in the Light of Evolution

PREDICTING FEMALE CONTRACEPTIVE USE: AN APPLICATION OF LIFE HISTORY THEORY

2014-02-07T08:52:00.000-08:00

After some motivation from various researchers on Twitter: why don't we put studies we know won't be published on the internet somewhere..., I decided I would put my thesis up. Until I can re-analyze the new data I collected & actually attempt to have this thing published, of course. In the mean time, feel free to fill your mind and your time.

If you are lucky enough to have access, the full document can be found here or here.

ABSTRACT

Sexual behavior and activity began to lose its sole purpose as sex for reproduction early in the evolution of the genus Homo (Benagiano et al., 2007). Consequently, humans have tried to utilize contraception ever since they could document their existence. When used correctly, contraceptives are enormously effective at preventing unintentional pregnancy. However, most studies conducted on contraceptive behaviors have not been driven by theory. The purpose of this research was to apply a theory to predict the contraceptive use of females. Since life history theory is concerned predominantly with the timing of reproduction, being that it is the final stage organisms can allocate resources to in their lifetime (Griskevicius et al., 2011), life history theory was applied for the current research. Participants (N = 283) completed surveys regarding their life history strategies and contraception behaviors. Results revealed a two-factor solution that represented r and K strategies. Multiple hierarchical regressions were utilized to determine if the K and r strategy measures independently predict the contraceptive measures as well as relevant life history outcomes. Although lacking significance, results trended in the hypothesized direction. Results did significantly predict a few relevant life history outcomes, including age, delay of sex, sex frequency in the past year, religiosity, and current health. Conclusions regarding future applications of this study’s measurement of contraceptive effectiveness and the two-factor solution for measurement of life history strategies are discussed.

Predicting Female Contraceptive Use: An Application of Life History Theory

Reproductive strategies of humans changed dramatically from that employed by some of our common ancestors, for whom sexual activity was restricted to sex for reproduction (Benagiano, Bastianelli, & Farris, 2007). Conceptive sex still remains as a valid reason for copulation; however, non-conceptive copulations in primates are well documented. Bonobos (Pan paniscus), a species of chimpanzee, engage in two distinct non-conceptive copulations: exchange sex, in which a female acquires non-reproductive resources, and communication sex, sexual activity to develop social relations or to reduce strain and aggression among group members (deWaal, 1987). As illustrated by bonobos, sexual behaviors incorporate another facet of sexuality that is considered typical of sexuality in human beings: sexual behavior becomes something that can be employed for reasons other than reproduction (Benagiano et al., 2007). These findings suggest that sexual behavior and activity began to lose their sole purpose as sex for reproduction early in the evolution of the genus Homo (Benagiano et al., 2007). Once “men and women were able to separate with fairly high accuracy the ‘reproductive’ from the ‘non- conceptive’ aspects of reproduction, a real sexual revolution took place” (Benagiano, Carrara, & Filippi, 2010, p. 97). This sexual revolution occurred long before that of the 1960’s.

Contraception

Humans have tried to utilize contraception ever since they could document their existence. Coitus interruptus, withdrawal with ejaculation afterwards, was performed since ancient times. In the 19th century, individuals started to monitor fluctuations in basal body temperature and changes in cervical mucus consistency to time ovulation; implementation of these combined indicators was used for natural family planning. In ancient Egypt and Rome, a device soaked with different extracts, juices, lactic acid, or honey was placed in the vagina to prevent women from conceiving; these recipes, noted in the Kahun Papyrusm, date as far back as 1850 BC. Three thousand years ago, dung from elephants or crocodiles was used in India and Egypt to be introduced in the vagina prior to intercourse, acting as an unsophisticated barrier and perhaps as spermicide. A physician was purported to have invented the condom between 1660-1685 for King Charles II, who was stricken with a large number of illegitimate offspring. Since the 18th century, condoms made of cloth or animal by-products including skin or intestinal tissue were mass-produced. The 19^th century brought rubber condoms while the 20^th century brought latex condoms (Li & Lo, 2005).

The notion of placing a foreign object in the uterus was first portrayed in fabled stories about African nomads who put tiny stones into the uterus of their camels to prevent conception during lengthy journeys. After ascertaining the anti-fertility effect of intrauterine copper ions in rabbits, the first copper-T IUD was introduced in 1969. Exchanging the copper with progesterone or levonorgesterol offered surplus non-contraceptive benefits, such as decreasing pelvic infections (Li & Lo, 2005; Szarewski & Guillebaud, 1991).

It was not until the turn of the 20^th century that we gained knowledge of reproductive endocrinology that would eventually lead to applications in modern hormonal contraception. In the 1920s administration of estrogen would render experimental animals infertile; in the 1930s both estrogen and progesterone caused anovulation. Through the hard work of several renowned researchers in steroid chemistry, synthetic progesterone and estrogen were produced (Szarewski & Guillebaud, 1991). The first combined oral contraceptive preparation, called Enovid was available in 1959, but the estrogen dosage was high compared to pills used today (Szarewski & Guillebaud, 1991). The lower dose ethinyl-estradiol pills were available since 1961 and even lower dose pills were in regular use since 1972 (Li & Lo, 2005).

Depo-Provera was the first injectable hormone developed and it was approved for contraceptive purpose in the mid-1960s. Norplant was the first contraceptive implant approved in 1984 as a multi-rod subdermal implant. A single-rod implant, Implanon, was launched in 1998. The latest hormone delivery methods, specifically the transdermal patch, Ortho-Evra, and the vaginal ring, NuvaRing, have been recently introduced as different mechanisms of hormonal contraceptive transmission (Li & Lo, 2005).

Since the earliest times, unusual ploys such as sneezing and vaginal douching with diverse substances such as lemon juice or Coca-Cola have been used for post-coital contraception. The modern hormonal method for emergency contraception (EC) probably started in the 1920s when post-intercourse administration of estrogen in animals prevented pregnancy; the first account of EC use for humans was in the early 1960s, when high-dose diethylstilbestrol was administered (Li & Lo, 2005). Sterilization for both men and women is also a very effective form of contraception and has replaced the Pill as the most used method for women over thirty (Szarewski & Guillebaud, 1991).

Contraceptive Facts

As of July 2012, 62 million women in the United States are between the ages of 15-44: women who are sexually active and able to bear children (Centers for Disease Control, 2009). Individuals who did not apply a contraceptive method had an 85% chance of becoming pregnant in the past year, intended or not (Trussell, 2011). One in ten females are at risk for an unintended pregnancy, the highest proportion of women being adolescents, between 15 and 19 year olds. Ninety-two percent of women with at least a bachelor’s degree are currently using a contraceptive method, compared to only 89% of females who do not have a high school diploma (Mosher & Jones, 2010). Only 88% percent of women living below the federal poverty line are using contraceptives, compared to 92% of women living above the poverty line (Mosher & Jones, 2010).

Fifty-four percent of 2.9 million teenage women rely on the pill as their primary method of contraception (Mosher & Jones, 2010). The pill is one of the most effective methods, with an eight percent failure rate with typical use (Trussell, 2011). When used correctly, contraceptives are enormously effective at preventing unintentional pregnancy. Females who use contraceptives consistently and correctly account for only 5% of unplanned pregnancies (Gold, Sonfield, Richards, & Frost, 2009). Using contraception consistently and correctly is equated to having high contraceptive use skills. If an individual uses contraception consistently and correctly, not only are they more skilled at using the contraception, they are more skilled at preventing pregnancy. Several hormonal methods, such as the pill, patch, vaginal ring, IUD, and implant provide numerous health benefits beyond just preventing pregnancy. Prevention of pregnancy is cited as the most common reason for using oral contraceptives like the pill; however, over half of pill users also mention the additional health benefits, including treatment for excessive menstrual bleeding, pain associated with menstruation, or acne as reasons for use (Jones, 2011).

