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Children's early language exposure impacts their later linguistic skills, cognitive abilities, and academic achievement, and large disparities in language exposure are associated with family socioeconomic status (SES). However, there is little evidence about the neural mechanism(s) underlying the relation between language experience and linguistic/cognitive development. Here, language experience was measured from home audio recordings of 36 SES-diverse 4-6 year-old children. During a story-listening fMRI task, children who had experienced more conversational turns with adults-independent of SES, IQ, and adult/child utterances alone-exhibited greater left inferior frontal (Broca's area) activation, which significantly explained the relation between children's language exposure and verbal skill. This is the first evidence directly relating children's language environments with neural language processing, specifying both environmental and neural mechanisms underlying SES disparities in children's language skills. Furthermore, results suggest that conversational experience impacts neural language processing over and above SES and/or the sheer quantity of words heard.
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Beyond the “30 Million Word Gap:” Children’s Conversational Exposure
is Associated with Language-Related Brain Function
Rachel R. Romeo1,2*, Julia A. Leonard2,3, Sydney T. Robinson2,3, Martin R. West4,
Allyson P. Mackey2,3,5, Meredith L. Rowe4, John D. E. Gabrieli2,3,4
1Division of Medical Sciences, Harvard University
2McGovern Institute for Brain Research at the Massachusetts Institute of Technology
3Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
4Harvard Graduate School of Education
5Department of Psychology, University of Pennsylvania
*Author to whom correspondence should be addressed:
Rachel Romeo, MIT (Office 46-4037), 43 Vassar St, Cambridge, MA 02139;
Children’s early language exposure impacts their later linguistic skills, cognitive abilities, and
academic achievement, and large disparities in language exposure are associated with family
socioeconomic status (SES). However, there is little evidence about the neural mechanism(s)
underlying the relation between language experience and linguistic/cognitive development.
Here, language experience was measured from home audio recordings of 36 SES-diverse 4-6
year-old children. During a story-listening fMRI task, children who had experienced more
conversational turns with adultsindependent of SES, IQ, and adult/child utterances alone
exhibited greater left inferior frontal (Broca’s area) activation, which significantly explained the
relation between children’s language exposure and verbal skill. This is the first evidence directly
relating children’s language environments with neural language processing, specifying both
environmental and neural mechanisms underlying SES disparities in children’s language skills.
Furthermore, results suggest that conversational experience impacts neural language
processing over and above SES and/or the sheer quantity of words heard.
Keywords: language, socioeconomic status, fMRI, LENA, turn-taking
Children’s early life experiences during sensitive periods of neural plasticity shape the brain
structures and functions underlying their cognitive aptitudes. One critical experience is language
exposure. Specifically, the language quantity (e.g., number of words) and quality (e.g., sentence
complexity, lexical diversity) that young children hear are the foundation of later language and
literacy skills (Hirsh-Pasek et al., 2015; Rodriguez & Tamis-LeMonda, 2011; Rowe, 2012) and
non-verbal capacities including executive functioning (Sarsour et al., 2011), math ability (Levine,
Suriyakham, Rowe, Huttenlocher, & Gunderson, 2010), and social skills (Connell & Prinz,
Children’s language exposure varies substantially in relation to their socioeconomic status
(SES). SES represents the social and economic resources of an individual or group, and
children from lower-SES backgrounds on average hear fewer and less complex utterances than
their more advantaged peers (Hart & Risley, 1995; Rowe, 2008). In a landmark study, Hart and
Risley (1995) estimated that by age 3, children from higher-SES backgrounds had heard 30
million more words than children from lower-SES backgrounds, and other studies report similar
trends (Hoff, 2006). Until recently, such studies required time-consuming transcription of parent-
child exchanges that limited the amount of data that could be collected. Technological advances
now allow for longer, more comprehensive, and less intrusive recordings of naturalistic
language exposure. One such device, the Language Environment Analysis (LENA) system,
records 16-hour days from the child’s perspective and automatically characterizes
children’s language environments. Studies using LENA have confirmed substantial variation
in the amount of language children experience in association with SES (Gilkerson et al., 2017).
This broad or distal association between SES and children’s language development must be
distinguished from the direct or proximal association between language exposure and language
development (Bronfenbrenner & Morris, 2007). SES is a broad characterization of many
correlated factors including income, educational access, other environmental resources, stress,
health, and nutrition. Development, however, depends upon specific, proximal factors that
directly affect the child, such as immediate language exposure. Indeed, the separability of distal
SES from proximal language experience is evident in the considerable variation in early
language exposure within each SES band (Gilkerson et al., 2017; Hirsh-Pasek et al., 2015;
Rowe, Pan, & Ayoub, 2005; Weisleder & Fernald, 2013). When SES is controlled, children’s
language exposure remains strongly associated with variation in their language abilities (Rowe,
2012; Weisleder & Fernald, 2013), and differences in exposure partially or fully explain the SES-
related gap in language skills (Hoff, 2006).
Despite considerable behavioral research linking children’s language exposure to their language
abilities, there is currently no evidence about the neural mechanisms underlying this
relationship. There is, however, a growing body of evidence that SES disproportionately affects
language ability and language neural systems compared to other neurocognitive domains
(Farah, 2017). Structurally, lower SES is associated with reduced gray matter in left perisylvian
regions underlying phonological, semantic, and syntactic components of language
comprehension and production (Noble et al., 2015; Noble, Houston, Kan, & Sowell, 2012), as
well as with bilateral occipitotemporal regions involved in reading (Jednorog et al., 2012;
Mackey et al., 2015). Additionally, functional neuroimaging with language tasks has revealed
SES-related differences in left inferior frontal (Raizada, Richards, Meltzoff, & Kuhl, 2008),
superior temporal, and fusiform regions (Noble, Wolmetz, Ochs, Farah, & McCandliss, 2006).
Although these studies provide valuable insight on the relation between brain development and
SES, they have not aimed to relate brain measures directly to children’s language
environments—the proximal factor presumed to directly influence children’s linguistic abilities
(Noble et al., 2012; Perkins, Finegood, & Swain, 2013). Relating specific and objectively
measureable language experiences to brain development is of particular interest because such
experiences can become practical and efficacious targets for intervention (Roberts & Kaiser,
2011). Only two neuroimaging studies have related home language experiences to brain
functions. One study using fMRI with children ages 8-12 reported a relation between videotaped
home language and right prefrontal activation on a complex nonverbal task (Sheridan, Sarsour,
Jutte, D'Esposito, & Boyce, 2012). Another study with infants reported a relation between
LENA-measured adult word counts and event related potentials (ERPs) to phonetic contrasts in
left frontal electrodes (Garcia-Sierra, Ramírez-Esparza, & Kuhl, 2016). However, neither study
examined the joint roles of SES and language input in relation to linguistic brain functions.
