Four successful tests of the Cognitive Differentiation–Integration Effort hypothesis

Intelligence 41 (2013) 832–842

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Intelligence

Four successful tests of the Cognitive Differentiation–Integration Effort hypothesis Michael A. Woodley a, b,⁎, Aurelio José Figueredo c, Sacha D. Brown c, Kari C. Ross c a

Department of Psychology, Umeå University, Umeå, Sweden Center Leo Apostel for Interdisciplinary Studies, Vrije Universiteit Brussel, Brussels, Belgium Graduate Program in Ethology and Evolutionary Psychology, Department of Psychology, School of Mind, Brain, and Behavior, College of Science, University of Arizona Tucson, AZ, USA b c

a r t i c l e

i n f o

Article history: Received 19 June 2012 Received in revised form 10 December 2012 Accepted 10 February 2013 Available online 26 March 2013 Keywords: Cognitive differentiation Cognitive integration Lynn–Flynn effect General intelligence Life history strategy

a b s t r a c t The Cognitive Differentiation–Integration Effort (CD–IE) hypothesis predicts that the dimension of life history speed (K) regulates the strength of the correlation among cognitive abilities, such that individuals with higher K exhibit more weakly integrated abilities than those with lower K. It is predicted that this effect takes place independently of the level of g owing to the absence of an individual differences level correlation between K and g. CD–IE was examined using two student samples: (1) an all female sample (N =121), using the ALHB as a measure of K and the two SILS subtests of g; and (2) a combined male and female sample (N= 346), using a shorter threeindicator (“Trifecta”) measure of K, a general creativity measure comprised of two subscales (writing and drawing “creative performance”), and the APM-18 measure of fluid cognition. A third, population-representative sample was obtained from the NLSY (N =11,907). A K-Factor was constructed from convergent measures of subjective well-being, sociability, interpersonal trust, internal locus of control, and delay of gratification, and a g-factor was constructed from the 10 subscales of the ASVAB. A fourth sample, addressing the question of ethnic differences was collected encompassing eight different ethnic groups with a combined 107 specific ability correlations with g. An aggregate K-Factor was constructed for this sample based on convergent population-level indicators of longevity, total fertility rates and infant mortality. Utilizing the Continuous Parameter Estimation Model, in student sample 1 a significant CD–IE effect was found on the SILS Abstract subtest (β =−.215), but not on the SILS Verbal subtest (β =.069). In student sample 2, CD–IE was observed on the general creativity measure (β=−.127), but not on the fluid cognitive ability measure (β =−.057). Significant effects were also observed on both the written and drawing creative output subscales (β =−.189 and −.183 respectively). In sample 3 (the NLSY), generally statistically significant but small-magnitude CD–IE effects were observed among all 10 ASVAB subtests (mean effect size β =−.032). In sample four, a near-significant CD–IE effect was detected (β =−.167). Controlling for subtest skew reduces the mean effect sizes across individual differences samples (β=−.071 in the student samples, −.027 in the NLSY), but boosted it to significance in the ethnic differences sample (β=−.179). Controlling for the skew of residuals reversed the signs of the CD–IE effects on the ASVAB Words and Comprehension subscales, and also on the SILS Verbal subscale, but amplified the magnitudes of the mean effects in the student and NLSY samples (β =−.036 and −.131), while reducing the effect size slightly in the ethnic-differences sample (β= −.172). In the individual differences samples, these effects were demonstrated to be unconfounded with sex of respondent and also unrelated to the Jensen effect. The apparent independence of the effect from both level of g and subtest g-loading suggests intriguing commonalities with the Lynn–Flynn effect. © 2013 Elsevier Inc. All rights reserved.

⁎ Corresponding author. Department of Psychology, Umeå University, Umeå, Sweden. E-mail address: [email protected] (M.A. Woodley). 0160-2896/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.intell.2013.02.002

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1. Introduction Life history theory describes the ways in which different organisms allocate bioenergetic and material resources among various components of fitness, given differing environmental constraints on survival and reproduction; life history strategies range from those based on maximizing reproductive outcomes as a buffer against environmental unpredictability, to those based on maximizing longevity and parenting, so as to enhance the quality and competitiveness of organisms in stable environments (Ellis, Figueredo, Brumbach, & Schlomer, 2009). Life history strategies have traditionally been characterized by the dimension of speed, with r-selected (r denoting the maximum population reproductive rate) and K-selected (which saturate their environment to carrying capacity or K) representing opposite poles of a continuum from fast (e.g., rabbits) to slow (e.g., elephants). A high mating effort strategy represents a fast life history owing to the associations between enhanced reproduction, rapid maturation and diminished longevity, whereas the high somatic and parental effort strategy represents a slow life history, owing to its theoretical and empirical associations with slower ontogenetic development and enhanced longevity (Figueredo & Rushton, 2009). Rushton (1985, 2000, 2004) proposed the differential-K model, based on the idea that hierarchically organized and heritable individual and group differences variables, such as personality, health and intelligence should share a common source of variance stemming from an overarching life history strategy, which coordinates tradeoffs among its constituent sources of individual and group differences. Consistent with predictions from the model, measures of personality, general health and behavioral life history measures exhibit strong genetic correlations at the individual differences' level, giving rise to a Super-K Factor of individual differences in life history speed (Figueredo, Cabeza de Baca, & Woodley, in press; Figueredo, Vásquez, et al., 2005). With regard to the general factor of intelligence (g) and life history speed (K), the relationships are not as clear-cut. At the group differences level (i.e. at the level of interethnic or international comparisons) there exists a strong correlation between the two, such that high-K populations typically exhibit higher levels of g than low-K populations (Meisenberg & Woodley, in press; Templer, 2008). At the individual differences level, however, the correlation is absent, with a recent meta-analysis (Woodley, 2011a) involving 10 studies and 12 effect sizes finding a non-significant positive population correlation between the two of ρ = .023 (N = 2056). Furthermore the same study revealed that the correlations between g and K are significantly heterogeneous. A potential solution to this “Rushton Paradox” involves the idea that individual differences in g and K are controlled by largely separate sources of genetic variance, which are currently in linkage equilibrium within populations under rough conditions of stabilizing selection for both traits (Woodley, 2011a), however at the level of ethnic groups, there has historically been a strong and parallel directional selective pressure for both traits in the climatically colder regions of the world, hence the cross-population cline of g is extrinsically correlated with the cline of K. Evidence for this comes from the study of Meisenberg and Woodley (in press), which found that racial composition accounts for the overwhelming preponderance