Less than 10% of women of reproductive age use a dual-method (most often the condom in addition to another method; Eisenberg, Allsworth, Zhao, & Peipert, 2012). Use of emergency contraception is a technique to avoid pregnancy after unprotected intercourse or contraceptive failure (Trussell & Raymond, 2011). Only one percent of women of reproductive age have used emergency contraception. The age group with the highest possibility for accidental pregnancy, females aged 18–29, are more apt than other aged women to have used emergency contraception, which is considered a backup method (Frost, Henshaw, & Sonfield, 2010). Again, emergency contraception is viewed as a method to use in cases where other methods during or prior to intercourse were not employed.

Knowledge about contraceptive methods is a robust predictor of use. Of unmarried women aged 18–29, for every correct response on a contraceptive knowledge scale, the likelihood of currently using a hormonal or long-acting reversible method (i.e., the most effective methods) increased by 17%, and of using no method decreased by 17% (Frost, Lindberg, & Finer, 2012). If an individual knows more about contraception, specifically the information regarding effectiveness of each method, they are more likely to choose the most effective method as their own method. Interestingly, 19% of women perceive they are infertile, regardless of whether this belief is medically supported (Polis & Zabin, 2012). This could explain the frequency of unplanned pregnancies within this population if proper contraceptive methods are perceived as not needed and thus not utilized.

Correlates of Contraceptive Use

Most studies conducted on contraceptive behaviors have not been driven by theory. Previous studies have examined a multitude of factors correlated with contraceptive behaviors. For example, one-third of women did not use contraception because they believed they could not get pregnant at the time of intercourse (Nettleman, Chung, Brewer, Ayoola, & Reed, 2007). If a woman doesn’t think there was a risk of pregnancy during intercourse, she wouldn’t employ a method. Another study revealed about 50% of coital events would be protected if women expressed that they were committed to not becoming pregnant (Bartz, Shew, Ofner, & Fortenberry, 2007). If a woman was explicitly devoted to preventing pregnancy, she would be committed to protecting herself for at least half of her coital events. Furthermore, if women reported that they communicate with their sexual partners about contraceptive choices and issues infrequently, they would be inconsistent contraceptive users (Davies et al., 2006). Also, increased relationship quality was associated with decreased condom use (Sayegh, Fortenberry, Shew, & Orr, 2006).

In addition, Asian, Latina, African American women and women who were raised with a religion are less likely to use any contraceptive method (Raine, Minnis, & Padian, 2003; Kost, Singh, Vaughan, Trussell, & Bankole, 2008). Contraceptive failure risk most severely affects the poorest females, women living below the 100% poverty level (Kost et al., 2008). These women might have to rely on more affordable, less effective methods, which are more likely to fail. Cultural beliefs, values, and influences of the individual’s surrounding community can act as barriers for the female to obtain contraceptives (Sable et al., 2000). Frequent discussions with friends about contraceptives foster positive attitudes towards contraception and increased satisfaction (Forrest & Frost, 1996).

Transportation often affects women’s abilities to obtain contraception; women who had no insurance cite transportation difficulties as a barrier more often than insured females (Sable, Libbus, & Chiu, 2000). If a woman’s community isn’t as accepting of contraceptive behaviors, this might stigmatize women attempting to prevent pregnancy; consequently, they might not be able to obtain their preferred method. Method satisfaction is most likely in women who acknowledge the pill’s non-contraceptive benefits, experienced few to no side effects, and had used the pill previously (Rosenberg, Waugh, & Burnhill, 1998). African Americans report lower use of hormonal contraceptives and increased rates of sexually transmitted infections, while Caucasians are less likely to use condoms for intercourse (Buhi, Marhefka, & Hoban, 2010).

Likewise, African American students show an increased incongruence in using effective methods of contraception compared to Caucasian students (Gaydos, Neubert, Hogue, Kramer, & Yang, 2010). Abused women do not use their preferred method compared to non-abused women (Williams, Larsen, & McCloskey, 2008). Furthermore, research conducted on contraceptive behaviors remains predominately located within the health fields (Skouby, 2010; Frost & Darroch, 2008; Buhi, Marhekfa, & Hoban, 2010; Gaydos et al., 2010; Ersek, Huber, Thompson, & Warren-Findlow, 2011).

It is somewhat apparent that predominately, the studies conducted on contraceptive behaviors are not driven by theory. Upon further examination of the variables correlated with contraceptive behaviors, one theory that is a likely candidate for application in this area of research is life history theory (LHT).

Life History Theory Literature

Charles Darwin proposed the theory of evolution by natural selection over 150 years ago (Darwin, 1859). It has only been in the past 20 years that the psychological, social, and behavior sciences have integrated the principles of evolution already deeply incorporated in biology and ecology. The goal of evolutionary psychology is not only to incorporate evolutionary biology, anthropology, neuroscience, and cognitive psychology to map human nature, but also to rewrite existing literature, reframing previous efforts into an evolutionary perspective (Tooby & Cosmides, 1992). It is worth mentioning that evolutionary psychology is not to be acknowledged as a subset of psychology, like social psychology or cognitive psychology, but rather as “a way of thinking about psychology,” available as a lens for viewing any topic therein (Cosmides & Tooby, 2000, p. 1). One evolutionary approach to the study of individual differences is life history theory (LHT). Initially, biologists and zoologists used life history theory to study species differences in reproductive strategies (Promislow & Harvey, 1990, 1991).

Evolution is the outcome of a process in which organisms compete to acquire resources from the environment to produce offspring (Kaplan & Gangestad, 2004). Individual organisms “‘capture’ energy from the environment (through foraging, hunting, or cultivating) and ‘allocate’ it to reproduction and survival-enhancing activities” (Kaplan & Gangestad, 2004, p.2). Selection will favor individual organisms that efficiently capture resources and effectively allocate these resources to increase fitness within their environment. Resources are not free; in reality, individuals must live within fixed resource budgets, only spending what is available. Allocation of a fixed budget necessitates trade-offs and consequently, decisions must be made about the ways to spend this budget. Essentially, LHT provides a framework addressing how individuals allocate resources to tasks that maximize fitness in the presence of trade-offs. Allocations vary across the lifespan and, consequently, LHT is concerned about the evolutionary forces that shape the scheduling of events involved in growth, maintenance, and reproduction (Kaplan & Gangestad, 2004).

Gadgil and Bossert (1970) established the first modern LHT framework, hypothesizing tradeoffs as fixed resource budgets. Organisms capture resources from the environment, the rate at which they capture resources determines their resource budget, and they can spend their resource budget on three distinctly different behaviors: growth, maintenance, and reproduction. Through growth, individuals can increase their resource capture rates for the future, thus maximizing future fertility; organisms typically have an adolescent stage in which reproduction is not possible and the primary resource allocation is on growth. Through maintenance, individuals repair tissue and allocate resources to increase immune function. Through reproduction, or reproductive effort, individuals replicate their genes into offspring (Gadgil & Bossert, 1970).

Deciding whether to spend resources on current reproduction versus future reproduction is at the heart of the somatic effort versus reproductive effort compromise. Somatic effort is akin to investing in one’s self and reproductive effort is akin to spending those investments that will allow for gene replication. Investment in somatic effort is investing in future reproduction (Griskevicius, Delton, Robertson, & Tybur, 2011). Individuals do not invest in themselves (somatic effort) as an end in itself; rather, they invest for the sake of future reproduction. This is exemplified in the individual that postpones reproduction immediately after puberty, attends a university, acquires a degree, becomes employed where she or he can accrue financial stability, and then finally decides to reproduce; with the increased financial stability and higher education, this individual can invest heavily in her or his offspring. Resources allocated to somatic effort, including the growth and maintenance of body and mind and the accumulation of embodied capital, cannot concurrently be allocated to reproductive effort, including sexual competition, gestation, and birth (Griskevicius et al., 2011).

A second dilemma is the allocation of resources to mating effort or parental effort (Figueredo et al., 2006). The procurement and retaining of mating partners is described as mating effort; that is, the time spent looking for potential mates and the time spent keeping these mates around until reproduction is successful. Enhancement of survival of offspring is described as parental effort; that is, the time spent feeding, nurturing, educating, and protecting offspring until they reach maturity. However, a trade-off also exists between these two efforts. For example, effort spent parenting cannot be spent acquiring new mates.

