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\begin{document}

\title{Short-term exposure to second language apps modulates brain responses in
pre-schoolers}



\author[1]{Sumie Leung}%
\author[2]{Conrad Perry}%
\author[3]{Jessica Guy}%
\author[3]{Deborah Loats}%
\author[4]{Kate Highfield}%
\author[3]{Jordy Kaufman}%
\affil[1]{The University of Melbourne Melbourne School of Psychological Sciences}%
\affil[2]{The University of Adelaide}%
\affil[3]{Swinburne University of Technology}%
\affil[4]{Australian Catholic University}%


\vspace{-1em}



  
  \date{June 14, 2024}


\begingroup
\let\center\flushleft
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\selectlanguage{english}
\begin{abstract}
Aims: Previous research showed that short-term second language training
modulates children's brain responses. However, little is known about how
well young children's brains process second language-related information
acquired from short-term usage of language-immersive apps. Methodology:
To examine this, we compared the auditory event-related potential (ERP)
to non-native language words learnt via language-immersive applications
(apps), as compared to those learnt via digital flash cards, in
3-5-year-old children. We also compared their auditory ERPs to known and
unknown words. Data analysis: Thirty-two participants have completed the
(audio) word-picture pair experiment while their electroencephalography
(EEG) was recorded. We compared the auditory ERPs in the 200-300ms
window and the 400-600ms window between the apps group and the
flash-card group, and the response to the known and unknown words.
Results: We found that the early positive potential (of the whole group)
to the known words was significantly larger than that to the unknown
words. Further, the early negative potential of the language immersed
group was significantly larger than that of the flash card group.
Conclusions: Short-term training from language-immersive apps has a
positive impact on the developing brain.%
\end{abstract}%



\sloppy


\textbf{Short Title:} Second Language Apps and pre-schooler's brain
response modulation

\textbf{Full Title} : Short-term exposure to second language apps
modulates brain responses in pre-schoolers

\textbf{Keywords:} second language learning, pre-schoolers, auditory
event-related potentials, EEG, language-immersive apps



\textbf{Abstract}

Aims:

Previous research showed that short-term second language training
modulates children's brain responses. However, little is known about how
well young children's brains process second language-related information
acquired from short-term usage of language-immersive apps.

Methodology:

To examine this, we compared the auditory event-related potential (ERP)
to non-native language words learnt via language-immersive applications
(apps), as compared to those learnt via digital flash cards, in
3-5-year-old children. We also compared their auditory ERPs to known and
unknown words.

Data analysis:

Thirty-two participants have completed the (audio) word-picture pair
experiment while their electroencephalography (EEG) was recorded. We
compared the auditory ERPs in the 200-300ms window and the 400-600ms
window between the apps group and the flash-card group, and the response
to the known and unknown words.

Results:

We found that the early positive potential (of the whole group) to the
known words was significantly larger than that to the unknown words.
Further, the early negative potential of the language immersed group was
significantly larger than that of the flash card group.

Conclusions:

Short-term training from language-immersive apps has a positive impact
on the developing brain.

\textbf{Highlights}

\begin{itemize}
\tightlist
\item
  The aim of the study was to examine how well young children's brains
  process second language-related information acquired from short-term
  usage of language-immersive apps.
\item
  Early positive ERP to the known words was larger than that to the
  unknown words, and early negative ERP of the apps group was larger
  than that of the flash card group.
\item
  Short-term training from language apps has a positive effect on brain
  development.
\end{itemize}

\textbf{Introduction}

Preschool children as young as 5 years old can learn from educational
touchscreen apps, as shown by numerous studies and a recent
meta-analysis (Xie et al., 2018). This benefit accrues in multiple
domains, including science, telling time, word learning, and story
comprehension (Alad\selectlanguage{ngerman}é et al., 2016; Wang et al., 2016; Piotrowski \&
Krcmar, 2017; Russo-Johnson et al., 2017). Among these domains,
touchscreen language learning has garnered considerable research
attention in the area of emergent literacy in the preschool age range
(e.g. Neumann, 2014; 2016; 2018; O'Toole \& Kannass, 2018). For example,
researchers have suggested the multisensory features of some literacy
applications (apps) contributes to the enhanced learning of letter names
and sounds in 2--5-year-old children (Neumann, 2018) and facilitates
word learning in preschool children (Smeets \& Bus, 2015). This benefit
extends to second language learning, with the interactional and haptic
feedback features of touchscreens providing an immersive-style learning
experience (Nassaji, 2016; Minogue \& Jones, 2006).

