JOURNAL OF MEMORY AND LANGUAGE
ARTICLE NO.
37, 438–461 (1997)
ML972524
Auditory Processing of Prefixed English Words
Is Both Continuous and Decompositional
Lee H. Wurm
State University of New York at Stony Brook
Two experiments compared continuous and discontinuous models of word recognition. Participants heard prefixed words whose full-form and root uniqueness points (UPs) differed, in either
a gating or lexical decision paradigm. Identification points and reaction times were analyzed
using multiple regression. Full-form UPs predicted performance better than root UPs did. Fullform frequency measures had reliable facilitative relationships with performance while root
frequency measures were not consistently significant. Prefix frequency had a reliable, inhibitory
effect. Judged prefixedness, semantic transparency, and prefix likelihood were related to performance, alone or in interaction. The results provide evidence for both kinds of word recognition
procedures. A model is proposed with two parallel recognition routines: a whole-word routine
and a decompositional routine that considers only unbound roots that can combine with the
prefix in question. A preliminary rating study provides stimulus values on several dimensions
and can be used as a database by other researchers. q 1997 Academic Press
Spoken-word recognition is complex, and
particularly little is known about cases where
words are composed of more than one morpheme. There are two ways in which lexical
This research was supported by Augmentation Award
for Science and Engineering Research Training Grant
93NL174 from the Air Force Office of Scientific Research, Grant R01 MH51663-03 (to Arthur G. Samuel)
from the National Institute of Mental Health, and National
Research Service Award 1 F32 MH11721-01, also from
NIMH. This article is based on the author’s doctoral dissertation (State University of New York at Stony Brook).
A portion of this work was presented at the 37th annual
meeting of the Psychonomic Society (Chicago, IL, October 31–November 3, 1996). I thank Arthur Samuel, Mark
Aronoff, Susan Brennan, Richard Gerrig, John Robinson,
and Cynthia Connine, all of whom made comments on
an earlier version of this paper. I also thank Donna Kat for
her invaluable help with computer programming. Arthur
Aron, David Cross, and the Statistics Lunch group at
Stony Brook provided a great deal of help with data analysis, and Harald Baayen helped in the computation of morpheme frequencies. Annmarie Cano and Douglas Vakoch
provided emotional support and help with many of the
more tedious aspects of this project. Marcus Taft, Dennis
Norris, and an anonymous reviewer made very helpful
comments on an earlier version of this paper.
Correspondence concerning this article should be addressed to Lee H. Wurm, Department of Psychology,
State University of New York at Binghamton, Binghamton, NY 13902-6000. E-mail: [email protected].
0749-596X/97 $25.00
access of such ‘‘morphologically complex’’
words might occur. First, complex words
might be recognized without decomposing
them into their constituent morphemes. Lexical entries would correspond to whole words.
Alternatively, complex words might be decomposed, with individual morphemes represented lexically.
Each of these access strategies corresponds
to one of the main schools of thought among
psychologists. I will call one of these the discontinuous (or decomposition) approach. In
models of this type, complex words must be
decomposed into root and affixes prior to lexical access. Lexical access can proceed only
via the root because complex words do not
have lexical entries. Although there have been
several discontinuous models suggested (e.g.,
Cutler, Hawkins, & Gilligan, 1985; Jarvella &
Meijers, 1983; MacKay, 1978; Morton, 1969;
1979), this approach has been chiefly associated with Taft and his colleagues (1979a;
1981; 1985; Taft & Forster, 1975; Taft, Hambly, & Kinoshita, 1986).
The main finding from the work of Taft and
his colleagues is that nonword decisions take
longer when the nonwords carry a prefix than
when they do not, and they take longer still
438
Copyright q 1997 by Academic Press
All rights of reproduction in any form reserved.
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when the ‘‘stems’’ of the prefixed nonwords
are genuine stems in English. Whether or not
the stems of such nonwords are genuine English stems makes no difference when the
nonwords do not begin with genuine prefixes.
These stems are not even recognizable as
stems because there is no prefix to strip off.
Bergman, Hudson, and Eling (1988) also
found support for the affix-stripping model
(Taft, 1981; 1985; Taft & Forster, 1975).
Taft (1994) addressed some of the more
persistent criticisms of his earlier work, and
the results completely supported the earlier
conclusions in favor of an affix-stripping approach. Still, Taft (1994) modified his position
somewhat, writing that an interactive-activation model provided the best explanation of
morphological processing. In Taft’s new
model, prefixes are independent activation
units, separate from roots (i.e., prefixed words
are stored in decomposed form), but there is
no prefix-stripping per se. In this type of
scheme the equivalent of a prefix-stripping
procedure is a part of the access process.
The opposing general approach is to access
morphologically complex words on a strictly
continuous, or left-to-right, basis. I will also
use the term full-listing to refer to models of
this type, because on this type of account, all
words are listed in the lexicon. Words are accessed as complete units, whether or not they
contain affixes. The lexicon in this case would
contain separate entries for the words
‘‘cover,’’ ‘‘uncover,’’ ‘‘covering,’’ and so on,
resulting in considerable redundancy (e.g.,
verbs in Finnish and Georgian can take thousands of distinct surface forms that are essentially the same vocabulary item although they
differ by inflection [Anderson, 1988]). This
redundancy is the reason people first proposed
a decompositional approach; the storage required by a morphemic lexicon is much
smaller than that required if all items are to
be listed. However, Bybee (1988; see also
Sandra, 1994) believes that the emphasis on
storage efficiency is misguided, given the
huge capacity of the human brain. She also
notes that linguists are relaxing their insistence on maximally efficient storage princi-
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ples, as the extent of the idiosyncracies of all
languages is coming to light.
The Cohort model (Marslen-Wilson &
Welsh, 1978; Marslen-Wilson, 1984), although not specifically designed to address issues relating to morphological complexity, is
a continuous processing model. Network
models, too, have most often aligned themselves with this view (e.g., McClelland & Elman, 1986; Norris, 1994). Other arguments
for a continuous processing model have been
made by Henderson, Wallis, and Knight
(1984), Rubin, Becker, and Freeman (1979),
and Tyler, Marslen-Wilson, Rentoul, and
Hanney (1988).
An important third class of model is driven
by stress (Cutler, 1976; Cutler & Norris, 1988;
Grosjean & Gee, 1987). According to the metrical segmentation strategy (MSS) of Cutler
and Norris (1988), a strong syllable initiates
a lexical search. The unstressed syllables immediately surrounding the strong syllable are
checked by an acoustic pattern-matching routine, to see if they can combine with the
stressed syllable. While not involving affixstripping per se, a stress-driven model is an
example of a discontinuous model; words are
not processed in a strict left-to-right manner.
Furthermore, stress-driven and affix-stripping
models make similar predictions in many
cases. Affixes will play a peripheral role in
lexical access because they are typically unstressed. In fact, stressed prefixes could cause
processing difficulties by triggering a lexical
look-up on the prefix (or the prefix plus the
initial phoneme[s] of the root) rather than on
the root itself.
There are also mixed models that incorporate both a continuous and a discontinuous
approach. For example, Bergman et al. (1988)
suggested a possibility based on the race
model of Cutler and Norris (1979): the speech
processor might attempt to access morphologically complex words both as full-forms and
as analyzed separate parts. Data will be presented below that are suggestive of this kind
of model; other variations on this idea have
also been proposed (e.g., Anshen & Aronoff,
1981; Caramazza, Laudanna, & Romani,
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LEE H. WURM
1988; Frauenfelder & Schreuder, 1992;
Schriefers, Friederici, & Graetz, 1992).
In recent years, researchers have begun to
examine the possible effects of linguistic and
computational variables on complex word
processing. Using the crossmodal priming
technique, Marslen-Wilson, Tyler, Waksler,
and Older (1994) found that derivationally
suffixed words primed and were primed by
their roots, but only if they were semantically
transparent (i.e., only if there was an obvious
semantic relationship between the two, as in
‘‘government’’ and ‘‘govern’’). Marslen-Wilson et al. (1994) concluded that semantics determines which words are morphologically related to each other (as well as which words
are complex). This was the first study to show
the importance of semantic transparency, a
variable that has interested linguists for some
time.