Within parental effort lies yet another trade-off: offspring quality versus quantity. This fundamental trade off assumes that resources are limited so as investment in one offspring increases, investment in each other individual offspring decreases; increased investment in offspring enhances the offspring’s reproductive success; and maternal fitness is calculated by the quantity of offspring that reach maturity and their ensuing lifetime reproductive success (Gillespie, Russell, & Lumma, 2008). Therein lies an evolved trade off for females between offspring quality versus quantity (Lack, 1947). Although, in comparison to other species, humans tend to favor quality over quantity in offspring, there are still individual differences in this reproductive decision. Increased maternal embodied capital increases child survival (Lawson et al., 2012). Birth, difficult and painful in humans, is risky for both the fetus and the mother (Mace, 2000). Although risks for maternal mortality are high and vary by region, risks to the baby are still greater. The earlier a child is weaned, the greater the risk of infant mortality (Mace, 2000); cessation of weaning is most likely due to impending pregnancy. Again, the quality of offspring is threatened when quantity of offspring increases. Offspring competition for resources is also a source of risk and mortality; larger quantities of siblings competing for food leads to decreased height in adolescence, further diminishing their future reproductive success (Lawson et al., 2012).

Life History strategies. How and when an individual decides to reconcile these resource allocation trade-offs comprises that individual’s LH strategy (Griskevicius et al., 2011; Gadgil & Bossert, 1970). Several environmental features influence LH strategies; factors include harshness (e.g., the age and sex-specific rates of mortality), unpredictability (e.g., the uniformity of harshness from one time to another), and resource scarcity (e.g., the obtainability of resources and level of competition for resources; Ellis et al., 2009). Species that evolved in harsh, unpredictable, resource scarce environments are inclined to implement distinctive LH strategies (Griskevicius et al., 2011). For example, a species that evolved in a harsh and unpredictable environment will invest less in somatic effort, reach sexual maturity quickly, and start reproducing at a high rate; this fast strategy or r strategy is adaptive for members of such species since reproducing quickly mitigates the risk of death without leaving behind any offspring. These individuals invest in reproductive effort at the cost of somatic effort. In contrast, investment in somatic effort heightens an individual’s capacity to compete for mates and invest in offspring; a species that evolved in a predictable environment follows a slower strategy or K strategy, investing more in somatic effort and delaying reproduction (Griskevicius et al., 2011). These individuals invest in somatic effort at the cost of investment in current reproduction effort, opting for future reproductive effort.

LHT can be exemplified by comparing the strategies displayed by r-selected species, the fast individuals, which invest heavily in reproduction with short life spans, with K-selected species, the slow individuals, which invest heavily in their own development and that of their offspring. Fast, r-selected species evolved in unstable, harsh, and unpredictable environmental conditions, leading to a strategy focused on producing high offspring quantity. Rabbits demonstrate quick sexual development, low parental investment, short life expectancy, and high competition for resources because they evolved in environments where short-term strategies increased fitness. Conversely, K-selected species evolved under stable and predictable conditions, leading to a strategy focused on survival of high offspring quality. Elephants demonstrate slow sexual development, high parental investment, long life expectancy, and low competition for resources because they evolved in stable environments where long-term strategies increased fitness (Figueredo, Vasquez, Brumbach, & Schneider, 2004).

Humans are characteristically defined by the substantial investments they make in somatic development at the cost of early reproduction. Compared with chimpanzees, humans reach physical maturity much later and reproduce at an even later age (Kaplan, Hill, Lancaster, & Hurtado, 2000). Originally, comparative biologists concentrated on describing LH strategies typical of a certain species, and then comparing the strategy of that species with the strategy of another (Lack, 1950); accumulated evidence, however, indicated adaptive within-species variation in LH strategies exist in many taxa (Daan & Tinbergen, 1997; Tinbergen & Both, 1999). Organisms appear to implicitly monitor current and expected states of their environments, regulating the LH strategies they engage (Ellis et al., 2009). Instead of exhibiting a fixed strategy for life, LH strategies show environmental exigency in reply to precise cues during childhood and adulthood. According to LHT, the environmental factors linked to different LH strategies in modern human environments are cues such as the local mortality rate and the availability of resources in the local environment (Kaplan & Gangestad, 2005; Quinlan, 2007; Worthman & Kuzara, 2005).

LHT suggests that since an individual’s resources are limited, the individual must choose between r and K strategies; the individual must choose to invest in their offspring or not, put off mating or not, and have few or many offspring (Kruger & Nesse, 2004, 2006). According to LHT, a continuum for humans exists from a slower course, high K, to a faster course, low K (Bielby et al., 2007; Ellis et al., 2009). An unpredictable and harsh environment, low investment from mother and father when growing up, father absence, stepfather presence, low social support, and high mortality risk are factors that contribute to the manifestation of a low K strategy, similar to the r strategy of other species (Figuredo et al., 2006). A predictable environment, high mother and father nurturing while growing up, high social support, and low mortality risk are factors that could contribute to the manifestation of a high K strategy (Figueredo et al., 2006). These choices in strategy manifest in behaviors existing within two clusters; mating strategies form a continuum, with most individuals falling somewhere along this range (Figueredo et al., 2004). The high K strategy involves clusters of behaviors such as delayed puberty, slow sexual maturation, selective mating, sexual restraint, extensive parental investment, consideration of risks, long lifespan, increased inclusive fitness, extensive somatic investment, high cooperation, high altruistic behaviors, monogamy, long term thinking, and intensive offspring investment. The low K strategy involves clusters of behaviors such as preference for sexual variety, sexual promiscuity, unrestrained sexuality, risk taking, aggression, early puberty, quick sexual maturation, impulsivity, low romantic attachment, low cooperation, rare altruistic behaviors, disregard of social rules, manipulative, exploitative, short term thinking, and increased fecundity (Figueredo et al., 2006).

The assumption, however, that an individual’s resources are limited, forcing them to choose between traditional r and traditional K strategies has been challenged. Rowe, Vazsonyi, and Figueredo (1997) state: “…it may be possible for an unusually resource rich male to pursue a mixed strategy combining certain elements of both” (p.106). It is reasonable to think that individuals with limited resources are forced to choose between r and K strategies, but individuals that have plentiful resources might be able to exhibit traditional r and traditional K strategies. An individual with increased health, energy, and wealth might be able to exhibit both traditional r and K strategy specific behaviors simultaneously.

It is proposed that the traditional view of the K strategy be maintained with respect to future time perspective, long-term mating strategy, and environmental input. Concurrently, sexual drive, sexual desire, sexual flexibility, and lack of sexual restraint (Figueredo et al., 2006) should be removed from K and placed on a separate factor; this second factor could be described as r. Figueredo and others (2006) include sexual restraint as dimension of the K Factor: high K individuals are more likely to delay sexual activity and engage in less risky sexual behaviors. Indicators of sexual restrictedness include increased religiosity, endorsing value and health reasons for abstaining from sex, perceived abilities to refuse sex, sexual decision making skills, positive endorsement of teenage abstinence, acceptance of norms prohibiting sex before marriage, intentions to abstain, and pro-social behaviors (Figueredo et al., 2006; Brumbach, Walsh, & Figueredo, 2007). According to this traditional view, high K individuals will experience sexual feelings and attractions later than low K individuals; concurrently, low K individuals will be more impulsive, sociable, risk-taking, and emotional in sexual relationships compared to their high K counterparts (Figueredo et al., 2006). However, this traditional view does not account for individuals that exhibit high K strategies in non-sexual clusters of behavior while exhibiting traditional low K strategies in sexual clusters of behavior. Individuals that show low “sexual restraint,” such as engaging in intercourse at a younger age and more frequently, can simultaneously engage in high K behaviors, such as attending a university with long term career goals.

