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Auditory Processing of Polymorphemic Pseudowords

Lee H. Wurm

Wayne State University

This study compared models of auditory word recognition as they relate to the processing of

polymorphemic pseudowords. Semantic transparency ratings were obtained in a preliminary rating

study. The effects of morphological structure, semantic transparency, prefix likelihood, and morphe-

mic frequency measures were examined in a lexical decision experiment. Reaction times and errors

were greater for pseudowords carrying a genuine prefix, and this effect was largest for pseudowords

that also carried a genuine root. While results were grossly similar for bound and free root types, there

were also some important differences. Regression analyses provided additional support for decom-positional models: semantic transparency, prefix likelihood, prefix frequency, and root frequency all

affected pseudoword rejection times. The results are most compatible with a modification of Tafts

(1994) interactive-activation model or a dual-route model. 2000 Academic Press

Key Words: lexical decision; morphology-language; semantic transparency; speech perception;

word recognition.

Morphological effects in spoken word recog-

nition have been receiving increasing attention.

English is considered to have only limited and

irregular morphological structure (e.g., Hender-son, 1985; Jarvella & Meijers, 1983), but recent

studies have shown that morphological infor-

mation is used in perception (e.g., Marslen-

Wilson, Tyler, Waksler, & Older, 1994; Wurm,

1997). There is of course some morphological

structure to the language, and different ap-

proaches could be used by the perceptual sys-

tem in dealing with that structure.

The traditional view in formal linguistics isthat nonarbitrary items do not need to be stored

in the lexicon (Bloomfield, 1933; Chomsky,

1965; Lyons, 1977). According to this view, it

is unnecessary to store built, builds, rebuild, and

other complex relatives of these, because the

lexicon would already contain the root mor-

pheme build. The complex forms can be gener-

ated as needed through the use of word forma-

tion rules. Reduction of redundancy is the mostattractive feature of this approach; some lan-

guages have verbs that can assume thousands of

distinct surface forms even though they differ

only by inflection and are essentially the same

vocabulary item (Anderson, 1988). Only the

base form of such verbs needs to be stored.

A class of word-recognition models that cor-

responds to this view can be referred to as

decompositional (or discontinuous). Although

there are several examples of discontinuous

models (e.g., Cutler, Hawkins, & Gilligan,

1985; Cutler & Norris, 1988; Grosjean & Gee,

1987; Jarvella & Meijers, 1983; MacKay, 1978;

Morton, 1969, 1979), the most visible one has

been the prefix-stripping model of Taft and his

colleagues (1981, 1985; Taft & Forster, 1975;

Taft, Hambly, & Kinoshita, 1986). This modelwas developed to explain visual lexical decision

times for various classes of morphologically

complex pseudowords.

According to this model, saying NO should

take longer for pseudowords with genuine pre-

fixes than for those without. The difference

should be even larger when the root of the

Portions of this research were supported by a National

Research Service Award from the National Institute of

Mental Health (Grant F32 MH11721). I thank Cynthia

Connine, Albrecht Inhoff, Arthur Samuel, Robert Schreu-

der, Marcus Taft, and an anonymous reviewer for making

helpful criticisms of a previous version of this paper. Mark

Aronoff and Mark Pitt also provided useful advice.

Correspondence and reprint requests concerning this ar-

ticle should be addressed to Lee H. Wurm, Department of

Psychology, Wayne State University, 71 West Warren Av-

enue, Detroit, MI 48202. E-mail: [email protected].

wayne.edu.

255 0749-596X/00 $35.00Copyright 2000 by Academic Press

All rights of reproduction in any form reserved.

Journal of Memory and Language 42, 255271 (2000)

doi:10.1006/jmla.1999.2678, available online at http://www.idealibrary.com on

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pseudoword is a real English root. This is be-

cause there is a successful prefix strip for Pre-

fix, Root pseudowords that requires time; for

Prefix, Root stimuli there is a successful

prefix strip plus a successful root look-up,

which requires still more time (deciding that thetwo legitimate morphemes cannot be combined

with each other to make a word also slows the

process).

One interesting aspect of this predicted pat-

tern concerns pseudowords that begin with non-

prefix strings (i.e., Prefix stimuli). Roots

should not even be recognizable as roots when

there are no prefixes to strip off, so root status

should not have an effect here (see Taft et al.,1986): reaction times (RTs) for the two condi-

tions should be equal. Taft (1994) later con-

cluded that it is theoretically possible to observe

a RT disadvantage for the Prefix, Root

items if the root is very common and easily

recognized, as in the visually presented

pseudoword IBPEOPLE (his example). I will

have more to say about this following the main

RT experiment.Some theorists feel that lexical redundancy

can be an advantage to be exploited, rather than

a burden (Henderson, 1985). Such authors pre-

fer the full-listing view, which states that all

words are stored in the lexicon (Bybee, 1985,

1995a, b; Jackendoff, 1975). Bybee (1988) feels

that theorists should not be concerned with stor-

age efficiency given the capacity of the human

brain and the widespread idiosyncrasies presentin all languages (see also Sandra, 1994).

Continuous processing models correspond to

this view. Words are processed on a strict left-

to-right basis, with no regard for internal struc-

ture. Morphological structure and morphologi-

cal variables cannot affect RTs or error rates.

The Cohort model (Marslen-Wilson, 1984,

1987; Marslen-Wilson & Welsh, 1978) is one

such model, and there have been several other

arguments in favor of continuous processing

(e.g., Henderson, Wallis, & Knight, 1984; Ru-

bin, Becker, & Freeman, 1979; Tyler, Marslen-

Wilson, Rentoul, & Hanney, 1988).

Pseudoword rejection should occur as soon as

the input becomes inconsistent with all words.

Some interactive-activation models explicitly

deny the existence of morpheme or word units

(e.g., McClelland & Elman, 1986; Rueckl,

Mikolinski, Raveh, Miner, & Mars, 1997; Sei-

denberg, 1987; 1989; for critical views, see

Dennet, 1987; Forster, 1994). On this view,

so-called morphological effects are in fact dueto semantic and form-based similarity, fre-

quency of occurrence of sublexical letter

strings, and so on. Most interactive-activation

models are characterized as continuous, but Taft

(1994) proposed an interactive-activation ver-

sion of the earlier prefix-stripping model. The

new model is behaviorally very similar to the

earlier one, but Taft found the interactive-acti-

vation framework more plausible and appealing(the major difference is that a prelexical prefix

store is not needed). The model has distinct

word and morpheme units and exhibits decom-

positional behavior. The equivalent of prefix

stripping takes place as a consequence of the

mapping process (acoustic-phonetic or visual-

orthographic).