Laudanna, Burani, and Cermele (1994)
found that a pair of little-examined variables
affect lexical decision performance for visually presented Italian nonwords: the number
of word-types beginning with a given prefix
and the success rate of prefix-stripping for that
prefix (i.e., the proportion of encountered
words beginning with that letter string that are
in fact truly prefixed—a quantity I will refer
to as the ‘‘prefix likelihood’’). Although the
two variables are highly correlated, Laudanna
et al. (1994) found that prefix likelihood was
the more important one. They concluded that
a word beginning with a prefix that has a high
prefix likelihood is likely to be stored and accessed in decomposed form.
The movement of some researchers toward
the theoretical middle ground in recent years
is a major advance. Researchers are recognizing the possibility that some words may be
decomposed while others may not. In accord
with this, researchers have realized that in order to specify the conditions under which various lexical access strategies may be used, they
must be concerned with concepts like semantic transparency. These were largely ignored
in early research efforts.
The present study used prefixed English
words in an attempt to clarify recent findings
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concerning prefix likelihood and semantic
transparency. The predicted identification
points (IPs) for continuous and discontinuous
processing models were directly tested. The
effects of prefix likelihood, degree of prefixedness, and semantic transparency were examined for their individual contributions to
the recognition process, and importantly, for
their joint or interactive contributions. This
has not previously been done and, as we will
see, there are interesting and theoretically important interactions between these variables
(and others). Furthermore, in the present study
these independent variables were treated in
ways that are more natural and powerful than
in previous research.
The main goal of the preliminary rating
study was to provide information to be used
in interpreting experimental data from the subsequent experiments. For each stimulus, values were obtained on several different dimensions of interest, including prefix likelihood,
judged prefixedness, semantic transparency,
root morpheme frequency, prefix frequency,
and neighborhood size. This study also provides a database for other researchers to use;
these values have not previously been available, and some of the computations are timeconsuming.
PRELIMINARY RATING STUDY
This study consisted of two parts, the general procedure of which was the same; participants were presented with auditory stimuli and
gave a rating of some kind. In Part I, participants rated the stimuli on semantic transparency, and in Part II, they rated the stimuli on
prefixedness.
The semantic transparency of an item might
influence the perceptual strategies used to process it. Words like ‘‘rebuild’’ have an obvious
compositional meaning (‘‘re’’ plus ‘‘build’’)
that is lacking from words like ‘‘prepare.’’
However, it is only within the very recent past
that researchers have recognized this, and the
few researchers to study this variable have
artificially dichotomized it. One of the purposes of this rating study was to get continuous semantic-transparency ratings for stimu-
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lus items, since no published norms exist.
These ratings were used as predictors of performance in the two main experiments of this
study.
A second purpose was to get ratings of the
prefixedness of the items. ‘‘Which words are
prefixed?’’ is just as much a psychological
question as a linguistic one. Noncircular definitions in this area are uncommon, and linguists have not been able to reach a consensus
about exactly what is or is not an affix. However, even if linguists were to reach such a
consensus, it is not clear that their definition
would have psychological relevance.
Method
Participants. Twenty-one students from the
Department of Psychology subject pool at the
State University of New York at Stony Brook
provided prefixedness ratings. Thirty students
from the same subject pool provided semantic-transparency ratings. All participants were
native speakers of English who received
course credit for their participation.
Materials. Seventy-two prefixed and pseudoprefixed words were selected from a
150,000-word computerized phonetic dictionary (Moby Pronunciator 1.01, 1989) that met
the following criteria: (1) carried a root that
is a free-standing monomorphemic English
word or homophonous with one; (2) began
with a string of phonemes that constitutes an
English prefix; (3) varied (as a set) as much
as possible along a continuum from high to
low semantic transparency; and (4) had fullform and root uniqueness points (UPs) that
differed by at least one phoneme (the UP is
the point in the acoustic signal where the word
in question diverges from all other words in
a language—see Marslen-Wilson, 1984; Marslen-Wilson & Welsh, 1978).
In determining UPs, I searched the same
computerized phonetic dictionary (Moby Pronunciator 1.01, 1989) used for initial stimulus
selection. I excluded any suffixed forms that
were related (by inflection or derivation) to the
word in question (see, for example, MarslenWilson, 1984; Tyler et al., 1988). Thus, the
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UP of ‘‘distaste’’ is the /aV /, in spite of the
existence of the related word ‘‘distasteful.’’
Full-forms were digitized at a sampling rate
of 10 KHz, low-pass filtered at 4.8 KHz, and
stored in disk files. Stimuli were spoken by a
female native English speaker who was not
familiar with the purpose of the study. Roots
were digitally spliced away from the fullforms using visual and auditory inspection,
and the resulting roots sounded quite natural.
For the prefixedness ratings, two stimulus
lists were created. A randomly-determined
half of the full-forms were assigned to list A
and half were assigned to list B. The root
corresponding to a given full-form was assigned to the opposite list. For example, if
‘‘rebuild’’ was in list A, ‘‘build’’ was in list
B. For the semantic transparency ratings, the
same stimuli were used, but they were arranged in pairs that consisted of a full-form
and its root (e.g., ‘‘rebuild’’ and ‘‘build’’).
Procedure. Participants were tested in
groups of one to four. Stimuli were presented
over headphones in a sound-attenuating chamber. For the prefixedness ratings, participants
were randomly assigned to hear either list A
or B. After each word, participants had 2500
ms to give a rating. A Likert scale with anchor
points ‘‘Not at all prefixed’’ (1) and ‘‘Very
prefixed’’ (8) was used. Participants were free
to decide for themselves what ‘‘prefixed’’
means; no example or further instructions
were given. For each list, words were presented in a different random order.
For the semantic-transparency ratings, on
each trial, participants heard a full-form and
its corresponding root and were asked to rate
‘‘. . . how related in meaning the two words
are’’ on an 8-point Likert scale. Anchor points
were labeled ‘‘Not at all related’’ (1) and
‘‘Very related’’ (8). On half of the trials, the
full-form was presented first, and on half the
root was presented first. Participants had 4000
ms to make their rating. Item pairs were presented in a random order.
Results and Discussion
Median judged prefixedness values are
listed for each stimulus in Appendix A. The
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ratings distribution had moderate negative
skew, so the recommended ‘‘reflect and
square-root’’ transformation was performed
(Tabachnick & Fidell, 1989). Thus, items that
participants rated highly prefixed had lower
transformed prefixedness scores. This transformed measure is the one that will be used
in all analyses reported in this study.
Median semantic-transparency values are
also listed for each stimulus in Appendix A.
Because the distribution of transparency
scores was positively skewed, the square-root
of the transparency scores was used in all analyses reported in this study.
Calculation of Other Regressor Variables
Several other variables were used to predict
participants’ performance in Experiments 1
and 2. Prefix likelihoods for each prefix were
calculated in the following way (I used pronunciation rather than spelling in selecting
words). I first identified for each prefix all of
the words from Webster’s Third New International Dictionary of the English Language
(1993) that were, in fact, truly prefixed. A
word was considered truly prefixed if the prefix contributes to the meaning and syntax of
the full-form. This definition implies three criteria: First, when the prefix is removed, what
remains must be a freestanding word. Second,
the meaning of the full-form must be reasonably transparent. Finally, the semantic relationship should be fairly constant across combinations; for example, truly prefixed words
beginning with ‘‘re-’’ will mean roughly ‘‘to
do (whatever the stem means) again’’ (M. Aronoff clarified these issues for me in a personal communication, November 30, 1994).
Note that words carrying bound roots are
excluded from the above definition, even
though such words can subjectively appear to
be prefixed (e.g., ‘‘supersede,’’ which is not
counted as truly prefixed by the above criteria,
had a high prefixedness rating [see Appendix
A]). In some cases, the classification of a root
as either bound or unbound is somewhat ambiguous, but the exclusion of words with
bound roots from the calculation of prefix
likelihood is justified along several lines. First,
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Schreuder and Baayen (1994) explained the
requirement of reasonably high semantictransparency, which is almost always missing
from words carrying bound roots, this way:
‘‘The only way in which the language learner
can discover that a certain string actually is a
stem (free or bound) is when that string occurs
in at least one semantically fully transparent
[italics added] combination’’ (p. 360). Even
in the case of very common bound roots that
combine with several different prefixes (e.g.,
‘‘-mit’’ or ‘‘-vent’’), any reliable covariance
between form and meaning (the importance
of which is also discussed by van Orden, Pennington, & Stone, 1990) is likely to be noticed
only by experts in linguistics. In addition,
Henderson (1985) asserted that the morphological complexity of words with bound roots
is synchronically meaningless; their affixes
are ‘‘fossils’’ and not psychologically relevant
at all. In short, although they appear to be
prefixed, they are not (the data of MarslenWilson et al. [1994], which I have already
discussed, agree with this conclusion).