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of the cross-country variance in both g and K. Due to the number of large-scale migrations and virtual population replacements that occurred starting in the early modern era (ca. 1500 AD), this observation probably reflects the selective pressures ancestrally present in the ultimate regional origins of each subpopulation rather than the climatic and ecological conditions that they currently experience. 1.1. The Cognitive Differentiation–Integration Effort hypothesis To account for the anomaly presented to life history theory in relation to variation in general intelligence at the individual differences level (as opposed to population level) by the Rushton Paradox, a new model was developed in the form of the Cognitive Differentiation–Integration Effort (CD–IE) hypothesis, which posits that while there is no “main effect” of K on g, there might nonetheless be an effect of K on the structure of the mental abilities constituting g. It is argued that individuals with slow life histories might develop a more specialized cognitive ability profile characterized by differentially developed abilities, which exhibit a weaker positive manifold of correlations. Slow life history (high-K) populations tend to exist at or near the carrying capacity of their environments, hence the individuals within them need to be competitive. According to CD–IE, one way of coping with increased intraspecific competition would be to become a cognitive specialist and seek out underexploited socio-economic niches. The resultant “cognitive polymorphism” enhances the division of labor within a high-K population, which in turn raises the carrying capacity further. This prediction is also consistent with the “coral reef model” of personality diversification, as derived from selectionist models of evolution and development (e.g. Figueredo, Gladden, Vásquez, Wolf, & Jones, 2009; Figueredo, Jacobs, Burger, Gladden, & Olderbak, 2011; Figueredo, Sefcek, et al., 2005; Figueredo, Vásquez, & Sefcek, 2008; Figueredo et al., 2010). Possessing highly integrated cognitive abilities, on the other hand, might provide an advantage to fast life history (low-K) individuals coping with either unpredictable natural as well as social environments (such as those that might be encountered in a short term mating market), as integrated mental abilities allow for efficient contingent switching between socio-cultural niches. The CD–IE hypothesis does not require an intrinsic association between variables that are indicative of genetic quality, such as those comprising Miller's (2000a, 2000b) F (“General Fitness”) factor on which the g-factor loads (Arden, Gottfredson, & Miller, 2009), and variables associated with life history strategy. The former have uniformly positive effects on fitness across environments, and are believed to be maintained by mutation–selection balance, whereas the latter typically have no homogeneous or cross-situationally consistent effect on fitness, or rather their effects are contextspecific (having low-K is advantageous in unstable environments but disadvantageous in more stable ones and vice versa). Therefore individual differences in life history traits, such as personality are believed to be maintained instead by balancing selection operating via a variety of routs and on various genetic polymorphisms (Del Giudice, 2012; Figueredo & Gladden, 2007; Penke, Denissen, & Miller, 2007a, 2007b). This suggests that there are two sources of genetic variance in human intelligence, one related to genetic quality that

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controls the “vertical” variance in the trait, meaning the levels of g which might correspond to the sorts of physiological neuronal properties that present pleiotropic mutations with large target sizes, such as plasticity and processing efficiency (e.g. Garlick, 2002; Jensen, 2006); and one which controls the “horizontal” variance in the trait, meaning the degree to which separate brain systems associated with specific cognitive abilities are bought into or out of close correlation with one another based on life history strategy (Woodley, 2011a). The CD–IE hypothesis also provides a potential explanation for the Lynn–Flynn effect, which based on some studies appears to be associated with both a decline in the strength of g over time and large gains in specific abilities, such as fluid rather than crystallized ability (see Woodley (2012a) for an overview). This is based on the observation that the demographic transition experienced by many Western nations in the last 100–200 years is a result of slowing life history in response to factors that would have reduced the levels of instability in the environment (i.e. harshness and unpredictability; Ellis, Figueredo, Brumbach, & Schlomer, 2009), such as improvements in nutrition, the development of standardized education, diminution of pathogen stress and decreasing household size (fewer offspring permitting more parenting effort per offspring; Mace, 2000). Life history therefore provides a meta-theoretical framework within which the influences of a variety of proposed causes of the Lynn–Flynn effect can be better understood. Consistent with this model, an aggregate of measures of impulse control (homicide rates) and somatic capital investment (literacy rates) has been found to strongly predict temporal variation in the Lynn–Flynn effect (Woodley, 2012b). This model of the Lynn–Flynn effect also partially accounts for the tendency of national IQs to load on cross-national K-Factors, as in addition to the co-selection model discussed previously, national IQs might substantively capture differential rates of secular gains between nations — hence, with respect to non-g sources of cross-national variance in IQ, cognitive differentiation effort may be contributing to the shared clinality between national IQs and K. This is another potential resolution to the ‘Rushton paradox’ (Meisenberg & Woodley, in press; Woodley, 2011b). Another implication of this model is that the so-called Great Divergence (Clark, 2007) between more-developed countries (MDCs) and less-developed countries (LDCs) in economic productivity might be more parsimoniously attributable to differences in mean life history speed than in mean general intelligence (Figueredo, 2009; Woodley, 2012b), with the slower life-history MDCs, representing cooperating teams of cognitive specialists, economically outcompeting the faster life-history LDCs, representing disorganized groups of cognitive generalists, by the straightforward and inevitable operation of Ricardo's (1891) Law of Comparative Advantage. Consistent with this model is the finding that GDP per capita relates more strongly to a National-K Factor constructed from convergent measures of social, sexual and economic behavior, than it does to national IQ (Meisenberg & Woodley, in press). Indirect support for the CD–IE hypothesis comes from four major converging lines of evidence: 1. Some studies observe that the g-factor weakens as a function of level of Neuroticism, such that emotionally stable individuals exhibit weaker g-factors than neurotic ones

