Abstract
Young men with superior upper-body strength typically show a greater proclivity for physical aggression than their weaker male counterparts. The traditional interpretation of this phenomenon is that young men calibrate their attitudes and behaviors to their physical formidability. Physical strength is thus viewed as a causal antecedent of aggressive behavior. The present study is the first to examine this phenomenon within a developmental framework. We capitalized on the fact that physical strength is a male secondary sex characteristic. In two longitudinal cohorts of children, we estimated adolescent change in upper-body strength using the slope parameter from a latent growth model. We found that males’ antisocial tendencies temporally precede their physical formidability. Boys, but not girls, with greater antisocial tendencies in childhood attained larger increases in physical strength between the ages of 11 and 17. These results support sexual selection theory, indicating an adaptive congruence between male-typical behavioral dispositions and subsequent physical masculinization during puberty.
Sexual dimorphism in upper-body strength far exceeds the disparity in overall body mass between men and women (Lassek & Gaulin, 2009). The presence of such a profound sex difference is indicative of different selection pressures on ancestral men and women. There are strong grounds to infer that men’s greater physical strength was shaped by direct male-male competition for access to mates (Darwin, 1871; Puts, 2010; Sell, Hone, & Pound, 2012). Indeed, many of the traits that make up the male suite of secondary sex characteristics (e.g., deep voice, jaw-augmenting facial hair, and greater stature and lean body mass) signal an enhanced capacity to physically intimidate one’s rivals (Dixson & Vasey, 2012; Hodges-Simeon, Gurven, Puts, & Gaulin, 2014; Puts, Apicella, & Cardenas, 2012). A physical capacity to dominate others may be facilitated when complemented by psychological adaptations that foster willingness to engage in conflict. This implies a linkage between physical formidability and behavioral traits of competitiveness (e.g., aggression, boldness, self-centeredness, and low empathy).
An association between physically aggressive tendencies and physical strength is strikingly intuitive, given the well-established sex difference favoring males on both counts. There is considerable evidence that young men with superior upper-body strength exhibit greater aggressive propensities and are more experienced with physical fighting than their physically weaker male counterparts (Gallup, White, & Gallup, 2007; Sell, Tooby, & Cosmides, 2009). Furthermore, a mesomorphic somatotype—which is partly based on the perception of high muscularity—characterizes male youth who are persistently delinquent (Glueck & Glueck, 1956).
There are several mechanisms that might account for the link between physical strength and aggression. The traditional interpretation is that somatic characteristics have causal priority, insofar as stronger individuals receive greater positive reinforcement from engaging in confrontation (Glueck & Glueck, 1956; Raine, Reynolds, Venables, Mednick, & Farrington, 1998; Sell et al., 2009). According to a facultative-calibration view of personality (Lukaszewski & Roney, 2011), the tendency to engage in interpersonal conflict should be tailored to one’s ability to inflict physical costs on others. Conversely, it is possible that aggressive individuals choose to invest in greater physical strength. This view recognizes the fact that aggressive individuals often desire to enhance their physical formidability, whereas the facultative-calibration hypothesis posits that aggressive tendencies are a reaction to one’s physical formidability. A third possibility is that aggression and physical strength co-occur because of common biological factors that masculinize the brain and physique independently (i.e., the neuroandrogenic hypothesis; Ellis, Das, & Buker, 2008). Given that previous studies have been cross-sectional, it is difficult to weigh the relative merits of these competing hypotheses. A prospective longitudinal framework could help clarify the mechanism (or mechanisms) at work. In particular, a question that has been unresolved by past studies is whether muscular young men were already aggressive before they attained their physical formidability.
The remarkable stability of aggression between childhood and adulthood suggests that aggressive men were likely to be aggressive as children (Huesmann, Eron, Lefkowitz, & Walder, 1984). Moreover, the developmental timing of sex differences in male-typical behavioral dispositions (e.g., physical aggression) unfolds differently than the emergence of male secondary sex characteristics (e.g., upper-body muscularity and deep voice). Males already surpass females in aggressive and rule-breaking tendencies in early childhood—a period during which the sex difference in physical strength is modest or negligible (Butterfield, Lehnhard, Loovis, Coladarci, & Saucier, 2009; Molenaar et al., 2010). This suggests that the masculinizing effects of androgens on antisocial tendencies are realized well before puberty. Given that androgenic influences on secondary sex characteristics are not fully expressed until adolescence, we predicted a temporal pattern in which greater levels of male-typical behavioral traits in childhood presage greater development of physical strength following the onset of puberty.
