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Australian Journal of Psychology 2012; 64: 199–208
doi:10.1111/j.1742-9536.2012.00053.x
Novelty and processing demands in conceptual combination
ajpy_53
199..208
Brentyn J. Ramm1 and Graeme S. Halford2
1
School of Psychology, University of Queensland, St. Lucia, and 2School of Psychology, Griffith University, Mt Gravatt,
Queensland, Australia
Abstract
In this article, we sought to isolate the processing demands of combining the concepts of modifier-noun phrases from those of other
language comprehension processes. Probe reaction time (RT) was used as an indication of the processing resources required for
combining concepts. Phrase frequency (as measured by Google hit rates) was used as a metric of the degree of conceptual combination
required for each phrase. Participants were asked to interpret modifier-noun phrases using a sense-nonsense decision (Experiment
1) and a phrase meaning access task (Experiment 2). Experiment 2 also used a lexical decision task to activate the word’s individual
meanings. Regression analyses for both experiments indicated that phrase frequency (indicating novelty) predicts a significant portion
of the probe RT variance, such that low-frequency phrases required more processing resources than high-frequency phrases, when
controlling for associative strength, word frequency, letter length, and lexical-semantic activation. Overall, this study indicates that
conceptual combination requires processing resources beyond those of other language processes.
Key words: conceptual combination, familiarity, probe reaction time, processing demands, working memory
Combined concepts, such as road rage, carbon footprint, and
beer garden are a common and everyday occurrence in language. Humans are also adept at interpreting novel combinations such as pet human. Creating new meanings such as
these, on-the-fly, would seem to rely upon working memory
resources; however, it is not clear whether such demands
stem from the integrative processes themselves or merely
from the concurrent activation of the concepts in working
memory, syntactic processing, etc. The aim of this study
was to investigate this question by isolating the processing
demands of conceptual combination from those of other
language comprehension processes. One advantage of using
two word phrases, in contrast with sentences (Daneman &
Carpenter, 1983; Just & Carpenter, 1992; MacDonald, Just,
& Carpenter, 1992; Miyake, Just, & Carpenter, 1994), is
that the processing costs of lexical-semantic activation of
the individual words, syntactic processing, and sentence
meaning updating can be held fixed, while the amount of
conceptual combination required for interpretation varies.
In particular, we used phrase frequency as a measure of the
amount of integration required for a phrase. It was assumed
Correspondence: Brentyn J. Ramm, PhD, School of Philosophy, HC
Coombs Bldg, The Australian National University, Canberra, ACT
0200, Australia. Email: brentyn.ramm@anu.edu.au
Received 8 November 2011. Accepted for publication 26 March
2012.
© 2012 The Australian Psychological Society
that interpreting low-frequency phrases (e.g., moth signals)
would involve more integrative processing than familiar
phrases (e.g., mobile phone), the latter of which are more
likely to involve the retrieval of a pre-stored meaning than
conceptual combination.
Modifier-noun compounds such as zebra crossing can be
broadly divided into property interpretations (e.g., a striped
crossing) and relational interpretations (e.g., a crossing
for zebras) (Wisniewski, 1996). In this study, we focused on
modifier-noun phrases that tend to elicit relational interpretations (Gagne & Shoben, 1997). Wisniewski (1997) proposes
that creating relation-based interpretations in conceptual
combination involves the construction of a scenario based on
background knowledge. For example, truck soap can be interpreted as soap used for washing trucks because a truck can be
washed, while plant soap is more likely to be interpreted as
soap made of plants because a plant does not easily fit into
the role of an object that can be washed. Background knowledge is also important in the selection of relations and the
elaboration of the combined concept (Medin & Shoben, 1988;
Murphy, 1988; Murphy & Medin, 1985). Thus, truck soap
could be inferred to be rougher and likely to dissolve grease,
while plant soap may be inferred to be more natural, sweetsmelling (e.g., rose, lemongrass etc), and softer on the skin
(e.g., it may contain Aloe Vera). Furthermore, access to
background knowledge may also be constrained by the historical frequency with which a relation has been activated
by the modifier-noun (as proposed by the Competition
200
B.J. Ramm and G.S. Halford
Amongst Relations in Nominals theory: Gagne 2001; Gagne
and Shoben 1997). For example, LOCATION is a highfrequency relation for mountain (e.g., mountain stream) and
may be more likely to be activated (more available) than a
low-frequency relation for mountain such as ABOUT (e.g.,
mountain magazine). While conceptual combination is an
essential process in human cognition, there do not appear to
be any previous studies that have investigated the role that
working memory resources play in combining concepts. It is
important to investigate this so that conceptual combination
can be related to broader cognitive systems.