Some researchers have argued that some

words are decomposed while others are not.Wurm (1997) proposed a dual-route model

based on the idea of parallel, competing pro-

cesses [cf. the Race model of Cutler and Norris

(1979)]. In his model, morphologically complex

words are processed simultaneously as full-

forms and as analyzed constituent morphemes.

The decompositional route of the model is sen-

sitive to variations in semantic transparency, the

likelihood that a given string is a prefix, andmorpheme frequencies (see below). Other vari-

ations on the dual-route theme have also been

proposed (e.g., Anshen & Aronoff, 1981; Berg-

man, Hudson, & Eling, 1988; Caramazza, Lau-

danna, & Romani, 1988; Frauenfelder &

Schreuder, 1992; Laudanna, Burani, & Cer-

mele, 1994; Laudanna & Burani, 1995; Lau-

danna, Cermele, & Caramazza, 1997; Schreuder

& Baayen, 1995; Stanners, Neiser, & Painton,

1979).

Dual-route models have often provided the

context for the initial exploration of previously

ignored variables, such as semantic transpar-

ency (Bergman et al., 1988; Henderson, 1985;

Smith, 1988; Smith & Sterling, 1982). Words

such as unhappy are highly transparent, while

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those such as relate are opaque. There is also a

sizable middle ground (Wurm, 1997). Recent

data (Libben, 1998; Schreuder & Baayen, 1995;

Wurm, 1997) have shown that this variable

plays a role in word recognition and have more

generally called into question the defensibilityof an all-or-none theoretical position on com-

plex word recognition. For example, Marslen-

Wilson et al. (1994) found that suffixed words

that do not have a semantic relationship that is

obvious to current language users are treated as

monomorphemic.

In an investigation of visual processing of

Italian pseudowords, Laudanna et al. (1994)

introduced another important concept: the pro-portion of tokens beginning with a given letter

string that are prefixed (e.g., retold is prefixed,

realize is not). Schreuder and Baayen (1994)

found that the average value for this variable in

English was very low and rejected the notion of

prefix-stripping. Wurm (1997) reported a simi-

lar average value for this variable (which he

called prefix likelihood), but found that it inter-acted with several other variables in the recog-

nition of auditorily presented prefixed English

words. The nature of the interactions suggested

decompositional processing for some items.

The current study extends previous work in

many ways. First, most previous studies have

presented stimuli visually. Auditory presenta-

tion can inform theory in a unique way, because

the pieces of a polymorphemic stimulus arriveat the listener at different, specifiable times (cf.

Butterworth, 1983; Grosjean & Gee, 1987;

Henderson, 1985; Kempley & Morton, 1982;

Marslen-Wilson, 1984; Morton, 1979; Radeau,

Morais, Mousty, Saerens, & Bertelson, 1992).

Second, the critical stimuli in most experi-

ments have carried bound roots (e.g., -ceive in

receive and conceive). Overreliance on bound

roots is a potential problem given recent find-

ings about the importance of semantics; bound

roots are semantically empty, at least to nonlin-

guists, and thus they are not subject to phenom-

ena like semantic drift (Aronoff, 1976) to the

same extent that free roots are (free roots are

those that can stand alone as words, such as the

build in rebuild). Bound roots are also less

productive than free rootsthey cannot com-

bine with prefixes to make novel words.

Finally, studies of auditory pseudoword pro-

cessing have not included prefix likelihood or

semantic transparency, nor have they looked at

interactions between these variables and mor-phemic frequency measures. This is important,

because pseudowords are simply potential

words that happen not to be used; speakers and

writers coin new combinations as needed, but

this almost never causes problems for listeners

and readers (provided the new combination is

phonotactically legalsee Baayen, 1994;

Coolen, van Jaarsveld, & Schreuder, 1991;

Schreuder & Flores dArcais, 1989).Because many studies have used pseudowords

as critical stimuli (e.g., Caramazza et al., 1988;

Laudanna et al., 1994; Taft, 1994; Taft & Forster,

1975; Taft et al., 1986), the use of pseudowords

allows contact with a large body of literature. The

current study examines whether the same vari-

ables that influence word recognition also affect

the processing of pseudowords.

PRELIMINARY RATING STUDY

This study provides values on semantic trans-

parency for polymorphemic pseudowords.

Method

Participants

Twenty students from the Department of Psy-

chology subject pool participated. All were na-tive speakers of English. Participants received

extra credit in a psychology course.

Materials

Critical pseudowords in this study fell into

one of four groups, defined by crossing the

presence or the absence of a genuine prefix and

a genuine root (see Appendix A). Only the

Prefix, Root pseudowords with free roots

were included in this rating study. Pilot ratings

collected for Prefix, Root pseudowords with

bound roots were uniformly low. These items

were dropped from the rating study, but will be

included in the main part of the lexical decision

experiment.

Stimulus construction is described more fully

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under the next Method section. The stimuli to

be rated were printed in a rating packet in two

different random orders.

Procedure

Participants made their ratings by writing anumber from 1 to 7 in a blank next to each

pseudoword. Anchor points on the scale were

labeled Impossible to put this in a sentence

(1) and Very easy to put this in a sentence (7).

Participants were given an example of a

pseudoword that can easily be put into a mean-

ingful sentence: The band was interrupted mid-

song by a power failure (a sentence heard by

the author on a radio station in Binghamton,

NY) and one that cannot easily be put into a

meaningful sentence (transplay). This indirect

method is one way of getting at the construct of

semantic transparency, which in the case of

pseudowords concerns how easily interpretable

each stimulus is (see Caramazza et al., 1988;

Coolen et al., 1991).

Results

Median semantic transparency ratings are

shown in Appendix B. There was significant

variation on this dimension, even though the

stimuli were created by the random concatena-

tion of a prefix and a root. Median ratings

ranged from 2 (e.g., transfrost) to 7 (e.g., re-

bolt).

