Learning and Consolidation of Novel Spoken Words

Abstract

Two experiments explored the neural mechanisms underlying the learning and consolidation of novel spoken words. In Experiment 1, participants learned two sets of novel words on successive days. A subsequent recognition test revealed high levels of familiarity for both sets. However, a lexical decision task showed that only novel words learned on the previous day engaged in lexical competition with similar-sounding existing words. Additionally, only novel words learned on the previous day exhibited faster repetition latencies relative to unfamiliar controls. This overnight consolidation effect was further examined using fMRI to compare neural responses to existing and novel words learned on different days prior to scanning (Experiment 2). This revealed an elevated response for novel compared with existing words in left superior temporal gyrus (STG), inferior frontal and premotor regions, and right cerebellum. Cortical activation was of equivalent magnitude for unfamiliar novel words and items learned on the day of scanning but significantly reduced for novel words learned on the previous day. In contrast, hippocampal responses were elevated for novel words that were entirely unfamiliar, and this elevated response correlated with postscanning behavioral measures of word learning. These findings are consistent with a dual-learning system account in which there is a division of labor between medial-temporal systems that are involved in initial acquisition and neocortical systems in which representations of novel spoken words are subject to overnight consolidation.

Full text

Learning and Consolidation of Novel Spoken Words
Matthew H. Davis
1
, Anna Maria Di Betta
2
, Mark J. E. Macdonald
3
, and M. Gareth Gaskell
2
1
Cognition and Brain Sciences Unit, Cambridge, UK
2
University of York, UK
3
University of Pennsylvania
Abstract
Two experiments explored the neural mechanisms underlying the learning and consolidation of
novel spoken words. In Experiment 1, participants learned two sets of novel words on successive
days. A subsequent recognition test revealed high levels of familiarity for both sets. However, a
lexical decision task showed that only novel words learned on the previous day engaged in lexical
competition with similar-sounding existing words. Additionally, only novel words learned on the
previous day exhibited faster repetition latencies relative to unfamiliar controls. This overnight
consolidation effect was further examined using fMRI to compare neural responses to existing and
novel words learned on different days prior to scanning (Experiment 2). This revealed an elevated
response for novel compared with existing words in left superior temporal gyrus (STG), inferior
frontal and premotor regions, and right cerebellum. Cortical activation was of equivalent
magnitude for unfamiliar novel words and items learned on the day of scanning but significantly
reduced for novel words learned on the previous day. In contrast, hippocampal responses were
elevated for novel words that were entirely unfamiliar, and this elevated response correlated with
postscanning behavioral measures of word learning. These findings are consistent with a dual-
learning system account in which there is a division of labor between medial-temporal systems
that are involved in initial acquisition and neocortical systems in which representations of novel
spoken words are subject to overnight consolidation.
INTRODUCTION
New word learning is a critical component of the human language system. By adulthood,
language users have approximately 30,000 words in their mental lexicon (Altmann, 1997;
Waring & Nation, 1997), with this lexical knowledge deployed automatically and efficiently
during listening and speaking (Levelt, Roelofs, & Meyer, 1999; Marslen-Wilson, 1984).
Retrieval of stored word knowledge is also reflected in differential neural responses evoked
by familiar words and unfamiliar pseudowords (Binder et al., 2000; Ziegler, Besson, Jacobs,
Nazir, & Carr, 1997; Bentin, McCarthy, & Wood, 1985). Here, we explore the learning
mechanisms by which novel spoken words obtain the equivalent cognitive and neural status
as preexisting, familiar words.
One cognitive marker of word knowledge is competition between lexical representations
(Gaskell & Marslen-Wilson, 1997; McClelland & Elman, 1986). We not only learn to
recognize new words such as
blog
but must also distinguish them from similar sounding
© 2008 Massachusetts Institute of Technology
Reprint requests should be sent to Matthew H. Davis, MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge, CB2
7EF, UK, or via matt.davis@mrc-cbu.cam.ac.uk, or to M. Gareth Gaskell, Department of Psychology, University of York, York,
YO10 5DD, UK, or via g.gaskell@psych.york.ac.uk..
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Published in final edited form as:
J Cogn Neurosci
. 2009 April ; 21(4): 803–820. doi:10.1162/jocn.2009.21059.
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competitors like
blag, bog
, and
clog
. Recent work has demonstrated a temporal dissociation
between these two aspects of word learning. Participants rapidly become familiar with
fictitious novel words such as
cathedruke
(measured by a recognition memory test), whereas
an effect of new learning on existing words (slowed identification of a competitor
cathedral
)
is only observed after a delay (Bowers, Davis, & Hanley, 2005; Gaskell & Dumay, 2003).
Furthermore, engagement in lexical competition is associated with sleep: A 12-hr delay
between learning and testing produces lexical competition when participants sleep in the
intervening period but not when they remain awake (Dumay & Gaskell, 2007). Such data fit
well with research demonstrating sleep-related memory consolidation in other domains (see
Walker, 2005); however, evidence of consolidation-induced changes in the neural
representation of novel words is lacking.
In the current article, we present convergent behavioral and fMRI evidence advancing a
two-stage neural model for learning the spoken form of new words: (1) initial learning is
supported by medial-temporal systems that rapidly adapt as novel words become familiar;
and (2) long-term cortical representations of new words are altered by slow, off-line
consolidation (cf. O’Reilly & Norman, 2002; McClelland, McNaughton, & O’Reilly, 1995).
Existing neuropsychological evidence suggests that the acquisition of new words depends on
hippocampal structures (Gooding, Mayes, & van Eijk, 2000; Verfaellie, Croce, & Milberg,
1995; Gabrieli, Cohen, & Corkin, 1988), whereas long-term knowledge of spoken words is
supported by cortical systems (Tyler, Marslen-Wilson, & Stamatakis, 2005; Bates et al.,
2003; Tranel, Adolphs, Damasio, & Damasio, 2001). Neuroimaging investigations
demonstrate both hippocampal (Breitenstein et al., 2005) and neocortical (Majerus et al.,
2005; Cornelissen et al., 2004) changes during exposure to new words. However, no
previous study has compared immediate and longer-term consequences of learning new
spoken words.
A further critical aspect of learning a new spoken word is that knowledge acquired must be
employed in a number of different tasks or contexts. For instance, we might have to produce
in speech a word that we have only read previously or interpret a recently learned word in an
unfamiliar sentential context. To know a word, then, implies both appropriate usage and
generalization across multiple different tasks and situations. For experimental investigations
of word learning, we can similarly distinguish between learning-induced changes that occur
when both the stimulus and the task or context are repeated (which might reflect task-
specific repetition priming; cf. Orfanidou, Marslen-Wilson, & Davis, 2006; Dobbins,
Schnyer, Verfaellie, & Schacter, 2004) and changes that imply long-term, stable, and
flexible representations of novel words. Although task-specific repetition effects may indeed
reflect aspects of word learning, a critical test is nonetheless to assess whether newly
acquired knowledge of spoken words generalizes to tasks that differ from those that were
used during training. Existing neuroimaging investigations of word learning either do not
include a test of generalization (Breitenstein et al., 2005; Majerus et al., 2005) or have failed
to showed wordlike generalization to an untrained task (Mestres-Misse, Rodriguez-Fornells,
& Munte, 2007). Here we assess the hypothesis that generalized knowledge of newly
learned spoken words involves overnight consolidation by using test tasks that differ from
those employed during initial learning.
Both behavioral and fMRI experiments reported here were conducted over 2 days. On each
day, participants learned fictitious novel words in a phoneme-monitoring task. Effects of
initial learning and overnight consolidation were assessed in behavioral and fMRI test
sessions on the second day, comparing the two sets of words trained on Day 1 or Day 2 with
untrained control items (see timelines, Figure 1). By using two sets of items learned in
separate study periods, we can test for effects of overnight consolidation within a single test
session. This is an efficient design given the potential variability of functional imaging
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results from different sessions in the same subject (McGonigle et al., 2000; Noll et al.,
1997). A further advantage of this design is that differences in word knowledge for items
learned on the same or previous day cannot reflect procedural learning of test tasks because
these are only administered once. Previous demonstrations of off-line consolidation of word
knowledge (Dumay & Gaskell, 2007; Bowers et al., 2005; Gaskell & Dumay, 2003) used a
single study period and multiple test sessions. These studies therefore confounded overnight
changes in word knowledge and practice at the test task. To assess whether procedural
learning could account for previous observations of off-line consolidation of newly learned
words, our first experiment replicates and extends behavioral findings on the overnight
emergence of lexical competition from newly learned words (Dumay & Gaskell, 2007).
