Semantic Advantage for Learning New Phonological
Form Representations
Erin Hawkins1, Duncan E. Astle2, and Kathleen Rastle1
Abstract
■ Learning a new word requires discrimination between a
novel sequence of sounds and similar known words. We inves-
tigated whether semantic information facilitates the acquisition
of new phonological representations in adults and whether this
learning enhancement is modulated by overnight consolidation.
Participants learned novel spoken words either consistently
associated with a visual referent or with no consistent meaning.
An auditory oddball task tested discrimination of these newly
learned phonological forms from known words. The MMN, an
electrophysiological measure of auditory discrimination, was only
elicited for words learned with a consistent semantic association.
Immediately after training, this semantic benefit on auditory
discrimination was linked to explicit learning of the associations,
where participants with greater semantic learning exhibited a
larger MMN. However, although the semantic-associated words
continued to show greater auditory discrimination than non-
associated words after consolidation, the MMN was no longer
related to performance in learning the semantic associations.
We suggest that the provision of semantic systematicity directly
impacts upon the development of new phonological representa-
tions and that a period of offline consolidation may promote the
abstraction of these representations. ■
INTRODUCTION
Learning new words is an ability that persists into adult-
hood. A critical feature of learning a new spoken word is
the development of a sufficiently well-specified phonolog-
ical representation to allow discrimination from similar-
sounding existing words. Phonological specification of
a new spoken word is hence an early yet critical compo-
nent of the full acquisition process (Page & Norris, 2009;
Baddeley, Gathercole, & Papagno, 1998; Papagno & Vallar,
1992). Although learning new phonological form represen-
tations can occur rapidly in the brain (Shtyrov, Nikulin, &
Pulvermüller, 2010), it remains relatively unknown what
factors modulate the acquisition of new words in adult-
hood. Importantly, as influential models of human spoken
word recognition argue for an interaction between phonol-
ogy and meaning in recognizing known words (Gaskell &
Marslen-Wilson, 1997, 2002; McClelland & Elman, 1986),
and as these links between phonology and meaning must
at some point have been acquired, one possibility is that
meaning also facilitates the learning of new phonological
form representations. Furthermore, although overnight
consolidation has been established as an important factor
in some aspects of word learning including lexical inte-
gration (e.g., Gaskell & Dumay, 2003) and generalization
(e.g., Tamminen, Davis, Merkx, & Rastle, 2012), it is un-
known how consolidation might impact on the develop-
1Royal Holloway, University of London, 2MRC Cognition and
Brain Sciences Unit, Cambridge, UK
ment of lower-level phonological form representations or
how this influence might be modulated by the provision
of systematic semantic information during learning. Here
we use the exquisite temporal precision of ERPs to identify
the exact moment at which participants distinguish newly
learned spoken words from known words. Combining this
with a novel learning paradigm enabled us to investigate
two possible influences on acquiring a new phonological
form representation: (i) the role of offline consolidation
and (ii) the provision of systematic semantic information.
It is well known that offline consolidation, possibly re-
lated to sleep, can improve perceptual and motor abilities
learned during wake (e.g., Korman et al., 2007; Karni,
Tanne, Rubenstein, Askenasy, & Sagi, 1994). Recent years
have seen great interest in the related possibility that con-
solidation may play a critical role in some aspects of word
learning. In a series of studies on the integration of novel
spoken words into the mental lexicon, Gaskell and Dumay
(2003) demonstrated that newly learned words (e.g.,
cathedruke) can come to compete with similar existing
words (e.g., cathedral ), but only if the initial learning
phase is followed by a period of offline consolidation (see
also Bowers, Davis, & Hanley, 2005, for an analogous study
using visual presentation). Sleep appears to provide an op-
timal state for these consolidation processes (Tamminen,
Payne, Stickgold, Wamsley, & Gaskell, 2010; Dumay &
Gaskell, 2007), but the integration of novel words into the
mental lexicon is also possible during wakefulness under
certain conditions (Lindsay & Gaskell, 2013; Szmalec, Page,
& Duyck, 2012; Fernandes, Kolinsky, & Ventura, 2009). In
© 2015 Massachusetts Institute of Technology Published under a
Creative Commons Attribution 3.0 Unported (CC BY 3.0) license
Journal of Cognitive Neuroscience 27:4, pp. 775–786
doi:10.1162/jocn_a_00730
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
t
t
f
/
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
–
p
–
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
t
/
f
.
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
addition to lexical integration processes, consolidation
also appears to be critical for the abstraction of newly
learned information, in such a way that promotes linguistic
generalization. For example, Gomez, Bootzin, and Nadel
(2006) demonstrated that infants who took a nap after a
spoken learning task were able to extract an abstract rule
relating elements in the training set that could be applied
to untrained stimuli in a manner that infants who failed to
nap could not. Similarly, Tamminen et al. (2012) showed
that adults who learned a series of words with an internal
morphological structure (e.g., teachnule, buildnule, sleep-
nule) could apply their knowledge of the element [-nule]
to untrained stimuli, but only following a period of over-
night consolidation. These types of effects have been
characterized within a complementary learning systems
(CLS) account (e.g., Davis & Gaskell, 2009; McClelland,
McNaughton, & OʼReilly, 1995), suggesting that newly
learned words are initially stored as distinct episodic repre-
sentations and that one function of consolidation may be to
transfer these episodic representations to abstract lexical
representations. However, although consolidation appears
to be very significant in these higher-level word learning
processes, it is unknown how consolidation may impact
on lower-level phonological form learning processes (e.g.,
Shtyrov et al., 2010) that are a necessary prerequisite.
Previous investigations of the influence of semantic
information on novel word learning are far less consistent.
In studies testing explicit memory for learned whole
words (e.g., Rueckl & Olds, 1993), the provision of meaning
has been shown to be broadly advantageous. Associative
learning between a word and visual referent (Breitenstein
et al., 2005), semantic richness of implicitly learned words
(Rabovsky, Sommer, & Abdel Rahman, 2012), and semantic
relatedness of new word meanings (Rodd et al., 2012) have
a beneficial impact on measures of word recall and recogni-
tion memory. However, this consistently beneficial effect
of semantic information on explicit measures of word learn-
ing does not always translate to measures of online lexical
processing such as speeded naming (e.g., Hultén, Vihla,
Laine, & Salmelin, 2009; Sandak et al., 2004). Furthermore,
it is difficult to reconcile with the lexical integration litera-
ture. Dumay, Gaskell, and Feng (2004) trained participants
on novel words (e.g., cathedruke), which were presented
in either a meaningful sentential context or in isolation in
a phoneme monitoring task. Although novel words intro-
duced in both conditions came to compete with existing
words (suggesting lexical integration), those introduced
in a sentential context required a longer period of con-
solidation to do so. Similarly, Takashima, Bakker, van Hell,
Janzen, and McQueen (2014) trained participants on a set
of novel spoken words, half of which were associated with
a picture. They observed that only those trained without a
picture engaged in lexical competition the subsequent day.
Conversely, Henderson, Weighall, and Gaskell (2013)
found that words trained in both semantic and nonsemantic
contexts yielded competition effects in children the day
after learning. Similarly, although some studies have
reported that the provision of semantic information is
necessary to achieve generalization in adult word learning
paradigms (Tamminen et al., 2012; Merkx, Rastle, & Davis,
2011, both for morphological rule learning), others have
reported generalization effects even in the absence of
semantic information (Taylor, Plunkett, & Nation, 2011, in
the case of artificial orthography learning). Overall, then,
although the provision of semantic information has a
strong influence on explicit memory for learned words,
the findings regarding higher-level word learning processes
such as lexical integration and generalization are much less
clear. In examining how the provision of semantic informa-
tion influences the acquisition of lower-level phonological
form representations before and after consolidation, our
study will go some way to beginning to resolve these con-
tradictory effects.
