Answer these questions according to 5 articles and Ann Fernald talk’s video about child development

http://pss.sagepub.com/ Psychological Science

http://pss.sagepub.com/content/24/11/2143 The online version of this article can be found at:

DOI: 10.1177/0956797613488145

2013 24: 2143 originally published online 10 September 2013Psychological Science Adriana Weisleder and Anne Fernald

Talking to Children Matters: Early Language Experience Strengthens Processing and Builds Vocabulary

Published by:

http://www.sagepublications.com

On behalf of:

Association for Psychological Science

can be found at:Psychological ScienceAdditional services and information for

http://pss.sagepub.com/cgi/alertsEmail Alerts:

http://pss.sagepub.com/subscriptionsSubscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

What is This?

– Sep 10, 2013OnlineFirst Version of Record

– Nov 8, 2013Version of Record >>

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

Psychological Science 24(11) 2143 –2152 © The Author(s) 2013 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0956797613488145 pss.sagepub.com

Research Article

At any given age, children show wide variability in their levels of language proficiency (Fenson et al., 1994). Although differences in verbal abilities among individuals are influenced to some extent by genetic factors (Oliver & Plomin, 2007), the contributions of early experience to such differences are also substantial. Factors associated with socioeconomic status (SES) are strongly related to variation in language outcomes. By the time they enter kindergarten, children from disadvantaged backgrounds differ significantly from their more advantaged peers in verbal and other cognitive abilities (Ramey & Ramey, 2004), and these disparities are predictive of later aca- demic success or failure (Hart & Risley, 1995; Lee & Burkam, 2002). Identifying the environmental factors that shape these consequential differences in early language proficiency is critical for remediating the growing achieve- ment gaps between children from impoverished and affluent families (Duncan & Murnane, 2011).

What accounts for differences among children in early language growth? One source of variability in rates of lan- guage learning is differential access to language and

gesture from caregivers. Some parents talk more and use richer vocabulary and gestures in interactions with infants than do others, and such differences in the quantity and quality of language input account in part for later dispari- ties among children in lexical and grammatical develop- ment, both within and between SES groups (Hart & Risley, 1995; Hoff, 2003b; Huttenlocher, Waterfall, Vasilyeva, Vevea, & Hedges, 2010; Pan, Rowe, Singer, & Snow, 2005; Rowe & Goldin-Meadow, 2009). A second source of vari- ability in language learning relates to infants’ speech-pro- cessing skills. Differences among infants in phonological discrimination (Tsao, Liu, & Kuhl, 2004) and spoken- word recognition (Fernald, Perfors, & Marchman, 2006; Singh, Reznick, & Xuehua, 2012) predict early vocabulary growth. In experimental studies in which infants look at pictures of familiar objects as one object is named, the

488145 PSSXXX10.1177/0956797613488145Talking to Children MattersWeisleder, Fernald research-article2013

Corresponding Author: Adriana Weisleder, Department of Pediatrics, New York University School of Medicine, 550 First Ave., OBV A529, New York, NY 10016 E-mail: adriana.weisleder@gmail.com

Talking to Children Matters: Early Language Experience Strengthens Processing and Builds Vocabulary

Adriana Weisleder and Anne Fernald Department of Psychology, Stanford University

Abstract Infants differ substantially in their rates of language growth, and slow growth predicts later academic difficulties. In this study, we explored how the amount of speech directed to infants in Spanish-speaking families low in socioeconomic status influenced the development of children’s skill in real-time language processing and vocabulary learning. All- day recordings of parent-infant interactions at home revealed striking variability among families in how much speech caregivers addressed to their child. Infants who experienced more child-directed speech became more efficient in processing familiar words in real time and had larger expressive vocabularies by the age of 24 months, although speech simply overheard by the child was unrelated to vocabulary outcomes. Mediation analyses showed that the effect of child-directed speech on expressive vocabulary was explained by infants’ language-processing efficiency, which suggests that richer language experience strengthens processing skills that facilitate language growth.

Keywords language development, poverty, environmental effects, individual differences, cognitive processes

Received 1/23/13; Revision accepted 3/27/13

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

2144 Weisleder, Fernald

infants’ speed and accuracy in recognizing the object name and identifying the correct picture in real time pre- dicts both early vocabulary development and later lan- guage and cognitive skills (Fernald et al., 2006; Fernald & Marchman, 2012; Marchman & Fernald, 2008).