(Austin, Deary, & Gibson, 1997; Austin, Hofer, Deary, & Eber, 2000; Austin et al., 2002; Eysenck & White, 1964). As a personality indicator Neuroticism is a component of Super-K (Figueredo, Sefcek, et al., 2005; Figueredo, Vásquez, et al., 2005) hence this could be a CD–IE effect. 2. Those with autistic-like personalities exhibit a distinct ability profile characterized by a tilt towards visuo-spatial and away from verbal ability. Autistic-like personality may be linked with high K (Del Giudice, Angeleri, Brizio, & Elena, 2010); hence this effect may result from CD–IE. 3. Higher K populations such as Ashkenazi Jews and East Asians (MacDonald, 1994; Rushton, 2000) exhibit more strongly tilted ability profiles relative to lower K populations. In the case of the former the ability tilt is towards verbal and quantitative and away from visuo-spatial ability (Cochran, Hardy, & Harpending, 2006; Lynn, 2011), whereas in the case of the latter, the tilt is towards visuo-spatial and away from verbal ability (Lynn, 1987). This could indicate the operation of an inter-population CD–IE effect. 4. The study of Draper and Harpending (1982), which was based on Carlsmith's (1964), revealed that among males taking the SAT, tilt towards verbal ability was predicted by father absence, whereas tilt towards mathematical ability was predicted by father presence. They interpret this finding in the context of a life history model predicated on the assumption that early perception of opportunities for stable pair-bonding encourages both slower life history and specialism with respect to resource acquiring abilities among males. Early perception of a lack of opportunities for stable pair-bonding encourages males to develop faster life histories and also interests in the sorts of interpersonal and competitive manipulative skills which relate more strongly to the relatively more social verbal ability. As the SAT Verbal subtest is substantively more g-loaded than the Math subtest (e.g. Jensen, 1998), this tilt would be consistent with the prediction from CD–IE that those with relatively faster life histories tend towards stronger g, relative to those with slow life histories. Thus far, CD–IE had not been directly tested; however, a major prediction is that individuals and groups with slow life history speeds should exhibit more differentiated abilities coupled with lower g variance than those with fast life history speeds (Woodley, 2011a). This prediction will here be tested using individual differences samples of convenience (college students) in addition to samples that are more representative of the general population of young people in the USA (the National Longitudinal Survey of Youth) and also samples that are representative of diverse ethnic groups from various countries. 2. Methods The following subsections are broken down by sample, presenting the measures administered and the measurement models estimated for the constructed latent variables. This is because these vary by specific sample and analysis. A variety of different measures of the theoretically-specified constructs of interest were used to show the generality of the CD–IE effect, in that detecting it was not dependent upon the use of

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any specific set of measures. As the samples used varied in size considerably, from a little over N = 100 to well over N = 10,000, unit-weighted factor scoring was applied, which avoided the well-documented sample-specificity of factor scoring coefficients produced by standard errors of inconsistent magnitudes across the different samples (Gorsuch, 1983). The unit-weighted factor scores were estimated as the averaged z-score of the non-missing indicators for each case, hence the aggregate was always estimable despite occasional missing data (Figueredo, McKnight, McKnight, & Sidani, 2000; McKnight, McKnight, Sidani, & Figueredo, 2007). Cases with occasional missing data were therefore included in the final analyses. The unit-weighted factor structures presented are no more than the part-whole correlations between the latent composites constructed and each of their component indicator measures. Although some of the samples were of sufficient size to reliably estimate differentially-weighted factor scores and factor loadings, this one method was applied throughout for the sake of consistency. As this is a set of secondary analyses, details of the data collection procedures are cited from the original sources rather than reproduced in their entirety here. 2.1. Student samples Two student samples were collected from a Southwestern US University. Student Sample 1 was comprised of 121 female undergraduates. For this sample all data were collected inperson (see Ross, 2010 for complete description of procedures). Student Sample 2 was a mixed-sex sample of 346 undergraduates. For this sample data collection was partially online and partially in person (see Brown, 2011 for complete description of procedures). 2.1.1. Analysis 1 Data on intelligence were collected using the Shipley Institute of Living Scales (SILS), which are decomposable into a fluid cognition analogue abstract factor and a crystallized intelligence analogue vocabulary factor. Data on life history speed were obtained using the 199-item Arizona Life History Battery (Figueredo, 2007). In this study, an attempt was made to detect CD–IE effects on the correlation between the two SILS subtests and their unit-weighted g factor as a function of K. Tables 1 and 2 present the unit-weighted factor structures of the g and K factors constructed, respectively, for Analysis 1.

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Table 2 Unit-weighted factor loadings for the indicators of the Slow Life History (K) factor derived from the ALHB. K.ALHB (“Slow LH”)

N

r(K)

Mini-K short form Insight, planning, and control Parental investment Family support Friends' support Partner attachment General altruism Religiosity

110 110 110 110 110 110 110 110

.689⁎ .493⁎ .597⁎ .590⁎ .289⁎ .619⁎ .340⁎ .443⁎

⁎ p ≤ .05.

(Gladden, Figueredo, & Jacobs, 2008). This was complimented with a broad creativity measure in the form of panel-rated creative output. This measure involved the use of three tasks, which tap different aspects of creativity. The three tasks were verbal writing, abstract drawing and representational drawing (Miller & Tal, 2007). Responses were scored on a five point Likert scale (1 = not creative at all–5 = very creative). Panel ratings (involving three raters) of the various task sets were averaged to determine overall creative output. In Analysis 2, life history speed was assessed using three convergent indicators of Slow Life History strategy (the “Trifecta”): (1) the Mini-K (Figueredo, 2007), a 20-item short form of the ALHB; (2) the 22-item HKSS (Giosan, 2006), or High-K Strategy Scale; and (3) the SF-36 (Ware & Sherbourne, 1992), the 36-item MOS short form health survey. An attempt was made to detect CD–IE effects on the correlation between the fluid cognitive ability measure (APM-18) and the general creativity factor (creative drawing + creative writing output) with their unit-weight cognitive performance or P-Factor. Tables 3 and 4 present the unit-weighted factor structures of the P and K factors constructed, respectively, for Analysis 2.

2.1.2. Analysis 2 Data on intelligence were collected using the APM-18, an 18-item short form of the Raven's Advanced Progressive Matrices, which is a measure of fluid cognitive ability

2.1.3. Analysis 3 Analysis 3 employed the same sample as was employed in Analysis 2. In Analysis 3, however, an attempt was made to detect CD–IE on the correlation between the two creativity subtests, as indicated by panel-rated “creative performance” on writing and drawing tasks, respectively, and their unit weighted general creativity or C-Factor. In Analysis 3, as in Analysis 2, life history speed was assessed using the “Trifecta” measure of Slow Life History strategy. Table 5 presents the unit-weighted factor structure of the C-Factor constructed for Analysis 3, the unit-weighted factor structure of the K-Factor being the same as for Analysis 2.

Table 1 Unit-weighted factor loadings for the indicators of the general intelligence factor derived from the Shipley Institute of Living Scales (g.SILS).