Measuring the extent to which children benefit from language learning
programs can be done a number of ways (see review by Bialsytok, 2007,
Barac et al., 2014). The majority of research has assessed children's
learning outcomes using simple behavioural tests. More complicated
cognitive neuroscience methodologies including electroencephalography
(EEG) may provide useful information that behavioural studies cannot
measure. Notably, the temporal order effects during speech processing
can be directly examined (see Steinhauer \& Connolly, 2008). Early
components of event-related potentials (ERPs) around 100-200ms after
word onset have been linked to phonetic/phonological processing (e.g.
Bonte \& Blomert, 2004). Following these are intermediate ERP components
at around 200-300ms, which have been linked to the early lexical access
(e.g. Kast et al., 2010; Bonte et al., 2009) or phoneme recognition
(\selectlanguage{polish}Č\selectlanguage{english}eponien\selectlanguage{ngerman}é et al., 2005; 2008). Later on, lexical/semantic processing is
reflected by ERP components at around 300-600ms window (e.g. Benau et
al., 2011). These are all important parts of normal language processing.

Developmental-EEG studies have also examined how young children's brains
register different stages of second language acquisition, including
phonological and lexical processing (e.g. Cheour et al., 2002;
Shestakova et al., 2003; Peltola et al., 2005; Tong et al., 2014; Jost
et al., 2015; Rinker et al., 2010; 2017; Bovo et al., 2018; Marques et
al., 2020). While some of these studies have focussed on children's
electrophysiological responses from long-term second-language learning
(e.g. Rinker et al., 2010; 2017), others have examined the effects of
short-term 2\textsuperscript{nd} language training (e.g. Shetsakova et
al., 2003; Moreno et al., 2015). Shetsakova et al., (2003) observed
larger early and late auditory ERP components (in 200-500ms and
350-700ms) in 3-6 years-old Finnish-speaking children after they learnt
French for 2-months, and also about 12-16 weeks after learning French.
Similarly, four-to-six-year-old English-speaking children showed an
enhanced late ERP component (450-850ms) after 4 weeks of French language
training (Moreno et al., 2015). Further, this training-effect persisted
even after one year, indicating that even short-term early childhood
training could have lasting functional benefits on the developing brain.

The advantages of learning a second language in language-immersion
programs in a traditional classroom setting, as compared to regular
programs, are well established (e.g. Bialystok et al., 2014; Hermanto et
al., 2012; Barbu et al., 2019). In the digital space, it is unclear how
well children can learn from language-immersive apps, as compared to
learning from traditional language learning methods, such as flash
cards. In addition, little is known about how well young children's
brains process second language-related information acquired from
short-term usage of language-immersive apps. The current research is the
electrophysiological component of the multi-faceted evaluation of the
Early Learning Languages Australia (ELLA) program (see Learning
materials for a description of the ELLA program). The ELLA apps provide
an immersive environment for children to learn a second language in the
digital space.

To examine how much the digital immersive environment contributes to
children's acquiring new language knowledge, we used digital flash cards
as control. This allowed us to compare ELLA, which is an activity-based
method with an immersive environment, to flash cards, which is a more
traditional word-learning and less interactive method, delivered by the
same technology (touchscreen). In this study, we compared the auditory
event-related potential (ERP) to Indonesian words learnt via the ELLA
apps, as compared to Indonesian words that the preschool children had
not been exposed to. Given that previous research has shown enhanced ERP
component after short-term training and the advantages on language
learning from language-immersion programs, we hypothesised that children
who learned from the ELLA apps would exhibit larger ERP components in
both early and late time-windows, as compared to the group who learned
from digital flash cards. We also hypothesised that children would
exhibit larger ERP components in both early and late time-windows to the
words that they were exposed to, as compared to the words that they were
not exposed to.