We thus investigated these two critical issues of semantic
exposure and consolidation in a single design. We asked,
first, whether the provision of systematic semantic informa-
tion about a novel word would enhance learning of the
low-level phonological form of that novel word; second,
we wanted to know whether a period of offline consolida-
tion would impact upon the emergence of phonological
representations or indeed modulate any semantic influ-
ence on this acquisition process. CLS accounts predict that
consolidation can both strengthen access to new word rep-
resentations and promote their abstraction from episodic
knowledge (Davis & Gaskell, 2009; McClelland et al.,
1995). Enhanced access to new word representations has
been observed in faster responses to the phonological
features of new words (Snoeren, Gaskell, & Di Betta,
2009) and gradually improving recognition and recall
of new phonological forms over consolidation (Tamminen
et al., 2010; Davis, Di Betta, Macdonald, & Gaskell, 2009;
Dumay & Gaskell, 2007; Dumay et al., 2004). Abstraction
from episodic knowledge after consolidation has been
observed for higher-level aspects of word learning such
as semantic integration (Tamminen & Gaskell, 2013), mor-
phological and grammatical rule learning (Tamminen et al.,
2012; St. Clair & Monaghan, 2008), and for nonlinguistic
statistical learning (Durrant, Taylor, Carney, & Lewis, 2011;
Ellenbogen, Hu, Payne, Titone, & Walker, 2007). However,
it is not established whether abstraction may also operate
on earlier phonological form learning processes. Hence,
we looked for a change both in access to new phonological
form representations and in their dependence on episodic
knowledge.
Our first question regarding a semantic benefit on
phonological form learning raised the critical issue of how
to manipulate the provision of semantic information. In
a key study in which the provision of semantic informa-
tion was actually disadvantageous to lexical integration
(Takashima et al., 2014), participants were required to
learn novel words via phoneme monitoring, where some
were also presented with a visual referent. One possibility
is that the semantic disadvantage in this study arose
because learning two novel pieces of information (a new
776
Journal of Cognitive Neuroscience
Volume 27, Number 4
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
f
/
t
t
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
–
p
–
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
t
f
/
.
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
phonological form and a new meaning) is more cognitively
demanding than learning just one novel piece of informa-
tion. It is possible that, when the amount of information
and learning goals are equated, the acquisition of phonolog-
ical representations will be benefited by systematic seman-
tic associations during training, because of associative links
between forms and referents leading to a stronger memory
trace (e.g., Leach & Samuel, 2007). Thus, we employed a
new paradigm in which participants learned novel spoken
words that were always accompanied by a picture. How-
ever, in the correlated condition, there was a strong relation-
ship between the novel words and their visual referent
across trials, whereas in the uncorrelated condition, there
was no relationship between the novel words and their
visual referents across trials. Task goals were thus perfectly
equated across conditions, and critically, participants were
unaware of two categorically different learning conditions.
Following learning, we tested the precision of newly
acquired phonological form representations using the
MMN potential as an electrophysiological measure of audi-
tory discrimination, both immediately (Day 1) and 24 hr
after participants acquired a novel vocabulary (Day 2).
The MMN has been shown to be a sensitive index of novel
word learning and discrimination from known words and is
critically elicited in the absence of attention to the speech
stream (and thus is not contaminated by specific process-
ing goals). Shtyrov et al. (2010) used the evoked MMN as
an index of novel word learning: A pseudoword was pre-
sented infrequently against a stream of known words,
where the infrequent pseudoword differed by one pho-
neme from the known word (i.e., pipe–pite). By the end
of a 14-min exposure session, the pseudoword elicited an
MMN response, which Shtyrov et al. (2010) suggested was
the result of rapidly forming a neural memory trace of the
novel pseudowords. The MMN was elicited in response
to a precise recognition point in the speech signal, where
the novel pseudoword could be discriminated from the
known word standard, and it therefore measured the per-
ceived phonological contrast between the novel pseudo-
word and known word. Using the MMN in similar design
in our test phase therefore allowed us to address whether
systematic semantic information enhances the acquisition
of phonological representations and to test whether the
acquisition of these phonological representations (or any
semantic influence therein) was modulated by overnight
consolidation.
METHODS
Participants
Twenty-four right-handed native English speakers (mean
age = 21.5 years, SD = 2.59 years, range = 18–27 years;
15 women) completed the study. The participants had no
known auditory, language, or learning difficulties. All par-
ticipants were recruited from Royal Holloway and were
paid for their participation. The study received ethical
approval from the Psychology Department ethics com-
mittee at Royal Holloway.
Materials and Design
Learning Task
There were three conditions in the learning task: the
correlated condition, where there was a strong association
between the novel words and picture referents; the un-
correlated condition, where there was no association
between the novel words and picture referents; and the
known word condition, which contained existing words
and their corresponding referents. Participants were ex-
posed to six monosyllabic spoken pseudowords in each
learning condition (therefore 12 novel pseudowords in
total) and six known words. The novel pseudowords were
assigned to each learning condition as shown in Table 1.
The pseudowords consisted of six minimal pairs drawn
from a larger pool of items for each subject. Two of the
pseudoword minimal pairs made up minimal triplets,
which consisted of two pseudowords and one known
word; these triplets were included to be later used in the
MMN test. Each item consisted of a consonant–vowel token
taken from the naturally spoken known word recording
(e.g., /kɑı/, as in kite) cross-spliced onto a /t/, /p/ or /k/
voiceless stop consonant. These were taken from the
onset of the final voiceless stop consonant in /kɑıt/, /pɑıp/,
and /bɑık/, respectively. This cross-splicing meant that
each minimal set was identical until the final stop consonant
(e.g., /kɑıt/ or /kɑıp/), with no acoustic or coarticulatory dif-
ferences before this disambiguation point (in the sub-
sequent MMN sessions, these points would be the trigger
to which we locked our ERP waveforms). Each item could
thus only be uniquely recognized at the final phoneme.
All spoken stimuli were recorded and edited in Cool Edit
2000, and peak sound energy was equated across items.
Table 1. An Example Stimulus Set Used in the Learning Task
Correlated Words
Known Words
Uncorrelated Words
boap /boʊp/
kipe /kɑıp/
jep /dʒεp/
vate /veıt/
pite /pɑıt/
clep /klεp/
boat /boʊt/
kite /kɑıt/
jet /dʒεt/
stick /stık/
pipe /pɑıp/
bike /bɑık/
boak /boʊk/
kike /kɑık/
clet /klεt/
stit /stıt/
vape /veıp/
bipe /bɑıp/
The IPA transcription is shown beside each word. The middle column
shows the known words (in bold). The column to the left shows the min-
imal pairs with these known words (in bold) that would be used in the
correlated learning condition. The column to the right shows a similar
list that would be used for the uncorrelated condition. In the subsequent
MMN sessions, we would only use the minimal triplets (the top two lines).
However, to make the learning task sufficiently challenging, we used
extra known words that had a minimal pair (which we could allocate to
either learning condition) and novel words that were minimal pairs with
each other (these are shown in italics).
Hawkins, Astle, and Rastle
777
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
t
t
f
/
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
–
p
–
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
.