EXPERIMENT 1—BEHAVIORAL EVIDENCE FOR LEARNING AND
CONSOLIDATION OF NOVEL SPOKEN WORDS
Materials and Methods
Participants—Fifty-seven students from the University of York were tested under the
supervision of the University of York Psychology Department Ethics Committee. All were
native English speakers with no known hearing or language impairment and received either
course credits or payment for their participation. Participants were randomly assigned to
three different groups.
Materials—We used 54 stimulus triplets taken from Gaskell and Dumay (2003;
n
= 33)
and Tamminen and Gaskell (2008;
n
= 21). Each triplet contained a familiar base word (e.g.,
alcohol
), a nonword to be learned as a “novel” word (e.g.,
alcohin
), and a second nonword
(e.g.,
alcohid
) that was used as a foil in the recognition memory task. Base words were
bisyllabic (
n
= 21) and trisyllabic (
n
= 33) with mean length 8.2 phonemes (
SD
= 1.2) and
frequency 5.8 per million (range = 0–19 from CELEX; Baayen, Piepenbrock, & van Rijn,
1993). Their phonemic uniqueness point (cf. Marslen-Wilson, 1984) was 6.0 (
SD
= 1.5).
Nonwords diverged from the base word on the final vowel and from each other on the final
consonant, ensuring that full lexicalization of the novel word would have the effect of
extending the uniqueness point of the associated base word (by adding a new close
competitor) and slow down the recognition of the base word (cf. Gaskell & Dumay, 2003).
The 54 triplets were divided into three matched lists of 18 items with each group of
participants trained on a set of 18 novel words on Day 1 and another set on Day 2. The
remaining 18 novel words acted as control items during testing. Thus, we can compare
responses to untrained control items with responses to novel words that have been trained
and (potentially) consolidated (Day 1 items) or that remain unconsolidated (Day 2 items).
The assignment of item lists to training conditions was counterbalanced across three groups
of participants. All the spoken words used in this study were recorded on CD-R by a native
speaker of British English in a sound-proof booth at a sampling rate of 44.1 kHz. Sound files
were digitally transferred to a computer, divided into single sound files using Cool Edit
software (Syntrillium Software, Phoenix, AZ), and trimmed to length.
Procedure—The experiment was carried out in two sessions on two consecutive days. On
Day 1, participants undertook 216 trials of a phoneme-monitoring test with each of 18 novel
words presented 12 times in total in a training session lasting approximately 15 min. Prior to
each block of 18 trials, a visual display signaled a target phoneme that participants should
listen for (/n/, /t/, /d/, /s/, /p/, /m/ used twice each). Participants were required to indicate
with a button-press if the target phoneme was present or absent and had 4 sec to respond. On
Day 2, approximately 24 hr after the first session, the same phoneme-monitoring paradigm
was used with a new list of 18 novel words. After that, participants carried out in fixed order
a
lexical competition test
, a
repetition test
, a two-alternative forced-choice
recognition test
,
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and finally a
word meaning rating test
; each of these tasks is described below. The
lexical
competition test
involved making timed lexical decisions (word/nonword) to the 54 base
words (e.g.,
alcohol
) testing whether responses were significantly slowed by competition
from newly learned novel words (such as
alcohin
). The 54 base words along with 206 filler
items (76 words and 130 nonwords) were presented in random order with a response
deadline of 3 sec and intertrial interval of 1 sec. Response latencies were recorded from the
onset of the spoken stimulus. The
repetition test
examined whether the speed with which
novel words are produced is affected by prior learning. All 54 novel words were presented
in random order, including 18 untrained items, 18 trained on the same day, and 18 trained on
the previous day. The order of presentation of the stimuli was randomized for each
participant, and each novel word was followed after a variable interval of 500, 1000, or 1500
msec by a tone that cued participants to repeat the word. Response latencies were recorded
from the beginning of the cue tone with deadlines and intertrial interval as before. The
forced-choice
recognition test
required participants to distinguish the 36 trained novel words
(e.g.,
alcohin
) from their corresponding foils (e.g.,
alcohid
). A two-alternative procedure
was employed with participants choosing whether the first or second item presented was a
learned novel word. The
word meaning rating task
involved presentation of all 54 novel
words in a random order with participants rating the extent to which each word had become
associated with meaning or acquired an invented meaning on a 7-point scale. Low ratings
(1–2) indicated that no meaning had been attached to the novel word, whereas high ratings
(6–7) indicated that a meaning had clearly been attached to the new word. Although the
instructions emphasized responding based on meaning, they also acknowledged that
differences in familiarity might also arise and so this task might reflect both subjective
familiarity and meaningfulness.
1
There was no time limit for responses in the recognition or
the meaning rating tests.
All participants were tested individually in separate cubicles, with auditory stimuli delivered
via high-quality headphones, and button-press and vocal responses were recorded using
DMDX software (Forster & Forster, 2003).
Results
The results of these behavioral tests were averaged over items for each participant, and
effects of word learning and consolidation were assessed using repeated measures ANOVAs
with SPSS. For this fully counterbalanced design, we report the results of analysis by
participants only (following Raaijmakers, Schrijnemakers, & Gremmen, 1999) and include
dummy variables to encode the subject-specific assignment of item groups to training
conditions (Pollatsek & Well, 1995).
Lexical Competition Test—Response times were inverse transformed to reduce the
influence of response time outliers (Ulrich & Miller, 1994). Mean response times shown in
Figure 2A are retrans-formed for ease of presentation (harmonic means). Analysis of
variance revealed significant differences between the three training conditions [
F
(2,108) =
3.635,
p
< .05]. Planned pairwise comparisons showed that, compared with controls for
which no novel neighbor had been presented, there was significant slowing of responses to
base words due to competition from consolidated novel items that were learned on Day 1
[
F
(1, 54) = 7.095,
p
< .01], whereas existing neighbors of unconsolidated items learned on
Day 2 showed no such slowing (
F
< 1). Critically, there was a significant difference between
responses to neighbors of consolidated (Day 1) and unconsolidated (Day 2) items [
F
(1, 54) =
1
Recent work suggests behavioral differences between words taught with and without associated meanings (Leach & Samuel, 2007).
Our intention was, therefore, that this rating task should index the degree to which participants “invented” a meaning for novel spoken
words that were presented without an associated meaning allowing comparison with other studies in which word meanings are taught
to participants.
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5.287,
p
< .05]. Thus, significant lexical competition only arises for existing word neighbors
of novel words on the day after initial acquisition. There were no significant differences in
error rates between the three training conditions (all
F
< 1).
Repetition Test—Response latencies when producing the spoken form of novel words in
the three training conditions (shown in Figure 2B) were assessed by hand-marking speech
onsets with the assistance of CheckVocal software (Protopapas, 2007). Response times
showed a marginally significant difference between repetition responses in the three
conditions [
F
(2, 108) = 2.571,
p
< .1] and no significant difference in error rate (
F
< 1).
Similar to the lexical decision results, planned pairwise comparisons revealed a (marginal)
difference in response latencies for consolidated items compared with untrained controls
[
F
(1, 54) = 3.780,
p
< .1] and no difference between unconsolidated items and untrained
controls (
F
< 1). Critically, though, there was again a significant difference between
repetition latencies for consolidated and unconsolidated items [
F
(1, 54) = 4.279,
p
< .05].
Production speed for novel words is, therefore, not affected by perceptual training on the
same day as testing but receives further significant facilitation if a period of overnight
consolidation intervenes between an initial perceptual acquisition and a speech production
test.
Recognition Memory Test—Recognition memory performance (Figure 2C) was
substantially above chance for both sets of trained items with fewer than 10% errors in this
two-alternative forced-choice test. However, this test of explicit memory was also affected
by overnight consolidation with better recognition memory for consolidated items trained on
Day 1 than unconsolidated items learned on Day 2 [
F
(1, 54) = 20.59,
p
< .001].
Meaning Rating Task—Due to a failure to follow instructions to respond to all items,
data from four participants were uninterpretable. Repeated measures ANOVAs on data from
the remaining 53 participants (Figure 2D) showed a significant effect of training condition
on strength of meaning ratings [
F
(2, 100) = 75.79,
p
< .001], with significant pairwise
differences between all three training conditions (all comparisons
p
< .001). This finding
further confirms that changes to the mental representation of novel words are produced both
by initial familiarization and by subsequent, overnight consolidation. Subjectively, novel
words become more meaningful or familiar a day after initial presentation.
