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Running Head: Poverty and Children’s Effortful Control
Family Instability and Children’s Effortful Control in the Context of Poverty:
Sometimes a Bird in the Hand is Worth Two in the Bush
Melissa L. Sturge-Apple, Patrick T. Davies
University of Rochester & Mt. Hope Family Center
Dante Cicchetti
University of Minnesota & Mt. Hope Family Center
Rochelle F. Hentges & Jesse L. Coe
University of Rochester
Melissa L. Sturge Apple, Patrick T. Davies, Department of Psychology, University of
Rochester and Mt. Hope Family Center; Dante Cicchetti, Institute of Child Development,
University of Minnesota and Mt. Hope Family Center; Rochelle F. Hentges and Jesse L. Coe,
Department of Psychology, University of Rochester. This research was supported by the
National Institute of Mental Health (MH071256) awarded to Patrick T. Davies and Dante
Cicchetti and the Eunice Kennedy Shriver Institute for Child and Human Development
(HD065425) awarded to Patrick T. Davies and Melissa L. Sturge-Apple. Both projects were
conducted at Mt. Hope Family Center. The authors are grateful to the children, parents, and
community agencies who participated in this project and to the Mt. Hope Family Center staff.
Correspondence concerning this article should be addressed to Melissa Sturge-Apple,
Department of Clinical and Social Sciences in Psychology, University of Rochester, Rochester,
New York, 14627. E-mail: melissa.sturge-apple@rochester.edu.
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Abstract
Effortful control has been demonstrated to have important ramifications for children’s self-
regulation and social-emotional adjustment. However, there are wide socioeconomic disparities
in children’s effortful control, with impoverished children displaying heightened difficulties. The
current study was designed to demonstrate how instability within the proximal rearing context of
young children may serve as a key operant on the development of children’s effortful control in
the context of poverty. Two separate studies were conducted that included samples of children
living within homes characterized by heightened economic risk. In Study 1, we tested the
differential prediction of family instability on two domains of children’s effortful control: cool
effortful control and hot effortful control. Consistent with hypotheses, elevated instability was
associated with decreased hot effortful control but not cool effortful control over the span of two
years. In Study 2, we examined how children’s basal cortisol activity may account for
associations between heightened instability and effortful control in reward tasks. Results were
consistent with sensitization models, suggesting that elevated cortisol activity arising from
increased uncertainty and unpredictability in rearing contexts may influence children’s hot
effortful control. Findings are interpreted within emerging evolutionary-developmental
frameworks of child development.
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Effortful control involves the modulation of attention and reactive modes of responding
to more volitional, controlled responding (Kochanska, Murray, & Harlan, 2000). As such,
effortful control requires the ability to plan, inhibit, and detect errors towards enacting a
subdominant response (Rothbart & Bates, 2006). Children’s effortful control has been related to
a range of positive indicators of adjustment, including academic competence (Razza &
Raymond, 2013); cognitive development (Shoda, Mischel, & Peake, 1990); social-emotional
competence (e.g., Eisenberg et al., 2003; Raver, Blackburn, Bancroft, & Torp, 1999); reduced
externalizing (Krueger, Caspi, Moffitt, White, & Stouthamer-Loeber, 1996) and internalizing
problems (Eisenberg et al., 2001; Lengua, 2006); and greater ego-resilience, conscientiousness,
and agreeableness (Krueger, et al., 1996). Recent investigations have documented wide
socioeconomic disparities in children’s effortful control, with impoverished children displaying
greater difficulties compared to more affluent counterparts (e.g., Cowell, Cicchetti, Rogosch, &
Toth, 2015; Evans & English, 2002; Lengua, Honorado, & Bush, 2007; Raver, et al., 2011).
Given the implications for effortful control on a host of socioemotional sequelae, this
discrepancy highlights the critical need for a greater understanding of the proximal factors
shaping the control abilities of children experiencing impoverishment.
Recent experimental research suggests that unpredictability may play a key role in
children’s reduction in effortful control. Building on work demonstrating that children’s
decreased expectancies about receiving a delayed reward influenced their preferences for
immediate rewards (Mahrer, 1956), Kidd and colleagues (2012) tested whether children who
were primed to see an unknown experimenter as either reliable/trustworthy or
unreliable/untrustworthy evidenced differences in delay of gratification. Within the “reliable
experimenter” condition, 9 out of 14 children waited for the experimenter to return. In contrast,
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only 1 out of 14 children in the unreliable condition waited. Thus, predictability may strongly
influence whether children choose to delay immediate rewards for later gain. Given that low-
income children are disproportionately exposed to higher levels of unstable rearing environments
(Ackerman, Kogos, Youngstrom, Schoff, & Izard, 1999), instability in the context of poverty
may be a potent factor in understanding reductions in children’s effortful control over and above
demographic risks.
As a central index of children’s experiences with unpredictability in socialization
contexts, family instability is conceptualized as heightened exposure to greater levels of
disruptive family events (e.g., caregiver changes, residential moves, changes in people within the
household). As such, instability reflects a general breakdown in the ability of the family to
provide an expectable, consistent, and safe socialization environment for the child (Ackerman,
Kogos, Youngstrom, Schoff, & Izard, 1999). Research has documented the pernicious effects of
instability on children’s socio-emotional development (e.g., Ackerman, et al., 1999; Bachman,
Coley, & Carrano, 2011; Cavanagh & Huston, 2008; Raver, Blair, Garrett-Peters, et al., 2014).
Elevated chaos within the household has also been linked to children’s cognitive functioning
including IQ (Deater-Deckard et al., 2009; Petrill, Pike, Price, & Plomin, 2004) and inhibitory
control (Hardaway, Wilson, Shaw, & Dishion, 2012). Furthermore, previous work has examined
parental and ecological risk factors and associations with children’s effortful control (e.g.,
Lengua, Honorado, & Bush, 2007; Lengua et al., 2014; Martin, Razza, & Brooks-Gunn, 2012).
However these samples were diverse with respect to family income levels. In the single study to
date utilizing a sample of children with elevated risk for poverty, Martin and colleagues (2012)
demonstrated an association between lack of family routine and delay of gratification. No
association for instability was reported. This might be a function of exclusionary criteria which
5
resulted in a generally more advantaged sample for analysis. Thus, the potential implications for
instability and children’s delay within the context of poverty remain unknown.
Towards addressing this, the current study presents two empirical questions. First, we
examined how instability within the proximal rearing context may operate as a potent predictor
of children’s effortful control. Towards increasing precision, we specifically compare two
domains of effortful control, namely ‘hot’ or delay control with ‘cool’ or executive control. We
hypothesized that instability may be more primarily linked to the hot domain of EC. To build on
Study 1, we further tested whether children’s stress response system activity within the context
of poverty operates as a physiological pathway linking family instability with children’s hot
effortful control. To provide an authoritative test of our hypotheses, we utilized two separate
longitudinal studies of preschool children living within economically impoverished
environments. We explored our questions during the period of early childhood. Effortful control
has been demonstrated to emerge around the age of three with large individual differences in
ability at this age (Green, Fry, & Myerson, 1994). In addition, early childhood is a particularly
vulnerable period for children’s exposure to unstable rearing environments (e.g., Cicchetti, 2015;
Edwards & Liu, 2002), suggesting the importance of examining these processes within this
developmental period.
Study 1 – Family Instability and Hot vs Cool Effortful Control
As a first step, Study 1 sought to determine whether family instability in economically
distressed families was a specific prognosticator of children’s hot effortful ability within the
broader constellation of effortful control. Effortful control has primarily been examined as a
single unitary construct operationalized through a variety of laboratory paradigms and tasks
designed to elicit a predominant response (e.g., Zalewski et al., 2012). However,
6
conceptualizations of effortful control also propose a more refined model including two distinct
processes that depend upon the emotional valence of the task (Allan & Lonigan, 2011; Kim,
Nordling, Yoon, Boldt, & Kochanska, 2013; Li-Grining, 2007). On one side of this distinction,
hot EC or delay control, is conceptualized as an affectively charged domain of EC in which tasks
elicit approach motivation through the offering of a potential prize or enhanced reward
associated with decision making. (Bronson 2000; Eisenberg & Fabes, 1995; Mischel, et al.,
1988, 1989). On the other hand, executive control or “cool” EC consists of response inhibition
to stimuli that are neutral, decontextualized, and abstract. Within cool effortful control tasks,
there are no specific extrinsic and proximal rewards associated with delay performance.