These studies have shown that children’s language outcomes are linked both to early experience with lan- guage and to speech-processing skills in infancy, but it is not well understood how these two influences work together during development to promote vocabulary growth. In the research reported here, we investigated two alternative possibilities. One is that language experi- ence and language-processing skill are separate factors that contribute independently to lexical development. That is, variation in children’s vocabulary growth could result from differences in children’s exposure to speech— and, thus, in their opportunities to learn new words—as well as from preexisting differences in children’s ability to process speech efficiently, whereby some children are better able to take advantage of the learning opportuni- ties available to them.

Another possibility is that early experience with lan- guage influences the development of efficiency in real- time language processing. That is, experience in hearing language from caregivers may sharpen infants’ skill in processing speech and, hence, improve their ability to learn from future language input. Our recent study com- paring infants from higher- and lower-SES families showed that significant disparities in language-process- ing efficiency were already present when children were 18 months of age (Fernald, Marchman, & Weisleder, 2013), which suggests that experiential factors associated with SES may contribute to differences in processing skill. In addition, one previous study showed that infants exposed to richer language input were more efficient in language processing (Hurtado, Marchman, & Fernald, 2008). However, in this latter study, the relation between language experience and processing efficiency could be explained by children’s vocabulary size. To address this gap, we asked the following questions: Is early experi- ence with language linked to the development of effi- ciency in language processing and, if so, do differences in processing efficiency mediate the well-established relation between early language experience and later vocabulary knowledge? Answers to these questions will further the understanding of the developmental path- ways linking early language experience, speech-process- ing efficiency, and vocabulary growth.

Method

We focused on infants from low-SES Latino families, a rapidly growing population of children in the United States at risk for academic difficulties (Reardon & Galindo, 2009). Rather than relying on short samples of mothers’

speech with an observer present (Hurtado et al., 2008; Pan et al., 2005), we collected more extensive and repre- sentative recordings of infants’ interactions with family members during a typical day at home. We examined how these naturalistic measures of caregiver speech related to experimental measures of language processing and to parent reports of expressive vocabulary.

Participants

Participants were 29 Spanish-learning infants (19 females, 10 males) tested at the ages of 19 and 24 months. Parents reported that all infants were full term and typically developing. An additional 6 children were excluded from the sample because the home recordings were not con- ducted properly (n = 3), the computer malfunctioned during testing (n = 2), or the infant received a diagnosis of developmental delay during the course of the study (n = 1). Family income ranged from less than $25,000 to $75,000 per year, with 79% of families reporting a yearly income below the federal poverty line. Although parents varied in years of education, most had not completed high school. Maternal education ranged from 4 to 16 years (M = 10, SD = 3) and was used as the primary index of SES, controlled in all analyses. All parents were native Spanish speakers, and Spanish was the primary language in the homes of all of the children, with English constitut- ing less than 25% of the language spoken in the home.

Measures of the home language environment

To measure adult speech accessible to infants in different families, we made audio recordings during a typical day at home when the child was 19 months old. A digital recorder in the chest pocket of specialized clothing worn by the child enabled unobtrusive recording of both child- directed and overheard speech in daily interactions among family members (Ford, Baer, Xu, Yapanel, & Gray, 2009). Parents were asked to record their child during a typical day in the home and to keep a log of the locations in which the recording was conducted, who was present, the main activities the child was engaged in, and whether anything atypical occurred.

Families were recorded for an average of 11 hr (range = 4–16) over the course of 1 to 6 days. Using infor- mation recorded in parents’ logs, we selected for each family the longest available recording that represented a typical day. Estimates of adult word counts based on these recordings were highly correlated with adult word counts aggregated over all days of recording (r = .84, p < .001). After we eliminated nap times, the final sample of recordings had an average duration of 7 hr (range = 3–13). Differences in the length of these recordings were controlled for in all analyses.

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

Talking to Children Matters 2145

The home recordings were analyzed using LENA anal- ysis software (Xu, Yapanel, & Gray, 2009). This software was used to process the audio files and yield estimates of different components of the infant’s language environ- ment, including the number of adult word tokens and the number of child vocalizations. The accuracy of these esti- mates for English-language recordings has been estab- lished in previous studies (Oetting, Hartfield, & Pruitt, 2009; Oller et al., 2010; Xu et al., 2009). To assess the accuracy of the adult word estimates in Spanish-language environments, we asked native Spanish speakers other- wise uninvolved in this research to transcribe 60-min samples from 10 of the home recordings. Our analysis of these transcriptions revealed a high correlation between automated estimates of adult words and transcriber- based word counts (r = .80), which confirmed that the LENA system provided reliable estimates of adult words in Spanish-language environments (further details can be found in Supporting Methods in the Supplemental Material available online).