I computed prefix likelihood, then, as a ratio: the numerator was the summed frequency
(from Francis and Kučera, 1982) of the truly
prefixed words beginning with a given phonetic string, and the denominator was the
summed frequency of all words beginning
with that string in which removal of the putative prefix leaves a pronounceable syllable or
syllables. The decision to consider syllabification is also consistent with the criteria of
Schreuder and Baayen (1994). For example,
although ‘‘coat’’ begins with ‘‘co-,’’ this word
was not considered a prefix-stripping failure
because the remainder of the word (simply the
phoneme /t/ in this case) does not constitute
a syllable.
The notion behind prefix likelihood is that
decomposing words may make lexical access
less efficient, if the majority of words beginning with a given prefix are not in fact prefixed
words. Values for the prefixes used in this
study are listed in Appendix B and ranged
from the theoretical minimum of .000
(‘‘hyper-’’) to the theoretical maximum of
1.000 (‘‘twi-’’). To illustrate what these num-
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bers mean, consider the prefix ‘‘dis-,’’ which
has a prefix likelihood of .092. This means
that 9.2% of the time a reader or listener encounters a word beginning with ‘‘dis,’’ that
word is prefixed. At the upper extreme, all
instances of words beginning with ‘‘twi-’’
(phonetically, /twai/) are truly prefixed (words
such as ‘‘twice’’ do not count as failures because removing the prefix leaves the nonsyllabifiable phonetic segment /s/).
With few exceptions, these prefix-likelihood values are quite low (M Å .19). This
value is quite comparable to the .17 reported
by Schreuder and Baayen (1994) for the seven
English prefixes they examined, using the
CELEX database (Baayen et al., 1993; Burnage, 1990). On the face of it, prefix likelihoods like these would suggest that a decomposition strategy has such a low payoff that it
would make lexical access less efficient,
which is what those authors concluded. As
will become clear, their conclusion may have
been premature.
The research of Laudanna et al. (1994), in
which the notion of prefix likelihood (what
they called the ‘‘success rate’’) was initially
explored, was discussed earlier. Although they
concluded that prefix likelihood was more important, they also found that the number of
words beginning with a given prefix was related to performance. This is essentially an
unweighted measure of the density of each
word’s lexical neighborhood. Lexical neighborhood is a metaphor for understanding how
words are organized or stored in memory. For
example, each of the four words in English
that begin with the phonemic sequence /twai/
have only the other three as neighbors in lexical space. They reside in a sparsely populated
neighborhood. Some neighborhoods have
hundreds or thousands of inhabitants (e.g.,
over 500 words begin with the phonemic sequence ‘‘pro-’’).
The denominator of the prefix-likelihood
ratio is a frequency-weighted measure of
neighborhood density. Regardless of the particular value of the prefix likelihood, a large
denominator means that a word resides in a
dense lexical neighborhood, and a small de-
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nominator means a sparse lexical neighborhood. Recognition is expected to be easier if
the surrounding lexical neighborhood is
sparsely populated, because items would have
relatively little competition from neighbors
(e.g., Goldinger, Luce, & Pisoni, 1989; Luce,
1987; Luce, Pisoni, & Goldinger, 1990).
Neighborhood sizes for each prefix are listed
in Appendix B. The neighborhood size of
‘‘pro-,’’ for example, is the sum of the frequencies of the more than 500 words beginning with that sequence.
Several different frequency measures were
also needed. Individual full-form and root token frequencies were taken directly from
Francis and Kučera (1982). The corresponding family frequencies were calculated by
summing values from Francis and Kučera
(1982) for each word and its morphological
relatives (family frequencies were included
because a consensus as to which frequency
measure is more important, token or family,
has not been reached). A continuous model
predicts that root frequency will have no role
in word recognition, and a decompositional
model predicts that root frequency will explain a large proportion of the variance (Taft
[1979b] found that both full-form and root
frequencies influence performance).
Morpheme frequencies for each root and
each prefix had to be calculated in a different
way. The first step in calculating root frequencies was a non-position-specific string search
for each root in the Birmingham/Cobuild corpus (18 million tokens) of the CELEX database (Baayen et al., 1993; Burnage, 1990).
Frequencies were then summed across all
cases where that root was a morpheme (e.g.,
‘‘lead’’ is a morpheme in the word ‘‘mislead,’’ but not in the word ‘‘plead’’). This
measure is similar to the family frequency
measure already calculated for each root, the
key difference being the importance of position. The root-morpheme frequency measure
included cases where the morpheme in question was not the first morpheme of a complex
word, whereas the family frequency measure
for each root did not. For example, the family
frequency measure for ‘‘lead’’ included words
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TABLE 1
SUMMARY STATISTICS
FOR
Frequency measure
EXPERIMENT 1
FREQUENCY MEASURES
M (SD)
Full-form measures
Tokena
Familya
Morphemic measures
Token (root)a
Family (root)a
Morpheme (root)b
Prefixb
8 (26)
22 (66)
121
227
235
1572
(188)
(354)
(409)
(4358)
Range
0–195
0–529
0–807
0–1786
0–2379
1–21963
Note. All values are per million tokens.
a
From Francis and Kučera (1982).
b
From the CELEX database (Baayen, Piepenbrock, &
van Rijn, 1993; Burnage, 1990).
such as ‘‘lead,’’ ‘‘leading,’’ and ‘‘leader’’
(among others). The root frequency measure
included these words plus words such as
‘‘mislead.’’ Root-morpheme frequency values
are shown in Appendix A.
Prefix frequencies were computed in essentially the same way. Counts of all words in the
Birmingham/Cobuild corpus beginning with
each prefix string were obtained. From these,
the frequencies for those cases that were, in
fact, instances of prefixation were summed
(e.g., ‘‘preview’’ counts but ‘‘preen’’ does
not). Prefix frequencies are shown in Appendix B.
Summary statistics for all of the frequency
measures are shown in Table 1. Distributions
of prefix-likelihood values and all six frequency measures had severe positive skew.
For all of them, the logarithm of each value
was used in the analyses.
This rating study provided values on several
variables that were chosen as predictors of
word recognition performance. The focus of
Experiment 1 shifted away from the stimuli
per se and toward the recognition performance
of participants. Experiment 1 provided the first
direct tests of the competing models: Would
root or full-form UPs be more closely related
to participants’ IPs? Would morphemic variables like prefix likelihood, prefixedness, or
semantic transparency be related to IPs?
Which frequency measures would matter?
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This experiment tested the predictions of
continuous and discontinuous models with respect to the time at which word identification
should occur. A strictly continuous model predicts that identification should occur at the
full-form UP, while a strict affix-stripping
model predicts that identification cannot occur
before the root UP. The inclusion of several
different frequency measures, some of which
are predicted to be irrelevant by a continuous
model, was intended to shed additional light
on the issue. Finally, this experiment tested
whether or not variables such as prefix likelihood, semantic transparency, judged prefixedness, and stress influence the identification of morphologically complex words.
Method
Participants. Thirty-eight students from the
Department of Psychology subject pool at the
State University of New York at Stony Brook
participated. All were native speakers of English with normal hearing. Participants received course credit or cash for their participation.
Materials. The 72 prefixed and pseudoprefixed words from the preliminary rating study,
along with their associated roots, were used.
These items are listed in Appendix A.
Procedure. The full-forms were randomly
divided into two sets of 36. The full-forms
from one set, along with the roots corresponding to the unpresented full-forms, were presented to one group of participants. A different
group of participants heard the other half of
the stimuli.