The present study aims to elucidate how variation in children’s natural language experience
relates to brain function underlying language processing, and in turn to linguistic abilities.
Specifically, we hypothesized that LENA measures of language exposureover and above
SES—would be associated with children’s language skills and language-related brain activation,
especially in left perisylvian neocortices known to support language.
Thirty-six children (22 male) aged 4 years, 6 months to 6 years, 10 months (M = 5.8 years, SD =
0.63 years) and their parents completed this study (see Supplement for justification of sample
size). Boys and girls did not significantly differ on any behavioral (all p > 0.15), demographic (all
p > 0.33), language exposure (all p > 0.76), or neural measure (maximum z = 1.2). Children
were native English speakers and typically developing, with no history of premature birth,
neurological disorders, developmental delay, speech/language therapy, or grade repetition, and
all bilaterally passed a 4 pure-tone hearing screening (0.5 KHz, 1 KHz, 2 KHz, 4KHz) on the day
of assessment. Nineteen children were initially assessed and excluded for not meeting these
inclusion criteria.
Twenty of the 36 participants additionally participated in a larger randomized controlled
intervention study on parenting practices; only their baseline data (before learning of group
assignment) was used here. Twenty-seven other children participated but did not have complete
data sets, either because they did not complete the home recordings (n = 6), did not participate
in the fMRI scan (n = 11), fell asleep during the fMRI scan (n = 3, details below), or exhibited
excessive movement during the fMRI scan (n = 7, details below). These participants did not
differ from the included sample on any behavioral scores, language exposure measures, or
SES. All procedures were approved by the Institutional Review Board at the Massachusetts
Institute of Technology, and written informed consent was obtained from parents.
Behavioral and Demographic Assessments
Children completed standardized behavioral assessments to characterize verbal and nonverbal
cognitive skills (see Supplement for additional info on executive function assessments). These
included the Matrix Reasoning, Picture Memory, and Bug Search subtests of the Wechsler
Preschool and Primary Scale of Intelligence (WPPS-IV) (Wechsler, 2012), the Peabody Picture
Vocabulary Test (PPVT-4 (Dunn & Dunn, 2007), and the Sentence Comprehension, Word
Structure, Formulated Sentences, and Recalling Sentences subtests of the Clinical Evaluation
of Language Fundamentals (CELF-5) (Wiig, Semel, & Secord, 2013), which together form the
CELF-5 Core Language Score (CLS). Age-normed scaled scores from the three WPPSI-IV
subtests were averaged to create a “nonverbal composite score.” Inclusion criteria required all
participants to have nonverbal composite score, PPVT-4 standard score, and CELF-5 CLS
greater than or equal to one standard deviation below the mean (16th percentile). Because the
CELF-5 only provides age-based norms for children aged 5 years or more, four-year-olds were
required to score greater than or equal to the age equivalent for their raw scores on each of the
four subtests. Composite Verbal Scores were created by averaging the PPVT-4 standard score
and the CELF-5 CLS.
Additionally, parent(s) filled out questionnaires about the child’s developmental history and
family demographics, including highest level of education obtained by both parents and annual
household income. When a father was present in the home, maternal and paternal years of
education were averaged to create a parental education metric (1 = high school or less, 2 =
some college/associate’s degree, 3 = bachelor’s degree, 4 = master’s/professional degree, 5 =
doctoral level degree).
Neuroimaging Data Acquisition
Neuroimaging took place at the Athinoula A. Martinos Imaging Center at the McGovern Institute
for Brain Research, at the Massachusetts Institute of Technology. First, children were
acclimated to the MRI environment and practiced lying still in a mock MRI scanner. Data were
then acquired on a 3 Tesla Siemens MAGNETOM Trio Tim scanner equipped for echo planar
imaging (EPI; Siemens, Erlangen, Germany) with a 32-channel phased array head coil. An
automated scout image was acquired, and shimming procedures were performed to optimize
field homogeneity. A whole-head, high-resolution T1-weighted multi-echo MPRAGE structural
image was acquired using a protocol optimized for movement-prone pediatric populations (TR =
2530 ms, TE = 1.64 ms/3.5 ms/5.36 ms/7.22 ms, TI = 1400 ms, flip angle = 7°, resolution = 1-
mm isotropic). Whole-brain functional images were acquired with a continuous gradient
echoplanar T2*-weighted sequence (T2*-weighted images, TR = 2500 ms, TE = 30 ms, flip
angle = 90°, bandwidth=2298 Hz/Px, echo spacing= 0.5 ms, 41 transverse slices with FoV =
192 × 192, in-plane resolution of 3 mm × 3 mm). Before each scan, six dummy volumes were
acquired and discarded to reach equilibrium, and online prospective acquisition correction
(PACE) was applied to the echo planar image sequence throughout the scan.
Functional MRI Task
Children passively listened to short, simple stories derived from the Narrative Language
Measures (Petersen & Spencer, 2012), the content of which includes events that young children
are likely to be familiar with (e.g., playing games, getting hurt). All stories had consistent
narrative structure, word count, and language complexity, and were recorded by a female
native-English speaker. A block design paradigm presented fifteen-second long trials consisting
of a single story either played normally or played in reverse (backward speech), followed by 5
seconds of silent rest. A third condition (not analyzed here) involved dichotic speech with a
different story played in each ear. One run consisted of 6 trials of each condition (18 trials total),
such that the run lasted 6 minutes, with condition order pseudo-randomized such that the same
condition never repeated twice in a row. Participants were randomly assigned to hear one of two
stimulus lists containing all different stories with equal story interest ratings. A female stick figure
appeared on a gray screen throughout auditory stimulation to remind children to listen. During
the scan, an experimenter stood at the foot of the bore and monitored participants’
attentiveness. If the participant closed their eyes for more than 5 seconds, they were considered
asleep and their data was discarded (n = 2, mentioned above). Before entering the scanner,
children completed a short practice with stories not heard in the scanner, and were required to
correctly answer 2 of 4 free-response comprehension questions to ensure familiarity with the
task. In the scanner, participants were reminded to listen carefully to the stories to earn prizes
upon task completion. Participants were not instructed to memorize the passages, because the
goal was to record brain responses during natural language comprehension. Pilot data from
children and adults indicated that participants had very low levels of incidental memory for the
passages; as such, no post-MRI comprehension/retention test was administered to avoid
burdensome additional testing that would be uninformative. All stimuli and scripts are available
for download at
Neuroimaging Analysis
Functional MRI data preprocessing and analysis was executed with Nipype, utilizing FSL
version 5.0.9 and FreeSurfer version 5.3.0. Functional images were re-aligned to the first
volume of the run, co-registered to the corresponding anatomical image (which had been
processed and manually edited as necessary in FreeSurfer to ensure correct gray and white
matter boundaries), and then to a standard MNI152 template. Functional time-series outliers
(global mean intensity > 3 standard deviations, or volume-to-volume motion > 2 mm) were
identified by ART and removed from the analysis by adding one regressor per outlier to subject-
level general linear models (GLMs). Participants with outliers in more than 20% of volumes were
excluded from the study (n = 7, mentioned above). Time-series data were high-pass filtered at
120 seconds, spatially smoothed using 6 mm FWHM Gaussian kernel, and convolved with the
canonical double-gamma hemodynamic response function (HRF) in FSL, and GLMs were used
to create contrast maps for each subject. Subject-level results were combined in mixed effects
models using FSL’s FEAT with FMRIB's Local Analysis of Mixed Effects (FLAME) stage 1.