In summary, these results confirm that the competitive environment of newly acquired
spoken words changes during the first 24 hr after initial training. Only those novel words for
which overnight consolidation was possible significantly slowed identification of lexical
competitors (cf. Dumay & Gaskell, 2007). Although explicit memory for novel words was
also enhanced by overnight consolidation, the emergence of lexical competition did not
reflect a failure of memory encoding for nonconsolidated items. Lexical decision responses
were similarly slowed for consolidated relative to unconsolidated novel words if analysis
was restricted, for each participant, to novel words that they correctly recognized in the
memory test [
F
(2, 108) = 3.506,
p
< .05]. Furthermore, these differences in response times
for consolidated items in the lexical decision tests were observed despite there being only a
single testing session. This finding provides valuable confirmation that previous
observations of sleep-association lexicalization (Dumay & Gaskell, 2007) are not dependent
on repeated presentation of test tasks. The results also provide more direct evidence for
changes in the representation of novel words following overnight consolidation. We found
significant changes in both the speed of spoken productions of novel words and the strength
of meaning ratings for those novel words that are subject to overnight consolidation.
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EXPERIMENT 2—NEURAL EVIDENCE FOR LEARNING AND
CONSOLIDATION OF NOVEL WORDS
Materials and Methods
Design—Experiment 2 used fMRI to measure neural responses to novel spoken words at
different stages of learning and consolidation using the same 2-day training procedure (see
Figure 1B). In this fMRI study, we assessed the neural representation of novel words
directly during the test phase (as in the repetition and meaningfulness rating tasks) rather
than by indirectly assessing responses to competitors of newly learned words (as in the
lexical competition test). A further goal in assessing neural responses to novel words was to
determine whether and when novel words evoke an equivalent neural response to
preexisting, familiar words. To avoid confounds due to recent training in comparing novel
and existing words, we included both novel words (e.g., “alcohin”) and matched
phonologically unrelated existing words (e.g., “legend”) in the training regime. In this way,
changes in the neural responses to novel words due to learning and consolidation can be
separated from effects of recent training on items with which participants are already
familiar. Thus, all three levels of preexposure (consolidated, unconsolidated, and untrained
controls) in Experiment 2 included both novel nonwords and phonologically unrelated
preexisting words. To increase statistical power in assessing fMRI responses, we presented
all stimulus items four times over the course of the experiment (three times intact and once
with an inserted pause for a detection response). Comparisons of neural responses for
untrained novel words across the three intact presentation provides a neural correlate of
initial familiarization with novel words.
Participants—Sixteen right-handed, native speakers of British English aged between 18
and 40 years participated in an fMRI study approved by the Cambridge NHS Research
Ethics Committee. All participants reported normal hearing and no neurological or language
impairment.
Materials—Because of the need to obtain robust fMRI responses to novel words in a
smaller number of participants, each training session included more repetitions of a larger
stimulus set in each condition. Experiment 2, therefore, assessed neural responses to 90
“novel” spoken words and 90 “existing” real words, both divided into three matched groups
of 30 items that varied in their time and level of preexposure prior to scanning: (1)
“untrained” items were first presented to participants during the first fMRI scanning session;
hence, the novel words in this condition were entirely unfamiliar at the start of scanning; (2)
“unconsolidated” items were presented to participants during a training session on the same
day as fMRI scanning such that novel items would be familiar to participants but not subject
to overnight consolidation; (3) “consolidated” items were presented to participants during
training on the day prior to scanning such that there was an opportunity for overnight, sleep-
associated consolidation of this set of novel words. On the basis of the previous behavioral
data and the results of Experiment 1, it is only this set of consolidated novel words that we
predicted to have a neural status approaching that of existing words.
The novel words were again taken from previous behavioral studies used to demonstrate
novel word lexical competition effects. There were 29 novel items (e.g.,
alcohin
) derived by
altering the final vowel and consonant(s) of existing base word (e.g.,
alcohol
; Gaskell &
Dumay, 2003). A further 61 items from Dumay and Gaskell (2007) were used in which the
novel word (e.g.,
fellowks
) was generated by adding one consonant (55 items) or two
consonants (6 items) to the existing word (e.g.,
fellow
). Items with two additional
consonants had offset segments that existed in monomorphemic English words (e.g., the
final syllable of “fellowks” rhymes with hoax or coax).
2
For each novel word, two matched
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items were selected: a preexisting English word and a nonword. The existing words were
matched to the novel words on the number of syllables, phonemes, and consonant–vowel
structure to the novel words and were matched for lemma frequency to the source word.
These matched words were phonologically unrelated to the base words from which the novel
items had been derived (e.g., the novel word
fellowks
, derived from
fellow
, was matched to
the existing word
legend
). The matched nonwords were used as foil items in recognition
memory tests and were highly similar to the novel words used in training (e.g., for
fellowks
the nonword
fellowkt
was used).
Stimuli were divided into three groups, matched for word frequency, length in phonemes/
syllables, and uniqueness/divergence points (cf. Marslen-Wilson, 1984). Each group of
items was assigned to the three training conditions in a counterbalanced fashion for each
volunteer. Stimuli were recorded and digitally transferred as before. To create target items
for the pause-detection task used during scanning, we inserted a 200-msec silent period into
each speech stimulus using Cool Edit software.
Procedure—The two training sessions were similar to those used in Experiment 1. In each
session, participants were exposed to one of the three sets of novel and existing words (for
details, see Figure 1B). One training session occurred on the previous day to scanning
(consolidated items: mean = 27 hr before scanning; range = 25 to 31 hr) and another session
on the same day as scanning (unconsolidated items: mean = 4 hr before scanning; range = 2
to 8 hr). Participants were trained on both novel and existing words described above using
the same phoneme-monitoring task as previously with six consonants (/p/, /b/, /k/, /m/, /n/, /
s/), which occurred with equal frequency in the three groups of 30 novel and 30 existing
words. Participants were presented with a randomly ordered sequence of items (intertrial
interval 2 sec) and instructed to press a button when they detected a prespecified phoneme.
Each participant monitored for each target phonemes five times, such that each stimulus was
presented 30 times for a total of 1800 phoneme-monitoring trials (30 trials for 30 novel
words and 30 familiar words) in a training session that lasted approximately 60 min.
During scanning, participants heard all the novel and existing words in a constrained,
pseudorandom order generated using Mix software (van Casteren & Davis, 2006). In each of
three 13-min scanning runs, all 180 stimulus items (90 novel and 90 existing words) were
presented along with 60 trials (30 novel and 30 existing words, drawn equally from the three
training conditions), in which a 200-msec silent pause was inserted into each speech
stimulus. Participants performed a pause-detection task (cf. Mattys & Clark, 2002), pressing
a button with their right index finger when they heard one of the 25% trials with an inserted
pause. Our fMRI analysis focused on the majority pause-absent trials in which words were
presented intact, and no button press was made. A further 60 null event trials (when no
stimulus was presented) were included in each scanning run to provide a resting baseline
against which responses to heard stimuli can be assessed (Josephs & Henson, 1999) and an
initial search volume for fMRI data analysis. Auditory stimuli were presented over high-
quality headphones (Resonance Technology, Commander XG system) using DMDX
software (Forster & Forster, 2003). Participants were given an opportunity to practice the
pause-detection task before entering the scanning.
2
An anonymous reviewer suggested that the inclusion of these two segment offsets made certain of our items phonological unusual.
We quantified this by computing biphone probabilities for the entire set of 90 items, which showed a small yet reliable difference in
the predicted direction: mean (
SD
) biphone frequency, words = 10,487 (5336), pseudowords = 8424 (5095),
t
(89) = 3.09,
p
< .01.
Although this small difference in phonological typicality might contribute to the overall response differences between words and
pseudowords, it cannot explain changes in word/pseudoword differences due to learning and consolidation because items were
assigned to training conditions in a counterbalanced fashion.
