Journal of Experimental Psychology:

# Difficulty in Learning Similar-Sounding Words: A Developmental Stage or a General Property of Learning?

Bozena Pajak
Duolingo Inc., Pittsburgh, Pennsylvania

Sarah C. Creel and Roger Levy
University of California, San Diego

How are languages learned, and to what extent are learning mechanisms similar in infant native-language
(L1) and adult second-language (L2) acquisition? In terms of vocabulary acquisition, we know from the
infant literature that the ability to discriminate similar-sounding words at a particular age does not
guarantee successful word–meaning mapping at that age (Stager & Werker, 1997). However, it is unclear
whether this difficulty arises from developmental limitations of young infants (e.g., poorer working
memory) or whether it is an intrinsic part of the initial word learning, L1 and L2 alike. In this study, we
show that adults of particular L1 backgrounds—just like young infants— have difficulty learning
similar-sounding L2 words that they can nevertheless discriminate perceptually. This suggests that the
early stages of word learning, whether L1 or L2, intrinsically involve difficulty in mapping similarsounding words onto referents. We argue that this is due to an interaction between 2 main factors: (a)
memory limitations that pose particular challenges for highly similar-sounding words, and (b) uncertainty
regarding the language’s phonetic categories, because the categories are being learned concurrently with
words. Overall, our results show that vocabulary acquisition in infancy and adulthood shares more
similarities than previously thought, thus supporting the existence of common learning mechanisms that
operate throughout the life span.
*Keywords:* word learning, spoken word recognition, nonnative speech perception, second language

acquisition

Humans are able to learn languages throughout their life spans.
But how similar are the learning mechanisms for infants acquiring
their native language (L1) and adults learning a second language
(L2)? Little work has been done to connect these two literatures,
reflecting the underlying assumption of a lack of developmental
continuity in terms of language learning (see, e.g., White, Yee,
Blumstein, & Morgan, 2013, for discussion). Instead, infants and
adults have been assumed to use qualitatively different mecha-

nisms to process and learn languages, largely following the critical
period hypothesis (Lenneberg, 1967; Johnson & Newport, 1989).
However, recent work has shown that while age of L2 acquisition
negatively correlates with achieved proficiency, there are signs of
developmental continuity in language learning and similarities
between infant and adult acquisition (Birdsong, 2009; Birdsong &
Molis, 2001; Hakuta, Bialystok, & Wiley, 2003; Werker & Tees,
2005; White et al., 2013). For example, it has been shown that
infants and adults rely on similar statistical learning mechanisms to
segment words out of a continuous speech stream (e.g., Saffran,
Aslin, & Newport, 1996; Saffran, Newport, & Aslin, 1996)orto
learn phonetic categories (e.g., Maye & Gerken, 2000; Maye,
Weiss, & Aslin, 2008; Maye, Werker, & Gerken, 2002; Pajak &
Levy, 2011; see Pajak, Fine, Kleinschmidt, & Jaeger, in press, for
a review and further discussion), and are similarly affected by
word familiarity during lexical processing of newly learned words
(White et al., 2013). White et al. argued that these parallel results
for infants and adults might reflect common mechanisms that
operate throughout development, thus highlighting the need for
greater interaction between the infant and the adult language
learning literatures.
Herein, we compared infant and adult language learning by

This article was published Online First March 10, 2016.
Bozena Pajak, Duolingo Inc., Pittsburgh, Pennsylvania; Sarah C. Creel,

Department of Cognitive Science, University of California, San Diego;
Roger Levy, Department of Linguistics, University of California, San
This document is copyrighted by the American Psychological Association or one of its allied publishers.Diego.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.Supported by the National Institutes of Health (Training Grants T32-

DC000041 and T32-DC000035) from the Center for Research in Language
at the University of California San Diego and the Center for Language
Sciences at University of Rochester, respectively, and research grants from
the University of California San Diego Academic Senate and the Alfred P.
Sloan Foundation. For their helpful feedback, we thank Eric Bakovic,
Klinton Bicknell, the Computational Psycholinguistics Lab at the University of California San Diego, the audience of LSA 86, and four anonymous
CogSci 2012 reviewers. Rafi Feliciano, K. Michael Brooks, and Hannah
Byers-Straus helped with data collection. We are grateful to Eugene Carsey
for permission to use his photographs of mushrooms (http://www
.eugenecarsey.com/).
Correspondence concerning this article should be addressed to Bozena

## http://www.eugenecarsey.com/ .eugenecarsey.com/).

considering one aspect that is crucial at the initial stages of
acquisition: the encoding of phonetic detail during word learning.
Learning words requires not only remembering a label for a given
referent, but also forming a phonetically rich representation of that
label by segmenting the word into individual sounds. The detailed
phonetic representation is especially important for similarsounding words (e.g., *bin* vs. *pin*), because successful learning
1377

Pajak, Duolingo Inc., 5533 Walnut Street, 3rd Floor, Pittsburgh, PA 15232.
E-mail: bozena@duolingo.com

## E-mail: bozena@duolingo.com mailto:bozena@duolingo.com

---

crucially relies on the ability to distinguish between the words
based on subtle acoustic–phonetic cues. Thus, the learner must be
able to perceptually discriminate the sounds that distinguish between words (e.g., [b] vs. [p]), and ignore any irrelevant variability
between instances of the same phonetic category (e.g., multiple
exemplars of the word *bin*). The ability to discriminate among
similar sounds is thus a necessary condition for successfully learning words distinguished by those sounds. But is it a sufficient
condition? In this article, we investigated this question for adult
learners by taking advantage of the influence of L1 background
(here, Mandarin and Korean) on adult perceptual discrimination
abilities. We examined to what extent the L1-driven differences in
adult speech sound discrimination are associated with differences
in word learning ability, building on a small existing body of work
in this area (Creel & Dahan, 2010; Creel, Aslin, & Tanenhaus,
2006; Silbert et al., 2015).
In the case of infant language learning, we know from the

literature that discrimination does not guarantee successful learning of similar-sounding words: Despite the ability at age 14
months to perceptually discriminate between similar sounds (e.g.,
*b* and *d*), 14-month-olds have been shown to confuse newly
learned words differentiated by those sounds (e.g., *bih* and *dih*
Pater, Stager, & Werker, 2004; Stager & Werker, 1997), unless
there is additional contextual information, or less demanding learning conditions (Ballem & Plunkett, 2005; Fennell, Waxman, &
Weisleder, 2007; Fennell & Werker, 2003; Rost & McMurray,
2009; Swingley & Aslin, 2002; Thiessen, 2007; Yoshida, Fennell,
Swingley, & Werker, 2009). The initial explanation proposed for
this result was a limited resource hypothesis (Stager & Werker,
1997; Werker, Fennell, Corcoran, & Stager, 2002): Because attending to fine phonetic detail while learning new words is computationally very demanding, young infants—who have limited
attentional and cognitive resources—might have difficulty accessing full phonetic detail when focusing their attention on learning
meaning. Other explanations have emphasized the role of increased lexical competition in learning similar-sounding words
(Swingley & Aslin, 2002; Swingley & Aslin, 2007), or suggested
that the difficulty might arise from poorly defined phonetic category boundaries at that stage of infant development (Rost &
McMurray, 2009) and limited experience with phonological categorization (Yoshida et al., 2009).
Regardless of the exact explanation, the consensus is that chil-

dren outgrow this initial difficulty, and by 17–20 months of age
succeed at learning new similar-sounding words (Werker et al.,
2002). However, despite this acquired sensitivity to minimal dif-
This document is copyrighted by the American Psychological Association or one of its allied publishers.
ferences between words in the learners’ L1, phonological similar-
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
ity continues to play a role in lexical processing in both older
children and adults. This is indicated, for example, by robust and
automatic activation of words that sound similar to the target word
(e.g., Andruski, Blumstein, & Burton, 1994; Allopenna, Magnuson, & Tanenhaus, 1998; Magnuson, Dixon, Tanenhaus, &
Aslin, 2007; Mani & Plunkett, 2011; Swingley & Aslin, 2000;
White & Morgan, 2008). Adults are also slower at processing
words that have a high neighborhood density (i.e., have a large
number of similar-sounding words, generally defined as a onephoneme distance; e.g., Luce & Pisoni, 1998) compared to words
in sparse lexical neighborhoods (e.g., Vitevitch & Luce, 1998), and
have increasing difficulty distinguishing phonologically nativelike nonsense words as the word similarity increases (Creel &

Dahan, 2010; Creel et al., 2006). All these results suggest gradient
effects of phonological similarity, where the encoding and the
retrieval of similar-sounding minimal pair words are impaired
relative to dissimilar words. (But see, e.g., Storkel, 2004; Storkel,
Armbrüster, & Hogan, 2006, for evidence that children and adults
learn new dense-neighborhood words in their native language
more readily than new sparse-neighborhood words, suggesting that
partial phonological overlap with known words may help
strengthen newly formed lexical representations.)
Thus, both children and adults are known to have difficulty

learning novel similar-sounding words whose phonological form
resembles their native language. These results do not, however,
answer the question whether adults are affected by phonological
similarity during learning of an *unfamiliar* language, a situation
more parallel to the case of 14-month-old infants learning their
native language.
As we mentioned earlier, it is known that adult L2 learners have

extreme difficulty distinguishing—and therefore also learning—
similar-sounding words that involve novel sound contrasts not
found in their native language, such as *rake* versus *lake* for native
speakers of Japanese (e.g., Escudero, Broersma, & Simon, 2013;
Escudero, Hayes-Harb, & Mitterer, 2008; Hayes-Harb & Masuda,
2008; Weber & Cutler, 2004). In those cases, L2 learners have to
override the L1 phonetic category information that is incompatible
with the L2 information (e.g., the acoustic–phonetic range occupied by the English “r” and “l’ roughly corresponds to a single
category in Japanese; e.g., Miyawaki et al., 1975; see also, e.g.,
Escudero, Simon, & Mulak, 2014, for how orthography may help
or hinder learning in these cases). Indeed, it is already known that,
within a given L1, listeners’ ability to discriminate a nonnative
contrast predicts how well they learn words that differ by that
contrast (Silbert et al., 2015). It is also known that individual
differences in learning may in these cases arise from variability in
purely auditory abilities (Kidd, Watson, & Gygi, 2007), as well as
variability in phonological short-term memory (Silbert et al.,
2015). We know less, however, about listeners’ overall abilities—as a group—to learn similar-sounding words when (a) they
can reliably perceive the perceptual contrast and (b) the L1
phonetic– category information does not strongly interfere with L2
perception (cf. Pajak & Levy, 2014). This is the topic of the
present study. That is, instead of trying to predict *an individual’s*
ability to learn words from that individual’s ability to discriminate
those words, as in prior work (e.g., Silbert et al., 2015), we
investigated the relation between perceptual discrimination and
word learning in listeners as a function of one of two different
native-language backgrounds. We used two native-language populations that we know have complementary expertise in perceptual
discrimination: Mandarin speakers, who are sensitive to nonnative
sibilant place-of-articulation distinctions, and Korean speakers,
who are sensitive to nonnative consonant length distinctions (Pajak
& Levy, 2014).
In particular, we examined two specific questions. First, is the