In the present study, we used a prospective longitudinal design to clarify the nature of the relationship between physical strength and aggression. We examined multiple measures of aggressive-antisocial tendencies at approximately age 11. We also assessed hand-grip strength (HGS) at three time points, namely, ages 11, 14, and 17. HGS is highly correlated with other indices of muscular strength (Wind, Takken, Helders, & Engelbert, 2010) and is thus an excellent measure of overall body strength. Latent growth models were fit to the HGS data in order to capture the pubertal-change (slope) component of HGS development. This strategy allowed us to determine whether antisocial tendencies are tied to preexisting levels of physical strength or to secondary sex development of physical strength. In particular, an association between the slope factor and age-11 antisocial tendencies would demonstrate that individual differences in antisociality precede changes in HGS. According to sexual selection theory, sex differences in physical aggression emerge at a young age in order to prepare boys for future intrasexual competition (Archer, 2004). We therefore hypothesized that greater childhood antisociality would predict superior HGS development in males, but not females.
Method
Participants
We analyzed data from two large samples of twins from the 11-year-old cohorts of the Minnesota Twin Family Study (Iacono & McGue, 2002; Keyes et al., 2009). 1 Individuals from these cohorts were recruited at approximately the age of 11 and were subsequently invited for follow-up assessments at 3-year intervals. The purpose of the Minnesota Twin Family Study is to investigate the antecedents of substance-use disorders and related externalizing characteristics. A wide range of cognitive, somatic, personality, and clinical assessments have been administered in order to address this research goal. HGS was assessed during three laboratory visits when participants were ages 11, 14, and 17. Mean ages at these visits were 11.78 (SD = 0.43), 14.84 (SD = 0.51), and 17.91 (SD = 0.53), respectively.
One sample (n = 1,527) was recruited during the years 1990 through 1996; the other sample (n = 998) was recruited in 1999 through 2006. We refer to these two samples as the original cohort and newer cohort, respectively. Most participants returned to the laboratory for their follow-up visits; total return rates were 83% and 71% at ages 14 and 17, respectively. A major cause of attrition was that families relocated from the state of Minnesota and therefore could participate only via phone assessment. Each cohort was subdivided into male and female samples. A large majority of participants (95%) were of European American background.
HGS data at some point during the study were available for a total of 2,513 individuals. The vast majority (98.8%) were assessed for HGS at age 11. Furthermore, for 2,206 individuals (87.8%), HGS data from one or both of the two follow-up visits were available. Participants who failed to return for laboratory visits were slightly (and nonsignificantly) weaker at age 11 than those who subsequently returned, Cohen’s d = −0.05. Information about age-11 antisociality was available for all but 18 individuals for whom we had HGS data; thus, all analyses were performed on data from at least 2,495 participants.
Measures and procedure
Hand-grip strength and pubertal development
At each assessment, HGS was measured a total of four times—twice for each hand—using Lafayette hand dynamometers (Models 78010 and 78011; Lafayette Instrument Company, Lafayette, IN). Participants were instructed to squeeze the dynamometer as hard as possible while standing upright and keeping their arms to the side in a neutral position. The maximum grip strength from these four trials was selected as our HGS variable (measured in kilogram-force units). We collected these data during daylong laboratory visits at the Minnesota Center for Twin and Family Research. These measurements were obtained during the course of a detailed anthropometric assessment. Additionally, physical stature and body mass were measured using a Detecto (Webb City, MO) mechanical physician scale with a height rod. Surveys of pubertal development were also administered during these visits.
