Experiments on Processing Lexical Blends*
Adrienne Lehrer and Csaba Veres
University of Arizona
University of Bergen
Lexical blends have existed in English for a long time, but in recent years
their creation has accelerated greatly, so much so that, at least in the US, it is
hard to avoid new ones. Yet mainstream morphologists have treated them as
marginal in spite of their increasing popularity. When they appear in context,
however, they seem easy to process, so we decided to carry out some
experiments to tap into the mechanisms of processing them. In 1997 and
1998 we carried out several experiments on how subjects process lexical
blends under time pressure. Although this work was never published, the
International Conference on Lexical Blending has provided us with an
opportunity to present our results and to suggest ways that we might have
continued with our project.
Our goal was to gain some insight into processing through
psycholinguistic experiments. There is a large literature on how much
speakers of a language decompose complex words, including compounds (the
basis of lexical blends), and our hypothesis was that similar decomposition
may take place with blends. The work built on earlier work of Lehrer (1996),
in which subjects were presented with blends and asked to identify the targets
(the two-word compounds underlying the blends) and to provide meanings.
There were no time constraints in those studies.
The point of these new experiments was to try to find evidence for rapid,
automatic processing in the decomposition of the blends. Thus, while the
conscious task of overtly identifying the components of meaning and
assigning an interpretation requires time and effort, the process might involve
an initial, possibly preconscious stage where candidate words are
automatically activated for further consideration. Since these are not reported
through conscious introspection, more subtle experimental techniques are
required.
After establishing some basic properties of timed blend
identification, we present two experiments. The first is a variation of the stem
completion task and the second a lexical decision task.
Timed Responses and Stem Completions
Experiment 1 consisted of two parts: (1) identifying the underlying words in
a blend and (2) a stem completion task. The first task that we carried out was
basically a replication of Lehrer's earlier experiment with the addition of time
constraints. We based our method on lexical decision tasks although our task
was not a lexical decision task but rather a lexical interpretation task.
Experiment 1: First task. Pilot.
Method
A pilot test was carried out with four subjects who were told that the purpose
of the experiment was to identify the target words and interpret the blends.
They were given several examples and also had some practice before the
experimental stimuli were presented. Subjects sat in front of a computer
screen and pushed a foot pedal to bring up an item on the screen. They were
told to press the yes button as soon as they could identify and understand the
blend, which ended the trial and recorded the elapsed time from the
presentation of the blend. Although the apparatus also had a no button,
subjects were told not to use it for this experiment. A tape recorder was
placed beside the computer, into which subjects spoke the two target words
and a gloss which they felt was appropriate for the blend. The four subjects
needed a long time to respond, an average time of 2222ms. (Null responses
were never included in our calculations.) In addition, the response time was
quite variable. We hypothesized that much of the time might involve finding
a plausible interpretation of the blend itself, so we eliminated this step from
the instructions in the main experiment and told subjects only to identify the
target words.
Experiment 1 Identifying the targets.
Subjects were 38 undergraduate students in linguistics courses, and they were
given extra credit for their voluntary participation Subjects saw one of two
lists of 40 blends with different blends on each list. The purpose of the two
lists was for the stem-completion task, described below. Each list each
contained four types of blends: word + splinter (oildralic < oil + hydraulic)
splinter + word (narcoma < narcotic + coma), two splinters (dramedy <
drama + comedy), and complete overlap (slanguage < slang + language).
Some blends were novel, such as bombphlet and others not. As before,
subjects brought up an item onto the screen by pushing a foot pedal,
Table 1 presents the time that subjects took, which was even longer than
response times for the pilot. The mean response time for he 21 subjects who
saw List 1 and the 17 who saw List 2 required almost three seconds.
Table 1 Response times for identification of blend targets.
Pilot N = 4
List 1 N = 21
Mean response time
2222 ms
2986 ms
Mean standard deviation
680 ms
828 ms
List 2 N = 17
2994 ms
595 ms
An ANOVA was carried out to compare the response time for different blend
types, but the F value of .69 was not significant.
