Childhood Intelligence and the Emergence of Self-Control

Abstract

Self-control represents, perhaps, one of the most robust predictors of antisocial behavior uncovered by behavioral scientists. What remains more unclear, however, are the exact sources of individual differences in levels of self-control. Emergent evidence along these lines is beginning to suggest that levels of intelligence—another robust correlate of antisocial behavior—may play an important role in predicting the development of self-control. Moreover, the influence of intelligence may begin to manifest very early in development. Building on prior work, the current study seeks to explore the role of intelligence in predicting levels of self-control in children. Our findings suggest that higher levels of intelligence predict higher levels of self-control beyond other traditional criminological and sociological variables including parenting practices and parental levels of self-control. These findings further underscore the relevance of intellectual functioning for a host of impactful traits in humans.

Keywords

self-control intelligence parenting early childhood development

Given its standing as a social science, the field of criminology is generally void of anything resembling a physical “law”: a fact or indelible truth concerning the nature of reality. Even so, perhaps the closest that researchers of crime can come regarding an axiom of antisocial behavior is that as an individuals’ level of impulse control, attention span, and ability to delay gratification decline, the odds of engaging in various types of imprudent, reckless, and even illegal behavior increase (M. Gottfredson & Hirschi, 1990; Moffitt et al., 2011; Pratt & Cullen, 2000; Vazsonyi & Crosswhite, 2004). Put differently, self-control—a trait broadly referring to one’s ability to regulate impulsive intentions—represents an important correlate of offending behaviors. Indeed, the concept of self-control is so important to research on antisocial behavior that studies on the topic may be considered misspecified if a researcher fails to account for individual differences in levels of self-control (M. Gottfredson & Hirschi, 1990; Pratt & Cullen, 2000).

What has eluded consensus among researchers, however, are the sources of individual differences in self-control. M. Gottfredson and Hirschi (1990)—the progenitors of self-control theory in the field of criminology—contend that variation in parenting is the most salient predictor of self-control (with schools and other neighborhood factors perhaps contributing in minor ways). A very different conception regarding variation in self-control, however, can be extracted from biosocial research in the area of impulse regulation and its link to neurological functioning (Beaver, Wright, & DeLisi, 2007). Examination of brain structure in concert with brain function, for example, clearly suggests that regulation of impulses, emotion, and behaviors are housed largely in the prefrontal cortex of the brain (Damasio, 1994; Goldberg, 2001; Ishikawa & Raine, 2003; Raine, 2002). Collectively, these coordinated operations are referred to as “executive functions” and they fully encompass the nature of self-control as described by M. Gottfredson and Hirschi (Damasio, 1994; Goldberg, 2001).

Indeed, there is an emergent and convincing body of research linking neuropsychological functioning and levels of self-control (Jackson & Beaver, 2013). Less effort, however, has been aimed at exploring the nexus between self-control and another critical product of neural activity: intelligence (Boisvert, Stadler, Vaske, Wright, & Nelson, 2013; Boutwell & Beaver, 2010). This lack of research is surprising primarily because intelligence, like self-control, is a robust correlate of criminal and antisocial behavior (Herrnstein & Murray, 1994; Lynam, Moffitt, & Stouthamer-Loeber, 1993; Moffitt, Gabrielli, Mednick, & Schulsinger, 1981). It stands to reason that self-control and intelligence should be highly intertwined, especially given the neurological underpinnings to both (Koenen, Caspi, Moffitt, Rijsdijk, & Taylor, 2006; Plomin & Spinath, 2004; Ratchford & Beaver, 2009). The current study therefore aims to explore the unfolding of intelligence and self-control early in the life-course, a time period thought by scholars to be of particular importance for understanding enduring individual differences in traits (i.e., self-control) that might influence behavior for decades (M. Gottfredson & Hirschi, 1990).

From Self-Control to Executive Function: A Trait Reconceptualized

As mentioned above, M. Gottfredson and Hirschi (1990) suggested that variations across specific parenting techniques are entirely responsible for the development of self-control in children. If parents adequately supervise and monitor their children while concurrently recognizing and correcting their deviant behavior, then the child should develop higher levels of self-control (M. Gottfredson & Hirschi, 1990). Despite some supportive findings (see Cullen, Unnever, Wright, & Beaver, 2008, for a review), the parental management hypothesis is incongruous with results from other disciplines demonstrating that impulse control, self-regulation, and temperament are highly—but not entirely—heritable, with only the most minimal of contributions from the shared (i.e., family) environment (Beaver, Ferguson, & Lynn-Whaley, 2010; Beaver et al., 2007; Price, Simonoff, Waldman, Asherson, & Plomin, 2001).

