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RESEARCH ARTICLE
How statistical learning interacts with the
socioeconomic environment to shape
children’s language development
Leyla EghbalzadID 1*, Joanne A. Deocampo1, Christopher M. Conway1,2¤*
1 Neurolearn Laboratory, Department of Psychology, Georgia State University, Atlanta, Georgia, United
States of America, 2 Neuroscience Institute, Georgia State University, Atlanta, Georgia, United States of
America
¤ Current address: Center for Childhood Deafness, Language, and Learning, Boys Town National Research Hospital, Omaha, Nebraska, United States of America
* [email protected] (LE); [email protected] (CMC)
Abstract
Language is acquired in part through statistical learning abilities that encode environmental
regularities. Language development is also heavily influenced by social environmental fac-
tors such as socioeconomic status. However, it is unknown to what extent statistical learning
interacts with SES to affect language outcomes. We measured event-related potentials in
26 children aged 8–12 while they performed a visual statistical learning task. Regression
analyses indicated that children’s learning performance moderated the relationship between
socioeconomic status and both syntactic and vocabulary language comprehension scores.
For children demonstrating high learning, socioeconomic status had a weaker effect on lan-
guage compared to children showing low learning. These results suggest that high statistical
learning ability can provide a buffer against the disadvantages associated with being raised
in a lower socioeconomic status household.
Introduction
The seeming ease with which typically developing children learn to comprehend and produce
language suggests the existence of universal biological learning mechanisms [1, 2]. However,
language development also relies on external interactions with social and environmental con-
texts [3, 4]. A combination of these perspectives, therefore, is needed to fully characterize lan-
guage development [5]. In particular, it is important to study language development in
children by focusing on the interaction between intrinsic (e.g., cognitive) and extrinsic factors
(e.g., the social/linguistic environment).
The brain’s ability to detect and encode probabilistic patterns in the environment, and then
use such knowledge to predict upcoming stimuli and events, is thought to be a crucial compo-
nent of cognition [6–13]. This type of learning, referred to as statistical learning, can take place
without conscious awareness [14] and may unfold simultaneously as in the visual domain (e.g.
pictures; [15]) or sequentially as in the auditory domain (e.g. musical tones; [16]. Statistical
learning is a crucial component of visual perception [17], music perception and production
[18], and language processing in infants [19, 20], children [21, 22], and adults [23, 24]. Several
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OPEN ACCESS
Citation: Eghbalzad L, Deocampo JA, Conway CM
(2021) How statistical learning interacts with the
socioeconomic environment to shape children’s
language development. PLoS ONE 16(1):
e0244954. https://doi.org/10.1371/journal.
pone.0244954
Editor: Aaron Jon Newman, Dalhousie University,
CANADA
Received: June 20, 2019
Accepted: December 18, 2020
Published: January 22, 2021
Peer Review History: PLOS recognizes the
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https://doi.org/10.1371/journal.pone.0244954
Copyright: © 2021 Eghbalzad et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All data are available
from the Open Science Framework database
(https://osf.io/8cm2v/).
studies have linked such learning abilities to various aspects of language including both syntax
[21, 25] and vocabulary [20].
In the classic statistical learning study conducted by Saffran, Aslin, and Newport, after only
2 minutes of exposure, infants were able to segment words from an auditory artificial speech
stream by learning the transitional probabilities between syllables, providing an “existence
proof” that such abilities might be used to detect word boundaries in natural speech [19]. In a
longitudinal study, Shafto and colleagues investigated the relationship between serial learning
and verbal and nonverbal language comprehension in infants 8–13 months. Using infants’
looking time as a measure of learning, they reported that the infants’ learning was correlated
with vocabulary comprehension at the time of testing and with their gesture comprehension 5
months later [20]. Furthermore, in a study investigating the relationship between children’s
(ages 6–8) performance on a non-linguistic visual statistical learning task and language ability,
Kidd and Arciuli [21] reported that statistical learning predicted performance on grammatical
tasks involving passive and object-relative clauses. These studies demonstrate the important
role of statistical learning in children’s language development.
However, very little if any research has investigated how statistical learning abilities interact
with social environmental factors such as the quality of the environment children are raised in.
One of the most important indicators of the quality of environment is socioeconomic status
(SES). SES consists of many distinct, but interrelated components which can individually and/
or cumulatively influence children’s development. These components include, but are not lim-
ited to, parental education level [26–40], household income [27, 29, 33, 37, 39, 41–44], home
environment [45, 46], neighborhood safety, maternal mental health [35, 44], school type [37,
38], and stress level [28, 33, 38].
Various studies have found a relationship between children’s poverty level and academic
outcomes mediated by behavioral and/or neural measures [35, 36, 47]. Children who live in low
SES families are reported to have less exposure to cognitive and linguistic stimulation and experi-
ence more stress in their environment [38, 48]. Consequently, children with low SES experience
challenges with language proficiency as early as 18 months and by 24 months of age there is a
6-month vocabulary gap between groups of high and low SES children [49]. Low parental educa-
tion, as an indicator of SES, is reported to predict language difficulties in children [40]. Hupp and
colleagues [31] found that twenty-month-old children with well-educated mothers demonstrated
better language production skills compared to those whose mothers were not as well-educated.
Furthermore, variations in language exposure (e.g. quantity and quality of caregiver’s input) are
associated with changes both in neural function [37] and neural structure [50] of left lateralized
language networks. Thus, socioeconomic disparity leads to an impoverished language environ-
ment, which in turn has a negative impact on children’s language development.