\textbf{Materials and methods}

\textbf{2.1 Learning materials}

The Early Learning Languages Australia (ELLA) program is a set of
language-learning touchscreen apps designed to enhance interest in
language learning amongst children in preschool services (ELLA website).
This Australian Government initiative was established in alignment with
their Early Years Learning Framework (EYLF). The apps feature characters
called `polyglots'. Each polyglot character is multilingual and is
involved in several language-learning activities within the apps. Each
app consists of several language-learning activities of the same theme
(e.g. the Polyglots in the Playroom; the Polyglots at the Beach). These
activities range from greetings, making a virtual cake to counting
during an egg-race (see Figure 1 top row for example screenshots of
three ELLA apps activities). Whilst the program has been implemented in
many languages, here only Indonesian (\emph{Bahasa Indonesia)} was
examined.

{[}INSERT FIGURE 1 APPROXIMATELY HERE{]}

The digital flash cards were made and presented on FC Maker on an iPad
(see Figure 1 bottom row for example screenshots of a digital flash card
stimulus). The words used in the flash cards were structured in the same
way as the Apps, such that there was not a single set of flash cards,
but rather a number of different sets. Each Indonesian word sound file
was embedded in each slide and was activated by a finger tap on the
slide. The word lists used in the Flash Card apps were words that could
easily be associated with pictures, for example, ``ball'' and
``ice-cream'' rather than ``Hi, my name is \ldots{}'', as phrases such
as greetings could not be easily displayed as a flash card. Thus, the
list of words in the Flash Card apps was not as long as the list of
words in the ELLA apps. However, only the words shared by both ELLA apps
and Flash Card apps were used as stimuli in the experiment.

{[}INSERT FIGURE 1 APPROXIMATELY HERE{]}

\textbf{2.2 Participants}

Sixty-five pre-school children who had no previous exposure \emph{Bahasa
Indonesia} participated in the experiment. Thirty-two participants (14
male, 18 female; mean age = 49.6 months, SD = 6.0 months; range 37-59
months) have completed the EEG experiment. Sixteen of them (7 female)
were allocated to the ELLA group and sixteen (11 female) to the Flash
Card group. There was no significant mean age difference between the two
groups (ELLA: 49.5 months, Flash Card: 49.7 months; \emph{p} = 0.937).
To control for differences in language aptitude, participants' verbal
ability was assessed by the Peabody Picture Vocabulary Test IV (PPVT-IV;
Dunn and Dunn, 2007). Of the 32 EEG participants, 1 participant did not
complete the PPVT-IV test as he/she did not co-operate, although
researchers were able to informally confirm that his/her verbal ability
was very likely to be age normal through conversations. The group mean
percentile of PPVT scores for the remaining EEG participants was 73.13
(SD = 23.96).

\textbf{2.3 Experiment paradigm}

Participants were randomly assigned to either the ELLA (ELLA) or Flash
Card group (FLASH). During the first lab visit, participants learnt the
same set of Indonesian words from either ELLA App 1 or a digital Flash
Card app for 20 minutes (or less if participants were unwilling to
continue). Following the 20-min ELLA or Flash Card activity,
participants were asked to complete a few tasks, including behavioural
word task, eye-tracking word task, and the PPVT-IV.

After the first lab visit, the ELLA group took an iPad home loaded with
two ELLA apps (App1+App2 or App1+ App3 or App1+ App4). The Flash Card
group took an iPad home loaded with two `decks' of digital flash cards
containing words and pictures from the two ELLA apps. Parents of both
groups were given the same instructions: to ask their child to play on
their given apps for 15-20 minutes per day for approximately two weeks.
Parents were instructed that children could stop sooner if they
expressed the desire not to continue for the whole 15 minutes. Parents
were asked to record the duration of use and rate their child's
engagement during each home use session.

After two weeks of home-based learning, participants visited the lab and
were asked to complete the behavioural word task and the eye-tracking
word task. The tasks were the same as those in the first lab visit,
except contained materials from two apps. During this visit, EEG data of
a subset of participants was also collected. The current paper focused
on the EEG data and results of the remaining tasks and parent reported
data will be published elsewhere.