Given the apparent import of genetic factors for understanding variation in levels of self-control, it immediately suggests an alternative explanation for how self-control emerges over the life-course. Variation in structure and functioning of the prefrontal cortex, for instance, is highly heritable (Thompson et al., 2001) and has been linked to a host of functions analogous to self-control, including behavior and emotion regulation, decision-making, and goal motivation, commonly referred to by researchers as “executive functions” (Damasio, 1994; Goldberg, 2001; Ishikawa & Raine, 2003; Raine, 1993). Executive functions comprise an individual’s ability to make decisions, properly process emotions, inhibit impulses, problem-solve, and maintain progress toward goals (Baker, Bezdjian, & Raine, 2006). What this suggests, then, is that accounting for cognitive ability is central to understanding the ability to regulate impulse drives.

Neurological Functioning, General Intelligence, and the Origins of Self-Control

The prefrontal cortex is not only crucial for self-regulatory processes. Mounting evidence continues to implicate this particular region of the brain with many abilities characterized by human intelligence (Jung & Haier, 2007). A century ago, statistician Charles Spearman described the concept of general intelligence, or g, to propose the existence of a latent trait capable of accounting for mastery across a variety of cognitive tasks (e.g., verbal tasks, spatial rotation tasks, memory recall tasks, etc.; Herrnstein & Murray, 1994; Hunt, 2011; Jensen, 1998; Spearman, 1904). With the advent of more advanced neuroimaging techniques, researchers have since been able to directly link variations in gray matter within the prefrontal cortex to g, as well as cognitive functioning more broadly defined (Thompson et al., 2001). Additional research has continued to uncover the neurological substrates that underpin intelligence, linking traits analogous to g to functioning in a variety of cortical structures (Barbey, Colom, & Grafman, 2013; Barbey et al., 2012; Gläscher et al., 2010; Jung & Haier, 2007; Penke et al., 2012; Thompson et al., 2001).

What should not be overlooked at this point, though, is that intellectual functioning and g in particular has relevance for human life success in the natural world. Scores on g-loaded tasks have been termed the “most powerful single predictor of overall job performance,” for example (L. S. Gottfredson, 1997; p. 83). Higher levels of g are associated with occupational prestige, advancement, managing others, and trainability (L. S. Gottfredson, 1997). The ability of g to predict job performance surpasses even that of work experience, surprisingly enough, which decreases in predictive validity as job complexity increases (L. S. Gottfredson, 1997). Beyond employment success, extant evidence has also uncovered a relationship between general intelligence and health outcomes over the life-course (Der, Batty, & Deary, 2009; Wrulich et al., 2014), mortality rates (Barnes, Beaver, & Boutwell, 2013; Batty, Deary, & Gottfredson, 2007; Shipley, Der, Taylor, & Deary, 2006), and even psychiatric disorders, such as schizophrenia (Moffitt et al., 2011; Woodberry, Giuliano, & Seidman, 2008). In regard to criminal justice issues, intelligence may even help explain, at least in part, differences in arrest and incarceration rates across population groups. Beaver, DeLisi, et al. (2013), for example, found that controlling for individual differences in intelligence and lifetime violence completely accounted for group differences in arrest and incarceration rates

Similar to low self-control, low intelligence has been consistently associated with delinquency and antisocial behavior across the life-course (Koenen et al., 2006; Moffitt et al., 1981; Raine et al., 2005). Findings which support the close association between intelligence and self-control extend even to an incarcerated sample (Myers & Ellis, 1992). Low levels of intelligence have been found to predict contact with the criminal justice system (i.e., arrest, conviction, incarceration, and probation) for both adolescents and adults even after controlling for self-control (Beaver, Schwartz, et al., 2013). In fact, the relationship between intelligence and criminality may manifest very early in life; intelligence at age 3 was predictive of criminality up to age 30 (Stattin & Klackenberg-Larsson, 1993). More important, though, what this suggests is that failure to account for intelligence when exploring research questions related to antisocial outcomes (including low impulse control) may result in a misspecified model.

Certainly, intelligence is intricately related to a wide range of behaviors, and may act as either a risk or protective factor for criminological outcomes. High levels of intelligence have been found to protect against antisocial behavior in those who otherwise would be considered at high risk (e.g., those with highly criminal fathers; Kandel et al., 1988). Equally important, a related line of criminological research has identified diminished cognitive abilities as a key risk factor for antisocial behavior over the life-course (Moffitt, 1993, 2006). Among even serious delinquents, for example, degree of intelligence plays a role in defining behavior; those with higher intelligence exhibit the lowest levels of impulsivity and lowest numbers of delinquent acts (Koolhof, Loeber, Wei, Pardini, & D’Escury, 2007).