Although it is known that language development is highly dependent on both the environ-
ment in which the child is raised as well as his or her own learning abilities, it is not known to
what extent these two factors might interact to impact language development. For instance, it
is possible that having better intrinsic pattern learning abilities could help offset the deleterious
effects of being raised in an impoverished social environment. Alternatively, it is possible that
learning ability cannot compensate for such environmental limitations. Answering this ques-
tion is important as it can bring clarity into what factors can offset negative social environmen-
tal conditions, which in turn could inform intervention approaches.
Current study
The specific aim of this study, therefore, was to examine the relationship between SES, statisti-
cal learning, and language performance in typically developing children. There were two main
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Funding: This research was funded by National
Institute on Deafness and other Communication
Disorders, Grant number: R01DC012037 (URL:
https://www.nidcd.nih.gov). The funders had no
role in study design, data collection and analysis,
decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.
research questions for this study: (a) to what extent does SES affect statistical learning perfor-
mance in children? and (b) does statistical learning ability moderate the relationship between
SES and language development?
We measured statistical learning by using the event-related potential (ERP) technique while
children were performing a computerized visual, non-linguistic statistical learning task. The
learning task used here is somewhat simple compared to other tasks used in prior statistical
learning studies but has the advantage of being adapted to ERP methodology and is usable
across a range of ages [51]. The task involves learning probabilistic relations among sequen-
tially presented stimuli, in which some stimuli predict a target stimulus to varying degrees.
Children are told to press a button every time they see a specific “target” stimulus (the exact
target stimulus is randomly determined for each participant). Children view a serially-pre-
sented stream of stimuli, with the target occurring occasionally, similar to classic “oddball”
tasks. However, what elevates this task above the simple oddball task is that unknown to the
children, the target is partly predictable based on the preceding stimulus. One specific stimulus
(the “high predictor”) is followed by the target 90% of the time. Another stimulus (the “low
predictor”) is followed by the target only 20% of the time. Preceding the low or high predictors
are a series of “filler” stimuli. As children learn the predictor-target associations, their response
time to the target should be quicker when the target follows the high predictor relative to the
low predictor, a finding which has been borne out in our previous work [52, 53].
In addition to reaction times, the use of ERPs provides a direct measure of neural respon-
siveness that may give a more sensitive measure of learning–or at least an additional, converg-
ing measure–compared to behavioral measures alone (e.g., [54]). Using ERPs, Jost et al. [51]
observed a late positive component (400–700 milliseconds) in posterior electrode sites that
was greater for the high predictor stimuli relative to the low predictor stimuli and was observed
only in the 2nd half of the task, once learning was expected to have occurred. In a follow up
study with adults using this same paradigm, Singh et al. found that this late positivity effect
was significantly correlated with participants’ explicit awareness of the predictor-target contin-
gencies as assessed through subjective reports following the experiment [53]. Finally, Singh
et al. observed this same component in typically developing children but an atypical profile in
children with developmental dyslexia. In sum, this late positivity likely reflects perceptual or
attentional processes associated with making a prediction about the upcoming stimulus [54].
Based on these previous findings [51, 52, 55], in the current study we predicted that if chil-
dren learned the probabilistic relationships between the two types of predictor stimuli and the
target, there should be significant differences in their response times (RTs) to the targets as
well as differences in the ERP amplitudes to the predictors based on whether a trial was high
or low probability. Specifically, we expected that reaction times would be quicker to the target
when it was preceded by the HP predictor compared to the LP predictor and that ERP ampli-
tudes would be significantly larger for the HP compared to the LP predictor. However, these
effects are expected only to be present (or to be larger in magnitude) in the second half of the
task, as the previous studies revealed that learning only occurred once a sufficient number of
trials have been experienced [51].
In regard to the first research question of this study (does SES affect statistical learning per-
formance?), research suggests that low SES may be associated with lower scores on a variety of
cognitive abilities including working memory, cognitive control, and language [56]. However,
no previous research has examined the effect of SES on statistical learning and therefore it is
possible that statistical learning as measured by RTs and ERPs (quicker reactions and higher
amplitudes to HP predictor compared to LP) could remain robust (i.e., unaffected) in the face
of environmental influences, a perspective that is common in the implicit learning literature
(e.g., [57]). In regard to the second research question (does statistical learning performance
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moderate the effect of SES on language development?), we predicted that statistical learning as
measured by RTs and ERPs (quicker reactions and higher amplitudes to HP predictor com-
pared to LP) would provide a “buffer” against the effects that low SES has on language develop-
ment. This would be shown by an interaction between statistical learning performance and
parental education such that participants with low SES would show better language perfor-
mance if they demonstrate high statistical learning relative to those demonstrating low
learning.
Method
Participants
We recruited 42 typically developing, monolingual English-speaking children aged 7–12 from
the Atlanta metropolitan area. Initially, two participants were excluded, one due to computer
failure and one due to a hearing impairment in one ear. In addition, 14 participants, consisting
of mostly 7-year-olds, did not meet the EEG criteria and were excluded from further analyses
(see section on Electroencephalography (EEG) recording and processing). Consequently, our final sample included 26 participants ages 8–12 (age mean = 10.12 years, SD = 1.48; 17 males). We chose this age range for four reasons: (a) there has been relatively less research on statistical
learning and other forms of implicit learning in middle childhood, with the bulk of the
research having been done on infancy through preschool and adolescence through adulthood;
(b) there are major changes in the development of both statistical learning and language at the
endpoints of this age-range (around 7–8 years and around 12 years), but statistical learning
seems to show little change within this age range [54, 58, 59]; (c) after 12 years, statistical learn-
ing appears to have reached adult levels [52] and thus seems less relevant to our focus here on
child development; and (d) the pattern learning task (with EEG measures) used in this study
has been difficult to use with younger children, but has successfully been used with this age
range [51, 55].