mismatch between discrimination and word learning a developmental phenomenon, or is it driven by the information being
learned? That is, will adult L2 learners—who, like infants, are
concurrently learning the language’s phonological categories, but
unlike infants, have vastly greater working memory capacity—
show greater difficulty in word learning tasks relative to discrimination tasks when acquiring new L2 vocabulary? Second, how does phonological similarity moderate discrimination versus learning of similar-sounding L2 words?
Answers to these questions for the two different L1 populations

we studied can potentially provide a key missing link connecting
theories of adult and infant language learning. If adults of
particular L1 background—just like 14-month-old infants—are
found to have difficulty learning similar-sounding words that they
can nevertheless discriminate perceptually, then this would provide evidence in favor of the existence of common language
learning mechanisms that operate throughout development and
into adulthood. Furthermore, a more detailed examination of the
role of phonological similarity in word discrimination and learning, and how similarity interacts with different task demands, can
help us shed more light on the nature of those common mechanisms that underlie language learning.
Before we continue, however, we first examine factors that might

contribute to the difficulty of learning the correct label/referent pairing. One such factor is that beginner learners, both infant and adult,
might have noisy phonetic representations, reflecting low confidence
in the fidelity of phonetic encoding of the newly learned words or in
the exact location of phonetic category boundaries (Rost & McMurray, 2009; Yoshida et al., 2009). Another factor might involve taskspecific memory limitations, which pose particular challenges for
highly similar-sounding words. In the next section, we describe in
detail how these factors might lead to potential difficulty in word
learning relative to discrimination.

## A Conceptual Model of Word–Referent Mapping Difficulties Learners acquiring words need to rely on their memory repre-

sentations of label/referent pairs, where each label can be described as a sequence of sounds sampled from the language’s
phonetic categories. Precise encoding of the label’s phonetic form
thus requires establishing from which categories the sounds were
sampled, a difficult task at the early stages of language learning.
Word learning is then likely affected by two sources of noise: (a)
noise and uncertainty associated with categorization of each individual sound and (b) noise associated with retrieving a memory
trace of the phonetic input and of the label/referent pairing. Thus,
one way of thinking about the difference between the discrimination and the word learning tasks is that the quality of phonetic
representations for individual input exemplars is lower for word
learning than for discrimination due to heavier long-term memory
demands in the word learning task: In word learning, listeners have
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to simultaneously keep track of the referent and try to form
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phonetic representations, while a discrimination task only requires
comparing short-term memory traces of phonetic input, without
the need to link them to referents. As more data are obtained over
time, successful word learning requires integrating multiple memory traces to arrive at the correct label/referent pairing. This would
correspond to narrowing the effective variance around the memory
representation of the label’s phonetic form.
In cases when the words being learned are composed of highly

dissimilar sequences of sounds—that is, the phonetic distance
between sound categories is large relative to the variance of the
label’s phonetic form—the learner’s performance should not be
impeded because a small number of samples would be sufficient to
learn the distinctions among the categories. However, when the

words are highly similar—that is, the phonetic form variance is
large relative to the distance between sound categories—it should
be much harder for learners to separate the sounds into categories
and pinpoint the right label/referent pairings. It is expected that
much more data (i.e., more learning instances) would be needed in
this case before learners can accumulate a sufficient number of
exemplars to learn phonetic category distinctions and form correct
label/referent mappings. Furthermore, if the learner is unable to
integrate the information accumulated from a number of exemplars
of the label/referent pairing, he or she will have great difficulty
learning the pairing reliably at all.
This conceptual model is consistent with prior suggestions that

additional cognitive load, such as simultaneous presentation of
visual stimuli, lowers the resolution of auditory processing of
phones (Mattys & Palmer, 2015). Such lower resolution processing may be due to missing some temporal pulses in the auditory
signal (Casini, Burle, & Nguyen, 2009) or to reduced cochlear
sensitivity (Lukas, 1980; Puel, Bonfils, & Pujol, 1988) during
concurrent attention to visual stimuli. Interestingly, perceptual
sensitivity seems to linearly decrease as the effort involved in the
simultaneous visual task increases (Mattys, Barden, & Samuel,
2014). This type of disruption in auditory processing may be
understood as an increased tolerance to imprecise acoustic encoding, and, as a result, to an increased perceptual overlap between
similar-sounding phones (Mattys et al., 2014; Mattys & Palmer,
2015).

## The Current Study In the current study, we examined how adults learn vocabulary

in a new language that is phonologically unfamiliar (i.e., an L2),
but composed of discriminable speech–sound categories. In particular, we compared two populations of participants with differential perceptual sensitivities to certain speech sound contrasts that
are due to their different L1 backgrounds. We tested one participant group on a discrimination task and another group on a word
learning task, and examined whether the known L1-backgrounddriven differences in sound discrimination would also be observed
in the word learning task when participants learned words that
differed by those sounds (as described in more detail below). The
situation of learning phonologically novel words that are similar
sounding, but that adults can nevertheless discriminate perceptually, is analogous to the situation of 14-month-old infants observed
in Stager and Werker (1997). This allowed us to assess whether the
good-discrimination-without-learning pattern observed in Stager
and Werker (1997) reflects a purely developmental phenomenon
or reflects general mechanisms of (language) learning. In addition,
we included multiple sets of word pairs that differed in their degree
of similarity, which let us investigate how phonological similarity
modulates discrimination and learning of L2 words.
We constructed a miniature language with pairs of words at

three levels of similarity: (a) *dissimilar* (e.g., [tala]–[kenna]), (b)
*similar* (e.g., [tala]–[taja]), and (c) *highly similar*, where the words
differed either in consonant *length* (e.g., [taja]–[tajja]) or in *place*
of articulation between alveolopalatal and retroflex sounds (e.g.,
[got a]–[got a]). We chose the *length* and the *place* dimensions
because they have been shown to be differentially discriminable by
two different L1-speaker populations: L1 Korean and L1 Mandarin
(Pajak, 2012; Pajak & Levy, 2014). In particular, Korean speakers have an advantage over Mandarin speakers in discriminating consonant length contrasts, while Mandarin speakers have an advantage over Korean speakers in discriminating alveolopalatal and
1
retroflex consonant contrasts. Therefore, we were able to investigate whether these differential L1-based perceptual advantages
on *highly similar* word pairs occurred not only in discrimination
but also in word learning. Note that we were not asking whether
performance on the discrimination task *predicted* performance in
the word learning task at the individual level. Rather, we investigated whether between-groups differences in discrimination ability
arising from differences in native language would also be reflected
in between-groups differences in word learning ability. There
might certainly be some individual variation in how well learners
take advantage of their L1-based perceptual abilities when learning
words, but the group-level comparisons reveal the overall trends in
the population as a whole, and this is the question we addressed
here. We did not expect differences in word learning performance
between L1-Korean and L1-Mandarin participants for *dissimilar*
and *similar* items because those contrasts were acoustically more
salient (relative to the contrasts in *highly similar* pairs) and there is
no reason to believe that participants’ language background would
affect their discrimination in a differential way. Crucially, the
language was phonologically novel to all participants in that all
phonetic properties of the stimuli (e.g., voice onset time, vowel
quality, stress) were taken from an unrelated language, Polish.
Therefore, this scenario was more comparable to the situation of
the 14-month-old infants than many previous studies in which
adults learned phonologically native-like words.
We had two main sets of predictions. The first concerned the

overall effect of phonological similarity on discrimination versus
learning of L2 words that are all discriminable by learners, either
because the differences are salient (*dissimilar*, *similar*) or because
a related distinction is used in the learners’ L1 (*highly similar*
*length* for Korean, *place* for Mandarin). Given the gradient acoustic similarity between the different sets of word pairs, we expected
discrimination also to be gradient: best for *dissimilar* words, intermediate for *similar* words, and poorest for *highly similar* words.
As for word learning, prior work using native-like words has
shown gradience in performance as a function of words’ phonological similarity (e.g., Creel & Dahan, 2010; Creel et al., 2006).
But how does phonological similarity interact with word learning
in the case of learning a new language with overall nonnative
phonology? If it works similarly to learning vocabulary in an L1
phonological system, then we would expect learners’ performance
to change as a function of similarity between the word pairs,
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matching the discrimination performance: best when identifying
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the referent in the context of two *dissimilarly* named possible
referents, intermediate for *similarly* named referents, and poorest
for *highly similar* pairs. On the other hand, it is possible that
learning words with an unfamiliar phonology is not affected by
similarity in the same way that discrimination is. That is, we might
expect a mismatch between the discrimination versus the word
learning task: For example, gradient performance for participants
in the discrimination task, but no differences in performance for
participants identifying word referents; or more exaggerated gradient effects in one task than the other.
Our second set of predictions concerned *highly similar* words.