Upon completion of the experiment, participants and their guardians were
reimbursed AUD 50-100 for their time and travelling costs. The study was
approved by the Human Research Ethics Committee at Swinburne University
of Technology, and written informed consent was obtained from the
parents of all child participants.

\textbf{2.4 Stimulus paradigm}

An (audio) word-picture pair paradigm was used. The audio words are
Indonesian words that participants had either been exposed to (KNOWN)
and had not been exposed to (UNKNOWN). The pictures were either
congruent or incongruent to the audio words. Each pair (trial) consisted
of 1) an audio word file; 2) a blank screen for 1 second; and 3) a
picture for 1.5 seconds. The inter-trial interval between each
word-picture pair was two seconds.

All audio word and picture stimuli were extracted from the ELLA apps.
The mean loudness, duration and fundamental frequency (F0) of the audio
sound stimuli were calculated by the software Praat (Boersma, 2001) and
is presented in Table 1. The unknown words were slightly louder than the
known words for Apps1+2 and Apps 1+3 (3.32dB and 5.17dB respectively).
The F0 of unknown words was higher than that of the known words for Apps
1+2 and for Apps 1+4 (35.71Hz and 47.99Hz respectively). The duration
was not different between the known words and the unknown words, as
shown by the 2-tailed t-tests in Table 1.

{[}INSERT TABLE 1 APPROXIMATELY HERE{]}

Participants were instructed to inform the researcher verbally if the
audio word matched the picture, after each trial. At the start of the
experiment, participants were given three word-picture pairs to
practice, which were discarded from the analyses. After this, a total of
95 word-picture pairs were presented to the participants. The total time
of the presentation was approximately nine minutes. Note that the task
was designed to maintain participants' attention to the audio words,
hence the ERP responses would not be error-rate-corrected.

\textbf{2.5 EEG data acquisition}

During the task, one researcher sat in a dimly-lit electrically shielded
room with the participant. Participants were seated approximated 60 cm
in front of a video monitor on which visual stimuli were presented. The
pictures were presented on a black background.

EEG was recorded with Netstation 4.4.2 acquisition software, and a NA300
amplifier with a Hydrocel Geodesic Sensor Net comprised of 124 sensors
(Electrical Geodesics, Inc.). Online EEG data were sampled at 500Hz and
were referenced to the vertex electrode.

\textbf{2.6 EEG data analysis}

EEG data was examined in two parses. The first was designed to identify
participants with particularly poor data for exclusion and to
interpolate bad channels for the remaining participants. The second was
designed to remove trials with artefacts after participants with
potentially reasonable data was found.

In the first data examination stage, 32 participants (ELLA = 16; FLASH =
16) completed the task without removing the cap in the task or similar
major mishap, and thus had a full dataset. Data from these participants
was first filtered between .1 and 30 Hz. To examine the data, we first
removed all electrodes that were external to the layer the mastoid was
on (see Figure 2). These electrodes were not located on the scalp.
Epochs for each participant were extracted from -200 to 800ms at the
stimulus onset of the audio words. Data was then re-referenced to the
mastoids.

{[}INSERT FIGURE 2 APPROXIMATELY HERE{]}

For each trial of each subject at each electrode, we computed a number
of different measures: variance, kurtosis, maximum range, mean z values,
and maximum z values, using code from the Fieldtrip visual toolbox. The
scores for each of these measures for each participant were sorted from
highest to lowest. For each electrode, for each measure, and for each
participant, if more than 30\% of the epochs on any given score were
above these criteria (variance: 5000; kurtosis:16, range:
400\(\mu\)V, mean Z: 16, maximum Z: 16), a score of 1 was
added to a total score. If a score was greater than 20, the subject was
discarded. This stage led to 5 subjects being discarded entirely.

In the second data examination stage, for each subject, any channel
which was bad on any measures was recalculated using the triangulation
method in Fieldtrip with a Neighborhood distance of 1. The average
number channels recalculated was .55 (SD=1.09). Independent Component
Analysis (ICA) was then used to eliminate ocular artefacts and other
artefacts. This was done using only 20 components to simplify component
identification. Components were removed if they were associated with eye
blinks, ocular movements, and likely muscle artifacts. For each subject,
individual epochs were then rejected if they were over a new set of
cut-offs (variance: 4000; kurtosis:16; range: 350\(\mu\)V;
maximum Z: 16) or if they were 4.5 SDs over the mean of any of those
measures. This procedure left one participant with only 21\% of their
original trials, and the data of this participant were discarded.