During their visit to our lab in the Psychology Department at Georgia State University,
parents/guardians provided written informed consent and children assent to participate. The
entire session lasted about 3 hours. Participants were offered a toy, worth $10 for participating
and parents received $50. This study was approved by the Institutional Review Board (IRB) of
Georgia State University and is in accordance with ethical standards described within the Dec-
laration of Helsinki.
Measures
The statistical learning task will be described in detail below. SES was measured through the
use of a demographic questionnaire. To assess language, we included two measures of chil-
dren’s language development (receptive vocabulary and grammaticality judgment) using stan-
dardized assessments because both aspects of language have been reported to be related to
statistical learning [20, 21, 25, 52]. Finally, we incorporated several measures of general cogni-
tive ability to control for the effect of other cognitive abilities such as working memory and
selective attention.
Socioeconomic status (SES). Parents completed a questionnaire providing socioeco-
nomic status (SES) and demographic information. This questionnaire consisted of questions
about their individual and household income, education, and other demographics of the pri-
mary and secondary caregivers and child. We used the average of both caregivers’ education
level as the measure of SES. Caregivers’ education level was rated using the following scale:
0 = Less than High School; 1 = High School; 2 = Some college; 3 = Associate’s degree; 4 = Bache-
lor’s degree; 5 = Master’s degree; 6 = PhD/Professional degree. Household income was not
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used in the analyses due to more than half of participants opting not to report income. A sum-
mary of average education levels for participants’ caregivers is reported in Table 1.
Statistical learning task. The visual statistical learning task was based on a computer task
developed by our research group [51] that in turn was based on the classic visual oddball para-
digm, but with probabilistic regularities (transitional probabilities) embedded in the stimulus
stream. We made the task child-friendly by making it into a game with a background story.
Children were told a story about a magician who tried to make food for his children using his
magic hat. Children were instructed to “catch” the food by pressing a button as quickly as
possible.
The participants viewed a stream of flashing stimuli consisting of hats of different colors
presented one at a time with a black background. Each stimulus was presented for 500
Table 1. Participant characteristics and descriptive statistics.
N = 26 Low SES group High SES group
N = 15 N = 11
Age in years 10.12 ± 1.48a 10.53 ± 1.45 9.55 ± 1.39 Gender
Male 17 10 7
Female 9 5 4
Race
American Indian or Alaskan Native 1 0 1
lack or African American 8 7 1
White/Caucasian 12 6 6
More than one race 5 2 3
Parents/Caregivers Education average
0. Did not graduate high school 3 3
1. High school 5 5
2. Some college 1 1
3. Associate’s degree 2 2
4. Bachelor’s degree 4 4
5. Master’s degree 8 8
6. Ph.D./ Professional degree 3 3
PPVT st 123.27 ± 21.33 107.8 ± 24.02 120.73 ± 14.93 Grammaticality Judgement st 104.15 ± 14.76 97.8 ± 14.76 112.82 ± 9.84 Block Design st 10.85 ± 3.89 10.13 ± 4.09 11.82 ± 3.55 Digit Span st 10.69 ± 3.53 10.73 ± 4.06 10.64 ± 2.84 Stroop st 50.69 ± 8.97 50.2 ± 10.07 51.36 ± 7.62 ERP H-L 1st half 2.22 ± 1.66 2.24 ± 1.61 2.19 ± 1.81 ERP H-L 2nd half 2.93 ± 2.78 2.95 ± 2.78 2.92 ± 2.91 RT L-H 1st half 40.03 ± 50.22 28.15 ± 43.98 54.08 ± 55.49 RT L-H 2nd half 88.1 ± 117.6 47.01 ± 93.98 136.66 ± 128.14
a mean ± SD.
st = Standard scores.
PPVT = Peabody Picture Vocabulary Task.
ERP = Event-related potential.
H-L = Difference in amplitude between High and Low probability conditions.
RT = Response time.
L-H = Difference in response time between Low and High probability conditions.
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milliseconds and was followed by a black screen for 500 milliseconds. Occasionally, a target
hat with food, to which children were instructed to press a button, was presented within the
stream. Participants were not told that hats of different colors differentially predicted the prob-
ability of occurrence of the target hat (which was indicated by food presence above the hat, not
hat color). Targets followed predictors with varying transitional probabilities (see Fig 1): the
target hat followed the high predictor hat (HP) 90% of the time and the low predictor hat (LP)
20% of the time. In addition, targets occasionally followed “filler” (or standard) stimuli without
a predictor. This was done to decrease the overall predictability of the target’s occurrence and
make the task more difficult.
Each experimental condition (HP and LP) contained 60 trials, and there were 60 filler trials
as well (half with a target and half without). Six blocks of 30 trials each (10 trials per predictor
Fig 1. Schematic representation of the statistical learning task. The low predictor and high predictor stimuli were presented with equal
frequency (i.e., the same number of trials); however, the target followed the high predictor on 90% of high predictor trials but only followed the
low predictor on 20% of low predictor trials.
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condition and 10 filler trials) were separated by 30-second breaks during which children
watched a short cartoon related to the magician story. Task duration was roughly 20 minutes.
Thus, although from the perspective of the participant the presentation of stimuli seemed to be
a continuous stream of mostly one color of hat (the filler) occasionally punctuated by another
color hat (one color for HP and one for LP) which might or might not be followed by the target
(a hat of the same color as the filler with food above it), trials were constructed to include a
random number of fillers from 1 to 7 followed by a high predictor or low predictor (or another
filler if it was the occasional target appearing without a predictor). The high predictor was then
followed by the target 90% of the time (or another filler 10% of the time) and the low predictor
was followed by a filler 80% of the time (or the target 20% of the time). The trial ended with
one more filler stimulus, however, to the participant, the stream just continued. Color of filler,
HP, and LP were randomly assigned to each participant.