Previous research has demonstrated that grip strength is related to biological maturity in males, even after controlling for height and weight (Jones, Hitchen, & Stratton, 2000). Therefore, it was possible that any observed association between HGS and antisociality would be confounded by pubertal status, given that early-maturing boys are at increased risk for delinquency (Cota-Robles, Neiss, & Rowe, 2002). At the age-11 assessment, we administered a modified version of the Pubertal Development Scale (Petersen, Crockett, Richards, & Boxer, 1988). Participants reported their level of maturity using a 4-point rating scale ranging from not yet started growing/not yet started changing to growth is complete/finished changing. Males were asked about voice changes, facial hair, underarm hair, and pubic hair. Females were asked if their growth had spurted and reported on their skin changes (pimples), body hair, and breast development. We averaged responses across the four characteristics to create a composite scale for pubertal development. This scale showed good internal consistency in females, Cronbach’s α = .75. Reliability was somewhat lower in males, Cronbach’s α = .66.
Pubertal development in boys was very positively skewed (and kurtotic). A plurality of boys showed a complete lack of pubertal development at age 11, with 44% responding to all four items on the questionnaire with the lowest possible rating (i.e., they had not yet started to change or grow). As a result, we subjected these scores to a log transformation in addition to treating these data categorically using a median split. Because results were essentially the same regardless of whether we conceived pubertal development as categorical or continuous, we report only the results obtained using the log-transformed scores. In females, physical-development scores were symmetrically distributed.
Aggressive-antisocial tendencies
We looked at our data set to identify measures of antisociality that were continuously distributed and available for the age-11 assessment. Two measures showed adequate sensitivity to individual differences: teacher ratings of antisocial personality features and self-reported aggression.
Teacher ratings of antisocial personality features
At age 11, participants nominated up to three teachers with whom they were acquainted at school. A rating form was mailed to each nominee soon after the laboratory visit. Twenty-eight traits (identified by sets of personality adjectives) were included in the rating form, along with 5-point rating scales. For each trait, the teacher compared the participant with his or her other classmates and indicated whether the participant was in the lowest 5%, lower 30%, middle 30%, higher 30%, or highest 5% of students in class. On average, 2.20 rating forms were returned per participant (more than 75% had at least two teacher ratings).
Of the traits in the teacher rating form, we selected eight that tap into the broader construct of antisociality and low agreeableness (e.g., “tough, unforgiving, aggressive”). These items are listed in Table 1. A composite index of antisocial personality was created by averaging scores for these traits across all raters. Although the eight traits were selected on a rational rather than empirical basis, intertrait correlations were moderate in magnitude for both sexes. Internal consistency was high, with Cronbach’s alphas ranging from .85 to .90 across the four samples.
Teacher-Rated Antisocial Personality Features
Self-reported aggression
For most participants, willingness to engage in physical aggression was assessed with a 40-item survey on opinions and attitudes that focused on school and home life. This survey was introduced into the protocol in 1993, and thus the males in the original cohort (whose age-11 assessments began in 1990) did not respond to it (the instrument used to measure aggression in these participants is described in the next paragraph); females in the original cohort did complete this survey because their age-11 assessments began in 1993. Seven items from the survey tapped into participants’ views about aggression, and all specifically dealt with direct (often physical) forms of confrontation (e.g., “If a person challenges you, you have to be ready to fight back”; see Table 2). Participants responded to these items using a 4-point rating scale: 1 = disagree a lot, 2 = disagree a little, 3 = agree a little, and 4 = agree a lot. A summary score was created by averaging across the seven items. This measure showed good reliability; Cronbach’s α ranged from .81 to .86 in the three samples who completed this survey.
Survey Items Assessing Attitudes Toward Physical Aggression
Males in the original cohort underwent a slightly different protocol at their intake assessment. Instead of rating their opinions and attitudes, they completed a self-report personality inventory. (None of the other samples received this inventory.) They were instructed to compare themselves with their same-age peers on 34 personality facets (adjectives) and to report their rankings on a 5-point rating scale: lowest 5%, lower 30%, middle 30%, higher 30%, or highest 5%. The personality inventory contained 3 aggression-related adjectives: tough, aggressive, and conciliatory (reverse-scored). To ensure that participants interpreted the adjectives appropriately, we provided statements describing the behavior and feelings that corresponded to high and low scores for each adjective. For example, the instructions indicated that participants should give themselves a high rating for tough if “you will sometimes pursue your own advantage even if someone else gets hurt; you seem to get a kick out of teasing or frightening others.” A high score for aggressive was warranted if “you enjoy watching a good brawl; you sometimes like to get into fights and are ready to hit people when you’re angry.” And a low score for conciliatory meant, “you are ready for a show-down or a fight when you think you’ve been criticized or taken advantage of.” Trait aggression was measured by averaging ratings across these three items.