Conceptual models of delay discounting drawn from a large body of empirical work with
adults may provide a potential framework for hypothesizing differences in the antecedents of
these two domains of effortful control (e.g., Green & Myerson, 2004). In particular, these models
suggest that under higher levels of environmental constraints, preferences are shifted toward
immediate rewards even as delayed rewards are increased (Bixter & Luhmann, 2013; Keren &
Roelofsma, 1995). Translated to the concept of effortful control, heightened levels of family
instability in the context of poverty may operate as a cue to children that outcomes are uncertain
and stochastic. In turn, this unpredictability should increase children’s sensitivity to immediate
reward cues and lead them to discount future losses resulting in lower delay or “hot” effortful
control (Eisenberg et al., 2003; Fawcett, McNamara, & Houston, 2012). However the “cool”
element of delay or inhibition may not be similarly affected given the lack of an affective
motivational component. In support of this, Lengua and colleagues (2015) examined risk factors
associated with both hot and cool effortful control in a heterogeneous sample of preschool
children with respect to socio-economic status. They found that cumulative risk scores
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representing aggregated scores on demographic and psychosocial risk factors, accounted for the
effect of income on children’s hot effortful control but not cool effortful control (Lengua, Moran,
Zalewski, Ruberry, Kiff, et al., 2014). However, this sample was heterogeneous with respect to
income including very impoverished to more middle-income participants and it is not clear that
these effects are specific to poverty. To test this hypothesis, we examined associations between
family instability and both of these domains of effortful control over time in a sample of children
living in families experiencing elevated impoverishment.
Participants.
Participants were drawn from a larger sample of 243 families (i.e., mother, intimate
partner, and preschool child) residing in a moderate-sized metropolitan area in the Northeast.
The average age of children at Wave 1 was 4.6 years (SD = .44), with 44% of the sample
consisting of boys. To obtain a sample exhibiting higher levels of economic risk, we recruited
through multiple local agencies including Head Start, Women, Infants, and Children (WIC)
programs, and public and private daycare providers. Although the sample was largely
impoverished, given our focus in the current study, we only included families who indicated that
they were receiving public assistance (n = 177) or reported incomes below the federal poverty
guidelines (n = 17) for a resulting sample of 194 families. Mean household per-capita earned
income of the families was $6,305 per year (range = $37 - $15,670). Although median education
levels for the sample consisted of having a GED/high school diploma, approximately 23% of the
parents had an education level below this. Ethnicity in the sample was diverse, with family
members reporting as Black or African American (49%), White (46%), multi-racial (3%), or
another race (2%). Approximately 15% of the family members were Latino. At Wave 1, 99% of
the mothers and 74% of their partners were biological parents. Parents lived together an average
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of 3.36 years and had, on average, daily contact with each other and the child (range = daily to
two or three days a week).
The retention rate in the selected sample across the waves was 85%. To test for selective
attrition, we conducted statistical comparisons between the families that participated through the
third measurement occasion and those that dropped out during the longitudinal component of the
study along the primary, covariate, and demographic variables at the first assessment (e.g.,
family income, family instability). No significant differences were identified in the analyses.
Procedures.
Parents and children visited our research center laboratory at two waves of data
collection, which were spaced two years apart. All research procedures were approved by the
Institutional Review Board prior to conducting the study. Families were compensated monetarily
for their participation.
Measures.
Effortful Control - Age 4 and 6. Children participated in two different procedures
designed to capture hot and cool dimensions of effortful control. To assess cool effortful control,
children participated in the Peg Tapping Task (Bierman et al., 2008; Diamond & Taylor, 1996).
Children were instructed to enact the rule of tapping a peg once on the table when the
experimenter tapped it twice and vice versa over 16 trials. The number of correct responses to
the Peg Tapping task over the 16 trials was used as an indicator of children’s ability to suppress
an automatic response in favor of a subdominant, contextually appropriate response (Bierman,
Nix, Greenberg, Blair, & Domitrovich, 2008; Diamond & Taylor, 1996).
To assess hot effortful control, children participated in the Reward Dominance task
adapted from a standardized computerized paradigm (O’Brien & Frick, 1996). Similar to
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standard hot effortful control tasks such as a gift wrapping tasks, this task is designed to elicit or
test impulsive and under-controlled behavior in the context of potential rewards (e.g., a prize at
the end). In the computer paradigm, a fisherman with a fishing pole appeared on the screen and
participants chose to either press a key to have him drop his fishing line or press a different key
to stop the game. If the key was pressed to drop his line, the fisherman would either catch a fish
(earn a point) or not (lose a point). Whether or not a fish was caught was programmed using an
increasing ratio of punishment to reward and if a child played the entire game, they would lose
all the points they had earned.
To adapt the task for the current study of pre-school aged children, a cardboard screen
with a fishing pond painted on the front of it was used and the participant child was given a
fishing pole to throw the line over the screen to “fish.” Children began with 10 tokens and were
told that they could use their tokens at the end of a game towards a prize. The experimenter sat
behind the screen giving instructions verbally to the child for each trial (“Do you want to fish or
stop?”) and the child either could “reel” in the line to reveal the outcome (a fish or a boot) or
choose to stop the game. The proportion of successful outcomes across each of successive 10
trials decreased from 90% to 0% over 100 trials.
Consistent with previous research utilizing the reward dominance task as an indicator of
children’s impulsivity and difficulty with control (e.g., Eisenberg, et al., 2007; Mezzacappa,
Kindlon, Saul, & Earls, 1998), the total number of trials played was used an index of hot EC
through reward dominance over control. For ease of interpretation, these scores were reversed
such that higher levels indicated higher hot effortful control.
Family Instability – Age 4. At the first measurement occasion, family instability was
measured through parental report on the Family Instability Questionnaire (FIQ; Ackerman et al.,
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1999; Forman & Davies, 2003). On the FIQ, caregivers answer questions that assess the
cumulative number of occurrences of eight disruptive family events over the past three years
across domains of (a) caregiver changes, (b) residential changes, (c) caregiver intimate
relationship changes, (d) job/income loss, and (e) family member deaths. Family and caregiver
reports of family instability with the FIQ have shown strong associations with child adjustment,
supporting its importance as a theoretical construct in ecological models of child development
(Forman & Davies, 2003). In the current study, both mothers and fathers completed the
questionnaire and demonstrated modest agreement (r = .27*, p < .001, χ
2
= 354.67, p = .31).
Responses were averaged to create the composite score.
Verbal Ability –Age 6. The Vocabulary subtest of the Wechsler Preschool and Primary
Scale of Intelligence (WPPSI; Wechsler, 2002) was administered to assess child verbal ability.
Children’s scores on the subtest were entered as a covariate to determine that effects on effortful
control are independent from verbal ability since the effortful control task requires the child to
demonstrate understanding of verbal instructions
Results
Table 1 provides the raw means, standard deviations, and ranges for the variables in our
primary analyses obtained. Consistent with the broader literature, children’s effortful control
abilities increased over time.
A path analysis within the structural equation modeling framework was used to test study
hypotheses. The path model was estimated using full-information maximum likelihood (FIML)
in AMOS 22.0 (Arbuckle, 2008) to account for missing data (maximum of 13% across all
measures) and retain the full sample for primary analyses (Enders, 2001). The determination of
model fit was made using 3 widely-used fit indices (Kline, 2006). First, the relative chi-squared
11
statistic (χ
2
/df ratio) denotes the minimal sample discrepancy divided by the degrees of freedom
with values between 1 and 3 indicating acceptable fit. Additionally, the Comparative Fit Index
(CFI) compares the model being tested to the independence model, with values above .90
indicating good fit. Finally, the Root Mean Square Error of Approximation (RMSEA) is an
absolute measure of fit based on the non-centrality parameter. RMSEA values below .08 signify
adequate fit.