Perhaps the most critical association to consider is that lower levels of intelligence have been tied to diminished capacity to delay gratification, curtail impulses, control behavior, and generally exhibit the types of traits encompassed by the term “self-control” (Beaver, Wright, & Maume, 2008; Finch, Spirito, & Brophy, 1982; Moffitt et al., 1981; Nussbaum, Choudhry, & Martin-Doto, 1996). To further illustrate the point, Russo, De Pascalis, Varriale, and Barratt (2008) uncovered a relationship between cognitive ability and impulsivity by an examination of brain wave patterns, termed “event-related potential.” Not surprisingly, participants with higher levels of impulsivity also tended to possess a reduced ability to ignore irrelevant or distracting information. This diminished ability to focus attention, moreover, was associated with lower overall cognitive ability. Finally, a very recent study conducted by Boisvert and her colleagues (2013) produced direct evidence of an association between cognitive abilities and self-control-related outcomes. Taken together, there is good reason to suspect a close overlap between intelligence and the emergence of self-control in the first few years of life.

The Current Study

Both self-control and intelligence represent key traits which influence numerous important outcomes in the human species (Herrnstein & Murray, 1994). Indeed, success in a social society appears to correlate closely with ones’ ability to regulate impulses and to function at a high level, cognitively speaking (L. S. Gottfredson, 1997). What has generally been lacking to this point, though, is a directed effort aimed at exploring the mutual development of self-control and intelligence, especially in the first few years of life. This study aims to expand the existing amount of prior research in this area by exploring the association between intelligence and self-control early in childhood (Beaver et al., 2007; Boisvert et al., 2013; Boutwell & Beaver, 2010). To this end, we examined data drawn from a large national data set of children and their families. Our findings, as well as a thorough description of the data, follow.

Method

Sample

Data drawn from the Fragile Families and Child Wellbeing Study (FFCWS) were utilized in the current study to examine the IQ-self-control nexus. The FFCWS included approximately 5,000 children born in large U.S. cities between 1998 and 2000. Data collection was designed to target “at-risk” families (those with a greater likelihood of experiencing poverty and unstable family conditions) by oversampling newborns of low-income, unwed couples. Respondents for the FFCWS were selected via the use of a three-stage process. First, a stratified sample of 20 U.S. cities with a population more than 200,000 was randomly selected. Second, a sample of 75 hospitals was taken from each of the 20 selected cities. Third, a random sample of both married and unmarried couples, which met the at-risk criteria, were selected within each hospital and asked to participate in the study. The final sample of respondents is comprised of approximately 1,100 married couples and 3,600 unmarried couples (Reichman, Teitler, Garfinkel, & McLanahan, 2001).

The first wave of data collection began within 48 hr of the child’s birth, and includes medical records for mother and child, as well as interviews with the mother in the hospital. Data from the fathers were also collected in the hospital or in their home residence. Follow-up surveys were conducted when the children were 1, 3, 5, and 9 years old. Interviews and surveys were completed by both the mother and the father at each wave (and/or the primary caregiver). The FFCWS contains data about the attitudes, behaviors, and health of all family members, including a focus on at-risk factors, such as infidelity, substance use, risk-taking behavior, criminal behavior, and others. For more detailed descriptions of the data, as well as sampling procedures, see Reichman et al. (2001).

Measures

Low Self-Control

During the 9-year wave, the focal child’s teacher was asked a series of questions about the child’s behavior during class and when interacting with other students. Teacher ratings are advantageous primarily to help avoid issues of shared methods variance which can exist when parents report on the outcome measures of their children, while also providing information regarding their own behavior (Harris, 1998). Previous research has utilized teacher ratings of self-control in the child (Beaver et al., 2007). In the case of the FFCWS, the teachers of the focal children were asked a series of items related to how the child behaved in class. For the current study, we selected 18 of these items to serve as the current measure of self-control in the child.¹ Generally speaking, these items reflected the child’s ability to transfer and retain attention, to finish tasks, tendency toward impulsivity, temper, and treatment of others. Responses used a 4-point scale, rating if the behavior occurs 1 = never, 2 = sometimes, 3 = often, or 4 = very often. The items were summed so that higher scores represent lower levels of self-control. Additional psychometric analysis suggested that each of the items coalesced on a single factor with satisfactory internal reliability estimates (Cronbach’s α = .94). Descriptive statistics for each of the variables included in the current study are presented in Table 1. An inventory of items for this and all other scales, with the exception of the intelligence measures, can be found in the appendix.