The entire statistical learning task consisted of a continuous learning task with no separate
learning and testing phases as is often found in other statistical learning tasks. EEG was col-
lected continuously throughout as children were incidentally learning the relationships
between HP and the target and LP and the target. They were expected to show learning over
time as demonstrated throughout the task without any explicit probe. Learning was operation-
ally defined as differences in ERPs and response times between conditions (HP or LP) that
developed over time, which was measured by examining the first half versus the second half of
the task.
Electroencephalography (EEG) recording and processing. ERPs reflecting stimulus-
time-locked changes in electrical potential on the scalp during the statistical learning task were
collected using an Electrical Geodesic, Inc. 32-channel sensor net in a 132 square foot double-
walled, sound-proofed acoustic chamber. Impedances were kept below 50 kO. Continuous
EEG was acquired with a .1 to 100 Hz band-pass filter digitized at 250 Hz with a vertex refer-
ence, later re-referenced to the average reference and resampled at 256 Hz. ERPs were time-
locked to the onset of each predictor stimulus (epochs: -200 ms to +900 ms; see below). This
resulted in 60 trials for each of the two predictor conditions (HP and LP).
EEG data were pre-processed, including 0.1 Hz high-pass filtering, 30 Hz low-pass filtering,
and baseline correction to the 200 ms pre-stimulus EEG, using Net Station Version 4.5.4 (Elec-
trical Geodesics, Inc.). The remainder of processing was done using a combination of custom
scripts and pre-programmed GUI functions in MATLAB (versions R2012b 8.0.0783, R2018a
9.4.0, and R2020a 9.8.0; MathWorks) [60] and the EEGLAB Toolbox (versions 10.2.2.24a and
2019.1 [61]) for MATLAB. Bad channel data were removed and then replaced using a spherical
interpolation of data from surrounding channels. Independent component analysis (ICA) pro-
cedures were applied to the continuous EEG to find and remove eye blink and eye movement
components. The EEG was subsequently epoched to a 1100 ms window from 200 ms before
stimulus presentation to 900 ms after stimulus presentation. Other movements and artifacts
were removed manually after epoching. Participants were required to have a minimum of 15
good epochs per condition in the first half of the SL task and 15 per condition during the sec-
ond half of the SL task to be included in further analyses; all of the participants in this sample
were well above this threshold. On average, 27.7% of epochs were rejected.
Data from 6 sensors in the posterior region of interest were extracted for analysis, an a pri- ori decision based on the findings of Jost et al. [51]. Data from perimeter sensors (2 sensors) and the two sensors directly behind the isolated common sensor (COM) were not included in
analyses due to electromyogram (EMG) noise and other excessive noise. All other posterior
region sensors were included (see Fig 2). Data were grand averaged across trials, electrodes,
and participants for each condition and each half of the acquisition session to produce ERP
waveforms. Mean amplitudes were extracted for the 400–700 ms post-stimulus time window,
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also an a priori decision based on the findings from Jost et al. [51]. To reiterate, region of inter- est (ROI) and time window for analysis were chosen a priori based on previous research in the literature as recommended by Keil et al., [62] and Luck and Gaspelin [63] in their guidelines
for the publication of EEG studies. Based on the ERPs observed in the statistical learning task
described by Jost et al. [51], we chose to analyze the ERP time window 400–700 ms post-stimu-
lus presentation from the posterior ROI. This is the time window and ROI in which Jost et al.
[51] and Singh et al. [55] found ERP effects of predictor condition in a similar statistical learn-
ing paradigm and with children of similar ages.
Language assessments. We used 2 standardized language assessments. The Peabody Pic-
ture Vocabulary Test, Fourth Edition (PPVT-4) [64] assessed receptive vocabulary using pic-
tures. The Grammaticality Judgment subtest of the Comprehensive Assessment of Spoken
Language (CASL) [65] assessed grammar by orally presenting a sentence with or without a
grammatical error, and the child was asked whether it sounded correct and if not to fix it by
changing one word. Age-based standard scores were used to score both of these tests (see
Table 1).
Cognitive assessments. We used 3 cognitive assessments to measure participants’ general
cognitive ability: the Stroop Color and Word Test: Children’s Version [66] as a measure of
executive function and 2 subtests of the Wechsler Intelligence Scale for Children Fourth Edi-
tion Integrated (WISC-IV Integrated) [67]: Block Design as a measure of spatial ability and
Digit Span as a measure of verbal short term and working memory. Standard scores for inter-
ference between color word reading and color naming were used in the Stroop Test. Standard
scores were also used for Block Design and Digit Span (see Table 1).
Fig 2. Sensor map for the EEG 32-sensor net. The region of interest is shaded in gray.
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The tasks and assessments were administered in the same order for all participants: PPVT,
visual learning task (about 20 minutes long), CASL, Stroop, Digit Span, and Block Design.
Results
Behavioral evidence of statistical learning
We used a two-way repeated-measures ANOVA with the factors of probability (high vs. low)
and block (first half vs. second half) to determine whether there were differences in the behav-
ioral response times for each of the 2 probability conditions across the first half (first 60 trials)
and second half (last 60 trials) of the task. Due to technical issues, the response times for 2 par-
ticipants were not recorded during the ERP data acquisition; however, the ERP responses for
these participants were intact. Therefore, we excluded these participants only in analyses that
include response time data (N = 24; 15 males). The results showed that there was a significant main effect of predictor condition (F(1,23) = 16.65, p < .0001); however, the main effect of block was not significant (F(1,23) = 0.11, p = ns). More importantly, there was a significant interaction between predictor condition and block, F(1,23) = 6.14, p < .05. Results of post-hoc paired-sample t-tests (using adjusted Bonferroni alpha levels of 0.025 per test) showed that in
the first half of the task participants responded significantly quicker to the targets in the high
(M = 404.68, SD = 73.4) compared to the low-probability condition (M = 444.71, SD = 66.55),
t(23) = -3.91, p = .001. The results for the second half of the task also revealed significantly
quicker responses to the target for the high (M = 377.22, SD = 86.06) compared to the low-
probability condition (M = 465.32, SD = 77.85,), t(23) = - 3.67, p = .001 which suggest that
learning was evident in both halves of the task.