We simultaneously entered family instability along with our other proximal variables as
predictors of children’s hot effortful and executive control over time (Figure 1). Autoregressive
pathways from W1 to W2 effortful control constructs were estimated in order to co-vary out
initial status. We also included child gender as well as W2 verbal ability as covariates in the
model in order to control for potential effects on children’s control ability. Finally, all
covariances between exogenous predictors were estimated, however these are not presented in
the figure for clarity as they are available in Table 1. In our initial model, we did not estimate a
covariance between verbal ability and other variables given the time-ordered nature of their
assessment (Verbal Ability at W2). However this model provided a poor fit to the data given the
strong association between verbal ability and W1 constructs. Thus we included this covariance in
our model to improve model fit. Substantive findings across the two models did not differ in any
significant manner.
The final model fit the data well, χ
2
(4) = 2.39, p = .66, χ
2
/df ratio = 0.59 , RMSEA =
.01, CFI = 1.0. First, findings for our covariates revealed that children’s verbal ability was
significantly associated with change in cool effortful control performance. With respect to our
substantive pathways, only one significant predictor emerged. As hypothesized, higher levels of
family instability was associated with reduced hot effortful control over time (β = -.20, z = 2.48,
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p < .05). Testifying to differential prediction in the domains of effortful control, family
instability was not significantly associated with cool effortful control over time (β = .05, z =
0.59, p = .56). AMOS’s Critical Ratio of Differences provides a test of the equivalence of model
parameters. Pairwise parameter comparisons calculate the difference between the two estimates
divided by the estimated standard error of the difference. The resulting difference statistic is
normally distributed and tested against the z-score distribution (CR > 1.96). Inspection of the
pairwise parameter comparison of these two predictive paths revealed that instability was a
significantly stronger predictor of changes in hot effortful control compared to cool effortful
control (z = 2.38). It is also noteworthy that other proximal variables were not associated with
children’s delay. Given our findings suggesting that elevated levels of family instability were
associated with decreases in children’s hot effortful control over time, we next proceeded with
Study 2.
Study 2 – Family Instability, Basal Cortisol and Children’s Hot Effortful Control
Our second question centered on understanding how children’s basal adrenocortical
activity may operate as a mechanism through which environmental instability shapes children’s
hot effortful control in the context of poverty. In particular, neurobiological models propose that
the hypothalamic-pituitary-adrenocortical (HPA) axis and its end product cortisol is a potential
pathway for the effects of early adversity on child development (Frodl & Keane, 2013). The
HPA axis is one of the primary systems that respond to environmental stress through mobilizing
metabolic resources and modulating the processing, encoding, and memory consolidation of
emotionally significant events (Munck & Naray-Fejes-Toth, 1994). HPA-axis activity can be
examined at different levels. Given our interest in HPA-axis activity over time, we focused on
basal functioning which reflects resting metabolism. Thus, basal activity has been suggested to
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function in a more trait-like manner and is considered a homeostatic set-point for system
activation (e.g., Helhammer et al., 2007; Lupien et al., 1998). Research has documented that
early-developing basal activity within the HPA may be calibrated in the context of
environmental risk (e.g.; Lupien, King, Meaney, & McEwen, 2001; Suor, Sturge-Apple, Davies,
Cicchetti, & Manning, 2015). For example, research supports the notion that basal cortisol is
shaped by early caregiving experiences (e.g., Doom, Cicchetti, Rogosch, & Dackis, 2013;
Leucken & Lemery, 2004; Tarullo & Gunnar, 2006) and elevated instability in the context of
poverty (Blair, Berry, Mills-Koonce, Granger, et al., 2013).
Towards understanding how children’s basal cortisol activity may underlie these
associations, the present study examined two potential hypotheses. First, the sensitization
hypothesis proposes that early exposure to environmental adversity may eventuate in the HPA
system becoming increasingly sensitive and overactive in marshaling resources to cope with
threat. Consequently, repeated exposure to unstable rearing contexts may lead to upward
modifications in the HPA system and elevations in resting levels of basal cortisol, with the
adaptive function of facilitating processing in risky contexts. In contrast, the attenuation
hypothesis proposes that exposure to chronic environmental stressors may result in the
suppression, rather than amplification, of adrenocortical activity. Down-regulation serves the
adaptive function of prohibiting chronic arousal and excessive expenditure of metabolic
resources. As a result of HPA axis attenuation, children would display lower levels of basal
cortisol.
With respect to how these hypotheses might operate in the present study, a small group of
studies has examined associations between children’s basal cortisol and effortful control and
findings have been mixed. In a sample of kindergarten children from middle to upper-class
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families, Davis and colleagues found no association between in-home or laboratory assessments
of cortisol and children’s delay of gratification (Davis, Bruce, & Gunnar, 2002). In a
heterogeneous sample of children with respect to income, (Lengua et al. 2014) reported that low
morning cortisol was not associated with children’s delay ability. However, within samples
experiencing elevated poverty similar to the present study, higher basal cortisol has been linked
to lower executive functioning (e.g., Blair et al., 2011).
In summary, the first aim of Study 2 was to test the replicability of our findings in Study
1 through demonstrating that higher levels of family instability within impoverished families was
associated with lower levels of children’s hot effortful control in a standard task. Upon
demonstrating consistent findings, our next set of analyses tested whether children’s basal
cortisol activity may operate as a potential mechanism of this association.
Method
Participants. Participants included 201 two-year-old children and their mothers who
were recruited in a moderately sized Northeastern metropolitan area. In order to obtain a sample
of families experiencing elevated levels of socio-demographic adversity, mothers and children
were recruited through community agencies such as Women, Infants, and Children assistance
offices, Temporary Assistance to Needy Families rosters from the Department of Human and
Health system, and the county family court system. Median annual income for the family
household among the participants in the sample was $18,300 (US) per year. A substantial portion
of mothers (30%) and their partners (24%) did not complete high school. Most families were
receiving public assistance (95%) and were living below the US Federal Poverty line (99.5%).
Furthermore, based on the computed Hollingshead Four Factor Index (Hollingshead, 1975), the
15
majority of families (77%) were rated in the two lower social strata (i.e., unskilled or semiskilled
workers).
The mean age of children at the first wave of assessment was 26 months (SD=1.69), with
nearly half of the sample consisting of girls (44%, n = 92). Of the 201 two-year-old children and
mothers in the sample, the majority identified themselves as Black (56%), with smaller
proportions of family members identifying as White (23%), Latino (11%), Multiracial (7%), and
Other (3%). Mothers also answered questions about their marital status, and 63% reported living
with someone, 23% were married, 5% were widowed, and 9% were separated. Of the children in
the sample, 73% of them lived with both their biological mother and biological father. The
remaining children in the study lived with their mother and the target partner was either a
stepfather or current romantic partner.
The cumulative retention rate across the three annual measurement occasions was 87%.
To test for selective attrition, we conducted statistical comparisons between the mother-child
dyads that participated through the third measurement occasion and dyads that dropped out
during the longitudinal component of the study along the primary, covariate, and demographic
variables at the first assessment (e.g., family income, maternal education). No significant
differences were identified in the analyses.
Procedures.
Mothers and their toddlers made two visits to our laboratory within a one- to two-week
time period at three annual measurement occasions spaced one year apart. The research
procedures were approved by the Institutional Review Board at the research site prior to
conducting the study. Assessments were spaced accordingly to minimize potential overlap across
16
paradigms. Mothers also completed questionnaires and interviews across the three visits.
Procedures were standardized across participants.
Saliva Collection. During the first two waves of data collection, saliva samples were
obtained on two different visits within a two-week window of time. Per visit, experimenters
collected one saliva sample from children within 20 minutes after their arrival to the laboratory.
A total number of four saliva samples, two per annual time-point, were collected as baseline
measures of children’s cortisol. Visit times were limited to a narrow period in the morning,
which ensured uniformity in sampling procedures at each visit. The mean collection times per
wave were as follows: Wave 1 (Age 2: M = 9:27 a.m., SD = 31 min.), Wave 2 (Age 3: M = 9:24
a.m., SD = 34 min.). Additionally, to avoid effects of dynamic cortisol awakening response, all
toddlers had been awake at least one hour and had not consumed any beverages or food at least
30 minutes prior to providing the morning saliva samples (Susman et al., 2007). Experimenters
were careful to follow identical saliva sampling procedures across all four visits, which included
developing rapport with the families and inviting children to play with toys and get acquainted
with the laboratory prior to saliva collection. A sorbette was held under the child’s tongue by a
research assistant for one minute to ensure a sufficient quantity of saliva was obtained. Each
sorbette was placed in a 2 mL cryovial and immediately stored at -80
◦
C until shipped on dry ice
to Salimetrics, LLC. (State College, PA).