These predictions are in some sense a more focused version of the
first set of predictions, because both are examining the influence of

perceptual *similar*ity. However, here we focused on the highly
similar words whose underlying speech sound categories are
acoustically overlapping, thus being most comparable to infants’
nascent speech sound categories. More specifically, we compared
two populations of speakers with differential perceptual sensitivities to these word differences: Korean speakers better on *length*
and Mandarin speakers better on *place*. We made the Koreanversus Mandarin-speaker comparison for two different tasks, word
learning and discrimination, where each task was completed by
independently recruited subjects. Therefore, we tested word learning versus discrimination of *highly similar* words that either (a)
were relatively easily discriminable because the differences were
based on a phonetic dimension informative in L1 (even though the
learners did not actually know any words distinguished by some of
the specific sounds used, thus resembling the situation of young
infants) or (b) were not easily discriminable because the differences were based on a phonetic dimension not informative in L1.
The contrasts in (a) were *length* for Korean speakers and *place* for
Mandarin speakers, while the contrasts in (b) were *place* for
Korean speakers and *length* for Mandarin speakers.
We expected the word learning task to be harder than the

discrimination task, which would be reflected in overall worse
performance for the group performing the word learning task.
However, overall task differences do not tell us about participants’
*use* of their native-language-based perceptual abilities in word
learning. The goal of our discrimination task was twofold: (a) to
replicate prior results (Pajak, 2012; Pajak & Levy, 2014) with
materials more comparable to our word learning task materials,
and (b) to show how much perceptual advantage Korean speakers
have over Mandarin speakers in *length* contrasts, and how much
perceptual advantage Mandarin speakers have over Korean speakers in *place* contrasts. Comparing the *degree* of this group-level
asymmetry in the discrimination versus the word learning tasks let
us assess how much participants use their L1-based perceptual
abilities in word learning. Therefore, we expected both Korean and
Mandarin speakers to perform worse on *highly similar* trials in
word learning than in discrimination (i.e., overall task differences).
However, the critical question was: Is the relative difference between the two L1 populations (Korean better at *length*; Mandarin
better at *place*) also observed in the word learning task? If it was,
then it would suggest that L1-based perceptual abilities are used in
word learning (whether or not the overall performance was lower
than in discrimination). If it was not, then it would suggest that the
word learning task makes it difficult for participants to use their
L1-based perceptual abilities. The extreme version of the latter
would be no group-level difference between Korean and Mandarin
speakers on *length* or *place* in the word learning task, showing that
*the whole* relative perceptual advantage observed at the group level
has been eliminated during word learning. It is also the latter case
(no use of perceptual advantages evident during word learning)
that would be most analogous to the results reported for L1-
learning 14-month-olds.

Pajak and Levy (2014) argued that these differential perceptual sensitivities follow from the fact that Korean has some length distinctions, while
Mandarin has none, whereas Mandarin has some alveolopalatal and retroflex sounds, while Korean only has alveolopalatal but no retroflex sounds
(Lin, 2001; Sohn, 1999).

---

## Method

## Participants

Ninety undergraduate students at the University of California
San Diego participated in the experiment for course credit or
payment. Half were speakers of Korean and other half were
speakers of Mandarin. We recorded participants’ language background information, including self-reported proficiency in both
Korean/Mandarin and English, current language exposure, as well
as the scores from the Shipley Vocabulary Test (Shipley, 1967)as
a measure of English proficiency. Participants varied in length of
residence in the United States: some were born in the United
States, while others immigrated at some point after birth or were
international students who arrived very recently. Consequently,
they varied in English proficiency. However, they all learned
Korean or Mandarin from birth, reported high proficiency in those
languages, and still used them regularly, predominantly with family. In most cases, they had some high school and/or college
exposure to Spanish or French. Some Mandarin speakers were also
familiar with Taiwanese Hokkien, mostly through family exposure. No exposure to any other languages was reported, including
Polish, the language that provided source material for novel words.
All participants reported no history of speech or hearing problems. We collected individual measures of participants’ nonverbal IQ using the Matrices subtest of the Kaufman Brief
Intelligence Test (Kaufman & Kaufman, 2004). All participant
characteristics are shown in Table 1. More detailed comparisons between participants depending on language background
are provided in the Appendix A.
To avoid potential carryover effects from one task to the other,

we tested discrimination and word learning in a betweenparticipants design, investigating whether between-L1-population
differences in discrimination ability arising from differences in
native language would also be reflected in between-L1-population
differences in word learning ability. Task cross-contamination in a
within-subjects design would be a serious barrier in interpreting
the results: whichever order of tasks we might choose, participants
would be biased in the second task because (a) their attention
would be directed to the tested contrasts and (b) they would have
received a great deal of perceptual exposure to those distinctions.
In fact, there is ample evidence from the perceptual learning
literature that even relatively brief exposure can affect adults’
perception (e.g., Clayards, Tanenhaus, Aslin, & Jacobs, 2008;
This document is copyrighted by the American Psychological Association or one of its allied publishers.Kraljic & Samuel, 2005; Norris, McQueen, & Cutler, 2003). This
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.design choice is analogous to infant studies, where the results
regarding the dissociation of discrimination and word learning
abilities are based on differences between groups.
Fifty-four participants were assigned to the word learning task

2
and 36 to the discrimination task. In each group, half were
speakers of Korean and the other half were speakers of Mandarin.
Comparing participants assigned to the discrimination versus the
word learning task revealed no significant differences in any of the
measures we collected (see Table 1).

contrasts tested by Pajak, 2012; Pajak & Levy, 2014). The words
were constructed such that there were eight minimal pair words
differing only in the middle consonant; these were the *highly*
*similar* word pairs. The complete list of trial types is described in
the Procedure section. More specifically, the minimal pairs differed either in *length* (a short vs. a long middle consonant) or *place*
of articulation (an alveolopalatal vs. a retroflex sibilant consonant
3
in the middle position). The materials were constructed using the
sound inventory and other phonological properties of Polish, and
were recorded by a phonetically trained Polish native speaker.
The inventories of Korean and Mandarin include some sound

materials consisted of 16 bisyllabic consonant–vowel–
consonant-vowel (CVCV) nonce words (see Table 2; a subset of

distinctions along the dimensions of *length* and *place*, respectively,
that are similar but not identical to the distinctions used in the
experiment. Korean employs the dimension of *length*, distinguishing between short and long sounds, but not the dimension of *place*
We found previously that Korean speakers are better than Mandarin speakers at discriminating consonant *length* contrasts, while
Mandarin speakers are better than Korean speakers at discriminating alveolopalatal versus retroflex *place* contrasts (Pajak, 2012;
Pajak & Levy, 2014). This follows from the fact that Korean has
some length distinctions, while Mandarin has none; whereas Mandarin has some alveolopalatal and retroflex sounds, while Korean
does not (Sohn, 1999; Lin, 2001).
More specifically, Korean uses length distinctions mostly on

## Materials The

vowels (e.g., [pul] “fire” vs. [pu:l] “blow”), but some long consonants ([ll], [nn], [mm]) arise from phonological assimilation pro-

The difference in the number of participants in the two tasks was due
to the fact that we have had more experience with discrimination experiments, and so we had a better sense of how many participants we would
need to obtain good statistical power. Studying word learning in this type
of task was relatively novel to us, and we expected more between-subjects
variability, which is why we decided to collect data from more participants.
<sup>3</sup>
We chose the middle consonants in our stimuli in such a way that half

of the corresponding sound distinctions had their analog in the listener’s
L1, and the other half did not. This was done to compare performance
between distinctions that were relatively familiar to our participants from
their L1s versus completely unfamiliar distinctions that yet varied along
familiar dimensions. In previous work on *length* and *place* discrimination
by Korean and Mandarin speakers (Pajak, 2012; Pajak & Levy, 2014),
Korean speakers outperformed Mandarin speakers on discriminating all
*length* contrasts, whether familiar or not, while Mandarin speakers outperformed Korean speakers on discriminating all *place* contrasts (note that the
stimuli in that study were also based on the Polish *length* and *place*
contrasts). However, there was a trend in that earlier study for both groups
to perform slightly better at the distinctions familiar from their L1s compared to the unfamiliar distinctions that varied along familiar dimensions
(e.g., familiar [m]–[mm] unfamiliar [j]–[jj] for Korean speakers). Therefore, we expected that a similar difference might hold in a word learning
task: that is, both Korean and Mandarin speakers would be better at
learning similar-sounding words that included familiar categories than
those that included unfamiliar categories which nevertheless varied along
a familiar dimension. (See Table 2 for the list of word contrasts based on
(a) familiar categories, where the specific distinction exists in Korean/
Mandarin, and (b) unfamiliar category contrasts, but familiar phonetic
dimensions.) To obtain sufficient power for such a comparison, but at the
same time keep the total number of words relatively small to ensure their
learnability in a single experimental session, we decided to focus our
analysis on *length* words (12 words in total), and included a much smaller
number of *place* words (four words in total). Length was chosen as the
dimension of main interest because length contrasts are possible for many
more types of segments than the alveolopalatal versus retroflex contrasts
(of which Polish only has four). No difference between familiar categories
versus unfamiliar category contrasts but familiar dimension was borne out
in the current results.

---

Table 1
*Individual Measures: Participants in the Discrimination vs. the Word Learning Task*
Discrimination task

|  | Discrimination task participants |  | Word learning task participants |  |  |
| --- | --- | --- | --- | --- | --- |
| Measure | M | SD | M | SD | t test |
| Age (years) | 20 | 1.6 | 21 | 2 | t(84.1)=-1.23,p=.22 |
| L1 proficiency: speakinga | 8.1 | 1.7 | 7.8 | 1.9 | |t|＜1 |
| L1 proficiency: understandinga | 8.3 | 1.6 | 8.3 | 1.5 | |t|＜1 |
| % time current L1 exposure | 33 | 19 | 30 | 19.3 | |t|＜1 |
| Age when regular English exposure began | 6.3 | 4 | 5.5 | 4.1 | |t|＜1 |
| Age of arrival in United States | 8.6 | 7 | 7.5 | 7.5 | |t|＜1 |
| Length of residence in United Statesb | 11.6 | 7.6 | 13.1 | 6.7 | |t|＜1 |
| English proficiency: speakinga | 8.1 | 1.7 | 8.3 | 1.9 | |t|＜1 |
| English proficiency: understandinga | 8.4 | 1.4 | 8.8 | 1.5 | t(78.8)=-1.24,p=.22 |
| English vocabulary test (% correct) | 72 | 10.8 | 72 | 13.7 | |t|＜1 |
| % time current English exposure | 66 | 19.1 | 69 | 19.5 | |t|＜1 |
| Nonverbal IQ test (% correct) | 88 | 7 | 88 | 7.8 | |t|＜1 |

Note.L1 native language.
<sup>a</sup>
On a 0 –10 scale (0 *none*

and 10 perfect). If born in the United States, coded as 0.