A conjecture for why reaction times are so long is that speakers
automatically wait for an interpretation before responding, so the reaction
times overestimate the amount of time it takes simply to recognize the
component words Whether the two processes of identifying and then
interpreting the two targets are done sequentially, overlapping, or in parallel is
something for future research.
Identifying the targets.
The spoken responses were transcribed and classified as correct. "Correct"
responses were determined by their identity to the ads, magazine headings,
and brand names which provided the blends we used, or they were
conventional items, like infomercial. There were a small number of additional
responses which were equally good (as judged by AL). For example, the
target words for biologue were biography and dialogue, but biology was
classified as plausible. However, when the additional items were added to the
"correct" one, the results were not affected much. Table 2 summarizes the
results.
Table 2: Percentage correct for each types of blend
Word + splinter Splinter + word Two splinters
List 1
52%
50%
34%
List 2
79%
77%
54%
Overlap
69%
59%
Subjects who saw List 2 did better than those who saw List 1, and this might
be due to the fact that List 2 was easier. Note that in spite of this, the reaction
times did not differ in the previous analysis. Perhaps this is because even
though the component words in List 2 were easier to recognize than in List 1,
the reaction time is controlled by the time it takes to generate a gloss, as we
have already suggested.
There was no attempt to match the two lists, and in retrospect we should
have done this to explore more variables. The number of items with complete
overlap was small, but the items were easy: On List 1: guestimate,
slanguage, and clandestiny. On List 2 were palimony, globaloney, and
selectric. Palimony < pal + alimony was easily identified because at the time
that word, coined by journalists, was in the news to describe a scandal. The
actor Lee Marvin, who had a house in Tucson, had broken up with his partner.
She was suing him because she claimed that he gave her a verbal promise to
share his wealth as he would a wife in case of separation. She said he had
broken his promise.
Experiment 1 Second task Stem completions and implicit memory
The stem completion task attempted to determine if subjects remembered the
parts of the blends, even in the absence of explicit recognition of those parts.
That is, can we find evidence for the activation of a lexical item involved in a
blend that is never explicitly recognized? The experiment was suggested by
work on implicit memory. Graf and Mandler (1982), Graf and Schacter
(1985), and Schacter (1987) summarize data showing evidence for two
memory systems: implicit memory and explicit memory. Implicit memory is
defined by Graf and Schacter (1985: 501) as facilitation on a task "in the
absence of conscious recollection," whereas explicit memory is revealed when
performance requires conscious recollection of previous experiences.
Graf and Mandler (1982) devised a study-task on memory. Subjects were
given a stack of cards with one word on each card. There were two
conditions: a semantic one, in which subjects were to decide how much they
liked or disliked the words, and a non-semantic one where they had to decide
if the letters on one card were similar to those on the previous card. Then all
subjects were given a list of stems, three-letter words that could be completed
by an English word. Subjects were told to complete the stems with the first
word that came to mind (implicit memory instructions). They were then
asked to recall as many of the words from the cards as they could (explicit
memory). Only those who performed the semantic task could recall some of
the words; the other subjects recalled few. However, on the stem
completions, subjects from both groups did equally well, and both completed
the stems with the words they saw five times as often as a control group
which had not seen the words.
Returning to our own experiment, immediately after the computer work
was finished, subjects performed the stem completion task. They were given
a list of stems, a sequence of two or thee letters that could be the beginning of
possible English words. Most stems were three letters; two were used only
when three would certainly trigger the target. Half the stems were from
blends on List 1 and half from those on List 2. In addition, half of each of
these could be the beginning of the first part of the blend and half the
beginning of the second. Table 3 gives examples of the stems we used.
Table 3: Examples of stems
Stem
Blend
Target
COM__ dramedy
comedy
BRA__ branchizing branch
PAM__ bombphlet
pamphlet
DES__ deskercize
desk
INC__
coaccidental incidental
HUR__ hurricoon
hurricane
Subjects were instructed to complete the stem with the first word that came to
mind beginning with those letters.
Stem completion results
There were significantly more stem completions based on the blends seen by
subjects than chance, where chance was defined by the responses of those
who had not seen the blend. (Each group served as the control for the other
group.)
Table 4: Stems completions with blend target.