Statistical analyses
Latent growth curve models were fit to the time series of HGS data in order to estimate change in strength during adolescence (Meredith & Tisak, 1990). This type of model was appropriate given that both the mean and the variance of HGS increased monotonically between ages 11 and 17 (see Isen, McGue, & Iacono, 2014). Two latent growth parameters were estimated: intercept and slope. The intercept represented participants’ initial HGS (assessed at age 11), whereas the slope was the amount of change occurring between the initial and final assessments (i.e., between ages 11 and 17). Interindividual variation in the slope was the main parameter of interest.
Interpretation of the slope was dependent on how we conceptualized time. We had the choice to estimate time as a function of individuals’ chronological age or as a fixed measurement wave. Given the restricted age range at each assessment, we found it efficacious to regard the three measurement waves as time units. HGS was then residualized with respect to participants’ chronological age at each assessment. Unstandardized residuals from the resultant regression equations were used as input in the latent growth curve models. (In order to retain information about mean level of HGS, we added the mean predicted HGS values to these residual scores.) Additionally, we regressed HGS on participants’ height and weight in order to ensure that individual differences in body size were not confounding the results. In practice, this correction had minimal impact on the structural relations between HGS and aggressive-antisocial tendencies.
Figure 1 illustrates the parameters of the latent growth curve model. Slope factor loadings were fixed at 0 and 1 for the age-11 and age-17 HGS variables, respectively. This allowed for easier interpretation of the slope mean, which was simply reduced to the difference in HGS between the beginning and end of the study. The slope factor loading of the age-14 HGS measurement (denoted as t) was freely estimated in each sample to allow for cohort- and sex-specific nonlinear growth. The model also allowed for a potential correlation between individual differences in the intercept and slope (i.e., random effects). One might assume that individuals who are biologically precocious, and hence stronger, at age 11 will show less increase in HGS growth during their teens. If later-maturing peers eventually catch up in strength, then the intercept-slope correlation would be negative. However, the correlation was negligibly positive in both sexes (Isen et al., 2014).
Path diagram of the latent growth model. Rectangles represent observed hand-grip strength (HGS) at the age-11, age-14, and age-17 assessments. Circles denote latent variables, and triangles represent the grand means of the intercept and slope parameters. Residual variances (RVs) are time-specific and represent variation unexplained by the growth model.
As should be done in any latent growth model, we estimated time-specific measurement error, which represents (residual) variance unexplained by the intercept and slope factors. The intercept parameter was reliable inasmuch as it accounted for a sufficient percentage of the age-11 HGS variance (Hertzog, Lindenberger, Ghisletta, & von Oertzen, 2006). Thus, it was desirable for residual variances to be small. However, we observed a substantial sex difference in the residual variance at age 11 (Isen et al., 2014). The intercept factor accounted for a larger proportion of the age-11 HGS variance in males than in females (although the heritability of the HGS intercept was similarly high in both sexes). Because this could have reduced statistical power to detect relationships between the model parameters and antisociality in females, we performed follow-up analyses to examine whether girls’ observed (raw) HGS values were related to any of the antisociality variables.
Estimation of the intercept and slope parameters was conducted using structural equation modeling in Mplus Version 6 (Muthén & Muthén, 2010). One of the major advantages of latent growth modeling is that it can handle missing data. For example, some participants underwent laboratory assessments only at ages 11 and 14, whereas others were assessed only at ages 11 and 17. Imputation of the slope parameter is appropriate if the data are assumed to be “missing at random” (Little & Rubin, 2002). (In a nonelderly sample, it is implausible that physical strength would be related to the reasons for missing data.) We used a full-information maximum likelihood approach in order to include the greatest number of observations.