cesses (Sohn, 1999), and Korean tense obstruents ([p=], [t=], [k=],
[s=], [t =]) have sometimes been analyzed as long (Choi, 1995). In
terms of place of articulation, Korean has some alveolopalatal
sounds ([ ], [t ]), but no retroflex sounds, thus lacking the *place*
contrast as defined in this article.
Mandarin, on the other hand, has both alveolopalatal and retro-

$$
[\mathfrak{s}^{\prime}],[\mathfrak{t}\mathfrak{c}^{\prime}]\big)
$$

flex sounds that are distinguished by spectral shape in the frication
noise (the *place* dimension), but does not use the *length* dimension.
In particular, Mandarin has voiceless alveolopalatals ([ ], [t ]) and
retroflexes ([ ], [t ]) as allophones of the same phonemic category.
In addition, the voiced retroflex fricative ([]) is a betweenspeaker variant of the retroflex approximant ([ ]). Other voiced
sibilants are assumed to be absent because Mandarin has obstruent
distinctions in aspiration, not in voicing (Lin, 2001). Note, however, that the analogous *place* distinction in Polish, which we used
in the stimuli, is not exactly the same as that in Mandarin, differing
somewhat in the placement of the tongue tip.
Note that all participants spoke American English, where *length*

and alveolopalatal versus retroflex *place* are not used contrastively. While vowel length varies in English, it correlates with
other cues (e.g., the tense–lax distinction), and native speakers of
English identify vowels relying predominantly on spectral properties (e.g., Hillenbrand, Clark, & Houde, 2000). Long consonants
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are sometimes attested, but only at morpheme boundaries (e.g.,
*dissatisfied*; Benus, Smorodinsky, & Gafos, 2003), and only produced as long by some speakers (Kaye, 2005). English has neither
alveolopalatal nor retroflex obstruents, although some speakers
produce the alveolar approximant [ɹ] as retroflex (Ladefoged &
Maddieson, 1996; Westbury, Hashi, & Lindstrom, 1998). While it
is possible that knowledge of English might affect discrimination
of the Polish *length* and *place* contrasts (and, in particular, help
with the *length* contrasts), Pajak and Levy (2014) found no evidence to support that hypothesis when testing discrimination of
these contrasts by a variety of bilingual listeners (English–Korean,
English–Vietnamese, English–Cantonese, and English–Mandarin),
whether their dominant language was English or their native
language.
The materials were recorded in a soundproof booth by a pho-

netically trained native speaker of Polish. There were 10 tokens
recorded for each word. For *length* words, two tokens of each word
with long consonants were chosen for the experiment. Subsequently, words with short consonants were created by shortening
the tokens with long consonants in a way that, for each word and
each recording, the naturally recorded long consonant was reduced
to half its duration so to maintain a constant 2:1 duration ratio
(cross-linguistically, the long-to-short consonant ratio varies be-

Table 2
*Stimuli (in International Phonetic Alphabet)*
<u>Length</u> <u>words</u>

| Length words |  |  | Place words |  |  |
| --- | --- | --- | --- | --- | --- |
| Short | Long | Specific distinction exists in Korean? | Alveolopalatal | Retroflex | Specific distinction exists in Mandarin? |
| tala | talla | Yes(?)a,b |  |  |  |
| kema | kemma | Yesb | gotça | gotşa | Yesc |
| kena | kenna | Yesb |  |  |  |
| diwa | diwwa | No,but familiar dimension |  |  |  |
| difa | diffa | No,but familiar dimension | goz a | goz a | No,but familiar dimension |
| taja | tajja | No,but familiar dimension |  |  |  |

$$
\ {\mathrm{Y e s\ (})^{\mathrm{a,b}}}
$$

$$
\mathrm{Y e s^{b}}
$$

$$
\mathrm{Y e s^{c}}
$$

$$
\mathrm{Y e s^{b}}
$$

general distinction exists, the exact place of articulation in Mandarin differs for both the alveolopalatal and the retroflex, and the distinction is only
allophonic.

---

tween 1.5 and 3; Ladefoged & Maddieson, 1996). For place words,
given that the alveolopalatal versus retroflex distinction is intrinsically already very subtle, even in natural speech (Nowak, 2006),
we used natural recordings of both alveolopalatals and retroflexes
with no additional manipulations. Two tokens each were chosen
for the experiment with the goal of maximizing the similarity
between the words in minimal pairs with regard to how vowels
were pronounced, but at the same time choosing tokens with
clearly enunciated sibilants. This was a departure from how the
stimuli were constructed by Pajak (2012) and Pajak and Levy
(2014), where both alveolopalatals and retroflexes were spliced
into an identical word frame. Pajak and Levy’s procedure removed
one of the cues to the contrast (the formant transition into the
following vowel), thus making it extremely subtle. In the current
study, we left this cue intact so that stimuli in the word learning
task were not overly difficult.
These auditory stimuli were used for both the discrimination and

the word learning task. For the word learning task, each word was
paired with a picture of a different kind of mushroom (see two
examples in Figure 1), which were chosen to include objects
unfamiliar to our participants, but not so unfamiliar that participants would find them bizarre and hard to remember. We selected
pictures that varied in shape and color so to maximize visual
differences between them. We created four different one-to-one
word-to-picture mappings that were counterbalanced between participants to ensure that the results were not driven by any peculiarities in the mappings we chose.

## Procedure

Participants sat in front of a computer and responded by using a
mouse. They were instructed that, in this experiment, they would
be listening to a novel language, and, specifically, either (a) learn
to distinguish this language’s sounds (in the discrimination task) or
(b) learn the language’s words for different types of mushrooms
(in the word learning task). The experiment was completed in a
single session, and each participant took part in only one of the
tasks. The discrimination and the word learning tasks were made
equal in the total auditory exposure to each stimulus in order to
keep them as parallel as possible.

## Discrimination Task Discrimination was tested in an ABX task. In each trial, three

words were presented auditorily through headphones: A [500 ms]
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1 1 2
whether X sounded more like A or more like B. As indicated by
subscripts, the X word was always acoustically different (i.e., a
physically different recording) from both A and B words to ensure
that the matching of X onto A or B was not based on pure
acoustical identity of two tokens. This procedure differed from that
in Pajak (2012) and Pajak and Levy (2014), where an AX task was
used. In this study, however, we wanted to maintain a close
parallel between the discrimination and the word learning tasks,
which was achieved with the ABX procedure. The AB word order
was counterbalanced, and the trial order was randomized for each
participant. There were four blocks, each with 64 trials and lasting
about 5 min. Note that this meant that each block included exposure to 192 words (64 trials 3 words per trial). Blocks were
separated by self-terminated breaks. There were four types of trials
depending on the AB contrast, as illustrated in Table 3: (a) *dis-*
*similar* word pairs (e.g., [tala]–[kenna]), which differed in all
sounds but the last vowel (16 trials; 8 AB word pairs 2 trials:
one trial with X A and one trial with X B), (b) *similar* word
pairs (e.g., [tala]–[taja]), which shared the initial CV sequence, but
the middle consonants differed along multiple phonetic dimensions (16 trials; 8 AB word pairs 2 trials: one trial with X A
and one trial with X B), (c– d) *highly similar* word pairs (e.g.,
*length*: [tala]–[talla] or *place*: [got a]–[got a]), where the initial
CV sequence was identical and the middle consonants differed
minimally, either in *length* or in *place* (32 trials per block: 8 AB
word pairs 4 trials: two trials with X A and two trials with
X B).
**Word Learning Task**

## In the word learning

task, participants learned to associate
auditorily presented words (one word per trial) with pictures of
mushrooms. There were four training blocks (each with 128 trials,
about 10 –15 min long) and four testing blocks (each with 64 trials,
about 5 min long), interleaved. Thus, each train plus test combination contained 128 training trials 64 test trials 192 auditory
exposures to the words, the same number of exposures as a block
of the discrimination task (64 trials 3 words per trial 192).
Blocks were separated by self-terminated breaks. In each trial, two
pictures were presented on a computer screen (see Figure 1), and
after a delay of 500 ms, a word was played through headphones.
Participants were asked to click on the picture that they thought
went with the word. In training, feedback was provided after the
response in the form of the correct picture staying on the screen. A
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Figure 1. Example of a screen shot from the word learning task. See the online article for the color version of
this figure.

---

Table 3
*Trial Types in Discrimination (AB Words Presented*
*Auditorily) and Testing in Word Learning (AB Labels for*
*Visually Presented Pictures)*
*Dissimilar Similar Highly similar*

| Dissimilar | Similar | Highly similar |
| --- | --- | --- |
| AB | AB | AB |
| tala-kenna | diwa-difa | diwa-diwwa(length) |
| talla-goz a | diwwa-diffa | difa-diffa(length) |
| taja-gotça | taja-tala | taja-tajja(length) |
| tajja-kema | tajja-talla | tala-talla(length) |
| diwa-kemma | kema-kena | kema-kemma(length) |
| diwwa-goz a | kemma-kenna | kena-kenna(length) |
| diffa-kena | gotşa-goz a | gozа-goz a(place) |
| difa-gotşa | gotşa-goz a | gotşa-gotşa(place) |

Note. For each pair, both orders of presentation were tested.

mouse click triggered the start of the next trial. Because participants were learning via feedback presented after each response,
early responses were necessarily random. Participants were told to
guess at first, and that through feedback they would eventually
learn the correct word-to-picture mappings. In testing, no feedback
was provided.
The training trial types consisted of picture pairs that were

always associated with *dissimilar* word pairs (e.g., [taja]–[diwa],
[got a]–[kemma]; see Table 3) so that participants were not directly alerted to the distinctions of interest. Each word was played
eight times per training block (8 16 words 128 total), and
each time it was accompanied by a different set of two pictures.
None of the training picture pairings appeared in later testing.
The Testing trial types always differed from the training trials,

and were completely analogous (in form and number) to trials in
the discrimination task, as illustrated in Table 3. Specifically, each
picture pair in the word learning task test was an analog of an AB
word pair in the discrimination task, and the auditorily presented
word in the word learning task corresponded to the X word in the
discrimination task. This meant that there were the following trial
types: (a) *dissimilar* picture pairs (e.g., picture of [tala] and picture
of [kenna]) (16 trials; 8 picture pairs 2 trials: one trial where the
auditorily presented word corresponded to the picture on the left,
and one trial where the auditorily presented word corresponded to
the picture on the right), (b) *similar* word pairs (e.g., picture of
[tala] and picture of [taja]) (16 trials; 8 picture pairs 2 trials: one
trial where the auditorily presented word corresponded to the
picture on the left, and one trial where the auditorily presented
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word corresponded to the picture on the right), (c– d) *highly similar*
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(e.g., *length*: picture of [tala] and picture of [talla]; or *place*
picture of [got a] and picture of [got a]) (32 trials per block; 8
picture pairs 4 trials: two trials where the auditorily presented
word corresponded to the picture on the left, and two trials where
the auditorily presented word corresponded to the picture on the
right).
Picture position was counterbalanced for both training and test-

ing trials. Trial order was pseudorandomized: we created four
randomized lists and then altered them manually so that the same
word was never repeated in two consecutive trials. Furthermore,
minimal pair trials were always separated by at least two other
trials. Each participant heard each list once, with a different list for
each block. Block order was counterbalanced across participants.

