Evolutionary Psychology
www.epjournal.net – 2010. 8(3): 492-505
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Original Article
The Effect of Life Expectancy on Aggression and Generativity: A Life
History Perspective
Curtis S. Dunkel, Department of Psychology, Western Illinois University, Macomb, IL, USA. Email:
C-Dunkel@wiu.edu (Corresponding author).
Eugene Mathes, Department of Psychology, Western Illinois University, Macomb, IL, USA.
Dennis R. Papini, Department of Psychology, Middle Tennessee State University, Murfreesboro, TN, USA.
Abstract: Following a model that is inclusive of both dispositional and situational
influences on life-history behaviors and attitudes, the effect of life expectancies on
aggression and generativity was examined. Consistent with the hypotheses it was found
that shorter life expectancies led to an increase in the desire to aggress and a decrease in the
desire to engage in generative behaviors. The results are discussed in terms of how life
history theory can be used to frame research on person-situation interactions.
Keywords: Life-history theory, aggression, generativity
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Introduction
With the growing recognition that evolutionary psychology offers an opportunity to
unify disparate subfields in psychology (Buss, 1995), there has been a reevaluation of the
relationship between the study of individual differences and evolutionary psychology.
While individual differences were dismissed in the early ascendency of evolutionary
psychology, this is no longer the case (Buss, 2009b; Michalski and Shackelford, 2010).
One evolutionary approach, life history theory, is especially promising because it offers a
powerful explanatory framework. Life history theory originated as an explanation for
species differences (Promislow and Harvey, 1990, 1991) but it is increasingly being used to
explain individual differences.
Life history theory posits that the competing needs of maintenance, growth, and
reproduction lead to trade-offs so that resources directed toward one end diminish the
resources available for use toward the other ends. Not only is it thought that different
strategies for the allocation of resources between these competing demands lay at the heart
of many group (Rushton, 1985), sex (e.g., Kruger and Nesse, 2004, 2006), and age
Life expectancy
differences (e.g., Kruger and Nesse, 2004, 2006), but these strategies are thought to form a
composite or suite of traits leading multiple individual differences to co-vary (e.g.,
Figueredo, Vásquez, Brumbach, and Schneider, 2007; Rushton, Bons, and Hur, 2008).
The life history strategies (LHSs) are often viewed as being at opposite ends of a
single spectrum. One LHS is defined by fast development, early and unrestrictive
reproductive behavior, and relatively less parental investment in offspring. At the opposite
end of the spectrum is a strategy defined by slow development, delayed initiation and more
restrictive reproductive behavior, with greater parental investment in offspring. As
predicted these strategies have been found to co-vary with a large number of individual
differences variables.
Accounting for Differences in LHS
LHSs are complex and multifaceted and the origins of the different strategies are
multifactorial. Genes, a variety of environmental factors, and gene x environment
interactions have all been the focus of research on the genesis of life history strategies.
Genetic variance explains a substantial amount of strategy variance. Utilizing a
large sample of middle-aged adult twins, Figueredo et al. (2004) found strong correlations
and high heritability among indicators of the LHSs. A recent reanalysis of the same data
(Figueredo and Rushton, 2009) suggests that the strategies, the general factor of
personality, and indicators of physical health share common variance that is explained by
genetic variance. More specific indicators of LHS (e.g., age at menarche) have also been
found to be largely explained by genetic variation (e.g., Belsky et al., 2007; Comings,
Muhleman, Johnson, and MacMurray, 2002; Rowe, 2002) and specific alleles related to life
history characteristics have also been discovered (Eisenberg et al., 2007).
Research on the environmental contributors to human LHSs can be traced back to
Draper and Harpending’s (1982) integration of psychological research on father absence
(Carlsmith, 1964; Heatherington, 1972) with life history theory. It was proposed that father
absence is an important environmental cue directing development toward a fast LHS and
there is a cache of research findings supporting this contention (e.g., Ellis et al., 2003;
Surbey, 1990).
The ideas expressed by Draper and Harpending (1982) were expanded on by
Belsky, Steinberg, and Draper (1991) in which the specific cue of father absence was
replaced by attachment security. Attachment security, itself being under the influence of
environmental stress and insensitive parenting, was posited to be the key environmental
antecedent to the developing strategy. Research on early exposure to stress, resource
availability, attachment, and father absence has continued and the results have varied as to
which factors are influential (Bogart, 2005; Chisholm, Quinlivan, Petersen and Coall, 2005;
Davis and Were, 2007; Hoier, 2003; Maestripieri, Roney, BeBias, Duarante, and Spaepen,
2004; Moffit, Caspi, Belsky, and Silva, 1992; Quinlan, 2003; Tither and Ellis, 2008).