While all theories hold that conceptual combination
involves additional processes to those of semantic and syntactic processing (e.g., Gagne & Shoben, 1997; Medin &
Shoben, 1988; Murphy & Medin, 1985; Wisniewski, 1996,
1997), not all cognitive processes impose working memory
demands. Working memory has been traditionally divided
into short-term memory components (e.g., phonological,
visual-spatial, etc) that are non-capacity-limited (but open to
decay and/or interference) and a central limited-capacity
component (e.g., Baddeley, 1986; Baddeley & Hitch, 1974;
Cowan, 2001; Oberauer, 2009; although for a multiple
capacities view, see Martin, Shelton, & Yaffee, 1994;
Wickens, 1984). Whether conceptual combination imposes
processing demands seems to depend crucially upon
whether relation activation and the subsequent linking of
concepts occurs in parallel with lexical-semantic and syntactic processing, or also draws upon the capacity-limited
component. The Semantic Cognition model (Rogers &
McClelland, 2004) is an example of a neural network where
relations are represented subsymbolically and are processed in parallel. Although it is not a model of conceptual
combination, it is at least suggestive of how conceptual
combination may occur in parallel with other language
comprehension processes. As current models of conceptual
combination do not make predictions either way, it is important to investigate this question empirically.
In this study, we introduced a secondary task (probe reaction time (RT)) concurrently with standard conceptual combination tasks (dual-task design) to measure the processing
resources involved in combining concepts. We expected that
the higher the novelty of a phrase, the more processing
resources (indicated by longer probe RTs) would be required
to combine their concepts, thus providing evidence that
conceptual combination is resource-demanding. We also
used multiple regressions to statistically control for other
factors such as associative strength, word length, and word
frequency.
EXPERIMENT 1
The present experiment used a sense-nonsense decision to
measure the processing time of combining the concepts of
novel phrases (e.g., Gagne, 2001; Gagne & Shoben, 1997). In
the experiment, an auditory tone (probe) sounded randomly
during the sense-nonsense decision. Participant’s latency in
a vocal response to the probe (probe RT) was used as an
indication of the processing demands of each phrase (e.g.,
Kahneman, 1973; Kerr, 1973; Lansman & Hunt, 1982;
Maybery, Bain, & Halford, 1986; Posner & Boies, 1971;
Posner & Klein, 1973). These studies have shown that latencies in probe RT do not necessarily mirror those of a primary
task. For example, it has been found that processing negative
premises takes significantly longer than positive premises
during transitive inference (Maybery et al., 1986), and processing four letters takes significantly longer than processing one letter (Shwartz, 1976). Despite this difference in
response times for these primary tasks, no difference was
found in probe RT. On the other hand, Maybery et al. (1986)
used latencies in probe RT to show that premise integration
during transitive inference imposes more informationprocessing demands than holding both of the premises in
working memory. The assumption behind the task is that as
working memory has limited resources, latency in responding to the beep is indicative of the amount of resources
available at that point in the primary task. That is, the greater
the demands of the primary task, the fewer resources will be
left over for responding to the secondary task, and so probe
RT will increase (Posner & Boies, 1971). The auditory probes
appeared after the visual onset of phrase during the process
of conceptual combination. Based upon previous findings of
average gaze durations for reading single words of 239 ms
(Just & Carpenter, 1980) and 244–324 ms (Miyake et al.,
1994), two probe onset times of 400 and 500 ms were used.
It was expected that this would place probes around the
beginning of the combination process. Two probe onset
times were used so as to reduce probe predictability.
As a measure of phrase frequency, we entered each phrase
into Google in quotes and recorded the hit rate returned (for
a previous study using Google hits as indication of familiarity, see Wisniewski & Murphy, 2005). It was expected that
high-frequency phrases would require less conceptual combination than low-frequency phrases, and thus fewer processing resources would be drawn upon for the former than
for the latter phrases. While the phrases used in this study
were all considered to be novel by Gagne and Shoben
(1997), we felt that some were clearly more familiar than
others (for example, financial headache and flu pills vs moth
signals and sap remedy). One advantage of this measure was
that for all phrases, both words are processed together, and
thus joint lexical-semantic activation and syntactic processing were held constant while the amount of conceptual
combination varied.