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After leaving the scanner, participants completed three behavioral tasks from Experiment 1
(lexicalization, recognition memory, and meaning rating). Due to the lack of a suitable
environment for recording naming responses, the repetition task was omitted. The lack of a
quiet, sound-proof testing environment also impacted on response times in the lexicalization
test, which were more variable than previously. These tests were conducted using a Sony
VAIO laptop running DMDX software (Forster & Forster, 2003) interfaced with a two-
button response box. All behavioral procedures were identical to Experiment 1 but used the
larger set of novel words and competitors from Experiment 2.
fMRI Scanning and Data Analysis—MR Imaging was performed with a 3-T Bruker
Medspec MRI scanner using a head coil. Functional images were collected using 21 axial
slices angled away from the eyes and covering most of the brain (slice thickness 4 mm,
interslice distance 1 mm, matrix size 64 × 64, field of view 20 cm × 20 cm, in plane
resolution of approximately 3 × 3 mm) with an EPI sequence (TR = 2.506 sec). To avoid
interfering effects of scanner noise, we used bunched image acquisition in which a single
volume (TA = 1.1 sec) is acquired, followed by a silent period (1.406 sec) during which a
single stimulus item is presented (for details, see Figure 1C). Due to the slow evolution of
the stimulus-evoked BOLD response, the hemodynamic response to each stimulus was
sampled with an adequate temporal resolution over five subsequent scans. In each of three
experimental sessions, 306 functional EPI images were acquired (~13 min scanning time per
session). Six images at the start of each run were discarded before preprocessing to allow the
EPI signal to reach equilibrium. High-resolution anatomical images (SPGR) and fieldmaps
were also acquired for use in preprocessing and normalization.
Functional imaging data were preprocessed and analyzed using Statistical Parametric
Mapping software (SPM2, Wellcome Department of Cognitive Neurology, London, UK):
Images were corrected for motion by realigning them to the first image, and a magnetic field
map was used to correct for geometric distortions to the EPIs (Cusack, Brett, & Osswald,
2003). The mean of the realigned, undistorted images was coregistered with the structural
T1 volume, which was then spatially normalized to the standard MNI template image. The
same spatial transformation was then applied to the EPI volumes. Normalized images were
smoothed with a 12-mm FWHM Gaussian kernel suitable for random-effects analysis
(Xiong et al., 2000).
Data from each participant were entered into a general linear model for an event-related
analysis (Josephs & Henson, 1999) with 13 event types in each session (the factorial
crossing of pause-present and pause-absent trials, novel and existing words, untrained,
unconsolidated, or consolidated at the time of scanning, plus an additional event type for
rare pause-detection errors). Events were modeled using the SPM2 canonical hemodynamic
response function (HRF) with temporal and dispersion derivatives (Henson, Price, Rugg,
Turner, & Friston, 2002). Movement parameters estimated at the realignment stage of
preprocessing were added as regressors of no interest. A high-pass filter (128 sec) was
applied and AR1 correction for serial autocorrelation was made. Contrasts of parameter
estimates from the least-mean square fit of single-subject models were entered into random-
effects analyses (one-sample
t
tests) comparing the mean parameter estimate for the
canonical response to zero over subjects. Significant results that passed voxel-wise false-
discovery rate correction for multiple comparisons are reported (FDR,
p
< .05; Genovese et
al., 2002). To ensure consistent presentation despite changes in the FDR-corrected threshold
for different contrasts, figures show results thresholded at
p
< .001 uncorrected, with
probability scales marked to indicate the FDR-corrected threshold. Graphs showing signal
change in specific brain regions present the mean parameter estimate for the SPM canonical
HRF in each single condition with zero reflecting the fit relative to scans following
unmodeled null events (cf. Josephs & Henson, 1999).
Davis et al.
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Results
Behavioral responses collected in the scanner were scored for the accuracy and the speed of
pause-detection responses. Responses were fast and accurate throughout (see Table 1), with
faster and more accurate pause-detection responses for preexisting, real words [accuracy:
F
(1,16) = 8.418,
p
< .01; response time:
F
(1,16) = 51.766,
p
< .001]. Responses were also
significantly more accurate for both consolidated and unconsolidated trained items [
F
(2,32)
= 5.851,
p
< .01], although changes in response times were only marginally significant
[
F
(2,32) = 2.817,
p
= .075]. There was no interaction between effects of lexicality and
training condition on accuracy or response time (both
F
< 1).
Neural Effects of Consolidation—In assessing the fMRI data, we focus on pause-
absent trials so that neural responses can be assessed in the absence of overt behavioral
responses. We begin by contrasting responses to all pause-absent trials with the resting
baseline provided by the null events. This contrast highlights a bilateral network of superior
and middle temporal regions, motor cortex, SMA, cerebellum, and hippocampus (HC) as
well as a more lateralized response in the left inferior frontal gyrus, insula, and premotor
cortex. These regions provide an initial search volume for responses to spoken words as a
function of preexperimental familiarity (novel/existing words) and training (untrained/
unconsolidated/consolidated items).
We then examined brain areas that showed a differential response to novel and existing
words (see Figures 3A–3C; Tables 2A–2C) in each training condition individually. In none
of the three training conditions was additional activity observed for familiar compared with
novel words. Furthermore, no additional response to existing compared with novel words
was observed when data from all three training conditions were combined to increase
statistical power (no voxels reach
p
> .001 uncorrected or FDR corrected). As discussed
later, the absence of an elevated response to familiar words might reflect our use of an
acoustic-phonetic task (pause detection) that emphasizes phonological processing.
Phonological processes are more difficult for less familiar words. Consequently, we
consistently observed an elevated response to novel compared with existing words in this
study.
For unconsolidated and untrained items, we observed an elevated response to novel
compared with existing words in a bilateral region of the STG, extending into posterior
middle temporal gyrus on the left (see Figures 3A and 3B; Tables 2A and 2B). In addition to
these lateral temporal responses, there was an elevated response to novel words in bilateral
motor cortex, SMA, and cerebellum, although these effects were more robust for items
trained on the same day as the scanning (unconsolidated items) than for the items that had
not been presented prior to scanning (untrained). Whereas both unconsolidated and
untrained items showed a similar lexicality effect (confirmed by statistical comparisons
reported later), the same comparison of novel and existing words for items on which
participants had been trained on the day prior to scanning did not reach a corrected level of
significance (see Figure 3C; Table 2C).
The lack of a difference between the neural responses to existing and novel words trained on
the day prior to scanning (hence that had been consolidated) is striking because it suggests
that these novel words have acquired more wordlike representations. However, to be
confident that this null effect is meaningful, a significant difference between lexicality
effects for items trained on the same day and the previous day to scanning is required. That
is, we must show a signification interaction reflecting a reduction in the lexicality effect due
to consolidation: (unconsolidated novel–unconsolidated existing) > (consolidated novel–
consolidated existing). As shown in Figure 3D and Table 3, this contrast picks out a number
of regions in which the elevated response to novel words over existing words was
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significantly reduced for consolidated items: in bilateral motor and somatosensory regions
on the precentral and postcentral gyri, the left premotor cortex, the SMA, and the right
cerebellum. The response profile picked out by this interaction reflects a significant
reduction in the magnitude of the novelty effect specific to items that were trained on the
day prior to scanning (see, e.g., the peak voxel in the right cerebellum shown in Figure 3E).
A second interaction contrast tested for any differences between lexical responses to
untrained and unconsolidated items. This comparison (unconsolidated novel–unconsolidated
existing) > (untrained novel–untrained existing) only revealed a single, small cluster in the
left anterior insula, which failed to reach a corrected level of significance (30 voxels at
p
< .
001 uncorrected, peak voxel,
x
= −20,
y
= +32,
z
= +4,
Z
= 3.43,
p
= .611 FDR). Thus, in
contrast to the significant differences following overnight consolidation, repeated exposure
to novel words earlier in the same day has little immediate effect on their neocortical
representation.
We also conducted an ROI analysis using MarsBar (Brett, Anton, Valabregue, & Poline,
2002) to assess the response of the superior regions of the left temporal lobe, an area that
shows a robust additional response to novel compared with existing words both in the
current study and in the previous work (Kotz, Cappa, von Cramon, & Friederici, 2002;
Newman & Twieg, 2001). This ROI was defined using the cluster of voxels that showed a
main effect of lexicality (novel > existing words, averaged over all three training
conditions), generating a large cluster centered on the posterior superior and middle
temporal gyri (center of mass,
x
= −54,
y
= −30,
z
= +6). In assessing the response of this
STG region, we computed a positive
t
contrast, assessing the same interaction between
lexicality and consolidation shown in the whole-brain analysis [i.e., (unconsolidated novel–
unconsolidated existing) > (consolidated novel–consolidated existing)]. This showed that the
average response of the STG to novel words is significantly reduced for consolidated,
relative to unconsolidated items [
t
(15) = 1.78,
p
< .05; shown in Figure 3F].