/
t
f
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
Inclusion of known words in the learning task equated prior
exposure to both the pseudowords and known words that
would later be presented in the oddball task. All pseudo-
words were counterbalanced between the correlated and
uncorrelated learning conditions.
On each learning trial, the auditory presentation of a
word was followed by two pictures. In the correlated word
condition, one of these pictures was frequently a referent
object, and the other picture was a nonreferent foil object.
In the uncorrelated word condition, both pictures were
always nonreferent foil objects. In the known word condi-
tion, one picture was frequently the known word referent
(e.g., a picture of a kite), and the other picture was a non-
referent foil object (Figure 1). The visual stimuli consisted
of six known objects, which were prototypical referents of
the known words, and 30 novel objects, which were ob-
scure real objects. For each participant, six novel objects
were randomly selected as referents for the six correlated
words; the remaining 24 were nonassociated foil objects,
which were shown with a different word on each trial.
After participants had been exposed to all 18 words and
36 pictures, the foil pictures were reassigned to different
words on the following round of trials. A different foil pic-
ture was thus presented with each word on each round
of trials. One foil was presented beside the referent cate-
gory picture for correlated and known words, and two
foils were presented with the uncorrelated words. There
were 40 exposures to each word over the course of the
learning task.
The six correlated pseudowords were thus frequently
associated with the same novel object; the six uncorrelated
pseudowords were presented without a consistent pic-
ture association. The participantsʼ task was to respond as
to whether one of the two pictures was the referent for
that word or whether the referent was not present. In
the known and correlated word conditions, the referent
could either be present (2/3 trials) or absent (1/3 trials).
On referent-absent trials, a different referent object from
that condition was presented on every trial. This protocol
ensured that accuracy for the correlated words emerged
from learning a one-to-one mapping between a correlated
pseudoword and referent, rather than simply a category of
“referent” objects. After responding, participants received
feedback on whether they had selected the correct or
incorrect referent. To maintain response motivation and
attentiveness in the uncorrelated condition, positive feed-
back was randomly given at chance levels on each expo-
sure. Because chance levels were considered 1/3 for this
purpose (based on participants being able to respond “left
object,” “right object,” or “neither object” on each trial),
this meant that 1/3 of uncorrelated word responses were
followed by positive feedback. This positive feedback was
randomly interspersed with the 2/3 of negative feedback
trials over the course of the experiment.
Test of Phonological Form Learning
The MMN is an ERP measure most commonly evoked
in passive oddball paradigms to a rare “deviant” stimulus
within a stream of “standard” filler stimuli (Näätänen et al.,
1997). The MMN is suggested to measure a memory trace
evoked by the deviant (Pulvermüller & Shtyrov, 2006)
or prediction error from the standard auditory stream
(Winkler, 2007) and is highly sensitive to a range of lexical
variables (e.g., Shtyrov, Kimppa, Pulvermüller, & Kujala,
2011). We followed the design of Shtyrov et al. (2010) by
presenting novel word deviants against a background of
known word standards. Critically, in this design, the deviant
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
t
t
f
/
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
–
p
–
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
t
f
/
.
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
Figure 1. The layout of a single learning trial. After responding participants received positive (“correct”) feedback with a green square or
negative (“incorrect”) feedback with a red square of the same size.
778
Journal of Cognitive Neuroscience
Volume 27, Number 4
Figure 2. Schematic of stimulus presentation in the multifeature
oddball task (adapted from Näätänen et al., 2004). S denotes the
standard filler known word, DC denotes the correlated word deviant,
and DUC denotes the uncorrelated word deviant.
stimulus must be detected as phonologically distinct from
the standard to elicit an MMN (e.g., Shtyrov et al., 2010). It
hence provides a pure measure of relative discrimination
of newly acquired spoken words at a neural level, eliminat-
ing confounds of task goals and explicit memory processes
commonly evoked by behavioral testing.
To present both a newly learned correlated and uncor-
related word against a competitor environment of known
words in the oddball task, we employed a multifeature odd-
ball paradigm (Näätänen, Pakarinen, Rinne, & Takegata,
2004), in which we interspersed two repetitions of a known
filler (i.e., boat) with a newly learned pseudoword deviant.
The task started with 15 presentations of the known token
(e.g., boat) to habituate participants to the filler stimulus
(Fisher, Grant, Smith, & Knott, 2011). There were then
900 trials in total, constituting 300 pseudoword expo-
sures (150 correlated, i.e., boap; 150 uncorrelated, i.e.,
boak) and 600 known filler exposures (i.e., boat), with
an 800 msec SOA (Shtyrov et al., 2010). The pseudowords
and fillers thus had a 1/3 and 2/3 presentation probability,
respectively (Figure 2). A different minimal triplet was
used in the oddball task on each day. Counterbalancing
of the critical pseudowords between correlated and uncor-
related conditions and day of testing (Day 1/Day 2) meant
that the same sounds were present in each novel pseudo-
word category, ensuring an evoked neural response there-
fore emerged from the learned psycholinguistic properties
of that word rather than salient acoustic properties.
Recognition Memory Task
On Day 2, participants completed a recognition memory
test following the oddball task. The foil words diverged
from the novel pseudowords at the final consonant,
where items were voicing-contrast minimal pairs in all
but one case. Examples of the recognition foils are given
in Table 2.
Procedure
The learning task was run on Day 1, with stimuli deliv-
ered via headphones using E-Prime (Psychology Software
Tools, Sharpsburg, PA). There were 40 exposures to
all stimuli and 720 trials in total. Referent-present and
referent-absent trials were randomized, and the order
of items was randomized within each round of expo-
sures. On each trial, participants first heard the spoken
word followed by the presentation of two pictures.
Instructions explained that the task required learning
which words went with which objects. Participants re-
sponded using arrow keys based on whether the left,
right, or neither picture was the referent object.
The oddball task was run after the learning task on Day 1,
and on Day 2, when participants returned to the laboratory
after a 24-hr delay. Stimuli were presented through head-
phones while participants watched a silent video to detract
attention from the auditory stream. A questionnaire about
detailed events in the video at the end of each day yielded
a mean accuracy of 81.22% (SD = 7.61) on Day 1 and
81.94% (SD = 7.93) on Day 2, verifying participants had
been sufficiently engaged in the video.
In the recognition memory task on Day 2, participants
heard each pseudoword and foil presented in isolation
and responded via keyboard to indicate whether that item
was familiar or unfamiliar.
Finally, in the association recall task on Day 2, participants
were presented with all 12 trained pseudowords via the
headphones and were instructed to write each word under
its corresponding picture if they were confident the word
went with that picture. The task was self-paced, and partici-
pants made a key-press to advance to the next word. At the
presentation of each word, a number also appeared on the
screen, which participants were instructed to write beside
the word on their response sheet. This ensured accuracy in
coding responses in case of difficulties reading the handwrit-
ten responses, as the pseudoword forms were highly similar.
EEG Preprocessing and ERP Formation
The EEG data were acquired using a 64-channel Biosemi
(Amsterdam, the Netherlands) ActiveTwo system, using a
Table 2. Example Foil Words Used in the Recognition
Memory Task
Correlated Words
Recognition
Foils
Uncorrelated
Words
Recognition
Foils
Association Recall Task
After the oddball session and recognition memory task
on Day 2, participants were tested on their memory of
the word–picture associations learned on Day 1. Partici-
pants responded using a sheet of paper with an array
of 30 pictures from the learning task, six of which were
referents for the correlated words and 24 of which were
foil pictures.
boap
kipe
jep
vate
pite
clep
boab
kibe
jeb
vade
pide
cleb
boak
kike
clet
stit
vape
bipe
boag
kige
cled
stid
vabe
bibe
Hawkins, Astle, and Rastle
779
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
f
/
t
t
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
–
p
–
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
t
f
.