However, a limitation to using phrase frequency as a
measure of the amount of conceptual combination is that it
may also overlap with factors such as: (1) associative links
© 2012 The Australian Psychological Society
Processing demands and concept combination
between the two words; (2) the frequency of the two words;
and (3) letter length. Later, we use regression analyses
to assess the unique contribution of phrase frequency to
processing demands (probe RT).
Method
Participants
Sixty-seven first-year psychology students participated for
course credit.
Design
The experiment consisted of four blocks of trials: (1) Probe
Only (28 trials); (2) Sense-Nonsense Practice (20 trials); (3)
Sense-Nonsense Test (160 trials); and (4) Probe-Only (28
trials).
Procedure
The experiment was computer-based, and participants were
tested in groups of up to four. Instructions were displayed
on the screen preceding each block. The trial sequence was
blank screen (1,000 ms), fixation cross (1,000 ms), blank
screen (1,000 ms), and phrase (3,000 ms). Each phrase
appeared in the centre of the screen, and participants were
asked to decide if it was sensible or nonsense (p and q
keys respectively) as quickly as possible. Participants completed two blocks of sense-nonsense decisions, and the 56
test phrases (obtained from Gagne & Shoben, 1997) were
randomly distributed between these blocks. Each sensenonsense block consisted of 160 trials, with probes occurring
randomly on 56 of the trials, that is, 28 sensible and 28
nonsense phrases (see Appendix I). Thus, probes occurred
on 35% of occasions. On a probe trial, a 100 ms beep (probe)
sounded through headphones 400 or 500 ms after the
appearance of the word-pair stimuli. Participants were
required to respond by saying ‘beep’ into a microphone as
quickly as possible when the tone sounded. Probe RT was
recorded using a verbal button box that measured the
latency of the vocal response from the onset of the beep. The
sensitivity of the verbal button box was adjusted to suit each
participant’s voice.
The Probe Only blocks consisted of the same procedure as
the earlier conditions, except that a neutral symbol ‘********’
appeared in place of the word pairs, and participants were
not required to respond to the symbol. This condition, in
which no words are processed, was used as baseline for the
primary task probes.
Measures of phrase characteristics
Phrase frequency. Phrase frequency was used as a measure
of the amount of integrative processes that are required for
© 2012 The Australian Psychological Society
201
each phrase. To reduce the large variance obtained in the hit
rates, we transformed these scores into logs of ten.
Associative strength. We obtained the associative strength
between each phrase’s words from Nelson, McEvoy, and
Schreiber (1998) (we only used forward association, i.e.,
from modifier to header). According to these free association
norms, none of the phrases were composed of associated
words, and thus, this variable was not considered in the
following analyses.
Word frequency. As a measure of word frequency, we
obtained Google hit rates for each individual word. The hit
rates of the modifier and header noun were summed for
each phrase, and we again transformed these scores into logs
of ten.
Letter length. The length of the word is another factor that
may affect processing demands of interpreting a phrase. We
obtained this by simply summing the number of letters in
each phrase.
Results
Thirteen participants were removed for scoring 50% or
less on overall accuracy for sensible phrases. The remaining
sample size was 54 participants. The average accuracy for the
sense-nonsense decision for the remaining participants was
70.3%. Because of experimenter error, the data for the
probe-only (baseline) blocks were not recorded. The data
analyses were performed only on correct trials and only on
trials in which a probe occurred. Preliminary analyses
showed that the distribution of scores was significantly positively skewed for both probe RT (z = 4.0, p = .001) and
sense-nonsense RT distributions (z = 3.11, p = .005). To
correct for the skewness, we used the median probe RT and
sense-nonsense RT score for each phrase (for previous use of
median scores for Probe RT, see Maybery et al., 1986). As
there was no main effect of probe onset time (400 and
500 ms), this variable was collapsed for the following analyses. The correlations of the predictor variables and dependent variables for Experiment 1 are displayed in Table 1. The
correlations and analyses were conducted across items (i.e.,
the phrases) rather than participants.