CALCULATION OF OTHER REGRESSOR

VARIABLES

Prefix likelihood is a ratio: the numerator is

the summed frequency (Francis & Kucera,

1982) of the truly prefixed words beginning

with a given phonetic string, and the denomi-

nator is the summed frequency of all words

beginning with that string in which removal of

the string leaves a pronounceable syllable or

syllables. For example, although real begins

with re-, this word was not considered a prefix-

stripping failure because the remainder of the

word (simply the phoneme /l/ in this case) is not

a syllable. Prefix likelihoods for each prefix

were taken from Wurm (1997). The value for

ad-, which was not used in that study, was

calculated by the methods described in that pa-

per.

Prefix likelihoods can range from 0 to 1. A

value of 0 would indicate that no words begin-ning with the string are truly prefixed. A value

of 1 would indicate that all words beginning

with the string are truly prefixed. Values for the

prefixes used in this study are listed in Appen-

dix C. They ranged from .005 (per-) to .283

(un-), averaging .07. Wurm (1997) found that

this variable played a role in word recognition

despite the fact that most of these values are

small.A measure of root morpheme frequency

was needed for Root pseudowords. The Bir-

mingham/Cobuild corpus (18 million tokens)

of the CELEX database (Baayen, Piepen-

brock, & van Rijn, 1993; Burnage, 1990) was

searched for each root. Frequencies were

summed across all cases where that root was

found (e.g., the frequencies of repay, prepay,

and so on are all included in the root fre-quency for pay). Root frequencies are shown

in Appendix B.

Prefix frequencies were calculated in essen-

tially the same way. Counts for words in the

Birmingham/Cobuild corpus beginning with

each (orthographic) prefix string were obtained.

From these, the frequencies for cases that were

instances of prefixation were summed (e.g., re-

play counts but reach does not). Appendix C

includes the prefix frequency for each prefix.

Summary statistics for both frequency measures

are shown in Table 1.

LEXICAL DECISION EXPERIMENT

There have been few explicit discussions of

possible processing differences for bound vs.

TABLE 1

Summary Statistics for Frequency Measures

M (SD) Range

Prefix frequency 481 (610) 91881

Root frequency (free) 192 (216) 4870

Root frequency (bound) 119 (134) 0619

Note. Per million tokens, from the CELEX database

(Baayen et al., 1993; Burnage, 1990).

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free roots. Most researchers who have ad-

dressed the issue have concluded that bound

elements are represented in the same way as

free ones (Bergman et al., 1988; Emmorey,

1989; Stanners et al., 1979; Taft, 1994). How-

ever, Marslen-Wilson et al. (1994) concludedthat bound roots do nothave the same represen-

tational status as free roots because they lack

reliable meanings. This experiment includes

stimuli with both root types.

Method

Participants

Participants were 88 students from the De-

partment of Psychology subject pool. All werenative speakers of English with no known hear-

ing problems. Participants received extra credit

in a psychology course for their participation.

Materials

Yoked quartets of critical pseudowords were

constructed by crossing /Prefix with

/Root. These quartets are listed in Appendix

A. Four lists of stimuli were prepared, eachconsisting of 480 items (240 words and 240

pseudowords). Each list contained 120 critical

pseudowords: 30 Prefix, Root pseudo-

words; 30 Prefix, Root pseudowords; 30

Prefix, Root pseudowords; and 30 Prefix,

Root pseudowords. One member of each

stimulus quartet was assigned to each list, so

that no participant heard more than one member

of the quartet. In each of the four conditions,half of the pseudowords came from a quartet

with bound roots and half came from a quartet

with free roots.

The Prefix, Root critical pseudowords

were constructed by randomly concatenating 1

of 10 English prefixes with 1 of 60 bound and

60 free roots. Each root was used once, and each

prefix was used 12 times (combined 6 times

with bound roots and 6 times with free roots).

To create the other 3 conditions, prefixes were

made into nonprefixes by the substitution of one

of the phonemes to a different phoneme from

the same broad class. Readers may notice that

Prefix strings were repeated more often than

Prefix strings throughout the experiment. I

will address this point below.

Roots were changed into nonroots by the

same procedure. Phoneme substitutions were

balanced among early, medial, and late posi-

tions within the individual morphemes. Item

durations were well matched across the eight

conditions (see Table 2).

Each list also contained 120 fillerpseudowords with no apparent internal structure

(e.g., *chormal), 120 prefixed filler words (e.g.,

enslave), and 120 unprefixed filler words (e.g.,

glutton). The 360 filler items were identical in

each list. Across the 480 stimuli heard by a

participant, 49% of words and 53% of

pseudowords had weak first syllables (the stress

of all critical items was weakstrong).

Two- or three-syllable filler words were cho-sen at random from a dictionary, subject to the

constraint that they be of sufficiently high fre-

quency to be familiar to the participant popula-

tion. Filler pseudowords were chosen the same

way. A randomly-selected two- or three-sylla-

ble word was changed into a pseudoword by the

substitution of one or two phonemes with a

phoneme or phonemes from the same class.

Three quarters of the filler pseudowords had one

phoneme change, and a quarter had two

changes. The position of these substitutions

were randomly determined. This mixture ap-

proximated the proportions established by the

critical pseudowords; matching the proportions

exactly was not possible, because a quarter of

the critical pseudowords (i.e., those in the

TABLE 2

Mean Item Durations (SD) in Milliseconds

Total Prefix Root

Items with free roots

Prefix, Root 879 (69) 203 (55) 676 (61)

Prefix, Root 877 (68) 200 (60) 677 (59)

Prefix, Root 880 (72) 196 (61) 684 (66)

Prefix, Root 879 (73) 212 (61) 667 (67)

Items with bound roots

Prefix, Root 877 (62) 198 (56) 679 (57)

Prefix, Root 875 (68) 186 (56) 689 (61)

Prefix, Root 876 (63) 197 (57) 679 (54)

Prefix, Root 878 (63) 200 (57) 678 (62)

Note. n 60 items per condition.


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Prefix, Root condition) had no phoneme

substitutions.

Stimuli were digitized at a sampling rate of

10 kHz, low-pass-filtered at 4.8 kHz, and stored

in disk files. A practice list of similar composi-

tion, consisting of 100 items, was used prior tothe main experiment. Visual feedback about

accuracy was given to the participants after each

trial, but only during the practice list.