/
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
Figure 3. The learning
curve for the correlated word
associations over the learning
task. Each data point shows
the mean accuracy across
participants on that exposure,
and the error bars show the
standard deviation. Note
that because we measured
associative learning accuracy,
the uncorrelated items do
not have a learning curve.
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
t
t
f
/
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
–
p
–
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
.
/
f
t
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
10–20 setup. Two additional electrodes were placed on the
outer canthi of each eye, and two electrodes were placed
above and below the right eye to record saccadic and blink
oculomotor artifacts, respectively. Two electrodes were
placed on the right and left mastoid to re-reference the
data offline, and the EEG was recorded using a 2000-Hz
sampling rate.
EEG data were downsampled to 250 Hz and filtered
with a 1-Hz high-pass filter. An independent compo-
nents analysis, which used tools from both Fieldtrip and
EEGlab, removed oculomotor artifacts (Shimi & Astle,
2013; Oostenveld, Fries, Maris, & Schoffelen, 2011;
Delorme & Makeig, 2004; Ungureanu, Bigan, Strungaru,
& Lazarescu, 2004). To account for the different disambig-
uation points between the minimal pair triplets used on
each day, the data were epoched such that the disambigu-
ation point for each individual item occurred at exactly
0 msec in peristimulus time. Analyses were thus locked
to the relative disambiguation point for each item, per-
mitting a precise analysis of any MMN memory trace ac-
tivation as a function of psycholinguistic properties of the
newly learned words. EEG data were then epoched −600
to 200 msec (with “0” the relative disambiguation point
across items) and processed with a 30-Hz low-pass filter.
Epoched data were rebaselined to −50 to 0 msec before
the relative disambiguation point to account for the shift-
ing of the epoch point between items. Because of the
varying disambiguation point across items, different rela-
tive word intensities preceded the disambiguation point;
baselining the data immediately before disambiguation
thus ensured these acoustic differences did not contribute
to the MMN (e.g., Shtyrov et al., 2010). The removal of
excessively noisy trials was then implemented using the
Fieldtrip Visual Artifact Rejection tool (Oostenveld et al.,
2011); this measured the overall variance in voltage within
each trial, and trials with exceptionally high variance were
removed. This process removed 3.52% of trials overall. Fol-
lowing this, the remaining trials were averaged to form an
ERP for each condition over the oddball task on each day.
Midline electrodes Cz, CPz, Pz, POz, and Oz, showing the
most negative raw voltage in the grand-averaged topogra-
phies, were pooled together for spatial smoothing and to
increase the signal-to-noise ratio because of the relatively
low number of trials per condition (Shtyrov et al., 2010).
Mean amplitudes in a 50-msec time window from the first
negative peak in the grand-averaged waveform across both
days and pseudoword conditions (∼130–180 msec) were
analyzed. To isolate the MMN from other components, a
difference wave was computed by subtracting each partic-
ipantʼs known standard voltage from their correlated and
uncorrelated pseudoword voltage on each day (cf. Bishop
& Hardiman, 2010). This difference wave measured the
degree of critical pseudoword discrimination from the
competitor environment of known words.
To examine any quantitative consolidation-based changes
in discrimination, we analyzed the MMN difference wave
elicited by correlated and uncorrelated deviants over the
oddball task on each day. We reasoned that if there was a
facilitatory effect of consolidation on phonological form
learning there should be a greater evoked MMN magnitude
on Day 2 for one (or both) of the newly learned pseudo-
word types. Although an online increase in a pseudoword
MMN within a single session has been found previously in
a comparable oddball task (Shtyrov et al., 2010), we
reasoned that a consolidation-driven change in discrimina-
tion should yield an overall quantitative change in MMN
magnitude from Day 1 to Day 2.
RESULTS
Behavioral Data
Performance on the learning task indicated good knowl-
edge of the correlated word associations by the end
of the exposure session, with group-level accuracy aver-
aged over the final ten exposures of the learning task
at 74.38% (SD = 19.69), which was significantly above
chance levels [t(23) = −16.35, p < .001]. Figure 3 shows
780
Journal of Cognitive Neuroscience
Volume 27, Number 4
the learning curve for the correlated words over the course
of the experiment.
In the recognition memory test, conducted on Day 2,
six participants did not respond for more than 50% of
trials in one condition, meaning accuracy scores could
not be computed for those indivdiuals. Recognition mem-
ory accuracy scores for the remaining participants showed
above-chance recognition of both correlated and uncorre-
lated pseudowords [correlated: t(17) = 19.56, p < .001;
uncorrelated: t(17) = 6.42, p < .001]. A Condition (2: cor-
related vs. uncorrelated) × Item Type (2: Learned item vs.
Foil) ANOVA on percentage accuracy further yielded a sig-
nificant main effect of Condition only [F(1, 17) = 23.31,
p < .001, ηp
2 = .58], with correlated words exhibiting
significantly higher recognition accuracy than uncorrelated
words [t(17) = 4.83, p < .001; correlated M = 91.67%,
SD = 9.04; uncorrelated M = 71.3%, SD = 14.07]. There
was no effect of Condition on recognition memory RTs.
The association recall test, conducted on Day 2 after
the oddball and recognition memory test, was scored by
the percentage of correlated words correctly assigned to
their referent picture (out of the array of 30 pictures). Per-
centage accuracy showed that participants retained good
knowledge of the word and picture associations on Day 2
(M = 64.58%, SD = 22.15). Errors were predominantly
from “no object” responses (not assigning correlated
words to an object; 20.83%). Assigning a correlated word
to an incorrect picture constituted 9.03% of errors, and
labeling a picture with the uncorrelated minimal pair of
its correlated label (e.g., labeling the boap correlated
object as a boak) constituted 4.86% of errors.
ERP Data
Effects of Consolidation and Meaning on the MMN
To examine any consolidation-based changes in discrimina-
tion, the correlated and uncorrelated MMN difference wave
on each Day was submitted to a Condition (Correlated vs.
Uncorrelated) × Day (Day 1 vs. Day 2) repeated-measures
ANOVA. This comparison yielded a significant main effect
of Condition only [F(1, 23) = 14.02, p = .001, ηp
2 = .38],
with a significantly more negative correlated word MMN
(M = −0.34, SD = 0.63) than uncorrelated word MMN
(M = 0.06, SD = 0.55) (Figure 4). There was no main effect
of Day and no interaction between Condition and Day
(both Fs < 1 and ps > .4).
Relationship between the MMN and Semantic
Association Learning
The ERP analysis clearly showed enhanced phonological
form discrimination for correlated words relative to uncor-
related words and that this enhancement was present on
both days, suggesting that consolidation did not strengthen
access to the new phonological representations. However,
CLS accounts also predict that a second function of con-
solidation may be the transformation of episodic repre-
sentations to abstract lexical representations (e.g., Davis &
Gaskell, 2009). From this prediction, there are (at least)
two possible sources of knowledge about newly learned
phonological forms. One is episodic knowledge from re-
cent learning, whereas the other is via a lexical store inde-
pendent of episodic knowledge. Given this, we sought to
distinguish the contribution of these two sources of knowl-
edge to our MMN effects, to ascertain whether different
types of knowledge drove the MMN on each day. We thus
investigated the extent to which the explicit learning of
semantic associations on Day 1 underpinned the MMN for
correlated words on Day 1 and Day 2. One participant was
excluded from this analysis, because of having a learning
score >2.5 standard deviations below the mean accuracy
score. We ran a partial correlation, controlling for word list,
between each participantʼs accuracy score on the correlated
word associations at the end of the learning task (averaged
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
f
/
t
t
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
–
p
–
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
/
.
f
t
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
Figure 4. (A) MMNs to the correlated, uncorrelated, and known words on Day 1 and Day 2, averaged across electrodes Cz, CPz, Pz, POz, and Oz.