Saw
List 1 # = 21 List 2 # = 17
L1 Target 252
109
L2 Target 77
122
Х2 = 52 p. < .001
In addition we calculated responses that used a morphologically related word.
For example, in the blend successories < success + accessories, if a subject
completed the stem SUC with succeed, that counted as related. However,
most related words were plurals of singular nouns or inflectional variants of
verbs.
Since 21 subjects saw List 1 and only 17 saw List 2, the numbers in Table
4 are adjusted for a better comparison by multiplying the responses to List 2
by 1.24 to show the percentages in Table 5. As can be seen, subjects who had
been primed by seeing a blend on the computer screen were twice as likely to
use the underlying constituent to complete a stem as the unprimed subjects.
Table 5: Percentage of primed and unprimed stem completions.
Primed Unprimed
%
%
Target
68
34
Target + Related 65
35
In retrospect, we should have looked at the various subclasses of responses,
especially to learn if there were significant differences between stems that
could be completed by the first word of the target as opposed to the second
word in the target.
Stem completions and identification of targets.
So far, we have shown that stem completions can be used to measure the
effects of previously presented items in this task. But the really interesting
question is still unanswered. That is, what happens to items that were not
fully processed to the point where subjects could give a coherent gloss? To
answer the question we analyzed the oral response of subjects and correlated
them with stem completions. Responses were categorized into four classes:
(1) subjects identified the target and completed the stem with that word; (2)
they identified the word but did not choose it for the stem; (3) they did not
identify the word but completed the stem with it anyway, and (4) they neither
identified the word nor completed the stem with it. The fourth class contained
a subclass where the subjects misidentified the target word and used that in
the stem completion. There were five instances with subjects who saw List 1
in this subclass. Table 6 shows these results.1
Table 6: Correlation of stem completions and correct identification of targets.
Saw List 1
Saw List 2
Identified word
and chose for
stem
No. %
216 27
101 18
Identified word
but did not
choose for stem
No. %
294 37
271 50
Did not identify
word but chose
for stem
No. %
64 8
28 5
Did not identify
word or choose
for stem
No. %
226 28
147 27
The relevant case here is the items in the third column, cases where the
subjects did not identify the word but used it on the stem completion anyhow.
The total number for List 1 is 64, which is 8% of all responses, and for List 2,
28, which is 5% of all responses. Since the numbers are small, we feel these
data are preliminary.2
Familiarity
Finally subjects were given a list of all the blends and asked which ones they
had seen or heard before the experiment. Items were divided into those which
over half of the responses were correct and those in which fewer than half
were correct. Then the items in the first group were further divided into those
in which over half the subjects reported having seen or heard the blend
previously and those who had not. T-tests showed a significant difference in
response times for List 1 (p < .01) but not significant for List 2. This mixed
result suggests more investigation is needed, but even if there are significant
differences, the phenomenon is compatible with two explanations: (1) blends
are stored as parsed with pointers to target words, or (2) subjects had
previously decomposed them and could do so again, but more quickly than
the first time.
Unfortunately we left the project before completing more detailed analyses
of our data and/or replicating the experiment by controlling for more
variables, such as the frequency and neighbors of the target words. We could
have also looked at each subject's protocol, such as familiarity instead of
pooling the data. Also in retrospect we did not focus on the interesting
category 3 results in which subjects did not identify the targets but chose that
word for the stem anyway. Finally, we should have matched the two lists in
the ways described at the end of the paper.
Experiment 2. Lexical decisions task with masked priming.
The second set of experiments was intended to explore rapid, automatic
decomposition. Although the research showing that morphological
decomposition for complex words and compounds is mixed, it still suggests
that we might find evidence for automatic decomposition of blends.
This set of experiments used the lexical decision task with masked
priming. In a lexical decision experiment, subjects are presented with a string
of letters and they have to decide as quickly as possible whether the letters
spell a word in their language or not. They then press a yes or no button,
which records the time. Usually half the stimuli are words and the other half
nonwords.