Procedure

Participants (alone or in pairs) listened to stim-

uli over headphones in a sound-attenuating room.

Order of stimulus presentation was randomized

for each group of participants. An equal numberof participants heard each of the four stimulus

lists. On each trial, a participant heard a stimulus

and made a lexical decision by pressing a button

on a response board with his or her dominant

hand. Participants pressed one button for words

and another button for pseudowords.

RTs were measured from the acoustic offset

of each item. This approximates the measure-

ment method used by Taft et al. (1986) and wasnecessary given the goals of this paper. One

goal was to see if the RT pattern predicted by

the prefix-stripping model would emerge for

stimuli carrying free roots rather than bound.

Another goal, contingent on the first one, was to

see if models other than the prefix-stripping

model can explain that pattern of data. Taft et al.

(1986) reasoned that it did not make any differ-

ence where the RT measurement began, pro-vided that two conditions were met: First, the

RT measurement had to start somewhere in the

root portion of each stimulus, and second, the

starting position had to be the same point for

both stimuli that contained a given root. Thus,

Taft et al. (1986) chose an arbitrary point in

each root from which to measure RTs. The

current study uses the analogous method of

measuring from item offset: the offset of each

item equals the offset of each root, which is as

good a point as any according to this view (if

item durations are well matchedsee Table 2).

Results and Discussion

A participant was excluded from the experi-

ment if he or she had an error rate greater than

15% or a mean RT greater than 1000 ms. Eight

participants were excluded by these criteria.

Analyses reported here were conducted on the

remaining 80 participants. RTs for trials onwhich the participant incorrectly classified a

critical stimulus as a word were not included.

RTs were discarded if they were more than 2 SD

above the mean for a given participant in a

given condition (subject analyses) or for a par-

ticular item (item analyses).

Analyses of Variance (ANOVAs)

Mean RT as a function of root status, prefix

status, and root type (free vs. bound) is shown in

Fig. 1. The mean error rate for each condition is

shown above the bar in the figure.

A 2 (root type) 2 (prefix status) 2 (root

status) ANOVA was conducted. Pseudowords

with bound roots had slightly faster mean rejec-

tion times than those with free roots (292 ms vs.

FIG. 1. Mean reaction time (RT) as a function of root

status, prefix status, and root type, in milliseconds (ms).

Error bars show 1 SEM. Mean error rates are shown above

the bar for each condition. (A) Pseudowords with free roots;

(B) pseudowords with bound roots.

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271 ms), but this difference was not significant

by items: F1(1, 79) 11.67, p .001; F2(1,

472) 1.98, p .10.

Items with genuine prefixes (M 354 ms)

took longer to reject than those without [M

209 ms; F1(1, 79) 369.24, p .001;F2(1, 472) 165.35, p .001]. Items with

genuine roots took longer than those without

[321 ms vs. 242 ms; F1(1, 79) 120.54, p

.001; F2(1, 472) 55.86, p .001], and

the interaction between prefix status and root

status was also significant [F1(1, 79) 22.80,

p .001; F2(1, 472) 9.63, p .01]. As

can be seen in the figure, the disruptive effect of

a genuine root was even more pronounced in thecontext of a genuine prefix. These last two

effects are incompatible with continuous pro-

cessing models.1,2

Both portions of Fig. 1 fit the overall pattern

predicted by a prefix-stripping model, except

for one important difference: RTs for Prefix,

Root items (M 229 ms) were slower than

RTs for Prefix, Root items [M 189 ms;

F1(1, 79) 14.82, p .001; F2(1, 236)

10.40, p .001]. I will return to this point

under General Discussion.

The error rates shown in Fig. 1 follow the

same pattern as the RTs. The effect of root type

(bound vs. free, 3.6% vs. 4.4%, respectively)

was not significant: F1(1, 79) 3.58, p

.07; F2(1, 472) 1.67, p .10. There was

a 4.6% difference between Root items and

Root items [F1(1, 79) 84.59, p .001;

F2(1, 472) 49.68, p .001]. Similarly,

there was a 3.9% error rate increase for Prefix

items, compared to Prefix items [F1(1, 79) 71.10, p .001; F2(1, 472) 36.95, p

.001]. The interaction between prefix status and

root status was also significant [F1(1, 79)

36.41, p .001; F2(1, 472) 16.60, p

.001].

One of the more informative aspects of the

error data can be found in the Prefix condi-

tions, which we already focused on in the RT

analyses. Prefix, Root items had higher er-ror rates than Prefix, Root items [3% vs.

1%F1(1, 79) 23.38, p .001; F2(1,

236) 5.83, p .05]. This significant dif-

ference underscores the RT result: there appears

to be some activation of the root portion of a

Prefix, Root item, regardless of whether the

root is bound or free.

While the RT patterns were similar for both

free and bound roots, the prefix status roottype interaction was significant by subjects and

approached significance by items: F1(1, 79)

17.23, p .001; F2(1, 472) 3.28, p

.08. However, this test includes all items, and

the free vs. bound manipulation has no real

meaning for the Root items. Therefore, I reran

the interaction including only the Root items

(i.e., those in the right half of both panels of

Fig. 1).Looking first at the Prefix, Root items,

one sees a small RT advantage for items that

carried free roots (221 ms 238 ms 17

ms); for Prefix, Root items, the effect is

large and inhibitory (442 ms 381 ms 61

ms in the opposite direction). For this subset of

the data, the interaction was significant, but only

by subjects [F1(1, 79) 10.52, p .01;

F2(1, 236) 1.75, p .10]. The corre-

sponding interaction on error rates was also

significant in the subjects analysis only: F1(1,

79 ) 5.48, p .05; F2(1, 236) 1.30,

p .10.