Table 3 Unit-weighted factor loadings for the indicators of the cognitive performance (P) factor.

g.SILS (“General Intelligence”)

N

r(g.SILS)

P (“Cognitive Performance”)

N

r(P)

Vocabulary Abstraction

121 121

.618⁎ .640⁎

Fluid cognitive ability (“Intelligence”) General creativity (“Creativity”)

346 346

.960⁎ .407⁎

⁎ p ≤ .05.

⁎ p ≤ .05.

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Table 4 Unit-weighted factor loadings for the indicators of the Slow Life History (K) factor derived from the “Trifecta” (TRI). K.TRI (“Slow LH”)

N

r(K)

Mini-K HKSS SF-36

346 346 346

.776⁎ .826⁎ .729⁎

⁎ p ≤ .05.

2.2. Population-representative sample Analyses 4 through 6 used a third sample of 11,907 young adults that was sourced from the publicly available National Longitudinal Survey of Youth (NLSY79). 2.2.1. Analysis 4 Intelligence was assessed in the NSLY using the Armed Services Vocational Aptitude Battery (ASVAB), which is comprised of 10 subtests: Science Knowledge, Arithmetic Reasoning, Word Knowledge, Comprehension, Numerical Speed, Coding Speed, Auto-Shop Information, Mathematical Knowledge, Mechanical Comprehension and Electronics Information. A K-Factor was constructed from the following psychosocial indicators of “Slow Life History” that had been sampled in the NLSY: (1) Self-Esteem, measured using the ten-item Rosenberg scale, averaged between 1980 and 1987 (Rosenberg, 1965); (2) Happiness or Subjective Well-Being, which was simply reverse-scored depression, as measured using the seven-item Center for Epidemiological Studies depression scale, averaged between 1992 and on reassessment of individuals over 40; (3) Delay of Gratification, which were “time preferences” assessed using interest rates on deferred receipt of $1000 for a month and for a year, administered in 2006; (4) Sociability, which was based on a question administered in 1985 asking the adult respondents to rate their shyness vs. outgoingness; (5) Trust, which is based on a measure of the degree to which the respondent often trusts others, administered in 2008; (6) Self-Concept, using the seven-item Pearlin Mastery Scale (Pearlin, Lieberman, Menaghan, & Mullan, 1981) and (7) the four-item Rotter scale, which measures Internal Locus Of Control (Rotter, 1966). Cronbach's alphas have been calculated for all scales exhibiting four or more items elsewhere, and range from poor in the case of the Rotter scale (α= .35; Kahn, 2002), to good in the case of Pearlin mastery, CES Depression and Rosenberg self-esteem scales (α >.80 (Danziger, Anant, & Browning, 2004; Dooley & Prause, 2004; Kahn, 2002)). Both self-esteem and depression load oppositely on the General Factor of Personality (Rushton & Irwing, 2011), which in turn exhibits genetic correlations with the K factor (Figueredo & Rushton, 2009; Figueredo, Vásquez, Brumbach, & Schneider, 2004). Time preferences are a very fundamental Table 5 Unit-weighted factor loadings for the indicators of the general creativity (Creativity) C-factor. C (“Creativity”)

N

r(C)

Creative drawing Creative writing

346 346

.846⁎ .846⁎

⁎ p ≤ .05.

dimension of human life history (Figueredo et al., 2004). Sociability and Trust are also part of a prosocial orientation, which is fundamentally latent in the GFP (Rushton & Irwing, 2011). Self-concept or personal mastery is a measure of the degree to which individuals feel that they are in control of their lives and the forces that influence them. It is a similar measure to locus of control which has also been found to relate directly to the GFP such that internal locus of control is associated with the high pole, and external locus of control is associated with the low pole (Erdle & Rushton, 2010). This Slow Life History factor therefore functions as an adequate life history speed measure in lieu of a full NLSY K-Factor. (For a more detailed review of the relevant literature regarding the use of psychosocial and conative indicators as measures of K see Figueredo et al. (in press)). In Analysis 4, an attempt was made to detect CD–IE effects on the correlations between each of the cognitive ability subtests and the g-Factor among them. Tables 6 and 7 present the unit-weighted factor structures of the g and K-Factors constructed, respectively, for Analysis 4.

2.2.2. Analysis 5 The first component of Analysis 5, involved a determination of the degree to which the CD–IE effects were influenced by subtest skew. Recent research (Murray, Dixon, Johnson, & Bouchard, 2011) has highlighted the importance of controlling for subtest skew in studies examining similar “manifold inconstancy” type phenomena like Spearman's Law of Diminishing Returns (SLODR). It is argued that even small deviations from subtest normality can induce apparent weakening or strengthening of the subtest/latent factor correlation. The specific algorithms applied for controlling skew are discussed below under Statistical methods. The second component of this analysis involved investigating Sex of Respondent as a potentially moderating variable. In addition to testing the main effects of both K and Sex of Respondent, CPEM was used to generate a parameter estimate of the K ∗ Sex interaction (modeling a sexually dimorphic CD–IE effect) for all subtests in the individual differences samples. In all cases, the CD–IE effects were robust and the interactions with sex were non-significant, so the results of this additional analysis are not reported.

Table 6 Unit-weighted factor loadings for the indicators of the general intelligence factor derived from the Armed Services Vocational Aptitude Battery (g.ASVAB). g.ASVAB (“General Intelligence”)

N

r(g.ASVAB)

Science Arithmetic Words Comprehension Numerical speed Coding speed Automotive information Mathematical knowledge Mechanical comprehension Electronics information

11,907 11,907 11,907 11,907 11,907 11,907 11,907 11,907 11,907 11,907

.886⁎ .873⁎ .892⁎ .843⁎ .751⁎ .699⁎ .746⁎ .826⁎ .810⁎ .836⁎

⁎ p ≤ .05.

M.A. Woodley et al. / Intelligence 41 (2013) 832–842 Table 7 Unit-weighted factor loadings for the indicators of the Slow Life History (K) factor derived from the NLSY. K.NLSY (“Slow LH”)

N

r(K)

Self-esteem Happiness (reversed depression) Delay of gratification Sociability Trust Rotter self-concept Pearlin locus of control

9677 8458 6910 10,279 7272 11,779 8452

.535⁎ .502⁎ .411⁎ .450⁎ .520⁎ .584⁎ .538⁎

⁎ p ≤ .05.