Hot Effortful Control. At the third wave of data collection, Mischel’s delay of
gratification task was administered (Mischel & Ebbesen, 1970; Mischel et al., 1972). Children
were placed at a small table with two plates in front of them and a bell. On one plate the
experimenter placed two M&Ms and on the other plate placed five M&Ms. Children were
instructed on how to ring the bell. Then the experimenter pointed out the difference in the
17
amount of candy on each plate and told the child that if they could wait until the experimenter
returned, they would receive the five pieces of candy. If they couldn’t wait, they were to ring the
bell to signal the experimenter to return and then they could eat the two pieces of candy. The
experimenter then left the room for a 10-minute wait period.
Measures.
Family Instability (Age 2). Family instability was measured through maternal report on
the Family Instability Questionnaire used in Study 1 (FIQ; Ackerman et al., 1999; Forman &
Davies, 2003).
Salivary Basal Cortisol (Age 2, 3). All samples were assayed for salivary cortisol in
duplicate using a highly sensitive enzyme immunoassay (Salimetrics, PA). The test uses 25 µ l of
saliva per determination, has a lower limit of sensitivity of 0.003 µ g/dl, standard curve range
from 0.012 to 3.0 µg/dl, and average intra-and inter-assay coefficients of variation 3.5 % and 5.1
% respectively. Method accuracy, determined by spike and recovery, and linearity, determined
by serial dilution, were 100.8 % and 91.7 % respectively. Values from matched serum and saliva
samples showed the expected strong linear relationship, r (63) = 0.89, p < 0.0001 (Salimetrics,
2005).
Cortisol data were checked for possible outliers, and 12 subjects (6%) evidenced values
greater than 3.5 standard deviations away from the mean. These values were removed and their
cortisol assessment for that wave was based upon the second sample collected. For two
participants, cortisol samples across both assessments were unusable and were removed from
analyses. Intra-class correlations between cortisol assessments within each wave ranged from .57
to .64 (p < .001) and were averaged to create a composite basal cortisol score for each annual
assessment. The present study methods are in alignment with prior research that has similarly
averaged two morning cortisol measures to form an index of basal morning cortisol levels in
18
examinations of associations between cortisol levels and socioeconomic status (Lupien et al.,
1998) and cognitive functions (Lupien, King, Meaney, & McEwen, 2001).
Hot Effortful Control (Age 4). Children’s delay ability at age 4 was operationalized as
the length of time during a standard delay of gratification task (Mischel & Ebbesen, 1970;
Mischel et al., 1972). Time was marked if they either ate the M&M on their own in the room or
when they rang the bell to have the experimenter return. To co-vary out earlier levels of hot
effortful control, we utilized the “unable to delay gratification” item from the California Child Q-
Set (CCQ- Block & Block, 1980). Two primary experimenters who were responsible for
overseeing the activities and tasks during the visits completed ratings of child adjustment at age
3. Ratings were based on close observations of the children for approximately six to ten hours,
encompassing multiple visits to our laboratory and, in most cases, transportation of families to
and from the research center. Experimenters rated the child on a 9-point scale ranging from
“extremely uncharacteristic” to “extremely characteristic.” Internal consistency across the two
experimenters for the scale was α = .53. Ratings were averaged to create a composite score.
Covariate - Verbal Ability (Age 4). The Vocabulary subtest of the Wechsler Preschool
and Primary Scale of Intelligence (WPPSI; Wechsler, 2002) was administered to assess child
verbal ability. Children’s scores on the subtest were entered as a covariate to determine that
effects on effortful control are independent from verbal ability.
Results
Table 2 provides the raw means, standard deviations, and correlations for the variables in
primary analyses. Cortisol values evidenced significant skew over the two waves and data was
subjected to a square-root transformation in order to reduce non-normality (Tabachnick & Fidell,
19
2006). Relationships among study variables were in the expected directions. Our model was
estimated using the same procedure as in Study 1.
Primary Analyses.
In our first set of analyses, we examined change in children’s basal cortisol from ages 2
to 3. Inspection of the cortisol values at age 2 and 3 suggested a high degree of stability in
cortisol with wide variability around average point estimates (Table 2). We utilized an intercept-
only latent factor to parameterize children’s basal cortisol levels. This is consistent with
previous work examining basal cortisol activity over time in young children that demonstrates
the presence of high stability in levels (e.g., Blair, et al., 2011). Although laboratory visits were
constrained to the morning hours to minimize the effect of time of day and routines (e.g., naps)
on cortisol values, cortisol follows a steep decline from wake-up time. To account for this, we
followed previous recommendations to control for the effects of variability in the length between
child wake-up time and visit time on cortisol (e.g., Sturge-Apple, Davies, Cicchetti, & Manning,
2012). This was accomplished by regressing wake-up time on the manifest indictors of cortisol
levels in our structural model, which effectively parcels out variance attributed to wake time.
Our first model analysis tested the direct associations between family instability at age 2
with children’s cortisol levels and their hot effortful control at age 4 (Figure 2). We included
similar covariates as in Study 1 to examine the relative strength of family instability in the
constellation of other risk variables. Also consistent with Study 1, we controlled for earlier hot
effortful control ability at age 3, as observed by independent raters, as well as children’s verbal
IQ at age 4 on hot effortful control. Finally, given documented associations between ethnicity
and cortisol values (e.g., Blair, et al., 2011) we also entered ethnicity as a covariate in predicting
children’s cortisol levels over time. Ethnicity was coded as a binary variable with 0 = Non-
20
African-American and 1 = African-American. The model provided an adequate fit to the data, χ
2
(34) = 60.78, p < .01, RMSEA = .06, CFI = .88, χ
2
/df ratio = 1.79). The CFI was lower than cut-
off values, however as will be seen in the next series, this was due to not estimating the path
from cortisol to children’s hot effortful control in order to examine the direct effect of family
instability on this outcome. As can be seen from the figure, family instability at age 2 was
significantly associated with children’s basal cortisol activity (β = .33, z = 2.64, p < .05). In
particular, higher instability predicted elevated cortisol levels in children. In addition, age 2
family instability was associated with decreased hot effortful control at age 4 (β = -.16, z = -
2.09, p < .05). This finding is consistent with results from Study 1, and suggests that family
instability was a significant prognosticator of children’s hot effortful control.
We next tested the role of children’s basal cortisol in the association between early family
instability and hot effortful control. To accomplish this, we estimated the pathway from cortisol
activity to age 4 hot effortful control. The model fit the data well χ
2
(33) = 55.95, p < .01,
RMSEA = .06, CFI = .90, χ
2/min
= 1.70). Results indicated that higher basal cortisol was
associated with lower hot effortful control at age 4 (β = -.35, z = -1.98, p < .05). It is also
noteworthy that the pathway from family instability at age 2 was reduced with the inclusion of
cortisol in the model (β = -.05, z = -0.33, p = .74). We estimated the strength of the indirect
effect from family instability to hot effortful control through basal cortisol using RMediation
software (Tofighi & MacKinnon, 2011). Results indicated a significant indirect effect pathway
estimate of .44 (SE = .28; 95% CI [-1.105, -.007]).
Discussion
The overarching goal of the present study was to add to our understanding of the
proximal factors shaping children’s effortful control in the context of poverty. Socioeconomic
21
disparities in effortful control have been identified, with children from impoverished homes
demonstrating significant reductions in this developmental task. Towards delineating the
elevated risk associated with poverty, our first set of findings suggest that unstable and
unpredictable rearing environments may be a significant prognosticator of children’s delay
control or ‘hot’ effortful control specifically. Results from a second study further demonstrated
that children’s cortisol functioning may operate as one potential mechanism underlying early risk
in the form of family instability and later hot effortful control. A key question regarding the
results of our studies revolves around interpreting why instability within impoverished rearing
environments is a specific predictor of children’s hot effortful control and how HPA activity
supports this link.