Table 1:

Correlation Matrix and Descriptive Statistics

		X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	X11	X12	X13	X14
Child Self-Control	X1	1.00
WISC-IV Digit Span	X2	−.22**	1.00
WJ-III Subtest 9	X3	−.35**	.49**	1.00
WJ-III Subtest 10	X4	−.32**	.49**	.66**	1.00
PPVT-III	X5	−.26**	.36**	.63**	.57**	1.00
Overall Intelligence	X6	−.36**	.72**	.85**	.83**	.80**	1.00
Global Positive Parenting	X7	−.16**	.10**	.14**	.14**	.11**	.15**	1.00
Maternal Self-Control	X8	.08**	−.09**	−.09**	−.07**	−.10**	−.11**	−.04	1.00
Paternal Self-Control	X9	.11**	−.02	−.09**	−.07**	−.11**	−.10**	−.09**	.11**	1.00
Spanking	X10	.11**	−.05**	−.02	−.07**	−.05**	−.07**	−.09**	.09**	.03	1.00
Apgar at 5 min	X11	−.02	.05*	.06**	.07**	.03	.05**	−.00	−.03	−.01	.00	1.00
Child’s sex	X12	.25**	−.06**	−.11**	−.00	.04*	−.03	−.08**	.02	−.01	.06**	−.05**	1.00
Child’s age (years)	X13	−.01	.07**	.11**	.17**	.13**	.16**	.08**	.03	.01	−.07**	.02	−.02	1.00
Maternal race (Black/non-Black)	X14	.21**	−.02	−.13**	−.20**	−.24**	−.19**	−.05**	.01	−.02	.11**	−.04*	−.00	−.05**	1.00
M		24.38	0.00	0.00	0.00	0.00	0.052	39.55	12.19	11.92	0.69	8.85	0.52	9.30	0.56
SD		10.80	1.00	1.00	1.00	1.00	3.14	7.33	3.71	4.01	0.89	0.66	0.50	0.40	2.56

Note. WISC-IV = Wechsler Intelligence Scale for Children–IV; WJ-III = Woodcock Johnson–III; PPVT-III = Peabody Picture Vocabulary Test–III.

Significant at the .05 level, two-tailed. **Significant at the .01 level, two-tailed.

Intelligence

The FFCWS included several measures intended to assess cognitive functioning in the participants. Four intelligence scales administered during the 9-year wave were included in the current analysis. During data collection, respondents were administered the Peabody Picture Vocabulary Test–III (PPVT-III), the Wechsler Intelligence Scale for Children Digit Span subtest (WISC-IV Digit Span), and two subtests from the Woodcock Johnson–III (WJ-III) battery: the Passage Comprehension test (WJ-III Subtest 9) and the Applied Problems test (WJ-III Subtest 10). The PPVT-III screens for verbal ability by having the child identify a picture corresponding to the word stated by the interviewer (Dunn & Dunn, 1997; α = .95²). The s-IV Digit Span assessment requires the child to repeat a number, either forward or backward, after it is read by an interviewer (Wechsler, 2003). This test provides a measure of auditory short-term memory, attention, and concentration (α = .87).

Children completed two subtests from the full WJ-III battery, Subtests 9 and 10 (Woodcock, McGrew, & Mather, 2001). The first part of the WJ-III Subtest 9 tests for symbolic learning by asking the child to match a pictograph of a word to an actual image of the object. The test increases in difficulty, next requiring that the child identify which item correctly represents a phrase and then by having the child fill in the blanks to written passages. The WJ-III Subtest 10 requires the child to solve math problems after listening to and recognizing the problem (for both tests, α = .81–.94). Prior to the analysis, each of the intelligence measures was converted to standardized z scores. In addition, the z scores from each test were summed to create a measure of overall intelligence (i.e., g).

Maternal and Paternal Involvement–Attachment

Two scales were used to create a measure of parental involvement and attachment. First, the focal child responded to six items which sought to describe the degree of involvement each parent had in the child’s life. Second, two items were included to measure the child’s attachment to their mother and their father. The child indicated how close they felt to their mother and father and how well their parents share ideas with them. The child was asked to respond to both sets of items in regard to each parent separately during the 9-year wave. These items were scored on a 4-point scale and coded such that high scores reflected more parental involvement and attachment (Cronbach’s α = .52 for maternal measures; α = .74 for paternal measures).