Results were corrected for multiple comparisons using a conservative cluster-forming threshold
of p < 0.001, connectivity of 26 (voxels must be connected by at least a point), and a family-wise
error rate of p < 0.05, and fractionally projected orthogonally to the surface for visualization
purposes. Average activations were extracted from subject-level cortical parcellations
(according the Desikan-Killiany gyral-based atlas) for mediation analysis.
Home Audio Recordings
Parents were given two LENA Pro digital language processors (DLPs), which are 2-ounce
digital recorders that fit in a child’s shirt pocket and store up to 16 hours of digitally recorded
audio. Parents were instructed to collect full-day recordings from a consecutive Saturday and
Sunday, beginning when the child woke up. The average number of days between
assessment/MRI and LENA recording was was 8.97 (SD = 5.81), with a maximum of 21
intervening days. Upon return of the DLPs, the LENA Pro processing system automatically
analyzed the audio and provided estimates of the total number of adult words spoken in the
recording (i.e. word tokens), the total number of child utterances, and the total number of adult-
child conversational turns, defined as a discrete pair of an adult utterance followed by a child
utterance, or vice versa, with no more than 5 seconds pause between the two. Whereas adult
words and child utterances are simple linguistic measures, conversational turns incorporate
both linguistic information and non-verbal communicative aspects such as temporal contiguity,
adult responsiveness, joint social attention, and exchange of communicative information. As
such, conversational turns may represent a more holistic measure of interpersonal
conversational engagement.
LENA speech-identification algorithms have been determined to be highly reliable, yielding
measures approximately 82% accurate for adult speech and 76% accurate for the speech of
infants and young children up to 3 years old (Gilkerson et al., 2017; Zimmerman et al., 2009).
Although primarily designed to analyze speech of children younger than four years old, the
same algorithms were applied to recordings from all participants, such that any potential
inaccuracies would be consistent. Running totals for each speech category were calculated for
each consecutive 60 minutes across the two days in 5 minute increments (e.g., 7:00 AM 8:00
AM, 7:05 AM 8:05 AM, etc.), and the per-participant highest hourly total of adult words, child
utterances, and conversational turns were separately extracted for further analysis. This metric
helped minimize differences in daily totals due solely to different recording lengths and/or loud
activities that may have masked speech and misrepresented language input. It also attempted
to reduce the amount of “overheard speech” that was not child-directed, since peak language
periods are shown to be more similar to engaged structured play situations (Tamis-LeMonda,
Kuchirko, Luo, Escobar, & Bornstein, 2017). Such measures of peak naturalistic observations
are consistent with other studies utilizing LENA (Garcia-Sierra et al., 2016; Ramírez-Esparza,
García-Sierra, & Kuhl, 2014).
Behavioral Results
All data (to the extent that they are available to share) are freely available for download at Children’s verbal and nonverbal ability, according to
standardized assessments, ranged from low average to above average (verbal composite
standard score: Range = 86-139, M = 114, SD = 15; nonverbal composite scaled score: Range
= 7.3-14.7, M = 10.6, SD = 2.1). Parental education ranged from partial high school to doctorate
level degrees (M = some college), and familial income ranged from $6,000 to $250,000 per
year, with median of $85,500 per year, consistent with the median familial income in
Massachusetts of $90,590. Parental education, but not income, was positively correlated with
children’s nonverbal ability (education: r = 0.34, 95% CI = [.02, .67], p < 0.05; income: r = 0.11,
p = n.s.; Fig 1a). Although both education and income were correlated with children’s verbal
ability (education: r = 0.69, 95% CI = [.44, .94], p < 0.00001; income: r = 0.48, 95% CI = [.17,
.79], p < 0.01; Fig 1b), linear regression revealed that income predicted no unique variance in
child verbal ability after accounting for parental education (education:
= 8.25, 95% CI = [4.07,
12.44], p < 0.001, income:
= < .01, p = n.s.).
Fig. 1. Scatterplots of composite (a) nonverbal and (b) verbal scores as functions of parent education
level (mother and father averaged) and household income. Standardized nonverbal assessments
evaluated fluid reasoning, nonverbal working memory, and processing speed. Standardized verbal
assessments evaluated vocabulary, receptive and expressive morphosyntax, and verbal working memory
skill. Dotted lines indicate the average range of scores (within 1 standard deviation of population mean).
There was great individual variability in language exposure measures, including the number of
adult words per peak hour (M = 4260, SD = 1225, range = 1953-6991), the number of child
utterances per hour (M = 743, SD = 261, range = 300-1275), and the number of conversational
turns per hour (M = 181, SD = 56, range = 86-330). Higher parental education and income
correlated significantly with more adult words (education: r = 0.41, 95% CI = [.09, .73], p < 0.05;
income: r = 0.39, 95% CI = [.06, .71], p < 0.05) and more conversational turns (education: r =
0.34, 95% CI = [.02, .67], p < 0.05; income: r = 0.37, 95% CI = [.04, .69], p < 0.05; Fig. 2), but
neither SES measure was significantly correlated with child utterances (education: r = 0.25, p =
n.s.; income: r = 0.24, p = n.s.). If these peak-hour measures are extrapolated, children in the
top and bottom SES quartiles would experience an annual adult word gap of 5 million words,
which could accumulate to approximately 30 million words by age of enrollment in this study,
similar to the gap originally reported by Hart and Risley (1995). However, SES only explained a
moderate share of the variability in language exposure (11-17%), indicating that there was wide
variability of language exposure within families of similar SES.