To differentiate between speech directed to the child and speech overheard by the child, we had native Spanish-speaking coders listen to each of the home recordings and classify each 5-min segment as containing speech that was predominantly child directed or over- heard. The number of adult word tokens in segments classified as child directed, divided by the duration of the recording, served as our measure of child-directed speech; the number of adult word tokens in segments classified as overheard, divided by the duration of the recording, served as our measure of overheard speech; and the number of speechlike vocalizations produced by the target child in segments classified as child directed, divided by the duration of the recording, served as our measure of child vocalizations (see Supporting Methods in the Supplemental Material for further details). From these measures, we estimated the number of words or vocalizations per hour and in a 10-hr waking day.

Measures of expressive vocabulary

When the children were 24 months old, parents com- pleted the MacArthur-Bates Inventario del Desarrollo de Habilidades Comunicativas: Palabras y Enunciados (Inventario II; Jackson-Maldonado et al., 2003), the Spanish-language version of the MacArthur-Bates Com- municative Development Inventories (MCDI). Productive- vocabulary scores were based on the number of words parents reported that their child understood and said (“comprende y dice”).

Measures of language-processing efficiency

In the looking-while-listening task (Fernald, Zangl, Portillo, & Marchman, 2008), infants were presented with

pairs of images (e.g., of a dog and a baby) while hearing sentences naming one of the pictures. Children were tested on words that are frequent in child-directed speech and are familiar to most children in the participants’ age range, based on the MCDI lexical norms (Dale & Fenson, 1996). When children were 19 months old, the eight tar- get nouns were el perro (dog), el libro (book), el jugo (juice), el globo (balloon), el zapato (shoe), el plátano (banana), la pelota (ball), and la galleta (cookie). When children were 24 months old, four additional familiar words were included: el caballo (horse), el pájaro (bird), la cuchara (spoon), and la manzana (apple). All of the words were presented in simple sentence frames ending with the target noun, for example, “Mira el perro” (“Look at the dog”).

The speech stimuli were recorded by a native Spanish- speaking adult female and edited for prosodic compara- bility. Visual stimuli consisted of digital pictures of objects presented in yoked pairs. The pairs were matched for visual salience, the grammatical gender of the object name, and lexical familiarity on the basis of MCDI lexical norms (Dale & Fenson, 1996). Each object was presented an equal number of times as a target and as a distracter. Table S1 in the Supplemental Material lists the word pairs as presented in the experiments at 19 and 24 months.

Children sat on their parent’s lap approximately 60 cm from the screen, and parents wore opaque sunglasses to block their view of the images. On each trial, two pic- tures were presented in silence for 2 s, followed by an approximately 3-s speech stimulus and a 1-s silent period during which the pictures remained on-screen. When children were 19 months old, the 8 target nouns were presented four times each for a total of 32 test trials; when children were 24 months old, the 12 target nouns were presented three times each for a total of 36 test tri- als. Side of target presentation was counterbalanced across trials, and trial order was counterbalanced across participants. The entire test session lasted 4 to 5 min.

Children’s looking patterns were video recorded. Subsequently, highly trained coders blind to target loca- tion coded each child’s gaze patterns. For each frame, coders noted whether the child was fixating the left or right picture, in transition between the two pictures, or looking away from both. A second coder independently recoded all trials for 28% of the participants at each age. The proportion of frames on which observers agreed was 99%.

Speech-processing efficiency was calculated as the proportion of time the infant spent fixating the target picture out of total time spent looking at either the target or the distracter picture, within 300 to 1,800 ms of target- word onset (Fernald et al., 2008). Only those trials on which the child was looking at either the target or the distracter picture at the onset of the target noun were included in these analyses. This measure of efficiency captured children’s tendency to shift rapidly toward the

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

2146 Weisleder, Fernald

target picture after initially looking at the distracter, as well as their tendency to maintain attention to the target when they were already looking at it. See Videos S1 and S2 and Legends for Supplemental Videos in the Supplemental Material to view videos of children partici- pating in the looking-while-listening task.

