used elaboration to produce a scenario to explain why the bones were
fragmented. Responses for the literal
context, “At the academy, young FBI officers are taught to target the backbone,”
include VULNERABLE, IRREPARABLE, and DAMAGING, which involves the integration
of information about FBI officers with what it means to target a backbone,
and completion of the integrated scenario. Other examples of features generated
are shown in Table 2.
In
sum, we found that there are differences but also similarities in the types of
features generated in each context. In particular, metaphorical sentences
elicited more unique features than the other two conditions, but the overall
high proportion of unique features generated in all sentential contexts
suggests a good deal of context-sensitivity.
At the same time, though, we have to acknowledge that the similarity
across the feature sets was quite high.
Approximately 60% of the features listed in each sentential context were
also listed in the null context, indicating some degree of constancy in the
conceptual structure available for meaning construction. Therefore, we can
assume that when a word appears in a sentential context, the presence of the
word and its interaction with the context can alter or drive certain aspects of
conceptual structure, which are exploited in meaning construction. We attribute
the systematic differences in the types of features produced in various
sentential contexts to differences in blending operations. In particular, as noted, literal and literal
mapping stimuli tended to engender completion, while metaphorical stimuli were
more likely to engender elaboration.
5.
As time goes by
In
positing continuity between literal and nonliteral meaning construction, the
SSM is supported by the consistent finding that when contextual factors have
been equated, literal and metaphoric meanings take the same amount of time to
compute. The SSM is also supported by
research that indicates that variables such as familiarity and contextual
support influence the processing difficulty of both literal and nonliteral
language. Further, ERP data suggest
that the same set of brain regions mediate the construction of both literal and
nonliteral meanings. However,
continuity between literal and nonliteral language processing is a feature of
most modern models of metaphor comprehension.
Consequently, evidence that supports the SSM also supports the direct
access model in which metaphorical meanings can be activated independently of
literal ones (Gibbs, 1994), a parallel model in which neither the literal nor
metaphorical interpretation has priority (Cacciari & Glucksberg, 1994;
Glucksberg, 1991), and an underspecification model in which the processor
initially activates the same underspecified representation for literal and
figurative meanings, and only later does it fill in the details (Frisson &
Pickering, 1999, and this volume).
However,
the SSM finds more support in the ERP data reported by Coulson and Van Petten
(2000, submitted). Although the direct
access model is supported by the similar timecourse of ERPs elicited by
metaphoric and literal uses of the same words, it is undermined by quantitative
differences in the N400 that indicate metaphors are harder to process. This finding also argues against the
underspecification model (Frisson & Pickering, 1999).[1]
If the parser employs a single underspecified representation each time it
encounters a word, processing difficulty should be independent of figurativity,
and thus predicts equivalence in N400 amplitude as well as in gaze
durations. While the gradient of
processing difficulty, from literal to literal mapping, to metaphorical, might
be consistent with other models of metaphor comprehension, it is most directly
implied by the theories of blending and mental spaces.
Interestingly, the processing difficulty
gradient observed by Coulson and Van Petten (2000, submitted) was paralleled to
a certain extent by the similarity gradient of the different feature sets
participants generated in the current study.
Comparing features that people generated for words used in contexts that
promote the same range of figurative meanings as in Coulson and Van Petten
(2000, submitted), we found that literal meanings were most similar to the
information associated with a word in the null context, literal mappings the
next-most, and metaphorical meanings were the least similar. This presents the possibility that the
observed difficulty gradient relates to blending operations needed to activate
the features that were unique to each context.
Qualitative analysis of these unique features indeed suggests that while
there is evidence for all of the blending processes in each of the conditions,
literal uses tend to engender composition and completion, while metaphorical
uses were more likely to promote elaboration.