We segregated the two cohorts when performing most statistical analyses, as it afforded us the opportunity for replication and gave us greater confidence in the validity of our conclusions. There were also practical reasons for separating the two cohorts (e.g., differences in the availability of survey information). In all statistical analyses, we accounted for the nonindependence of observations (i.e., the clustered nature of twin data) by using a sandwich estimator in Mplus. This permitted us to compute nonbiased standard errors, which would have been too small if we had used standard (nonrobust) maximum likelihood estimation. Finally, prior to inferential analyses, scores for all antisociality measures were subjected to a Blom transformation in order to normalize the data.
Results
Descriptive statistics
Table 3 shows the sample sizes and raw scores for pubertal development and each of the antisociality measures at the intake (age-11) assessment. As expected, compared with females, males received higher teacher ratings of antisocial personality and (in the case of the newer cohort) endorsed more aggressive attitudes. In the interest of succinctness, we henceforth refer to trait aggression (assessed only among original-cohort males) and endorsement of aggressive attitudes as “self-reported aggression.”
Descriptive Statistics for Pubertal Development and Antisociality in the Two Cohorts
Correlations (rs) between teacher ratings and self-reported aggression ranged from .15 to .28 (all ps < .001) across the four samples, indicating that the two measures provided overlapping, but largely unique, information about antisociality. Pubertal development was not significantly associated with any of the antisociality measures in females, ps > .05. However, these correlations were statistically significant in males, rs = .09–.19.
Descriptive statistics for body stature and mass are not reported here, as they have been reported elsewhere (Isen et al., 2014). However, it should be noted that girls, because of their earlier growth spurt, were taller and heavier than boys at age 11. To obtain a measure of overall body size (i.e., bulk), we computed the product of height (in centimeters) and weight (in kilograms) at each assessment (see Raine et al., 1998). These values were then rank-normalized to adjust for positive skew. At age 11, girls had modestly larger body sizes than did boys (Cohen’s d = 0.20). By middle adolescence, boys were bulkier than girls (ds = 0.29 and 0.88 at ages 14 and 17, respectively).
We next examined whether body size was related to aggression-antisociality. To reduce the number of statistical tests, we averaged self-reported aggression and teacher ratings (both rank-normalized) into a single score. We then computed the correlation between this composite aggression-antisociality measure and body size at each age, separately within each of the four samples. Out of a total of 12 correlations, only 3 were statistically significant—and all of the significant correlations were for males in the newer cohort. Greater aggression-antisociality predicted greater body size at all ages in this cohort, r = .14 (p < .01) at age 11, r = .19 (p < .001) at age 14, and r = .21 (p < .001) at age 17. The corresponding correlations for the males in the original cohort were nonpositive (rs = −.05, .00, and .00), which suggests a cohort-by-body-size interaction. Although the reasons for this discrepancy are unclear, it is noteworthy that the newer-cohort males (born in the early 1990s) were consistently heavier than the original-cohort males (born in the late 1970s and early 1980s), a difference reflecting the increase in body mass in the U.S. population during that period. We regressed out the effects of body size when performing HGS analyses.
Modeling hand-grip strength
Raw HGS values were symmetrically distributed at each time point in all four samples. (The median value was always the integer closest to the mean.) The HGS data were further residualized with respect to age, height, and weight in each sex separately. After adding the absolute HGS means to these unstandardized residuals, we submitted the data to latent growth modeling. For model identification purposes, the slope loadings of the age-11 and age-17 time points were fixed at 0 and 1, respectively (see Fig. 1). The slope loading for the middle time point (which represented the proportion of growth occurring by age 14) was freely estimated in each of the four samples and ranged from .51 in original-cohort males to .74 in newer-cohort females. Hence, HGS growth was relatively linear between the ages of 11 and 17 in males, but clearly decelerated in females after age 14.