Life history theory is concerned predominantly with the timing of reproduction, since that is the final stage organisms can allocate resources to in their lifetime (Griskevicius et al., 2011). The question of how individuals time their reproduction can be answered: they use contraception. For example, many insist prior to the sexual revolution of the 1960’s that the female’s only way of preventing pregnancy was by acting as a gatekeeper: to say no to any unworthy mate; this however does not seem to make sense (see also Ryan & Jethá, 2010; Pillard, 2007; Hayes & Carpenter, 2010; Wiederman, 2005; Schick, Zucker, & Bay-Cheng, 2008). Humans have known the benefits of non-conceptive sex since the beginning of the genus Homo (Benagiano et al., 2007; 2010). Our animal kingdom relatives have also found ways to employ contraception in order to prevent pregnancy, seen in wooly spider monkeys (Glander, 1994; Strier, 1993; Strier & Ziegler, 1994), red colobus monkeys (Wasserman et al., 2012), and even elephants (Shuker, 2001). Furthermore, the history of contraception, contraceptive practices, and the extensive amount of information collected on how to prevent pregnancy supports the notion that women did not have to serve as gatekeepers (Meena & Rao, 2010; Li & Lo, 2005).

If women wanted to time their reproduction they would go to great lengths to prevent pregnancy. Also, it does not seem logical for an early human female to insert one of many of the previously mentioned substances into her vagina (Li & Lo, 2005), a rather dangerous and sometimes deadly task, just to keep her mate happy; her drive for sex must have been just as strong. As previous literature has indicated, a traditionally high K woman that wished to maintain her desires for future reproduction would have the only option of abstinence (Buss & Schmitt, 1993). Humans however, are highly sexual; this is even more apparent when researching all the methods of contraception that have ever been invented and utilized.

In this light, a low sexually restrained woman isn’t forced to life the life of a low K female: she can engage in high-K nonsexual clusters of behavior and “low K” sexual clusters of behavior simultaneously. To that end, a second factor must be examined. This new factor, r, is sexual clusters of behavior. Humans have non-sexual clusters of behavior and sexual clusters of behavior. Those sexual clusters of behavior are sex drive, attitudes towards sex, sexual excitability, and sexual sensation seeking; those behaviors also lie on a continuum. Individuals can be low in sexual restraint (r) or high, just as much as they can be high or low on the K spectrum. With contraception, individuals that engage in high r behaviors, such as engaging in intercourse at a younger age and more frequently, can simultaneously engage in high K behaviors, such as attending a university with long term career goals because intercourse does not lead to reproduction.

Concluding, to measure life history strategy on one continuum, with sexual and non-sexual clusters of behavior within the single continuum, does not reveal the whole picture of human life. Specifically, high K, high r individuals are being missed. It is through the use of contraceptives females can pursue a mixed strategy of combining both r and K strategies. Although women have been practicing contraception since they could keep written records, it was only recently that highly effective hormonal methods allowed women to almost perfect their contracepting behaviors (Szarewski & Guillebaud, 1991). Since sex with contraception lowers the risk of pregnancy, women can combine r and K strategies. High K specific strategies such as high personal investment (attending a university, pursuing a career, etc.), delay of reproduction, and increased investment in offspring quality with birth spacing can be achieved by utilizing an effective form of contraception. However, these women can also engage in a high frequency of intercourse both inside and outside the context of relationships, a strategy often displayed by high r individuals. On the contrary, individuals that are seen as traditionally high K, possessing high sexual restraint, would thus exhibit low r clusters of behavior: sex only for reproduction and selective, restricted long-term mating (see Table 1).

Table 1

Hypothesized results for contraceptive effectiveness

Strategy	High K	Low K
Low r	Sex only for reproduction, high sexual restraint, committed and not active. Selective, restricted long term mating. Method: very effective birth control and highly skilled use (Purposeful Abstinence).	Non-normal population, not interested in sex, high sexual restraint, and potential floor effect. Varied, unrestricted short term mating. Method: no contraceptive method used since not engaging in sexual intercourse (Indifferent Abstinence).
High r	Frequent sex for non-reproduction, low sexual restraint, committed and very active. Selective, restricted long term mating. Method: very effective birth control and highly skilled use (pill, IUD, implant, shot, etc.)	Frequent sex for non-reproduction, low sexual restraint, promiscuous and very active. Varied, unrestricted short term mating. Method: no method, least effective method and low skilled use (withdrawal, fertility awareness, etc.)

Hypotheses

The purpose of this research is to investigate the relationships between r and K strategies and the use of contraception. Specifically, the following hypotheses will be tested:

1. An interaction between r and K strategies will be found to predict method effectiveness, such that high r and high K women will utilize the most effective methods.

2. An interaction between r and K strategies will be found to predict contraceptive knowledge, such that high r and high K women will be most knowledgeable about contraception.

3. An interaction between r and K strategies will be found to predict general attitudes towards contraceptives, such that high r, high K women will have the most positive attitudes.

4. An interaction between r and K strategies will be found to predict morality attitudes towards contraceptives, such that high r, high K women will be the most morally accepting of contraceptives.

5. An interaction between r and K strategies will be found to predict contraceptive self-efficacy, such that high r, high K women will have the highest contraceptive self-efficacy

Method

Participants

Two hundred and eighty-three women participated in the study. Participants were either recruited using the SONA system within the Western Illinois University (WIU) Psychology Department or through convenience sampling, using the software survey data collection tool SurveyMonkey (SurveyMonkey, Inc.).

The mean age of total participants was 23.75 (SD = 7.35), and 82.7% of the sample was Caucasian, 8.8% African American/African, 1.8% Asian American/Asian, 2.8% Hispanic, and 2.8% Multiple Ethnicity/Mixed. Of the participants, 42.8% were single and dating exclusively, 17.3% were single and not dating, 13.4% were married, 12.0% were single and dating casually, 8.1% were engaged to be married, and 5.3% were single and cohabiting. The majority of participants identified as heterosexual (90.1%), 6.4% identified as bisexual, and 1.4% identified as homosexual. Of the participants, 26.9% identified as Catholic, 26.9% identified as Christian, 19.1% identified as having no religious affiliation, 9.2% identified as Atheist, and 3.5% identified as Agnostic. The majority of participants were insured by their parents (54.1%), 15.2% of the participants were insured by their employers, 9.5% of the participants were insured by their university, 5.3% of the participants were insured by the government, and 5.3% of the participants were uninsured. Lastly, 84.1% of participants identified their most recent sex partner as being male and 2.5% identified their most recent sex partner as female. For the highest education level of participants, 17% were Freshman, 22.1% were Sophomore, 18% were Junior, 7% were Senior, 12% were Graduate Student, 14% graduated college, 6% held a professional degree, 4% completed graduate school, 5% received a high school education or less.

Contraceptive method. Two hundred and seventy-six participants reported a primary method, or their primary method was determined by their responses on Current Contraceptive Method and Recent Contraceptive Method questions of the CMQ. The majority of participants reported the pill as their primary contraceptive method (35. 8%), 23% reported using no method, 21.2% reported using the condom as their primary method, 6.9% reported using the IUD as their primary method, and 3.3% reported using the vaginal ring as their primary method.

Participants could denote any methods they use, including more than one method. For both Current and Recent Methods reported on the CMQ, 11.4% of participants indicated they used no method, 27.3% of participants indicated they used the pill, 28.9% indicated they used the condom, 14.0% indicated they used withdrawal, .05% indicated they used fertility awareness, .04% indicated they used the IUD, .03% indicated they used the ring, .02% indicated they used the shot, .01% indicated they used the patch, .01% indicated they use the implant, .008% indicated they use male partner sterilization, .006% indicated they use the female condom, .004% indicated they used spermicide, and .002% indicated they used female sterilization.

Participants could also denote if they were dual method users (e.g., pill and condom, vaginal ring and condom, etc.); participants must have reported using the condom in addition to another method. Dual method use was inferred from their responses on the Current or Recent Methods portion of the Contraceptive Methods Questionnaire as well as their responses on the two dual method intention items on the General Errors in Contraceptive Use Scale. The majority of participants were single method users (N = 208), while 68 participants reported being dual method users. Interestingly, many participants reported using the withdrawal method in addition to other methods listed in the Current or Recent Methods on the CMQ. While majority of participants did not denote using the withdrawal method in addition to another method, 68 participants reported using withdrawal in conjunction with other methods.