2.2.3. Analysis 6. Ethnic differences in CD–IE In his comprehensive overview of ethnic differences in intelligence, Lynn (2006) lists several hundred studies conducted on a variety of racial and ethnic groups. Also listed are estimates of three major abilities measured in these studies (Reasoning, Verbal and Visuospatial), in addition to full IQ and g measures. From this list it was possible to isolate 19 reasoning ability correlations with g, 36 verbal ability correlations with g and 32 visuospatial ability correlations with g, which when combined, yielded 87 specific ability correlations with g. Combining these data with studies conducted on Jewish populations from Lynn (2011) increased the number of specific ability correlations with g to 107. These data were available for representative samples of eight ethnic groups (Amerindians, Arctic peoples, Australian Aboriginies, Ashkenazi Jews, East Asians, Hispanics, Pacific Islanders and Sub-Saharan Africans). Unit-weighted g scores were simply derived by averaging across abilities. Direct measures of the K-Factor were not available for each sample, therefore population-level K-Factor scores were created from a composite of three life history indicators; infant mortality (reversed), total fertility (reversed) and longevity. In instances where the ethnicities were representative of specific countries (i.e. Japanese, Taiwanese etc.), country level statistics were used from the CIA World Factbook 2012 (CIA, 2011). In instances where the ethnicities were a minority within a particular country, specific data sources were used, such as http://aihw.gov.au in the case of Australian Aboriginies, http:// stats.gov.nz in the case of New Zealand Maoris, Hamilton (2011) and http://minorityhealth.hhs.gov in the case of US resident Hispanics and Amerindians/Arctic peoples and http:// cbs.gov.il in the case of North American Jews (the data used are from Israel whose Jewish population is directly comparable in terms of the level of development to the North American Jewish population). The three measures used here are reasonable indicators of cross-population differences in K, as they capture differences in mating effort (total fertility rate), environmental and social harshness (infant mortality) and somatic effort (longevity) respectively. Furthermore they have been demonstrated to share a common variance with a cross-national K-Super factor incorporating additional life history indicators (Templer, 2008; Woodley, 2011b). Among the observations used here, they exhibit a high mean correlation (r ¼ :675, N= 107). As aggregated K-Factor scores were employed the sample sizes could not be used to delimit N. Instead the number of specific ability correlations with g was used to delimit N (107).

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2.2.4. Analysis 7 In Analysis 7, we attempted to determine whether or not the CD–IE effects detectable in the individual differences samples are associated with the Jensen effect. The Jensen effect describes the tendency for the vector (or rank) of a tests g-loading to be significantly and positively associated with the effect magnitude vector of other (typically biological) variables (Rushton, 1998), including reaction times, brain pH, evoked potentials (Jensen, 1998, 2006), Black–White cognitive ability differences, inbreeding depression scores, subtest heritabilities (Rushton & Jensen, 2010), brain size (Rushton & Ankney, 2009), fluctuating asymmetry (Prokosch, Yeo, & Miller, 2005) and dysgenic fertility (Woodley & Meisenberg, in press-a). By contrast, some phenomena are not Jensen effects, in that they are either most pronounced on tests exhibiting the lowest g-loadings, or show no significant association with test g-loadings. Examples would include SLODR (Jensen, 2003), the Lynn–Flynn effect (Rushton, 1999; Rushton & Jensen, 2010; te Nijenhuis, in press) and IQ gains due to retesting (te Nijenhuis, van Vianen, & van der Flier, 2007). It is predicted that CD–IE tradeoffs occur primarily on non-g sources of variance hence the effect should not be associated with a Jensen effect. To determine if this is the case the vector of the unit weighted factor score derived g-loadings for each of the subtests used in each of the individual differences level studies (both student and NLSY respondents) were correlated with the vector of their respective CD–IE effect magnitudes. 2.3. Statistical methods A recently developed analytical tool was employed for these analyses: the Continuous Parameter Estimation Model (Gorsuch, 2005). This model permits the change in the strength of the correlation between two variables (such as intelligence subtest and g-factor scores) to be determined throughout the full range of a third variable (such as K). Pearson's Product-moment Correlation Coefficient is defined as the mean cross-product of the standardized scores: Σ(Zx ∗ Zy) / N. Thus, for all intellectual and creative performance domains sampled, correlation coefficients were estimated at the individual level by taking the cross-product of the standardized subtest scores (Zs) and the standardized unit-weighted factor scores (Zf) for each domain: Zs ∗ Zf. By definition, the group mean of these individual-level crossproducts inevitably provides the correlation coefficient for each group under consideration. Therefore, the cross-product itself (Zs ∗ Zf) can be used as the individual-level “raw score” in CPEM to estimate the varying amount of integration or differentiation “effort” in each group. Thus, computing and comparing the group means of these cross-products using ANOVA automatically calculates and compares group-level Pearson Correlation Coefficients. This tests the degree to which the strength of this relationship varies between any discrete groups. When using more traditional methods for identifying changes in the strength of the correlation coefficient between groups, it is necessary to acquire samples of at least 75–100 respondents in each group, so as to stabilize the correlation coefficients for comparison. However, as a graded method, CPEM does not require the polytomization of continuous distribution, by ill-advised tactics such as the median split, which has long been known

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to be problematic (Cohen & Cohen, 1983; MacCallum, Zhang, Preacher, & Rucker, 2002). Skew is defined as the mean cube of the standardized scores of any variable: Σ(Zx 3)/N. Skew was therefore operationalized using CPEM by statistically controlling the CD–IE effects for any effects of subtest skew by including the cube of the standardized subtest scores (Zs 3) as an additional predictor in the regression equations predicting the individuallevel cross-products. In addition to controlling for subtest skew, we also controlled for the differences in skew between the subtest and the latent factor. This was done by taking the cube of the difference between the corresponding standardized scores: (Zs − Zf) 3. This provides an estimate of the skew of the residuals. Contrary to popular misconception, parametric statistics does not assume that the raw data need be normally distributed: the normality assumption applies exclusively to the residuals. Otherwise, methods such as ANOVA would automatically violate normality by employing categorical (non-normal) predictors. For the residuals to be normally distributed, it is only necessary that the distributions of the predictor(s) and criterion be of the same form (isomorphic), meaning that if the former is skewed then the latter should be similarly skewed and subtracting the weighted predictor(s) from the criterion score will leave nothing but a symmetrical pattern of random errors, centered around zero. This makes sense if one variable, which happens to be skewed, is in fact causally influencing the other. The skew of the residuals therefore reflects any mismatch between the distribution forms of the criterion with respect to the predictor(s), and is probably a more important indicator of a potential problem than the separate skew of either of the two raw distributions (in the present case, those of the subtest and factor scores). 3. Results Each set of results will be presented as discrete tables, again presented in the order of the tests conducted on the different samples. Table 8 indicates the presence of statistically significant CD–IE effects on the SILS Abstract subtest, which persist if the skew of the residuals is controlled for, but not when subtest skew is controlled. A negative sign indicates that the subtest/g correlation weakens as the level of K increases consistent with CD–IE, a positive sign indicates the opposite. No CD–IE effects were detected on the SILS Verbal subtest, however the effect reverses its sign and trends in the expected direction when the skew of the residuals is controlled. These null-results could be a consequence of insufficient statistical power due to a low sample size. Table 9 indicates that in Analysis 2, a statistically significant CD–IE effect exists on the general creativity measure,