Probe RT
A multiple hierarchical regression was used to examine
whether phrase frequency predicted probe RT after controlling for word frequency and letter length. The variables word
frequency and letter length were entered in the first step,
and phrase frequency was entered in the second step. In the
first step, the combined variables explained a significant
202
B.J. Ramm and G.S. Halford
Table 1 Experiment 1 Pearson’s r correlations of dependent variables (probe reaction time (RT), sense-nonsense RT, and accuracy) with
predictor variables
Probe RT
Response time
Accuracy
Phrase frequency
Word frequency
Letter length
-0.57
p < .001
-0.45
p = .001
0.27
p = .043
-0.47
p < .001
-0.31
p = .022
0.13
p = .431
0.37
p = .005
0.42
p = .001
0.013
p = .925
portion of the probe RT variance (r2 = 0.31, F(2,53) = 11.75,
p < .001). At the second step, phrase frequency made a significant additional contribution (b = -0.40, r2 change = 0.07,
F(1, 52) = 6.11, p = .017). The only other significant individual contributor to the final model was letter length
(b = 0.23, t(52) = 2.04, p = .047). Overall, then, it was found
that the less frequently a modifier-noun phrase appears on
the Internet, the more processing resources that are required
to integrate its constituent meanings, and this is the case
when controlling for the frequency of the phrase’s individual
words and letter length.
Sense-nonsense RT
Similar analyses were used to examine the relationship
between phrase frequency and sense-nonsense RT, controlling for letter length and word frequency. Letter length and
word frequency were added at the first step and combined to
explain a significant portion of the sense-nonsense RT variance (r2 = 0.24, F(2,53) = 8.28, p = .001). At the second step,
phrase frequency made a significant additional contribution
(b = -0.36, r2 change = 0.06, F(1, 52) = 7.34, p = .041). In
this final model, letter length also made a significant unique
contribution to sense-nonsense RT (b = 0.32, t(52) = 2.65,
p = .011). Thus, it was found that phrase frequency made a
unique contribution to sense-nonsense RT when controlling
for letter length and word frequency.
Sense-nonsense accuracy
When sense-nonsense accuracy was used as the criterion,
letter length and word frequency added at the first step did
not explain a significant portion of the accuracy variance
(r2 = 0.02, F(2,53) = 0.48, p = .619). At the second step,
however, phrase frequency made a significant unique
contribution to predicting accuracy scores (b = 0.40, r2
change = 0.07, F(1,52) = 4.25, p = .044).
EXPERIMENT 2
The results of Experiment 1 indicated that the less frequently
a phrase appears on the Internet, the more processing
resources are required to interpret it. However, as it is
subjective and ambiguous as to whether a phrase is sensible
or not, this task included considerable decision uncertainty
that may have imposed additional processing loads to that
of conceptual combination. The present experiment assessed
the resource demands of processing modifier-noun phrases
using a phrase meaning access task in which participants
pushed a button when they had generated an interpretation for the phrase. This eliminates the uncertainty of the
decision.
In the previous experiment, phrase frequency was used as
a measure of amount of conceptual combination; particularly, it was expected that more familiar phrases such as
financial headache would require less combinatory processes
than more novel phrases such as mountain magazine. In the
present experiment, we also employed a lexical decision task
to gain an independent measure of the lexical-semantic activation of the individual words (i.e., when processed one at
a time).
The mean sense-nonsense decision time from Experiment
1 was 1,201 ms, suggesting that conceptual combination
may be a relatively late-occurring process. To sample processing demands from a slightly later period in the processing of the phrases, we changed the probe onset times from
400 and 500 ms (Experiment 1) to 500 and 600 ms (Experiment 2). It should be noted that probe RT took an average of
657 ms, thus a probe onset of 600 ms should approximately
sample processing demands ranging from 600 to 1,257 ms.
We also added highly familiar phrases (e.g., stop sign) to
increase the range of familiarity to novelty for the stimuli. It
was expected that if the combination of concepts is capacitydemanding, then the frequency of the phrase would show
a negative correlation with phrase probe RT even when
controlling for lexical-semantic access, associative links
between the two words, letter length, and the frequency of
the phrase’s individual words.
Method
Participants
Data were collected from 61 first-year psychology students
who participated for course credit.
© 2012 The Australian Psychological Society
Processing demands and concept combination
Design
The experiment consisted of six blocks of trials: (1) Probe
Only (28 trials); (2) Lexical Access Practice (20 trials); (3)
Lexical Decision Test (112 trials); (4) Phrase Meaning Access
Practice (20 trials); (5) Phrase Meaning Access Test (112
trials); and (6) Probe-Only (28 trials). The order of the
Lexical Access and Phrase Meaning Access blocks was counterbalanced between subjects.