Analysis participants. Participants recruited outside of the WIU Department of Psychology’s Human Subject Pool only made up 12.6% of participants used in analysis. The mean age for participants in analysis was 21.56 (SD = 5.13). Of analysis participants, 70.5% were Caucasian, 22.1% were African American/African, 3.2% were Hispanic, and 4.2% were Multiple Ethnicity/Mixed. Of analysis participants, 28.4% were college freshman, 20.0% were college sophomores, 17.9% were college juniors, 17.9% were college seniors, 6.3% were graduate students, 3.2% completed graduate school, 1.1% graduated from college, and 2.1% held a professional degree. Of analysis participants, 21.1% were not dating, 11.6% were dating casually, 50.5% were dating exclusively, 8.4% were engaged, and 8.4% were married. Of analysis participants, 94.7% were heterosexual, 1.1% were homosexual, and 4.2% were bisexual. Of analysis participants, 13.7% reported no religious affiliation, 2.1% identified as Atheist, 1.1% identified as Agnostic, 27.4% identified as Catholic, and 37.9% identified as Christian. The Institutional Review Board of WIU reviewed and approved this study before administration.

Procedure

Participants read an Informed Consent before any data was collected; further participation signified informed consent. Time of completion ranged between 45 minutes to one hour. The measures administered through the SONA system and through SurveyMonkey were identical, with the exception that SurveyMonkey allowed a skip question formation for ease of survey completion. In addition, titles of measures were not included as to avoid any priming effects of the scale title. Participants were given a debriefing form that reminded them of the continued privacy of their responses and described the purpose of the study. Eligible WIU participants were awarded PURE credit, a requirement for the completion of General Education Psychology courses, upon completion of the study.

Measures

Demographics. A demographic questionnaire was created to gather demographic information from the participant. Items include age, year in school, ethnicity, religion, religiosity, current relationship status, present sexual orientation, current living arrangements, health insurance provider, current health, sexual status, intercourse frequency in the past year, age at first intercourse, number of children, frequency of miscarriages, frequency of abortions, gender of the most recent sexual partner, sterilization, and infertility (see Appendix A).

Contraceptive Methods Questionnaire. The Contraceptive Methods Questionnaire consists of a checklist for known methods of contraception (see Appendix B). The questionnaire addresses contraceptive methods (i.e., birth control pills, condoms, vaginal ring, etc.) participants are currently using and used during their most recent intercourse. The questionnaire includes a list of possible known methods of contraception as well as the informal terms, often a brand name (e.g., The Pill, Depo-Provera, NuvaRing, etc.).

Contraceptive Use Questionnaire. The Contraceptive Use Questionnaire (CUQ) consists of six subscales: General Knowledge of Contraception Scale; General Errors in Contraceptive Use Scale; Intention, Consistency, and Perfection of Contraceptive Use Scale; and Method Specific Errors in Contraceptive Use Scale. The CUQ was created through modification of the Contraceptive Utilities, Intentions, and Knowledge Scale (CUIKS; Condelli, 1984). The CUIKS addresses the contraceptive behaviors of the participants, including how “well” they use their method. The scale was modified for the current study’s specific needs.

General Knowledge of Contraception Scale. The General Knowledge of Contraception portion was modified from the CUIKS for brevity and to include more relevant forms of contraception used today (see Appendix C). Cronbach’s alpha revealed satisfactory reliability (α = .86). The section consists of 21 multiple-choice questions assessing general knowledge regarding contraception. A sample knowledge item is: “The pill: (a) prevents ovulation, (b) keeps cervical mucous very thin, (c) changes the lining of the uterus to make implantation unlikely, (d) both A & C, (e) all of the above;” the correct answer being A. The General Knowledge of Contraception Scale has a total score of 21 points, with each correct answer earning the participant one point and each incorrect/no response earning the participant zero points. Higher scores on the General Knowledge Scale indicate greater knowledge of contraception. Two hundred and forty seven participants completed the General Knowledge of Contraception Scale; the mean score was 12.40 (SD = 2.66).

General Errors in Contraceptive Use Scale. Participants completed the General Errors in Contraceptive Use Scale that assessed their contraceptive use behaviors (see Appendix D). A sample item is: “I have engaged in unprotected sex because the event was unplanned or undesired;” participants responded to seven items on a five-point Likert scale from 1 (definitely not true of me) to 5 (definitely true of me). Total scores were calculated by reversing four items then summing over items; higher scores indicate greater general contraceptive use skill. Cronbach’s alpha was low for this scale (α = .68), which could be attributed to two of the items specifically assessing dual use behaviors, while four specifically assessed their engagement in unprotected intercourse. Two hundred and thirty-seven participants completed the General Skills in Contraceptive Use Scale; the mean score was 23.06 (SD = 5.13).

Intention, Consistency, and Perfection of Contraceptive Use Scale. The Intention, Consistency, and Perfection of Contraceptive Use Scale (ICPCUS) consists of items specific to different forms of contraception that measure intention to use, consistency in use, and perfection in use for the pill, the condom, the IUD, the vaginal ring, or other methods (see Appendix E). For each method, two items measured intention, two items measured consistency, and two items measured perfection. For example, for the Pill, an intention item is: “I will use the pill during my next sexual intercourse.” A consistency item is: “I would describe my pill contraceptive behavior in the past 3 months as consistent (administering the pill daily at the same time, etc.).” A perfection item is: “I would describe my pill contraceptive behavior in the past 3 months as perfect (administering the pill daily at the same time, etc.).” Responses were made on a five-point scale from 1 (definitely not true of me) to 5 (definitely true of me); participants were marked N/A (not applicable) for methods they were not employing. For participants’ primary method, the scores were summed for each area and higher scores indicate greater intention, consistency of use, and perfection of use by the participant, comprising their Primary Method General Use Skills score. Two hundred and seventy-five participants completed the Intention, Consistency, and Perfection of Contraceptive Use Scale; the mean score was 17.88 (SD = 11.95).

Method Specific Errors in Contraceptive Use Scale. Participants completed a Method Specific Errors in Contraceptive Use Scale that assessed method specific contraceptive use behaviors (see Appendix F). Eleven items measured method-specific errors; participants answered questions specific to the pill (2 items), the condom (6 items), the IUD (1 item), the vaginal ring (2 items), and answered not applicable (N/A) for methods they were not using. A sample item is: “During the past year, I took my pill at the same time every day;” participants responded to the 11 items using a five-point Likert scale from 1 (definitely not true of me) to 5 (definitely true of me), and participants not using that specific method reported not applicable. For the participants’ primary method, after reverse scoring, scores were averaged; higher scores indicate greater method specific use skill, comprising their Primary Method Specific Use Skill score. Two hundred and seventy-four participants completed the Method Specific Errors in Contraceptive Use Scale; the mean was 4.93 (SD = 6.60).

Sociosexual Orientation Scale. The Sociosexual Orientation Scale (SOI; Simpson & Gangestad, 1991) measured differences in willingness for or endorsement of casual, promiscuous sex (see Appendix G). The seven-item scale has two past behavioral items and one future behavioral item; one item assesses fantasies with participants responding on an eight-point Likert from 1 (never) to 8 (at least once a day). Three items assess attitudes, with participants responding on a nine-point Likert scale from 1 (strongly disagree) to 9 (strongly agree). The weighted total was computed using scoring from Simpson and Gangestad (1991); of the 139 participants that completed the SOI, the mean was 43.97 (SD = 30.52). Higher scores represent an unrestricted sociosexual orientation. Cronbach’s alpha (.77) demonstrated the internal consistency of the scale.