Parameter estimates of the intercept and slope factors are reported in Table 4. The intercept mean was modestly higher for males than for females in both cohorts. As many as one third (32–33%) of females were stronger than the average male at age 11. By age 17, only a single female (0.1% of the total female sample) was stronger than her average male peer. Males’ HGS more than doubled between ages 11 and 17, whereas females’ mean HGS increased by 40%. This difference is reflected in Table 4, which shows that the average increase in strength (i.e., slope mean) was approximately 22 kg in males and 9 kg in females. Initial HGS levels failed to predict individual differences in HGS growth; the intercept-slope correlation (r) was .03 in males and .02 in females.
Latent-Growth-Model Parameters of Grip-Strength Development in Adolescence
Note: Grip-strength values (in kilogram-force units) were adjusted for age, height, and weight. Standard errors are presented in parentheses. The intercept is an estimate of initial (age-11) grip strength. The slope represents the change in grip strength between ages 11 and 17. Means are fixed (constant) values in a given sample; variances reflect the extent of interindividual differences in initial strength (intercept) and subsequent growth (slope).
Variances around the slope and intercept factors were substantially larger in males than females (see Table 4). The larger intercept variance in males stems from the fact that the latent growth model accounted for 87% to 98% of the observed age-11 HGS variance in boys, but only 66% to 68% of the corresponding variance in girls. (There was greater residual variance in females, perhaps because their HGS values did not display the same monotonic linear increase found in males; for a minority of females, HGS decreased slightly between ages 14 and 17.) Males’ excess variance around the slope was nonspurious, as it reflected the fact that the sex difference in HGS variance becomes more marked as children advance through adolescence (Isen et al., 2014). Males’ variance around the slope was approximately 4 times that of females.
Relations with antisociality
Next, we regressed the intercept and slope factors on age-11 pubertal development and our measures of antisociality. Table 5 summarizes the results for males. Boys with greater self-reported aggression and higher teacher ratings showed enhanced gains in physical strength during adolescence (i.e., a steeper slope), but were not consistently stronger than their peers at age 11. As the table shows, this phenomenon was apparent in both the original and the newer cohorts. Individual differences in the slope (but not intercept) were positively associated with every measure of antisociality. Although there was some evidence that age-11 HGS (i.e., intercept) was related to teacher ratings, this effect was confined to the newer cohort.
Regression of the Latent-Growth-Model Parameters on Age-11 Predictors in Males
Note: Intercept represents individual differences in age-11 grip strength; slope represents growth in grip strength between ages 11 and 17. The aggression-antisociality composite is the average of teacher ratings and self-reported aggression. CI = confidence interval.
p < .05. **p < .01. ***p < .001.
The incremental utility of employing different methods of assessing antisociality was borne out in a multiple regression framework. In both cohorts, self-reported aggression and teacher ratings of aggression were uniquely associated with HGS slope, βs > 0.13, ps < .05. The core of our findings is represented in Figure 2, which presents results obtained using median splits. Participants who scored in the top 50% for teacher ratings and the top 50% for self-reported aggression formed our high-antisocial group. Those who scored in the bottom half on both variables were classified in our low-antisocial group. (Individuals scoring above the median on one variable but below the median on the other were not placed into a group.) As Figure 2 illustrates, these groups differed only modestly with respect to their observed HGS at age 11, Cohen’s d = 0.12 (pooled across cohorts). Six years later, however, these groups differed markedly with respect to their HGS, Cohen’s d = 0.50.
Hand-grip strength of male participants in the original and newer cohorts at age 17 (top) and age 11 (bottom). Results are shown separately for participants in the low- and high-antisocial groups (defined by a median split on age-11 scores). The ranges of values on the y-axes are deliberately truncated in order to allow for adequate resolution of the error bars, which represent 95% confidence intervals.
The observed pattern of results cannot be due to the confounding influences of age, weight, and height, as these were partialed out of HGS. A possible concern is that precocious biological maturity might have contributed to greater antisocial propensities as well as accelerated HGS development in boys. This concern is unwarranted, however, as pubertal development was related only to the HGS intercept, and not to individual differences in HGS slope (see Table 5). When we included pubertal development and teacher ratings as joint covariates in a multiple regression model, the relationship between the HGS intercept and teacher ratings was reduced to nonsignificance in the newer cohort, β = 0.11, p = .08. Similarly, the relationship between the HGS intercept and the aggression-antisociality composite in the newer cohort was reduced to nonsignificance when we included pubertal development as a covariate, β = 0.10, p = .06.