Materials
The novel test phrases were the same 56 modifier-noun
phrases from Gagne and Shoben (1997) (e.g., tax pressure)
used in Experiment 1. We also selected 56 familiar modifiernoun phrases (e.g., mobile phone) from the Oxford English
Dictionary. These phrases were randomly distributed
between the Phrase Meaning Access and Lexical Access test
blocks for each participant, such that each block had 28
novel phrases and 28 familiar phrases. There were also 14
novel and 14 familiar filler phrases, and 28 word–non-word
phrases in each block. The word–non-word pairs (e.g., thunk
kitten) were constructed by changing the vowel of existing
words and by the pairing of random syllables. Non-words
appeared equally often in the modifier and head-noun positions (see Appendix II).
Procedure
Except for the following details, the procedure was the same
as Experiment 1. For the Phrase Meaning Access block,
participants were asked to read the two words and to push
the P key on the keyboard (or the Q key for left-handers) as
quickly as possible when they had accessed a meaning for
the phrase as a whole. If one of the strings was a non-word,
then they were asked to refrain from pushing the button.
For the Lexical Decision block, participants were asked to
push a button if both of the strings were words and again to
refrain from responding if one of the strings was a non-word.
The probe sounded randomly on 28 of the trials of each test
block (25% of occasions), half of which were novel and half
of which were familiar modifier-noun phrases.
Measures of phrase characteristics
Phrase characteristics were measured using the same
method as Experiment 1.
Phrase frequency. As expected novel phrases appeared with
significantly lower frequency on the Internet (M = 3.97,
standard deviation (SD) = 1.17) than familiar phrases
(M = 6.57, SD = 0.69), t(110) = 14.29, p < .001.
Associative strength. According to Nelson et al.’s (1998)
free association norms, many familiar phrases such as beach
© 2012 The Australian Psychological Society
203
sand were composed of associated words (e.g., there was a
39% probability that participants would produce sand given
beach), while none of the words of the novel phrases such
as chocolate plant were associated. A between-groups t-test
revealed that familiar phrases (M = 0.057, SD = 0.09) were
significantly more associated than novel phrases (M = 0.0),
t(110) = 4.71, p < .001.
Word frequency. A between-groups t-test revealed that
novel phrases were composed of significantly lower
frequency individual words (M = 16.42, SD = 0.95) than
familiar phrases (M = 16.96, SD = 0.79), t(110) = 3.24,
p = .002.
Lexical-semantic access. We used probe RT for a lexical
decision to the phrases as a means of controlling for the
processing demands of lexical access to the two individual
words, thereby isolating the demands of conceptual combination from lexical-semantic activation.
Results
Two participants were removed from the data set for not
following the instructions. The only accuracy score for this
experiment was the requirement that participants refrain
from making a response when one of the words of the
phrase was a non-word (that is the lexical decision task for
both novel and familiar phrases). A further two participants
were removed for scoring outside two SDs in accuracy for the
lexical decision task. For the remaining 57 participants, the
overall average accuracy rate on word–non-word trials was
87.83%.
To reduce skewness in probe RT and phrase meaning
access scores, we again used the median scores for each
phrase. Preliminary analyses showed that probe RT was
significantly faster for the probe-only task (baseline)
(M = 475 ms) than for the experimental conditions
(M = 565 ms), showing that probe RT was effectively indicating processing load differences, t(61) = 6.08, p < .001. As
there was no main effect or interactions with probe onset
time (500 and 600 ms), this variable was collapsed. The
correlations of the predictor variables and dependent variables for the experiment are displayed in Table 2. Pearson’s
r correlations revealed that phrase frequency, associative
strength, and word frequency were all negatively correlated
with phrase meaning access probe RT. It was also found
that lexical probe RT to the phrases and letter length were
positively correlated with phrase meaning access probe RT.
The purpose of the following two multiple regressions was to
investigate the unique contribution of phrase frequency to
the variance of phrase probe RT and phrase meaning access
response time.
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B.J. Ramm and G.S. Halford
Table 2 Experiment 2 Pearson’s r correlations of dependent variables (phrase probe reaction time (RT) and phrase meaning access response
time) with predictor variables
Phrase probe RT
Response time
Phrase
frequency
Lexical
probe RT
-0.61
p < .001
-0.75
p < .001
0.36
p < .001
Lexical
response time
Word
frequency
Association
strength
Letter
length
0.76
p < .001
-0.43
p < .001
-0.48
p < .001
-0.21
p = .025
-0.25
p = .009
0.31
p = .001
0.36
p < .001
Note. Cells are left empty for variables that were not used as predictors in the multiple regression analyses.