Brief Sexual Attitudes Scale. The Brief Sexual Attitudes Scale (BSAS; Hendrick, Hendrick, & Reich, 2006) assessed sexual attitudes (see Appendix H). The overall Cronbach’s alpha for the BSAS was .83, (N = 140). The BSAS has four subscales consisting of Permissiveness (10 items; α = .90) with a mean of 2.16 (SD = .90), Birth Control (3 items; α = .86) with a mean of 4.56 (SD = .73), Communion (5 items; α = .80) with a mean of 3.87 (SD = .85), and Instrumentality (5 items; α = .75) with a mean of 3.01 (SD = .84). A sample item from the Instrumentality subscale is: “The main purpose of sex is to enjoy yourself;” 140 participants responded on a five-point Likert scale from 1 (strongly disagree with the statement) to 5 (strongly agree with the statement).

Sexual Desire Inventory. The Sexual Desire Inventory (SDI; Spector, Carey, & Steinberg, 1996) assessed sexual desire (see Appendix I). The SDI is a 14-item scale that includes two subscales: Dyadic Sexual Desire (8 items; α = .90), which measures the desire to engage in sexual behaviors with another individual, and Solitary Sexual Desire (6 items; α = .91), which measures the desire to engage in sexual behaviors in solitary. A sample Solitary Sexual Desire item is: “Compared to other people of your age and sex, how would you rate your desire to behave sexually with a partner?” and 139 participants responded on a 9-point Likert scale from 0 (much less desire) to 8 (much more desire). Higher scores are indicative of higher sexual desire; the mean for SDI Dyadic Sexual Desire was 4.75 (SD = 1.67) and the mean for SDI Solitary Sexual Desire was 2.37 (SD = 2.04).

Sexual Sensation Seeking Scale. The Sexual Sensation Seeking Scale (Kalichman et al., 1994) assessed the need for varied, novel, and complex sexual experience and risk-taking behaviors to enhance the sexual experience (see Appendix J). The ten-item scale has a Cronbach’s alpha of .83. A sample item is: “I enjoy watching ‘X-rated’ videos;” 138 participants responded on a four point Likert scale from 1 (not at all like me) to 4 (very much like me). Higher scores were indicative of higher sexual sensation seeking; the mean was 2.13 (SD = .58).

High-K Strategy Scale. The High-K Strategy Scale assessed the life history strategy of individuals (Giosan, 2006; see Appendix K). The 26-item scale tapped into health, attractiveness, social capital, and upward mobility; the scale is scored in the slow strategy direction. The scale had a high internal consistency reliability, α = .96. A sample item is: “I live in a community to which I am well suited;” participants responded on a five point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Of the 222 participants that completed the scale, the mean score was 5.03 (SD = 1.08).

Mini-K. In the context of life history theory, Figueredo and associates developed the Mini-K in 2006 (see Appendix L). The Mini-K is a 20-item scale that measured the extent to which participants endorse slow life history strategies. The Cronbach’s alpha of the scale was moderate, α = 83. A sample item is: “I am often in social contact with my blood relatives.” One hundred and thirty nine participants responded to the items on a seven point Likert scale from 1 (disagree strongly) to 7 (agree strongly). The mean for the Mini-K was 5.41 (SD = .67).

Contraceptive Attitudes Scale. The Contraceptive Attitudes Scale measured attitudes towards the use of contraceptives in general (Black & Pollack, 1987; see Appendix M). The scale consists of 17 positive items and 15 negative items; sample items from the scale include: “It is no trouble to use contraceptives” and “I believe it is wrong to use contraceptives.” Two hundred and twenty two participants responded on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Higher scores indicate more positive attitudes towards using contraceptives after reverse scoring; the mean was 3.27 (SD = 1.54). The scale’s internal consistency reliability was high (α = .93).

Contraceptive Immorality Scale. The questions pertaining to contraceptive immorality were adapted from A Scale to Assess University Women’s Attitudes About Contraceptives (Fisher et al., 1998; see Appendix N). Only five items are utilized from this measure to ensure brevity. A sample item is: “Using contraception is immoral;” 169 participants responded on a five-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). All 5 items were reverse scored; higher scores indicated higher belief that contraception is moral. Internal consistency reliability was high α =.88, (M = 4.67, SD = .61).

Contraceptive Self-Efficacy Scale. The Contraceptive Self-Efficacy Scale (CSE) assessed motivational barriers to contraceptive use among sexually active adolescent women (see Appendix O). Levinson (1986) created the scale to measure the ability of a female adolescent to control sexual and contraceptive situations. Cronbach’s alpha for this measure was adequate, (α =.64). A sample item is: “When I have sex, I can enjoy it as something that I really wanted to do;” 214 participants responded on a five-point Likert scale from 1 (not at all true of me) to 5 (completely true of me). The mean for the CSE was 3.96 (SD = .55).

Hurlbert Index of Sexual Excitability. The Hurlbert Index of Sexual Excitability (HISE; Apt & Hurlbert, 1993) assessed a female’s ability to become sexually excited (see Appendix P). The scale consists of 25 items and participants responded on a five-point Likert scale from 0 (never) to 4 (all of the time). A sample item is: “I find sex with my partner to be exciting.” Higher scores indicate higher sexual excitability (M = 3.90, SD = .63). Two hundred and ten participants completed the HISE and Cronbach’s alpha for this measure was high, (α =.90).

Consideration of Future Consequences Scale. The Consideration of Future Consequences Scale (Strathman, Gleicher, Boninger, & Edwards, 1994) assessed an individual’s consideration of distant outcomes of current behaviors and the extent of the influence of these outcomes (see Appendix Q). Only the Future Orientation subscale was used for the purpose of this study. The Future Orientation subscale has a mediocre Cronbach’s alpha of .64. The subscale has five items; a sample item from the Future Orientation subscale is: “I consider how things might be in the future and try to influence those things with my day-to-day behavior.” One hundred and thirty-nine participants responded on a five-point Likert scale from 1 (extremely uncharacteristic) to 5 (extremely characteristic). Higher scores on the subscale indicate greater consideration of future consequences (M = 3.79, SD = .57).

Data Screening

The data were checked for univariate outliers by examining box plots. Two participants were noted as being outliers on three or more scales and removed from analysis; likewise, individuals that self-reported as being abstinent were removed from analysis (N = 10). The minimum amount of data needed for subsequent factor analyses were marginally satisfied, with a final sample size of 96 (using listwise deletion). The skewness and kurtosis for each scale were within the tolerable range for assuming a normal distribution and examination of the histograms and stem-and-leaf plots revealed the distributions to look normal.

Life History Strategies Factor Analysis

First, the Brief Sexual Attitudes Scale was divided into the four subscales (Permissiveness, Communion, Birth Control, and Instrumentality), the Sexual Desire Inventory was divided into the two subscales (Solitary and Dyadic), and only the Future Orientation subscale was used from the Consideration of Future Consequences. The HKSS, Mini-K, Sexual Sensation Seeking Scale, Hurlbert Scale of Sexual Excitability, and Sociosexuality Inventory, and the aforementioned subscales were utilized in the factor analysis. See Table 2 for summary of scales used in the Life History strategies factor analysis. Second, the factorability of the 12 scales was examined. The factorability of the 12 scales was assessed using the Kaiser-Meyer-Olkin measure of sampling adequacy, which was above the recommended value of .6 (.62), and Bartlett’s test of sphericity was significant, χ² (66) = 231.98, p < .01. The diagonals of the anti-image correlation matrix were all over .5 (with the exception of Brief Sexual Attitudes Birth Control, at .42), supporting the inclusion of each item in the following factor analysis. Brief Sexual Attitudes Birth Control and Brief Sexual Attitudes Instrumentality had initial communalities below .2 (see Table 3). Sexual Desire Inventory Dyadic, Brief Sexual Attitudes Communion, and Future Orientation had initial communalities below .3 (see Table 3). HKSS, Mini K, Brief Sexual Attitudes Permissiveness, Sexual Desire Inventory Soloitary, Sexual Sensation Seeking, Hurlbert Scale of Sexual Excitability, and Sociosexuality had initial communalities above .3 (see Table 3).