Results

Among these low-SES families, there was striking vari- ability in the total amount of adult speech accessible to the infant, which ranged from almost 29,000 adult words to fewer than 2,000 words over the course of 10 hr (see Fig. 1 for variability across the 29 participating families). When only talk addressed directly to the child was con- sidered, these differences were even more extreme: In one family, caregivers spoke more than 12,000 words to the infant, whereas in another family, the infant heard only 670 words of child-directed speech during an entire day—an 18-fold difference in the amount of child- directed speech available to these two children. These differences in parental engagement were uncorrelated with maternal education (r = .29, p = .13). In addition, amount of child-directed speech was not correlated with amount of overheard speech (r = .17, p = .38), which suggests that the observed differences in speech directed to children were not due to overall differences in talk- ativeness among families but, rather, to caregivers’ degree of verbal engagement with their infants.

Links between language experience and vocabulary

We next asked whether differences among families in amount of speech available to infants predicted children’s vocabulary 6 months later. Those children who heard more child-directed speech at 19 months had larger vocabularies at 24 months (r = .57, p < .01), a result con- sistent with previous findings (Hoff, 2003b; Hurtado et al., 2008). In contrast, differences in exposure to over- heard speech directed to other adults and children were not related to infants’ vocabulary size (r = .25, p = .2), which suggests that language spoken directly to infants is more supportive of early lexical development than is speech simply overheard by infants.

One alternative possibility is that infants with more precocious language skills tend to vocalize more often, eliciting more speech from their caregivers. If this is true, and if infants who produce more speech early on have larger productive vocabularies at 24 months, this might account for the relation between child-directed speech and later vocabulary (Newport, Gleitman, & Gleitman, 1977). To examine this possibility, we first analyzed the relation between infant vocalizations and child-directed speech at 19 months. Infants who vocalized more often did hear more child-directed speech (r = .41, p < .05), which suggests some degree of concordance between infants’ and caregivers’ vocalizations. However, even after controlling for infant vocalizations at 19 months, we

0

5,000

10,000

15,000

20,000

25,000

30,000

1 8 15 22 29 Families With 19-Month-Old Infants

Overheard Speech

Child-Directed Speech

Ad ul

t W or

ds in

a 1

0- Hr

D ay

Fig. 1. Mean number of words that infants heard adults speak in a typical day at home for each family and each type of speech.

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

Talking to Children Matters 2147

found that the relation between child-directed speech and infants’ vocabulary at 24 months remained robust (r = .51, p < .01). This result suggests that, over and above differences in infants’ expressive language skill early on, exposure to child-directed speech predicted later vocab- ulary size.

Links between language experience and language processing

These results support previous findings showing that early language experience predicts later vocabulary knowledge. But are children who hear more child- directed speech also more efficient in processing familiar words in real time? Amount of exposure to child-directed speech was reliably correlated with children’s processing efficiency at 19 months (r = .44, p < .05) and at 24 months (r = .51, p < .01; see Figs. 2 and 3b for illustrations of these relations). Moreover, controlling for differences in processing at 19 months, we found that children who heard more child-directed speech were more efficient in language processing at 24 months than were those who heard less child-directed speech (r = .47, p < .05). This result indicates that amount of exposure to child-directed

speech explained gains in processing efficiency from 19 to 24 months. A particularly important finding was that the relation between child-directed speech and process- ing efficiency at 24 months remained significant when controlling for differences in vocabulary size at 24 months (r = .39, p < .05). This result indicates that over and above differences in vocabulary knowledge, children who were exposed to more child-directed speech were better able to identify familiar words during real-time language processing.

Can differences in processing explain the link between language experience and vocabulary?

Next, we asked whether the effect of language experi- ence on processing efficiency helps explain the well- established relation between child-directed speech and vocabulary. We used mediation analysis to examine whether processing skill at 19 months accounted for the link between child-directed speech and 24-month vocabulary (while controlling for maternal education, recording length, and infant vocalizations at 19 months). The scatter plots in Figure 3 illustrate the first three steps

.25

.50

.75

1.00

0 300 600 900 1,200 1,500 1,800 Time (ms) From Target-Noun Onset

Infants Who Heard More Child-Directed Speech at 19 Months Infants Who Heard Less Child-Directed Speech at 19 Months

M ea

n Pr

op or

tio n

of T

im e

Sp en

t L oo

ki ng

to T

ar ge

t P ic

tu re

a t 2

4 M

on th

s

M i r a e l P E R R O

Fig. 2. Mean proportion of trials on which 24-month-old children looked to the target picture, measured from the onset of the target noun. Infants who heard more child-directed speech at 19 months and infants who heard less child-directed speech at 19 months were grouped on the basis of a median split. The dashed vertical line represents target-noun offset; error bars represent standard errors across participants.