Parental Permissiveness

The focal child responded to five items during the 9-year wave which indicated how often their primary caregiver, typically their mother, knew what was occurring in the child’s life. Items were scored on a 4-point scale, and coded such that high scores reflected less permissive, or stricter, parental behavior (Cronbach’s α = .47).

Global Parenting

Each of the three scales just mentioned—Maternal Involvement–Attachment, Paternal Involvement–Attachment, and Permissiveness—were summed to create a global score of parenting, which has been found to have increased reliability and validity compared with individual scales (Wright & Cullen, 2001). Higher scores on the global parenting measure indicate a greater degree of positive parenting in the child’s life (Cronbach’s α = .73).

Maternal and Paternal Impulsivity

Six items from the Dickman (1990) impulsivity scale were completed by the focal child’s mother at the 3-year wave and by the father at the 1-year wave. Both parents responded to questions asking whether or not they think before they act, and whether or not consequences arise because they act impulsively. Each item was rated on a 4-point scale, and all items were summed so that high values indicated higher levels of impulsivity (i.e., lower levels of self-control; father scale Cronbach’s α = .84; mother scale α = .84). This scale has been used in previous research as an indicator of self-control for both mothers and fathers (Boutwell & Beaver, 2010).

Spanking

Although debate exists regarding the causal influence of spanking on behavior, little question remains regarding the correlation between corporal punishment and behavioral problems (Barnes, Boutwell, Beaver, & Gibson, 2013; Mulvaney & Mebert, 2007). In light of these findings, a measure of spanking was included in the analyses. At the 5-year wave, mothers were asked whether or not they spanked their children, and if so, how frequently they had done so in the previous month. These measures were condensed into one 5-point scale, including a score for mothers who did not report spanking, ranging from 0 (did not spank) to 4 (spanked nearly every day).

Apgar Score

The focal child’s Apgar score at 5 min was included to control for any health deficits experienced at birth. The Apgar test assigns a score to the newborn based on the condition of their breathing, muscle tone, pulse rate, complexion, and reflexes. Higher scores indicate healthier newborns. Although Apgar scores are indicators of a newborn’s general health, deficits so early in life may be indicators or correlates of neurologic disability, including cognitive deficits (Ehrenstein, 2009). In addition, low Apgar scores (less than 7) at 5 min have been found to correlate to increased risk of neurologic disability for several years after birth.

Control Variables

Several variables were included in the analysis as controls, including the child’s sex (1 = male and 0 = female) and age in years. The mother’s race was also included as a control, as there is no direct measure of the child’s race in the currently available data. Maternal race was coded as 1 = Black, 0 = non-Black.

Plan of Analysis

The analysis for the current study was conducted through the use of a two-step sequence. The first step was intended to untangle the relationship between intellectual functioning and levels of self-control. Specifically, Ordinary Least Squares (OLS) regression is utilized to test the effect of each of the intelligence measures—as well as composite measure of overall intelligence—on the child’s level of self-control. The second step was intended to further explore the interrelationship between self-control, intelligence, and the types of parenting environments experienced by the focal children.

This phase of the analysis becomes particularly interesting given the focus placed on parenting behavior as a predictor of self-control (M. Gottfredson & Hirschi, 1990). Less has been discussed, though, regarding the possibility that early emerging individual differences in intelligence (and even self-control) might predict variation in the types of parental experiences that children have (Scarr, 1989, 1992). In other words, it may be the case that positive parenting correlates with the emergence of self-control. A simple correlation, however, does nothing to address the possibility that children with higher levels of intelligence might elicit different responses from their parents compared with other children with lower levels of intelligence. Using both path analysis and mediation tests (Sobel, 1982, 1986), we examined whether levels of intelligence and childhood self-control predict the types of parenting interactions encountered by the respondents in the FFCWS.

Results

Table 2 presents the findings from the first phase of the analytical process. Beginning with Model 1, intelligence exerted a significant impact on the outcome measure of self-control. In this case, as levels of intelligence increased, individual levels of self-control tended to also increase (recall that the self-control scale was coded such that higher scores represent lower levels of self-control, thus the coefficient is negative in the tables). A virtually identical pattern of findings emerged for each of the other models in Table 2. In each case, the influence of intelligence was negative and significant in the prediction of low self-control. Of additional interest, however, was the effect of parenting. Despite the influence of intelligence, the parenting variable was a statistically significant predictor of self-control in two regression models. This is interesting, yet it does not fully address the question of whether traits in the child (such as intelligence) might partially account for the type of parenting the child experienced.