Another possible and more speculative, environmental variable influencing the
development of LHSs is the male-to-female sex ratio. Given sex differences in sexual
strategies (Buss and Schmitt, 1993) in which males tend to favor short-term mating and
females tend to favor long-term mating, sex ratios could tilt behaviors toward the preferred
strategies of either sex. For example, when sex ratios are high, to attract a mate men must
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respond to women’s preferences for long-term mating. A large cross-cultural analysis by
Schmitt (2005) showed that variance in sociosexuality across countries was strongly
correlated to sex ratio. In another cross-cultural analysis of 185 countries Barber (2000)
found that sex ratios were predictive of teen birth rates. Because sociosexuality and age at
first birth are indicative of LHSs, it is reasonable to infer that sex ratio influences LHSs.
This integration of sexual selection and life history theory is advancing (Gangestad and
Simpson, 2000; Jackson and Ellis, 2009; Kruger and Schlemmer, 2009) and promises more
fine-tuned predictions concerning LHS (Del Giudice, 2009).
A third line of research on the origins of the LHSs can be traced to Promislow and
Harvey’s (1990, 1991) comparison of life history traits across several species. They found
that mortality rates formed a hub at the center of a large number of species differences.
Chisholm (1993) suggested that humans have the ability to adjust their LHSs based on the
perception of extrinsic cues concerning life expectancy. The importance of life expectancy
cues was confirmed by Wilson and Daly’s (1997) analyses of Chicago neighborhoods in
which life expectancy was strongly related to indicators of LHS. Continuing along this
vein, Hill, Ross, and Low (1997) found that the shorter lifespan estimates of young adults
were predictive of risk-taking behavior and Kruger, Reischl, and Zimmerman (2008) found
that for inner city youth discounting the future mediated the relationship between aspects of
the neighborhood environment and externalizing behaviors.
Finally, the increased focus on the importance of gene x environment interactions
(Champagne and Mashoodh, 2009) in determining dispositional differences in LHSs has
emerged (Belsky and Pluess, 2009; Ellis and Boyce, 2008). There are a number of possible
ways in which gene x environment interactions could play a role in the development of
dispositional LHSs. For example, it could be that genes code for “if-then” responses to the
environment or that individuals vary based on their susceptibility to environmental
influences.
Proposed Model
The position tested in the current set of studies is consistent with the theoretical
positions on the development of dispositional differences in LHSs, but includes the role of
environmental contexts in explaining behavior and attitudes. While the focus of most
research has been on the antecedent causes of dispositional differences in LHS, we
propose, citing the exceptionality of human plasticity (MacDonald, 1988) and the utility of
conditional strategies (Winterhalder and Smith, 2000), that a significant amount of
behavioral flexibility remains throughout development, allowing individuals to adjust life
history behaviors around a dispositional set point. It is thought that a dispositional set point
is formed through the combined influence of genetics and early experience and those
situations that relay important life history information sway present life history behaviors
and preferences.
This proposition is very similar to what Voland (1998) described as the “Three
Levels of Adaptability” when explaining the root of differences in LHSs. He described
level one as genetic differences setting the boundaries of the LHSs, level two as phenotypic
plasticity within those boundaries as associated with the environmental effects on
dispositional differences described above, and level three as:
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Situative context. Human strategies are conditional. Our regulatory
machinery can lead to situatively different solutions to the same adaptive
problem. The change from one option to another would then be understood
as accommodation or as adjustment within an adaptive strategy (Voland,
1998, p. 365).
For example, following the research on life expectancy cues on LHS, Dunkel,
Mathes, and Decker (2010) found that mating preferences were partially contingent upon
both dispositional LHS and imagined life expectancy. When participants imagined shorter
life expectancies, in comparison to imagining longer life expectancies, they exhibited
increased interest in short-term mating and decreased interest in long-term mating.
However, if LHS are a composite of multiple behaviors it would be expected that shifts in
behaviors would not be limited to the realm of sexuality, but that numerous behaviors or
preferences would respond to life expectancy cues.
Aggression and Generativity
The two traits that are a part of the suite of traits that covary with individual
differences in LHS and are the focus of the current pair of studies are aggression and
generativity.