Participants in groups of one to four listened to stimuli over headphones in a soundattenuating chamber. For roots, the first gate
consisted of the first 50 ms of the word, the
second gate consisted of the first 100 ms of
the word, and so on, until the entire stimulus
was presented. For full-forms, the first gate
was between 50 and 99 ms long, depending
on the duration of the word’s prefix (I wanted
to have the acoustic onset of one of the gates
of the full-form coincide exactly with the
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acoustic onset of corresponding root). For example, because the full-form ‘‘belong’’ had a
duration of 723 ms and the root ‘‘long’’ had
a duration of 645 ms (giving a prefix duration
of 78 ms), the first gate of ‘‘belong’’ was 78
ms. Gate #2 then began at the acoustic onset
of the root.
After each gate, participants were given
7000 ms to write down what they thought the
word was. Then the next gate, consisting of
the already-heard portion plus 50 ms more,
was heard, and so on. Stimuli were presented
in a random order.
Results and Discussion
One item (‘‘ultrasound’’) was discarded
from all analyses because of digitization problems. A response was considered an error if
the participant never did correctly identify the
presented word. Two participants’ data were
excluded because of very high error rates
(22% and 25%—all other participants had error rates less than 5%). The IP, the point at
which the participant correctly identified the
presented word without subsequently changing his or her mind, was found for each trial.
Trials on which the participant never did arrive at the correct word comprised 3.9% of
the data. These trials were discarded.
IPs were analyzed using multiple regression. The independence of observations assumed by linear regression models did not
hold in the current experiment (i.e., each participant provided more than one observation).
In repeated-measures regression analyses, this
is controlled by the inclusion of N 0 1 dummy
variables (35 in this case) that represent the
participants. In addition, item analyses cannot
be conducted in a repeated-measures regression design. The regressor variables (e.g., prefixedness, semantic transparency, and so on)
are intrinsic, nonvarying aspects of the items
themselves—each item is essentially its own
condition. This is reflected in the large df
value in the denominator, which equals the
number of participants times the number of
critical stimuli, minus the number of incorrect
critical trials and the number of previous factors in the model. The relevant question be-
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comes: Were the items selected appropriately
(randomly, representatively, or whatever else
‘‘appropriately’’ might mean)? The interested
reader can refer to Cohen and Cohen (1983).
The analysis of IPs proceeded at three different levels, the first of which was a direct
comparison of the two competing classes of
models. The models make specific predictions
that can be tested directly—I simply determined which of the two UPs (full-form vs
root) better predicted performance. The second level of analysis involved what I will call
prior effects (prefix stress and the six frequency measures). I use the term ‘‘prior effects’’ because of the importance of accounting for these variables before making any
strong claims about factors such as prefix likelihood and semantic transparency. The prioreffects variables provided another test of the
two classes of models—decompositional
models predict that root measures should be
at least as important as full-form measures.
Conversely, continuous models predict that
only full-form frequency measures should
matter. Stress, too, is irrelevant from the
standpoint of a strictly continuous model. The
final level of analysis looked at what I will
call decomposition variables. These include
prefix likelihood, prefixedness, and semantic
transparency, along with their interactions
(with each other, with prefix and root frequency measures, and with stress). Any significant effects from level 2 (the prior effects)
were partialed from this analysis because
those variables had moderate to strong correlations with the variables in this level. The
analysis will proceed in exactly this way for
Experiment 2, as well. Since this level of the
analysis contained a fairly large number of
statistical tests (19), p-values were adjusted
using a Bonferroni correction.
UP analyses. The mean IP for the fullforms in this experiment was 441 ms (SE Å
4.12 ms), which is much closer to the mean
full-form UP (480 ms) than to the mean root
UP (714 ms, measured within the full-form
tokens). One would expect actual performance
in a gating experiment to be slightly earlier
than the theoretically critical point because of
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guessing; on a certain proportion of the trials,
participants will stumble onto the correct response by chance before they have heard input
sufficient to isolate a unique word candidate.
On average, then, IPs were fairly close to the
values predicted by a full-listing model and
preceded the decomposition values by an average of 273 ms. This result is problematic
for discontinuous models.
Regression analyses supported this conclusion. Two analyses were performed. After entering the N-1 dummy variables, the second
step in each analysis was to enter the relevant
UP and see whether it produced a significant
increase in the total explained variance. Both
UPs were significant predictors of performance (F(1,1213) Å 131.68 and F(1,1213) Å
44.00 for full-form UPs and root UPs, respectively—both ps õ .0001).
Both full-form and root UPs can be significant because they were correlated with each
other (r[70] Å .57, p õ .001). When the
shared variance between them was accounted
for, however, only the full-form UPs were significant. The partial correlation between fullform UPs and IPs, controlling for participants
and for root UPs, was .26 (p õ .001), while
the correlation between root UPs and IPs disappeared when participants and full-form UPs
were partialed (r Å .01, p Å .66). As noted
above, this result supports continuous models
over discontinuous ones.
Therefore, in subsequent analyses, IPs were
measured not from word onset but from the
full-form UP of each stimulus. This was necessary because full-form UPs were correlated
with other variables under investigation. Any
conclusion about the importance of word frequency (for example) that did not account for
the fact that each word has its own UP would
be meaningless.
Prior effects. The next level of the analysis
assessed the role of prefix stress and the frequency measures. After the dummy variables
were entered, prefix stress was also found to
be a significant predictor of IPs (F(1,1213) Å
4.77, p õ .05). Higher prefix stress (this was
an ordinal variable taking values of 0 [unstressed], 1 [secondary stress], or 2 [primary
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TABLE 2
RESULTS
OF
FREQUENCY ANALYSES (EXPERIMENT 1)
Regressor
Full-form measures
Log token frequency (full-form)
Log family frequency (full-form)
Morpheme measures
Log token frequency (root)
Log family frequency (root)
Log morpheme frequency (root)
Log prefix frequency
Sign
F(1, 1212)
0
0
10.42*
12.08**
/
/
/
/
õ1
õ1
1.37
25.81***
* p õ .01.
** p õ .001.
*** p õ .0001.
stress]) was associated with later IPs. This
could be easily explained if IPs were measured
from word onset: stressed prefixes have longer
durations. However, IPs were measured from
the full-form UP. The MSS (Cutler & Norris,
1988), while not addressing such variables as
semantic transparency, predicts that lexical
access should be root-driven when items have
unstressed prefixes. This would mean later IPs
for those items because root UPs were always
later than full-form UPs. The observed relationship runs in the opposite direction.
However, the MSS makes a second prediction that is consistent with the observed relationship: an effect like this could appear if
participants mistakenly treated stressed prefixes as the first portion of roots. An attempt
to access a root morpheme, using the prefix
as the assumed first syllable (because it is
stressed), will fail. For example, participants
might hear the initial portion of the word ‘‘copilot’’ and mistakenly access words like
‘‘cope.’’ Items like these might have late IPs,
because participants are likely to perseverate
on a mistaken hunch in a task like gating.
Which frequency measures were related to
IPs? Six regression analyses were performed.
In each one, the dummy variables were entered first, along with prefix stress, and then
one of the frequency measures described
above. As can be seen in Table 2, both of the
full-form frequency measures had significant
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facilitative relationships with IPs. This is the
type of relationship generally expected: higher
frequency values were associated with earlier
IPs. Of the four morpheme-relevant measures,
only prefix frequency was a significant predictor of performance, and it had an inhibitory
relationship with the dependent variable: more
common prefixes led to slower performance.
This effect has a natural explanation in terms
of neighborhood density—prefix frequency
was significantly associated with the density
measure described earlier (r[70] Å .66, p õ
.001). This suggests that earlier IPs should be
found when prefix frequency is low because
these items reside in relatively sparse lexical
neighborhoods.
Decomposition variables. This group consisted of prefix likelihood, judged prefixedness, and semantic transparency, as well
as interactions involving them. Continuousprocessing models predict that none of these
analyses will be significant. As will be seen,
that prediction does not hold. Prefix likelihood
turned out to play a major role in mediating
the effects of other variables.
A separate analysis was conducted for each
regressor. As the first step, the dummy variables, prefix stress, prefix frequency, and fullform family frequency (the stronger of the two
significant full-form measures) were entered.
Then the variable being considered was entered.