Table 2:

Ordinary Least Squares (OLS) Regression Models Predicting Child Self-Control

	Model 1	Model 2	Model 3	Model 4	Model 5
	b/β	b/β	b/β	b/β	b/β
Intelligence measures
WISC-IV Digit Span	−2.243/−0.201***
SE	0.385
WJ-III Subtest 9		−3.171/−0.286***
SE		0.381
WJ-III Subtest 10			−2.810/−0.258***
SE			0.384
PPVT-III				−2.264/−0.207***
SE				0.393
Overall Intelligence					−1.121/−0.318***
SE					0.122
Important covariates
Global Positive Parenting	−0.111/−0.077*	−0.085/−0.059	−0.082/−0.057	−0.102/−0.070*	−0.080/−0.056
SE	0.050	0.050	0.050	0.050	0.049
Maternal Self-Control	0.075/0.026	0.103/0.036	0.087/0.030	0.081/0.028	0.064/0.022
SE	.102	.099	.100	.102	.099
Paternal Self-Control	0.284/0.102**	0.222/0.080*	0.248/0.089**,^a	0.242/0.087*	0.246/0.089**
SE	0.096	0.094	0.096	0.097	0.094
Spanking	0.620/0.050	0.928/0.076*	0.823/0.067*,^b	0.902/0.073*	0.745/0.061
SE	0.425	0.414	0.419	0.423	0.410
Apgar at 5 min	0.263/0.019	0.215/0.015	0.288/0.020	0.241/0.017	0.263/0.019
SE	0.484	0.473	0.479	0.486	0.469
Child’s sex	4.766/0.225***	4.393/0.208***	4.923/0.233***	5.304/0.250***	4.820/0.228***
SE	0.730	0.718	0.725	0.732	0.713
Child’s age (years)	−0.295/−0.009	0.049/0.001	−0.139/−0.004	−0.393/−0.012	0.189/0.006
SE	1.157	1.138	1.156	1.157	1.131
Maternal race (Black/non-Black)	3.850/0.182***	3.066/0.145***	2.661/0.126***	2.789/0.132	2.572/0.122***
SE	0.723	0.718	0.739	0.756	0.723
n	730	721	721	726	714
R²	.169	.207	.190	.171	.225

Note. WISC-IV = Wechsler Intelligence Scale for Children–IV; WJ-III = Woodcock Johnson–III; PPVT-III = Peabody Picture Vocabulary Test–III.

p = .01.

p = .05.

p < .05. **p < .01. ***p < .001.

The second phase of the analytical plan was intended to test this question, and those results are presented in Table 3. As can be seen, we examined the relationship between the child’s level of intelligence and the parenting measure. The pattern of findings that emerged was consistent, in that the measure of child intelligence remained a significant predictor of parenting, regardless of the covariates included in the analysis. Interestingly, paternal self-control was associated with parenting outcomes, yet maternal self-control failed to reach statistical significance. While the results presented in Table 3 are important, they lack the capacity to address several additional questions that are also of interest. Could it be, for instance, that the effect of intelligence on parenting operates via an effect on self-control? To further unpack the association between intelligence and parenting, we utilized path modeling and the results are presented in Figure 1.

Table 3:

Ordinary Least Squares (OLS) Regression Models Predicting Global Positive Parenting

	Model 1	Model 2	Model 3	Model 4	Model 5
	b/β	b/β	b/β	b/β	b/β
Intelligence measures
WISC-IV Digit Span	0.361/0.047
SE	0.226
WJ-III Subtest 9		0.620/0.081**
SE		0.229
WJ-III Subtest 10			0.833/0.111***
SE			0.226
PPVT-III				0.374/0.050
SE				0.233
Overall Intelligence					0.226/0.094**
SE					0.074
Important covariates
Paternal Self-Control	−0.158/−0.085**	−0.148/−0.080**	−0.153/−0.082**	−0.149/−0.080**	−0.152/−0.082**
SE	0.055	0.055	0.055	0.055	0.055
Maternal Self-Control	−0.073/−0.038	−0.072/−0.037	−0.065/−0.033	−0.071/−0.037	−0.060/−0.031
SE	0.057	0.057	0.057	0.057	0.058
Spanking	−0.645/−0.078**	−0.729/−0.088**	−0.653/−0.079**	−0.662/−0.080**	−0.699/−0.084**
SE	0.244	0.244	0.243	0.244	0.245
Child’s sex	−1.201/−0.083**	−1.109/−0.077**	−1.283/−0.088**	−1.259/−0.087**	−1.193/−0.082**
SE	0.422	0.425	0.423	0.424	0.427
Child’s age (years)	2.083/0.092**	2.036/0.090**	1.837/0.081**	2.045/0.090**	1.942/0.086**
SE	0.662	0.665	0.668	0.664	0.671
Maternal race (Black/non-Black)	−1.184/−0.082**	−0.961/−0.066*	−0.839/−0.058	−0.975/−0.067*	−0.794/−0.055
SE	0.422	0.429	0.435	0.446	0.439
n	1,147	1,130	1,134	1,139	1,117
R²	.045	.051	.055	.044	.052

Note. WISC-IV = Wechsler Intelligence Scale for Children–IV; WJ-III = Woodcock Johnson–III; PPVT-III = Peabody Picture Vocabulary Test–III.

p < .05. **p < .01. ***p < .001.