The vertical dashed line shows the relative disambiguation point across items. (B) The difference scores for the correlated and uncorrelated words
from the known word standards, plotted for Day 1 and Day 2. The error bars show the SEM for a within-subject design (Cousineau, 2005).
Hawkins, Astle, and Rastle
781
Figure 5. The relationship
between the correlated
word MMN and semantic
learning accuracy from
the training task on Day 1
(before consolidation; left)
and Day 2 (after consolidation;
right). MMN magnitude is
plotted on the y axis, and
semantic learning accuracy
is plotted on the x axis.
The polarity of the y axis
is reversed for ease of
interpretation.
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
f
/
t
t
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
–
p
–
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
t
.
/
f
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
over the final 10 exposures) and the correlated word MMNs
on Day 1. This analysis revealed a significant negative cor-
relation between semantic learning accuracy and the corre-
lated word MMNs [r(20) = −.59, p < .005]. This analysis
indicated that, as semantic learning accuracy improved, cor-
related word discrimination improved, which was indexed
by a more negative MMN voltage. We then tested whether
this benefit of episodic knowledge extended to enhanced
discrimination of the correlated words on Day 2 and found
no significant relationship [r(20) = .18, p = .44]. Mengʼs
Z test (Meng, Rubin, & Rosenthal, 1992) confirmed that
the correlations differed significantly between Day 1 and
Day 2 (Z = 2.56, p = .01). Figure 5 presents scatterplots
of these correlations.1 Our correlational analyses thus sug-
gested that, in the correlated word condition, phonological
discrimination (indexed by the MMN) was initially tied to
semantic learning accuracy, but following a period of offline
consolidation, there was no relationship between semantic
learning and phonological discrimination.
DISCUSSION
We sought to establish whether the provision of systematic
semantic information facilitates the learning of phonological
representations and how overnight consolidation impacts
on these representations. Participants learned spoken novel
words accompanied by a novel visual referent, which was
either systematically associated with the novel word (corre-
lated condition) or differed on every trial (uncorrelated
condition). We subsequently tested newly acquired phono-
logical representations using the MMN potential as an index
of auditory discrimination. Results showed a main effect
of semantic condition only, with those words in the cor-
related condition yielding enhanced discrimination from
known words. There was no strengthened access to phono-
logical representations by overnight consolidation. How-
ever, although discrimination performance did not change
as a function of consolidation, correlational analyses
suggested that it was underpinned by different sources
of knowledge across the two days of testing. Explicit knowl-
edge of the semantic associations in the learning task was
reflected in discrimination of the correlated words on
Day 1 (r = −0.59) but not on Day 2 (r = .18). The critical
finding of this study is thus that semantic information can
enhance the acquisition of new phonological representa-
tions, with the possibility that a period of offline consolida-
tion may assist in the abstraction of these representations.
As shown by the correlated word MMNs on both Day 1
and Day 2, semantic knowledge facilitated the learning of
phonological representations. Although previous conclu-
sions regarding the role of semantic exposure on aspects
of word learning have been mixed (cf. Leach & Samuel,
2007; Breitenstein et al., 2005; Dumay et al., 2004), the
current study demonstrates that the provision of system-
atic semantic information confers a selective benefit for
acquiring new phonological form representations. This
result poses important constraints on models of word
learning and memory, which must account for a semantic
influence on phonology not only during known word rec-
ognition (e.g., Tyler, Voice, & Moss, 2000) but also during
the relatively early stages of learning these words in the
first place. Distributed connectionist models can perhaps
best account for this interactive influence between lan-
guage subsystems (e.g., Davis & Gaskell, 2009; Gaskell &
Marslen-Wilson, 1997; McClelland et al., 1995). In par-
ticular, a key behavior of such models is that novel words
characterized by systematic mappings between word
forms and meanings are learned with greater ease than
novel words lacking this systematicity (Rueckl & Dror,
1994). The current study extends these findings by sug-
gesting that novel words with a degree of systematicity
(i.e., a semantic association) are not only learned more
readily than those without, but that this systematicity
directly impacts upon phonological form learning itself,
rather than simply word level recall of new items.
However, such models may benefit from considering
the impact of learning goals on the outcome of the acqui-
sition process. The learning task in the current study em-
phasized associative learning, but it is equally plausible that
an emphasis on phonological learning would minimize
782
Journal of Cognitive Neuroscience
Volume 27, Number 4
the recruitment of semantic information during training
and consequently not afford such a semantic benefit (see
Takashima et al., 2014; also cf. Yoncheva, Blau, Maurer, &
McCandliss, 2010; Forster, 1985). The impact of learning
goals on initial acquisition also has implications for the time
course of consolidation; for example, Szmalec et al. (2012)
suggested that the implicit learning of new word forms via
a repetition task led to more efficient lexical consolidation
than the explicit learning of word forms (as in phoneme
monitoring paradigms; e.g., Dumay & Gaskell, 2007).
Learning goals are thus a central factor in evaluating the
extent to which semantic information is recruited during
training and its subsequent impact on consolidation.
The conclusion that semantic systematicity impacts on
phonological form learning in particular is supported by
the temporal precision of the MMN we used to measure
learning. Similar to Shtyrov et al. (2010), we observed an
evoked MMN for the discrimination of the novel (correlat-
ed) words from existing words following a defined recogni-
tion point in the speech signal, where the MMN indexed
the perceived phonological contrast between the novel
word and known word. Furthermore, as the MMN was
elicited automatically in the absence of attention to the
speech stream and thus without any specific processing
goals, it provided a precise measure of the degree of pho-
nological discrimination of the newly learned words.
Notably, we employed a multifeature MMN paradigm that
required the fine-grained discrimination of two minimal
novel words from a known word, which could be substan-
tially more taxing than the learning and discrimination of a
single minimal novel and known word pair as in Shtyrov
et al. (2010). The increase in phonological learning de-
mands in the current study could have thus contributed
to observing no stable MMN response for the uncorrelated
words on Day 2. It is also notable that the MMN is sensitive
to the familiarity of linguistic stimuli, where it is evoked for
familiar words rather than simply in response to a pho-
nemic contrast. The MMN can distinguish native phonemic
contrasts, where discrimination between native phoneme
categories (e.g., /bɑ/-/dɑ/) elicits an MMN without any
training (e.g., Shestakova et al., 2002; Phillips et al., 2000;
Dehaene-Lambertz, 1997; Näätänen et al., 1997). However,
when native phonemic contrasts are presented within novel
words the MMN is significantly reduced (Pulvermüller et al.,
2001). Shtyrov and Pulvermüller (2002) tested whether this
reduction of MMN for phonemic contrasts in novel words
was because of the unfamiliarity of novel word stimuli by
comparing the MMN responses for (i) word deviants against
word standards, (ii) word deviants against pseudoword
standards, and (iii) pseudoword deviants against word stan-
dards. The MMN elicited by word deviants (conditions i
and ii) was significantly greater than for the pseudoword
deviants (condition iii). This suggested a critical factor in
MMN magnitude to linguistic stimuli was familiarity of the
deviant stimulus rather than simply a phonemic or lexicality
difference between the deviant and standard stimuli,
in which case the pseudoword deviant versus word stan-
dard should have elicited a comparable MMN (see also
Korpilahti, Krause, Holopainen, & Lang, 2001, for similar
results). Interestingly, this suggests that the MMN evoked
by word stimuli may be based at least partly on a top–down
influence of word representations benefiting discrimination.