The major difference between Prefix items

and Prefix items is that in the former case, the

perceptual system is not expected to attempt

1

To ensure that the results shown are not due to differ-ences in the number of auditory neighbors each kind of

pseudoword has, I calculated the number of words that

differ from each pseudoword by a single phoneme substi-

tution. Zero was the median and modal value for all com-

binations of/Prefix and /Root (78.3% of the stimuli

had 0 neighbors). Furthermore, number of neighbors did not

differ significantly across the eight types of pseudowords,

whether the analysis included only the number of

pseudowords having 0 neighbors (Kolmogorov-Smirnov

Z 1.18, p .10) or data for all of the pseudowords

[2 9.38 (df 7), p .10].2 As mentioned previously, the stimulus-initial phoneme

strings in the Prefix conditions were repeated more often

(12 times each) than those in the Prefix conditions (M

2.4 times each). To ensure that the results obtained were not

due to this difference in repetition, I recalculated perfor-

mance in the Prefix conditions using only each partici-

pants first two encounters with each prefix. While perfor-

mance on the early trials was slower and more variable, the

data patterns are consistent with the results shown in Fig. 1.


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decomposition. Therefore, root type should not

have an influence here. Decomposition is ex-

pected in the Prefix cases, and that is where a

large RT difference was observed. The fact that

it was items with free roots that suffered such a

large inhibitory effect in the context of a genu-ine prefix may illustrate the importance of se-

mantics, discussed earlier in this paper (Libben,

1998; Marslen-Wilson et al., 1994; Schreuder &

Baayen, 1995; Wurm, 1997). The regression

analyses to be reported below lend additional

support to this idea.3

Regression Analyses

RTs were also analyzed using hierarchicalmultiple regression. Only Prefix, Root

pseudowords were analyzed, because these are

the only pseudowords for which it was possible

to get values on all of the regressors. Regression

models assume independence of observations,

which does not hold for the current experiment

because each participant provided more than

one observation. In repeated-measures regres-

sion analyses, this is controlled by the inclusionofN-1 dummy variables (79 in the present case)

that represent the participants. The interested

reader can refer to Cohen and Cohen (1983) for

more details.

After entering the 79 dummy variables, prefix

frequency and root frequency were found to

have inhibitory effects on RTs [F(1, 1967)

8.80, p .01; and F(1, 1967) 8.44, p

.01, respectivelythe large df value in the de-nominator equals the number of participants

times the number of relevant stimuli minus the

number of incorrect critical trials and the num-

ber of previous factors in the model].

The prefix frequency effect can be viewed

one of two ways, both of which rest on the idea

that processing is more difficult in portions of

lexical space that are densely populated (see

Goldinger, Luce, & Pisoni, 1989; Luce, Pisoni,

& Goldinger, 1990). The inhibitory effect of

prefix frequency may be a byproduct of contin-uous processing. Prefix frequency is necessarily

correlated with neighborhood density, so words

with high-frequency prefixes have more neigh-

bors than words with low-frequency prefixes.

Alternatively the prefix frequency effect may

have decompositional underpinnings. A high-

frequency prefix usually attaches to more roots

than a low-frequency prefix does [Wurm (1996)

found a correlation of .75 for these quantities].In general, then, a pseudoword response should

require more time if its prefix has high fre-

quency, because the pool of root candidates

would be relatively large.

The root frequency effect agrees with

Wurms (1997) finding for real prefixed words

and fits with his conclusion that high-frequency

roots compete with the full-forms that carrythem. This conclusion, if correct, would suggest

that the prefix frequency effect is due to the size

of the pool of root candidates and is not simply

a byproduct of continuous processing.

The next effect assessed was that of root type.

Included in the model ahead of root type were

the N-1 dummy variables, prefix frequency, and

root frequency. Pseudowords with free roots

had slower RTs than those with bound roots[439 ms vs. 374 ms; F(1, 1965) 20.48, p

.001; this analysis only considers items from

the Prefix, Root conditionthe overall ad-

vantage for items with bound roots was 21 ms,

significant only by subjects].

The next effect assessed was that of prefix

likelihood. Items higher on prefix likelihood

had slower RTs [F(1, 1965) 4.12, p

.05]. This agrees with the finding of Laudanna

et al. (1994) for Italian pseudowords, presented

visually. Higher semantic transparency was also

associated with slower RTs [F(1, 926)

9.49, p .01this analysis was done for

items with free roots only]. These effects argue

against strict continuous and strict decomposi-

tional models. The behavior of the perceptual

3 The possibility that the prefix status root type inter-

action was due in part to some unidentified aspect of the

materials cannot be completely ruled out, because the in-teraction was also present in the subjects analysis of Root

items [i.e., those in the left half of Fig. 1: F1(1, 79) 7.79,

p .01; F2(1, 236) 1.57, p .10]. This was

unexpected, because as noted above, the root type manipu-

lation is meaningless for Root items. However, the effect

was weaker for these items (3 ms and 42 ms 45 ms)

than it was for the Root items (17 ms and 61 ms 78

ms), and both F ratios were less than 1.0 for the correspond-

ing effect on error rates.

262 LEE H. WURM

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system seems to be more flexible than those

models suggest.

The next three analyses assessed the interac-

tions between root type and the main effects of

prefix frequency, root frequency, and prefixlikelihood. Root type (bound vs. free) interacted

with prefix likelihood [Fig. 2: F(1, 1963)

6.13, p .05] and root frequency [Fig. 3: F(1,

1963) 13.51, p .001]. The figures show

high and low values based on median splits, but

these dichotomies were not used in the analyses.

This is merely a convenient way to show the

nature of each interaction. A significant interac-

tion indicates that the slope of the relationshipbetween one independent variable and RT

changes as a function of the other independent

variable (Aiken & West, 1991; Cohen & Cohen,

1983, Tabachnick & Fidell, 1989).

Figure 2 shows that the cost in processing

time for items that are good candidates for de-

composition (by virtue of their high prefix like-

lihoods) is more pronounced if the accompany-

ing root is free rather than bound. As was

suggested in connection with Fig. 1, free roots

tend to pay a price for their meaningfulness; the

exact price depends on whether the carrier item

is a good candidate for decomposition, as de-

termined by high or low prefix likelihood.

Figure 3 shows the interaction between root

type and root frequency. The interaction sug-

gests that any free root will slow down rejection

times, but a more complicated situation holds

for bound roots. First, bound roots never slow

down processing to the same extent that free

ones do. Second, the amount of interference

caused by a bound root is related to that rootsfrequency: the higher the frequency, the more

interference.

One three-way interaction was also signifi-

cant. Figure 4 [F(1, 1960) 8.07, p .01]

shows that the two-way interaction shown in

Fig. 3 depends additionally on prefix likelihood.