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

2148 Weisleder, Fernald

of the mediation analysis: Exposure to child-directed speech at 19 months predicted vocabulary at 24 months (Fig. 3a); exposure to child-directed speech also pre- dicted processing efficiency at 19 months (Fig. 3b); and processing efficiency at 19 months predicted vocabulary at 24 months (Fig. 3c), even when we controlled for child-directed speech.

Finally, a critical condition for mediation is that the path coefficient between the predictor variable (child- directed speech) and the outcome variable (vocabulary)

be significantly reduced when the mediator variable (processing efficiency) is included in the model. As shown in Figure 3d, the parameter estimate for the effect of child-directed speech on vocabulary was reduced from 12.61 to 7.41 when processing efficiency was included in the model. A bootstrap analysis (Preacher & Hayes, 2004) of the significance of the indirect effect yielded a 95% confidence interval (corrected for bias) of 0.44 to 13.61. This result confirmed that the mediation was significant and suggests that language experience

0

200

400

600

0 300 600 900 1,200 1,500 0

200

400

600

40 50 60 70 80

Child-Directed Speech: Number of Adult Words Per Hour

Processing Efficiency at 19 Months

Child-Directed Speech: Number of Adult Words Per Hour

40

50

60

70

80

0 300 600 900 1,200 1,500

r2 = .25 r2 = .30r2 = .27

a b c

d

Vo ca

bu la

ry a

t 2 4

M on

th s

Pr oc

es si

ng E

ffi ci

en cy

a t 1

9 M

on th

s

Vo ca

bu la

ry a

t 2 4

M on

th s

Processing Efficiency (19 Months)

Vocabulary Size

(24 Months)

Child-Directed Speech (19 Months)

β = 0.76* β = 6.85*

β = 12.61**

β = 7.41

Fig. 3. Results. The three scatter plots (with best-fitting regression lines) show zero-order correlations between (a) vocabulary size (number of words) at 24 months and child-directed speech at home, (b) processing efficiency (mean percentage of time spent looking to the target picture) at 19 months and child-directed speech, and (c) vocabulary size at 24 months and processing efficiency at 19 months. Vocabulary size was measured as the number of words produced on the MacArthur-Bates Inventario del Desarrollo de Habilidades Comunicativas: Palabras y Enunciados (Jackson- Maldonado et al., 2003). The mediation model (d) shows the effect of child-directed speech at 19 months on vocabulary size at 24 months, as medi- ated by processing efficiency at 19 months. Along the lower path, the solid and dashed arrows show results when the mediator was not included and was included in the model, respectively. Asterisks indicate significant paths (*p < .05, **p < .01).

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

Talking to Children Matters 2149

promotes vocabulary development at least in part via its influence on processing efficiency. The final model explained 47% of the variance in children’s vocabularies at 24 months.

Are differences in processing efficiency explained by infants’ knowledge of the target words?

One potential concern is that some children may have been unfamiliar with certain target words used in the study, in which case variability in processing efficiency might simply reflect differences in children’s knowledge of these words. To control for this possibility, we col- lected an independent measure of each participant’s familiarity with the target words. Using a list of only the words used in the study, we asked parents whether their child understood each target word. According to parents’ reports, all of the target words were understood by 66% of the children at 19 months and by 72% of the children at 24 months. For each child, we removed those trials containing target words that the child was reported not to understand and then recomputed the processing effi- ciency measures. After rerunning the mediation model reported earlier, we found that the pattern of results remained the same: Child-directed speech was related to processing efficiency at 19 months (r = .40, p < .05), and processing efficiency at 19 months predicted vocabulary at 24 months (r = .53, p < .01), even when controlling for child-directed speech (r = .41, p < .05). Finally, the param- eter estimate for the effect of child-directed speech on vocabulary was significantly reduced from 12.61 to 8.75 when processing efficiency was included in the model, which indicates that processing efficiency mediated the link between child-directed speech and vocabulary.

In a final analysis, we included only those children whose mean level of accuracy was greater than 50% at 19 months (n = 22), thus excluding those whose overall level of performance was at or below chance level. This analysis revealed even stronger correlations between child-directed speech and processing efficiency (r = .58, p < .01) and between processing efficiency and later vocabulary (r = .62, p < .01). Moreover, even in this smaller sample, processing efficiency mediated the link between child-directed speech and vocabulary (i.e., the parameter estimate for the effect of child-directed speech on vocabulary was significantly reduced from 15.61 to 8.86 when processing efficiency was included in the model). These results provide further evidence that dif- ferences in processing efficiency do not simply reflect variability in children’s all-or-nothing knowledge of the target words. Instead, differences in how quickly and reli- ably children interpret familiar words in real time reflect

variability in a cognitive skill that facilitates further lan- guage learning.