## Results

## We investigated two main questions in our data analysis, based

on the two sets of predictions that guided our study’s design. First,
how does phonological similarity moderate discrimination versus
learning of similar-sounding words in an L2 with clearly nonnative
phonology? Second, when testing phonologically highly similar
word pairs, is an L1 background that confers good discrimination
sufficient for good learning of similar-sounding words in adult L2
learners, or is there a disconnect between discrimination and word
learning like that observed in infant L1 learners (Stager & Werker,
1997)?
To answer these questions, we analyzed accuracy scores from

both discrimination and testing in word learning with mixedeffects logit models (Jaeger, 2008). Following Barr, Levy, Scheepers, and Tily (2013), who recommended maximal random-effects
structures for mixed-effects models as best practice, we included
random intercepts for participants and items, and random slopes
for participants and items for all effects of interest (including
interaction effects) that were respectively manipulated within par-
4
ticipants or within items. There was no stepwise model selection.
We controlled for participants’ nonverbal IQ and L1 proficiency
and use—through a combined score of (a) proficiency of L1 in
speaking, (b) proficiency in L1 understanding, and (c) the percentage of time of current L1 exposure— by including them as fixed
5
effects in the models. All the binary and continuous predictor
variables were centered; three-level variables were coded using
successive differences contrast coding. The reported *p* values came
from the *z* statistic. For *highly similar* trials, the difference between
familiar categories and familiar dimensions only (as shown in
Table 2 and discussed in footnote 5) was not significant in either
task, so we did not report it in the analysis.

## Gradient Phonological Competition in L2 Learning? We begin by addressing the second question concerning the

effects of phonological similarity on discrimination and learning of
L2 words. We analyzed trials from both discrimination and word
learning tasks for word pairs that were all expected to be *discrim-*
*inable* by learners due to their familiarity with the tested sound
contrasts from their L1s. That is, the analysis included the following trials: *dissimilar* (e.g., [tala]–[kenna]), *similar* (e.g., [tala]–
[taja]), and a subset of *highly similar* pairs, depending on participants’ L1: *length* (e.g., [tala–talla]) for Korean speakers and *place*
(e.g., [got a]–[got a]) for Mandarin speakers. Crucially, we withheld from this analysis the following *highly similar* pairs: *place* for
Korean speakers and *length* for Mandarin speakers, which, based
on prior work, we knew would not be easily discriminable by the
learners due to their native language backgrounds. These trials
were analyzed separately, and the results are reported in the next
section, where we address the main question regarding the discrimination versus word learning performance.

analysis of variance procedures.
<sup>5</sup>
We checked the fit of our two main models (Task Trial Type

Language and Task Feature Type
and the L1 proficiency/use factors. InLanguage) with and without the IQ both cases, adding these factors
significantly improved the model fit (*p*s.05); the other effects remained
unaffected across the models.

---

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Based on prior work, we expected discrimination to be gradient:
best for *dissimilar* words, intermediate for *similar* words, and
poorest for *highly similar* words. Of most interest was the comparison between discrimination and word learning in order to
evaluate how phonological similarity moderates performance
across different tasks.
The results are illustrated in Figure 2 (see Appendix Figure B1

for the results by block). We began the analysis by evaluating a
model with fixed effects of Task (*discrimination*, *word learning*
Trial Type (*dissimilar, similar, highly similar*), and Language
(*Korean, Mandarin*). Trial Type was coded with *similar* trials as
the reference level so that we would be able to directly compare
*similar* and *dissimilar* trials, as well as *similar* and *highly similar*
trials. As expected, there was a significant effect of Trial Type in
that accuracy, pooled across the discrimination and the word
learning tasks, varied in accordance to the similarity between
words: the responses on *dissimilar* trials were significantly higher
than on the *similar* trials (*p*.001), which in turn were higher
than *highly similar* trials (*p*.001).
Furthermore, there were significant Task Trial Type interac-

tions (*p*s.001; note that there were two interaction terms due to
the contrast coding of Trial Type), suggesting that accuracy on
each type of trial was moderated by the task: discrimination versus
word learning. To examine this further, we directly compared
performance in discrimination and word learning separately for
each Trial Type using models with the fixed effect of Task (*dis-*
*crimination, word learning*). For *dissimilar* trials, there were no
main effects of task (*p*.59), suggesting that performance did not
differ across tasks (although it is possible that an underlying
difference between tasks was masked by ceiling effects, given that
overall performance was above 95%). However, for both *similar*
and *highly similar* trials, we found significant main effects of Task
(*p*s.001): higher overall performance in discrimination than in
the word learning task.
No other effects in the full model were significant, including

effects or interactions involving Language (*p*s.2). This suggests
that the two L1 populations had similar overall response patterns
on the word pairs that were predicted to be relatively well discriminated by all participants (i.e., all word pairs excluding *place*
trials for Korean speakers and *length* for Mandarin speakers).

In sum, these results suggest that there is a gradient effect of
phonological similarity in both word discrimination and word
learning: performance decreases as similarity grows. However,
this effect is moderated by the specific task: relative to discrimination, performance in word learning suffers substantially more as
similarity increases.

## Discrimination Versus Word Learning: Are L2 Learners Like Infants?

## Addressing whether L2 learners show a discrimination–word

learning asymmetry like that observed in L1-learning infants
(Stager & Werker, 1997) entails a specific comparison between the
two different *highly similar* trial types—*length* and *place*—for
both L1 populations and across the two different tasks. Given
previous studies with similar stimuli (Pajak, 2012; Pajak & Levy,
2014), we expected differential discrimination of *length* and *place*
contrasts by the two L1 populations: Korean speakers more accurate than Mandarin speakers on discriminating *length* trials, and
Mandarin speakers more accurate than Korean speakers on discriminating *place* trials (a Feature Type Language interaction).
The question was whether this interaction would also extend to the
word learning data.
The results are illustrated in Figure 3. We analyzed these data in

a model with fixed effects of Task (*discrimination, word learning*),
Feature Type (*length, place*), and Language (*Korean, Mandarin*).
(See Appendix B for additional analyses that include Block as a
fixed effect, demonstrating that the main result of a Feature
Type Language interaction was consistent throughout the experiment.) First, there was a significant main effect of Task (*p*
.001): performance was overall higher in discrimination than in
word learning, indicating that the latter task was more difficult.
Furthermore, there was a significant Feature Type Language
interaction (*p*.001): as expected, Korean speakers performed
better on *length* trials and Mandarin speakers performed better on
*place* trials. Critically, however, there was also a significant threeway Task Feature Type Language interaction (*p*.001),
indicating that performance as a function of Feature Type differed
across the two tasks. To interpret this interaction, we analyzed
each task separately.

Figure 2. Results for dissimilar, similar, and highly similar (length for Korean, place for Mandarin) trials.
Accuracy scores indicate proportion of correct responses and error bars are standard errors.

---

Figure 3. Results for all highly similar trials: length and place. Accuracy scores indicate proportion of correct
responses and error bars are standard errors.

For the discrimination task (see Figure 3, left), there was a
significant Feature Type Language interaction (*p*.001): as
predicted, Korean listeners performed better on *length* trials, while
Mandarin listeners performed better on *place* trials. For the word
learning task (see Figure 3, right), however, there was no interaction between the two variables (*p*.21; nor any main effects,
*p*s.5), suggesting that the respective group-level perceptual
advantages of Korean and Mandarin speakers did not translate into
an advantage during word learning.
Therefore, the three-way interaction in the main model indicated

$$
(p<.00)
$$

$$
p{\mathrm{s}}\,>.,55)
$$

that good discrimination does not necessarily yield better learning.
Overall, these results suggest that learners did not take full advantage of their L1-based perceptual abilities in a word learning task.
This pattern is highly similar to 14-month old infants learning their
native language (Stager & Werker, 1997), suggesting that discordant performance between discrimination and mapping is not a
developmental phenomenon, but a more general feature of learning
words in a new phonology.

## Do Individual Differences Affect Use of Perceptual Advantages in Word Learning? Word learning is a complex task that involves a combination of

cognitive abilities and attention. Therefore, it is possible that there
is a high degree of individual differences in how efficiently learners make use of their L1-based perceptual abilities in the word
This document is copyrighted by the American Psychological Association or one of its allied publishers.
learning task. To answer that question, we examined the word
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
learning data separately for two groups of participants: higher and
lower performers.
We split participants into higher and lower performer groups

based on their accuracy scores on *dissimilar* and *similar* trials.
These trials consisted of more salient distinctions (see Table 3),
and were independent of the variables of interest. Median accuracy
6
on all these trials combined was 94.5%. Seven participants scored
exactly at 94.5%. We performed two separate analyses of the data,
where the seven participants were either all included in the higher
performer group or in the lower performer group, later referred to
as Split 1 and Split 2, respectively. The results were equivalent in
both cases, but, for simplicity reasons, we only illustrate the Split
1 results. The distribution of participants was fairly equal across

language background (see Table 4). The table also shows scores on
*dissimilar* and *similar* trials for both higher and lower performers.
Both groups were highly accurate on these trials, but there was
much more variability among lower performers, as indicated by
the higher standard deviations.
Figure 4 illustrates the word learning results for length and place

trials split into higher and lower performers. By visual inspection
alone, it can be seen that participants in the higher performer group
were clearly learning the minimal pair words, as indicated by their
much higher levels of accuracy. In the lower performer group, on
the other hand, participants’ responses were close to chance.
We analyzed these results with models with fixed effects of

Feature Type (*length*, *place*), Language (Korean, Mandarin), and
Performer Type (*high*, *low*), separately for Split 1 and Split 2. In
both cases, we found a significant effect of Performer Type (*p*s
.001), reflecting higher accuracy in the higher performer group
than in the lower performer group. Critically, there were also
significant three-way Feature Type Language Performer
Type interactions (in both Split 1 and Split 2; *p*s.05), indicating
distinct response patterns for Korean versus Mandarin speakers on
*length* and *place* trials depending on their overall success rate in
learning, as measured by their accuracy on *dissimilar* and *similar*
trials.
To assess the nature of the three-way interaction, we analyzed

the effects of Feature Type and Language separately for each
Performer Type. Higher performers showed a pattern more consistent with taking advantage of their perceptual biases: Korean
speakers were more accurate on *length* trials than Mandarin speakers, but the reverse was true on *place* trials, as indicated by
significant Feature Type Language interactions (in both Split 1

A reviewer pointed out that a median level of performance of 94.5%
suggests ceiling effects, such that distinguishing good learners from poor
learners ceases to be very meaningful. We agree, in principle, that this is
a valid concern, but because this median split revealed a significant and
interpretable interaction, we did not think it was a concern in practice.