The earliest theoretical work applying life history theory to the development of
human individual differences included the expectation that the strategies included
differences in aggression with a fast strategy associated with higher levels of aggression
(Belsky, Steinberg, and Draper, 1991; Draper and Harpending, 1982). There is also a
wealth of research on the relationship between LHS and aggression (e.g., Figueredo,
Gladden, and Brumbach, 2009; Kruger and Nesse, 2004, 2006; Wolf, van Doorn, Leimar,
and Weissing, 2007) with the evidence supporting the theorized relationship between LHS
and aggression. Wilson and Daly’s (1997) analyses of Chicago neighborhoods are a good
example of this relationship and is the most relevant to the current investigation. They
found that variance in life expectancy explained variance in the life history characteristics
of age at first birth and aggression. For example, they found that life expectancy explained
a large amount of variance in homicide rates across the neighborhoods. This finding
suggests that cues to a shorter life expectancy increase aggression.
Altruistic behavior also has a strong theoretical and empirical association with the
LHSs (e.g., Bogaert and Rushton, 1989). For example, the most extensive effort to measure
the LHSs, the Arizona Life History Battery (Figueredo et al., 2006), includes a number of
items related to altruism. Over fifty of the 199 items composing the battery measure
altruism. Many of these items are similar to a specific form of altruism (e.g., altruism
toward children, altruism toward community) called generativity.
Generativity is the desire to help create and positively influence future generations
(Erikson, 1968; McAdams and de St. Aubin, 1992). This often takes the form of having and
rearing children, but can be much broader in scope. However, this definitive generative act
of investing in one’s own offspring appears to be strongly aligned with a slow LHS.
Supportive of this proposed association, Dunkel and Sefcek (2009) found that generativity
was associated with a slow LHS.
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Summary
To summarize; (1) life history strategies include a suite of traits (including
aggression and generativity), (2) research suggests that cues to life expectancy direct
organisms toward either a slow or fast strategy, (3) a degree of behavioral flexibility
centering around dispositional differences remains throughout development, (4) individuals
continue to use cues to life expectancy to adjust life history behaviors, and (5) therefore the
manipulation of cues to life expectancy will result in a shift in the suite of traits that
compose the LHSs (including aggression and generativity).
Study 1
Materials and Methods
Participants
A total of 153 (77 females) 18-48 year olds (M = 21.74, SD = 4.88) participated in
return for course credit. The sample was composed of 110 whites, 26 blacks, four AsianAmericans, five Hispanics, six answered other, and three failed to respond to the question
concerning ethnicity.
Procedure
LHS. The Mini-K (Figueredo et al., 2006) was used to measure LHS. The Mini-K is
a 20-item self-report measure in which participants rate a diverse set of questions each of
which is indicative of a slow LHS. The Mini-K utilizes a 7-point Likert-type scale
anchored at -3 and +3. The internal consistency for the Mini-K was, α = .66.
Life expectancy manipulation. Life expectancy was manipulated through the
instructions presented to participants. The use of hypothetical vignettes has been used
previously to examine the adjustment of life history behaviors to ecological conditions
(Cohen and Belsky, 2008). There were three expectancies: five months left to live, five
years left to live, and at least 50 years left to live. Each of the expectancies was presented to
each participant, and counter balanced, making for a within subjects design. Participants
were given the following instructions for the 5 months condition and these were slightly
modified in the other conditions to reflect the specific life expectancies:
Imagine that you have just been to the doctor for your annual checkup and
find out that you are very ill and have 5 months to live. You will be pain
free, have no symptoms, and be able to do all the things you do now during
those 5 months. Please answer the following questions with the
understanding that the doctor told you that you will have 5 months to live.
Aggression. The Aggression Questionnaire (Buss and Perry, 1992) was used to
measure aggression. It is a self-report measure that consists of four subscales: Physical
Aggression, Verbal Aggression, Anger, and Hostility. Since our research requires
participants to state how aggressive they would be given three different situations (five
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months left to live, five years left to live, and at least 50 years left to live) the questionnaire
was modified to become a state measure. First, only the Physical Aggression, Verbal
Aggression, and Anger subscales were used because they appear to be more responsive to
situational differences than Hostility, which is a more general attitude toward life involving
resentment and suspicion. A sample Hostility item is, “I am suspicious of overly friendly
strangers.” Second, instead of responding to the items in terms of whether they were
“characteristic of me,” participants were instructed to respond to the items in terms of the
three different situations. Thus, for the five months situation participants were instructed:
“Given you will have 5 months to live how likely are you to engage in the following
activities?” Responses were made on a five-point Likert-type scale ranging from “highly
unlikely” (1) to “highly likely” (5). To conform to the altered instructions some of the items
had to be reworded. Thus, the Physical Aggression item, “Once in a while I can’t control
the urge to strike another person,” was rewritten as follows, “If I have the urge to strike
another person I will.” The Verbal Aggression item, “I tell my friends openly when I
disagree with them,” was rewritten as follows, “If I disagree with friends I will tell them
openly.” The Anger item, “When frustrated, I let my irritation show,” was rewritten, “If
frustrated, I will let my irritation show.”