which persists when both subtest skew and the skew of the residuals are controlled. No CD–IE effect was detected on the fluid cognitive ability measure despite the effect trending in the expected direction in all cases. Table 10 indicates the presence of statistically significant CD–IE effects on both creative drawing and creative writing, which persist even when controls for skew and the skew of the residuals are used. Table 11 presents the results of the tests conducted in Analysis 4 on the population-representative sample provided by the NLSY data. A potential concern in these data was that the “Slow Life History” (K) factor was found to correlate significantly with the general cognitive ability (g) factor of the ASVAB (r = .399, N = 10767). CD–IE is predicted to occur independently of the level of g. This was not a problem in the student samples used in Studies 1 through 3 as none of the cognitive ability indicators correlated significantly with K. To control for this potential confound in the NLSY data, we residualized the Slow Life History scores for any shared variance with g, thus creating an adjusted index of Slow Life History that was orthogonal to g, and therefore amenable to testing. Table 11 indicates the presence of either unambiguously or borderline statistically significant CD–IE effects on all ASVAB subtests. These effects are all negative, which means that they trend in the expected direction. Eight of the ten effects were diminished in magnitude, but retained their significance when the subtest skew was controlled. Controlling for the skew of the residuals enhanced the effect on eight of the ten subtests and also reversed the sign of the effect on the Words and Comprehension subtests. Table 12 indicates the presence of a near significant CD–IE effect in the ethnic group differences data, which increases in magnitude and becomes significant when the subtest skew is controlled. When the skew of the residuals is controlled, the effect reduces in magnitude slightly and returns to being near significant. It is argued that when interpreting data at high levels of aggregation, null-hypothesis significance testing should be substituted for tests of robustness and stability (Rindermann, 2008). By the inductive generalization criterion, the population-level effects presented here should therefore be considered potentially meaningful given their persistence to the skewness controls. In this analysis the aggregate level, K and g are highly correlated (r = .697, N = 107). Despite this, so as to keep this analysis consistent with the individual differences analyses, the K aggregate was residualized for shared variance with g. Finally, the results of Analysis 7 indicate that the vector correlation between g-loadings and non-skew controlled CD-IE effect magnitudes (i.e where CD-IE effects were scaled

Table 8 Testing for CD–IE in the correlation between the SILS subfactors and g in relation to a K-Factor derived from the ALHB; β(K)-β(Zs3) corresponds to the CD–IE effects controlled for subtest skew only and β(K)-β(Zs−Zf)3 corresponds to the CD–IE effects controlled for differences in subtest skew. H(H0) corresponds to the F-statistic, p(H0) corresponds to the significance level. K.ALHB (“Slow LH”)

β(K)

β(K)-β(Zs3)

β(K)-β(Zs − Zf)3

z(g.SILS) ∗ z(Verbal) F(H0), p(H0) z(g.SILS) ∗ z(Abstract) F(H0), p(H0)

.069 (.125) F(1,108)=.57, p=.450 −.215 (.094) F(1,108) = 5.75, p = .018

.036 (.060) F(1,107) = 199.18, p = .417 −.070 (.075) F(1,107) = 44.20, p = .327

−.015 (.116) F(1,107) = 13.67, p = .861 −.203 (.082) F = (1,107) = 22.89, p = .011

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Table 9 Testing for CD–IE in the correlation between general creativity and fluid cognitive ability and the p-Factor in relation to K; β(K)-β(Zs3) corresponds to the CD–IE effects controlled for subtest skew only and β(K)-β(Zs–Zf)3 corresponds to the CD–IE effects controlled for differences in subtest skew. H(H0) corresponds to the F-statistic, p(H0) corresponds to the significance level. K.TRI (“Slow LH”)

β(K)

β(K)-β(Zs3)

β(K)-β(Zs − Zf)3

z(p) ∗ z(Fluid cognitive ability) F(H0), p(H0) z(p) ∗ z(General creativity) F(H0), p(H0)

−.057 (.073) F(1,344) = 1.11, p = .294 −.127 (.109) F(1,344) = 5.67, p = .018

−.038 (.071) F(1,343) = 14.71, p = .465 −.063 (.052) F(1,343) = 592.30, p = .014

−.055 (.074) F(1,343) = .81, p = .308 −.135 (.106) F(1,343) = 13.91, p = .010

positively, and reverse effects were scaled negatively) across all of the individual differences samples (both students and NLSY) r = −.120. In the case of the subtest skew controlled effects, r = .100, and in the case of the skew of residuals controlled effects, r = -.275. In no cases did these vector correlations reach significance when this parameter was computed using Spearman's Rank. These findings strongly indicate that the CD–IE effect is not a Jensen effect, as subtests with higher g-loadings are not significantly associated with bigger CD–IE effects relative to subtests with lower g-loadings when this parameter is computed using the Spearman's Rank Order correlation. Furthermore, what directional tendency there is in the vector correlations suggests an overall anti-Jensen effect. This tentatively indicates that CD–IE is largely “hollow” in terms of g, and can therefore be placed into the same class of psychometric phenomena as the Lynn–Flynn effect and IQ gains via the retesting effect. 4. Discussion To sum up, in the student “convenience” samples, statistically significant CD–IE effects were detected on the SILS Abstract factor, the general creativity measure and its two constituent measures (creative writing and drawing output). No CD–IE effects were detectable on the SILS Verbal factor or the measure of Fluid cognition. Statistically significant and consistent CD–IE effects were also detected on all ASVAB subtests. Furthermore, while not statistically significant in two out of three cases, CD–IE effects of a magnitude and directionality comparable to those found in the individual differences samples were extracted from the ethnic-differences data. Population stratification and a history of parallel directional selection increases the magnitude between groups of correlations that are substantially smaller at the individual differences level — such as in the case of g and K (Meisenberg & Woodley, in press). One possible reason for the relatively small magnitudes of the ethnic-differences effects is that the