One variation uses a prime, which is an item preceding the target item
which is either identical to the target or related, either phonologically,
morphologically, orthographically or semantically. For example, if the target
word is operate, then presenting operate again or operation or doctor earlier
in the list will speed up a subject's response to operate. Forster (1985) has
shown that an identical or related word presented can serve as a prime for up
to 10 following words.
A modification is the use of a masked prime, which is a repetition effect
that occurs when the same lexical item is accessed twice in rapid succession,
usually for a very short time, e.g., 50 milliseconds. This effect is independent
of word frequency and word type. Although difficult to detect, a masked
prime produces a reliable effect in speeding up the response time for words,
but not for nonwords (Forster, 1985).
Our hypothesis was that when words are primed by blends, subjects would
correctly respond faster to words than would subjects who were not primed.
Identical primes < Blend primes < Unrelated primes
Fastest
Intermediate
Slowest
Method
Three lits of stimuli were constructed for three groups of subjects (none from
the previous experiment). Each list had a total of 126 randomized items, half
of which were words and half nonwords. In each list of words there were 63
targets: 21 words where the masked prime was a blend and the target was one
of the words for the blend, 21 words with identical primes, and 21 words with
unrelated primes. In each list of the 21 blends, 7 consisted of a word followed
by a splinter, 7 of a splinter followed by a word, and 7 of two splinters. If the
masked prime was a blend, subjects would see the following, one line at a
time at the exactly the same place on the computer screen and taking up
exactly the same amount of space:
a string of hash marks
a masked prime in small letters
a target word in capital letters
####### 1000 ms
d yne ti c 100 ms
DYNAMIC 1000 ms
The three lists were counterbalanced, so that all subjects saw the same 63
targets, and each group saw only one of the masked primes. 21 saw an
identical prime, 21 saw a blend prime, and 21 saw an unrelated prime.
Condition 1
##########
fruitopia
##FRUIT##
Condition 2
##########
##fruit##
##FRUIT##
Condition 3
##########
stillborn
##FRUIT##
The hash marks, serving as a forward mask, call attention to the screen and
appeared for one second. Since both the primes and targets were long, often
10 letters or more, we used masked primes of 100ms. In cases where the
blend contained more or fewer letters, hash marks were used so that each line
was the same length. Then hash marks were present for all groups. About
half the subjects reported that they did not notice anything between the hash
marks and the target. Many of the others noticed a flicker but did not realize
that they were seeing letters. A few subjects could read short words. Only
one subject reported being able to read the primes, and he had the highest
error rate (24%) since he was frequently responding to the primes.
Subjects were 59 undergraduate students in linguistics classes who were paid
for their time and in some cases given extra credit as well. By pushing a foot
pedal, subjects brought up an item on the screen to begin the timing. First the
hash marks appeared, followed by the masked prime, then followed by the
target, which remained on the screen for one second. A yes and no button
were placed on the table at which the subjects were seated. Subjects were to
decide as quickly and accurately as possible whether the letters spelled a word
and then to press the appropriate button. Pressing a button stopped the timing.
Results
If a subject's error rate was over 15%, we eliminated that person. This
removed one subject each in groups 1 and 2 and 5 in group 3, leaving 17
subjects in each of the three groups. The averages for all groups for the
primed words, counting correct responses only, are in Table 7.
Table 7: Results of lexical decision task
Identical primes Blend Primes
Unrelated primes
879 ms
942 ms
949 ms
The order of reaction time was as predicted, with identical primes the fastest
and unrelated primes the slowest, with blends as primes in between. But an
analysis of variance showed that the differences did not reach significance at
the .05 level. Analyzing various subgroups and correlating reaction times
with other factors such as stem completions or familiarity did not result in our
finding any significant differences either.
In retrospect, we could also have run the experiment in the opposite
direction. That is, rather than looking for traces of automatically activated
words, we should have tried to pre-activate the candidate words ourselves and
see if that had any effect on the blend identification success. The logic is that
if a preconscious lexical identification phase is involved, facilitating this
phase should enhance the performance of the whole task. This would be
shown if masked priming with a word would facilitate blend identification
speed and accuracy.
Problems and possibilities
Since our experiments were done many years ago, lexical blends have become
even more common that they were then, so subjects might be even better at
processing them now.