Table 2

Descriptive Statistics Summary for Life History Strategies Scales

Scale	N	# of Items	Mean	SD	α
Sociosexuality Inventory	139	7	43.97	30.52	.77
Brief Sexual Attitudes Permissiveness	140	10	2.16	.90	.90
Brief Sexual Attitudes Birth Control	140	3	4.56	.73	.86
Brief Sexual Attitudes Communion	140	5	3.87	.85	.80
Brief Sexual Attitudes Instrumentality	140	5	3.01	.84	.75
Dyadic Sexual Desire	139	8	4.75	1.67	.90
Solitary Sexual Desire	139	6	2.37	2.04	.91
Sexual Sensation Seeking Scale	138	10	2.13	.58	.83
Hurlbert Inventory of Sexual Excitability	139	25	3.90	.63	.90
High K Strategy Scale	222	23	5.03	1.08	.96
Mini K	139	20	5.41	.67	.83
Future Orientation Subscale	139	5	3.79	.57	.64

Table 3

Life History Strategies Factor Analysis Initial Communalities

	Initial Communalities
High K Strategy	.40
Mini K	.31
Future Orientation Factor of CFC	.24
Brief Sexual Attitudes Permissiveness	.50
Brief Sexual Attitudes Birth Control	.15
Brief Sexual Attitudes Communion	.27
Brief Sexual Attitudes Instrumentality	.15
Sociosexuality	.51
Sexual Sensation Seeking	.34
Hurlbert Scale of Sexual Excitability	.33
Sexual Desire Inventory Solitary	.30
Sexual Desire Inventory Dyadic	.29

A Principal Axis Factoring method of extraction with an Oblimin rotation was utilized. The initial eigenvalues, 2.70 and 1.98 respectively, showed a clear two-factor solution, with the first factor explaining 22.58% of the variance and the second factor explaining 16.52% of the variance. Two other factors explained 11.92 and 9.35 percent of the variance, with 1.43 and 1.12 eigenvalues respectively. Due to apparent “leveling off” as examined in the scree plot and theoretical considerations, only the first two factors were analyzed further. Both factors explained a total of 39.35% of the variance. There was little difference between an Oblimin and Varimax rotation; the two factors correlated at -.11.

The results of the factor analysis confirmed the two-factor hypothesis; the factor-loading matrix is presented in Table 4. The scales assessing sexual behaviors loaded strongly on the first factor (e.g., .69 and .57) and subsequently loaded weakly (e.g., .27) or negatively (e.g., -.03) on the second factor, with scales assessing non-sexual behaviors; illustrating the distinct nature of these two factors. Likewise, the scales assessing non-sexual behaviors loaded strongly on the second factor (e.g., .57) and loaded negatively (e.g., -.15) on the first factor; this reiterates the distinct and separate nature of these two factors. Contrary to hypotheses, the Hurlbert Scale of Sexual Excitability loaded moderately on the second factor, .46, and weakly on the first factor, .23, where it was hypothesized to load strongest. Also, Brief Sexual Attitudes Instrumentality loaded moderately on the first factor as predicted, .22, but also loaded moderately and negatively on the second factor, -.33, not as predicted. Brief Sexual Attitudes Birth Control loaded weakly on both factors, .05 for the first and .28 for the second. Brief Sexual Attitudes Permissiveness cross-loaded, but in the hypothesized direction: positively on the first factor, .56 and negatively on the second factor, -.40. Brief Sexual Attitudes Communion loaded weakly on the first and second factor, .17 and .01 respectively.

Table 4

Life History Strategies Factor Matrix

	Factor 1	Factor 2
High K Strategy	-.02	.52
Mini K	-.26	.57
Future Orientation Factor of CFC	-.15	.46
Brief Sexual Attitudes Permissiveness	.60	-.40
Brief Sexual Attitudes Birth Control	.05	.28
Brief Sexual Attitudes Communion	.17	.01
Brief Sexual Attitudes Instrumentality	.22	-.33
Sociosexuality	.51	-.38
Sexual Sensation Seeking	.69	.27
Hurlbert Scale of Sexual Excitability	.23	.46
Sexual Desire Inventory Solitary	.57	-.03
Sexual Desire Inventory Dyadic	.48	-.14

The factor labels suited the hypothesized predictions; Factor 1 was labeled the “r Factor” and Factor 2 being labeled the “K factor.” However, when factor scores were computed to use in subsequent hierarchical regression analyses, the scales that did not follow in line with the theoretical framework were eliminated. The Hurlbert Scale of Sexual Excitability, Brief Sexual Attitudes Birth Control, Brief Sexual Attitudes Communion, and Brief Sexual Attitudes Instrumentality scales were dropped from the subsequent factor analyses, due to the lack of theoretical support and the presence of weak or cross loadings.

K and r Factor scores. To compute factor scores, Principal Axis Factoring method of extraction with a Varimax rotation with Kaiser Normalization was utilized. HKSS, Mini K, Brief Sexual Attitudes Permissiveness, Sexual Desire Inventory Solitary, Sexual Desire Inventory Dyadic, Sexual Sensation Seeking, Sociosexuality, and Future Orientation were inputted into the analysis. The factorability of the eight scales was examined using the Kaiser-Meyer-Olkin measure of sampling adequacy, which was above the recommended value of .6 (.65), and Bartlett’s test of sphericity was significant, χ² (28) = 172.80, p < .01. Examination of the anti-image correlation matrix revealed correlations above .5, with the exception of the HKSS (.49), supporting their inclusion in the analysis. With the exception of Future Orientation, HKSS, Sexual Desire Inventory Solitary, Mini K, and Sexual Sensation Seeking, the initial communalities were above .3. Variance is accounted for by the two-factor solution was 52.78%. The rotated factor matrix is presented in Table 5. Factor scores were created for each of the two factors, based on the mean of the items, which had their primary loadings on each factor. Higher scores on the r Factor indicated greater endorsement of sexual behaviors and higher scores on the K Factor indicated greater endorsement of non-sexual behaviors. Descriptive statistics are presented in Table 6. The skewness and kurtosis were within the tolerable range for assuming a normal distribution and examination of the histograms revealed the distributions to look normal.

Table 5

Life History Strategies Factor Scores Factor Matrix

	r Factor	K Factor
High K Strategy		.62
Mini K		.60
Future Orientation Factor of CFC		.52
Brief Sexual Attitudes Permissiveness	.72
Sociosexuality	.66
Sexual Sensation Seeking	.58
Sexual Desire Inventory Solitary	.49
Sexual Desire Inventory Dyadic	.51

Note: Factor loadings < .40 are suppressed

Table 6

Descriptive Statistics for r Factor and K Factor Scores (N = 99)

	M (SD)	Skewness	Kurtosis
r Factor	.00053 (.87718)	.34	.44
K Factor	.00146 (.81292)	.44	-.40

Effectiveness Score Factor Analysis

In order to test hypothesis one, a factor analysis was ran to compute the participants’ contraceptive method effectiveness score. First, Primary Method Specific Errors, Primary Method General Errors, General Errors in Contraceptive Use, and General Knowledge of Contraception was factor analyzed and one factor was extracted using Principal Components Analysis. Examination of initial communalities and the anti-image correlation matrix revealed General Knowledge of Contraception did not correlate with the other three scales, and was consequently dropped from analysis. The factorability of the three scales was examined using the Kaiser-Meyer-Olkin measure of sampling adequacy, which was satisfactory (.53). Also, Bartlett’s test of sphericity was significant, χ² (3) = 16.83, p < .01. A Principal Components Analysis was utilized to extract one factor from the three scales, and 44.72% of the variance was explained by this single factor. The component matrix is presented in Table 7. Component scores were created from the single factor solution; higher scores indicated higher general and method specific use skills. In the next step, participant’s scores on the General Knowledge of Contraception Scale were converted into z scores and subsequently averaged with the contraceptive use skills factor scores. This represented a composite effectiveness score of their contraceptive use skill and contraceptive knowledge. Lastly, participants’ specific method was taken into account by multiplying their composite score of use skills and knowledge by a percentage. This percentage was derived from known contraceptive efficacy (see Trussell, 2007): with typical use, eight women (of 100) experience an unintended pregnancy within the first year of use of the pill or ring, therefore participants using either the pill or ring received 92% of their composite effectiveness score. To calculate what percentage of their score they received, pill and vaginal ring users’ composite scores were multiplied by .92, condom users’ composite scores were multiplied by .85, and IUD users’ composite scores were multiplied by .992. The resulting score was known as their overall contraceptive effectiveness score. Descriptive statistics are presented in Table 8 and 9, separated by single method users and dual method users.