Fig. 2. Scatterplots of peak hourly (a) adult words and (b) conversational turns as functions of parent
education level (mother and father averaged) and household income.
All three measures of language experience correlated with children’s scores on behavioral
language assessments, although conversational turns most strongly predicted the verbal
composite score (conversational turns: r = 0.51, 95% CI = [.022, .81], p < 0.001; adult words: r =
0.36, 95% CI = [.04, .69], p < 0.05; child utterances: r = 0.34, 95% CI = [.01, .66], p < 0.05).
Multiple regression models were constructed to predict verbal composite scores as a function of
parental education, family income, and each of the three language experience measures. In all
three models, parental education significantly predicted verbal scores [all
> 7.70, p < 0.001,
partial r > 0.55] whereas income did not [all
< 0.1]. Only conversational turns significantly
predicted additional variance in verbal scores after education and income were partialled out (
= 0.09, 95% CI = [.02, .16], p = 0.01, partial r = 0.43, R2 change = 0.10; Fig. 3). Thus, children’s
composite verbal score increased by one point for every additional 11 conversational turns
experienced per hour, independent of SES. The relation between conversational turns and
verbal scores remained significant (all
> 0.08, p < 0.05) when adult words and/or child
utterances was added to the model, suggesting that conversational turns was not just a proxy
for adult speech or child talkativeness. Furthermore, a bootstrap mediation analysis revealed
that the number of conversational turns significantly mediated the relationship between parental
education and verbal composite scores (indirect effect = 1.16, 95% CI = [0.22, 2.92],
indirect/total effect = 0.16), such that variation in conversational turns could account for 16% of
the total relationship between parental education and children’s verbal scores.
Fig. 3. Relationship between children’s composite verbal score (controlled for parent education level and
income) and the number of hourly conversational turns.
Neuroimaging Results
The contrast of interest was activation during the comprehensible forward speech condition
versus the incomprehensible backward speech condition, which yields activation specific to
higher-level language processing involved in comprehending heard stories, roughly controlling
for auditory characteristics. As a group, this task yielded significant activation along bilateral
superior temporal sulci (STS), with a leftward lateralization (Fig. 4, Table S1); in the left
hemisphere, a cluster extended from the temporal pole to supramarginal/angular gyri, while in
the right hemisphere, a cluster was restricted to the anterior portion of the STS.
Fig. 4. Regions where activation was significantly greater while listening to forward speech versus
backward speech, averaged across all participants. Clusters include the whole of the left superior
temporal sulcus and the anterior portion of the right superior temporal sulcus.
Whole brain correlations with the three LENA measures were conducted to detect individual
differences in activation related to language exposure. While there were no significant
correlations with the number of adult words or child utterances, the number of conversational
turns correlated positively with activation in a single cluster (Fig. 5a, Table S2, 766 total voxels)
spanning left pars triangularis (Brodmann area 45) extending into pars opercularis (Brodmann
area 44), which together comprise “Broca’s area.” This cluster remained significantly correlated
with conversational turns after controlling for parental education and income (Fig. 5b), verbal
and nonverbal composite scores (Fig. 5c), adult words and child utterances counts (Fig. 5d), or
all of these covariates together (Fig. 5e), indicating that this relationship was not driven simply
by any of these factors. In other words, the more conversational turns a child experienced, the
greater their activation in Broca’s area during language processing, independent of the child’s
SES, cognitive ability, or sheer numbers of adult words and child utterances. There were no
clusters exhibiting significant correlations with any demographic variables (age, gender, parent
education, income) or cognitive (verbal, nonverbal scores) variables.
Fig 5. Correlations between activation during language processing and the number of hourly
conversational turns children experienced. (a) Zero-order correlation between the number of
conversational turns and activation in the forward > backward speech contrast. Correlations remained
significant when controlling for (b) parental education and income, (c) verbal and nonverbal assessment
scores, (d) individual numbers of adult words and child utterances, or (e) all of these covariates.
We then asked if Broca’s area activation helped explain the relation between children’s
language exposure and verbal scores. The magnitudes of children’s Broca’s area activations,
(averaged over anatomically-defined opercular and triangular regions, as shown in Fig. 6)
significantly mediated the relation between the number of conversational turns and verbal
composite scores (indirect effect = 0.065, 95% CI = [0.02, 0.11], indirect/total effect = 0.48),
rendering the relation between conversational turns and verbal scores insignificant. This
suggests that conversational turns may support children’s verbal skills in part by influencing
Broca’s area activation during language processing. Further, this neural pattern explained 48%
of the relation between children’s conversational turns and their verbal scores.
Fig. 6. Mediation model showing the effect of conversational turns on language assessment scores as
mediated by activation in the left inferior frontal gyrus, shaded in yellow. Activation significantly mediated
the relation between the number of conversational turns children experience and their language scores.
Solid arrows represent direct paths, whereas the dotted arrow represents the indirect (mediated) path.
coefficients represent unstandardized regression coefficients. *p < 0.05, **p < 0.01, ***p < 0.001
Finally, conversational turns and Broca’s activation jointly mediated the relationship between
parent education and children’s language scores, (indirect effect = 1.69, 95% CI = [0.24, 3.75],
indirect/total effect = 0.23), indicating that conversational turns and Broca’s activation during
language processing could account for 23% of the total relationship between SES and children’s
language skills.
This study provides the first evidence of the neural activation patterns underlying the relation
between children’s early language exposure and verbal skills. Using at-home, real-world audio
recorders, we replicated behavioral findings that higher SES is correlated with both greater
language experience and verbal abilities in children ages 4-6 years. Specifically, it was the
number of conversational turns between children and adults (and not the sheer number of adult
words) that significantly mediated the SES-verbal ability relationship. Further, neuroimaging
revealed a neural mechanism by which language experience may influence brain development;
namely, children who experienced more conversational turns exhibited greater activation in left
inferior frontal regions (“Broca’s area”) during language processing, which explained nearly half
the relationship between children’s language exposure and verbal abilities. Finally,
conversational turns and Broca’s activation jointly mediated the relationship between SES and
children’s language abilities, demonstrating both environmental and neural mechanisms
underlying SES disparities in early language skills.