Figure 1:

Examining the Interconnections of Parenting, Intelligence, and Self-Control

As can be seen, the effect of intelligence remained strong and in the expected direction controlling for other relevant covariates. What should not be missed is that the effect of childhood intelligence (Note: the measure of overall intelligence was utilized) appeared to flow partially through self-control—which also exerted a significant impact on parenting experiences.³ These results suggest that self-control mediates the association between intellectual functioning and parenting outcomes. To directly test this assumption, we utilized the “sgmediation” command contained in STATA 12.0 (Preacher & Hayes, 2004). Our findings in this regard suggested that self-control captured roughly 23% of the effect of intelligence on parenting.⁴

Discussion

The current study sought to contribute to the recently expanding literature exploring the association between intelligence and self-control (Boisvert et al., 2013; Boutwell & Beaver, 2010). Our analysis of the FFCWS revealed two broad findings that warrant further attention. First, higher levels of intelligence in children were consistently related to higher levels of self-control. The association between intelligence and self-control persisted regardless of the covariates included in the analysis (including parental impulsivity and parenting quality). This suggests that cognitive growth early in the life-course—assessed via both individual intelligence measures as well as the more global construct of general intelligence—is a robust predictor of impulse control. In light of these findings, the omission of intelligence measures from studies examining the development of self-control could represent a misspecified model.

The second broad finding that bears mentioning concerns the predictors of parenting experienced by children in the study. Recall that the parenting measure retained statistical significance in only two of the regression equations predicting self-control, while intelligence maintained a robust relationship with self-control in all models. Intelligence was also found to predict a positive parenting experience in the child—to some degree via its influence on self-control. The types of parenting practices experienced by the child are not only influenced by characteristics of their parents, then, but also by the child’s behavior (Scarr & McCartney, 1983). In other words, a child may report low levels of attachment and involvement with their parents which may be generated, at least in part, by diminished cognitive functioning in the child. As a result, the model could be—and likely is—tapping into an evocative parenting experience (Scarr & McCartney, 1983), whereby the child’s natural temperament and personality elicit certain responses from their caregiver (Harris, 1998). Moving forward, it will become all the more important to further understand the associations between child traits and parenting outcomes.

For criminological scholars in particular, it is important to emphasize that our findings pose a direct challenge to M. Gottfredson and Hirschi’s (1990) seminal work on self-control. Specifically, the current study adds to an ever-growing body of evidence demonstrating that parenting styles may have little or nothing to do with childhood acquisition and development of self-control (Boutwell & Beaver, 2010; Wright & Beaver, 2005; Wright, Beaver, DeLisi, & Vaughn, 2008). The highly heritable nature of intelligence (Erlenmeyer-Kimling & Jarvik, 1963; Plomin, 2003; Plomin & Spinath, 2004) combined with the growing evidence that the development and stability of self-control is partially accounted for by genetic variation (Beaver et al., 2010; Beaver, Wright, DeLisi, & Vaughn, 2008) is directly incongruous with the claims made by M. Gottfredson and Hirschi (1990). Does this fully negate the relevance of the environment to the development of self-control? No, but what our findings do suggest is that parenting might not represent the juggernaut force that M. Gottfredson and Hirschi suggest it is.

More broadly speaking, the findings presented in this study add to the literature supporting the understanding of self-control as an executive (i.e., brain-based) function (Beaver et al., 2007). Given that intellectual and cognitive functioning is the product of brain-based operations, the robust relationship between self-control and intelligence only bolsters the connection of self-control to the brain. As the theory of low self-control is popular and well-supported in the literature, establishing a link between self-control and brain structure and function serves not only to advance the theory, but also to promote the importance of biology in criminological scholarship overall.