After these two data examination stages, EEG data of 12 participants in
the ELLA group and 14 in the FLASH group remained. On average, the
number of epochs left in each condition of the two groups was: ELLA
KNOWN: M = 30.8, SD 8.663; ELLA UNKNOWN = 31.42, SD = 9.87. FLASH KNOWN
= 26.29, SD = 7.54; FLASH UNKNOWN = 26.92, SD = 5.81.

\textbf{2.7 Statistical analysis}

We examined the results using cluster-based permutation tests (Maris and
Oostenveld, 2007), as implemented in Fieldtrip. This technique uses a
data driven method to identify significant electrodes and thus does not
require areas to be a-priori defined and compared. We applied this test
on each data point and used a two-tailed distribution. With this
technique, we examined the main effects of Presentation group (ELLA vs.
FLASH), Word Novelty condition (KNOWN vs. UNKNOWN), and the interaction
between the Presentation group and Word Novelty condition in the
200-300ms window and the 400-600ms window.

\textbf{Results}

Among the 32 participants who have participated in the EEG study, we
received 21 parental reports of home usage, with 10 from the ELLA group
and 11 from the FLASH group. Results from parental reports showed that
the ELLA group spent more time on the apps (mean = 26.3 minutes, SD =
17.0 minutes) than their peers on the digital Flash Cards (mean = 10.7
minutes, SD = 5.8 minutes) at home.

The EEG results showed that within the 200-300ms window, there were
significant electrodes in the Presentation (\emph{p} = .045), and Word
Novelty conditions (\emph{p} = .028), but no interaction. As shown in
the topographic difference map between the ELLA and the FLASH groups,
the negative potential of the ELLA group is significantly larger than
the FLASH group in the frontal and right lateralized electrodes (as
indicated by the crosses on the map of Figure 3 and `*EvF' on Electrode
16, 123, 109 and 97 on Figure 5). As shown in the topographic difference
map between the KNOWN and the UNKNOWN conditions, the positive potential
of the KNOWN condition is significantly larger than that of the UNKNOWN
condition in the left central electrodes (as indicated by the crosses on
the map of Figure 4 and `*KvU' on Electrode 40 on Figure 5). There were
no significant effects and no significant interaction in the 400-600ms
window.

{[}INSERT FIGURE 3 APPROXIMATELY HERE{]}

{[}INSERT FIGURE 4 APPROXIMATELY HERE{]}

{[}INSERT FIGURE 5 APPROXIMATELY HERE{]}

\textbf{Discussion}

The early positive (200-300ms) potential (of the whole group) to the
known words was significantly larger than that to the unknown words in
the left central electrodes, but there was no difference between the
known and unknown words for the late potential (400-600ms). By comparing
the ERPs between the children who learnt via the ELLA apps versus via
the digital Flash Cards, the early negative potential (200-300ms) of the
ELLA group was significantly larger than that of the Flash Card group in
the frontal and right regions, but there was no difference observed in
the late time-window (400-600ms).

Our enhanced positive response to the known words was similar to the
enhanced P3a exhibited by 3-6-year-old children after learning French
for several weeks (Shestakova et al., 2003). Positive responses at
around 200-350ms have been associated with involuntary attention shifts
to novel sounds (e.g. Escera et al., 1998; 2000) and to spoken stimuli
(Shtyrov et al., 2012). Further to this, our centrally dominant response
was similar to the topography and the latency of the early portion of
P3a (Escera et al., 1998). The left-hemisphere dominance of our response
provided further support that the response is of linguistic nature,
especially when neural modulations in left hemisphere, such as the left
middle frontal gyrus and inferior frontal gyrus, have been associated
with increases in second language proficiency (Stein et al., 2012;
Mårtensson et al., 2012; Yang et al., 2015). Taken together, the
observed enhanced positive response to known foreign words in the
current study could be an indication of children switching their
attention to the known foreign words due to their increased relevance
compared to the unknown words.