The internal consistency reliabilities for Physical Aggression subscale for the three
situations were: five months, α = .89; five years, α = .89; and 50 plus years, α = .91. The
internal consistency reliabilities for the Verbal Aggression subscale for the three situations
were: five months, α = .72; five years, α = .71; and 50 plus years, α = .74. The internal
consistency reliabilities for the Anger subscale for the three situations were: five months, α
= .90; five years, α = .88; and 50 plus years, α = .87.
Participants completed the questionnaires in groups in a classroom setting. The
participants were administered the Mini-K prior to the life expectancy manipulation and the
administration of the aggression scale.
Results
To test the relationship between dispositional LHS and aggression, correlations
between the Mini-K and the indices of aggression for each life expectancy were computed.
The means and standard deviations of the indices of aggression and the results of the
correlations with LHS can be seen in Table 1. Although the effect sizes are small,
consistent with expectations, a general trend was observed in which the Mini-K was
negatively correlated with the indices of aggression.
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Table 1. Means for aggression by life expectancy and correlations between aggression and
LHS
Aggression
5 months
Life Expectancy
5 years
50 years plus
Means and Standard Deviations
Physical
21.72 (8.39)
19.95 (7.93)
19.13 (8.11)
Verbal
11.08 (3.50)
10.23 (3.42)
9.84 (3.56)
Anger
14.00 (6.14)
12.92 (5.28)
12.20 (5.10)
Correlations with LHS
Physical
-.22**
-.19*
-.16
Verbal
-.16*
-.07
-.00
Anger
-.28**
-.18*
-.15
Note: *p < .05, **p < .01.
Following the suggestions of Thomas et al. (2009) when testing for moderation of a
continuous between-factor in repeated measures, one should first run an ANOVA to
examine the main effects and then run a secondary analysis including the continuous
variable as a covariate and look at the interaction between the repeated factor and the
covariate. Therefore initial mixed analyses of variance (ANOVA) were performed with the
three life expectancies as the three levels of the within factor and sex as the between factor.
After the initial analyses, three analysis of covariance (ANCOVA) were run with life
history strategy (Mini-K) as the covariate allowing for the examination of the possible
interaction of dispositional LHS, sex, and life expectancy on aggression.
The first ANOVA was conducted for physical aggression. The assumption of
sphericity was violated and therefore Greenhouse-Geiser estimates were used. The main
effect for life expectancy was significant, F(1.40, 211.57) = 16.13, p < .001, partial η2 =
.10. Bonferroni corrected paired comparisons showed that the five month life expectancy
resulted in higher scores for physical aggression in comparison to both the five year and 50
plus life expectancies. No interactions were significant, but there was a main effect for sex,
F(1, 151) = 18.41, p < .001, partial η2 = .11, with males exhibiting higher scores (males; M
= 21.57, SD = 9.08. females; M = 16.73, SD = 6.20). In the follow-up ANCOVA when
LHS was added as a covariate the interaction term was not significant.
The second ANOVA was conducted for verbal aggression. Once again the
assumption of sphericity was violated and the Greenhouse-Geiser estimates were used. The
main effect for life expectancy was significant, F(1.52, 230.10) = 13.09, p < .001, partial η2
= .08. Bonferroni corrected paired comparisons showed that the five month life expectancy
resulted in higher scores for verbal aggression in comparison to both the five year and 50
plus life expectancies. The main effect and interaction terms for sex were not significant. In
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the follow-up ANCOVA when LHS was added as a covariate the interaction term between
LHS and life expectancy was not significant.
The third ANOVA was conducted for anger. Once again the assumption of
sphericity was violated and the Greenhouse-Geiser estimates were used. The main effect
for life expectancy was significant, F(1.54, 232.96) = 12.62, p < .001, partial η2 = .08.