One interpretation of this interaction, based on

the results of Wurm (1997) for prefixed real

words with free roots, takes as its starting pointthe assumption that the perceptual system learns

over time to associate high prefix likelihood (in

conjunction with other variables) with success-

ful decomposition. Low prefix likelihood would

therefore signal an item that the perceptual sys-

tem should not be inclined to decompose.

We can understand this interaction by look-

ing at the fastest and slowest RTs. The fastest

RTs were for items that are low on prefix like-lihood and carry bound, low-frequency roots.

These are items that the perceptual system

should be disinclined to decompose because of

the low value of prefix likelihood. In addition,

the roots of these items are bound and low in

frequency. Therefore, these stimuli can be re-


type and root frequency, in milliseconds. Error bars show

1 SEM.


type and prefix likelihood, in milliseconds. Error bars show

1 SEM.


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jected quickly. The slowest RTs were for items

high on prefix likelihood that carry high-fre-

quency free roots. These are items that the per-

ceptual system should be inclined to decom-pose, and the resulting root is easily

recognizable. Pseudowords like this are partic-

ularly difficult to reject.

GENERAL DISCUSSION

One finding of the current study that should

be explored more fully is the significant perfor-

mance disadvantage for Prefix, Root items,

compared to Prefix, Root items. It is hard to

determine whether the roots used in the current

study meet Tafts (1994) underspecified crite-

rion for root recognizability: the mean fre-

quency for free roots was 192 (range 4 to

870), while the mean frequency for bound roots

was 119 (range 0 to 619). For comparison,

people [Tafts (1994) example root] has a fre-

quency of 847. It is also worth noting in this

context that the observed performance disad-

vantage was just as large for roots that are

bound (47 ms, vs. 33 ms for free roots).4 This

may be inconsistent with Tafts (1994) hypoth-

esis, insofar as bound roots in general do not

appear to be as recognizable as free ones. In any

event, one specific part of the pattern predicted

by the prefix-stripping model appears to be in-

correct: genuine roots elevate both RTs anderror rates even for pseudowords that do not

begin with genuine prefixes.

The later version of Shortlist (Norris, Mc-

Queen, & Cutler, 1995) might be able to predict

this effect. The Metrical Segmentation Strategy

of Cutler and Norris (1988) was implemented in

Shortlist to accommodate experimental findings

(e.g., Vroomen & de Gelder, 1997; see also

McQueen, Norris, & Cutler, 1994; McQueen,Cutler, Briscoe, & Norris, 1995). Strong sylla-

bles help determine alignment, which is rele-

vant because roots in the current study were

stressed. If strong syllables are used in deter-

mining alignment between a word candidate

and the stimulus input, and if such alignment is

not absolutely crucial in determining activation,

then Shortlist might indeed predict partial acti-

vation for the root of a Prefix, Root item. Aswith Tafts (1994) account, though, this expla-

nation becomes less attractive when we con-

sider that the root effect held for bound roots,

too. Individual morphemes are not represented

in Shortlist unless they also happen to be words,

so it would be hard to explain the origin of this

effect for bound roots.

Another question addressed by this study was

whether pseudowords with free roots are pro-cessed in the same way as those with bound

roots. At a fairly gross level of analysis, one

finds that the performance data in the current

study were quite similar across root types. How-

ever, it would be premature to conclude any-

thing on the basis of those results alone. The

interactions shown in Figs. 24 and the 65-ms

main effect of root type in the regression anal-

ysis indicate that there is something different

about the processing of the two root types. In

addition, the potentially very interesting inter-

action between prefix status and root type4 These effects may in fact be nearly identical in magni-

tude, because of a small difference in the deviation points

for stimuli in these two conditions (the deviation point is the

point at which a pseudoword diverges from all real words in

the language). If RTs are adjusted to reflect this difference,

the sizes of the root effects for Prefix stimuli become 34

ms for items with bound roots and 35 ms for items with free

roots.


frequency, prefix likelihood, and root type (free or bound),

in milliseconds. PL stands for prefix likelihood. Error bars

show 1 SEM.

264 LEE H. WURM

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for Root items is worthy of further investiga-

tion.

The current study suggests that while bound

roots probably are recognizable entities, their

representations may not be as meaningful or

richly interconnected as those for free roots.The semantic fields normally associated with

lexical entries are essentially empty for bound

roots because they have no clear definitions.

This would predict less computation time for

rejecting an item carrying a bound root, which

is what was found for Prefix, Root

pseudowords.

Tafts (1994) interactive-activation proposal

offers an attractive starting position from whichto explain these data. That model has a level of

representation for bound morphemes (i.e., pre-

fixes and bound roots) and one where all free-

standing words (including polymorphemic

words) are represented. Elements that combine

to make larger words, whether free or bound,

are interconnected. This would predict the

muted effects observed for bound roots; they are

recognizable elements with their own represen-tations, but do not have the same degree of

interconnectedness or combinatorial flexibility

as free elements. A number of different dual-

route models can also accommodate the current

results (e.g., Caramazza et al. 1988; Frauen-

felder & Schreuder, 1992; Laudanna et al.,

1994, 1997; Schreuder & Baayen, 1995; Wurm,

1997).

Future research efforts might use a varietyof strategies to extend what has been learned.

Manipulating the stress pattern of the critical

stimuli in different ways would help deter-

mine whether a model such as Shortlist (par-

ticularly in its second version, which incor-

porates the Metrical Segmentation Strategy

Norris et al., 1995) can be reconciled with

perception data.

Another strategy that may prove useful

would be to use common word beginnings that

are not prefixes. This would help tease apartvarious classes of models. TRACE (McClelland

& Elman, 1986), Cohort (Marslen-Wilson,

1984, 1987; Marslen-Wilson & Welsh, 1978),

and Shortlist (Norris, 1994; Norris et al., 1995)

all predict that common, nonprefix word begin-

nings will have the same processing conse-

quences as prefixes do, because prefixation ef-

fects in those models are essentially cohort

effects. On the other hand, prefix-stripping and

dual-route models predict that there is some-

thing special about prefixes; common word be-

ginnings that are not prefixes will not have the

same perceptual consequences as actual pre-

fixes.