Phrase probe RT
In the first regression using phrase probe RT as the criterion,
the variables lexical probe RT, associative strength, word
frequency, and letter length were entered in the first step,
and phrase frequency was entered in the second step. In
the first step, the combined variables explained a significant portion of the phrase probe RT variance (r2 = 0.28,
F(4,107) = 10.49, p < .001). In the second step, phrase
frequency made a significant additional contribution
(b = -0.54, r2 change = 0.13, F(1,106) = 24.14, p < .001. In
this final model, letter length (b = -0.20, t(106) = 2.52,
p = .013) also made a significant unique contribution to predicting phrase probe RT. Overall, then, it was again found
that the more novel a modifier-noun phrase, the more processing resources that are required to combine its concepts,
and this is the case when controlling for lexical-semantic
access, the associative strength between the phrase’s words,
the frequency of the phrase’s individual words, and letter
length.
Phrase meaning access RT
In the second regression, phrase meaning access RT was
used as the criterion. The variables letter length, associative
strength, word frequency, and lexical response time were
entered in the first step, and phrase frequency was entered
in the second step. In the first step, the combined variables
explained a significant portion of the response time variance
(r2 = 0.61, F(4,107) = 41.84, p < .001). At the second step,
phrase frequency made a significant additional contribution
(b = -0.44, r2 change = 0.07, F(1,106) = 44.51, p < .001).
In this final model, a significant unique contribution to
response time was also made by lexical response time
(b = 0.41, t(106) = 4.71, p < .001). Thus, for phrase meaning
access RT, it was found that phrase frequency again made
the largest contribution, although a unique contribution was
also made by lexical response time.
GENERAL DISCUSSION
Even though conceptual combination plays a crucial role
in cognition, to our knowledge, no previous studies have
directly investigated the resource demands involved in this
process. In the present study, we sought to isolate the processing capacity-demands of combining the concepts of
modifier-noun phrases from that of other processes such as
lexical-semantic and syntactic processing. In both experiments, phrase frequency (as measured by Google hit rates)
was used as a metric of the amount of conceptual combination required for each phrase. We also controlled for
associative strength, word frequency, and letter length. In
Experiment 2, a lexical decision task was also used as an
independent measure of the lexical-semantic activation
of the two words. Both experiments indicated that conceptual combination imposes more processing loads as the
frequency of the phrase decreases, when controlling for
these factors. Overall, our findings clarify how conceptual
combination occurs within working memory in that they
provide converging evidence that the more combinatory
processes that are required to interpret a phrase, the more
processing resources that will be involved in constructing
the representation.
One likely source for the processing demands of conceptual combination beyond that of the lexical-semantic processing of the individual words is that conceptual integration
requires the simultaneous processing of both concepts and
their linking via a relation. One way in which this process
could be modelled is as the binding of concepts to a relational
symbol (e.g., Goodwin & Johnson-Laird, 2005; Halford,
Wilson, & Philips, 1998). For instance, interpreting mountain
magazine may involve the selection of a relational symbol
in which mountain and magazine play appropriate roles
(?(mountain, magazine)). This would be an example of a
binary relation, which have been found to impose more
processing demands than unary relations such as the binding
of an attribute to a single concept (e.g., ripe(apple)) (Halford
et al., 1998). The hypothesis that relational processing contributes to the processing demands of conceptual combination is also consistent with findings that the binding/relating
of representations is capacity-limited for a wide variety of
cognitive functions such as reasoning and general intelligence (e.g., Halford, Wilson, & Philips, 2010; Oberauer,
2009; Oberauer, Süß, Wilhelm, & Wittmann, 2008).
However, as we did not isolate relational processing from
non-relational processes, we acknowledge that our findings
© 2012 The Australian Psychological Society
Processing demands and concept combination
only support the general conclusion that combining concepts does not take place in parallel with other language
comprehension processes. Other non-relational processes
may also contribute to the processing costs of combining
concepts. For instance, it is likely that low-frequency phrases
(e.g., moth signals) require more background knowledge in
constructing a scenario (e.g., moth signals may be a flight
pattern used in a mating ritual) and elaborating the properties of the new concept (e.g., such moths may be more likely
to be social insects than ordinary moths).