Of
current models of metaphor comprehension, the SSM is most similar to the model
proposed by Cacciari and Glucksberg (1994), especially in being a parallel
model. Perhaps it is not surprising,
then, that the SSM is supported by findings in a study by Cacciari and
Glucksberg (1995) in which participants were asked to describe mental images
formed in conjunction with the comprehension of a number of Italian idioms
normed for familiarity and for opacity, or the extent to which its literal and
figurative readings were related to one another. Interestingly, Cacciari and
Glucksberg report descriptions of imagery judged as figurative which
seem to us to represent the sorts of images associated with a blended
space. For example, for the Italian
idiom lose one's head, which means to become crazy, so-called figurative
depictions of this idiom included, “I am laughing to tears and I lose my head
in a jump, the head jumps away,” as well as “A crazy person that no longer has
control over his actions, his head is empty, transparent, without its content,
the brain,” (Cacciari & Glucksberg, 1995: 50-51).
Both
these examples are characteristic of cognitive models represented in the
blended space in a conceptual integration network. While not all blends are
chimerical, it is not unusual for unrealistic, impossible events such as a
headless body, or a head without a brain, to be represented in the blended
space. The first example, in which we
have a head that spontaneously separates itself from its body, can be
represented in a conceptual integration network in which one input contains a
model of the realistic implications of a head falling off (death), and the
other contains a model of an unspecified cause resulting in erratic
behavior. The blended space inherits
the cause from the first input, and the effect from the second, such that the
head falling off the body is understood to cause erratic behavior. In the SSM, the meaning of metaphoric
language is not represented in any single space in the integration network, nor
in any single analogical link, but emerges from apprehension of the
relationships among the various elements in the network.
Cacciari
and Glucksberg (1994) argue that evidence for the activation of literal
meanings in metaphorical context, reflects the parallel activation of both
sorts of meanings. In contrast, the SSM
explains such data by pointing to the principled relationship between literal
and nonliteral meaning in conceptual blending theory. While researchers in CMT argued that the literal content of
metaphorical expressions indicates congruity between both the language and the
logic of the source and target domains, conceptual blending theory takes this
observation one step farther in arguing that the mixture of source and target
domain language in metaphoric utterances is mirrored in the logic of the
blend. Indeed, blending theory is in
part motivated by the observation that speakers often employ source domain
language without fully utilizing source domain logic.
Evidence
for the import of blending in metaphoric langauge can be found in examples that
contain partial disanalogy (e.g. Coulson, 1996). For example, the presence of disanalogy is particularly common in
idioms like digging your own grave, (discussed in Coulson, 1997;
Fauconnier & Turner, 1998). Coulson (1997) shows how various instances of
the metaphoric idiom digging your own grave, involve imagery from one
input (the source input of death and grave digging), but the causal structure
of the other input, in which the person is unwittingly contributing to his own
future failure. Although the mapping
might seem to draw an analogy between the grave digger and the fool, in fact
digging a grave doesn't cause anything (other than the grave itself), that
might be mapped onto the grave-digger's failure. Even in abhorrent instances such as that described in a 1995
Associated Press blurb (HEADLINE: YOUTH KILLED WITH SHOVEL, BURIED IN HOLE HE
HAD DUG; Crime: A man and a teen-ager who allegedly taunted the victim before
beating him to death are arrested)[2],
the digging itself does not lead to death.
We
suggest that the ready availability of literal meaning in idiom interpretation
is no accident, as it stems from the import of conceptual structure in one or
more of the input spaces in a conceptual integration network. Idiom interpretation requires the
construction of a number of cognitive models, one of which corresponds to the
source domain, and what would be dubbed a literal interpretation of the
metaphoric expression. Moreover, the activation of conceptual structure from
the source domain is not random, but seems to be limited to some
metaphor-relevant aspects, with metaphor-irrelevant aspects being actively
suppressed (Gernsbacher & Robertson, 1999). The context specificity of source domain activations may arise
from inherent constraints on the alignment of structure between spaces in the
network.
And
so we find ourselves telling a story reminiscent of that told by linguists of
old. While we reject a firm dichotomy
between literal and nonliteral language, and argue that qualitatively similar
processing operations underlie the comprehension of both sorts of meanings, our
proposal is not too far removed from the old suggestion that readers construct
a literal interpretation automatically as part of the parsing
process. However, on the space
structuring model, this grammatically cued meaning construction occurs
more-or-less in parallel with the structuring of other spaces in the network.