These findings are consistent with evidence that qualitative aspects of children’s language
experience (such as turn-taking) may have a greater impact on language development than
sheer quantitative measures (Hirsh-Pasek et al., 2015; Zimmerman et al., 2009). While the
conversational turn count likely includes more child-directed speech than the adult word count
(which also includes any “overheard” speech), it is unlikely that the quantity of child-directed
speech alone explains the significance of the conversational turn measure. Studies of child-
directed speech suggest that contiguity (temporal connectedness) and contingency (contextual
relevancy) with children’s utterances are critical for word-learning (Roseberry, HirshPasek, &
Golinkoff, 2014), and that the fluency, connectedness, and joint engagement of communication
predict later language skills over and above the number of adult words (Hirsh-Pasek et al.,
2015). In fact, conversational turns fully explain the effect of adult words on 2-to-48-month-old
children’s language skills (Zimmerman et al., 2009). The present results extend the importance
of conversational turns to language skills at age 6, suggesting a continued role for this
essentially social aspect of language development.
Conversational turns may be particularly important for language development because they
provide increased opportunities for children to practice language and receive feedback from
adults. Furthermore, this creates a feedback loop to help adults hone their own speech to the
optimal complexity to best support children’s language development (Zimmerman et al., 2009).
While it is possible that children with better language abilities may better engage in these
conversations, child utterances had the weakest relation to language scores and brain
functions, suggesting that the strong effect of conversational turns is not simply a reflection of
more talkative children. More broadly, the importance of conversational turns supports theories
that language development crucially relies on social interaction and social neural circuitry (Kuhl,
2007) and that pre-linguistic communicative turn-taking was essential for the evolution of
language (Levinson, 2016).
The present study is the first to provide evidence of a localized (left inferior frontal) neural
mechanism that underlies the relation between children’s direct language exposure and
language processing. This is consistent with findings that language input is related to infants’
ERP responses in left frontal regions during a phonological task (Garcia-Sierra et al., 2016).
Thus, linguistic experience appears to have a particular influence on language processes in left
prefrontal cortex, beginning in infancy and continuing through early childhood.
The finding that participants as a group yielded left-lateralized superior temporal activation is
likely indicative of a relative invariance in activation related to the acoustic/sub-discourse
aspects of language. However, variation in participants’ language experience correlated
exclusively with activation in Broca’s area. The localization of this brain-behavior relationship
may be related to the nature of conversational turns as a higher-level, supralexical language
process. Although Broca’s area is classically associated with speech production, research
suggests it plays a much broader role in both receptive and expressive language processing, as
well as a variety of non-linguistic functions. The specific role of Broca’s area in passive
language comprehension is still a matter of debate, although it may function as a convergence
zone, in which small, independent elements of language (e.g., phonemes, words) are unified
into a coherent overall representation (Hagoort, 2014). The present functional tasklistening to
meaningful, connected storiesrequires integration across phonological, semantic, and
syntactic units; thus, greater activation in Broca’s area may represent a deeper engagement
with the linguistic structure of the stories. Alternatively, regions of Broca’s area also support
several domain-general functions, including action perception, working memory, and executive
functioning/cognitive control (Fedorenko, Duncan, & Kanwisher, 2012); by this view, greater
activation could represent a neural representation of the speaker’s/characters’ movements,
and/or relating current verbal information to recently heard sentences/stories. Conversational
experience could plausibly contribute to either or both neural systems, and future studies are
needed to delineate the precise cognitive process(es) associated with language exposure.
Several limitations of the present study are noted. To study typical development, children with
language disorders/delays or language scores below the 16th percentile were excluded. Given
the strong relation between SES and language scores, this may have disproportionately
excluded lower-SES children, which some argue may itself be considered a “learning disability”
(Ryan, 2013). As such, future studies should delineate the generalization of these findings to
children with a greater variety of language abilities. Additionally, participants’ young age
required minimization of in-scanner tasks; as such, the functional task was passive in nature.
Although children were required to demonstrate listening comprehension before entering the
scanner, monitored for alertness during scanning, and incentivized to listen closely, children
could have varied in their level of task engagement. However, this is unlikely to wholly account
for activation differences, because there were no temporal-lobe differences in relation to
language experience and because this task has revealed robust perisylvian activation even in
young, sleeping children (Redcay, Haist, & Courchesne, 2008). Nevertheless, any functional
activation is constrained by the nature of the task and material used in an experiment, and
further studies will be needed to characterize the scope and limits of the present findings.
Finally, while LENA provides immense, naturalistic data on the quantity of speech experienced,
it does not parse what is said, and thus provides little information about other qualitative aspects
of language, such as lexical diversity and grammatical complexity. Future studies should
determine the precise relation between conversational quantity and quality on brain and
language development.
Although it has been theorized that the home language environment underlies the link between
SES and the structure and function of canonical language-related brain regions (Noble et al.,
2012; Perkins et al., 2013), this is the first study to reveal a direct relation between a specific
aspect of language exposure, namely conversational turns, and brain function during language
processing. While causation cannot be implied, results suggest that early language exposure, a
proximal aspect of children’s environment, may alter the way in which their brains process
language. These findings also have clear practical implications. While many early intervention
programs aim to increase the amount of language parents address to their children, these
findings suggest programs should also encourage parents to talk with their children by engaging
in more interactive, back-and-forth conversation (Leech, Wei, Harring, & Rowe, in press;
McGillion, Pine, Herbert, & Matthews, 2017). Future longitudinal studies may determine if
increasing the number of conversational turns affects the neural patterns supporting language
processing, and if there is a critical/sensitive period for such neural changes. Nevertheless, the
present study provides initial information on the neural mechanisms underlying the link between
children’s linguistic exposure and their language development.
Author Contributions
R. R. Romeo and J. D. E. Gabrieli developed the study concept. R. R. Romeo, J. A. Leonard, A.
P. Mackey, M. L. Rowe, and J. D. E. Gabrieli designed the study. R. R. Romeo, J. A. Leonard,
and S. T. Robinson collected the data. R. R. Romeo performed the data analysis and
interpretation under the supervision of J. D. E. Gabrieli and M. L. Rowe. R. R. Romeo drafted
the manuscript, and J. A. Leonard, M. R. West, A. P. Mackey, M. L. Rowe, and J. D. E. Gabrieli
provided critical revisions. All authors approved the final version of the manuscript for
We thank the Athinoula A. Martinos Imaging Center at the McGovern Institute for Brain
Research (MIT); Atshusi Takahashi, Steve Shannon, and Sheeba Arnold for data collection
support; Kelly Halverson, Emilia Motroni, Lauren Pesta, Veronica Wheaton, and Christina Yu for
assistance in administering behavioral assessments; Megumi Takada for help with data
collection/organization; Anne Fernald for insight on LENA data analysis; Joshua Segaran and
Hannah Grotzinger for MRI quality assurance; Tyler Perrachione for thoughtful conversations;
Andrea Imhof for manuscript comments; and Transforming Education, John Connolly and
Glennys Sanchez from 1647 Families plus Ethan Scherer from the Boston Charter Research
Collaborative for extensive recruitment support.