It is possible that the uncorrelated words did not establish
strong enough representations to influence discrimina-
tion on Day 1 or Day 2 in the current study and potentially
had a slower time course of establishing new phonological
representations.
One important question following this is whether the
inconsistent pictures in the uncorrelated word condition
unfairly disadvantaged the learning of the uncorrelated
phonological forms, thus potentially exaggerating the
systematic semantic benefit observed. Although we cannot
rule out this possibility, it is important to recognize that
the uncorrelated words had significantly above-chance be-
havioral recognition accuracy on Day 2, indicating that
participants had a degree of familiarity with the uncorre-
lated words, albeit less than the correlated words. Further-
more, varying the associative systematicity between the
correlated and uncorrelated condition arguably provided
a more realistic proxy of real-world learning than contrast-
ing the correlated word condition with a “form-only” con-
dition (e.g., as in Dumay et al., 2004; Takashima et al.,
2014). In real-world situations, we are rarely exposed to a
spoken word with no potential semantic meaning or goal to
acquire one, and it is not uncommon to experience a word
in different contexts across several exposures and thus
struggle to extract a specific meaning, such as in the case
of words with multiple meanings (e.g., bug). Finally, in ex-
perimental situations contrasting semantic and “form-only”
conditions (e.g., Takashima et al., 2014; Dumay et al.,
2004), there is not only a difference in semantic content be-
tween the two conditions but also a categorical difference in
learning goals, information load, and attentional demands.
We therefore suggest that the current learning paradigm
provides a contrast between associative semantic learning
and an ambiguous learning situation where words could be
treated as having either many potential referents or no refer-
ent, which is not unlike real-world word learning situations.
Recent research on consolidation effects in novel word
learning has drawn heavily on CLS theories of memory
(e.g., Davis & Gaskell, 2009; McClelland et al., 1995). The
central tenet of these theories is that newly learned words
are stored initially as episodic representations mediated
by a fast-learning hippocampal store and over a period
of consolidation become less dependent on this episodic
memory as they become integrated with existing knowl-
edge and therefore represented neocortically. If this in-
stantiation is correct, we should expect to see a greater
contribution of episodic knowledge to phonological form
representations immediately after learning, with decay in
this episodic contribution over time as newly learned words
become increasingly lexicalized (see Tamminen & Gaskell,
2013, for a similar argument). Our correlational analysis
suggested this was the case: Episodic knowledge of the
Hawkins, Astle, and Rastle
783
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
t
t
f
/
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
-
p
-
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
/
.
t
f
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
semantic associations was tied to correlated word dis-
crimination on Day 1 but was unrelated to it on Day 2.
Importantly, the lack of association between semantic
learning and the correlated word MMN on Day 2 is unlikely
to be the result of participants simply forgetting the asso-
ciations: The association recall accuracy data collected on
Day 2 showed that participants retained strong knowledge
of the correlated word–picture associations after con-
solidation, with 64.58% accuracy when selecting the correct
referent from an array of the novel pictures. It is important
to note that this is a substantially more difficult task than
selecting from the two pictures presented in the learning
task. Taken together, these data suggest that consolidation
decreased the reliance of the correlated word phonological
forms on learned associations from the training task and
that this decreased reliance may have been a specific con-
sequence of consolidation, rather than failure to retain
memory of the associations overnight. These data are
consistent with a CLS account (Davis & Gaskell, 2009;
McClelland et al., 1995) and extant literature suggesting
that consolidated knowledge can be represented indepen-
dently of episodic knowledge (e.g., Tamminen & Gaskell,
2013; Tamminen et al., 2012; see also Gomez et al., 2006).
Nonetheless, we recognize that because the consolidation-
based abstraction of the correlated words was based on a
correlational change, rather than more direct evidence
of independence from memory of semantic associations,
this finding requires support in future research.
Alternative accounts of lexical learning assert that words
learned in adulthood can only be represented episodically
(e.g., Qiao, Forster, & Witzel, 2009; Jiang & Forster, 2001).
Our data add to evidence that is inconsistent with this claim
(see also Dumay & Gaskell, 2012). If the newly learned words
could only achieve an episodic representation, we ought to
have observed a postconsolidation relationship between
the correlated word MMN and semantic learning. That this
was not the case suggests that new phonological forms
may be represented independently of episodic knowledge
and that this independent representation could require
offline consolidation. However, it is important to recog-
nize that we did not measure the engagement of the
newly learned words with existing lexical items (Leach &
Samuel, 2007). It thus remains to be established what
consequences this effect on phonological representations
has for the full lexical integration of newly learned words
and their engagement with existing knowledge.
The current findings thus pose several questions for spec-
ifying the impact of semantics on the word learning process.
Given the adverse effect of semantic exposure on the time
course of lexical integration (Takashima et al., 2014; Dumay
et al., 2004; cf. Henderson et al., 2013), one possibility is that
the semantic benefit on learning new phonological form
representations observed here does not transfer to their
offline integration with existing lexical items. This would
suggest that phonological form learning and lexical integra-
tion reflect two separate stages of word memory formation,
which are differentially impacted by semantic information.
Alternatively, it could be the case that a learning task with
semantic information must also recruit phonological in-
formation sufficiently well for the time course of lexical inte-
gration to be unimpaired by semantic knowledge. We note,
however, that acquiring new semantic knowledge in terms
of visual referents may differ from learning more seman-
tically rich meanings, which link to existing semantic knowl-
edge, and we have thus measured just one aspect of a
semantic influence on word learning. It is also the case that
our novel items had a large phonological neighborhood
size in contrast to the studies of Takashima et al. (2014)
and Dumay et al. (2004), which utilized items with few close
phonological neighbors (e.g., cathedruke–cathedral ).
Thus, it is also possible that semantic knowledge is beneficial
only in the acquisition of new words with high phonological
neighborhoods, akin to the impact of imageability (a seman-
tic variable) in skilled spoken word recognition for words
in high competition cohorts only (e.g., Tyler et al., 2000).
The way in which a semantic advantage for learning new
phonological form representations relates to the offline
impact of semantic knowledge in lexical integration there-
fore remains an important avenue for future work.
We have demonstrated that systematic semantic
knowledge facilitates the acquisition of new phonological
representations and that consolidation may provide an
opportunity for these phonological representations to
become less dependent on the episodic knowledge they
are linked to before consolidation. Given the mixed evi-
dence for the precise role of semantic information in word
learning, the current study provides an important ad-
vance by suggesting that this knowledge is advantageous
for the relatively low-level learning of phonological form
representations and that these representations may be
abstracted from episodic knowledge as a consequence
of offline overnight consolidation. We thus provide new
evidence elucidating the nature and time course of a se-
mantic influence on the development of new phonological
representations.
Acknowledgments
D. E. A. was supported by a British Academy Postdoctoral Fellow-
ship and by the Medical Research Council (United Kingdom)
intramural program (MC-A060-5PQ40). K. R. was supported by
a research grant from the Economic and Social Reseach Council
(United Kingdom; ES/L002264/1).