Initial Learning of Novel Spoken Words—To assess neural responses to initial
presentations of novel spoken words, we now focus on the untrained items. The novel words
in this condition were entirely unfamiliar before the first functional imaging scan but were
presented four times over the three scanning runs (one pause-present and three pause-absent
presentations). By the time of the postscan recognition memory test, participants were able
to distinguish these novel words from highly similar foils with above chance accuracy [mean
2AFC recognition = 75%;
t
(15) = 11.21,
p
< .001]. Nonetheless, these items differ from the
other novel items, which were presented 30 times prior to and four times during the
scanning session. Mean 2AFC recognition performance was 79% for unconsolidated and
94% for consolidated novel items trained on the day before scanning. Recognition memory
performance differed between the three training conditions [
F
(2,30) = 37.67,
p
< .001] with
planned pairwise comparisons confirming that (as in Experiment 1) recognition memory was
significantly enhanced by overnight consolidation [difference between consolidated and
unconsolidated items;
t
(15) = 6.70,
p
< .001]. Interestingly, there was only a marginal
enhancement of recognition memory by training on the same day as scanning when
compared with untrained items that were only presented during scanning [
t
(15) = 1.71,
p
< .
1 one-tailed].
To assess neural correlates of the initial learning of novel words, we compared untrained
novel items with the two sets of novel items that had been trained prior to scanning. Of
particular interest are neural responses during the first scanning run since only at this time
are these untrained items truly novel. Although some voxels showed an additional response
to untrained novel words compared with consolidated and unconsolidated novel words in
this first scanning run [including a cluster of 17 voxels at
p
< .001 uncorrected in the left HC
and anterior fusiform gyrus, peak voxel:
x
= −34,
y
= −10,
z
= −22;
Z
= 3.85,
p
= .112
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FDR], this comparison failed to reach whole-brain corrected significance. Given previous
imaging studies suggesting a role for the HC in learning new words (Breitenstein et al.,
2005) and theoretical accounts that emphasize a role for medial-temporal regions in rapid
learning (McClelland et al., 1995), we used a hippocampal ROI to further investigate the
initial acquisition of novel spoken words. This ROI was generated by assessing neural
activity for pause-absent trials compared with rest at
p
< .05 FDR within the left HC region
of the automated anatomical labeling (AAL) map of the MNI single subject brain (Tzourio-
Mazoyer et al., 2002). This procedure provides an ROI of 675 voxels, with center of mass at
x
= −22,
y
= −18,
z
= −12 (shown in Figure 4A), overlapping with an anterior HC activation
region that previously showed a reduction in HC activity over five presentations of
consistent nonword-picture pairings (Breitenstein et al., 2005; Saykin et al., 1999).
To assess the response of the HC, we used MarsBar (Brett et al., 2002) to extract the mean
response of the HC ROI to novel words in each training condition (untrained,
unconsolidated, and consolidated) for the initial and subsequent scanning runs (Figure 4B).
Statistical analysis of HC responses confirmed significant activity for untrained novel words
compared with recently trained novel words in the first scanning run [
t
(15) = 1.938,
p
< .05].
Paired comparisons suggested that recent training significantly reduces the hippocampal
response to novel words; untrained novel items produced an elevated response compared
with unconsolidated trained items [
t
(15) = 2.76,
p
< .05], whereas the equivalent comparison
with consolidated trained items was nonsignificant [
t
(15) = 0.662,
p
> .1].
3
Such a result
replicates other fMRI findings (Bosshardt et al., 2005) in demonstrating that hippocampal
responses following item familiarization change as a function of time since encoding. That
the elevated response to untrained novel words is reduced by training is confirmed by
equivalent statistical tests for subsequent scanning sessions. The difference between
untrained and unconsolidated novel words was nonsignificant in subsequent scanning
sessions [
t
(15) = 1.03,
p
> .1], and there was a significant reduction in this difference
between untrained and trained novel words between the first and the subsequent sessions
[
t
(15) = 2.54,
p
< .05]. Such results suggest that the HC contributes primarily to the initial
acquisition of novel spoken words.
Further evidence that the elevated HC response to novel words contributes to initial
acquisition of novel words comes from correlational analyses relating HC activity for
untrained novel words to individual’s recognition memory performance for those same
novel words after scanning. The magnitude of the HC response in Session 1 for untrained
compared with trained novel words is positively correlated with recognition memory
performance [
r
(16) = 0.440,
p
< .05; Figure 4C]. Furthermore, the degree of reduction in the
HC response between first and subsequent scanning runs shows a marginally significantly
correlation with recognition memory performance [
r
(16) = 0.408,
p
= .058] such that those
subjects with better subsequent recognition memory produced larger reductions in HC
activity. These results support previous findings that link changes in hippocampal activity to
successful acquisition of novel spoken words (Breitenstein et al., 2005).
Postscanning Behavioral Results—Lexical decision data were lost for one participant
due to a software error. Data from the remaining 15 participants failed to reveal an effect of
consolidation on responses to real-word neighbors of newly learned words (
F
< 1). This may
reflect a priming effect from recent presentations of novel words or more likely (and less
interesting) reduced power due to their being fewer participants and a nonoptimal testing
3
This pattern might suggest that the hippocampal response recovers following sleep, although direct comparison of responses to
unconsolidated and consolidated novel items does not reach statistical significance [
t
(15) = 1.65,
p
= .12] in the first scanning session.
This difference was significant in subsequent scanning sessions, although responses to unconsolidated novel items were then greater
than for consolidated items [
t
(15) = 2.20,
p
< .05].
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environment for this response time experiment. However, despite these problems with this
on-line test, the meaning/familiarity rating task showed the same effects of training and
consolidation observed previously [
F
(2,30) = 27.60,
p
< .001] with significant pairwise
differences (all
p
< .01) between ratings for previously untrained items (mean = 3.19 of 7),
unconsolidated items (3.63), and consolidated items (4.34). Hence, the same participants
that show differential fMRI responses as a function of learning and consolidation of novel
words also show changes in behavioral response to newly learned words equivalent to those
seen in Experiment 1.
DISCUSSION
We have demonstrated changes in the cognitive and the neural representation of previously
unfamiliar spoken words as a consequence of learning and overnight consolidation. Previous
behavioral demonstrations of word consolidation used multiple test sessions (Dumay &
Gaskell, 2007; Bowers et al., 2005; Gaskell & Dumay, 2003) hence confounded changes in
levels of word knowledge and test practice. In contrast, both our experiments employed a
single test session following 2 days of training with different items. The emergence of
lexical competition between consolidated novel words (
cathedruke
) and familiar words
(
cathedral
) in Experiment 1 must, therefore, reflect changes in the underlying
representations of novel words rather than practice at the test task. Although the current
research was not intended to test directly whether consolidation is associated with sleep,
previous research in which participants either remained awake or slept for equivalent
periods of time showed that the mere passage of time is insufficient to generate lexical
competition (Dumay & Gaskell, 2007). It seems likely, therefore, that the emergence of
lexical competition ordinarily requires overnight, sleep-associated consolidation of newly
learned words.
4
Our study also showed other behavioral and neuroimaging differences between words
learned on the same day as testing and those learned the day before. For these findings, a
similar comparison between groups that sleep or remain awake following training has not
previously been conducted. We, therefore, cannot rule out the possibility that certain of our
findings might be due to interference between novel words learned first and second or due to
the mere passage of time in the absence of sleep. However, existing data on proactive and
retroactive interference would predict relatively little between-list interference given that the
two sets of pseudowords taught to participants were entirely dissimilar to each other (cf.
Bower, Thompson-Schill, & Tulving, 1994). Recent evidence would further suggest that the
degree of between-list interference will be further reduced by postlearning sleep
(Drosopoulos, Schultze, Fischer, & Born, 2007). Although future studies that include a
between-groups assessment of words learned and tested on the same day or subsequent days
would be helpful, we consider it most likely that many of the response differences between
novel words learned on the same day and previous day to testing similarly reflect overnight
changes in the cognitive and neural representation of newly learned words. In support of this
conclusion, we note that comparisons between untrained and unconsolidated conditions
were nonsignificant in both the lexical competition and the repetition test of Experiment 1.