Bonferroni corrected paired comparisons showed that the five month life expectancy
resulted in higher scores for anger in comparison to both the five year and 50 plus life
expectancies. There was also a main effect for sex, F(1, 151) = 4.20, p < .05, partial η2 =
.03, but no interaction between sex and life expectancy. Males (M = 13.11, SD = 5.93)
scored higher on anger than females (M = 11.31, SD = 3.96) across life expectancies. In the
follow-up ANCOVA when LHS was added as a covariate, the interaction term between
LHS and life expectancy was significant, F(1.55, 231.90) = 4.91, p < .05, partial η2 = .03.
An examination of the correlations between dispositional LHS and all indices of aggression
show a trend in which the relationship between dispositional LHS and aggression weakens
as life expectancy increases. This trend may account for the significant interaction found
for the dependent variable of anger.
Study 2
Materials and Methods
Participants
A total of 125 (92 males) 18-35 year olds (M = 19.78, SD = 2.48) participated in
return for course credit. The sample was composed of 109 whites, nine blacks, four AsianAmericans, and four answered other to the question concerning ethnicity.
Procedure
Life history strategy. The Mini-K (Figueredo et al., 2006) was used to measure lifehistory strategy. The internal consistency of the scale was α = .69.
Life expectancy manipulation. The same life expectancy manipulation that was used
in Study 1 was used in Study 2.
Generativity. Generativity was measured using a modified version of the Loyola
Generativity Scale (McAdams and de St. Aubin, 1992). The LGS is 20-item self-report
measure in which items are judged using a 4-point Likert type scale. A sample item is,
“People come to me for advice,” and it was modified to “I will welcome people to come to
me for advice.” The internal consistencies associated with the various life expectancies
were as follows: five months, α = .89; five years, α = .91; 50 plus years, α = .91.
Results
To test the relationship between dispositional LHS and generativity correlations
between the Mini-K and the indices of generativity for each life expectancy were
computed. The means and standard deviations of the indices of generativity and the results
of the correlations with LHS can be seen in Table 2. As anticipated, the Mini-K was
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positively correlated with generativity at each level of life expectancy.
Table 2. Means for generativity by life expectancy and correlations between generativity
and LHS
Generativity
Life Expectancy
5 months
5 years
50 years plus
Means and SD
Correlations with LHS
71.81 (11.87)
73.54 (12.38)
75.70 (12.12)
.39**
.30**
.34**
Note: SD = standard deviations. Standard deviations are in parentheses.
A mixed ANOVA was performed with the three life expectancies as the three levels
of the within factor and sex as between factor. The assumption of sphericity was violated
and the Greenhouse-Geiser estimates were used. The main effect for life expectancy was
significant, F(1.41, 173.10) = 9.65, p < .005, partial η2 = .07. Bonferroni corrected paired
comparisons showed that the five month life expectancy resulted in lower scores for
generativity in comparison to both the five year and 50 plus year life expectancies. There
was not a significant main effect or interaction for sex. The interaction between
dispositional life history strategy and life expectancy tested in follow-up ANCOVA was
not significant.
Discussion
A person x situation perspective was taken (Buss, 2009a) in which individuals
retain a significant degree of behavioral flexibility centered on a dispositional LHS, so that
individuals can adapt their behavior to life history cues from the environment. LHSs are
thought to contain a suite of traits and have been found to vary based on genetics and an
identified set of experiences. Therefore, it stands that for there to truly be a shift in life
history behaviors, and not just a shift in specific behaviors to specific stimuli, then the suite
of behaviors that make up the LHSs should shift in response to those cues to which the
LHSs are sensitive.
Indicators of life expectancy have strong theoretical and empirical backing as
important life history cues. Thus, given the proposal, cues to life expectancy should act as
contextual cues shifting current behavior and preferences. And given that LHSs are made
up of a suite of traits, one should see a number of behaviors and behavioral preferences
shift in response to these cues. Dunkel, Mathes, and Decker (2010) have shown that the
LHS’s fundamental feature of short and long-term mating preferences shift in response to a
life expectancy manipulation. The results of the current set of studies extends these earlier
findings by showing that two behaviors, aggression and generativity, which are associated
with the suite of traits making up the LHS profiles, also shift in response to a life
expectancy manipulation.
There was also a trend across the indices of aggression in which the strength of the
relationship between aggression and LHS was stronger under the shorter life expectancy
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conditions. For anger, this trend was significant. We speculate that under conditions of
shorter life expectancies individuals may be more inclined to cast off societal constraints
and behave in a manner that is more in accordance with dispositional impulses. This is
especially likely with regards to anti-social behaviors and attitudes (e.g., aggression) as
opposed to prosocial behaviors and attitudes (e.g., generativity) and could be why this trend
did not appear with generativity as the dependent variable.