To further investigate the nature of the interaction and capture the magnitude of the learn-
ing effects in analyses, we created difference scores between high- and low-probability predic-
tor conditions for the RT means (L-H). As mentioned above, the participants exhibited
shorter response times for high- compared to low-probability conditions; therefore, we created
L-H difference scores to avoid analyzing negative response times. Results of paired-sample t-
tests showed a significant increase in the RT L-H difference score variable from the first
(M = 40.03, SD = 50.22) to the second half (M = 88.10, SD = 117.60), t(23) = - 2.48, p = .021. These results show that the magnitude of learning is greater in the second half which provides
behavioral evidence that prolonged exposure to the embedded probabilistic patterns increased
children’s learning of the underlying regularities. Fig 3 shows the change in participants’ RT
difference scores from the first half to the second half of the visual learning task.
Neurophysiological evidence of statistical learning
Based on the results of Jost and colleagues [51], we focused our analyses on the posterior
region of the scalp during the 400–700 ms post predictor time window, using a predefined set
of 6 electrodes (see Fig 2). Fig 4 displays the grand averaged ERP waveforms in this region for
the first half (Fig 4A) and second half (Fig 4B) of the task. To illustrate the distribution of EEG
activity across the scalp, we created topographical 2-D maps by using EEGLAB toolbox (ver-
sion 2019.1) [61] in MATLAB software (version R2012b 8.0.0783) [60]. Maps in Fig 5 show
the ERP wave amplitude averaged across all 32 electrodes for each probability condition (high,
low) and block (first vs. second half) in the 400–700 ms time-window.
A two-way repeated-measures ANOVA with the factors of probability condition (high,
low) and block (first vs. second half) in the 400–700 ms time-window did not show a signifi-
cant main effect of block (F(1,25) = 2.0, p = ns) or predictor condition (F(1,25) = 3.75, p = ns); however, there was a significant interaction between block and predictor condition, F(1, 25) = 14.71, p = 0.001. Post-hoc paired-samples t-tests (using adjusted Bonferroni alpha levels of
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0.025 per test) indicated that in the first half of the task, the ERP wave amplitudes were not sig-
nificantly different between the high (M = 2.14, SD = 2.74) and low-probability conditions (M = 2.55, SD = 2.87), t(25) = - 0.76, p = 0.46. However, in the second half of the task, the amplitudes were significantly higher for the high (M = 4.12, SD = 3.94) compared to the low- probability condition (M = 1.82, SD = 3.07), t(25) = 3.51, p = .002.
Overall, the ERP results show that the children’s ERPs demonstrated sensitivity to the dif-
ferent probability conditions only in the second half of the task, providing further evidence
that children had learned the probabilistic relationships between predictor and target stimuli
after this amount of exposure, similar to the findings by Jost and colleagues [51].
For the next set of analyses exploring the relationship among SES, statistical learning, and
language, we created difference scores between high- and low-probability predictor conditions
for the ERP amplitudes (H-L) to capture learning as a single variable. Due to statistically signif-
icant learning observed in the second half of the task, we focus these analyses on RT and ERP
difference scores in the second half of the task only. In addition, absolute values of the ERP dif-
ference scores were used because conceptually, any difference in amplitudes between high and
low predictor conditions is an indication that these conditions have been differentiated, i.e.,
that learning occurred. Exploratory analyses confirmed that the same general effects were
observed whether or not absolute values were used, but appeared to be more robust using this
approach.
Correlation analyses
The relationship between statistical learning ability (measured using the RT and ERP ampli-
tude difference scores in the second half of the task), parental education, and neuropsychologi-
cal assessments (raw language and cognitive measures scores) were examined using a partial
correlation analysis controlling for age of the participants. We found significant correlations
Fig 3. Behavioral change in learning. This figure shows change in RT (difference in response time between Low and
High predictor conditions) from the first half to the second half of the statistical learning task. Error bars represent
standard error.
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between parental education and performance on language assessments. Parental education
(M = 3.65, SD = 1.93) was positively correlated with raw scores on both PPVT (M = 168.81, SD = 32.45), r = 0.61, p = .002, and Grammaticality Judgment (M = 57.92, SD = 16.0), r = 0.60,
Fig 4. ERP average waveforms in the posterior region elicited during the statistical learning task. (a) average waveforms in the first half of the
task. (b) average waveforms in the second half of the task. High-probability: red; low-probability: blue.
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p = .002. These results suggest that higher parental education level is associated with children performing better on language assessments. On the other hand, neither of the statistical learn-
ing measures (RTs or ERPs) was significantly correlated with either of the language measures
nor with parental education level. A complete list of correlation results and descriptive statis-
tics of all measures are reported in Table 2.
Influence of SES on statistical learning
To answer our first research question to examine the possible effect that SES might have on
statistical learning, we used Pearson correlations to investigate a potential relation between the
neurophysiological and behavioral measures of learning in the second half of the task and
parental education level. As reported in Table 2, in our sample, parental education level did
not directly correlate with neurophysiological or behavioral performance on the statistical
learning task.