Reprint requests should be sent to Erin Hawkins, Department of
Psychology, Royal Holloway University of London, Egham, Surrey,
TW20 0EX, United Kingdom, or via e-mail: erin.hawkins.2011@
live.rhul.ac.uk.
Note
1. A reviewer wondered whether this correlation was driven by
a data point in the bottom left corner, which showed a learning
score of 45%. When this data point was removed, the pattern
of data was unchanged [Day 1: r(19) = −.57, p = .007; Day 2:
r(19) = .41, p = .07], and the correlations still differed signifi-
cantly between Day 1 and Day 2 (Mengʼs Z = 3.07, p = .002).
784
Journal of Cognitive Neuroscience
Volume 27, Number 4
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
t
t
f
/
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
-
p
-
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
/
.
f
t
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
REFERENCES
Baddeley, A., Gathercole, S., & Papagno, C. (1998). The
phonological loop as a language learning device.
Psychological Review, 105, 158–173.
Bishop, D. V. M., & Hardiman, M. J. (2010). Measurement of
mismatch negativity in individuals: A study using single-trial
analysis. Psychophysiology, 47, 697–705.
Bowers, J. S., Davis, C. J., & Hanley, D. A. (2005). Interfering
neighbours: The impact of novel word learning on the
identification of visually similar words. Cognition, 97,
B45–B54.
Breitenstein, C., Jansen, A., Deppe, M., Foerster, A.-F.,
Sommer, J., Wolbers, T., et al. (2005). Hippocampus
activity differentiates good from poor learners of a novel
lexicon. Neuroimage, 25, 958–968.
Cousineau, D. (2005). Confidence intervals in within-subject
designs: A simpler solution to Loftus and Massonʼs method.
Tutorials in Quantitative Methods for Psychology, 1,
42–45.
Gomez, R. L., Bootzin, R. R., & Nadel, L. (2006). Naps promote
abstraction in language-learning infants. Psychological
Science, 17, 670–674.
Henderson, L., Weighall, A., & Gaskell, G. (2013). Learning new
vocabulary during childhood: Effects of semantic training
on lexical consolidation and integration. Journal of
Experimental Child Psychology, 116, 572–592.
Hultén, A., Vihla, M., Laine, M., & Salmelin, R. (2009). Accessing
newly learned names and meanings in the native language.
Human Brain Mapping, 30, 976–989.
Jiang, N., & Forster, K. I. (2001). Cross-language priming
asymmetries in lexical decision and episodic recognition.
Journal of Memory and Language, 44, 32–51.
Karni, A., Tanne, D., Rubenstein, B. S., Askenasy, J. J., &
Sagi, D. (1994). Dependence on REM sleep of overnight
improvement of a perceptual skill. Science, 265, 679–682.
Korman, M., Doyon, J., Doljansky, J., Carrier, J., Dagan, Y., &
Karni, A. (2007). Daytime sleep condenses the time course
of motor memory consolidation. Nature Neuroscience, 10,
1206–1213.
Davis, M. H., Di Betta, A. M., Macdonald, M. J., & Gaskell,
Korpilahti, P., Krause, C. M., Holopainen, I., & Lang, A. H.
M. G. (2009). Learning and consolidation of novel spoken
words. Journal of Cognitive Neuroscience, 21, 803–820.
Davis, M. H., & Gaskell, M. G. (2009). A complementary systems
account of word learning: Neural and behavioural evidence.
Philosophical Transactions of the Royal Society B, 364,
3773–3800.
Dehaene-Lambertz, G. (1997). Electrophysiological correlates
of categorical phoneme perception in adults. NeuroReport,
8, 919–924.
Delorme, A., & Makeig, S. (2004). EEGLAB: An open source
toolbox for analysis of single-trial EEG dynamics. Journal
of Neuroscience Methods, 134, 9–21.
Dumay, N., & Gaskell, G. (2007). Sleep-associated changes in
the mental representation of spoken words. Psychological
Science, 18, 35–39.
Dumay, N., & Gaskell, M. G. (2012). Overnight lexical
consolidation revealed by speech segmentation. Cognition,
123, 119–132.
Dumay, N., Gaskell, M. G., & Feng, X. (2004). A day in the life
of a spoken word. In K. Forbus, D. Gentner, & T. Regier (Eds.),
Proceedings of the 26th Annual Conference of the Cognitive
Science Society (pp. 339–344). Mahwah, NJ: Erlbaum.
Durrant, S. J., Taylor, C., Cairney, S., & Lewis, P. A. (2011).
Sleep-dependent consolidation of statistical learning.
Neuropsychologia, 49, 1322–1331.
Ellenbogen, J. M., Hu, P. T., Payne, J. D., Titone, D., &
Walker, M. P. (2007). Human relational memory requires
time and sleep. Proceedings of the National Academy
of Sciences, U.S.A., 104, 7723–7728.
Fernandes, T., Kolinsky, R., & Ventura, P. (2009). The
metamorphosis of the statistical segmentation output:
Lexicalization during artificial language learning.
Cognition, 112, 349–366.
Fisher, D. J., Grant, B., Smith, D. M., & Knott, V. J. (2011).
Effects of deviant probability on the “optimal” multi-feature
mismatch negativity (MMN) paradigm. International
Journal of Psychophysiology, 79, 311–315.
Forster, K. I. (1985). Lexical acquisition and the modular
lexicon. Language and Cognitive Processes, 1, 87–108.
Gaskell, M. G., & Dumay, N. (2003). Lexical competition and
the acquisition of novel words. Cognition, 89, 105–132.
Gaskell, M. G., & Marslen-Wilson, W. D. (1997). Integrating
form and meaning: A distributed model of speech perception.
Language and Cognitive Processes, 12, 613–656.
Gaskell, M. G., & Marslen-Wilson, W. D. (2002). Representation
and competition in the perception of spoken words.
Cognitive Psychology, 45, 220–266.
(2001). Early and late mismatch negativity elicited by words
and speech-like stimuli in children. Brain and Language,
76, 332–339.
Leach, L., & Samuel, A. G. (2007). Lexical configuration
and lexical engagement: When adults learn new words.
Cognitive Psychology, 55, 306–353.
Lindsay, S., & Gaskell, M. G. (2013). Lexical integration of novel
words without sleep. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 39, 608–622.
McClelland, J. L., & Elman, J. L. (1986). The TRACE model
of speech perception. Cognitive Psychology, 18, 1–86.
McClelland, J. L., McNaughton, B. L., & OʼReilly, R. C. (1995).
Why there are complementary learning systems in the
hippocampus and neocortex: Insights from the successes
and failures of connectionist models of learning and memory.
Psychological Review, 102, 419–457.
Meng, X., Rubin, D, & Rosenthal, R. (1992). Comparing
correlated correlation coefficients. Psychological Bulletin,
111, 172–175.
Merkx, M., Rastle, K., & Davis, M. H. (2011). The acquisition
of morphological knowledge investigated through artificial
language learning. The Quarterly Journal of Experimental
Psychology, 64, 1200–1220.
Näätänen, R., Lehtokoski, A., Lennes, M., Cheour, M.,
Huotilainen, M., Iivonen, A., et al. (1997). Language-specific
phoneme representations revealed by electric and magnetic
brain responses. Nature, 385, 432–435.
Näätänen, R., Pakarinen, S., Rinne, T., & Takegata, R. (2004).
The mismatch negativity (MMN): Towards the optimal
paradigm. Clinical Neurophysiology, 115, 140–144.
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011).
FieldTrip: Open source software for advanced analysis
of MEG, EEG, and invasive electrophysiological data.