Research was funded by the Walton Family Foundation (to M.R.W.), National Institute of Child
Health and Human Development (F31HD086957 to R.R.R.), Harvard Mind Brain Behavior Grant
(to R.R.R.), and a gift from David Pun Chan.
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Supplementary Information
Sample size justification
Because this is the first study to examine individual relationships between children’s language
exposure and fMRI measures of language-related brain activation, effect size estimates were
not available to inform sample size. However, the behavioral correlations between language
input quantity/quality and children’s language scores are typically moderate to strong (0.4 < r <
0.6) (Hirsh-Pasek et al., 2015; Hoff, 2003; Rowe, 2012; Weisleder & Fernald, 2013). For 80%
power to detect such an effect in the expected direction at = 0.05, one would need to recruit
15-36 participants. Similarly, a majority of studies investigating correlations between behavioral
measures and fMRI activation using appropriate independent analyses report correlations in the
0.5 to 0.7 range, with a median of 0.6 (Vul, Harris, Winkielman, & Pashler, 2009, Figure 5).
Given that individual differences analyses (i.e., correlational analyses) have lower power than
within-subjects analyses (i.e., condition differences), common sample-size planning tools for
fMRI studies are not appropriate for the present power analysis. Instead, power curves specific
to Pearson’s correlations in the context of fMRI were consulted (Yarkoni & Braver, 2010). By
these estimates, one would need to recruit 15-30 participants for the same parameters stated
above. Combined, a sample size of 15-36 is recommended to find expected behavioral and
neural effects. However, because of the likelihood of publication bias in previously reported
effects (e.g., Anderson, Kelley, & Maxwell, 2017), we aimed for the highest end of this range (n
= 36).
Statistical Analysis
All statistical analyses (with exception of whole-brain fMRI analyses) were performed in IBM
SPSS Statistics version 24. The first approach was to conduct zero-order Pearson’s correlations
between children’s assessment scores, SES demographics, and LENA measures of language
exposure. Because all three variables were intercorrelated, we conducted linear regressions to
determine which independent variables predicted unique variance in children’s language scores,
while controlling for the other independent variables. Finally, we conducted bootstrapped
mediation with 10,000 iterations using the PROCESS macro for SPSS (Hayes, 2013) to
determine whether language exposure mediated the relationship between SES and children’s
language scores. The same bootstrapping approach was applied to neural activation measures
extracted from a region of interest (see main text).
Executive Functioning measure
In addition to the standardized assessments described in the main text, children also completed
a non-standardized executive functioning (EF) task. Because EF relies on prefrontal regions
adjacent to/overlapping with frontal language regions, EF was included to serve as a
covariate/nuisance variable. The Hearts and Flowers version of the dots task (Davidson, Amso,
Anderson, & Diamond, 2006) is commonly used to assess EF in both children and adults,
because it requires all three EF dimensions (working memory, inhibition, and cognitive
flexibility/switching) with simple instructions. Children rested their hands on a handlebar
adjusted to finger-distance away from a touch screen computer and completed a practice run of
quickly pressing on-screen buttons in this way. Then, a red heart or flower would appear on the
right or left side of the screen, and children were instructed to press the button on the same side
as a heart (congruent condition) and the button on the opposite side of a flower (incongruent
condition). The task consisted of three consecutive blocks: a congruent block of 12 trials, an
incongruent block of 12 trials, and a randomly mixed block (congruent and incongruent) of 49
trials. For all conditions, stimuli were displayed for 500 milliseconds (ms) with 1500 ms to
respond and an interstimulus interval of 500 ms. Any response faster than 200 ms were
considered to be anticipatory (Davidson et al., 2006) and excluded from analyses. Children
received up to 12 practice trials before the congruent and incongruent blocks to ensure
understanding of the rule. No practice was included before the mixed block, and thus the first
trial was additionally excluded from analyses. The main outcome measures were the average
accuracy across all trials in the mixed block and average reaction time (RT) in milliseconds
across all correctly answered trials in the mixed block. Although rare, accuracy scores below
50% were not discarded because they could have been obtained by rule reversal, indicating an
error in cognitive flexibility/switching.
Mean accuracy on the EF task was 72%, (SD = 22%) with a mean reaction time on correctly
answered trials of 1140 ms (SD = 207). None of the fMRI analyses including group mean task
activation and whole brain correlates with LENA measures changed with the inclusion of EF
scores as a nuisance variable. This suggests that correlations between conversational turns and
activation in Broca’s area are not driven by differences in executive functioning.
Supplementary References
Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample-size planning for more accurate
statistical power: A method adjusting sample effect sizes for publication bias and
uncertainty. Psychol Sci, 956797617723724.
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control and executive functions from 4 to 13 years: evidence from manipulations of
memory, inhibition, and task switching. Neuropsychologia, 44(11), 2037-2078.
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regression-based approach. New York, NY: The Guilford Press.
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(2015). The contribution of early communication quality to low-income children’s
language success. Psychol Sci, 26(7), 1071-1083.
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studies of emotion, personality, and social cognition. Perspect Psychol Sci, 4(3), 274-
Weisleder, A., & Fernald, A. (2013). Talking to children matters: Early language experience
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Table S1. Group mean task activations for forward > backward speech
MNI coordinates
Anatomical Description
Left Hemisphere Cluster (3552 voxels)
Anterior superior temporal sulcus
Posterior superior temporal sulcus
Temporal pole
Supramarginal gyrus
Right Hemisphere Cluster (1418 voxels)
Anterior superior temporal sulcus
Temporal pole
Coordinates and anatomical descriptions of local peak activations for the forward > backward
speech contrast, averaged over the entire sample (n = 36). Analyses revealed two significant
clusters, one in each hemisphere, visualized in Figure 4.
Table S2. Correlation between conversational turns and forward > backward activation
MNI coordinates
Anatomical Description
Left posterior pars triangularis
Left anterior pars triangularis
Left anterior pars opercularis
Coordinates and anatomical descriptions of local peak activations in a single cluster (Figure 5a,
766 voxels) exhibiting a significant correlation between the number of conversational turns
children experienced per hour and activation in the forward > backward speech contrast.
... Somos el fruto de nuestras conversaciones. Una afirmación que no solo sostiene la filosofía, sino que confirma la neurociencia: las conversaciones en la primera infancia tienen un poder determinante en la estructuración del cerebro y, en consecuencia, en el desarrollo de habilidades lingüísticas (Romeo et al., 2018;Merz et al., 2020). ...