However, several variables that we did not consider are important to
control for. The first change would be to have a much better way of
classifying blends in order to determine how easy or difficult they might be.
Frequency, lexical neighbors, context, missing material, and semantic
coherence, the factors that Lehrer (1996, 2008) found important, are still
relevant, but so are other things we neglected. For one thing some of the
splinters have become bound morphemes, disconnected from their source.
Examples are -(a)holic, -thon, -scape, -nomics, -tini, and -licious (Lehrer,
2007). These splinters are often entered in dictionaries as suffixes. Bauer
(1983), Adams (1973), Warren (1990), Stein (1977), and others argue that
they are combining forms, not suffixes. But classification aside, these items
seem to function as bound morphemes without regard to their etymology as
splinters. And some of them are in the process of becoming standard
clippings.
Secondly, we should have paid more attention to the phonological
properties of blends. Insights by Kubozono (1990), Kelly (1998), Plag (2003),
Gries (2004) and López-Rua (2004) relevant information on the phonemic
and orthographic aspects of blends. In addition, the relationship between the
phonology and orthography of the stimuli should be examined as well. One
important problem is where to divide the blend, especially in cases with two
splinters where more than one division is possible as with snizzle < snow +
drizzle or swacket < sweater + jacket.
Using the lexical decision task can be a useful tool, but the prime need not
be masked. Lexical blends can serve as primes for target words. Blends
could also be used as targets in lexical decision tasks. Our prediction is that it
will take significantly longer to respond to novel blends than to conventional
words and that there will be considerable variation in yes vs. no decisions.
For some people a string of letters is not a word until it appears in an
authoritative dictionary, while other individuals may use looser criteria.
However, instructions on what to count as a word could be an interesting
variable.
Masked priming could also be modified by varying the duration of the
masked prime. We selected 100 milliseconds, which is longer than the normal
duration of 50 or 60 milliseconds. During the discussion of our paper at the
Lyon conference, Susanne Borgwaldt suggested that the priming effect may
have been stronger with shorter masked primes because the longer duration in
our experiments may have interfered with the automatic implicit responses.
Some of the published work (Rastle et al 2000, Forster et al, 1984) may
support this conclusion, although results are mixed because the duration of
masked primes interacts with other variables.
There are possibilities for future work on stems, too. Our two lists of stems
described above were not matched in any way, and that was a lost
opportunity. The stem INF, however, could be completed by a different word
from each list. List 1 contained the blend infomercial < information +
commercial, while List 2 contained affluenza < affluence + influenza. Table 8
presents the results. Five of the subjects who saw List 1 responded with
information, but none of those who saw List 2 did. Of the subjects presented
with affluenza, one finished the stem with influenza, and no one from the
other group did. Of course, the two words beginning with INF are not
comparable, since information is a more common word than influenza, and
has info is a standard clipping.
Table 8 Results of stem completion task
Response
information influenza
Saw List 1
Saw List 2
infomercial
afffluenza
5
0
0
1
Summary and conclusions
The experiments reported here show our attempt to find evidence for
automatic processing of lexical blends by measuring the time subjects took to
respond, first of all directly by giving them blends with enough time for them
to find the targets (source words) and indirectly with masked primes in a
lexical decision task. Since that work was done, blends have become more
common, and presumably English speakers have become better at
decomposing blends and identifying the target words. We think that lexical
decision experiments provide a promising method, but future work will
require a better categorization of blends types than we used in order to control
for the many variables that need to be identified. We hope that our work will
motivate other to continue with this approach.
Endnotes
* We wish to thank Susanne Borgwaldt for her excellent comments and suggestions.
1. Because of equipment problems, we have oral responses for only 20 subjects
seeing List 1 and 15 seeing List 2.
2. Susanne Borgwaldt suggested that a good control group would be a group of
students who did no participate in the experiment, but complete the stem. The their
percentages were similar to 8% and 5% respectively, it could just be that the stem
completions were relatively plausible words that would pop up in everyone's mental
lexicon, regardless of the previous exposure to blends containing these stems. Similar
information might be obtained by examining frequencies and neighbors as well (AL).
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