Moderation analyses
To answer our second research question, we used hierarchical multiple regression models to
specifically examine the moderating effect of statistical learning performance on the
Fig 5. Topographical maps depicting ERP amplitudes averaged across electrodes in 400–700 millisecond time-
window after predictor onset. Maps are shown for the first half of the task for the high-probability (top left) and the
low-probability (top right) conditions. Likewise, maps are shown for the second half of the task for the high-probability
(bottom left) and low-probability conditions (bottom right). The 6 electrodes of the a priori posterior region used in the waveform analyses are marked by stars.
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relationship between SES and language. Before entering the variables in the hierarchical multi-
ple regression, parental education and statistical learning scores (moderator) were standard-
ized to reduce multicollinearity between the variables. We conducted separate regression
analyses for RT and ERP amplitude measures, using PPVT and Grammaticality Judgment
scores as outcome variables in each regression analysis (4 regression analyses total). As recom-
mended by Frazier, Tix, and Barron [68], in the first step of each regression, we entered paren-
tal education and statistical learning (RT or ERP) as the predictor and moderator, respectively,
and the language measure (performance on PPVT or Grammaticality Judgment test) was
entered as the outcome variable. In the second step of the model, we entered the interaction
term of parental education and statistical learning performance. Prior to conducting the hier-
archical multiple regressions, relevant assumptions were tested. First, the assumptions of sin-
gularity and collinearity were met as predictor variables (parental education and statistical
learning measures) were not combinations of each other and were not highly correlated (see
Table 2). Additionally, collinearity statistics (Tolerance and VIF) were within acceptable limits
[69, 70]. Second, the assumptions of normality, linearity, and homoscedasticity were met
according to the scatterplots and histograms of standardized residuals of the data [70]. Because
statistical learning was evident in the second half of the task, regression analyses were per-
formed for behavioral (RT) and ERP data from the second half only. Results showed that there
was no significant moderating effect of the behavioral measure of statistical learning on the
relationship between parental education and either language measure (PPVT: F(2,23) = 5.10, p = .23; Grammaticality Judgment: F(2,23) = 6.59, p = .16).
However, results of the regression analysis using the neurophysiological measure showed
that statistical learning performance as measured by the difference in ERP amplitudes between
probability conditions was a significant moderator of the relationship between parental educa-
tion and both language measures (PPVT & Grammaticality Judgment). With parental educa-
tion and H-L ERP as predictor variables, the model significantly explained 38% of the variance
in children’s performance on the PPVT test, R2 adj = .382, F(2, 23) = 8.73, p = .002. There was a
Table 2. Pearson correlations (controlling for age) and descriptive statistics.
1 2 3 4 5 6 7 8
1. Parental education level ---
2. Grammaticality Judgement 0.60 ��
---
3. PPVT 0.61 ��
0.89 ��
---
4. Block Design 0.40 0.72 ��
0.63 ��
---
5. Digit Span 0.06 0.61 ��
0.51� 0.49� ---
6. Stroop 0.06 -0.39 -0.12 -0.38 -0.17 ---
7. ERP H-L 2nd half of the task 0.02 -0.26 -0.14 -0.45� -0.34 0.52� ---
8. RT L-H 2nd half of the task 0.31 0.52 0.22 -0.06 -0.08 -0.12 0.11 ---
Mean 3.65 57.92 168.81 36.23 16.31 50.69 2.93 88.10
SD 1.94 16.0 32.48 14.86 4.12 8.96 2.78 117.60
�p< .05 ��p< .01 coefficients in bold remained significant after Bonferroni correction, p = 0.006.
PPVT = Peabody Picture Vocabulary Task.
ERP = event-related potential.
H-L = difference in amplitude between High and Low probability conditions.
RT = response time.
L-H = difference in response time between Low and High probability conditions.
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main effect of parental education level on PPVT performance, β = .65, p < .001, but not a main effect of statistical learning performance (H-L) β = -.19, p = ns. Adding the interaction term to the model significantly increased the variance explained to 47%, R2 adj = .475, F(3, 22) = 8.24, p = .044 (see Table 3).
Similarly, with parental education and H-L ERP as predictor variables, the model signifi-
cantly explained 46% of the variance in children’s performance on the Grammaticality Judg-
ment test, R2 adj = .46, F(2, 23) = 11.76, p .001. There was a main effect of parental education level on Grammaticality Judgment performance, β = 0.69, p < .001, but not a main effect of statistical learning ability (H-L) β = -0.25, p = ns. Adding the interaction term to the model sig- nificantly increased the variance explained to 55%, R2 adj = .55, F(3, 22) = 11.30, p = .027 (see Table 4).
The significant interactions between parental education and statistical learning perfor-
mance in the second half of the task are depicted in Fig 6A for PPVT scores and Fig 6B for
Grammaticality Judgement scores. According to simple slope analysis following regressions,
for children who showed high levels of statistical learning, parental education level did not
influence either PPVT (β = 0.32, t(24) = 1.48, p = ns) or grammaticality judgement (β = 0.32, t (24) = 1.80, p = ns) scores. However, for children who showed low statistical learning, parental education had a significant influence on both PPVT (β = 0.99, t(24) = 4.54, p < 0.001) and grammaticality judgement scores (β = 1.04, t(24) = 5.20, p < 0.001).
As an additional control, we included each of the cognitive measures (Stroop task, Block
design, & Digit span) as covariates in the moderation analyses with parental education and the
statistical learning ERP H-L variable for the second half of the task. After adding the Stroop
task to the analysis, the moderating effect of statistical learning performance on the relation-
ship between parental education and performance on PPVT (R2 adj = .60, F(4, 21) = 10.35, p = .017) and Grammaticality Judgment (R2 adj = .63, F(4, 21) = 11.85, p = .012) remained signifi- cant. Adding Block Design and Digit Span changed the significant moderating effect of statisti-
cal learning performance on the relationship between parental education and performance on
PPVT (Block design: F(4, 21) = 10.10, p < ns; Digit Span: F(4, 21) = 7.82, p < ns). However,
Table 3. Hierarchical regression analysis of the moderating effect of statistical learning (ERPs) on the relationship between parental education average and PPVT
scores.