In Study 1, results indicated that family instability was particularly associated with
children’s ‘hot’ or delay control and was not associated with children’s functioning within a task
eliciting the “cool” domain of effortful control. Within the effortful control literature, “cool”
domains do not include a salient reward-based motivational component and instead demand a
more abstract form of regulation. In contrast, “hot” effortful control typically involves
hedonically attractive and highly salient rewards. Thus, environmental cues of scarcity and lack
of resources were primarily associated with children’s impulsive decisions to continue playing
the game given the potential for a prize at the end, even in the face of mounting losses.
This is an interesting finding which begs the question as to why would family instability
within resource limited contexts be specifically impact children’s hot effortful control? Part of
the answer to this may lie in how children’s effortful control has been interpreted within
psychological research. Within normative psychological models, the ability to delay and control
impulses is considered healthy and indicative of optimal functioning. As such, the elevated
22
difficulties with hot effortful control for children living in poverty have been interpreted as
problematic when juxtaposed against more normative frameworks. However, principles of
developmental psychopathology stress the importance of understanding development within its
context as adaptive with respect to circumstances. (Cicchetti & Toth, 2009). In concert with this,
emerging evolutionary-developmental models emphasize that definitions of adaptive behavior
and “survival and success” vary depending on environmental conditions (e.g., Belsky & Pluess,
2013; Belsky, Ruttle, Boyce, Armstrong, & Essex, 2015; Bjorklund & Ellis, 2014; Blair &
Raver, 2012).
In particular, life history theory (e.g., Belsky et al., 1991; Ellis et al., 2009) may provide a
potential conceptual framework for understanding why children are more likely to reduce hot
effortful control when faced with heightened instability within the context of poverty and why
this may be ‘adaptive’ in this regard. Life history theory proposes that human organisms face
fundamental trade-offs in terms of how they invest energy and effort towards tasks necessary for
survival, with natural selection favoring strategies which optimize the use of resources within
immediate ecological niches (e.g., Ellis, Figueredo, Brumbach, & Schlomer, 2009; Roff, 1992).
In accordance with this, within a larger ecological context of poverty, elevated levels of
instability in the proximal rearing context may result in the higher likelihood of children
adopting a reward orientation given heightened uncertainty about future pay-offs. Thus, children
shift preferences for reward within this environmental condition and waiting may be costly in
that the proffered reward may never appear (Fawcett et al., 2012). This forward-shifting focus is
proposed to be the result a hard-wired implicit system associated with the primate brain that
evolved early with the function of accessing resources within resource-scarce contexts
(MacDonald, 2008). This interpretation is largely speculative and will require further
23
confirmation, however it aligns with emerging commentary suggesting that as a field we must
broaden our analysis to consider that behavior, which although maladaptive to society at large,
represents a competent and adaptive response to local environmental conditions (e.g., Dishion, in
press; Sturge-Apple, et al., in press).
Taking a process-oriented perspective, the second aim of the current paper was to
examine whether the stress-responsive adrenocortical system operates as a potential underlying
mechanism in the association between heightened instability and hot effortful control.
Supporting the sensitization hypothesis, early histories of heightened family instability were
linked to elevated basal cortisol in children. The HPA-axis has been shown to be highly activated
by the presence of unpredictable or uncontrollable challenges, particularly within the immediate
rearing context of young children (e.g., Belsky, et al., 2015; Flinn et al., 2006). Results of our
process model further revealed that elevated activity in the HPA system was associated with
children’s reduced hot effortfull control. Activation of the HPA axis has been associated with
increased reward-orientation as elevated levels of cortisol are thought to switch the balance
within the limbic system towards increased activity in brain regions associated with reward-
related behavior (e.g. Piazza & Le Moal, 1997). For example, prior research in which the
administration of cortisol prior to participation in a risky-decision gambling task demonstrated
that experimentally induced elevations in cortisol were associated with both increased reward-
sensitive behavior and reduced punishment-sensitive behavior (Putman, Antypa, Crysovergi, &
van der Does, 2010). Moreover, van den Bos and colleagues (2009) utilized an experimental
manipulation of stress with adults and reported that elevated cortisol levels in response to an
acute social stressor were associated with higher risk taking on the Iowa Gambling task. Taken
together, these findings suggest that children’s adrenocortical activity may operate as one
24
potential pathway in the association between environmental instability and difficulties in hot
effortful control in the context of poverty.
This interpretation is consistent with evolutionary-developmental models which propose
that the human stress response system is highly plastic, particularly within early developmental
periods (e.g., Boyce & Ellis, 2005; Del Guidice, Ellis, & Shirtcliffe, 2011), with the biological
function to respond to environmental threats that may have fitness-relevant consequences. This
plasticity allows for subsequent adoption of strategies that may promote success within certain
ecological conditions. Thus, elevated cortisol levels in the context of heightened instability
within the proximal rearing context of young child may serve the adaptive function of reducing
delayed gratification, as this may serve to facilitate short-term fitness-relevant outcomes (e.g.,
Frankenhuis, Gergely, & Watson, 2013). Recent work adopting a life history theory perspective
supports this interpretation through demonstrating that elevated cortisol early in childhood may
operate as an underlying mechanism for accelerated strategies within the context of harsh and
unpredictable rearing contexts (e.g., Belsky, et al., 2015; Doom & Gunnar, 2013; Saxbe, Negriff,
Susman, & Trickett, 2015).
Several limitations must be acknowledged in interpreting our results. First, the current
study utilized a single assessment of family instability and it would be important for future work
to include a broader range of assessments of this construct. Second, although the focus was to
examine resting or basal levels of cortisol, our sampling precludes examination of diurnal
patterns of cortisol levels at different points during the day. In addition, we did not examine
children’s cortisol reactivity during delay of gratification tasks, and this may offer another
potential avenue of influence. Third, our studies included other potential sources of risk in the
context of poverty (e.g., income) in order to test the relative strength of these factors with family
25
instability. However, there are other potential concurrent factors that may be important for future
work to consider (e.g., parenting, child maltreatment). Fourth, in Study 2 we were not able to
control for earlier levels of hot effortful control given the assessment was only conducted at the
final wave of data collection. Our use of experimenter ratings as a control is supported by the
significant autoregressive pathway suggesting some shared variance, however it would be a
stronger test if we had an earlier assessment of the hot effortful control task. In addition, the low
internal consistency of our experimenter ratings should be noted as this may indicate low
conformity on ratings. Finally, the results of the current study are specific towards understanding
risk factors within the context of poverty and findings may not translate to more heterogeneous
or higher income samples.
Despite these limitations, our results support the notion that children’s histories of
exposure to heightened family instability can exert a negative influence on children’s ability to
control impulses in the context of reward. In addition, these findings further demonstrate that the
adrenocortical system, in turn, may operate as a key underlying mechanism in children’s hot
effortful control. The results of this study also suggest that greater specificity may be needed
with respect to detailing how some of the key environmental factors within the context of
poverty influence children’s effortful control and how physiological systems may support this
link. Our findings also have implications for preventative interventions with children contending
with the reality of poverty with respect to identifying key elements that may shape behaviors
considered maladaptive within the broader ecology but adaptive within the immediate context
(e.g., Dishion, 2015; Toth & Cicchetti, 2011). Although speculative, we embed our findings
within emerging evolutionary-developmental frameworks, which stress the importance of
placing development within its context. We believe this is particularly true for understanding and
26
interpreting children’s developmental outcomes within highly stressful and impoverished rearing
environments, as behaviors may be honed to match the constraints of their surroundings and not
necessarily match with normative models of child development.
27
References
Ackerman, B. P., Kogos, J., Youngstrom, E., Schoff, K., & Izard, C. (1999). Family instability
and the problem behaviors of children from economically disadvantaged
families. Developmental Psychology, 35, 258-268.
Allan, N. P., & Lonigan, C. J. (2011). Examining the dimensionality of effortful control in
preschool children and its relation to academic and socioemotional
indicators. Developmental Psychology, 47, 905-915.
Arbuckle, J. L. (2008). Amos 17.0 user’s guide. Chicago, IL: SPSS.
Bachman, H. J., Coley, R. L., & Carrano, J. (2011). Maternal relationship instability influences
on children’s emotional and behavioral functioning in low-income families. Journal of
Abnormal Child Psychology, 39, 1149-1161.