residualization step was excessively strict. Some of the shared variance between K and g might have been legitimate, hence controlling for this could have reduced the magnitude of the ‘real’ CD–IE effects in this sample. It was found that the unresidualized Slow Life History factor extracted from the NLSY correlated positively and significantly with the ASVAB g (r = .399, N= 10767). As a higher-order life history construct, at the individual differences level K does not typically correlate nearly as strongly with g (Woodley, 2011a), whereas lower-order life history constructs (such as longevity and health) do (e.g., Rushton, 2004). Brunswick symmetry suggests that in comparing between correlations, it is necessary to control for the respective levels of aggregation of the two constructs involved (i.e., only compare higher-order latent factors with other higher-order latent factors, at the same levels within their respective hierarchies; Brunswick, 1952). This suggests that the finding of either no or only small positive correlations between g and K is likely a more reliable indicator of the real relationship between intelligence and life history than are positive correlations between indicators of life history exhibiting a more restricted nomological net (such as the NLSY Slow Life History factor) and g. The restricted nomological net of our Slow Life History factor may also have attenuated the CD–IE effects in the NLSY sample, as the effects were bigger in the student samples where broader measures of K were employed (mean effect size of β = − .032 in the NLSY vs. − .117 in the student samples). Another factor that could have reduced the NLSY CD–IE effect magnitudes concerns the fact that the ASVAB is primarily a measure of crystallized intelligence. Crystallized intelligence measures are predicted to give rise to smaller CD–IE effects for the same reason that they give rise to smaller Lynn–Flynn effects — they are typically more g-loaded than measures of fluid cognition (Johnson, Bouchard, Krueger, McGue, & Gottesman, 2004). This interpretation is also supported by the finding of a significant CD–IE effect on the SILS Abstract (fluid cognition analogue) subtest, compared with no CD–IE effect on the SILS Verbal (crystallized intelligence analogue) subtest. Furthermore, across all subtests

Table 10 Testing for CD–IE in the correlation between drawing and creative writing sub-factors and the c-Factor in relation to K; β(K)-β(Zs3) corresponds to the CD–IE effects controlled for subtest skew only and β(K)-β(Zs − Zf)3 corresponds to the CD–IE effects controlled for differences in subtest skew. H(H0) corresponds to the F-statistic, p(H0) corresponds to the significance level. K.TRI (“Slow LH”)

β(K)

β(K)-β(Zs3)

β(K)-β(Zs − Zf)3

z(c) ∗ z(Creative drawing) F(H0), p(H0) z(c) ∗ z(Creative writing) F(H0), p(H0)

−.183 (.067) F(1,325) = 11.86, p = .001 −.189 (.073) F(1,325) = 12.80, p = .000

−.168 (.066) F(1,324) = 10.89, p = .002 −.122 (.064) F(1,324) = 65.89, p = .009

−.184 (.066) F(1,324) = 8.22, p = .001 −.191 (.074) F(1,324) = 6.57, p = .000

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Table 11 Testing for CD–IE by predicting the correlations between the ASVAB subtests and the ASVAB g-factor using Slow Life History, residualized for g; β(K)-β(Zs3) corresponds to the CD–IE effects controlled for subtest skew only and β(K)-β(Zs − Zf)3 corresponds to the CD–IE effects controlled for differences in subtest skew. H(H0) corresponds to the F-statistic, p(H0) corresponds to the significance level. K.NLSY-β(g.ASVAB)

β(K)

β(K)-β(Zs3)

β(K)-β(Zs–Zf)3

z(g.ASVAB)∗ z(Science) F(H0), p(H0) z(g.ASVAB)∗ z(Arithmetic) F(H0), p(H0) z(g.ASVAB)∗ z(Words) F(H0), p(H0) z(g.ASVAB) ∗ z(Compreh) F(H0), p(H0) z(g.ASVAB)∗ z(NumSpeed) F(H0), p(H0) z(g.ASVAB)∗ z(CodSpeed) F(H0), p(H0) z(g.ASVAB)∗ z(AutoInfo) F(H0), p(H0) z(g.ASVAB)∗ z(MathKno) F(H0), p(H0) z(g.ASVAB)∗ z(MechComp) F(H0), p(H0) z(g.ASVAB)∗ z(ElectronInfo) F(H0), p(H0)

−.029 (.010) F(1,11905) = 10.23, p = .001 −.036 (.010) F(1,11905) = 15.07, p = .000 −.037 (.010) F(1,11905) = 16.50, p = .000 −.030 (.010) F(1,11905) = 10.53, p = .001 −.032 (.010) F(1,11905) = 12.37, p = .000 −.021 (.010) F(1,11905) = 5.28, p = .022 −.031 (.010) F(1,11905) = 11.16, p = .001 −.031 (.009) F(1,11905) = 11.77, p = .001 −.039 (.009) F(1,11905) = 17.72, p = .000 −.029 (.009) F(1,11905) = 9.68, p = .002

−.029 (.010) F(1,11904) = 250.78, p = .001 −.028 (.010) F(1,11904) = 667.96, p = .001 −.024 (.007) F(1,11904) = 4694.33, p = .001 −.027 (.007) F(1,11904) = 4604.49, p = .000 −.026 (.010) F(1,11904) = 2084.05, p = .001 −.017 (.010) F(1,11904) = 673.58, p = .051 −.028 (.009) F(1,11904) = 186.41, p = .002 −.025 (.008) F(1,11904) = 1489.64, p = .002 −.033 (.009) F(1,11904) = 408.43, p = .000 −.028 (.009) F(1,11904) = 36.64, p = .002

−.029 (.010) F(1,11904) = 5.27, p = .001 −.033 (.010) F(1,11904) = 279.84, p = .000 .074 (.012) F(1,11904) = 224.25, p = .000 .038 (.010) F(1,11904) = 120.68, p = .000 −.019 (.011) F(1,11904) = 14.68, p = .056 −.041 (.011) F(1,11904) = 24.26, p = .000 −.073 (.009) F(1,11904) = 73.18, p = .000 −.145 (.010) F(1,11904) = 383.41, p = .000 −.103 (.010) F(1,11904) = 131.82, p = .000 −.033 (.009) F(1,11904) = 20.61, p = .000

used, this conclusion is tentatively supported by the results of the Jensen effect analyses, which indicate negative but non-significant vector correlations between CD–IE effect magnitudes and subtest g-loadings in two out of three cases. Despite the relatively small effect magnitudes of the NLSY CD–IE effects, they are highly consistent in terms of both magnitude and direction, they are either unambiguously or borderline statistically significant, and are apparently not confounded with subtest skew, although controlling for this reduces the mean magnitude of the effects (β = − .027). Controlling for subtest skew also attenuates the mean CD–IE effect magnitude in the student data (β = − .071). Controlling for the skew of the residuals on the other hand enhances the mean strength of the CD–IE effects. In the NLSY, the mean effect magnitude increases to β = − .036, while in the student samples the mean effect magnitude increases to β = − .131. This finding suggests that when looking to control for the effects of skew on the integrity of “manifold inconstancy” type effects (such as SLODR and CD–IE), it is important to take into consideration the skew on the latent factor in addition to subtest skew. These results indicate that at least in the case of CD–IE, this parameter has an attenuating influence on the effect. Furthermore, controlling for this appears to be able to