An alternative interpretation of the present results is
in terms of a central attentional bottleneck, where certain
central processes must proceed one at a time rather than by
the sharing of limited processing resources (e.g., Pashler,
1994). In fact, both processing resources and bottlenecks
play an important role in a number of prominent models of
working memory (see Oberauer & Göthe, 2006). However,
given the large amount of evidence that many sentence level
processes draw upon a limited working memory capacity
(e.g., Daneman & Carpenter, 1980, 1983; Just & Carpenter,
1992; Miyake et al., 1994), we felt that a processing
resources interpretation (or both together) was the most
likely explanation for our results. Future research directly
investigating whether an individual’s general working
memory capacity affects their ability to interpret novel
modifier-noun phrases would help to clarify this issue.
Further studies are also needed to isolate the processing
demands of individual subprocesses in conceptual combination, such as relation competition and availability (Gagne &
Shoben, 1997), scenario construction (Wisniewski, 1997),
and accessing background knowledge and elaboration of
the representation (Medin & Shoben, 1988; Murphy, 1988;
Murphy & Medin, 1985). In conclusion, the present study
suggests that working memory resources should be considered an essential factor in any complete model of conceptual
combination and suggests some potentially fruitful lines of
research.
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APPENDIX I
Experiment 1 materials
Sensible test phrases (Gagne & Shoben, 1997)
adventure months
air power
birth rash
cable brake
chocolate plant
city committee
coal country
college publication
cooking treatment
cream bread
family cow
financial headache
finger nerve
fish gland
flu pills
gas cloud
growth range
gutter rodent
heat iron
historical elder
home light
horse toy
job conflict
juvenile swing
manual utensils
marine antique
marital instincts
maternal pains
morning prayers
moth signals
mountain magazine
murder film
musical town
nose sound
office charge
oil moisturizer
olive season
paper model
party toast
pine dust
sap remedy
servant language
smoke problem
sports pain
star drama
steam equipment
student scandal
summer money
tax pressure
traumatic tale
urban album
vapour drops
vegetable cube
voice vote
winter breeze
wood allergy
Sensible filler phrases
acid burn
alligator knife
alpine forest
anatomy guide
ankle bump
ant powder
aquatic mammal
arm exercise
art fund
ash bin
baby smile
bicycle dent
billing network
bitter peel
boar roast
bone weapon
bronze statue
budget list
butter pudding
cabin trail
cabinet photo
cake oven
casino loss
chalk artist
cider drum
cigarette ban
coin game
cookie feast
criminal file
crowd rumble
dinner music
drink swirl
enemy strength
farm wedding
fiancée vow
forest tiger
fracture swelling
gallows trial
garbage rake
gestation stage
glass pen
gold drill
harvest curse
heart design
history conference
honey perfume
ideological doctor
incense sneeze
industrial lock
jail years
lawyer story
letter folder
lightening rod
margin note
meditation corner
mining engineer
minnow stream
mirror cloth
mitten knitter
monkey puzzle
mouse phobia
muscle tear
neighbourhood bar
number science
ocean warning
office cup
oval sport
parking indecision
parrot veterinarian
planet mass
plastic report
pole height
political sticker
potato sauce
property law
radio communication
rain lake
reform meeting
restaurant review
rice picker
robin attack
royal massage
sand shield
show title
silk bug
social lounge
song review
spine inflammation
storm noise
tank transport
television shelf
tennis rage
tile pattern
tooth pain
torch holder
tractor shaft
train junk
truck factory
trumpet store
vacation humidity
valley village
war parade
water bird
wool string
© 2012 The Australian Psychological Society
Processing demands and concept combination
207
APPENDIX I Continued
Nonsense phrases
acquaintance monster
angel tube
anvil taste
anxiety canoe
applause bridge
apple gravel
argument brush
balcony coal
balloon threat
band noose
basin friend
bath carriage
beetle sponge
bell shirt
bleach moustache
bolt mushroom
boss well
box birthday
brain kiosk
brass blood
bun nuisance
bus temple
cabbage cord
cage smog
captive feather