In contrast to the regression models for males, the models for females showed that all associations between the latent growth parameters and the measures of antisociality were nonsignificant (see Table 6). Moreover, pubertal development was unrelated to the HGS intercept. Because overall body size may convey important information about females’ competitiveness, we also fit the latent growth model to raw HGS data (i.e., without adjusting for body weight and height); again, none of the antisociality covariates were significantly related to the intercept and slope parameters. This lack of association between antisociality and HGS persisted when we calculated correlations using the observed HGS data at each assessment, with and without adjustment for body size (rs < .10, ps > .07).
Regression of the Latent-Growth-Model Parameters on Age-11 Predictors in Females
Note: Intercept represents individual differences in age-11 grip strength; slope represents growth in grip strength between ages 11 and 17. The aggression-antisociality composite is the average of teacher ratings and self-reported aggression. CI = confidence interval.
An important question is whether sex moderates the association between antisociality and HGS development. Such an effect cannot feasibly be demonstrated in a multigroup latent growth model because the mean and variance of HGS slope is vastly greater for males than for females. We have presented standardized regression coefficients in Tables 5 and 6 in order to render the effect sizes comparable across sexes and cohorts, but these coefficients do not convey the raw impact of antisociality on HGS development. Unstandardized regression coefficients make the sex difference more apparent. For example, the unstandardized coefficient for the composite aggression-antisociality measure as a predictor of HGS slope in males was 1.28 (95% confidence interval, CI = [0.67, 1.90]) in the original cohort and 1.98 (95% CI = [1.17, 2.80]) in the newer cohort; in other words, each 1-SD unit increase in aggression-antisociality roughly predicted 1 to 2 kg of excess HGS gain during adolescence. The unstandardized coefficients for females were much lower, b = 0.23 (95% CI = [−0.34, 0.81]) in the original cohort and b = 0.52 (95% CI = [−0.08, 1.12]) in the newer cohort. Although the confidence intervals for males and females in the original cohort overlapped slightly, no overlap was evident for the newer cohort.
Discussion
The present study used longitudinal data to address a provocative question: Why are physically strong males more aggressive than their physically weaker peers? We framed this question within the broader goal of understanding the developmental processes by which male-typical behavioral traits complement masculine physical characteristics. We employed latent growth models of HGS to exploit the fact that puberty profoundly alters males’ physical strength. In two large samples of male youth, aggressive-antisocial tendencies were positively related to the pubertal-change (slope) component of physical strength. To our knowledge, this is the first study to demonstrate that individual differences in behavior or personality are linked to interindividual development of a secondary sex characteristic. As expected, there was no relationship between HGS and antisociality in females.
Sex-specific associations between aggressive propensities and upper-body strength are well established in the literature (e.g., Gallup et al., 2007; Sell et al., 2009). Previous studies have generally relied on cross-sectional designs, which render interpretation of the developmental mechanism (or mechanisms) difficult. Sell and his colleagues (Sell et al., 2012; Sell et al., 2009) interpreted their findings through the lens of facultative-calibration theory, according to which males adaptively tailor their aggressive behavior and attitudes to their physical formidability. Because physically formidable individuals possess an enhanced ability to inflict physical costs on others, it seems intuitive that physical strength facilitates the adoption of coercive tactics.
Although the facultative-calibration perspective is theoretically viable, it does not offer a complete picture of the ontogeny of aggression (nor was it advocated as doing so). For example, it remains silent on important developmental considerations. Male toddlers already demonstrate greater risk taking and aggressive propensities than female toddlers (Archer, 2009). It appears that neuroendocrine mechanisms are responsible for sexual dimorphism in physical aggression early in ontogeny, long before the emergence of sexual dimorphism in physical strength. Moreover, patterns of aggressive-antisocial behavior can be considered traitlike, as they show moderate stability from early childhood to adolescence (van Beijsterveldt, Bartels, Hudziak, & Boomsma, 2003).