Table 12 Testing for CD–IE by predicting the correlations between the ability measures and their g-factor using a K-Factor aggregate residualized for g, along with the results of the F-test and standard errors; β(K)-β(Zs3) corresponds to the CD–IE effects controlled for subtest skew only and β(K)-β(Zs−Zf)3 corresponds to the CD–IE effects controlled for differences in subtest skew; standard errors follow each corresponding model parameter in parentheses. H(H0) corresponds to the F-statistic, p(H0) corresponds to the significance level. K.Aggregate-β(g) β(K) z(g) ∗ z(Abilities) F(H0), p(H0)

β(K)-β(Zs3)

β(K)-β(Zs−Zf)3

−.167 (.096) −.179 (.086) −.172 (.095) F(1,105)=3.01, F(1,104)=16.157, F(1,104)=3.092, p=.086 p=.039 p=.074

alter the direction of the effects, such as in the case of the Verbal subtest in SILS which flips from positive to negative in sign, but remains non-significant, and the Words and Comprehension subtests in the ASVAB, which become both positive and significant. The meaning of these apparent NLSY reverse or anti-CD–IE effects is unclear however and must be subject to further study. We anticipate that in future research CD–IE could be investigated using more sophisticated model-based approaches such as moderated factor analysis where the moderation of the g-loadedness of subtests as a consequence of level of K could be modeled using a one step procedure in which relevant model parameters (such as skew) can be extracted directly from the data (Molenaar, Dolan, & van der Maas, 2011; Molenaar, Dolan, & Verhelst, 2010; Molenaar, Dolan, Wicherts, & van der Maas, 2010; Purcell, 2002). As these more sophisticated model-based methods require larger sample sizes, however, the present analyses relied on a more generally applicable method (CPEM) that permitted the direct comparison of convergent and divergent results across our various samples of widely varying sizes. Only one sample of sufficient size, the NLSY, was reported in the present paper. A further limitation of these data is that some of the NLSY indicators used in this study are single-item measures and therefore probably of limited reliability, which is a major disadvantage when using multivariate structural modeling approaches. This study provides compelling evidence for the existence of robust CD–IE effects detectable not only within both samples of convenience and population-representative ones at the individual differences level, but also between ethnic groups. This is an important finding, as the failure of general intelligence and life history speed to substantively correlate at the level of latent factors posed a significant theoretical challenge to the application of life history theory to humans. Here, we demonstrate for the first time the existence of a fundamentally novel life-history-dependent source of variance in human intelligence.

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The apparent independence of the CD–IE effect from both subtest g-loadings and level of g at the individual differences level furthermore makes it an excellent candidate explanation for the Lynn–Flynn effect, which exhibits precisely the same psychometric properties — i.e., the effect is unrelated to the Jensen effect, it is characterized by a weakening of g (in some studies) and also manifests itself independently of population level of g, which might even have been historically declining owing to dysgenic pressures (for an overview of the relevant literature see: Woodley & Figueredo, 2013; Woodley & Meisenberg, in press-a,b; Woodley, 2012b). As was mentioned in the Introduction, many diverse environmental factors have been posited as drivers of the Lynn–Flynn effect. CD–IE permits these potential causes to be integrated into an overarching causal framework such that any environmental improvement that slows the life history speed of a population is likely to trigger cognitive differentiation and hence reproduce the phenomenology of the Lynn–Flynn effect. Furthermore the initial effect of cognitive differentiation effort on reducing the strength of correlations among abilities does not need to be large in order for it to still be a fundamental cause of the Lynn–Flynn effect. We anticipate that cognitive differentiation effort only needs to function to modestly weaken the g-factor, such that social multiplier effects (Dickens & Flynn, 2001) can further pry the factor apart over time. The testing of other specific predictions of CD–IE pertaining to neurology and behavior genetics could form the basis for a comprehensive research program (see: Woodley, 2011a, pp. 238–239). Future research therefore needs to be concerned with replicating these effects using an even wider array of cognitive ability, life history measures, and analytical methods. Acknowledgments We would like to thank Gerhard Meisenberg, Guy Madison, Jelte Wicherts, Geoffrey Miller, and one anonymous reviewer for suggestions and shared expertise that improved this manuscript. References Arden, R., Gottfredson, L. S., & Miller, G. (2009). Does a fitness factor contribute to the association between intelligence and health outcomes? Evidence from medical abnormality counts among 3654 US Veterans. Intelligence, 37, 581–591. Austin, E. J., Deary, I. J., & Gibson, G. J. (1997). Relationships between ability and personality: Three hypotheses tested. Intelligence, 25, 49–70. Austin, E. J., Deary, I. J., Whiteman, M. C., Fowkes, F. G. R., Pedersen, N. L., Rabbitt, P., et al. (2002). Relationships between ability and personality: does intelligence contribute positively to personal and social adjustment? Personality and Individual Differences, 32, 1391–1411. Austin, E. J., Hofer, S. M., Deary, I. J., & Eber, H. W. (2000). Interactions between intelligence and personality: A study of Cattell scales in two large samples. Personality and Individual Differences, 29, 405–427. Brown, S. D. (2011). Creative performance, creative preference, and creative perception: Testing Fisher's runaway sexual selection theory, Miller's fitness indicator theory, and the role of social selection. Unpublished Masters Thesis, Department of Psychology, University of Arizona, Tucson, AZ. Brunswick, E. (1952). The conceptual framework of psychology (International encyclopedia of unified science, volume 1, number 10). Chicago, IL: The University of Chicago Press. Carlsmith, L. (1964). Effect of early father absence on scholastic aptitude. Harvard Educational Review, 34, 3–21. Central Intelligence Agency (2011). The CIA World Factbook 2012. New York, NY: Skyhorse Publishing.

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