Discussion

This research yielded three main results. First, we found that variation in experience with child-directed speech in low-SES Spanish-speaking families predicted children’s later vocabulary. This result replicates findings from other studies linking caregiver speech and vocabulary develop- ment in low-SES children (Hurtado et al., 2008; Pan et al., 2005), but our study went beyond earlier research by using all-day recordings of daily interactions in the home to sample children’s early language environments. Thus, our measures of child-directed speech minimized poten- tial artifacts introduced by the presence of an observer or by parents’ reactions to a laboratory setting. Second, by recording interactions with multiple family members and identifying different sources of adult speech accessible to the child, we found that only speech addressed directly to the infant, and not speech in adult conversations over- heard by the child, facilitated vocabulary learning at this age, a result consistent with recent findings from studies of children in middle-class English-speaking families in the United States (Shneidman, Arroyo, Levine, & Goldin- Meadow, 2013) and in Yucatec Mayan families (Shneidman & Goldin-Meadow, 2012).

Third, and most important, speech-processing effi- ciency mediated the relation between child-directed speech and vocabulary. This result shows that a critical step in the path from early language experience to later vocabulary knowledge is the influence of language exposure on infants’ speech-processing skill. In previous studies, one explanation proposed for the association between exposure to more child-directed speech and faster vocabulary growth has been that more diverse lan- guage from caregivers provides children with more mod- els to learn from as they begin to build a lexicon (e.g., Hoff, 2003b; Rowe & Goldin-Meadow, 2009). Our find- ings reveal an additional mechanism by which differ- ences in early language experience lead to differences in vocabulary size: Infants who hear more talk have more opportunities to interpret language and to exercise skills that are vital to word learning, such as segmenting speech and accessing lexical representations (Gershkoff-Stowe, 2002; Saffran, Newport, & Aslin, 1996). As a result, infants with more exposure to child-directed speech orient to familiar words more quickly and accurately when inter- preting speech in real time, which enables them to learn new words and facilitates rapid vocabulary growth.

Our results also give rise to a challenging question: What factors explain the striking disparities observed between families in the amount of verbal stimulation

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

2150 Weisleder, Fernald

provided to infants? Studies comparing advantaged and disadvantaged families have shown that SES differences are linked to variability both in speech and gesture directed to children and in children’s language outcomes (Hoff, 2003b; Huttenlocher et al., 2010; Rowe & Goldin- Meadow, 2009). However, in such between-group com- parisons, differences in caregiver input are confounded with many other factors associated with SES that could also lead to variability in language learning—such as parental education, access to resources, crowded living conditions, and family stress levels (Evans, 2004). By focusing on differences within a homogeneous group of disadvantaged families, rather than on differences between SES groups, we reduced variability in these con- founding factors.

Given this narrower focus, it was surprising to dis- cover differences in the amount of child-directed speech between families that were almost as large as those dif- ferences reported in the landmark study by Hart and Risley (1995), whose sample spanned a broad demo- graphic range from poverty-level to professional families. Although Hart and Risley found significant differences between SES groups, with a 20-fold difference in verbal stimulation between parents who were the most and the least verbally engaged with their infants, our findings revealed an 18-fold difference in caregiver talk to infants within a more demographically homogeneous group of disadvantaged families. Moreover, the differences in parental engagement observed within this low-SES sam- ple were not correlated with maternal education. An important implication of these findings is that although variability in parenting behaviors is consistently linked to factors related to SES, there is also considerable variabil- ity in parental verbal engagement that is independent of social class.

In ongoing research, we are exploring other factors that could explain observed differences in children’s lan- guage environments. Previous studies have discussed several such factors, including variability in parents’ own verbal abilities or conversational style (Hoff-Ginsberg, 1991), in the activities that parents tend to engage in with their children (Hoff, 2003a), and in parental stress and emotional well-being (Conger, McCarty, Yang, Lahey, & Kropp, 1984). In addition, some studies have found that parents from different sociocultural groups have different beliefs about the role they play in children’s communica- tive development (Heath, 1983), and Rowe (2008) found that parents’ knowledge and beliefs about child develop- ment mediated the relation between SES and caregiver speech to children. Although not assessed in the current study, parental beliefs are one important factor to con- sider in explaining differences in caregivers’ tendency to

engage infants in language-rich interactions, given that these beliefs may be more malleable than other influen- tial factors.