The children who learnt from the ELLA apps exhibited a larger early
negative response to all stimuli (regardless of whether the stimuli were
known or unknown) than the children who learnt from the digital Flash
Cards. Negative responses with latency circa 250ms have been associated
with various early cognitive processing, such as phonological (Connolly
and Philips, 1994), lexical selection (van den Brink et al., 2001),
sound content feature processing (Ceponiene et al., 2005) and word form
priming (Friedrich \& Friederici, 2008). Children who learnt from the
ELLA apps might be able to better detect some of these features of the
foreign language than the children who learnt from the digital flash
cards.

Another possible explanation for this enhancement is related to word
familiarity. Children as young as 11 months old have shown larger
negative responses within 250ms when they heard familiar compared to
unfamiliar words (Thierry et al., 2003), and 20-month-old children have
shown larger early negative responses to known words than to nonsense
words (Mills et al., 2004). Similar to our observed topography, the
responses circa 250ms in previous research were more negative on the
electrodes on the right hemisphere (Thierry et al., 2003) and in the
right frontal sites (Mills et al., 2004). Taken together, the
ELLA-related larger negative responses suggested that children learnt
from the ELLA apps were more familiar with the foreign words, hence they
might be more able to detect certain features of the foreign words
better than the children who learnt from the digital flash cards. This
could be related to the ELLA group having spent more time on the apps
than their peers on the digital Flash Cards at home. However, children
could have learnt less from the Apps than from the digital Flash Cards
in the same amount of time. This is because digital Flash Cards only
present words to be learnt, whereas the Apps require a reasonable amount
of non-linguistic actions to use (e.g., moving a polyglot around a
screen or performing moves in a puzzle).

There are a number of methodological issues that warrant discussion.
Firstly, the loudness of our Unknown Word stimuli was slightly louder
than those of the known words, and the fundamental frequency of our
Unknown Word stimuli was slightly higher than those of the known words
in two sets of our apps. These loudness and frequency differences might
affect the ERPs, and hence may be a possible explanation for the
observed larger positive potential of the known words. However, it
should be noted that louder and higher frequency stimuli usually produce
larger auditory event-related components in both adults and children
(e.g. Picton et al., 1974; Davies et al., 2010; Putkinen, 2012). Despite
these differences of the stimuli, our softer and lower frequency (known)
words stimuli actually produced a larger positive potential than the
louder and higher frequency (unknown) words, supporting the possibility
that the observed difference was not due to the physical differences of
the stimuli. Another reason that loudness difference would be unlikely
to confound the task is that the differences were less than 6 dB. Such a
small difference should have negligible effect on our results,
especially when previous research showed a much larger loudness
difference (i.e. 20 dB) led to a relatively small ERP amplitude
modulation (Davies et al., 2010).

A second methodological issue with this study is that the number of
accepted epochs in our EEG data did not allow us to further divide the
Known Word condition according to children's accuracy of the task or of
the behavioural word test results, as that would have severely impacted
the signal-to-noise ratio of the ERP responses. Thus, although our
behavioural data has shown that both groups of children learnt from both
the apps and the digital Flash Cards (see Guy et al., manuscript in
preparation), future study will be necessary to examine if children's
semantics and understanding to the foreign language contributed to the
modulation of the electrophysiological response rather than other
factors such as familiarity.

Two important points of this research warrant further clarification. The
first point is to clarify the underlying mechanism of the enhanced
response observed in the ELLA group: specifically, whether it was a
representation of familiarity to the words or because of the children's
enhanced ability to detect certain distinct features in the new
language. The second is to investigate whether well-designed foreign
language apps could lead to long term neural modulation in children,
especially when training-related enhancement in children's auditory ERP
components have been observed months to one-year post-training
(Shetsakova et al., 2003; Moreno et al., 2015). Although further
research is needed to clarify these points, either outcome would
typically be seen as positive, as it shows that short-term training from
the ELLA apps (which were designed to support second language learning
for young children) have a positive impact on the developing brain.