Higher values of semantic transparency
were associated with earlier IPs (F(1,1210) Å
51.98, p õ .001). This finding supports decompositional models because the semantic
transparency of a morphological combination
is irrelevant from a continuous perspective.
There was also a trend toward the same kind
of IP advantage for higher levels of prefixedness (F(1,1210) Å 8.97, p õ .10). By
itself, prefix likelihood had virtually no effect
on IPs (F(1,1210) õ 1).
Four analyses were conducted to assess the
interactions between these three variables
(three possible two-way interactions and the
three-way interaction). In all cases, the
dummy variables, prefix stress, prefix frequency, and family frequency of the full-form
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FIG. 1. Mean identification point (IP) as a function of
semantic transparency and prefix likelihood, in milliseconds (ms). An IP of 0 corresponds to the full-form uniqueness point of each stimulus.
were partialed. In addition, the main effects of
the variables making up the interaction were
partialed. For the three-way interaction, the
two-way interactions were also partialed.
These steps are all necessary to satisfy assumptions of the general linear model (Cohen & Cohen, 1983).
For ease of interpretation, readers should
note three things about the figures that will
be shown. First, untransformed prefixedness
ratings are shown so that values can be interpreted more naturally (transformed values
were used in the statistical tests). Second, negative IPs reflect the fact that, on average, identification of the items took place some 39 ms
before the full-form UP. Third, although the
Y-axes show mean IPs based on median splits,
readers are reminded that none of the predictor
variables were dichotomized in the analyses;
this is simply the best way to illustrate the
nature of each interaction.
The interaction between prefix likelihood
and semantic transparency was significant and
is shown in Fig. 1 (F(1,1208) Å 22.84, p õ
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.001). As can be seen in the figure, higher
semantic transparency was associated with
earlier IPs but only for items with high-likelihood prefixes. As with the significant main
effect of semantic transparency, this interaction supports a decompositional processing
account.
If we consider high values on decomposition variables to be cues associated with successful morphemic access, then the earliest
IPs on the graph indicate that multiple cues
result in enhanced performance. The latest IPs
on the graph seem to reflect a cost incurred
when the processing system deals with items
that, according to one variable (e.g., prefix
likelihood), should be decomposed but according to another (e.g., semantic transparency) should not.
The three-way interaction was also significant (F(1,1204) Å 13.32, p õ .01) due to the
fact that the two-way interaction between semantic transparency and prefix likelihood
shown in Fig. 1 was more pronounced the
lower judged prefixedness was. This may be
due to the moderately high correlation between prefixedness and semantic transparency
(r[70] Å .58, p õ .01).
Twelve additional regression analyses were
conducted, each of which looked at the interaction between two of the following variables:
prefix stress, prefix frequency, root morpheme
frequency, prefix likelihood, prefixedness, and
semantic transparency. As a first step, the
dummy variables, prefix stress, prefix frequency, and family frequency for the full-form
were partialed, along with the two variables
whose interaction was being assessed. The interaction term was entered next. It might be
that morphemic variables that do not account
for much variance overall (i.e., their main effects are not significant) are nonetheless important for certain kinds of words (i.e., they
interact).
One of these interactions was significant,
providing further evidence of decompositional
processing. This was the root frequency 1
prefixedness interaction, shown in Fig. 2
(F(1,1208) Å 9.11, p õ .05). One interesting
possibility concerning the positive slope of the
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line for highly prefixed items is competition:
a high-frequency root may compete for activation with the full-form that contains it, delaying recognition of the full-form. Schreuder
and Baayen (1994) assert that this type of
competition is possible, and further evidence
for it will be presented below.
EXPERIMENT 2
The gating paradigm has some distinct advantages. First of all, researchers can be precise about how much acoustic information is
available at each response point. In addition,
gating responses provide a picture of how the
group of word candidates forms and is narrowed down. In spite of this, gating has been
criticized for various reasons, the most important being that listeners hear multiple repetitions of each stimulus and that responses are
not speeded. These criticisms have been convincingly addressed by several studies (e.g.,
Cotton & Grosjean, 1984; Marslen-Wilson,
1984; Salasoo & Pisoni, 1985; Tyler, 1984;
Tyler & Marslen-Wilson, 1986; Tyler & Wes-
FIG. 2. Mean identification point (IP) as a function of
root frequency and judged prefixedness, in milliseconds
(ms). An IP of 0 corresponds to the full-form uniqueness
point of each stimulus.
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sels, 1983; 1985), but it is always wise to
ensure that a given result is not due to the
peculiarities of an experimental paradigm. Experiment 2 tests the same factors as Experiment 1, with a different methodology (auditory lexical decision). Participants heard each
stimulus uninterrupted and intact, and their
word/nonword responses were made as
quickly as possible.
Method
Participants. Participants were 110 students
from the Department of Psychology subject
pool at the State University of New York at
Stony Brook. All were native speakers of English with normal hearing. Participants received course credit or cash for their participation.
Materials. The words used in this experiment are listed in Appendix C. The full-forms
from Experiment 1 that carried a two-syllable
prefix were dropped in the interest of uniformity. Three hundred and sixty stimuli were
divided into two lists of 180. Each list contained: 30 of the full-forms from Experiment
1; the roots of the other 30 full-forms from
Experiment 1; 30 filler words; 30 filler nonwords; and 60 ‘‘morphological’’ nonwords.
These last 60 nonwords were of four different
types: no obvious morphemic structure (e.g.,
‘‘pangort’’); genuine English prefix with a
nonword root (e.g., ‘‘prezelp’’); nonprefix
with a real word root (e.g., ‘‘grevent’’); and
genuine English prefix with a real word root
(e.g., ‘‘precorrect’’). Fifteen of each type were
contained in each list.
In order to use the data on all 60 of the fullforms, it was necessary to assess the effect
of List 1 presentation on List 2 performance.
Certain stimulus items were included with this
in mind. First, the filler items (30 words and
30 nonwords) were identical in each list and
were included to assess the effect of exact
repetition. Second, a subset of the participants
(44/110) listened to the stimuli described
above plus an additional 40 words, 20 in each
of the two lists. These words were included
to assess practice effects.
Filler words were chosen from the same
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distributions as the other word stimuli, in
terms of word frequency, number of phonemes and syllables, and stress. To discourage
strategic responding, these words were of a
variety of different morphological types (monomorphemic, suffixed, and prefixed, including both bound and unbound roots). Stress was
approximately balanced for words and nonwords. A practice list of similar composition
was used prior to the main experiment. This
list consisted of 24 stimuli.
Procedure. Participants in groups of one to
four listened to stimuli over headphones in a
sound-attenuating chamber, and stimulus presentation was randomized for each group of
participants. Half of the participants heard list
A and then list B; the order was reversed for
the other half. On each trial, a participant
heard an auditory stimulus and made a
speeded lexical decision by pressing buttons
on a response board with his/her dominant
hand. Participants pushed one button for
words and another button for nonwords.
Results and Discussion
Participants were excluded if they had an
error rate greater than 15% or a mean RT
greater than 1000 ms on either of the two lists
they heard. Twelve participants were excluded
by these criteria. Analyses were conducted on
responses of the remaining 98 participants.
The lexical decision task is essentially a
kind of familiarity judgment. Participants
have some familiarity threshold above which
they respond ‘‘Word’’ and below which they
respond ‘‘Nonword.’’ Table 3, which shows
mean RTs for each type of word as a function
of whether it was in the first list a participant
heard or in the second, can be understood if
one assumes that because of the overall higher
familiarity after some experience with the various kinds of stimuli (particularly the quite
novel nonwords), participants’ familiarity
thresholds were higher in List 2. The familiarity interpretation explains the 50-ms facilitation for filler words, which was significant in
an ANOVA by subjects (F1(1,97) Å 54.84, p
õ .001) and by items (F2(1,29) Å 39.38, p õ
.001). These words were exactly repeated and
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TABLE 3
MEAN RTs (ms) AS A FUNCTION OF WORD TYPE
LIST ORDER (EXPERIMENT 2)
AND
List heard
Word type
First
Second
Difference
Fillersa
Full-formsb
Rootsb
Practice-effect wordsc
637
603
489
656
587
607
486
684
050**
/4
03
/28†
a
These items were exactly repeated in both lists.