Consequently, parallel activation of meaning does not reflect a blind
activation process to be followed by selection of the correct meaning. Rather, parallel activation is thought to
reflect the construction of cognitive models in various spaces in the network.
For this reason it is crucial for establishing the overall meaning, which
involves comprehension of the relationship between the cognitive models in the
source input, the target input, and the blended space. Continuity between literal and nonliteral
language comprehension consists in the space structuring, mapping, and blending
operations needed to construct literal and nonliteral meanings alike.
Acknowledgments
Many
thanks to Raymond Gibbs for use of his psycholinguistics lab at University of
California, Santa Cruz, and to research assistants Ashleigh Briggs, Jenny
Lederer, Tracy Lee, and Annelise Casaubon-Smith.
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Table 1
__________________________________________________
__________________________________________________
object ship movie ship
course voyage run voyage
outcome sunk wins-oscars sink
__________________________________________
Table 2
Examples of Some Features Generated with Metaphoric, Literal-Mapping, and Literal Contexts
offshoot
met You might think ambition is a
productive emotion, but jealousy is often its offshoot
Unique features: DOWN-SIDE,
UNWANTED, MOTIVATION, REASON
lit-map The way those two trees have grown together,
the left one looks like an offshoot
Unique features: FORK,
CONNECTION, LEAN
lit
The Rockies are the major mountain range around
here, this one is just an offshoot
Unique features: SMALL,
EXTRA, ADDITION, SUBSIDIARY, RANDOM
Shared features (appear in 3 different contexts):
BRANCH, GROW
met Spectacular and short-lived, the right
mix of gin and vermouth is a meteor
Unique features INTOXICATING, STRONG
lit-map Not well versed in astronomy, she mistakenly
thought the comet was a meteor
Unique features FALLING FROM THE SKY, FLASH, DANGER, BALL
lit She looked up into the night sky
and happened to see a meteor
Unique features DISTANT, UNIVERSE, EXPANSIVE
Shared features: FAST,
SHOWER, ROCK, BRIGHT, SHOOTING
met
She said it was serious but her
relationship with him was just a reststop
Unique features NOTHING SERIOUS, WAITING, IN BETWEEN, REBOUND
lit-map Looking
at the photo closely he realized the campground was actually a reststop
Unique features PARK,
PLACE ALONG THE ROAD, OPEN, RECREATION
lit After tracking him for days, the
police finally cornered the fugitive
Unique features INTERSTATE, STOPOVER, PITSTOP
Shared features: BREAK, BATHROOM, RESTROOM, RELAX,
HIGHWAY
met Blindly following orders, those cult
members were cattle
Unique features BLIND, STUPIDITY, DEPENDENT, UNTHINKING,
DEATH
lit-map He mistook the herd of gazelles for cattle
Unique features horns, wild, goats, DOMESTICATED
lit We grew some corn for ourselves but
more of it for the cattle
Unique features FOOD,
CHEWING, and VARIOUS STOMACHS
Shared features: COW, ANIMALS, MEAT
Figure
1. Classic N400 Effect. The solid line shows the event-related brain
response (ERP) from one electrode site for processing words that were highly
expected in the context. The dashed
line shows the ERP elicited by words that were unexpected in the context.
Figure
2. Percentages for unique features generated
per context type. Error bars represent
the standard error of the mean.
[1] Of course, the underspecification model could be resuscitated if it were found that brain activity underlying the N400 is correlated to measures of total reading time rather than to the first fixation measure used by Frisson & Pickering (see this volume for review). At present first fixation is the best sign of immediate processing difficulty in eye tracking studies of visual language comprehension. N400 is the best sign of immediate difficulty of lexical integration in the ERP. However, the exact relationship between the two measures is currently unkown.
[2] Thanks to Todd Oakley for bringing this (albeit gruesome) example to our attention.