Unstandardized Coefficients Standardized Coefficients t Δ R2 b SE β
Step 1 0.38��
PPVT 91.42 7.73 11.83��
Parent education 7.12 1.74 0.65 4.10��
ERP H-L 2nd half -1.42 1.21 -0.19 -1.17
Step 2 0.46��
PPVT 76.06 10.17 7.48��
Parent education 11.14 2.48 1.01 4.49��
ERP H-L 2nd half 3.93 2.75 0.51 1.43
Parent education X ERP H-L 2nd half -1.34 0.63 -0.87 -2.14�
N = 26. �p < .05 ��p < .01. ERP = event-related potential.
H-L = difference in amplitude between High and Low probability conditions.
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after adding Block Design and Digit Span the moderating effect of statistical learning on the
relationship between parental education SES and performance on Grammaticality Judgment
remained significant, R2 adj = .69, F(4, 21) = 14.16, p = .049 and R 2
adj = .72, F(4, 21) = 17.40, p = .025, respectively. These results suggest that the moderating effect of statistical learning per-
formance on the relationship between parental education and grammar ability is independent
of these other cognitive factors.
Discussion
In this study, we investigated the relationships among SES (measured by parental education
level), visual statistical learning ability (operationally defined by the difference in ERP ampli-
tudes and response times between High- and Low-probability conditions; [51, 52, 55]), and
language outcomes in children. In the statistical learning task, children demonstrated sensitiv-
ity to the different probability conditions, as measured by both RTs and ERPs, indicating
learning of the statistical probabilities. Statistical learning was especially pronounced in the
second half of the task. Note that in this task, the HP and LP stimuli occur with equal fre-
quency; thus, statistical learning is based on differences in the transitional probabilities
between the HP and LP stimuli and the target, not based on simple frequency discrimination.
Consistent with previous findings [40, 46, 47] there was a positive relationship between
children’s SES level (as measured by caregiver education levels) and both syntactic knowledge
(Grammaticality Judgment subtest of the CASL) and receptive vocabulary knowledge (PPVT).
Children with more highly educated caregivers demonstrated better receptive vocabulary and
grammar knowledge skills, compared to those children whose caregivers were not as highly
educated. On the other hand, SES was not correlated with statistical learning ability, nor did
SES group assignment (low or high) have a significant effect on statistical learning.
Although SES did not directly impact statistical learning, importantly, the results of the
moderation analyses revealed that children displaying high levels of statistical learning (as
measured by ERPs) appeared to have more robust syntactic language ability as well as higher
levels of vocabulary development that was less affected by their SES. In other words, the
Table 4. Hierarchical regression analysis of the moderating effect of statistical learning (ERPs) on the relationship between parental education average and gram-
maticality judgement scores.
Unstandardized Coefficients Standardized Coefficients t Δ R2 b SE β
Step 1 0.46��
Grammaticality Judgement 88.85 4.99 17.81��
Parental education 5.25 1.12 0.69 4.68��
ERP H-L 2nd half -1.32 0.78 -0.25 -1.69
Step 2 0.55�
Grammaticality Judgement 78.05 6.44 12.13��
Parental education 8.08 1.57 1.06 5.14��
ERP H-L 2nd half 2.44 1.74 0.46 1.40
Parental education X ERP H-L 2nd half -0.95 0.40 -0.89 -2.37�
N = 26. �p < .05 ��p < .001. ERP = event-related potential.
H-L = difference in amplitude between High and Low probability conditions.
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negative effect of low SES on syntactic and lexical language development appeared to be damp-
ened by high statistical learning performance. Conversely, for children with lower statistical
learning scores, their language scores were much more sensitive to the effects of SES. Thus,
Fig 6. The significant interaction between parental education and statistical learning performance in the second
half of the task. Scatter plots of (a) PPVT and (b) grammaticality judgement scores vs. parental education for low
statistical learning and high statistical learning (as measured by ERP amplitudes) during the second half of the task. For
illustration, the statistical learning variable was separated into low and high levels by a median split. Parents’ average
education level: 0 = Less than High School, 2 = Some college, 4 = Bachelor’s degree, 6 = PhD/professional degree.
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children who were raised in less advantaged families showed more typical language develop-
ment if they had good statistical pattern learning skills. To our knowledge, these results are the
first to suggest that statistical learning ability plays a moderating role in the relationship
between SES and language development in children.
Although previous studies have found visual statistical learning to predict language ability
(e.g., [21]), we did not find a main effect of statistical learning ability on language measures in
our sample. Previous work examining the relationship between statistical learning and lan-
guage has generally been done solely with participants of middle and upper SES levels; it may
be that the general relationship between statistical learning and language is different depend-
ing on SES. Thus, it is possible that a main effect of statistical learning ability is not apparent
because the effect is different for participants of different SES within our sample.
In contrast to the ERP measures, there was no such moderating effect observed using RTs
as the measure of statistical learning. On the one hand, the RT and ERP difference score mea-
sures were significantly correlated with each other, revealing a coupling between the behav-
ioral indication of statistical learning and the associated neural response. On the other hand, it
is likely that these two variables are measuring slightly different aspects of statistical learning.