Computational Intelligence and Neuroscience, 2011, 1–9.
Page, M. P. A., & Norris, D. (2009). A model linking immediate
serial recall, the Hebb repetition effect and the learning of
phonological word forms. Philosophical Transactions of
the Royal Society B, 364, 3737–3753.
Papagno, C., & Vallar, G. (1992). Phonological short-term
memory and the learning of novel words: The effect of
phonological similarity and item length. The Quarterly
Journal of Experimental Psychology Section A: Human
Experimental Psychology, 44, 47–67.
Phillips, C., Pellathy, T., Marantz, A., Yellin, E., Wexler, K.,
Poeppel, D., et al. (2000). Auditory cortex accesses
phonological categories: An MEG mismatch study.
Journal of Cognitive Neuroscience, 12, 1038–1055.
Hawkins, Astle, and Rastle
785
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
f
/
t
t
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
-
p
-
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
t
f
.
/
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
Pulvermüller, F., Kujala, T., Shtyrov, Y., Simola, J., Tiitinen, H.,
Alku, P., et al. (2001). Memory traces for words as revealed
by the mismatch negativity. Neuroimage, 14, 607–616.
Pulvermüller, F., & Shtyrov, Y. (2006). Language outside
the focus of attention: The mismatch negativity as a tool
for studying higher cognitive processes. Progress in
Neurobiology, 79, 49–71.
Qiao, X., Forster, K. I., & Witzel, N. (2009). Is banara really
a word? Cognition, 113, 254–257.
Rabovsky, M., Sommer, W., & Abdel Rahman, R. (2012).
Implicit word learning benefits from semantic richness:
Electrophysiological and behavioural evidence. Journal
of Experimental Psychology: Learning, Memory, and
Cognition, 38, 1076–1083.
Rodd, J. M., Berriman, R., Landau, M., Lee, T., Ho, C., Gaskell,
M. G., et al. (2012). Learning new meanings for old words:
Effects of semantic relatedness. Memory and Cognition,
40, 1095–1108.
Rueckl, J. G., & Dror, I. (1994). The effect of orthographic-
semantic systematicity on the acquisition of new words.
In C. Umiltà & M. Moscovitch (Eds.), Attention and
performance XV (pp. 571–588). Hillsdale, NJ: Erlbaum.
Rueckl, J. G., & Olds, E. M. (1993). When pseudowords acquire
meaning: Effect of semantic associations on pseudoword
repetition priming. Journal of Experimental Psychology:
Learning, Memory, and Cognition, 19, 515–527.
Sandak, R., Mencl, W. E., Frost, S. J., Rueckl, J. G., Katz, L.,
Moore, D. L., et al. (2004). The neurobiology of adaptive
learning in reading: A contrast of different training
conditions. Cognitive, Affective, & Behavioural Neuroscience,
4, 67–88.
Shestakova, A., Brattico, E., Huotilainen, M., Galunov, V.,
Soloviev, A., Sams, M., et al. (2002). Abstract phoneme
representations in left temporal cortex: Magnetic
mismatch negativity study. NeuroReport, 13, 1813–1816.
Shimi, A., & Astle, D. E. (2013). The strength of attentional
biases reduces as visual short-term memory load increases.
Journal of Neurophysiology, 110, 12–18.
Shtyrov, Y., Kimppa, L., Pulvermüller, F., & Kujala, T. (2011).
Event-related potentials reflecting the frequency of
unattended spoken words: A neuronal index of connection
strength in lexical memory circuits? Neuroimage, 55, 658–668.
Shtyrov, Y., Nikulin, V. V., & Pulvermüller, F. (2010). Rapid
cortical plasticity underlying novel word learning. Journal
of Neuroscience, 30, 16864–16867.
Shtyrov, Y., & Pulvermüller, F. (2002). Neurophysiological
evidence of memory traces for words in the human brain.
NeuroReport, 13, 521–525.
Snoeren, N. D., Gaskell, M. G., & Di Betta, A. M. (2009). The
perception of assimilation in newly learned novel words.
Journal of Experimental Psychology: Learning, Memory,
and Cognition, 35, 542–549.
St. Clair, M. C., & Monaghan, P. (2008). Language abstraction:
Consolidation of language structure during sleep. In
Proceedings of the 30th Annual Conference of the Cognitive
Science Society (pp. 727–732). Mahwah, NJ: Erlbaum.
Szmalec, A., Page, M. P. A., & Duyck, W. (2012). The development
of long-term lexical representations through Hebb repetition
learning. Journal of Memory and Language, 67, 342–354.
Takashima, A., Bakker, I., van Hell, J. G., Janzen, G., & McQueen,
J. M. (2014). Richness of information about novel words
influences how episodic and semantic memory networks
interact during lexicalization. Neuroimage, 84, 265–278.
Tamminen, J., Davis, M. H., Merkx, M., & Rastle, K. (2012).
The role of memory consolidation in generalisation of
new linguistic information. Cognition, 125, 107–112.
Tamminen, J., & Gaskell, M. G. (2013). Novel word integration
in the mental lexicon: Evidence from unmasked and masked
semantic priming. The Quarterly Journal of Experimental
Psychology, 66, 1–25.
Tamminen, J., Payne, J. D., Stickgold, R., Wamsley, E. J., &
Gaskell, M. G. (2010). Sleep spindle activity is associated with
the integration of new memories and existing knowledge.
The Journal of Neuroscience, 30, 14356–14360.
Taylor, J. S., Plunkett, K., & Nation, K. (2011). The influence
of consistency, frequency, and semantics on learning to read:
An artificial orthography paradigm. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 37, 60–76.
Tyler, L. K., Voice, J. K., & Moss, H. E. (2000). The interaction
of meaning and sound in spoken word recognition.
Psychonomic Bulletin & Review, 7, 320–326.
Ungureanu, M., Bigan, C., Strungaru, R., & Lazarescu, V. (2004).
Independent component analysis applied in biomedical
signal processing. Measurement Science Review, 4, 1–8.
Winkler, I. (2007). Interpreting the mismatch negativity.
Journal of Psychophysiology, 21, 147–163.
Yoncheva, Y. N., Blau, V. C., Maurer, U., & McCandliss, B. D.
(2010). Attentional focus during learning impacts N170
ERP responses to an artificial script. Developmental
Neuropsychology, 35, 423–445.
D
o
w
n
l
o
a
d
e
d
f
r
o
m
l
l
/
/
/
/
j
f
/
t
t
i
t
.
:
/
/
h
t
t
p
:
/
D
/
o
m
w
i
n
t
o
p
a
r
d
c
e
.
d
s
f
i
r
o
l
m
v
e
h
r
c
p
h
a
d
i
i
r
r
e
.
c
c
t
.
o
m
m
/
j
e
d
o
u
c
n
o
/
c
a
n
r
a
t
r
i
t
i
c
c
l
e
e
-
p
-
d
p
d
2
f
7
/
4
2
7
7
/
7
4
5
/
1
7
9
7
4
5
8
/
9
1
1
7
6
8
o
2
c
6
n
5
_
4
a
/
_
j
0
o
0
c
7
n
3
0
_
a
p
_
d
0
0
b
7
y
3
g
0
u
.
e
p
s
t
d
o
f
n
b
0
y
8
S
M
e
I
p
T
e
m
L
i
b
b
e
r
r
a
2
r
0
i
2
3
e
s
/
j
f
.
/
t
u
s
e
r
o
n
1
7
M
a
y
2
0
2
1
786
Journal of Cognitive Neuroscience
Volume 27, Number 4