Belsky, J., & Pluess, M. (2013). Beyond risk, resilience, and dysregulation: Phenotypic plasticity
and human development. Development and Psychopathology, 25, 1243-1261.
Belsky, J., Ruttle, P. L., Boyce, W. T., Armstrong, J. M., & Essex, M. J. (2015). Early adversity,
elevated stress physiology, accelerated sexual maturation, and poor health in
females. Developmental Psychology, 51, 816-822.
Belsky, J., Schlomer, G. L., & Ellis, B. J. (2012). Beyond cumulative risk: distinguishing
harshness and unpredictability as determinants of parenting and early life history strategy.
Developmental psychology, 48(3), 662.
Belsky, J., Steinberg, L., & Draper, P. (1991). Childhood experience, interpersonal development,
and reproductive strategy: An evolutionary theory of socialization. Child development,
62(4), 647-670.
28
Bierman, K. L., Nix, R. L., Greenberg, M. T., Blair, C., & Domitrovich, C. E. (2008). Executive
functions and school readiness intervention: Impact, moderation, and mediation in the
Head Start REDI program. Development and Psychopathology, 20, 821-843.
Bixter, M. T., & Luhmann, C. C. (2013). Adaptive intertemporal preferences in foraging-style
environments. Frontiers in Neuroscience, 7:93.
Bjorklund, D. F., & Ellis, B. J. (2014). Children, childhood, and development in evolutionary
perspective. Developmental Review, 34, 225-264.
Blair, C., & Raver, C. C. (2012). Individual development and evolution: Experiential
canalization of self-regulation. Developmental psychology, 48(3), 647.
Blair, C., Raver, C. C., Granger, D., Mills-Koonce, R., & Hibel, L. (2011). Allostasis and
allostatic load in the context of poverty in early childhood. Development and
Psychopathology, 23, 845-857.
Block, J. H., & Block, J. (1980). The role of egocontrol and ego-resiliency in the organization of
behavior. In W. A. Collins (Ed.), Minnesota Symposia on Child Psychology (Vol. 13, pp.
39-101). New York: Erlbaum.
Boyce, W. T., & Ellis, B. J. (2005). Biological sensitivity to context: I. An evolutionary–
developmental theory of the origins and functions of stress reactivity. Development and
Psychopathology, 17, 271-301.
Bronson, M. B. (2000). Recognizing and Supporting the Development of Self-Regulation in
Young Children. Young Children. 55, 32-37.
Carrascal, L. M., & Moreno, E. (1993). Food caching versus immediate consumption in the
nuthatch: the effect of social context. Ardea, 81, 135-135.
29
Cavanagh, S. E., & Huston, A. C. (2008). The timing of family instability and children’s social
development. Journal of Marriage and Family, 70, 1258-1270.
Cicchetti, D. (2011). Allostatic load. Development and Psychopathology, 23, 723-724.
Cicchetti, D. (2015). Neural plasticity, sensitive periods, and psychopathology. Development and
Psychopathology, 27, 319-320.
Cowell, R. A., Cicchetti, D., Rogosch, F. A., & Toth, S. L. (in press). Childhood maltreatment
and its effect on neurocognitive functioning: Chronicity and timing matter.
Cicchetti, D., & Toth, S. L. (2009). The past achievements and future promises of developmental
psychopathology: The coming of age of a discipline. Journal of Child Psychology and
Psychiatry, 50(1‐2), 16-25.
Davis, E. P., Bruce, J., & Gunnar, M. R. (2002). The anterior attention network: Associations
with temperament and neuroendocrine activity in 6‐year‐old children. Developmental
psychobiology, 40(1), 43-56.
Deater-Deckard, K., Mullineaux, P. Y., Beekman, C., Petrill, S. A., Schatschneider, C., &
Thompson, L. A. (2009). Conduct problems, IQ, and household chaos: A longitudinal
multiinformant study. Journal of Child Psychology and Psychiatry, 50, 1301-1308.
Del Giudice, M. (2014). An evolutionary life history framework for psychopathology.
Psychological Inquiry, 25, 261-300
Del Giudice, M., Ellis, B. J., & Shirtcliff, E. A. (2011). The adaptive calibration model of stress
responsivity. Neuroscience & Biobehavioral Reviews, 35(7), 1562-1592.
Diamond, A., & Taylor, C. (1996). Development of an aspect of executive control: Development
of the abilities to remember what I said and to “Do as I say, not as I do”. Developmental
Psychobiology, (29), 315-334.
30
Dishion, T. J. (in press). Social influences on executive functions development in children and
adolescents: Steps toward a social neuroscience of predictive adaptive responses. Journal
of Abnormal Child Psychology.
Donzella, B., Gunnar, M. R., Krueger, W. K., & Alwin, J. (2000). Cortisol and vagal tone
responses to competitive challenge in preschoolers: Associations with
temperament. Developmental Psychobiology, 37, 209-220.
Doom, J. R., Cicchetti, D., Rogosch, F. A., & Dackis, M. N. (2013). Child maltreatment
and gender interactions as predictors of differential neuroendocrine hormone profiles.
Psychoneuroendocrinology, 38, 1442-54.
Doom, J. R., & Gunnar, M. R. (2013). Stress physiology and developmental psychopathology:
past, present, and future. Development and psychopathology, 25, 1359-1373.
Edwards, C. P., & Liu, W. (2002). Parenting toddlers. In M. L. Bornstein (Ed.), Handbook of
parenting, Second edition. Vol. I: Children and parenting (pp. 45-72). Hillsdale, NJ:
Lawrence Erlbaum Associates.
Eisenberg, N., Cumberland, A., Spinrad, T. L., Fabes, R. A., Shepard, S. A., Reiser, M., ... &
Guthrie, I. K. (2001). The relations of regulation and emotionality to children's
externalizing and internalizing problem behavior. Child Development, 1112-1134.
Eisenberg, N., & Fabes, R. A. (1995). The relation of young children's vicarious emotional
responding to social competence, regulation, and emotionality. Cognition & Emotion, 9,
203-228.
Eisenberg, N., Michalik, N., Spinrad, T. L., Hofer, C., Kupfer, A., Valiente, C., ... & Reiser, M.
(2007). The relations of effortful control and impulsivity to children's sympathy: A
longitudinal study. Cognitive Development, 22, 544-567.
31
Eisenberg, N., Valiente, C., Fabes, R. A., Smith, C. L., Reiser, M., Shepard, S. A., &
Cumberland, A. J. (2003). The relations of effortful control and ego control to children's
resiliency and social functioning. Developmental Psychology, 39, 761-776.
Ellis, B. J., Del Giudice, M., Dishion, T. J., Figueredo, A. J., Gray, P., Griskevicius, V., ... &
Wilson, D. S. (2012). The evolutionary basis of risky adolescent behavior: implications
for science, policy, and practice. Developmental psychology, 48(3), 598.
Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & Van IJzendoorn, M. H.
(2011). Differential susceptibility to the environment: An evolutionary–
neurodevelopmental theory. Development and psychopathology, 23, 7-28.
Ellis, B. J., Shirtcliff, E. A., Boyce, W. T., Deardorff, J., & Essex, M. J. (2011). Quality of early
family relationships and the timing and tempo of puberty: Effects depend on biological
sensitivity to context. Development and psychopathology, 23, 85-99.
Enders, C. K. (2001). A primer on maximum likelihood algorithms available for use with
missing data. Structural Equation Modeling, 8, 128-141.
Evans, G. W., & English, K. (2002). The environment of poverty: Multiple stressor exposure,
psychophysiological stress, and socioemotional adjustment. Child Development, 73,
1238-1248.
Evans, G. W. (2003). A multimethodological analysis of cumulative risk and allostatic load
among rural children. Developmental Psychology, 39, 924-933.
Fawcett, T. W., McNamara, J. M., & Houston, A. I. (2012). When is it adaptive to be patient? A
general framework for evaluating delayed rewards. Behavioural Processes, 89, 128-136.
Flinn, M. V. (2006). Evolution and ontogeny of stress response to social challenges in the human
child. Developmental Review, 26, 138-174.
32
Forman, E. M., & Davies, P. T. (2003). Family instability and young adolescent maladjustment:
The mediating effects of parenting quality and adolescent appraisals of family
security. Journal of Clinical Child and Adolescent Psychology, 32, 94-105.