The ERPs are time-locked to the onset of the predictor stimuli before the target appears and
therefore seem to reflect the recognition–i.e., a modulation of attention [11, 71]—that certain
predictors are cues for the occurrence of the target (i.e., a form of predictive processing). Alter-
natively, the reaction times are a measure of the behavioral responses following the occurrence
of a target and therefore reflect the reaction to the target, and not a prediction that the target
will occur. Thus, in the current design, the ERPs likely index perceptual or attentional neural
processes associated with making a prediction about the upcoming stimulus as well as
response preparation whereas the RT measures reflect an (implicit) motor response (see [57]
for a similar perspective). While both measures appear to be related to each other, each one is
an index of slightly different aspects of statistical learning, which may be why one but not the
other shows a moderating effect of SES on language. In this case, attention-driven predictive
processing shows a moderating effect, but the reactive motor response does not. This in turn
suggests that predictive processing, such as predicting which syllables, words, or other linguis-
tic units will occur next in speech, may be the crucial link between statistical pattern learning
ability and language processing [8].
Although up to this point we have considered statistical learning ability an intrinsic or bio-
logical factor and SES an environmental one, it is also possible that children’s statistical learn-
ing may have been shaped by the environment in which they were raised, similar to other
studies showing that low SES is associated with atypical neural development [33, 56]. However,
the lack of a significant correlation between SES and statistical learning scores and the non-sig-
nificant effects of SES on statistical learning in the present study suggests that what drives vari-
ation in learning is not due to SES. In fact, what exactly drives variability in implicit and
statistical learning remains an open question, which is hindered by some learning tasks exhib-
iting poor psychometric properties [72, 73]. We believe that the current findings should be
considered in light of the fact that there are many different tasks and ways to measure statisti-
cal and related forms of learning, and it is not currently clear to what extent these different
tasks are or are not related to one another (see [74], for further discussion on this and other
challenges related to statistical learning research). Thus, future research should continue to
investigate the relationships among different aspects of learning, SES, and language using a
variety of tasks and methods.
Finally, we should note that parental education level is just one component of SES. Various
components of SES may be individually and/or cumulatively influencing children’s linguistic
abilities. The predictive validity of parent education has also been shown in other research
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demonstrating that it is a reliable predictor of linguistic outcome in children [36, 45, 75].
Future studies might fruitfully focus on using additional variables that are related to the gen-
eral construct of SES as well as exploring these effects in younger children. Such variables
include, but are not limited to, neighborhood context, household structure, socioemotional
stressors, and level of cognitive stimulation. It is important to note that we collected parent
education level as an ordinal variable, and we averaged both parents/caregivers’ education
level together to create the parental education level variable. We are aware that as with any
ordinal variable, the distance between each category is not known; However, in this case, due
to the lack of consensus regarding this issue and ease of interpretation we decided to assume
linearity between categories of this SES variable. We should also point out that, in our sample,
there is a slightly higher proportion of African American participants in the lower SES group
(N = 7) compared to the higher SES group (N = 1), raising the possibility that cultural differ- ences could also be playing a role in these analyses. Despite the multitude of factors that are
likely impinging on individual differences in children’s language and statistical learning ability,
it is striking that a significant moderating effect was observed with our variables, which sug-
gests that parent education and statistical pattern learning ability together impact language
outcomes. So, too, this study was limited by a small sample size (N = 26). Future research is needed to replicate and extend these findings using a larger sample.
In sum, this research provides an important examination of the relationship between statis-
tical learning abilities, the socioeconomic environment, and language development in chil-
dren. The results suggest that having good statistical learning abilities may confer some level of
resilience and can help ameliorate the language disadvantages associated with being raised in a
lower SES home environment, offering intriguing new ways to think about the relations
between learning, language development, and the social/linguistic environment in which a
child is raised. Importantly, this result was obtained using a visual non-linguistic measure of
learning. This implies a certain level of domain-generality as the statistical learning task was
visual and non-linguistic and yet it impacted spoken/auditory language development (for fur-
ther discussion of domain-generality and modality-specificity, see [11, 76, 77]. One possible
implication of these findings is the prospect of designing intervention programs for children
of families with low SES by taking statistical pattern learning abilities into account [78]. For
example, recent research has demonstrated that it may be possible to improve statistical learn-
ing abilities through targeted computerized training (e.g. [79, 80]). Such an approach may be
able to facilitate language development in children who are raised in low SES families by mini-
mizing the impact of being raised in a less than optimal social and linguistic home environ-
ment. Our results highlight the need to study language development in children by focusing
on the interaction between intrinsic (e.g., cognitive) and extrinsic factors (e.g., the social/lin-
guistic environment) for determining the most effective intervention programs for children
raised in impoverished environments.
Acknowledgments
We would like to thank the following individuals for their helpful feedback on an earlier ver-
sion of this manuscript: Adam Bosen, Angela AuBuchon, Cathy Carotta, Katherine Gordon,
Kaylah Lalonde, and Karla McGregor.
Author Contributions
Conceptualization: Leyla Eghbalzad, Christopher M. Conway.
Data curation: Leyla Eghbalzad.
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Formal analysis: Leyla Eghbalzad.
Funding acquisition: Christopher M. Conway.
Investigation: Joanne A. Deocampo.
Methodology: Leyla Eghbalzad, Joanne A. Deocampo, Christopher M. Conway.
Project administration: Leyla Eghbalzad, Joanne A. Deocampo, Christopher M. Conway.
Resources: Christopher M. Conway.
Software: Leyla Eghbalzad.
Supervision: Leyla Eghbalzad, Christopher M. Conway.
Validation: Leyla Eghbalzad.
Visualization: Leyla Eghbalzad.
Writing – original draft: Leyla Eghbalzad, Christopher M. Conway.
Writing – review & editing: Joanne A. Deocampo, Christopher M. Conway.
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