Any effect here would be essentially a repetition priming effect, caused by prior presentation of a shared root
morpheme. For example, if a given participant heard ‘‘rebuild’’ in the first list, ‘‘build’’ would be in the second
list (and vice versa).
c
This is strictly a practice effect, as none of the items
in this group was repeated. These items were heard by
only a subset of the participants (44 out of 110).
† p õ .10 for subjects, p õ .05 for items.
** p õ .001 for subjects and items.
b
thus highly familiar. The familiarity-threshold
account can also explain the 28-ms cost for
practice-effect words, which were new and
thus unfamiliar (significant by items, marginal
by subjects—F1(1,97) Å 3.70, p õ .10;
F2(1,39) Å 7.04, p õ .05). Full-forms and
roots more or less broke even, due to the raised
familiarity threshold’s canceling the moderate
increase in familiarity (all four F-ratios õ 1).
The corresponding effects for the nonwords
averaged 031 ms, with the negative sign reflecting the importance of increased familiarity for nonwords.
Data from the first and second list each participant heard were pooled, and ‘‘first or second list heard’’ was used as a regressor in the
analyses. However, this variable will not be
discussed further; it did not account for a significant proportion of the variance nor did it
interact with any of the variables under study.
Analysis of the word data parallels the analyses for Experiment 1. The same regressors
were used and the underlying logic was identical, but the dependent variable was RTs instead of IPs.
UP analyses. Measuring RTs from word
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onset, the importance of the two UPs in predicting performance was assessed as before.
As in the gating data, both were significant
(F(1,5332) Å 120.85, p õ .0001, and
F(1,5332) Å 55.16, p õ .0001, for the fullform and root UPs, respectively). The partial
correlation coefficients lead to the same interpretation as before: the correlation between
full-form UPs and RTs, partialing root UPs
and participants, was .12 (p õ .001), and the
analogous partial correlation for root UP,
though significant, was much smaller (r Å .03,
p õ .05). Thus, as in the gating analyses, RTs
for the subsequent levels of the analysis were
measured from the full-form UP of each stimulus.
Prior effects. Prefix stress was marginally
related to RTs (F(1,5332) Å 3.04, p õ .10);
items with stressed prefixes tended to have
somewhat slower RTs (consistent with the
gating result). Table 4 shows the results of the
frequency measure analyses. The four morphemic measures had inhibitory relationships
with RT, while the two full-form measures
had the expected facilitative relationships. As
discussed in Experiment 1, the effect of prefix
frequency could be interpreted in light of that
variable’s correlation with neighborhood density. The root frequency effects cannot be explained in this way but may indicate competition between roots and the full-forms that contain them (as discussed by Schreuder and
Baayen [1994] and in connection with Fig. 2
TABLE 4
RESULTS
OF
FREQUENCY ANALYSES (EXPERIMENT 2)
Regressor
Full-form measures
Log token frequency (full-form)
Log family frequency (full-form)
Morpheme measures
Log token frequency (root)
Log family frequency (root)
Log morpheme frequency (root)
Log prefix frequency
Sign
F(1, 5332)
0
0
9.72*
36.06***
/
/
/
/
48.43***
26.40***
30.96***
30.59***
* p õ .01.
*** p õ .0001.
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FIG. 3. Mean reaction time (RT) as a function of semantic transparency and prefix likelihood, in milliseconds
(ms).
of the present study). Such root effects support
decompositional models, while the UP results
and the full-form frequency effects fit the predictions of a continuous processing model (as
was true in Experiment 1).
From the analyses conducted to this point,
then, it was determined that prefix frequency,
token frequency of the root, and family frequency of the full-form would be partialed
from the further analyses. Again, the following p-values have been adjusted using a Bonferroni correction.
Decomposition variables. The results for
the decomposition variables mirrored those
from the gating experiment and once again
suggest decompositional processing. Higher
levels of prefixedness (F(1,5329) Å 35.46, p
õ .001) and semantic transparency (F(1,5329)
Å 66.60, p õ .001) were associated with faster
RTs, and once again prefix likelihood was not
significant.
As in the gating experiment, the prefix likelihood 1 semantic transparency interaction
was significant (F(1,5327) Å 10.75, p õ .05).
Figure 3 shows this interaction and resembles
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Fig. 1, at least as far as the slopes of the two
lines are concerned. In this figure, however,
performance on low prefix-likelihood items
relative to high prefix-likelihood items was
somewhat poorer than it was in the gating
experiment. This interaction supports decompositional models.
Five of the next set of 12 interactions were
significant, as well. All of these significant
interactions involved either prefix likelihood
or prefix stress (or both). Figures 4 and 5 show
the interactions between prefix stress and prefixedness and between prefix stress and semantic transparency (F(1,5327) Å 10.57, p õ
.05, and F(1,5327) Å 57.87, p õ .001, respectively). Figure 4 shows that when prefixedness
was low, the stress value of the prefix was
largely irrelevant. Theoretically, for low prefixedness values, listeners would not process
the prefix and root separately. For highly prefixed items, one would expect to observe a
RT advantage (recalling the main effect of
prefixedness); however, this advantage was
erased for those highly prefixed items that also
happened to have stressed prefixes. This suggests that stressed prefixes ‘‘fool’’ the lexical
FIG. 4. Mean reaction time (RT) as a function of prefix
stress and judged prefixedness, in milliseconds (ms).
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FIG. 5. Mean reaction time (RT) as a function of prefix
stress and semantic transparency, in milliseconds (ms).
access system, drawing processing away from
the root, a possibility that was discussed earlier.
Figure 5 shows the interaction between semantic transparency and prefix stress. Given
the strong correlation between prefixedness
and semantic transparency, the expectation
was that this figure would resemble Fig. 4. It
did, except for items low on semantic transparency with fully stressed prefixes. As predicted by the MSS (Cutler & Norris, 1988),
strong–weak words that are functionally monomorphemic (low on transparency) enjoyed
a RT advantage.
Figures 6 and 7 show the prefix stress interactions with root frequency (F(1,5327) Å
10.82, p õ .05) and prefix frequency
(F(1,5328) Å 12.92, p õ .01). The two show
the same general pattern. For items with lowfrequency roots or prefixes, prefix stress had
a weak facilitative association with RTs, while
for items with high-frequency roots or prefixes, prefix stress had a stronger, inhibitory
association. These effects fit the speculation
made earlier about the disruptiveness of words
with strong prefixes; the disruptiveness of
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high-frequency roots (Fig. 6) or prefixes (Fig.
7) was heightened by a potentially-disruptive
(i.e., fully-stressed) prefix.
Figure 8 shows the prefix frequency 1 prefix likelihood interaction (F(1,5328) Å 11.22,
p õ .05). The inhibitory effect of prefix frequency was strong for items with low prefix
likelihoods, while there was only a weak effect of prefix frequency for items with high
prefix likelihoods. One interpretation of this
pattern is that the perceptual system strips off
prefixes when the prefix likelihood is above
some threshold value. Prefix frequency cannot
have much of an effect if the prefix has been
stripped away, which would be the case for
items very high on prefix likelihood. The delayed word recognition for high-frequency
prefixes is consistent with the observation
made previously about high-density lexical
neighborhoods.
As I noted in Experiment 1, none of these
variables is predicted to have any relevance by
continuous processing models (and obviously,
no interactions between them are predicted).
Therefore, all of the significant interactions
FIG. 6. Mean reaction time (RT) as a function of prefix
stress and root frequency, in milliseconds (ms).
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Discontinuous processing is implied by the
results involving the decomposition variables
and root frequency. These variables are completely irrelevant from the standpoint of the
continuous, left-to-right processing model implicated by the UP and full-form frequency
results, yet all of these variables were significant either as main effects or in interactions.
In particular, it is clear that prefix likelihood
affects word-recognition performance, in
combination with other variables. Such results
cannot be accommodated within a strictly continuous framework.
Conditional Root UPs (CRUPs)
FIG. 7. Mean reaction time (RT) as a function of prefix
stress and prefix frequency, in milliseconds (ms).
constitute evidence for decompositional processing.
The original prediction concerning the decomposition variables was that they would be
positively related to IPs and RTs. Items high
on prefixedness or semantic transparency, according to the prediction, would be more
likely to be decomposed into morphemes and
accessed via their roots. They would therefore
have later IPs and slower RTs, because root
UPs were always later in the stimuli than fullform UPs.