Frankenhuis, W. E., & de Weerth, C. (2013). Does Early-Life Exposure to Stress Shape or
Impair Cognition? Current Directions in Psychological Science, 22, 407-412.
Frankenhuis, W. E., Gergely, G., & Watson, J. S. (2013). Infants May Use Contingency Analysis
to Estimate Environmental States: An Evolutionary, LifeHistory Perspective. Child
Development Perspectives, 7, 115-120.
Frodl, T., & O'Keane, V. (2013). How does the brain deal with cumulative stress? A review with
focus on developmental stress, HPA axis function and hippocampal structure in
humans. Neurobiology of Disease, 52, 24-37.
Graziano, P. A., Keane, S. P., & Calkins, S. D. (2010). Maternal behaviour and children's early
emotion regulation skills differentially predict development of children's reactive control
and later effortful control. Infant and Child Development, 19, 333-353.
Green, L., Fry, A. F., & Myerson, J. (1994). Discounting of delayed rewards: A life-span
comparison. Psychological Science, 5, 33-36.
Green, L., & Myerson, J. (2004). A discounting framework for choice with delayed and
probabilistic rewards. Psychological Bulletin, 130, 769-792.
Griskevicius, V., Tybur, J. M., Delton, A. W., & Robertson, T. E. (2011). The influence of
mortality and socioeconomic status on risk and delayed rewards: a life history theory
approach. Journal of Personality and Social Psychology, 100, 1015-1026.
33
Hardaway, C. R., Wilson, M. N., Shaw, D. S., & Dishion, T. J. (2012). Family functioning and
externalizing behaviour among lowincome children: Selfregulation as a
mediator. Infant and Child Development, 21, 67-84.
Hollingshead, A. B. (1975). Four factor index of social status. Unpublished manuscript,
Department of Sociology, Yale University, New Haven, CT.
Howse, R. B., Lange, G., Farran, D. C., & Boyles, C. D. (2003). Motivation and self-regulation
as predictors of achievement in economically disadvantaged young children. The Journal
of Experimental Education, 71, 151-174.
Keren, G., & Roelofsma, P. (1995). Immediacy and certainty in intertemporal
choice. Organizational Behavior and Human Decision Processes, 63, 287-297.
Kidd, C., Palmeri, H., & Aslin, R. N. (2013). Rational snacking: Young children’s decision-
making on the marshmallow task is moderated by beliefs about environmental
reliability. Cognition, 126, 109-114.
Kim, S., Nordling, J. K., Yoon, J. E., Boldt, L. J., & Kochanska, G. (2013). Effortful control in
“hot” and “cool” tasks differentially predicts children’s behavior problems and academic
performance. Journal of Abnormal Child Psychology, 41, 43-56.
Kirschbaum, C., & Hellhammer, D. H. (1989). Salivary cortisol in psychobiological research: an
overview. Neuropsychobiology, 22(3), 150-169.
Kirschbaum, C., Steyer, R., Eid, M., Patalla, U., Schwenkmezger, P., & Hellhammer, D. H.
(1990). Cortisol and behavior: 2. Application of a latent state-trait model to salivary
cortisol. Psychoneuroendocrinology, 15, 297-307.
Kopp, C. B. (1982). Antecedents of self-regulation: a developmental perspective. Developmental
Psychology, 18, 199-214.
34
Krueger, R. F., Caspi, A., Moffitt, T. E., White, J., & Stouthamer-Loeber, M. (1996). Delay of
Gratification, Psychopathology, and Personality: Is Low Self-Control Specific to
Externalizing Problems? Journal of Personality, 64, 107-129.
Landry, S. H., Miller-Loncar, C. L., Smith, K. E., & Swank, P. R. (2002). The role of early
parenting in children's development of executive processes. Developmental
Neuropsychology, 21, 15-41.
Lengua, L. J. (2006). Growth in temperament and parenting as predictors of adjustment during
children's transition to adolescence. Developmental Psychology, 42, 819-832.
Lengua, L. J., Honorado, E., & Bush, N. R. (2007). Contextual risk and parenting as predictors
of effortful control and social competence in preschool children. Journal of Applied
Developmental Psychology, 28, 40-55.
Lengua, L. J., Moran, L., Zalewski, M., Ruberry, E., Kiff, C., & Thompson, S. (2014). Relations
of growth in effortful control to family income, cumulative risk, and adjustment in
preschool-age children. Journal of Abnormal Child Psychology, 43, 705-720.
Lengua, L. J., Zalewski, M., Fisher, P., & Moran, L. (2013). Does HPA Axis Dysregulation
Account for the Effects of Income on Effortful Control and Adjustment in Preschool
Children? Infant and Child Development, 22, 439-458.
Li-Grining, C. P. (2007). Effortful control among low-income preschoolers in three cities:
Stability, change, and individual differences. Developmental Psychology, 43, 208.
Little, R. J. (1995). Modeling the drop-out mechanism in repeated-measures studies. Journal of
the American Statistical Association, 90, 1112-1121.
35
Lupien, S. J., de Leon, M., De Santi, S., Convit, A., Tarshish, C., Nair, N. P. V., Thakur, M.,
McEwen B.S., Hauger, R.L., & Meaney, M. J. (1998). Cortisol levels during human
aging predict hippocampal atrophy and memory deficits. Nature Neuroscience, 1, 69-73.
Lupien, S. J., King, S., Meaney, M. J., & McEwen, B. S. (2001). Can poverty get under your
skin? Basal cortisol levels and cognitive function in children from low and high
socioeconomic status. Development and Psychopathology, 13, 653-676.
MacDonald, K. B. (2008). Effortful control, explicit processing, and the regulation of human
evolved predispositions. Psychological Review, 115, 1012-1031.
Mahrer, A. R. (1956). The role of expectancy in delayed reinforcement. Journal of Experimental
Psychology, 52, 101-106.
Martin, A., Razza, R. A., & Brooks-Gunn, J. (2012). Specifying the links between household
chaos and preschool children's development. Early Child Development and Care, 182,
1247-1263.
Melby, J. N. & Conger, R. D. (2001). The Iowa Family Interaction Rating Scales: Instrument
summary. In P. K. Kerig & K. M. Lindahl (Eds.), Family observational coding systems:
Resources for systemic research (pp.33-58). Mahwah, NJ: Lawrence Earlbuam
Associates.
Mezzacappa, E., Kindlon, D., Saul, J. P., & Earls, F. (1998). Executive and motivational control
of performance task behavior, and autonomic heart-rate regulation in children:
physiologic validation of two-factor solution inhibitory control. Journal of Child
Psychology and Psychiatry, 39(04), 525-531.
Mischel, W., & Ebbesen, E. B. (1970). Attention in delay of gratification. Journal of Personality
and Social Psychology, 16, 329-337.
36
Mischel, W., Shoda, Y., & Rodriguez, M. I. (1989). Delay of gratification in
children. Science, 244, 933-938.
Mischel, W., Shoda, Y., & Peake, P. K. (1988). The nature of adolescent competencies predicted
by preschool delay of gratification. Journal of Personality and Social Psychology, 54,
687-696.
Munck, A., & Naray-Fejes-Toth, A. (1994). Glucocorticoids and stress: permissive and
suppressive actions. Annals of the New York Academy of Sciences, 746, 115-130.
O'Brien, B. S., & Frick, P. J. (1996). Reward dominance: Associations with anxiety, conduct
problems, and psychopathy in children. Journal of Abnormal Child Psychology, 24, 223-
240.
Petrill, S. A., Pike, A., Price, T., & Plomin, R. (2004). Chaos in the home and socioeconomic
status are associated with cognitive development in early childhood: Environmental
mediators identified in a genetic design. Intelligence, 32, 445-460.
Piazza, P. V., & Le Moal, M. (1997). Glucocorticoids as a biological substrate of reward:
physiological and pathophysiological implications. Brain Research Reviews, 25, 359-372.
Posner, M. I., & Rothbart, M. K. (1998). Attention, self–regulation and
consciousness. Philosophical Transactions of the Royal Society of London, B, 353, 1915-
1927.
Putman, P., Antypa, N., Crysovergi, P., & van der Does, W. A. (2010). Exogenous cortisol
acutely influences motivated decision making in healthy young
men. Psychopharmacology, 208, 257-263.