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Título: Niños que hablan de libros. Una reflexión sobre el aprendizaje de la literatura, la escucha y la conversación Resumen: Se aborda en el presente artículo una cuestión crucial para la educación literaria en la infancia y la adolescencia: cómo aprender a hablar de los libros leídos, cómo participar en una comunidad de lectores y lectoras que construyen juntos el sentido de un texto. La conversación, tan decisiva para el conocimiento de los seres humanos y el entendimiento social, resulta igualmente determinante en el proceso de comprensión de un texto literario. Palabras clave: lectura, conversación, escucha, educación literaria. Title: Children talking about books. A reflection on learning literature, listening and conversation Abstract: This article raises a key question for literary education in childhood and adolescence: how to learn to talk about the books read, how to participate in a community of readers who together construct the meaning of a text. Conversation, which is so decisive for the knowledge of human beings and social agreement, is equally decisive in the process of understanding a literary text.
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For shared book reading to be effective for language development, the adult and child need to be highly engaged. The current paper adopted a mixed-methods approach to investigate caregiver’s language-boosting behaviours and children’s engagement during shared book reading. The results revealed there were more instances of joint attention and caregiver’s use of prompts during moments of higher engagement. However, instances of most language-boosting behaviours were similar across episodes of higher and lower engagement. Qualitative analysis assessing the link between children’s engagement and caregiver’s use of speech acts, revealed that speech acts do seem to contribute to high engagement, in combination with other aspects of the interaction.
Despite the clear importance of a developmental perspective for understanding the emergence of psychopathology across the life-course, such a perspective has yet to be integrated into the Research Domain Criteria (RDoC) model. In this paper, we articulate a framework that incorporates developmentally specific learning mechanisms that reflect experience-driven plasticity as additional units of analysis in the existing RDoC matrix. These include both experience-expectant learning mechanisms that occur during sensitive periods of development and experience-dependent learning mechanisms that may exhibit substantial variation across development. Incorporating these learning mechanisms allows for clear integration not only of development but also environmental experience into the RDoC model. We demonstrate how individual differences in environmental experiences-such as early life adversity-can be leveraged to identify experience-driven plasticity patterns across development and apply this framework to consider how environmental experience shapes key biobehavioral processes that comprise the RDoC model. This framework provides a structure for understanding how affective, cognitive, social, and neurobiological processes are shaped by experience across development and ultimately contribute to the emergence of psychopathology. We demonstrate how incorporating an experience-driven plasticity framework is critical for understanding the development of many processes subsumed within the RDoC model, which will contribute to greater understanding of developmental variation in the etiology of psychopathology and can be leveraged to identify potential windows of heightened developmental plasticity when clinical interventions might be maximally efficacious. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
In this chapter, we examine reading outcomes and socioeconomic status (SES) using a developmental cognitive and educational neuroscience perspective. Our focus is on reading achievement and intervention outcomes for students from lower SES backgrounds who struggle with reading. Socioeconomic disadvantage is a specific type of vulnerability students experience, which is often narrowly defined based on parental income, education level, and/or occupational prestige. However, implications of socioeconomic status extend broadly to a suite of areas relevant for reading outcomes including a student's access to resources, experiences, language exposure, academic outcomes, and psychological correlates. Underlying this constellation of factors are brain systems supporting the processing of oral and written language as well as stress‐related factors. We review the implications of SES and reading achievement, and their intersectionality, for the science and practice of reading instruction.
To understand how infants become engaged in conversations with their caregivers, we examined who tends to initiate conversations between adults and infants, differences between the features of infant‐ and adult‐initiated conversations, and whether individual differences in how much infants engage in infant‐ or adult‐initiated conversations uniquely predict later language development. We analyzed naturalistic adult–infant conversations captured via passive recording of the daily environment in two samples of 6‐month‐old infants. In Study 1, we found that at age 6 months, infants typically engage in more adult‐ than infant‐initiated conversations and that adult‐initiated conversations are, on average, longer and contain more adult words. In Study 2, we replicated these findings and, further, found that infants who engaged in more adult‐initiated conversations in infancy had better expressive language at age 18 months. This association remained significant when accounting for the number of infant‐initiated conversations at 6 months. Our findings indicate that early interactions with caregivers can have a lasting impact on children's language development, and that the extent to which parents initiate interactions with their infants may be particularly important.
This study investigates the influence of the quantity, content, and context of screen media use on the language development of 85 Saudi children aged 1 to 3 years. Surveys and weekly event-based diaries were employed to track children’s screen use patterns. Language development was assessed using JISH Arabic Communicative Development Inventory (JACDI). Findings indicate that the most significant predictor of expressive and receptive vocabulary in 12- to 16-month-olds was screen media context (as measured by the frequency of interactive joint media engagements). In older children (17- to 36-month-olds), more screen time (as measured by the amount of time spent using screens, the prevalence of background TV at home, and the onset age of screen use) had the highest negative impact on expressive vocabulary and mean length of utterance. These findings support health recommendations on the negative effects of excessive screen time and the positive effects of co-viewing media with children.
The present study examines the language environments of bilingually-raised Latinx infants (n = 37) to characterize the relation between exposure to electronic media and infants’ language input, with a specific focus on parentese, a near-universal style of infant-directed speech, distinguished by its higher pitch, slower tempo, and exaggerated intonation. Previous research shows that parentese and parent-infant turn-taking are both associated with advances in children’s language learning. Here we test the hypothesis that exposure to electronic media is associated with a reduction in these two social features of language input. Using the Language Environment Analysis (LENA) technology, two daylong audio recordings were collected from each family. Exposure to electronic media was measured in three ways: 1) Through LENA’s automatic estimate; 2) Through manual annotation of LENA audio recordings; and 3) Through a parental questionnaire. Language of electronic media, parental language input, and child language output were quantified through automatic and manual analyses of LENA recordings. Infants’ estimated daily exposure to electronic media varied between the three methods used. There was a significant positive correlation between daily media exposure assessed via the two observational methods, but neither significantly correlated with parental report. Infants experienced electronic media in Spanish and English, and the language of electronic media correlated with the language of paternal and maternal child-directed speech. Linear regression analyses controlling for demographics (infant age, sex, socioeconomic status) demonstrated a negative association between exposure to electronic media and parentese, as well as between exposure to electronic media and turn-taking. Exposure to electronic media was also negatively associated with infants’ linguistic vocalizations. The present findings suggest that exposure to electronic media negatively impacts infant vocal activity by reducing parental parentese and parent-infant turn-taking, which are known to positively impact infants’ linguistic, socioemotional, and cognitive development. This analysis is an important step forward in understanding Latinx infants’ electronic media ecologies and their relation to language input and language development.