Limitations and Future Research
The most important limitations, and ones that should be addressed in future
research, are the reliance on both self-report measures and convenience samples.
Behavioral measures should be employed to examine the reliability and validity of the
results. Secondly, given that life history theory derives a great deal of power from the
ability to frame the vicissitudes of life, age differences are very important. Therefore, the
reliance on relatively homogeneous convenience samples is a limitation. It is quite likely,
that the effects of the current studies would be moderated by age. Alternatively, it might
not be as blunt as age, but other life history indicators that are important. For example, a
forty-year old with three children may respond to life expectancy cues by investing more in
his offspring while a forty-year old who is childless may allocate more resources toward
mating.
Future research should address the limitations of the studies and examine the scope
of the phenomenon. Are other LHS behaviors and attitudes susceptible to change based on
cues to life expectancy? Risk-taking, cooperation, time perspective, status seeking,
impulsivity, consumption, and health behaviors are some of the psychological and
behavioral characteristics that are associated with LHS and could be targeted for
subsequent investigation. Given that other experiences are thought to play a role in
determining dispositional LHS (e.g., Ellis, Figueredo, Brumbach, and Schlomer, 2009), do
cues reflecting these experiences also lead to adjustments in LHS behaviors? Could cues to
resource availability or sex-ratios act like life expectancy cues and lead to adjustments of
life history behaviors and attitudes? These questions set the ground for subsequent
research.
Received 6 April 2010; Revision submitted 3 September 2010; Accepted 13 September
2010
References
Barber, N. (2000). On the relationship between country sex ratios and teen pregnancy rates:
A replication. Cross-Cultural Research, 34, 26-37.
Belsky, J., and Pluess, M. (2009). The nature (and nurture?) of plasticity in early human
development. Perspectives on Psychological Science, 4, 345-351.
Belsky, J., Steinberg, L., and Draper, P. (1991). Childhood experience, interpersonal
development, and reproductive strategy: An evolutionary theory of socialization.
Child Development, 62, 647-670.
Belsky, J., Steinberg, L. D., Houts, R. M., Friedman, S. L., DeHart, G., Cauffman, E.,
Evolutionary Psychology – ISSN 1474-7049 – Volume 8(3). 2010.
-501-
Life expectancy
Roisman, G. E., Helpern-Felsher, B. L, and Susman, E. (2007). Family rearing
antecedents of pubertal timing. Child Development, 78, 1302-1321.
Bogaert, A. F. (2005). Age at puberty and father absence in a national probability sample.
Journal of Adolescence, 28, 541-546.
Bogaert, A. F., and Rushton, J. P. (1989). Sexuality, delinquency and r/K reproductive
strategies: Data from a Canadian university sample. Personality and Individual
Differences, 10, 1071-1077.
Buss, A. H., and Perry, M. (1992). The aggression questionnaire. Journal of Personality
and Social Psychology, 63, 452-459.
Buss, D. M. (1995). Evolutionary psychology: A new paradigm for psychological science.
Psychological Inquiry, 6, 1-30.
Buss, D. M. (2009a). An evolutionary formulation of person-situation interactions. Journal
of Research in Personality, 43, 241-242.
Buss, D. M. (2009b). How can evolutionary psychology successfully explain personality
and individual differences? Perspectives on Psychological Science, 4, 359-366.
Buss, D. M., and Schmitt, D. P. (1993). Sexual strategies theory: An evolutionary
perspective on human mating. Psychological Review, 100, 204-232.
Carlsmith, L. (1964). Effect of early father absence on scholastic aptitude. Harvard
Educational Review, 34, 3-21.
Champagne, F. A., and Mashoodh, R. (2009). Genes in context: Gene-environment
interplay and the origins of individual differences in behavior. Current Directions
in Psychological Science, 18, 127-131.
Chisholm, J. S. (1993). Death, hope and sex: Life-history theory and the development of
reproductive strategies. Current Anthropology, 34, 1-24.
Chisholm, J. S., Quinlivan, J. A., Petersen, R. W., and Coall, D. A. (2005). Early stress
predicts age at menarche and first birth, adult attachment, and expected lifespan.
Human Nature, 16, 233-265.