---

*Dissimilar and Similar Trial Scores (Standard Deviations) From the Word Learning Task Split by Higher and Lower Performers*
<u>Higher performers Lower performers</u>

| Trial type | Higher performers |  |  |  | Lower performers |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Trial type | Korean |  | Mandarin |  | Korean |  | Mandarin |  |
| Trial type | Split 1 | Split 2 | Split 1 | Split 2 | Split 1 | Split 2 | Split 1 | Split 2 |
| n | 16 | 11 | 15 | 13 | 11 | 16 | 12 | 14 |
| Dissimilar | .99(.10) | .99(.07) | .98(.11) | .99(.09) | .93(.26) | .93(.25) | .90(.30) | .92(.27) |
| Similar | .95(.21) | .96(.20) | .94(.24) | .95(.22) | .79(.41) | .81(.39) | .78(.41) | .82(.38) |

and Split 2; ps.05). The lower performers, on the other hand,
showed no significant Feature Type Language interactions (and
no other significant effects).
Therefore, analyzing the word learning data split by accuracy on

$$
2; p s < . 0 5). ^ {7}
$$

more salient distinctions revealed a difference between higher and
lower performers, with the former group taking more advantage of
their L1-based perceptual abilities than the latter group. What
might underlie such individual differences? One possibility is that
the lower performers in the word learning task simply had lower
discrimination abilities. Due to the between-subjects design, we
did not have any direct evidence about how discrimination related
to word learning at the level of individual participants. However,
we were able to examine a similar split of participants in the
discrimination task to see whether the difference between Korean
and Mandarin speakers would disappear for lower performers. We
found that, with a similar split of participants (see Figure 5), the
Feature Type Language interactions were significant for both
higher performer and lower performer groups (*p*s.01). Therefore, while we cannot rule it out, we did not find any evidence to
support the hypothesis that overall lower performers might have
lower discrimination abilities. Instead, these results suggest that
the higher performer advantage in the word learning task was
driven by something other than (or perhaps in addition to) better
discrimination abilities. We outline possible explanations in the
Discussion section.
To summarize, in the overall results, there was no effect (inter-

$$
(p\ <<<11)
$$

action) of language background in the word learning task, suggesting that discrimination is necessary but not sufficient for successful learning of similar-sounding words. While we found some
evidence of language background– based learning of highly similar
words in participants who scored better on the learning task
overall, the effect was confined to better word learners and far less
This document is copyrighted by the American Psychological Association or one of its allied publishers.robust than the effect we saw in the discrimination task.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

## Discussion

In this article, we asked two specific questions: (1) Do adult L2
learners have difficulty learning similar-sounding words that they
can nevertheless discriminate, just like 14-month-old infants?; and
(2) How does phonological similarity moderate discrimination
versus learning of similar-sounding L2 words? To answer these
questions, we asked participants of two different language backgrounds (Korean and Mandarin) either to discriminate similarsounding words in a new language or to map similar-sounding
words onto novel referents. That is, we investigated the relation
between perceptual discrimination and word learning across
speaker populations varying in native-language background. We

used three levels of word similarity: dissimilar word pairs (differing in all sound segments but the last vowel), *similar* word pairs
(same first syllable, but salient differences in the middle consonant), and *highly similar* word pairs (same first syllable and only
subtle differences in the middle consonant). Furthermore, the
*highly similar* word pairs consisted of two distinction types to
which the two participant populations had differential perceptual
sensitivities (as shown by previous work, and confirmed by our
discrimination task). In particular, some distinctions varied along
dimensions familiar to participants from their L1, and others did
not. Comparing the three levels of word pairs (*dissimilar, similar,*
*highly similar*) allowed us to assess the role of phonological
similarity in discrimination versus word learning (Question 2),
while investigating the differences between L1 populations on the
two types of *highly similar* word pairs allowed us to answer the
question about the potential group-level mismatch between discrimination and learning of similar-sounding words in adult L2
learning (Question 1).
Regarding the effects of phonological similarity on discrimina-

tion versus word learning, we found—as in prior work on learning
phonologically native-like words— gradient performance. In both
discrimination and word learning tasks, performance was best on
*dissimilar* word pairs, intermediate for *similar* word pairs, and
worst on *highly similar* word pairs. However, we also found a
mismatch between discrimination and learning: the gradient effects were more exaggerated for participants in the word learning
task than for participants in the discrimination task, with performance dropping disproportionally as word similarity increased.
Therefore, our results suggest that word learning is particularly
difficult when words are similar-sounding, even when the differences are fairly salient (as in our *similar* word pairs).
The results from *highly similar* word pairs revealed that partic-

ipants in the word learning task were unable to use their L1-based
perceptual abilities effectively during word learning: Despite
Korean- versus Mandarin-speaker differences in discrimination of
*highly similar* word pairs, there were no analogous Korean- versus
Mandarin-speaker differences for participants in the word learning
task. However, there was some evidence that higher performers (as
independently assessed by performance on unrelated trials) were

One might wonder if these results only showed up after better learners
were alerted to the nature of the task. However, the interaction was already
numerically present (marginal on Split 1, *p*.08; Split 2, *p*.11) in the
first block of testing. This suggests that the effect of Feature Type was
present before test trials “tipped off” participants to the presence of the
*length* and *place* minimal pairs, because the pictures corresponding to these
minimal pairs were never shown together in training.

---

This document is copyrighted by the American Psychological Association or one of its allied publishers.

Figure 4. Word learning task results (length and place trials) split by higher and lower performers (Split 1;
results for Split 2 were equivalent). Accuracy scores indicate proportion of correct responses and error bars are
standard errors.

somewhat more successful. This was in sharp contrast to the
discrimination results, where L1-based perceptual advantages were
observed for all participants. This result thus reveals an intermediate effect between failure to learn similar-sounding words (as
observed for 14-month-old infants) and a full ability to use existing
L1-based perceptual abilities in learning (which should mimic the
discrimination data).
What factors led to better performance in learning highly

similar-sounding words? It is possible that the observed differences in the word learning task performance for higher versus
lower performers simply arise from better discrimination abilities
of the higher performers; that is, higher performers might be better
at detecting subtle differences, especially when given the chance to
directly compare the highly similar words during test trials. However, our data did not provide any evidence that the lower performers were actually perceptually worse. In the conceptual model
we discussed in the Introduction, the difference between higher
and lower performers can be explained by higher performers being
able to keep track of and/or integrate evidence from more exem-

plars relative to the lower performers. In addition, higher performers might be able to store exemplars better (i.e., with higher
phonetic fidelity) than the lower performers from the outset of
language exposure, and thus be faster at forming new phonetic
categories. This would not hurt lower performers when asked to
identify dissimilar words, but would impact them more substantially when identifying label-referent pairings of similar-sounding
words. This account means that the observed differences between
higher and lower performers can, at least in part, be attributed to
individual differences between learners. To investigate this further,
we examined more closely the individual measures collected from
our participants, as shown in Table 5 (Split 1 only; minor differences between Split 1 and Split 2 are reported in table footnotes).
A series of *t* tests revealed that the higher performer and the

lower performer groups did not significantly differ in nonverbal IQ
or L1 proficiency. However, they did differ in English proficiency:
higher performers reported higher overall proficiency than lower
performers. One possibility, thus, is that better knowledge of
English benefitted the higher performers in this task. However, as

Figure 5. Discrimination task results (length and place trials) split by higher and lower performers. Accuracy
scores indicate proportion of correct responses and error bars are standard errors.

---

Table 5
*Higher vs. Lower Performers in the Word Learning Task*
<u>Higher performers</u>

|  | Higher performers |  | Lower performers |  | t test |
| --- | --- | --- | --- | --- | --- |
| Measure | M | SD | M | SD | t test |
| Age(years) | 20 | 1.6 | 21 | 2.3 | |t|＜1 |
| L1 proficiency:speakinga | 7.5 | 1.9 | 8.1 | 1.9 | t(47.3)=1.02,p=.31 |
| L1 proficiency:understandinga | 8.2 | 1.4 | 8.4 | 1.6 | |t|＜1 |
| %time currentL1 exposure | 23 | 16.6 | 39 | 19.2 | t(43.3)=3.03,p＜.01c |
| Age when regular English exposure began | 4.8 | 3.9 | 6.3 | 4.1 | t(46.4)=1.34,p=.18 |
| Age of arrival in United States | 5.6 | 6.5 | 10.2 | 8 | t(41.1)=2.19,p＜.05 |
| Length of residenceinUnitedStatesb | 14.9 | 6.2 | 10.8 | 6.7 | t(45.4)=-2.23,p＜.05 |
| English proficiency:speakinga | 8.9 | 1.4 | 7.4 | 2.1 | t(35.4)=-2.83,p＜.01 |
| English proficiency:understandinga | 9.2 | 1.2 | 8.1 | 1.7 | t(38.2)=-2.60,p＜.05 |
| English vocabulary test(%correct) | 75 | 12.7 | 68 | 14.1 | t(44.4)=-1.69,p=.10 |
| %time currentEnglish exposure | 75 | 16.2 | 62 | 21 | t(39.8)=-2.38,p＜.05d |
| Nonverbal IQ test(%correct) | 89 | 7.6 | 86 | 7.9 | t(46.5)=-1.26,p=.21 |

Note. Significant differences in boldface. L1 native language.
<sup>a</sup> b
On a 0 –10 scale (0 *none* and 10 *perfect*). If <sup>b</sup>orn in the United States, coded as 0.