Our results reveal that caregiver talk has direct as well as indirect influences on lexical development. More exposure to child-directed speech not only provides more models for learning words but also sharpens infants’ emerging lexical processing skills, with cascading bene- fits for vocabulary learning. If increased opportunities for verbal interaction can strengthen critical processing skills that enable more efficient learning, then interventions aimed at increasing parents’ verbal engagement with their infants have the potential to change the course of vocabulary growth and, in turn, to improve later out- comes for disadvantaged children.

Author Contributions

A. Weisleder and A. Fernald developed the study concept and designed the study. A. Weisleder performed the research and analyzed the data. A. Weisleder and A. Fernald drafted the manuscript.

Acknowledgments

We give special thanks to V. A. Marchman, R. Hoffmann Bion, R. D. Fernald, C. M. Fausey, and three anonymous reviewers for comments on earlier versions of the manuscript and to N. Hurtado, L. Rodriguez Mata, C. Coon, M. Barraza, J. Villanueva, A. Arroyo, N. Otero, V. Limón, L. Martinez, and the staff of the Center for Infant Studies at Stanford University for help with data collection and coding. We are grateful to the children and parents who participated.

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.

Funding

This research was supported by a grant from the National Institutes of Health (R01 DC008838) to A. Fernald.

Supplemental Material

Additional supporting information may be found at http://pss .sagepub.com/content/by/supplemental-data

References

Conger, R. D., McCarty, J. A., Yang, R. K., Lahey, B. B., & Kropp, J. P. (1984). Perception of child, child-rearing val- ues, and emotional distress as mediating links between environmental stressors and observed maternal behavior. Child Development, 55, 2234–2247.

Dale, P. S., & Fenson, L. (1996). Lexical development norms for young children. Behavior Research Methods, Instruments, & Computers, 28, 125–127.

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

Talking to Children Matters 2151

Duncan, G. J., & Murnane, R. J. (2011). Whither opportunity? Rising inequality, schools, and children’s life chances. New York, NY: Russell Sage Foundation.

Evans, G. W. (2004). The environment of childhood poverty. American Psychologist, 59, 77–92.

Fenson, L., Dale, P. S., Reznick, J. S., Bates, E., Thal, D. J., & Pethick, S. J. (1994). Variability in early communicative development. Monographs of the Society for Research in Child Development, 59(5, Serial No. 242).

Fernald, A., & Marchman, V. A. (2012). Individual differences in lexical processing at 18 months predict vocabulary growth in typically developing and late-talking toddlers. Child Development, 83, 203–222.

Fernald, A., Marchman, V. A., & Weisleder, A. (2013). SES dif- ferences in language processing skill and vocabulary are evident at 18 months. Developmental Science, 16, 234–248.

Fernald, A., Perfors, A., & Marchman, V. A. (2006). Picking up speed in understanding: Speech processing efficiency and vocabulary growth across the 2nd year. Developmental Psychology, 42, 98–116.

Fernald, A., Zangl, R., Portillo, A. L., & Marchman, V. A. (2008). Looking while listening: Using eye movements to moni- tor spoken language comprehension by infants and young children. In I. A. Sekerina, E. M. Fernandez, & H. Clahsen (Eds.), Developmental psycholinguistics: On-line methods in children’s language processing (pp. 97–135). Amsterdam, The Netherlands: John Benjamins.

Ford, M., Baer, C. T., Xu, D., Yapanel, U., & Gray, S. (2009). The LENA™ language environment analysis system: Audio specifications of the DLP-0121. Retrieved from http://www.lenafoundation.org/TechReport.aspx/Audio_ Specifications/LTR-03-2

Gershkoff-Stowe, L. (2002). Object naming, vocabulary growth, and the development of word retrieval abilities. Journal of Memory and Language, 46, 665–687.

Hart, B. M., & Risley, T. R. (1995). Meaningful differences in the everyday experience of young American children. Baltimore, MD: Brookes.

Heath, S. B. (1983). Ways with words: Language, life, and work in communities and classrooms. Cambridge, England: Cambridge University Press.

Hoff, E. (2003a). Causes and consequences of SES-related dif- ferences in parent-to-child speech. In M. H. Bornstein & R. H. Bradley (Eds.), Socioeconomic status, parenting, and child development (pp. 147–160). Mahwah, NJ: Erlbaum.

Hoff, E. (2003b). The specificity of environmental influence: Socioeconomic status affects early vocabulary development via maternal speech. Child Development, 74, 1368–1378.