Cohen, D. L., and Belsky, J. (2008). Individual differences in female mate preferences as a
function of attachment and hypothetical ecological conditions. Journal of
Evolutionary Psychology, 6, 25-42.
Comings, D., Muhleman, D., Johnson, J., and MacMurray, J. (2002). Parent-daughter
transmission of androgen receptor gene as an explanation of the effect of father
absence on age of menarche. Child Development, 73, 1046-1051.
Davis, J. and Werre, D. (2007). Agonistic stress in early adolescence and its effects on
reproductive effort in young adulthood. Evolution and Human Behavior, 28, 228233.
Del Giudice, M. (2009). Sex, attachment, and the development of reproductive strategies.
Behavioral and Brain Sciences, 32, 1-67.
Draper, P., and Harpending, H. (1982). Father absence and reproductive strategy: An
evolutionary perspective. Journal of Anthropological Research, 38, 255-273.
Dunkel, C. S., Mathes, E. and Decker, M. (2010). Behavioral flexibility in life history
strategies: The role of life expectancy. Journal of Social, Evolutionary and Cultural
Psychology, 4, 51-61.
Dunkel, C. S., and Sefcek, J. A. (2009) Eriksonian lifespan theory and life history theory:
Evolutionary Psychology – ISSN 1474-7049 – Volume 8(3). 2010.
-502-
Life expectancy
An integration using the example of identity formation. Review of General
Psychology, 13, 13-23.
Eisenberg, D. T. A., Campbell, B., MacKillop, J., Modi, M., Dang, D., Koji Lum, J., and
Wilson, D. S. (2007). Polymorphisms in the dopamine D4 and D2 receptor genes
and reproductive and sexual behaviors. Evolutionary Psychology, 5, 696-715.
Ellis, B. J., Bates, J. E., Dodge, K. A., Fergusson, D. M., Horwood, L. J., Pettit, G. S., and
Woodward, L. (2003). Does father absence place daughters at special risk for
early sexual activity and teenage pregnancy? Child Development, 74, 801- 821.
Ellis, B. J., and Boyce, W. T. (2008). Biological sensitivity to context. Current Directions
in Psychological Science, 17, 183-187.
Ellis, B. J., Figueredo, A. J., Brumbach, B. H., and Schlomer, G. L. (2009). Mechanisms of
environmental risk: The impact of harsh versus unpredictable environments on the
evolution and development of life history strategies. Human Nature, 20, 204-268.
Erikson, E. H. (1968). Identity: Youth and crisis. New York: Norton.
Figueredo, A. J., Gladden, P. R., and Brumbach, B. H. (2009). Sex, aggression, and life
history strategy. Behavioral and Brain Sciences, 32, 30.
Figueredo, A. J., and Rushton, J. P. (2009). Evidence for shared genetic dominance
between the general factor of personality, mental and physical health, and life
history traits. Twin Research and Human Genetics, 12, 555-563.
Figueredo, A. J., Vásquez, G., Brumbach, B. H., and Schneider, S. (2004). The heritability
of life history strategy: The K-factor, covitality, and personality. Social Biology, 51,
121-143.
Figueredo, A. J., Vásquez, G., Brumbach, B. H., and Schneider, S. M. R. (2007). The Kfactor, covitality, and personality: A psychometric test of life history theory. Human
Nature, 18, 47-73.
Figueredo, A. J., Vásquez, G., Brumbach, B. H., Schneider, S., Sefcek, J. A., Tal, I. R.,
Hill, D., Wenner, C. J., and Jacobs, W. J. (2006). Consilience and life history
theory: From genes to brain to reproductive strategy. Developmental Review, 26,
243-275.
Gangestad, S. W., and Simpson, J. A. (2000). The evolution of human mating: Trade-offs
and strategic pluralism. Behavioral and Brain Sciences, 23, 573-587.
Hetherington, E. M. (1972). Effects of father absence on personality development in
adolescent daughters. Developmental Psychology, 7, 313-326.
Hill, E. M., Ross, L. T., and Low, B. S. (1997). The role of future unpredictability in
human risk-taking. Human Nature, 8, 287-319.
Hoier, S. (2003). Father absence and age at menarche. Human Nature, 14, 209-233.
Jackson, J. J., and Ellis, B. J. (2009). Synthesizing life history theory with sexual selection:
Toward a comprehensive model of alternative reproductive strategies. Behavioral
and Brain Sciences, 32, 31-32.
Kruger, D. J., and Nesse, R. M. (2004). Sexual selection and the male:female mortality
ratio. Evolutionary Psychology, 2, 66-85.