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10.1016/j.chb.2016.03.080

Barac, R., Bialystok, E., Castro, D. C., and Sanchez, M. (2014). The
Cognitive Development of Young Dual Language Learners: A Critical
Review. \emph{Early childhood research quarterly} , 29(4), 699--714.
https://doi.org/10.1016/j.ecresq.2014.02.003

Barbu, C., Gonzalez, A., Gillet, S., and Poncelet, M. (2019). Cognitive
Advantage in Children Enrolled in a Second-Language Immersion Elementary
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\textbf{TABLE}

Table 1: The mean loudness, duration and fundamental frequency (F0) of
the known and unknown words, for each app groups. The t-tests represent
whether the loudness, duration, or F0 of the known words were
significantly different to that of the unknown words
(*\emph{p}\textless{} 0.05).\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Apps 1 and 2 & Loudness (dB) & Duration (seconds) & F0
(Hz)\tabularnewline
\midrule
\endhead
KNOWN & 69.12 & 0.80 & 315.21\tabularnewline
UNKNOWN & 74.29 & 0.71 & 350.92\tabularnewline
T-TEST & 0.01* & 0.12 & 0.00*\tabularnewline
Apps 1 and 3 & Loudness (dB) & Duration (seconds) & F0
(Hz)\tabularnewline
KNOWN & 70.59 & 0.75 & 339.03\tabularnewline
UNKNOWN & 73.91 & 0.76 & 355.12\tabularnewline
T-TEST & 0.02* & 0.86 & 0.19\tabularnewline
Apps 1 and 4 & Loudness (dB) & Duration (seconds) & F0
(Hz)\tabularnewline
KNOWN & 71.77 & 0.79 & 307.94\tabularnewline
UNKNOWN & 74.16 & 0.74 & 355.93\tabularnewline
T-TEST & 0.15 & 0.40 & 0.00*\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure captions:}

Figure 1: Top row: Example screenshots of three ELLA apps activities.
Bottom row: Example screenshots of a digital flash card stimulus

Figure 2: An electrode map of the Hydrocel Geodesic Sensor Net. The
greyed-out electrodes were not on the scalp and were not included in the
analysis. Figure adapted from Geodesic Sensor Net Technical Manual
(Electrical Geodesics, Inc.)

Figure 3: Topographic difference map between the ELLA and the Flash Card
groups. The crosses on the map indicate the electrodes where the
negative potential at 200-300ms of the ELLA group is significantly
larger than that of the Flash Card group.

Figure 4: Topographic difference map between the Known Word and the
Unknown Word conditions. The crosses on the map indicate the electrodes
where the positive potential at 200-300ms of the Known Word condition is
significantly larger than that of the Unknown Word condition.

Figure 5: Event-related potentials of the two Word conditions of the two
groups: Known ELLA; Unknown ELLA; Known Flash; and Unknown Flash. The
numbers in bold represents the electrode number. *KvU represents
significant difference between Known words and Unknown words. *EvF
represents significant difference between the ELLA and the Flash Card
groups.

\textbf{Hosted file}

\verb`Figure 1_Screenshots_ELLA_FlashCard.pptx`  available at \url{https://authorea.com/users/638290/articles/662381-short-term-exposure-to-second-language-apps-modulates-brain-responses-in-pre-schoolers}

\textbf{Hosted file}

\verb`Figure 2_ElectrodeMap.pptx`  available at \url{https://authorea.com/users/638290/articles/662381-short-term-exposure-to-second-language-apps-modulates-brain-responses-in-pre-schoolers}

\textbf{Hosted file}

\verb`Figure 3 Topo Map ELLA Flash.pptx`  available at \url{https://authorea.com/users/638290/articles/662381-short-term-exposure-to-second-language-apps-modulates-brain-responses-in-pre-schoolers}

\textbf{Hosted file}

\verb`Figure 4 Topo Map Known Unknown.pptx`  available at \url{https://authorea.com/users/638290/articles/662381-short-term-exposure-to-second-language-apps-modulates-brain-responses-in-pre-schoolers}

\textbf{Hosted file}

\verb`Figure 5 ERPs.pptx`  available at \url{https://authorea.com/users/638290/articles/662381-short-term-exposure-to-second-language-apps-modulates-brain-responses-in-pre-schoolers}

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