carrot necklace
chicken light
child metal
chin mission
clam hose
clown banana
colour ring
compass dock
cork reading
costume digestion
counter study
courage rack
credit scissors
crystal moisture
cub keyboard
day grave
debt rib
desert spray
dew fire
diagram axe
discretion snow
doctor soil
dog crayon
dwarf ray
ear smirk
edge gel
egg hook
elastic dawn
electric favour
elephant tyre
fabric scholar
fear shelf
felt barbecue
fiction ink
fire dolphin
flower prisoner
fox smoke
fuzz kettle
galaxy sail
© 2012 The Australian Psychological Society
gin fibre
grape golf
grit hospital
groceries energy
hammock paper
hate reed
helium carnivore
highway sheppard
hog cotton
horn canine
horse pope
hour ice
idea linen
jar disease
knot key
lemonade marriage
lion fin
lip hammer
lizard cotton
lung beer
map grass
mask toe
melon punishment
mental cart
mine meow
mineral girl
moon fork
nail fountain
nut satire
olympic pineapple
pack phone
park laser
partner climate
peace mosquito
pear wind
pebble thread
penny coat
piano cook
pizza whip
plan shop
plank dose
plate dark
platform diet
poem revolt
pork mine
possum sword
postage cat
pounce library
princess salmon
punch tablet
puppet vault
purple grace
rug pie
salt secretary
saw game
scotch shoe
sheep gymnast
shelf river
shell cop
ship vision
shoe disability
sky switch
slime race
slug school
song virus
space cherry
spear rose
spot team
spring cave
stone match
stork stitch
straw ladder
sugar gallery
sun pot
tantrum eagle
tap skull
teacher arms
television fur
thick responsibility
thigh candidate
tile hood
time tiger
tin weather
tooth seed
traffic bed
trail curtain
truck blindness
trumpet bank
vanilla comet
velvet account
violin swan
wall trousers
war toddler
wave cookie
wing laugh
word sprinkle
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B.J. Ramm and G.S. Halford
APPENDIX II
Experiment 2 materials
Familiar test phrases
academic year
art gallery
bank statement
beach sand
boat deck
book shelf
brand name
bronze medal
bus ticket
business hours
centre stage
chicken feed
circus tent
coffee cup
Familiar filler
bath towel
brain damage
camp ground
cave man
civil rights
fountain pen
ground floor
immune system
infant mortality
lamp post
mirror image
night school
nursery rhyme
pirate ship
pocket knife
prime minister
private life
rock music
rope ladder
snow boot
song bird
sound wave
space shuttle
stainless steel
sugar cane
theme park
time bomb
world record
computer game
cricket match
crystal ball
desert island
fire truck
hand grenade
insurance policy
intelligence score
junk mail
kitchen sink
labour camp
leather jacket
lemon juice
middle class
Novel filler
anatomy guide
aquatic mammal
ash bin
budget list
butter pudding
casino loss
cider drum
enemy strength
fracture swelling
grain law
jail years
margin note
mining engineer
minnow stream
mouse phobia
muscle tear
ocean warning
planet mass
political sticker
reform meeting
restaurant review
rice picker
spine inflammation
tank transport
vacation humidity
valley village
wax burn
wool string
mobile phone
parking space
place mat
police station
primary school
prison bars
public toilet
question mark
rain water
raw material
remote control
report card
room temperature
rubber band
screen saver
sex drive
skeleton key
slave labour
social security
speed camera
stock market
stop sign
table tennis
tea leaves
tobacco pipe
tomato sauce
traffic jam
treasure chest
Word non-word pairs
bear raplan
bitter pog
bont brush
breath bostle
cigarette insede
cike oven
clock diot
clothes lacone
courage ritch
criminal follod
crish helmet
curdail sport
dince floor
gold druct
gont monkey
guff machine
harch harvast
heak design
helmet collife
humson forest
industrial leck
jold snack
lamade marriage
lant powder
leif shovel
mactus veterinarian
milk bafire
mitten complun
mupe road
pabio knife
package telmer
pallow ban
paper ganem
perfume voney
princess famon
pule height
relice fabric
ribin knitter
river jeg
rocket frew
royal missage
salad attick
sameg comic
soofer justice
stanex oyster
surface humstag
suttid stain
sweat charote
syrup hension
tennis pone
thruel guard
trevol trunk
wemach pump
wheat motch
wrist selact
yater incense
Note: The Novel Test phrases used in Experiment 2 were the same as the ‘Sensible Test Phrases’ used in Experiment 1 (see Appendix I).
© 2012 The Australian Psychological Society