Effect only marginal in Split 2: t(51.4) 1.74, p
<sup>d</sup>
.087. Effect not significant in Split 2: *t*(51.8) 1.36, *p*.181.

outlined earlier in describing the stimuli, it is not entirely clear
what properties of English would produce this type of benefit. It is
true that English uses segmental length as a secondary cue to some
vowel contrasts (as well as coda voicing), but our stimuli only
included length differences for consonants, and prior work (Pajak
& Levy, 2014) found no benefit of higher English proficiency on
discriminating consonant length contrasts. Another possibility is
that the better performance of some participants in the word
learning task was an effect of early bilingualism: more balanced
bilinguals might be better equipped to use their L1-based perceptual abilities when learning novel words. Indeed, higher English
proficiency of the higher performers in our experiment was likely
the consequence of their earlier age of arrival to the United States
and a longer length of residence relative to the lower performer
group. Therefore, the higher performer group might have been
composed of early bilinguals who were perhaps more balanced in
both languages (i.e., fluent in both Korean/Mandarin and English),
while the lower performer group included more speakers strongly
dominant in their L1. This is consistent with other findings suggesting that early simultaneous bilingual adults have a general
cognitive advantage over monolinguals for novel word learning,
whether phonologically familiar or unfamiliar, and independently
of phonological memory capacity (Kaushanskaya, 2012; Kaushanskaya & Marian, 2009). Yet another alternative explanation is
This document is copyrighted by the American Psychological Association or one of its allied publishers.that higher English proficiency of some participants might be an
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.indicator (more precise than IQ) of better language-learning skills
that led to superior performance in our word learning task. However, this explanation seems unlikely given that the differences in
self-reported English proficiency between the higher and lower
performer groups disappeared after controlling for age of arrival or
length of residence in the United States.

## Why Is Word Learning Difficult? Irrespective of individual differences, the results we have re-

ported suggest that there is something inherently hard about the
early stage of word learning that precludes attention to fine phonetic detail that is otherwise available during phonetic processing—this is true for adult L2 learners in the same way it seems to

be true for 14-month-old L1 learners. Together with the results of
White et al. (2013), who showed that infants and adults are
similarly affected by lexical familiarity during word learning, these
findings provide evidence that there might be common learning
mechanisms operating throughout development. But what are
these mechanisms, and what is the source of difficulty in learning
similar-sounding words?
One answer is that learning novel words is simply a highly

complex task that leads to information processing overload.
This explanation was originally proposed by Stager and Werker
(1997) and Werker et al. (2002), who argued that young infants
struggle with accessing sufficient phonetic detail in their lexical
representations to successfully differentiate between words that
are phonetically highly similar. This means that—relative to
task difficulty— only individuals with better attentional or general cognitive abilities might effectively manage concurrent
information at multiple levels of processing. This would explain
why older, 17- to 20-month-old infants outperform 14-montholds, as well as why adults with some general cognitive advantages (such as early simultaneous bilinguals; e.g., Bialystok,
1999) might outperform other adults, as shown in this study.
This would also suggest that encoding and using phonetic detail
when learning similar-sounding words would be improved by
decreasing memory demands in the word learning task (consistent with prior work; e.g., Fennell & Werker, 2003), as well as
providing additional support for storing the phonetic detail of
word exemplars and forming new phonetic categories. The
latter idea is consistent with recent findings that auditory training on difficult phonetic distinctions improves subsequent word
learning, and is particularly beneficial for learners with low
pretraining auditory sensitivity (Cooper & Wang, 2013; Ingvalson, Barr, & Wong, 2013).
Note, however, that it does not seem to be the case that word

learning is simply overall harder than discrimination— our results
indicate that, for highly dissimilar word pairs, performance is
equally high in both tasks (although an underlying difference
might be hidden due to ceiling effects). Instead, performance in
word learning drops disproportionately as the word pairs get phonologically more similar, suggesting that the task is especially
taxing when detailed phonetic representations are needed to distinguish between words, even when the words are perceptually
easily discriminable (as our similar word pairs). This is consistent
with other work showing that confusability between newly
learned— but phonologically native-like—words is modulated by
the phonetic distance between the sounds that differentiate between them (e.g., Creel & Dahan, 2010; Creel et al., 2006; White
et al., 2013). Thus, the overall difficulty of word learning in
general lies most likely not just in the task itself, but also in
pinning down the correct label/referent pairings, as outlined in the
conceptual model we proposed in the Introduction.

## Implications for L2 Acquisition The results we have presented contribute to our understand-

ing of L2 learning, as well as provide more practical implications for second-language teaching. The current view is that L2
phonetic category learning is largely hindered due to perceptual
difficulties arising from prior acquisition of L1 phonology (e.g.,
Best, 1995; Flege, 1995; Kuhl & Iverson, 1995). While we do
not dispute the importance of L1 influence on L2 phonological
acquisition, our results suggest that at least some of the learners’ difficulties in distinguishing among novel L2 sounds might
be due to their introduction in the context of highly similarsounding lexical items. In such a context, when attention is
directed at forming label-referent mappings, learners might be
unable to properly separate similar-sounding categories. In fact,
it has been shown that, when discrimination between some L2
sounds is initially present but fragile, the mere act of learning
the word–referent mappings for similar-sounding words that
differ by those sounds makes their perceptual discrimination
even worse (Dobel, Lagemann, & Zwitserlood, 2009). It thus
seems that the introduction of highly similar L2 phonetic categories might be more effective either outside of a word learning task (e.g., as a focused nonnative sound discrimination
practice) or when learning words that are overall fairly dissimilar. The latter conclusion is independently supported by the
results from infant studies, which have shown that prior experience with sounds in nonminimal pair lexical contexts improves infants’ ability to later learn minimal pair words distinguished by those sounds (Thiessen, 2007). A similar conclusion
can be drawn from both behavioral and computational modeling
work, which has shown that nonminimal pair lexical contexts
improve distributional learning of overlapping sound categories
This document is copyrighted by the American Psychological Association or one of its allied publishers.
for both infants and adults (Feldman, Griffiths, Goldwater, &
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Morgan, 2013; Feldman, Myers, White, Griffiths, & Morgan,
2013).

## Conclusion The results presented in this article show that adults, just like

young infants, have difficulty learning similar-sounding words that
they can nevertheless distinguish perceptually, demonstrating that
discrimination is necessary but not sufficient for successful learning of similar-sounding words. This parallel between infants and
adults points to a common mechanism underlying the initial stage
of lexical acquisition throughout development, whether in the
native language or any additional language acquired in adulthood.

Together with recent results that have shown other parallels between infant and adult lexical acquisition (White et al., 2013), our
findings highlight the necessity for greater interaction between
infant and adult language learning literatures that would investigate the commonalities and differences between native-language
development in infancy and second-language learning in adulthood, thus shedding more light on the degree of developmental
continuity in language learning.

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LEARNING SIMILAR-SOUNDING WORDS

## Appendix A Individual Measures

Appendix A includes a more detailed version of the individual measures collected from participants that were provided in
Table 1.
Tables A1 and A2 compare participants of the discrimination

and the word learning tasks separately for each language background (see Table A1 for Korean speakers; see Table A2 for
Mandarin speakers). Just as reported in Table 1, there were no
significant differences between participants within each L1 population, with one minor exception: Korean speakers in the word
learning task reported slightly higher proficiency in understanding
English than Korean speakers in the discrimination task.

Tables A3 and A4 compare Korean and Mandarin speakers
separately for each task (see Table A3 for discrimination; see
Table A4 for word learning). There were no significant differences
between Korean and Mandarin speakers assigned to the discrimination task. There were, however, some differences between participants assigned to the word learning task. Namely, relative to
speakers of Mandarin, Korean speakers on average immigrated to
the United States earlier, had a longer length of residence in the
United States, and reported higher proficiency in English and
slightly lower proficiency in their L1. At the same time, they did
not differ on an objective measure of English proficiency (the

Table A1
*Individual Measures: Korean Speakers by Task*

|  | Discrimination task participants |  | Word learning task participants |  |  |
| --- | --- | --- | --- | --- | --- |
| Measure | M | SD | M | SD | t test |
| Age (years) | 20 | 1.7 | 21 | 1.4 | |t|＜1 |
| L1 proficiency: speakinga | 7.6 | 1.6 | 7.2 | 2 | |t|＜1 |
| L1 proficiency: understandinga | 7.8 | 1.5 | 8.1 | 1.6 | |t|＜1 |
| % time current L1 exposure | 32 | 15.4 | 29 | 16.8 | |t|＜1 |
| Age when regular English exposure began | 5.7 | 3.1 | 4.8 | 3.4 | |t|＜1 |
| Age of arrival in United States | 6.7 | 5.4 | 5.5 | 6 | |t|＜1 |
| Length of residence in United Statesb | 13.7 | 5.7 | 15.2 | 6 | |t|＜1 |
| English proficiency: speakinga | 8.5 | 1.3 | 8.9 | 1.4 | t(36.6)=-1.01,p=.31 |
| English proficiency: understandinga | 8.6 | 1.1 | 9.4 | 0.8 | t(28.8)=-2.58,p&lt;.05 |
| English vocabulary test (% correct) | 74 | 8.5 | 74 | 11.5 | |t|＜1 |
| % time current English exposure | 67 | 16 | 70 | 16.8 | |t|＜1 |
| Nonverbal IQ test (% correct) | 87 | 5 | 86 | 6.1 | |t|＜1 |

<sup>a</sup> On a 0-10 scale (0 = none and 10 = perfect). <sup>b</sup> If born in the United States, coded as 0.