Finally, it should be noted that roots cannot

always be classified as unambiguously as those

used in the current study (e.g., Scalise, 1984;Selkirk, 1982; Siegel, 1979). For example, al-

though English has a free-standing word vent,

many theorists consider it to be a different mor-

pheme than the one found in words like invent

and convent because there is no relationship in

meaning between those words. Taft and Forster

(1975) performed one experiment looking at

this type of root and concluded that the bound

morpheme -vent and the free morpheme ventwere separate entities, stored separately in

memory. It might prove interesting for fu-

ture studies to use not only clearly bound roots,

such as -ceive, but also some of these less clear

cases.

APPENDIX A: CRITICAL PSEUDOWORDS

Prefix Prefix

Root Root Root Root

Stimuli with free roots

adlay [&dleI] adloo [lu] aflay [&f ] afloo

adlead [&dlid] adlod [lAd] udlead [Vd] udlod

adlease [&dlis] adlose [lOUs] aklease [&k ] aklose


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APPENDIX AContinued

Prefix Prefix

Root Root Root Root

adlive [&dlIv] adlave [leIv] odlive [OUd] odlaveadseal [&dsi@l] adseaf [si@f] idseal [Id] idseaf

adstate [&dsteIt] adstote [stOUt] agstate [&g] agstote

cobend [kOUbend] cogend [gend] pobend[pOU] pogend

cobind [kOUbaInd] cobund [bVnd] dobind [dOU] dobund

cocast [kOUk&st] cocaft [k&ft] jocast [dZOU] jocaft

codate [kOUdeIt] codape [deIp] todate [tOU] todape

comix [kOUmIks] cobix [bIks] chomix [tSOU] chobix

coscreen [kOUskrin] coscrone [skrOUn] cooscreen [cu] cooscrone

decap [d@k&p] depap [p&p] pecap [p@] pepap

defit [d@fIt] defot [fAt] sefit [s@] sefot

dejoin [d@dZOIn] depoin [pOIn] doojoin [du] doopoin

depay [d@peI] depoe [pOU] kepay [k@] kepoe

detaste [d@teIst] dedaste [deIst] getaste [g@] gedaste

detreat [d@tSrit] detroot [tSrut] daytreat [deI] daytroot

disact [dIs&kt] diseect [ikt] dosact [dAs] doseect

disbrace [dIsbreIs] disblace [bleIs] kisbrace [kIs] kisblace

disclog [dIsklOg] disclig [klIg] doosclog [dus] doosclig

discook [dIskUk] discoop [kUp] tiscook [tIs] tiscoop

displug [dIsplVg] disklug [klVg] pisplug [pIs] pisklug

distest [dIstest] distesht [teSt] gistest [gIs] gistesht

enclaim [enkleIm] englaim [gleIm] onclaim [An] onglaimenfund [enfVnd] enfunt [fVnt] esfund [es] esfunt

enphrase [enfreIz] enphrooze [fruz] ekphrase [ek ] ekphrooze

enread [enrid] enreat [rit] elread [el] elreat

ensell [ensel] enchell [tSel] oonsell [un] oonchell

ensort [ensOUrt] ensart [sArt] ersort [er] ersart

percount [p@rkaUnt] perpount [paUnt] dercount [d@r] derpount

perjudge [p@rdZVdZ] perjadge [dZ&dZ] terjudge [t@r] terjadge

perlight [p@rlaIt] perlighp [laIp] gerlight [g@r] gerlighp

perplace [p@rpleIs] perprace [preIs] kerplace [k@r] kerprace

persearch [p@rsertS] perfearch [fertS] pensearch [p@n] penfearch

perset [p@rset] persep [sep] pelset [p@l pelsep

preblock [priblAk] preglock [glAk] dreblock [dri] dreglock

prebuild [pribIld] prevuild [vIld] trebuild [tri] trevuild

prechain [pritSeIn] precheen [tSin] plechain [pli] plecheen

prename [prineIm] prenane [neIn] brename [bri] brenane

preprove [pripruv] preprooz [pruz] greprove [gri] greprooz

pretouch [pritVtS] pretaich [teItS] kretouch [kri] kretaich

rebar [r@bAr] redar [dAr] lebar [l@] ledar

rebolt [r@bOUlt] rebalt [bAlt] sebolt [s@] sebalt

recool [r@kul] recoor [kU@r] tecool [t@] tecoor

respeak [r@spik] resteek [

stik] kespeak [k@

] kesteek restress [ristres] restreff [stref] roostress [ru] roostreff

retrust [ritrVst] reprust [prVst] getrust [gi] geprust

transtring [tr&nstrIN] transkring [krIN] tronstring [trOUn] tronskring

transcut [tr&nskVt] transvut [vVt] pranscut [pr&ns] pransvut

transfrost [tr&nsfrOst] transfrest [frest] kransfrost [kr&ns] kransfrest

transprint [tr&nsprInt] transprant [pr&nt] gransprint [gr&ns] gransprant

transtrace [tr&nstreIs] transkrace [kreIs] branstrace [br&ns] branskrace

transword [tr&nswerd] transwurt [wurt] troonsword [truns] troonswurt

unform [VnfOUrm] unforn [fOUrn] ainform [eIn] ainforn

unheat [Vnhit] unkeat [kit] ulheat [Vl] ulkeat

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APPENDIX AContinued

Prefix Prefix

Root Root Root Root

unplay [VnpleI] unploe [plOU] usplay [Vs] usploeunsoak [VnsOUk] unsoat [sOUt] udsoak [Vd] udsoat

unview [Vnviu] unvai [veI] onview [OUn] onvai

unweigh [VnweI] unzeigh [zeI] eenweigh [in] eenzeigh

Stimuli with bound roots

adlect [&dlekt] admect [mekt] aklect [ak ] akmect

adlude [&dlud] adluche [lutS] aflude [&f ] afluche

adnounce [&dnaUns] adnounch [naUntS] odnounce [OUd] odnounch

adstruct [&dstrVkt] adstroct [strAkt] alstruct [&l] alstroct

advince [&dvIns] adzince [

zIns] idvince [Id

] idzinceadvive [&dvaIv] adveve [viv] udvive [Vd] udveve

cofide [kOUfaId] cokide [kaId] pofide [pOU] pokide

cofuse [kOUfius] copuse [pius] tofuse [tOU] topuse

copone [kOUpOUn] copene [pin] dopone [dOU] dopene

coprive [kOUpraIv] coproav [prOUv] cooprive [cu] cooproav

coturb [kOUterb] coturp [terp] choturb [tSOU] choturp

cozert [kOUzert] cozerch [zertS] gozert [go] gozerch

defess [d@fes] dejess [dZes] kefess [k@] kejess

degress [d@gres] degless [gles] tegress [t@] tegless

demit [d@mIt] demip [mIp] gemit [g@] gemip

depel [d@pel] dekel [kel] sepel [s@] sekeldevade [d@veId] dezade [zeId] doovade [du] doozade