In Experiment 2, a relatively long delay of approximately 4 hr intervened between Day 2
training and scanning, yet we observed no effect of this training on cortical responses to
4
Our study does not demonstrate that overnight consolidation is necessary for enacting the changes in representation that we discuss
because we cannot rule out the possibility that some unspecified training regime might produce such changes in the absence of
overnight consolidation. However, we have demonstrated that in the context of a reasonably representative exposure session,
overnight consolidation facilitates the engagement of novel word representation into various aspects of normal word processing
including lexical competition. Indeed, behavioral experiments have assessed a range of training methods including supplying a
meaning and a sentential context and have found no evidence of engagement in lexical competition prior to sleep (e.g., Dumay et al.,
2004).
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novel words. This might suggest that our results do not arise from the same form of nonsleep
associated memory stabilization that has been suggested for motor skill learning (Walker,
Brakefield, Hobson, & Stickgold, 2003).
One interesting result consistent with consolidation-induced changes comes from the
speeded-repetition task. Production of novel words was unaltered by same-day training but
showed reliable facilitation following overnight consolidation. Strength of meaning ratings
also showed reliable increases for novel words learned on the previous day. These findings
have interesting parallels in the learning of other cognitive skills. Participants trained on
visual target sequences containing hidden probabilistic rules developed implicit, procedural
knowledge (shown by RT reductions; Fischer, Drosopoulos, Tsen, & Born, 2006). However,
they only showed explicit sequence knowledge after sleep. We demonstrate the reverse
dissociation, with participants showing explicit knowledge of newly learned words (good
recognition memory) but failing to show implicit procedural effects (in word recognition
and production tasks) until the following day. These results suggest that one facet of sleep’s
role in memory consolidation is to provide for bidirectional information flow between
explicit and implicit learning systems.
Evidence of overnight consolidation of newly learned words is also provided by differential
neural responses to novel words familiarized prior to or following sleep. Our fMRI
comparison between real and novel words assessed whether familiarization produces
pseudoword representations comparable with those of words. Previous fMRI studies
comparing real and novel words (without training) showed elevated responses for words in
brain regions supporting access to meaning including the supramarginal gyrus (Orfanidou et
al., 2006; Xiao et al., 2005; Kotz et al., 2002; Binder et al., 2000), inferior temporal, and
fusiform gyri (Orfanidou et al., 2006; Majerus et al., 2002; Binder et al., 2000). In contrast,
pseudowords increase phonological processing demands and evoke elevated activity
compared with words in phonologically associated brain regions, such as the superior and
the middle temporal gyri (Majerus et al., 2005; Xiao et al., 2005; Kotz et al., 2002; Newman
& Twieg, 2001).
Consistent with this previous literature and our use of an acoustic or a phonological task
(pause detection), we observed a stronger bilateral STG response for pseudowords than
matched words (for items that were not consolidated at the time of scanning). An additional
response to pseudowords was also observed in the inferior frontal, premotor, and motor
cortices and in the cerebellum, an elevated response shown most clearly for learned but not
consolidated items. Recent studies have shown that left frontal and premotor regions are
engaged in phonological processing of spoken language if motoric processes are not
concurrently engaged for overt decisions (Meister, Wilson, Deblieck, Wu, & Iacoboni,
2007; Pulvermuller et al., 2006; Wilson, Saygin, Sereno, & Iacoboni, 2004; Watkins,
Strafella, & Paus, 2003; Fadiga, Craighero, Buccino, & Rizzolatti, 2002). Functional and
anatomical connectivities between posterior temporal and prefrontal/premotor regions
(Wilson & Iacoboni, 2006; Buchsbaum, Olsen, Koch, & Berman, 2005; Catani, Jones, &
ffytche, 2005) support the recruitment of this distributed network for sensorimotor
integration of spoken language (for a discussion, see Davis & Johnsrude, 2007; Scott, 2005;
Hickok & Poeppel, 2004). In the present study, we thus see additional recruitment of
prefrontal and premotor regions for pseudowords.
Strikingly, these elevated neural responses were not reduced by 30 prior presentations of
pseudowords on the same day as scanning. Extensive prior familiarization does not lead to
wordlike neural responses in brain regions involved in phonological processing and speech–
motor integration. However, equivalent familiarization followed by nocturnal sleep does
result in a more wordlike neural response. Thus, behavioral and fMRI data converge in
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suggesting that overnight consolidation creates more efficient representations of newly
learned words in cortical systems involved in speech perception and production.
A Two-stage Account of Word Learning and Consolidation
Our findings suggest a two-stage account of word learning, with rapid initial familiarization
followed by slower, overnight consolidation, consistent with dual memory systems theories
(McClelland et al., 1995). One motivation for these dual-process theories is the “catastrophic
interference” suffered by distributed connectionist networks if new information is learned
too quickly (McClosky & Cohen, 1989). A proposed solution assumes functional and
anatomical separation between a hippocampal system that specializes in initial storage of
new memories and neocortical networks that use slower, interleaved learning to combine
new and existing knowledge without interference (O’Reilly & Norman, 2002; French, 1999;
McClelland et al., 1995). Evidence consistent with this theory suggests that sleep may play
an important role in the consolidation of hippocampal learning (e.g., Skaggs &
McNaughton, 1996; Wilson & McNaughton, 1994). For instance, single-cell recordings
provide evidence that neural activity in the HC, and visual cortex during awake exploration
is “replayed” during slow-wave sleep (Ji & Wilson, 2007). The present study supports an
extension of this dual-process theory to the acquisition of novel spoken words.
Our data showing the immediate consequences of novel word acquisition lend further
support to this dual-process account of word learning. The recognition memory data showed
that participants are sufficiently familiar with recently presented novel words to distinguish
them from similar-sounding foils. The neuroimaging data demonstrated stronger activity in
the HC for untrained novel words on their initial presentation, with lesser activation on
second and third presentation, and a particularly subdued response after extensive prior
training on the same day. Further evidence to link hippocampal responses to initial
acquisition comes from correlational analyses, which show that those participants that
produce a larger HC response to untrained words and a greater reduction in HC responses
over the three scanning sessions have better subsequent recognition memory for novel
words. We, therefore, propose a specific role for the HC in the initial acquisition of novel
spoken words such that learners can distinguish newly learned words from similar-sounding
foils after only three intact presentations. This conclusion is consistent with similar results
for the learning of written word to picture pairings (Breitenstein et al., 2005). Hippocampal
involvement in word learning is also supported by evidence that medial-temporal lobe
lesions substantially impair vocabulary acquisition (Gooding et al., 2000; Verfaellie et al.,
1995; Gabrieli et al., 1988).
The overnight changes that we have seen in cortical responses to novel words may appear at
odds with the fact that retrograde amnesia can impair recall of memories formed years or
even decades prior to hippocampal damage (Squire, 1992). To be clear, we are not
proposing that all representations of novel words are transferred from HC to neocortex
overnight. However, we do suggest that a substantial (and perhaps the largest) change in
hippocampal involvement occurs on the first night after learning. Thus, behavioral and fMRI
studies can demonstrate observable effects of just a single night of consolidation. This
profile of hippocampal involvement fits with a previous neuroimaging study of declarative
memory for photographs over the course of 90 days following learning (Takashima et al.,
2006). By far, the largest changes to hippocampal and ventral medial prefrontal activation
on subsequent recognition occurred within 24 hr of exposure, with more modest changes
occurring in the following 90 days. Furthermore, the time course of hippocampal
dependence on new learning may relate to both the task used to assess learning and the
degree of prior experience of a particular domain of knowledge. For instance, studies of
place–food learning in rats have shown that the duration of hippocampal dependence of
newly acquired associations depends on the degree of prior experience and schematization
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of place–food associations (Tse et al., 2007). For animals with the appropriate schema,
newly learned associations became independent of the HC in less than 48 hr—consistent
with the time span of sleep-associated consolidation proposed in this and previous studies of
word learning (e.g., Dumay & Gaskell, 2007). By analogy to lexical learning, then, we
might expect that participants could more rapidly learn and consolidate new word forms that
fit the phonotactic structure of their native language (as in the present study because all our
novel words contained legal phonological sequences) compared with “foreign” words that
include nonnative phonological sequences (cf. Warker & Dell, 2006).
Overnight transfer of representations from HC to neocortex may involve some process of
elimination of existing memories to make room for new memories the next day (cf.