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Background: Early language skills are critical for later academic success. Lower socioeconomic status (SES) children tend to start school with limited language skills compared to advantaged peers. We test the hypothesis that this is due in part to differences in caregiver contingent talk during infancy (how often the caregiver talks about what is in the focus of the infant's attention). Methods: In a randomised controlled trial with high and low SES families, 142 11-month olds and their caregivers were randomly allocated to either a contingent talk intervention or a dental health control. Families in the language intervention watched a video about contingent talk and were asked to practise it for 15 min a day for a month. Caregiver communication was assessed at baseline and after 1 month. Infant communication was assessed at baseline, 12, 15, 18 and 24 months. Results: At baseline, social gradients were observed in caregiver contingent talk to their 11-month olds (but not in infant communication). At posttest, when infants were 12 months old, caregivers across the SES spectrum who had been allocated to the language intervention group engaged in significantly more contingent talk. Lower SES caregivers in this intervention group also reported that their children produced significantly more words at 15 and 18 months. Effects of the intervention did not persist at 24 months. Instead expressive vocabulary at this age was best predicted by baseline infant communication, baseline contingent talk and SES. Conclusions: A social gradient in children's communication emerges during the second year of life. A low-intensity intervention demonstrated that it is possible to increase caregiver contingent talk and that this is effective in promoting vocabulary growth for lower SES infants in the short term. However, these effects are not long-lasting, suggesting that follow-up interventions may be necessary to yield benefits lasting to school entry.
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Methods can powerfully affect conclusions about infant experiences and learning. Data from naturalistic observations may paint a very different picture of learning and development from those based on structured tasks, as illustrated in studies of infant walking, object permanence, intention understanding, and so forth. Using language as a model system, we compared the speech of 40 mothers to their 13-month-old infants during structured play and naturalistic home routines. The contrasting methods yielded unique portrayals of infant language experiences, while simultaneously underscoring cross-situational correspondence at an individual level. Infants experienced substantially more total words and different words per minute during structured play than they did during naturalistic routines. Language input during structured play was consistently dense from minute to minute, whereas language during naturalistic routines showed striking fluctuations interspersed with silence. Despite these differences, infants' language experiences during structured play mirrored the peak language interactions infants experienced during naturalistic routines, and correlations between language inputs in the two conditions were strong. The implications of developmental methods for documenting the nature of experiences and individual differences are discussed.
Preschool children’s use of decontextualized language, or talk about abstract topics beyond the here-and-now, is predictive of their kindergarten readiness and is associated with the frequency of parents’ own use of decontextualized language. Does a brief, parent-focused intervention conveying the importance of decontextualized language cause parents to increase their use of these conversations, and as a result, their children’s? We examined this question by randomly assigning 36 parents of 4-year-old children into a decontextualized language training condition or a no-treatment control condition and used mixed effects modeling to measure change (from baseline) in parent and child decontextualized language at 4 subsequent home mealtimes during the following month (N = 174 interactions including the baseline). Compared with the control condition, training condition dyads significantly increased their decontextualized talk and maintained these gains for the month following implementation. Furthermore, trained dyads generalized the program content to increase their use of similarly decontextualized, yet untrained conversations. These results demonstrate that an abstract feature of the input is malleable, and introduces a simple, scalable, and replicable approach to increase a feature of child language known to be foundational for children’s school readiness.
Human beings differ in their socioeconomic status (SES), with accompanying differences in physical and mental health as well as cognitive ability. Although SES has long been used as a covariate in human brain research, in recognition of its potential to account for behavioral and neural differences among people, only recently have neuroscientists made SES a topic of research in its own right. How does SES manifest in the brain, and how do its neural correlates relate to the causes and consequences of SES? This review summarizes the current state of knowledge regarding these questions. Particular challenges of research on the neuroscience of SES are discussed, and the relevance of this topic to neuroscience more generally is considered.
The sample size necessary to obtain a desired level of statistical power depends in part on the population value of the effect size, which is, by definition, unknown. A common approach to sample-size planning uses the sample effect size from a prior study as an estimate of the population value of the effect to be detected in the future study. Although this strategy is intuitively appealing, effect-size estimates, taken at face value, are typically not accurate estimates of the population effect size because of publication bias and uncertainty. We show that the use of this approach often results in underpowered studies, sometimes to an alarming degree. We present an alternative approach that adjusts sample effect sizes for bias and uncertainty, and we demonstrate its effectiveness for several experimental designs. Furthermore, we discuss an open-source R package, BUCSS, and user-friendly Web applications that we have made available to researchers so that they can easily implement our suggested methods.
Purpose: This research provided a first-generation standardization of automated language environment estimates, validated these estimates against standard language assessments, and extended on previous research reporting language behavior differences across socioeconomic groups. Method: Typically developing children between 2 to 48 months of age completed monthly, daylong recordings in their natural language environments over a span of approximately 6-38 months. The resulting data set contained 3,213 12-hr recordings automatically analyzed by using the Language Environment Analysis (LENA) System to generate estimates of (a) the number of adult words in the child's environment, (b) the amount of caregiver-child interaction, and (c) the frequency of child vocal output. Results: Child vocalization frequency and turn-taking increased with age, whereas adult word counts were age independent after early infancy. Child vocalization and conversational turn estimates predicted 7%-16% of the variance observed in child language assessment scores. Lower socioeconomic status (SES) children produced fewer vocalizations, engaged in fewer adult-child interactions, and were exposed to fewer daily adult words compared with their higher socioeconomic status peers, but within-group variability was high. Conclusions: The results offer new insight into the landscape of the early language environment, with clinical implications for identification of children at-risk for impoverished language environments.
The present investigation explored the relation between the amount of language input and neural responses in English monolingual (N = 18) and Spanish-English bilingual (N = 19) infants. We examined the mismatch negativity (MMN); both the positive mismatch response (pMMR) and the negative mismatch response (nMMR), and identify a relationship between amount of language input and brain measures of speech discrimination for native and non-native speech sounds (i.e., Spanish, English and Chinese). Brain responses differed as a function of language input for native speech sounds in both monolinguals and bilinguals. Monolingual infants with high language input showed nMMRs to their native English contrast. Bilingual infants with high language input in Spanish and English showed pMMRs to both their native contrasts. The non-native speech contrast showed different patterns of brain activation for monolinguals and bilinguals regardless of amount of language input. Our results indicate that phonological representations of non-native speech sounds in bilingual infants are dependent on the phonetic similarities between their native languages.