devulse [d@vVls] develse [vels] dayvulse [deI] dayvelse

disdict [dIsdIkt] disdect [dekt] gisdict [gIs] gisdect

disfect [dIsfekt] dischect [tSekt] kisfect [kIs] kischect

displode [dIsplOUd] displud [plVd] tisplode [tIs] tisplud

dissume [dIssum] dissule [sul] doossume [dus] doossule

distect [dIstekt] dispect [pekt] pistect [pIs] pispect

distrive [dIstraiV] distroov [truv] dostrive [dAs] dostroov

encise [ensaIs] enfise [faIs] elcise [el] elfise

endain [endeIn] enzain [zeIn] ondain [An] onzain

enpand [enp&nd] engand [g&nd] espand [es] esgand

entain [enteIn] enyain [jeIn] ertain [er] eryain

entract [entSr&kt] entroct [tSrAkt] oontract [un] oontroct

envenge [envendZ] envenche [ventS] ekvenge [ek ] ekvenche

perflect [p@rflekt] perslect [slekt] derflect [d@r] derslect

perpute [p@rpiut] perpite [paIt] terpute [t@r] terpite

persult [p@rzVlt] pervult [vVlt] kersult [k@r] kervult

pervect [p@rvekt] pervoct [vAct] gervect [g@r] gervoct

pervise [p@rvaIz] pervose [vOUz] pelvise [p@l] pelvose

pervolve [p@rvOlv] pervolze [vOlz] penvolve [p@n] penvolze

preject [pridZekt] pregect [gekt] pleject [pli] plegectpreplore [priplOUr] preplere [plI@r] breplore [bri] breplere

preproach [priprOUtS] preproce [prOUs] greproach [gri] greproce

prespect [prispekt] preskect [skekt] krespect [kri] kreskect

prespond [prispAnd] prespond [spOUnd] trespond [tri] trespond

prevert [privert] preverp [verp] drevert [dri] dreverp

recept [r@sept] rechept [tSept] lecept [l@] lechept

reclude [r@klud] reclode [clOUd] rooclude [ru] rooclode

rejunct [r@dZuNkt] repunct [puNkt] gejunct [g@] gepunct

rerupt [r@rVpt] reroopt [rupt] kerupt [k@] keroopt

resorb [r@zOUrb] resork [zOUrk] tesorb [t@] tesork


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APPENDIX B: SEMANTIC TRANSPARENCY AND ROOT FREQUENCY

FOR PREFIX, ROOT PSEUDOWORDS

a

Rated on a 17 scale, for items with free roots only.b Per million tokens, from the CELEX database (Baayen et al., 1993; Burnage, 1990).

Pseudoword

Median semantic

transparencya Root frequencyb

adlay 2 144

adlead 2 555

adlease 2 8

adlive 2 526

adseal 2 18

adstate 2.5 152

cobend 3.5 77

cobind 5.5 26

cocast 5.5 148

codate 5 97

comix 6 133coscreen 4 48

decap 6 44

defit 4.5 151

dejoin 6 158

depay 4 441

detaste 4 22

detreat 5 168

disact 5 717

disbrace 5 49

disclog 6.5 5

discook 4 118

displug 5 16

distest 4 55

enclaim 5 202

enfund 4 70

enphrase 4 50

enread 3 571

ensell 3 153

ensort 4 40

Pseudoword

Median semantic

transparencya Root frequencyb

percount 3 153

perjudge 3 160

perlight 2.5 536

perplace 3 870

persearch 2 236

perset 3.5 347

preblock 5.5 83

prebuild 5.5 453

prechain 4 54

prename 5.5 407

preprove 6 290pretouch 5 120

rebar 4 114

rebolt 7 20

recool 6 79

respeak 6 402

restress 6.5 26

retrust 6 76

transcut 4 251

transfrost 2 16

transprint 4 103

transtrace 2.5 26

transtring 2 5

transword 2 4

unform 5.5 771

unheat 6 168

unplay 5 650

unsoak 5.5 23

unview 4 66

unweigh 5 35

APPENDIX AContinued

Prefix Prefix

Root Root Root Root

revide [r@vaId] revike [vaIk] dovide [dOU] doviketranceive [tr&nssiv] transfeive [fiv] tronceive [trOUns] tronsfeive

tranzide [tr&nzaId] tranzipe [zaIp] granzide [gr&n] granzipe

transcline [tr&nsklaIn] transcrine [kraIn] troonscline [truns] troonscrine

transflict [tr&nsflIkt] transfleect [flikt] bransflict [br&ns] bransfleect

transhort [tr&nshOUrt] transhorp [hOUrp] kranshort [kr&ns] kranshorp

transtinct [tr&nsstiNkt] transpinct [piNkt] pranstinct [pr&ns] pranspinct

uncess [Vnses] unpess [pes] aincess [eIn] ainpess

unfract [Vnfr&kt] unflact [fl&kt] eenfract [in] eenflact

ungest [Vngest ungesk [dZesk] usgest [Vs] usgesk

untrude [Vntrud] ungrude [grud] ultrude [Vl] ulgrude

unvoke [VnvOUk] unveke [vik] onvoke [An] onveke

unzerve [Vnzerv] unzorve [zOUrv] udzerve [Vd] udzorve

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APPENDIX C: PREFIX LIKELIHOOD

AND FREQUENCY

Prefix Prefix likelihood Prefix frequencya

ad- .023 9

co- .010 18de- .008 104

dis- .092 766

en- .092 371

per- .005 484

pre- .013 56

re- .067 1881

trans- .092 47

un- .283 1072

a

Per million tokens, from the CELEX database (Baayenet al., 1993; Burnage, 1990).

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(Received January 12, 1999)

(Revision received August 30, 1999)


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