Rosenzweig, Barnes, & McNaughton, 2002). Consistent with this idea, there was some
suggestion that the subdued hippocampal response to novel words that had been presented
repeatedly during the day then recovered on the first presentation following a night’s sleep
(see Figure 4B). However, the critical comparison of responses to unconsolidated and
consolidated novel items failed to reach statistical significance. Further investigations of
responses to recently and not-so-recently learned items would be valuable to address this
question.
Although our data are in line with a dual-systems account of novel word learning,
uncertainty remains as to the degree of interplay between neocortical and hippocampal
systems in the consolidation process for novel words. Despite some striking examples of
post-morbid vocabulary acquisition (e.g., Van der Linden et al., 2001), the
neuropsychological literature on retrograde amnesia suggests that lesions to hippocampal
and parahippocampal structures severely impair vocabulary learning (e.g., Verfaellie,
Koseff, & Alexander, 2000). However, most case studies have focused on retrieval or
recognition of meanings or familiarity of forms. Given that spoken word recognition is an
overlearned, automatized skill, it is reasonable to think of the engagement of a novel word in
lexical competition as procedural knowledge. Hence, the aspects of novel word learning that
lead to a delay in the recognition of existing words, as in Experiment 1, may still be evident
in cases where normal hippocampal functioning is lost. Support for this point of view comes
from research showing amnesics’ spared learning of “common ground” in communication,
which was viewed as a procedural tuning of existing systems (Duff, Hengst, Tranel, &
Cohen, 2006). Nonetheless, we would predict that in the absence of support from medial-
temporal lobe learning systems, it would take many more exposures of a novel word for an
amnesic to show lexical knowledge.
One potential challenge to this dual-process, hippocampal learning followed by cortical
consolidation account of word learning comes from previous functional imaging studies
reporting more immediate changes in cortical responses during repeated presentations of
nonwords (Mestres-Misse et al., 2007; Breitenstein et al., 2005; Majerus et al., 2005;
Cornelissen et al., 2004). However, response reductions can also result from task-specific
neural repetition priming (a form of procedural learning documented for pictures and for
spoken words and pseudowords; Orfanidou et al., 2006; Dobbins et al., 2004), which could,
in principle, be unrelated to word learning. For instance, response reductions for newly
learned words in the fusiform gyrus (Breitenstein et al., 2005) are inconsistent with more
wordlike representations because fMRI and PET studies show that this region most often
shows a heightened response to real words (Orfanidou et al., 2006; Majerus et al., 2002,
although other factors such as the semantic category of words and the task used can also
alter responses; for a review, see Binder et al., 2000). Therefore, apparent learning-related
reductions in cases where both stimulus and task are repeated might equally be ascribed to
the effect of task-specific repetition priming on neural responses. This ambiguity can be
resolved by using different tasks during training and testing (cf. Dobbins et al., 2004). The
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results of our imaging study satisfy two stringent criteria for demonstrating neural correlates
of word learning: (1) generalization to a novel test task should occur and (2) responses to
novel words should resemble responses to existing, familiar words. One previous study that
included a test of these two criteria immediately after learning failed to show ERP responses
to novel words that resemble real words in a generalization test (Mestres-Misse et al., 2007).
Although the consequences of swift learning and repetition priming are difficult to tease
apart, our work would suggest that one means of satisfying both the above criteria involves
learning followed by overnight consolidation.
Effects of Consolidation on Newly learned Words
What benefit does consolidation confer on neural representations of newly learned words?
The emergence of lexical competition in this and previous work (Dumay & Gaskell, 2007;
Gaskell & Dumay, 2003) shows that consolidation strengthens neural representation such
that newly learned words can be recognized more rapidly, hence compete with existing
words. We propose that the emergence of lexical competition effects from novel words
(Experiment 1) reflects an increase in the speed with which novel words can be identified
because it is only after consolidation that novel words are recognized quickly enough to
compete for identification with existing words. The results of our fMRI study (Experiment
2) and a previous study reporting a neural correlate of lexical competition for existing words
in superior temporal regions (Okada & Hickok, 2006) would, thus, be consistent with the
hypothesis that phonological representations in superior temporal regions are modulated by
consolidation. In our study, consolidation was such that superior temporal responses to
newly learned words more closely resembled responses to existing, familiar words. This
might reflect sharper, better-tuned neural representations following consolidation (cf. Grill-
Spector, Henson, & Martin, 2006). However, although mean activity levels are reduced in
cortical regions involved in phonological processing, the emergence of behavioral
competition following consolidation might suggest that representations of newly learned
words have more not less overlap with representations of existing words. Future work to test
for direct correlations between consolidation induced neural changes, and the behavioral
emergence of lexical competition would, therefore, be valuable. Of particular interest would
be studies that assess whether the emergence of lexical competition for spoken words alter
inferior frontal responses (as observed during written word recognition; Fiebach, Ricker,
Friederici, & Jacobs, 2007) or whether competition effects are confined to temporal lobe
regions (cf. Okada & Hickok, 2006).
Further evidence for the benefits conferred by overnight consolidation comes from the
speeded repetition task: Consolidation reduces the time taken for listeners to repeat a heard
nonword. This increase in the efficiency of perceptuomotor transformations of speech is
likely associated with the consolidation-induced reductions in neural activity in cerebellar,
precentral, and supplementary motor regions observed in Experiment 2. This conclusion is
supported by existing evidence concerning a role for these regions in verbal rehearsal (Chen
& Desmond, 2005; Smith, Jonides, Marshuetz, & Koeppe, 1998) and speech production
(Alario, Chainay, Lehericy, & Cohen, 2006; Bohland & Guenther, 2006). However, at
present we have only indirect evidence for links between these response time effects in
behavior and functional imaging results.
Effects of consolidation on cortical processes of perceptuomotor transformation highlight
the fact that word knowledge involves multiple sensory modalities and motor systems (e.g.,
we use different perceptual and motor systems in recognizing and producing spoken and
written words). Because both our experiments used a purely auditory training procedure,
links between perceptual and motor systems are required for newly learned words to be
efficiently produced. Our behavioral and neuroimaging findings suggest that this transfer of
knowledge requires overnight consolidation. We propose that on-line learning processes
Davis et al.
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without consolidation may be limited to altering neural responses in sensory systems and
tasks that were direct engaged during training (as in repetition priming studies). However, to
transfer newly acquired perceptual knowledge to other sensory modalities and tasks (for
instance, efficiently producing words that have only been perceived previously) requires
overnight consolidation. Whether this cross-modal transfer process also reflects a division of
labor between hippocampal and neocortical learning is unclear. Neuropsychological
evidence for deficits in supraspan phonological STM following medial-temporal lesions is at
least consistent with the proposal that binding perceptual and motor representations of
spoken words can involve medial-temporal regions (Knott & Marslen-Wilson, 2001).
Perhaps the clearest example of multimodal word knowledge is in the domain of semantics
(Hauk, Johnsrude, & Pulvermuller, 2004; Martin & Chao, 2001; Barsalou, 1999). Learning
the meaning of a novel word involves associating representations of word form with various
perceptual and motor representations that encode the likely referent of that word. Because
the present studies only assessed word-form learning, we can only speculate as to whether a
similar two-stage neural process is critical for learning form-meaning associations. Existing
behavioral evidence suggests that the automatic activation of associated meanings for newly
learned words (semantic priming) requires off-line consolidation (Dumay, Gaskell, & Feng,
2004). However, neuroimaging data have so far focused on initial acquisition of word
meaning (Mestres-Misse et al., 2007; Breitenstein et al., 2005; Cornelissen et al., 2004).
Further investigations are, therefore, required to assess the role of overnight consolidation in
learning form-meaning associations and to test for neural consequences of consolidation on
the activation of semantic representations for newly learned spoken words.
Acknowledgments
This work was supported by the UK Medical Research Council (U.1055.04.013.00001.01, G0000071) and the
Economic and Social Research Council (RES-063-27-0061). Mark MacDonald was supported by an Undergraduate
Research Bursary from the Nuffield Foundation. We would like to thank the staff at the Wolfson Brain Imaging
Center, University of Cambridge, for their help with data acquisition and our volunteers for their participation.
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Figure 1.