(Appendices continue

---

Table A2
*Individual Measures: Mandarin Speakers by Task*

|  | Discrimination task participants |  | Word learning task participants |  |  |
| --- | --- | --- | --- | --- | --- |
| Measure | M | SD | M | SD | t test |
| Age (years) | 20 | 1.5 | 21 | 2.4 | t(42.8)=-1.16,p=.25 |
| L1 proficiency: speakinga | 8.5 | 1.6 | 8.3 | 1.6 | |t|＜1 |
| L1 proficiency: understandinga | 8.8 | 1.5 | 8.5 | 1.4 | |t|＜1 |
| % time current L1 exposure | 34 | 22 | 30 | 22 | |t|＜1 |
| Age when regular English exposure began | 6.9 | 4.7 | 6.1 | 4.6 | |t|＜1 |
| Age of arrival in United States | 10.4 | 7.9 | 9.6 | 8.3 | |t|＜1 |
| Length of residence in United Statesb | 9.5 | 8.7 | 11 | 6.7 | |t|＜1 |
| English proficiency: speakinga | 7.6 | 2 | 7.6 | 2.1 | |t|＜1 |
| English proficiency: understandinga | 8.2 | 1.6 | 8.1 | 1.8 | |t|＜1 |
| English vocabulary test (% correct) | 69 | 12.2 | 70 | 15.3 | |t|＜1 |
| % time current English exposure | 65 | 21.7 | 69 | 21.8 | |t|＜1 |
| Nonverbal IQ test (% correct) | 90 | 8.4 | 90 | 9 | |t|＜1 |

Note.L1 native language.
<sup>a</sup>
On a 0 –10 scale (0 *none*

and 10 perfect). If born in the United States, coded as 0.

## Table A3

*Individual Measures: Participants in the Discrimination Task by Language Background*
L1 Korean L1 Mandarin

|  | L1 Korean participants |  | L1 Mandarin participants |  | t test |
| --- | --- | --- | --- | --- | --- |
| Measure | M | SD | M | SD | t test |
| Age(years) | 20 | 1.7 | 20 | 1.5 | |t|＜1 |
| L1 proficiency:speakinga | 7.6 | 1.6 | 8.5 | 1.6 | t(34.0)=-1.61,p=.12 |
| L1 proficiency:understandinga | 7.8 | 1.5 | 8.8 | 1.5 | t(33.9)=-1.83,p=.08 |
| %time current L1 exposure | 32 | 15.4 | 34 | 22 | |t|＜1 |
| Age when regular English exposure began | 5.7 | 3.1 | 6.9 | 4.7 | |t|＜1 |
| Age of arrival in United States | 6.7 | 5.4 | 10.4 | 7.9 | t(30.1)=-1.60,p=.12 |
| Length of residence in United Statesb | 13.7 | 5.7 | 9.5 | 8.7 | t(29.2)=1.68,p=.10 |
| English proficiency:speakinga | 8.5 | 1.3 | 7.6 | 2 | t(30.0)=1.53,p=.14 |
| English proficiency:understandinga | 8.6 | 1.1 | 8.2 | 1.6 | |t|＜1 |
| English vocabulary test(%correct) | 74 | 8.5 | 69 | 12.2 | t(30.5)=1.46,p=.15 |
| %time current English exposure | 67 | 16 | 65 | 21.7 | |t|＜1 |
| Nonverbal IQ test(%correct) | 87 | 5 | 90 | 8.4 | t(27.5)=-1.03,p=.31 |

Note.L1 native language.
<sup>a</sup>
On a 0 –10 scale (0 *none*

and 10 perfect). If born in the United States, coded as 0.

Shipley Vocabulary Test), and all were fluent speakers of both
English and Korean/Mandarin, with similar amounts of current
exposure to each language. Although it is possible that these
differences might have affected our results, prior work with sub-
This document is copyrighted by the American Psychological Association or one of its allied publishers.jects from the same populations has shown that, when subjects
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

use both languages regularly, their relative dominance in English
versus Korean/Mandarin does not affect the discrimination of the
contrasts tested in this study (Pajak & Levy, 2014). Furthermore,
and most critically, within each L1 population, participants did not
differ across the two tasks, as shown in Tables A1 and A2.

---

This document is copyrighted by the American Psychological Association or one of its allied publishers.

Table A4
*Individual Measures: Participants in the Word Learning Task by Language Background*
L1 Korean L1 Mandarin

|  | L1 Korean participants |  | L1 Mandarin participants |  | t test |
| --- | --- | --- | --- | --- | --- |
| Measure | M | SD | M | SD | t test |
| Age(years) | 21 | 1.4 | 21 | 2.4 | |t|＜1 |
| L1 proficiency:speakinga | 7.2 | 2 | 8.3 | 1.6 | t(49.2)=-2.20,p＜.05 |
| L1 proficiency:understandinga | 8.1 | 1.6 | 8.5 | 1.4 | |t|＜1 |
| % time current L1 exposure | 29 | 16.8 | 30 | 21.5 | |t|＜1 |
| Age when regular English exposure began | 4.8 | 3.4 | 6.1 | 4.6 | t(47.9)=-1.14,p=.26 |
| Age of arrival in United States | 5.5 | 6 | 9.6 | 8.3 | t(47.5)=-2.07,p＜.05 |
| Length of residence in United Statesb | 15.2 | 6 | 11 | 6.7 | t(51.3)=2.4,p＜.05 |
| English proficiency:speakinga | 8.9 | 1.4 | 7.6 | 2.1 | t(44.0)=2.6,p＜.05 |
| English proficiency:understandinga | 9.4 | .8 | 8.1 | 1.8 | t(36.8)=3.25,p＜.01 |
| English vocabulary test (% correct) | 74 | 11.5 | 70 | 15.3 | t(48.2)=1.11,p=.27 |
| % time current English exposure | 70 | 16.8 | 69 | 21.8 | |t|＜1 |
| Nonverbal IQ test (% correct) | 86 | 6.1 | 90 | 9 | t(45.8)=-1.59,p=.12 |

## Appendix B

## Learning During the Task

In both discrimination and word learning, it is reasonable to expect
that performance could improve with exposure to the materials and
greater experience in the test task; hence, we analyzed the results
including test block as an additional variable (see Figures B1–B4).
Throughout the analysis, Block was treated as a mean-centered continuous covariate, which is a simple way of imposing the constraint
that temporal changes should be monotonic.
**Word learning**

First, we examined the word learning results for dis*similar*
similar, and *highly similar* (Korean: *length*, Mandarin: *place*) trials
to test whether there was differential improvement over the course
of the experiment depending on the word similarity. In particular,
we expected relatively stable, good performance on *dissimilar*
trials throughout the experiment, with most dramatic improvements observed for *highly similar* trials. We tested this by evalu-
**Word learning**

Figure B1. Results for dissimilar, similar, and highly similar (length for Korean, place for Mandarin) trials by
block. Accuracy scores indicate proportion of correct responses and error bars are standard errors.

---

This document is copyrighted by the American Psychological Association or one of its allied publishers.

ating a model with fixed effects of Trial Type (dissimilar, similar
*highly similar*), Language (Korean, Mandarin), and Block (as a
continuous predictor). These results are illustrated in Figure B1.
Just as in the main analysis, Trial Type was coded with *similar*
trials as the reference level, and there was a significant effect of
Trial Type in that accuracy varied in accordance with the similarity
between words: The responses on *dissimilar* trials were signifi-

cantly higher than on similar trials (p.001), which in turn were
higher than on *highly similar* trials (*p*.001). We found
significant main effect of Block (*p*.001), indicating that participants improved throughout the experiment. There was also
significant Trial Type Block interaction (*p*.05) in that the
relative difference between *dissimilar* and *highly similar* trials
varied as a function of block: The difference was largest at the

$$
(p<.00)
$$

$$
(p<.05)
$$

## Discrimination

## Word learning

Figure B2. Results for all highly similar trials by block: length and place. Accuracy scores indicate proportion
of correct responses and error bars are standard errors.

---

This document is copyrighted by the American Psychological Association or one of its allied publishers.

beginning of the experiment, and it gradually decreased with time (no
such interaction was found for the difference between *similar* and *highly*
*similar* trials). This was in line with our prediction that learners’ performance should not be impeded in cases of learning very dissimilar words;

however, learning was expected to be slower for highly similar words
because more data would be needed before learners could accumulate a
sufficient number of exemplars to learn phonetic category distinctions
and form correct label/referent mappings.

**Higher performers**

**Higher performers**

**Lower performers**

**Lower performers**

Figure B3. Word learning task results by block (length and place trials) split by higher and lower performers
(Split 1; results for Split 2 were equivalent). Accuracy scores indicate proportion of correct responses and error
bars are standard errors.

---

PAJAK, CREEL, AND LEVY

**Higher performers Higher performers**

length feature place feature

1.0 1.0

|1.0||||1.0||||
|---|---|---|---|---|---|---|---|
|0.9||||0.9||||
|0.8||||0.8||||
|Accuracy||||Accuracy||||
|0.7||||0.7||||
|0.6|||Korean Mandarin|0.6|||Korean Mandarin|
|0.5||||0.5||||
|Test1|Test2|Test3|Test4|Te s t 1|Te s t 2|Te s t 3|Te s t 4|
||Block||||Block|||

**Lower performers Lower performers**

length feature place feature

1.0 1.0
0.9 0.9
0.8 0.8
Accuracy Accuracy

0.7 0.7
Korean Korean

0.6 0.6
Mandarin Mandarin

0.5 0.5 Test1 Test2 Test3 Test4 Te s t 1 Te s t 2 Te s t 3 Te s t 4
Block Block *Figure B4.* Discrimination task results by block (length and place trials) split by higher and lower performers (Split 1; results for Split 2 were equivalent). Accuracy scores indicate proportion of correct responses and error bars are standard errors.

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. (*Appendices continue*)

---

Next, we analyzed both discrimination and word learning sets of
*highly similar* trials (see Figure B2). For both tasks, we found
significant main effects of Block (discrimination: *p*.05; word
learning: *p*.001), indicating that participants improved throughout the experiment. There were also significant Feature Type
Block interactions (discrimination: *p*.05; word learning: *p*
.01); in word learning, the improvement was more prominent for
the *length* trials than for the *place* trials; the opposite seemed to be
the case for discrimination—more improvement on *place* than on
*length* trials. Crucially, adding the block information revealed that

$$
p<.05;
$$

$$
p<.001)
$$

$$
p<.05.
$$

$$
p<
$$

the main result—a difference between Korean and Mandarin
speakers in discrimination, but not in word learning—was consistent throughout the experiment. Figures B3 and B4 illustrate the
by-block results split by higher and lower performers in the word
learning and the discrimination tasks, respectively.

Accepted December 22, 2015

Received July 3, 2014
Revision received November 28, 2015

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