37
Raver, C. C., Blackburn, E. K., Bancroft, M., & Torp, N. (1999). Relations between effective
emotional self-regulation, attentional control, and low-income preschoolers' social
competence with peers. Early Education and Development, 10, 333-350.
Raver, C. C., Blair, C., Garrett-Peters, P., & Family Life Project Key Investigators. (2014).
Poverty, household chaos, and interparental aggression predict children's ability to
recognize and modulate negative emotions. Development and Psychopathology, 1-14.
Razza, R. A., & Raymond, K. (2013). Associations among maternal behavior, delay of
gratification, and school readiness across the early childhood years. Social
Development, 22, 180-196.
Salimetrics. (2005). HS cortisol kit information. Unpublished manuscript, State College, PA.
Saxbe, D. E., Negriff, S., Susman, E. J., & Trickett, P. K. (2015). Attenuated hypothalamic–
pituitary–adrenal axis functioning predicts accelerated pubertal development in girls 1
year later. Development and psychopathology, 27, 819-828.
Shoda, Y., Mischel, W., & Peake, P. K. (1990). Predicting adolescent cognitive and self-
regulatory competencies from preschool delay of gratification: Identifying diagnostic
conditions. Developmental Psychology, 26, 978-986.
Sturge-Apple, M. L., Davies, P. T., Cicchetti, D., & Manning, L. G. (2012). Interparental
violence, maternal emotional unavailability and children's cortisol functioning in family
contexts. Developmental Psychology, 48, 237-249.
Sturge-Apple, M.L., Suor, J.L., Davies, P.T., Cicchetti, D., Skibo, M. A., & Rogosch, F. A. (in
press). Vagal Tone and children’s delay of gratification: Differential sensitivity across
resource poor and resource rich environments. Psychological Science.
38
Suor, J. H., Sturge-Apple, M. L., Davies, P. T., Cicchetti, D., & Manning, L. G. (2015). Tracing
differential pathways of risk: Associations among family adversity, cortisol, and
cognitive functioning in childhood. Child Development, 86, 1142-1158.
Susman, E. J., Dockray, S., Schiefelbein, V. L., Herwehe, S., Heaton, J. A., & Dorn, L. D.
(2007). Morningness/eveningness, morning-to-afternoon cortisol ratio, and antisocial
behavior problems during puberty. Developmental Psychology, 43, 811-822.
Tabachnick, B. G., & Fidell, L. S. (2000). Using multivariate statistics (4th ed.). Boston: Allyn
& Bacon.
Taylor, Z. E., Eisenberg, N., Spinrad, T. L., & Widaman, K. F. (2013). Longitudinal relations of
intrusive parenting and effortful control to egoresiliency during early childhood. Child
Development, 84, 1145-1151.
Tofighi, D., & MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis
confidence intervals. Behavior Research Methods, 43, 692-700.
Toth, S. L., & Cicchetti, D. (2011). Frontiers in translational research on trauma. Development
and psychopathology, 23(02), 353-355.
Van den Bos, R., Harteveld, M., & Stoop, H. (2009). Stress and decision-making in humans:
performance is related to cortisol reactivity, albeit differently in men and
women. Psychoneuroendocrinology, 34, 1449-1458.
Watamura, S. E., Donzella, B., Kertes, D. A., & Gunnar, M. R. (2004). Developmental changes
in baseline cortisol activity in early childhood: Relations with napping and effortful
control. Developmental Psychobiology, 45, 125-133.
Wechsler, D. (2002). WPPSI-III: Technical and interpretive manual. San Antonio, TX: The
Psychological Corporation.
39
Zalewski, M., Lengua, L. J., Fisher, P. A., Trancik, A., Bush, N. R., & Meltzoff, A. N. (2012).
Poverty and single parenting: Relations with preschoolers' cortisol and effortful
control. Infant and Child Development, 21, 537-554.
Running Head: Poverty and Children’s Effortful Control
Table 1. Means, standard deviations, and intercorrelations of variables used in analysis for Study 1
Mean SD 1 2 3 4 5 6 7 8 9
1. Age 4 Family Instability 4.49 3.61 --
2. Age 4 Cool Effortful
Control
9.92 4.58 -.19* --
3. Age 4 Hot Effortful
Control
24.16 17.17 -.06 .11 --
4. Age 6 Verbal IQ 98.37 14.58 -.28* .24* .04 --
5. Age 6 Cool Effortful
Control
14.19 2.98 -.06 .23* .04 .24* --
6. Age 6 Hot Effortful
Control
26.43 17.71 -.08 .10 .15* .18* .18* --
7. Age 4 Maternal Education -- -- -.40* .32* .09 .29* .13 .12 --
8. Age 4 Child Age 4.13 .48 -.16* .25* .07 .07 .03 -.04 -.03 --
9. Child Gender -- -- .08 -.07 .01 -.13 -.06 -.22* -.01 -.08 --
Note. Child Gender (1 = Girl, 2 = Boy). SD = Standard Deviation, * p < .05.
41
Table 2. Means, standard deviations, and intercorrelations of variables used in analysis for Study 2.
Mean SD 1 2 3 4 5 6 7 8 9 10 11
1. Age 2 Maternal Education -- -- --
2. Age 2 Child Age 25.72 1.68 .01 --
3. Age 2 Family Instability 5.08 4.75 -.26* -.09 --
4. Age 2 Cortisol 0.24 0.17 -.20* -.13 .29* --
5. Age 2 Cortisol Wake Time 7:31AM :45 min
-.18* .03 .09 .31* --
6. Age 3 Cortisol 0.23 0.14 -.05 -.12 .11 .30* .25* --
7. Age 3 Cortisol Wake Time 7:41 AM :35min -.01 .10 -.01 .18* .45* .43* --
8. Age 3 Hot Effortful Control 4.76 2.20 .17* .02 -.02 -.15 -.07 -.10 .12 --
9. Age 4 Verbal IQ 59.13 7.08 .27* .10 -.32* -.30* -.07 -.02 -.17* .21* --
10. Age 4 Hot Effortful Control 1:55 min 3:40 .25* .15 -.28* -.19* -.06 -.09 .06 .22* .37* --
11. Ethnicity. -- -- -.21* .04 .19* .28* .09 .18* .14* .06 -.16* -.23* --
12. Child Gender -- -- .12 -.05 .08 -.09 -.06 .01 .01 .14 .11 .04 -.05
Note. Child Gender (1 = Male, 2 = Female). Ethnicity (1 = Non-African-American, 2 = African-American). SD = Standard Deviation,
* p < .05.
42
Age 4 Hot
Effortful Control
Age 4 Family
Instability
Age 6 Hot
Effortful Control
Age 4 Cool
Effortful Control
Age 6
Cool Effortful
Control
Child Gender
Age 4 Mother
Education
.19*
- .20*
.23*
.13ᵵ
r
=
-
.12
Age 6
WPPSI Verbal
Figure 1. Study 1 path model analysis examining associations between family context variables and children’s effortful
control over time. Correlations for all predictor variables as well as covariates were modeled but are not included for ease of
presentation. These associations did not differ from the bivariate associations presented in Table 2. Only significant
structural pathways are included in the model. ns = non-significant. * = p < .05. ᵵ = p < .10.
.15
ᵵ
Child Age
.17*
- .21*
43
Age 2
Basal
Cortisol
Age 2-3
Cortisol
µ = -.10 (.08) ns
V = .004 (.001)*
R
2
= .31
1 1
Age 3
Basal
Cortisol
Age 2
Wake-Up
Time
Age 3
Wake-Up
Time
β = -.16*; [-.05]
ns
β = -.29 *
Age 4
Hot Effortful
Control
R
2
= .20
Age 3
Hot Effortful
Control
Age 2
Family Instability
Child Ethnicity
Child Gender
Age 4 WPPSI Verbal
β = .36*
β = .33*
β = .14* β = .26*
Figure 2. Study 2 path model analysis examining associations between family context variables, children’s basal cortisol levels and
children’s delay control. Correlations for all predictor variables as well as covariates were modeled but are not included for ease of
presentation. These associations did not differ from the bivariate associations presented in Table 2. Only significant structural
pathways are included in the model. * = p < .05.
Child Age
Maternal Education
.34* .32*