(A) Timeline for Experiment 1. All participants were trained on two sets of novel spoken
words on successive days. On the second day, training was immediately followed by a
behavioral test session comprising four behavioral tasks assessing different forms of
knowledge of the newly learned words. (B) Timeline for Experiment 2. After training on
two sets of novel and existing spoken words on successive days, participants were fMRI
scanned and completed a behavioral test session assessing different knowledge of newly
learned words. (C) Timeline of the fast sparse fMRI procedure illustrating the rapid
alternation of stimulus presentation and single scan volumes. Dashed line shows an estimate
of the predicted BOLD response to a single stimulus (using the canonical hemodynamic
response in the SPM software), illustrating how the expected hemodynamic response is
sampled over subsequent scans (cf. Orfanidou et al., 2006; Jacquemot et al., 2003).
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Figure 2.
(A) Lexical decision response times for real-word competitors of novel words trained on
Day 1 (hence consolidated), Day 2 (unconsolidated), or untrained controls. Error bars show
1
SEM
after between-subjects variability is removed, suitable for repeated measures
comparisons (cf. Loftus & Masson, 1994). Statistical significance of planned pairwise
comparisons (***
p
< .001, **
p
< .01, *
p
< .05, (*)
p
< .1,
ns
= nonsignificant). (B)
Response times for novel words trained on Day 1 (consolidated), Day 2 (unconsolidated), or
untrained controls in the auditory repetition test. Error bars and statistical significance as
before. (C) Recognition memory performance for novel words trained on Day 1 or Day 2.
(D) Rated strength of meaning for novel words trained on Day 1, Day 2, or untrained
controls.
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Figure 3.
Brain regions showing response differences between novel and existing spoken words
(pause-absent trials) in the fMRI experiment rendered onto the MNI canonical brain. (A)
Untrained novel > existing words, thresholded at
p
< .001 uncorrected, all activations shown
exceed FDR corrected
p
< .05. No voxels show an elevated response to existing words. (B)
Unconsolidated novel > existing (comparison of items trained on Day 2) thresholded as
before. (C) Consolidated novel > existing words (comparison of items trained on Day 1),
thresholded at
p
< .001 uncorrected, no voxels approach FDR
p
< .05. (D) Brain regions
showing an interaction between novelty and consolidation (i.e., an additional response for
novel words trained on Day 2, not seen for novel words trained on Day 1), results displayed
at
p
< .001, all peak voxels in the left hemisphere and right cerebellum exceed FDR
corrected
p
< .05, as indicated by the arrow on the color scale. (E) Mean BOLD parameter
estimate (peak of the fitted canonical HRF function on an arbitrary scale) of the peak voxel
in the right cerebellum (
x
= 16,
y
= +60,
z
= −36) for novel and existing words in each of the
training conditions. Error bars show
SEM
after between-subject variance has been removed,
suitable for repeated measures comparisons between conditions (Loftus & Masson, 1994).
(F) Response profile of a cluster of voxels in the left STG (center of mass,
x
= −54,
y
= −30,
z
= +6), which show an elevated response to novel words and a significant interaction
between novelty and consolidation. Error bars as in panel E.
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Figure 4.
(A) Single slice of the MNI canonical brain showing the ROI within the left HC, defined as
the portion of the AAL left HC map (white outline), which shows an additional response to
pause-absent trials compared with rest thresholded at FDR
p
< .05 (red). (B) Response of
this left HC ROI to novel words presented in the first scanning run (i.e., the first
presentations for untrained items) and in later scanning runs (mean of second/third run).
Error bars as in Figure 3E. Significance of statistical comparisons shown with braces as in
Figure 2A. (C) Correlation between responses to untrained novel words during Session 1 in
the left HC ROI and subsequent recognition memory performance for single participants.
The dotted line shows the best fitting linear regression line (
y
= 2.66
x
− 1.73).
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Table 1
Accuracy (
d
’) and Response Time (msec from Word Onset) of Pause-Detection Responses as a Function of Lexicality and Training in Experiment 2
Accuracy (d’) Response Time (msec)
Consolidated Unconsolidated Untrained Consolidated Unconsolidated Untrained
Novel words 4.02 (0.13) 4.08 (0.16) 3.66 (0.17) 968 (8.68) 945 (8.10) 952 (10.44)
Existing words 4.27 (0.11) 4.15 (0.14) 3.82 (0.15) 912 (9.09) 919 (8.79) 932 (7.33)
Bracketed values show the
SEM
, after between-subject variation is removed, suitable for repeated measures comparisons (cf. Loftus & Masson, 1994).
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Table 2
MNI Coordinates for Peak Voxels (Maximum of Three Peaks/Cluster, Separated by >8 mm) Showing Increased Activity for Novel versus Existing
Words in the Three Training Conditions
Location Cluster Size
(Voxels)
Z x y z
(A) Untrained Words: Novel > Existing (Results
Thresholded at p < .001 Uncorrected, All Voxels
Exceed FDR p < .05)
Left posterior MTG 785 4.64 −48 −44 4
Left anterior STG 4.57 −60 −8 −4
Left posterior MTG 3.94 −62 −30 4
Right posterior MTG 107 3.71 60 −28 −6
Left cerebellum (Lobe 6) 61 3.56 −22 −62 −26
Right anterior STG 8 3.15 58 −6 −4
Right inferior frontal
gyrus
1 3.13 50 20 −10
(B) Unconsolidated Words: Novel > Existing (Results
Thresholded at p < .001 Uncorrected, All Voxels
Exceed FDR p < .05)
Right motor cortex 34 4.21 34 −12 56
Left SMA 224 4.18 −12 0 44
SMA 3.42 0 −4 54
Left posterior STG 250 4.11 −52 −46 14
Left posterior MTG 3.83 −64 −40 8
Left posterior MTG 3.26 −50 −56 0
Left anterior MTG 167 3.93 −62 −12 −2
Right posterior MTG 148 3.72 56 −26 0
Left putamen 48 3.72 −32 −20 −4
Left anterior insula 51 3.60 −20 36 8
Left anterior insula 3.28 −22 28 4
Right cerebellum
(Lobe 6)
25 3.59 20 −58 −22
Left dorsal PCG 63 3.48 −48 −4 46
Right dorsal PCG 5 3.38 30 −20 52
Right ventral PCG 8 3.36 36 −20 34
J Cogn Neurosci
. Author manuscript; available in PMC 2010 March 05.

Europe PMC Funders Author Manuscripts Europe PMC Funders Author Manuscripts
Davis et al. Page 28
Location Cluster Size
(Voxels)
Z x y z
Left ventral PCG 54 3.35 −58 0 24
Left PCG 3.16 −44 −2 26
Right posterior MTG 5 3.30 66 −40 10
Right posterior MTG 6 3.30 50 −42 −2
Right ventral PCG 6 3.26 52 −2 48
Right anterior STG 6 3.21 58 −4 −8
Left SMG 8 3.18 −44 −40 26
Left rolandic operculum 2 3.14 −46 −28 20
(C) Consolidated Words: Novel > Existing (Results
Thresholded at p < .001 Uncorrected, No Voxels Exceed FDR
p < .05)
Right anterior STG 5 3.44 58 6 −14
Left posterior MTG 4 3.20 −58 −32 4
MTG = middle temporal gyrus; PCG = precentral gyrus; SMA = supplementary motor area; SMG = supramarginal gyrus; STG = superior temporal gyrus.
J Cogn Neurosci
. Author manuscript; available in PMC 2010 March 05.

Europe PMC Funders Author Manuscripts Europe PMC Funders Author Manuscripts
Davis et al. Page 29
Table 3
Peak Voxels (Maximum of Three Peaks/Cluster, Separated by >8 mm) Showing a Significant Interaction between Novelty (Novel versus Existing Words)
and Consolidation (Unconsolidated versus Consolidated)
Location
Cluster Size
(Voxels)
Z x y z
Left precentral gyrus 319 5.48 −44 −4 52
Left postcentral gyrus 3.71 −52 −18 46
Left precentral gyrus 3.46 −56 −8 42
Left precentral gyrus 62 4.07 −56 0 26
Right precentral gyrus 16 3.71 54 0 38
Right cerebellum (Lobe 8) 25 3.56 16 −60 −36
Supplementary motor area 81 3.52 −4 −6 56
Right precentral gyrus
*
3 3.30 36 −14 56
Right postcentral gyrus
*
2 3.18 44 −20 38
*
Results thresholded at
p
< .001. All peak voxels reach
p
< .05 FDR corrected, except those marked with an asterisk.
J Cogn Neurosci
. Author manuscript; available in PMC 2010 March 05.