Hoff-Ginsberg, E. (1991). Mother-child conversation in dif- ferent social classes and communicative settings. Child Development, 62, 782–796.

Hurtado, N., Marchman, V. A., & Fernald, A. (2008). Does input influence uptake? Links between maternal talk, processing speed and vocabulary size in Spanish-learning children. Developmental Science, 11, F31–F39.

Huttenlocher, J., Waterfall, H., Vasilyeva, M., Vevea, J., & Hedges, L. V. (2010). Sources of variability in children’s language growth. Cognitive Psychology, 61, 343–365.

Jackson-Maldonado, D., Thal, D. J., Marchman, V. A., Newton, T., Fenson, L., & Conboy, B. T. (2003). MacArthur Inventarios del Desarrollo de Habilidades Comunicativas: User’s guide and technical manual. Baltimore, MD: Brookes.

Lee, V. E., & Burkam, D. T. (2002). Inequality at the starting gate: Social background differences in achievement as children begin school. Washington, DC: Economic Policy Institute.

Marchman, V. A., & Fernald, A. (2008). Speed of word recogni- tion and vocabulary knowledge in infancy predict cognitive and language outcomes in later childhood. Developmental Science, 11, F9–F16.

Newport, E. L., Gleitman, L. R., & Gleitman, H. (1977). Mother, I’d rather do it myself: Some effects and non-effects of maternal speech style. In C. E. Snow & C. A. Ferguson (Eds.), Talking to children: Language input and acquisition (pp. 109–149). Cambridge, England: Cambridge University Press.

Oetting, J. B., Hartfield, L., & Pruitt, S. (2009). Exploring LENA as a tool for researchers and clinicians. The ASHA Leader, 14, 20–22.

Oliver, B. R., & Plomin, R. (2007). Twins’ Early Development Study (TEDS): A multivariate, longitudinal genetic inves- tigation of language, cognition and behavior problems from childhood through adolescence. Twin Research and Human Genetics, 10, 96–105.

Oller, D. K., Niyogi, P., Gray, S., Richards, J. A., Gilkerson, J., Xu, D., . . . & Warren, S. F. (2010). Automated vocal anal- ysis of naturalistic recordings from children with autism, language delay, and typical development. Proceedings of the National Academy of Sciences, USA, 107, 13354–13359.

Pan, B. A., Rowe, M. L., Singer, J. D., & Snow, C. E. (2005). Maternal correlates of growth in toddler vocabulary pro- duction in low-income families. Child Development, 76, 763–782.

Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717–731.

Ramey, C. T., & Ramey, S. L. (2004). Early learning and school readiness: Can early intervention make a difference? Merrill- Palmer Quarterly, 50, 471–491.

Reardon, S. F., & Galindo, C. (2009). The Hispanic-White achievement gap in math and reading in the elementary grades. American Educational Research Journal, 46, 853– 891.

Rowe, M. L. (2008). Child-directed speech: Relation to socioeco- nomic status, knowledge of child development and child vocabulary skill. Journal of Child Language, 35, 185–205.

Rowe, M. L., & Goldin-Meadow, S. (2009). Differences in early gesture explain SES disparities in child vocabulary size at school entry. Science, 323, 951–953.

Saffran, J. R., Newport, E. L., & Aslin, R. N. (1996). Word segmentation: The role of distributional cues. Journal of Memory and Language, 35, 606–621.

Shneidman, L. A., Arroyo, M. E., Levine, S., & Goldin-Meadow, S. (2013). What counts as effective input for word learning? Journal of Child Language, 40, 672–686.

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from

2152 Weisleder, Fernald

Shneidman, L. A., & Goldin-Meadow, S. (2012). Language input and acquisition in a Mayan village: How important is directed speech? Developmental Science, 15, 659–673.

Singh, L., Reznick, J. S., & Xuehua, L. (2012). Infant word segmentation and childhood vocabulary development: A longitudinal analysis. Developmental Science, 15, 482– 495.

Tsao, F.-M., Liu, H.-M., & Kuhl, P. K. (2004). Speech perception in infancy predicts language development in the second year of life: A longitudinal study. Child Development, 75, 1067–1084.

Xu, D., Yapanel, U., & Gray, S. (2009). Reliability of the LENA™ language environment analysis system in young children’s natural home environment. Retrieved from http://www .lenafoundation.org/TechReport.aspx/Reliability/LTR-05-2

at Stanford University Libraries on December 9, 2013pss.sagepub.comDownloaded from