The facultative-calibration hypothesis of physical aggression cannot account for the present pattern of results. In particular, this hypothesis does not anticipate our finding that greater aggressive-antisocial propensities in childhood predict greater increases in physical strength during adolescence. The fact that age-11 aggression in boys was associated with the slope (pubertal-change) component of HGS, but not to contemporaneously measured HGS, challenges the notion that physical strength is a causal antecedent of physical aggression.
Alternatively, joint hormonal mediation may be responsible for both muscular strength and aggressive-antisocial traits. Androgen exposure is known to affect both brain functioning and somatic development (Ellis et al., 2008), which implies that male-typical behavioral traits and muscular strength might arise together indirectly (i.e., through a pleiotropic mechanism), rather than because one directly causes the other. This possibility is supported by observations that fetal testosterone exerts organizational influences on grip strength and physical aggression in young men (Bailey & Hurd, 2005; Butovskaya, Fedenok, Burkova, & Manning, 2013; Hone & McCullough, 2012). However, the hormonal mechanism (or mechanisms) responsible for the present findings are not necessarily prenatal or even gonadal.
Adrenal androgens are particularly relevant because they rise dramatically during middle childhood and are thought to influence sexually selected behaviors without grossly affecting morphology. Del Giudice, Angeleri, and Manera (2009) refer to this juvenile phase (adrenarche) as a period in which boys can hone their interpeer competitiveness without developing the physical masculinization that would otherwise elicit rivalry from adolescent or adult men. There is evidence that aggressive-antisocial boys have elevated levels of adrenal androgens even while possessing normal concentrations of testosterone (van Goozen, Matthys, Cohen-Kettenis, Thijssen, & van Engeland, 1998; van Goozen et al., 2000). In adults, adrenal androgens (particularly dehydroepiandrosterone) are important precursors to testosterone synthesis (Labrie et al., 2005), which, in turn, is important for the growth and maintenance of muscle mass. Thus, individual differences in adrenal androgen functioning may underlie the temporally lagged relationship between preadolescent antisociality and adult physical strength.
Given our lack of hormonal measures, we cannot conclude that androgen exposure organizes both aggressive propensities and pubertal increases in strength. Our results are not incompatible with the interpretation that antisocial boys engage in activities that facilitate greater development of strength. It is possible that more aggressive boys in the present samples were more likely to participate in violent, physically strenuous activities (e.g., martial arts and hunting), which could have fostered a greater increase in their HGS.
The present study offers several novel contributions to current understanding of the intersection between personality and somatic development. It appears that males’ physical strength is related to broad facets of the antisocial spectrum rather than to aggression specifically. Teacher ratings of risk taking, rule breaking, and low empathy predicted subsequent HGS growth during adolescence. Given that these male-typical traits facilitate competition, it is likely that they are part of the same adaptive complex as physical aggression.
Major strengths of this study include the large sample sizes and extensive range of antisociality measures. Statistical findings were both robust and replicable. That is, the association between HGS slope and antisociality was robust to differences in rating method (i.e., teacher ratings vs. self-reported attitudes) and was replicable (i.e., consistent across two cohorts of participants). Future studies may profit from using a wider array of physical strength measures (rather than relying on HGS only). It will also be beneficial to assess children at younger ages, well before the onset of puberty.
Concluding Remarks
Pubertal development is a salient and ubiquitous phenomenon in humans, but the functional role of secondary sex characteristics is often underappreciated. Features such as a deep voice, facial hair, and greater musculature may have rendered males more attractive to potential mates and more intimidating to same-sex rivals during the course of evolutionary history (Darwin, 1871). Investigators have recently garnered encouraging evidence that these secondary sex characteristics serve as aggressive display features in intermale competition (Dixson & Vasey, 2012; Hodges-Simeon et al., 2014). This implies that physical aggression and male secondary sex development have coevolved as part of an adaptive complex (Archer, 2009). Our results cohere with sexual selection theory and suggest that aggressive-antisocial dispositions in childhood may serve as preparation for future male-male competition in young adulthood, when physical strength is at its peak.
Footnotes
Declaration of Conflicting Interests
The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article.
Funding
Research reported in this manuscript was supported by the following grants from the National Institutes of Health: DA 013240, DA 05147, and AA 09367.