GENERAL DISCUSSION
The results of this study indicate that the
auditory processing of prefixed words has
both continuous and decompositional characteristics. The results of the two main experiments with respect to the full-form vs root
UPs, as well as the outcomes of the full-form
frequency analyses, support left-to-right processing models. Although the reliable inhibitory relationship between prefix frequency and
performance, observed in both experiments,
was not specifically predicted by either class
of model, it is best viewed as a by-product
of continuous processing. Decompositional
models would predict that if prefix frequency
were going to be used for word recognition,
higher frequency would be associated with
better performance, not worse (higher frequency, as I have noted, is generally associated with faster processing). Therefore, this
result also favors continuous processing models, though only mildly.
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FIG. 8. Mean reaction time (RT) as a function of prefix
frequency and prefix likelihood, in milliseconds (ms).
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The fact that the observed effects were in
the opposite direction (as well as the overall
earliness of IPs in Experiment 1, which occurred on average 39 ms before the full-form
UP) may be explained by a model that has
two parallel but nonindependent processes, a
whole-word process and a morphemic one,
but only if the morphemic process is selective
about the candidates it considers.
At word onset, both processes in such a
model would begin and run in parallel. The
whole-word process simply checks the accumulating input against the lexicon, in exactly
the same way as the class of continuous models I have described throughout this paper.
This process will achieve a match at the fullform UP. I will describe the operation of the
morphemic process in the following paragraphs.
The morphemic process, running in parallel, strips any prefix and then attempts to
make a lexical match using the portion of
the signal beginning just after the prefix. The
relevant UP for this process depends on the
root rather than the full-form, but it is not
the root UP I have been discussing throughout this paper. The process I am describing
is selective in that, in attempting to match
the input to the lexicon, it considers only
unbound roots that attach to this prefix. The
morphemic process will achieve a match at
what I will call the conditional root UP, or
CRUP. A word’s CRUP is the root UP given
this particular prefix.
Usually, the CRUP of a prefixed word will
be the same point as the full-form UP. However, there are many exceptions to this general
rule. For example, consider the word ‘‘discredit.’’ The full-form UP of this word is the
second /d/, because of words such as ‘‘discretion.’’ The root UP of ‘‘discredit’’ is the /t/,
because ‘‘credible’’ is still a competitor prior
to that point. The CRUP of ‘‘discredit,’’
though, is the /r/: the only words still consistent with ‘‘discr-’’ are ‘‘discretion,’’ ‘‘discrepant,’’ and their morphological relatives,
but because the morphemic process in this
model only considers unbound roots, those
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words do not count (‘‘-cretion’’ and ‘‘-crepant’’ are not unbound roots).
Such a morphemic process could be a historical by-product of the way language is
used. Speakers coin new words as they need
them, essentially continuously (Henderson,
1985). Writers do this, too: Baayen (1994)
found that approximately 15 new words
ending in ‘‘-ity’’ appear each month in The
New York Times. Although they are perfectly intelligible, they are nonwords in that
they have never been seen before by readers
and are not in any dictionary. Evidence
comes from more anecdotal sources, as
well: two examples I have recently heard
(one in the hallway and one on the evening
news) are ‘‘They were interrupted *midsong by a power outage,’’ and ‘‘Violence
threatens to *re-Balkanize the region.’’
These ‘‘nonwords’’ were quite easy to interpret, and the speakers presumably knew
that they would be.
Aronoff (1976) has noted that new word
forms tend to be quite highly transparent while
older ones may not be because of semantic
drift. I have argued above that unbound roots
are required for high semantic transparency.
To the extent that this argument is on the
mark, a decompositional process that only
considers unbound roots would prove extremely useful in the recognition of newly
coined forms, which we encounter extremely
often (Henderson, 1985).
In order to prevent the perceptual system
from committing prematurely to the wrong
word, the perceptual system would have to
wait for verification from the whole-word process before ultimately committing to a decision. Otherwise, the perceptual system might
decide at the CRUP that the word being heard
is ‘‘discredit,’’ when in fact it is ‘‘discretion’’
(for example). However, even though the perceptual system would not be allowed to fully
commit to ‘‘discredit’’ until ‘‘discretion’’ has
been ruled out, facilitated recognition performance would reflect an activation boost received by the root ‘‘credit’’ at the CRUP (a
boost that was not received by bound roots
like ‘‘-cretion’’). Furthermore, it is possible
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AUDITORY PROCESSING OF PREFIXED WORDS
that other kinds of computations can begin
once a CRUPs-based hypothesis has been generated, while the system is waiting for the
whole-word response. These might include
accelerated access of meaning, integration of
the lexical hypothesis with a sentence representation, and/or access of a grammatical category.
As a post hoc check of this account, all of
the words were analyzed to see if their
CRUPs differed from their full-form UPs. Of
the 71 full-forms in Experiment 1, nine
(12.7%) had CRUPs that were earlier than
their full-form UPs, as in the above example
of ‘‘discredit.’’ For the remaining 62 words,
the CRUP was the same point as the fullform UP. This suggests that even if premature commitment to an incorrect lexical hypothesis cannot be avoided in the way I just
described (i.e., even if the morphemic process does not wait for the whole-word process), the occurrence of such errors would be
fairly low; perhaps the computational advantages outweigh the occasional cost. The mean
CRUP across all 71 words was 456 ms, a
value that corresponds to the mean IP in Experiment 1 (441 ms) even better than the
mean full-form UP (480 ms) did.
However, there was conflicting evidence regarding CRUPs. In a regression analysis for
CRUPs just like that for full-form UPs and
root UPs, although CRUPs did account for a
significant increase in the explained variance
(F(1,1213) Å 81.52, p õ .0001, for the gating
experiment), the effect was not as large as that
for full-form UPs. Still, this notion is deserving of some direct investigation with stimuli
explicitly chosen to test it, because of the close
overall correspondence between CRUPs and
IPs and because several of the results in this
study are most easily interpretable from the
perspective of a selective parallel model.
One could argue that the CRUPs result is
artifactual and due to the combination of the
gating task (where prefixes might be argued
to be processed completely and independently
of what follows them) and the stimulus set
(there were no stimuli with bound roots or
nonroots). If this were the case, CRUPs should
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not predict RTs in Experiment 2, because such
strategic responding was unlikely: that experiment not only used a different methodology,
but also contained a variety of different types
of words and nonwords (suffixed, prefixed
[with bound as well as unbound roots], and
monomorphemic). The CRUPs result for
Experiment 2 was just like that for Experiment
1: CRUPs significantly predicted RTs
(F(1,5332) Å 54.11, p õ .0001), doing a better
job than root UPs but a somewhat worse job
than full-form UPs.
There are two findings in the literature that
may relate to CRUPs. Schriefers, Zwitserlood,
and Roelofs (1991) found that prefixed words
were identified earlier than unprefixed words
with identical predicted recognition points (in
Dutch). For example, both the prefixed word
‘‘opstaan’’ and the root ‘‘staan’’ have the final /n/ as their UP, but in a gating experiment
participants identified the prefixed words an
average of 37 ms earlier (compared to the 39
ms advantage found in Experiment 1 of this
study). This ‘‘general prefixation advantage’’
was replicated in two subsequent experiments
and could not be explained by either class of
model. It is not possible to say whether
CRUPs would explain this effect, but the possibility is intriguing. Taft (1988) also found
what appears to be a general prefixation advantage in lexical decision times, although he
provided relatively little methodological detail. To explain his nonword data, he suggested an ‘‘activate and check’’ model that is
similar to the smart morphemic half of the
dual-route model suggested in this paper, but
one that considers bound rather than unbound
roots. The model did not apply, however, to
his word data.
CRUPs may also help explain the prefix
stress interactions from Experiment 2 of the
current study. Specifically, if the basic stressdriven model is modified so that it is sensitive
to the decomposition variables and so that it
operates via CRUPs rather than root UPs,
there seem to be two possible predicted outcomes. One of these is illustrated fairly well
by Fig. 4: prefix stress should not matter for
items low on prefixedness (for example), but
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LEE H. WURM
should begin to affect RTs as prefixedness increases. The RTs that should stand out as
faster (because of the facilitative potential of
CRUPs) are those for words high on prefixedness with unstressed prefixes.