Along these lines, there is a body of literature regarding “hot” and “cool” executive functions which warrants some attention (Rubia, 2011). Evidence from this research arena demonstrates that the cognitive abilities under the umbrella of “executive functions” can be traced to different subregions within the prefrontal cortex. “Cool” executive functions include tasks such as attention, planning, and inhibition, whereas “hot” executive functions are those which regulate incentive and motivation. Due to this difference, some executive functions may be more closely related to certain behaviors and traits than others, potentially explaining behavioral differences between violent offenders with and without psychopathic traits (De Brito, Viding, Kumari, Blackwood, & Hodgins, 2013), or adolescents with conduct disorder with and without Attention Deficit Hyperactivity Disorder (ADHD; Dolan & Lennox, 2013). Differences in hot and cool executive functions may also explain factors related to intelligence, such as learning-related behaviors (including self-control, attention, and self-motivation) and academic achievement, particularly in math (Brock, Rimm-Kaufman, Nathanson, & Grimm, 2009).

Research on hot and cool executive functions also serves to promote the notion that the association between self-control and intelligence might be due to shared developmental origins. Beaver, DeLisi, Vaughn, Wright, and Boutwell (2008) suggested that self-control and language, an ability closely tied to verbal intelligence (Horwitz et al., 2003; Silva, Williams, & McGee, 1987; Stevenson, 1984), are driven by the same genetic factors. Interestingly enough, when genetic influences on self-control were held constant, language skills continued to predict variation in levels of self-control. Due to the close association between language and intelligence (Horwitz et al., 2003; Silva et al., 1987; Stevenson, 1984), the shared origins between self-control and language may also extend to intelligence more broadly defined. The possibility that intelligence and self-control could share a developmental origin is one for future researchers to address. For now, however, it appears that there is much to come concerning the mutual unfolding of self-control, intelligence, and the ways in which the brain functions to underpin each of these critical developmental constructs.

Several limitations of the current study must be addressed prior to concluding. First, the FFCWS data contain a disproportionate number of at-risk families, and may not be representative of the population as whole. Two thirds of the families included in the FFCWS, for example, were unmarried at the time of the child’s birth. Second, intelligence has been found to influence whether or not respondents choose to continue participating in longitudinal research. Beaver (2013) found that respondents who stopped participating in a large, longitudinal study generally had lower intelligence scores than those who participated throughout the study; on average, retained respondents differed from dropouts by about 4.5 IQ points. It may be possible that those who refused to participate in the Fragile Families study or those who opted out of subsequent waves were quantitatively less intelligent than respondents who were retained. Third, this study lacks the ability to control for any potential genetic influences. A growing body of evidence suggests that traits such as intelligence and self-control share, at least in part, a developmental origin (Beaver, DeLisi, et al., 2008). Future research could overcome this limitation by determining whether or not a shared genetic architecture can account for variation in both intelligence and self-control. However, there is at least some reason to believe that the relationship between intelligence and self-control may remain beyond the impact of genes. Research examining genetic influence on the mutual development of individual traits has found evidence of an independent impact of one trait on the development of another. This developmental impact is external to the influence of genetic factors, suggesting that some traits may be developmentally interrelated (Beaver, DeLisi, et al., 2008).

A final limitation is that this sample focuses on children, specifically those between the ages of 8 and 11. The findings from this research may not fully capture developmental outcomes in adolescence or adulthood. Ultimately, however, our findings underscore the importance of cognitive functioning—even early in the life-course—across a host of important outcomes. What should not be missed is that individual differences in intelligence emerge soon after birth and begin contributing to variation in traits such as self-control early on in a child’s development. In fact, recent research regarding psychometric testing of cognitive ability has demonstrated that intelligence can be effectively assessed as young as age 2 or 3 (Baron & Leonberger, 2012; Ward, Rothlisberg, McIntosh, & Bradley, 2011). As a result, failure to account for the importance of intelligence may yield tenuous and likely misspecified results. Blatant ignorance regarding the importance of intelligence will only serve to stunt our understanding of human development across the life-course.

Footnotes

Appendix

Notes

Melissa A. Petkovsek is currently an assistant professor in the Department of Criminal Justice at the University of Central Missouri. Her research agenda is focused in the area of biosocial criminology, centering primarily on the development of antisocial behavior during early childhood. Her work has appeared in a variety of peer-reviewed journals including Journal of Adolescence and Social Science and Medicine.

Brian B. Boutwell is an associate professor of criminology and criminal justice in the School of Social Work at Saint Louis University. His research interests span a variety of areas and include human intelligence, human mating, the evolution of complex traits, and the genetic underpinnings to antisocial and aggressive behaviors. His work has appeared in a variety of journals including Developmental Psychology, PLOS One, Social Science and Medicine, Criminology, Journal of Psychiatric Research, Intelligence, and Aggressive Behavior.

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