Kruger, D. J., and Nesse, R. M. (2006). An evolutionary life-history framework for
understanding sex differences in human mortality rates. Human Nature, 17, 7497.
Evolutionary Psychology – ISSN 1474-7049 – Volume 8(3). 2010.
-503-
Life expectancy
Kruger, D. J., Reischl, T., and Zimmerman, M. A. (2008). Time perspective as a
mechanism for functional developmental adaptation. Journal of Social,
Evolutionary, and Cultural Psychology, 2, 1-22.
Kruger, D. J., and Schlemmer, E. (2009). When men are scarce, good men are even harder
to find: Life history, the sex ratio, and the proportion of men married. Journal of
Social, Evolutionary, and Cultural Psychology, 3, 93-104.
MacDonald, K. B. (1988). Social and personality development: An evolutionary
synthesis. New York: Plenum Press.
Maestripieri, D., Roney, J. R., DeBias, N., Durante, K. M., and Spaepen, G. M. (2004).
Father absence, menarche and interest in infants among adolescent girls.
Developmental Science, 7, 560-566.
McAdams, D. P., and de St. Aubin, E. (1992). A theory of generativity and its assessment
through self-report, behavioral acts, and narrative themes in autobiography.
Journal of Personality and Social Psychology, 62, 1003-1015.
Michalski, R. L., and Shackelford, T. K. (2010). Evolutionary personality psychology:
Reconciling human nature and individual differences. Personality and Individual
Differences, 48, 509-516.
Moffit, T. E., Caspi, A., Belsky, J., and Silva. P. A. (1992). Childhood experience and the
onset of menarche: A test of a sociobiological model. Child Development, 63, 4758.
Promislow, D. E. L., and Harvey, P. H. (1990). Living fast and dying young: A
comparative analysis of life-history variation among mammals. Journal of Zoology,
220, 417-437.
Promislow, D. E. L., and Harvey, P. H. (1991). Mortality rates and the evolution of
mammal life histories. Acta Œcologia, 12, 119-137.
Quinlan, R. J. (2003). Father absence, parental care, and female reproductive
development. Evolution and Human Behavior, 24, 376-390.
Rowe, D. C. (2002). On genetic variation in menarche and age at first sexual intercourse.
Evolution and Human Behavior, 23, 365-372.
Rushton, J. P. (1985). Differential K theory: The sociobiology of individual and group
differences. Personality and Individual Differences, 6, 441-452.
Rushton, J. P., Bons, T. A., and Hur, Y. M. (2008). The genetics and evolution of the
general factor of personality. Journal of Research in Personality, 42, 1173-1185.
Schmitt, D. P. (2005). Sociosexuality from Argentina to Zimbabwe: A 48-nation study of
sex, culture, and strategies of human mating. Behavioral and Brain Sciences, 28,
247-275.
Surbey, M. (1990). Family composition, stress, and human menarche. In F. Bercovitch and
T. Zeigler (Eds.), The socioendocrinology of primate reproduction (pp. 71-97).
New York: Wiley-Liss.
Thomas, M. S. C., Annaz, D., Ansari, D., Serif, G., Jarrold, C., and Karmiloff-Smith, A.
(2009). Using developmental trajectories to understand developmental disorders.
Journal of Speech, Language, and Hearing Research, 52, 336-358.
Tither, J. M., and Ellis, B. J. (2008). Impact of fathers on daughters’ age at menarche: A
genetically- and environmentally-controlled sibling study. Developmental
Evolutionary Psychology – ISSN 1474-7049 – Volume 8(3). 2010.
-504-
Life expectancy
Psychology, 44, 1409-1420.
Voland, E. (1998). Evolutionary ecology of human reproduction. Annual Review of
Anthropology, 27, 347-374.
Wilson, M., and Daly, M. (1997). Life expectancy, economic inequality, homicide, and
reproductive timing in Chicago neighborhoods. British Medical Journal, 314, 12711274.
Winterhalder, B., and Smith, E. A. (2000). Analyzing adaptive strategies: Human
behavioral ecology at twenty-five. Evolutionary Anthropology, 9, 51-72.
Wolf, M., van Doorn, G. S., Leimer, O., and Weissing, F. J. (2007). Life-history trade-offs
favour the evolution of animal personalities. Nature, 447, 581-584.
Evolutionary Psychology – ISSN 1474-7049 – Volume 8(3). 2010.
-505-