CONCLUSION
Most existing models of word recognition
have too rigidly insisted on either continuous
processing with total disregard for decompositional variables or on strict decompositional
processing in every instance. The current
study demonstrates that both theoretical positions are wrong; there is evidence for both
full-form processing and at least some decomposition, the latter perhaps involving CRUPs.
In hindsight, the insistence of some researchers on one or the other kind of mechanism
seems difficult to understand. The ease with
which people coin and understand new words,
the idea of semantic drift, and the flexibility of
the perceptual system demonstrated in various
tasks should have suggested earlier that there
might be both types of processing mechanism.
In addition, as pointed out in the Introduction,
while nonword data from studies such as Taft
et al. (1986) suggest an important role for
roots, this does not imply that the system always uses root access for all words.
As I have noted throughout this paper, the
Cohort model (Marslen-Wilson, 1984; Marslen-Wilson & Welsh, 1978) cannot accommodate the results having to do with the decomposition variables. In addition, the suggested competition between high-frequency
roots and the full-forms that carry them flies
directly in the face of the basic cohort principle. The current results suggesting an important role for semantic transparency in decompositional processing is, however, consistent with the conclusion of Marslen-Wilson et
al. (1994). Their model may be able to accom-
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modate the current results, if modified to reflect the importance of prefix likelihood, for
example.
Cutler et al. (1985) argue in favor of a decompositional serial autonomous model,
based on process considerations rather than
storage and efficiency considerations. They
believe that a strict prefix-stripping account
is probably wrong, but that decomposition of
prefix and stem is a strategy that seems routinely available to the language processor.
However, one aspect of their framework that
would seem not to fit the current results is
their insistence that listeners compute the
meaning of stems before affixes. While this
makes sense in many situations, particularly
those involving suffixing (e.g., ‘‘sad’’ /
‘‘-ness’’), the current results indicate that prefixes are dealt with very early in the recognition process. Prefix likelihood should not play
a role in the early stages of recognition if stem
processing has to be completed first.
Network models (e.g., McClelland & Elman, 1986; Norris, 1994) might be able to
accommodate the current results without having two separate processes. Such models are
often claimed to have the ability to extract
structural (e.g., that ‘‘re-’’ is a separable unit)
or distributional (e.g., relating to prefix likelihoods) information based on covariance of
form and meaning. Van Orden et al. (1990)
provide a useful discussion of this issue.
A final interesting point concerns the fact
that the structure of the input itself determines
the specifics of perceptual processing. It may
well be profitable to apply the techniques developed here crosslinguistically, choosing languages with known differences in morphology
and affix structure. Such a test could illustrate
how the perceptual process gets instantiated
differentially in response to different language
environments.
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AUDITORY PROCESSING OF PREFIXED WORDS
APPENDIX A
Judged Prefixedness, Semantic Transparency, and Root Morpheme Frequency
Judged
prefixednessa
Full-form (root)
ablaze (blaze)
abreast (breast)
abridge (bridge)
acquire (choir)
aloft (loft)
antibody (body)c
antisocial (social)c
archbishop (bishop)
ascend (send)
aspire (spire)
atingle (tingle)
beget (get)
behold (hold)
belong (long)
beside (side)
circumscribe (scribe)c
concave (cave)
condense (dense)
copilot (pilot)
counterplot (plot)c
default (fault)
defrost (frost)
discredit (credit)
disfigure (figure)
disgust (gust)
distaste (taste)
embattle (battle)
embody (body)
engage (gauge)
engulf (gulf)
entitle (title)
forecast (cast)
foreshadow (shadow)
forewarn (warn)
hyperspace (space)c
increase (crease)
infringe (fringe)
insecure (secure)
intercourse (course)c
interstate (state)c
midyear (year)
misfit (fit)
mislead (lead)
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Semantic
transparencya
Root morpheme
frequencyb
6
2
2
1
2
2
8
7
2
2
3.5
2
4
1
5
2
2
4.5
8
7
3
7
7
4
1
7
4
3
2
1.5
3
2
2
7.5
5.5
1
3
8
2
6.5
7
3
7.5
17
83
71
8
11
705
537
40
72
20
3
2379
527
1508
1236
2
42
33
25
32
55
16
58
229
5
93
92
705
0
22
46
127
72
50
154
8
20
184
749
497
1420
145
557
6
6
4
1
5
8
8
6
6
3
6
6
5
3
5
5
6
5
8
7
7
8
8
8
4
8
6
6
5
7
7
6
7
7
7
3
7
8
7
7
7
7
8
a00f$$2524
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LEE H. WURM
APPENDIX A—Continued
Full-form (root)
Judged
prefixednessa
Semantic
transparencya
Root morpheme
frequencyb
mistrial (trial)
monorail (rail)c
perfume (fume)
persuade (suede)
preamble (amble)
prefix (fix)
preheat (heat)
proclaim (claim)
rebuild (build)
recite (cite)
rephrase (phrase)
reprint (print)
reword (word)
subsample (sample)
supersede (seed)c
surcharge (charge)
surname (name)
transcend (send)
twilight (light)
twinight (night)
ultrasound (sound)c
unbend (bend)
unborn (born)
unbuckle (buckle)
unbutton (button)
underscore (score)c
unplug (plug)
unscramble (scramble)
unusual (usual)
8
7
1
5
8
8
8
6
8
7
8
8
8
8
7
7
6
5
3
5.5
7
8
8
8
8
7
8
8
8
7
6
4
1
3
1
7
7
7
2
7
7
6.5
7
2
7
7
3.5
6
5
6
8
8
8
8
6
7.5
8
8
66
89
13
3
12
52
143
177
277
26
50
95
502
21
3
162
421
72
530
625
295
77
113
7
34
57
17
19
311
a
Values can range from 1–8.
Per million tokens.
c
These items were used only in Experiment 1.
b
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AUDITORY PROCESSING OF PREFIXED WORDS
APPENDIX B
Neighborhood Size, Prefix Likelihood, and
Prefix Frequency
Prefix
aantiarchbecircumconcocounterdedisemenforehyperinintermidmismonoperpreproresubsupersurtranstwiultraununder-
Neighborhood
size
16,803
72
18
6562
109
4828
1690
22
5733
2416
644
2017
372
7
9444
1101
163
243
48
2153
2214
3832
8561
608
109
556
346
4
9
3271
543
Prefix
likelihood
Prefix
frequencya
.090
.028
.722
.304
.018
.012
.010
.545
.008
.092
.003
.092
.212
.000b
.094
.212
.264
.181
.083
.005
.013
.001
.067
.061
.284
.009
.092
1.000
.889
.283
.232
2454
28
20
204
1
228
18
26
104
766
78
371
129
1
21963
256
60
79
22
484
56
152
1881
649
49
654
47
9
2
1072
234
a
Per million tokens.
The value of .000 for ‘‘hyper-’’ seems odd, given
words such as ‘‘hyperactive.’’ In fact, there were 24
words beginning with ‘‘hyper-’’ in Webster’s Third New
International Dictionary of the English Language (1993)
that qualified as truly prefixed, but none of them was listed
in the frequency count of Francis and Kučera (1982).
b
APPENDIX C
Words Used in Experiment 2
Critical items: Items from Appendix A not
denoted by a superscript c.
Fillers: alphabet, anthem, bedazzle, bridal,
bugle, curbside, deduction, dependent, dwin-
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dle, eclipse, frontier, gratify, grocer, hindrance, hockey, image, inert, infant, inspiring,
legal, linguist, minus, mission, mystique, operate, referee, subsequent, teeter, torpedo, velour.
Practice-effect words (these items were heard
by 44 of the 110 participants): acumen, audition, brutal, catapult, cognizant, compel, cutlets, despair, detachment, dinnerware, eyeball,
fluttering, frankly, heavenly, hesitance, instinct, iron, mazurka, mermaid, mistletoe,
murmur, nourish, partial, privilege, pseudonym, ranking, reinforce, shrubbery, slumber,
spaghetti, splatter, stainless, subpoena, sweetheart, thinning, towel, twitter, vernacular,
worthy, wristwatch.
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