Table 7

Contraceptive Use Skill Factor Analysis Factor Loadings for Effectiveness Score

	Factor Loadings
General Contraception Use Skill	.72
Primary Method General Use Skill	.78
Primary Method Specific Use Skill	.47

Table 8

Descriptive Statistics for Contraceptive Effectiveness Score, Single Users


	M(SD)	Skewness	Kurtosis
Effectiveness Score (skill)	-.24(.96)	-.67	.02
Effectiveness Score (skill & knowledge)	-.09(.80)	-.12	-.59
Effectiveness Score (skill, knowledge, & method)	-.09(.66)	-.28	-.42

Table 9

Descriptive Statistics for Contraceptive Effectiveness Score, Dual Users


	M(SD)	Skewness	Kurtosis
Effectiveness Score (skill)	.49(.91)	-.85	.74
Effectiveness Score (skill & knowledge)	.22(.72)	-.60	1.48
Effectiveness Score (skill, knowledge, & method)	.26(.58)	.09	-.58

Results

Contraceptive Effectiveness

It was hypothesized that an interaction between r and K strategies would be found to predict contraceptive method effectiveness, such that high r and high K women would utilize the most effective methods. Results did not support my first hypothesis, N = 71. To examine the contribution of the r Factor, the K Factor, and their interaction to explain contraceptive effectiveness (skill only), a hierarchical multiple regression analysis was performed. The r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). Before analysis was performed, the independent variables were examined for collinearity. Results of the variance inflation factor (VIF) were all less than 2.0, and collinearity tolerance were greater than .80, suggesting the estimated βs were well established in the model. The results were not significant, p > .05.

Results from analysis on contraceptive effectiveness (skill and knowledge) did not support my first hypothesis, N = 99. To examine the contribution of the r Factor, the K Factor, and their interaction to explain contraceptive effectiveness (skill and knowledge), a hierarchical multiple regression analysis was performed. The r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). Before analysis was performed, the independent variables were examined for collinearity. Results of the variance inflation factor (VIF) were all less than 2.0, and collinearity tolerance were greater than .92, suggesting the estimated βs were well established in the model. The results were not significant, p > .05.

Results from analysis on contraceptive effectiveness (skill, knowledge, and method) did not support my first hypothesis, N = 76. To examine the contribution of the r Factor, the K Factor, and their interaction to explain contraceptive effectiveness (skill, knowledge, and method), a hierarchical multiple regression analysis was performed. The r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). Before analysis was performed, the independent variables were examined for collinearity. Results of the variance inflation factor (VIF) were all less than 2.0, and collinearity tolerance were greater than .80, suggesting the estimated βs were well established in the model. The results of step three were not significant, p > .05. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 10. A graph illustrating the interaction of the two factors is presented in Figure 23.

Table 10

Summary of Hierarchical Regression Analysis for Contraceptive Effectiveness, N = 76

Step	Variable	ΔR²	β	b
1	r Factor	.00	.10	.10
2	K Factor	.01	.10	.09
3	Interaction	.01	.10	.13
	Intercept			-.09

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps. The overall model was not significant, R² = .02, F(3,72) = .49, p > .05.

Contraceptive Knowledge

It was hypothesized that an interaction between r and K strategies would be found to predict contraceptive knowledge, such that high r and high K women would be most knowledgeable about contraception. Results did not support my second hypothesis, N = 100. To examine the contribution of the r Factor, the K Factor, and their interaction to explain contraceptive knowledge, a hierarchical multiple regression analysis was performed. The r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results were not significant, p > .05. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 11. A graph illustrating the interaction of the two factors is presented in Figure 24.

Table 11

Summary of Hierarchical Regression Analysis for Contraceptive Knowledge, N = 99

Step	Variable	ΔR²	β	b
1	r Factor	.02	.12	.41
2	K Factor	.00	-.04	-.12
3	Interaction	.01	.08	.34
	Intercept			11.62

General Contraceptive Attitudes

It was hypothesized that an interaction between r and K strategies would be found to predict general attitudes towards contraceptives, such that high r, high K women would have the most positive attitudes. Results did not exactly support my third hypothesis, N = 100. To examine the contribution of the r Factor, the K Factor, and their interaction to explain general attitudes towards contraception, a hierarchical multiple regression analysis was performed. The r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) by the three variables equaled .05 (adjusted R² = .02), which was not significantly different from zero, F(3, 96) = 1.72, p >.05. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 12. A graph illustrating the interaction of the two factors is presented in Figure 25.

Table 12

Summary of Hierarchical Regression Analysis for Contraceptive Attitudes, N = 100

Step	Variable	ΔR²	β	b
1	r Factor	.01	.02	.02
2	K Factor	.04*	-.20	-.20^✚
3	Interaction	.00	.07	.08
	Intercept			4.09

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps. The F_Δ for Model 2 was significant, R² = .05, F_Δ (1,97) = 4.02, p < .05. * p < .05;^✚ p = .06

Contraceptive Morality Attitudes

It was hypothesized that an interaction between r and K strategies would be found to predict morality attitudes towards contraceptives, such that high r, high K women would be the most morally accepting of contraceptives. Results did not fully support my fourth hypothesis, N = 100. To examine the contribution of the r Factor, the K Factor, and their interaction to explain morality attitudes towards contraception, a hierarchical multiple regression analysis was performed. The r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the three variables equaled .07 (adjusted R² = .04), which was nearly significantly different from zero, F(1, 98) = 2.32, p = .080. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 13. A graph illustrating the interaction of the two factors is presented in Figure 26.

Table 13

Summary of Hierarchical Regression Analysis for Contraceptive Morality Attitudes, N = 100

Step	Variable	ΔR²	β	b
1	r Factor	.04^✚	.21	.17*
2	K Factor	.02	.14	.12
3	Interaction	.02	.13	.14
	Intercept			4.57

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps.The overall model was not significant, R² = .07, F(3, 96) = 2.32, p = .08. * p < .05;^✚ p = .06

Contraceptive Self-Efficacy

It was hypothesized that an interaction between r and K strategies would be found to predict contraceptive self-efficacy, such that high r, high K women would have the highest contraceptive self-efficacy. Results from single method users did not fully support my fifth hypothesis, (N = 71). To examine the contribution of the r Factor, the K Factor, and their interaction to explain contraceptive self-efficacy, a hierarchical multiple regression analysis was performed. For single method users (N = 71), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with variables entered equaled .08 (adjusted R² = .04), which was not significantly different from zero, F(3, 67) = 1.96, p >.05. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 14. A graph illustrating the interaction of the two factors is presented in Figure 27.

Table 14

Summary of Hierarchical Regression Analysis for Contraceptive Self-Efficacy from Single Method Users, N = 71

Step	Variable	ΔR²	β	b
1	r Factor	.00	.04	.03
2	K Factor	.05	.26	.17*
3	Interaction	.03	.19	.16
	Intercept			3.91

Additional Analyses

Age. To examine the contribution of the r Factor, the K Factor, and their interaction to explain age, a hierarchical multiple regression analysis was performed. For all participants (N = 100), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the three variables entered equaled .33 (adjusted R² = .31), which was significantly different from zero, F(3, 96) = 15.77, p < .01. Thirty-three percent of the variance in age can be accounted for by the two factors and their interaction. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 15. A graph illustrating the interaction can be found in Figure 28.

Table 15

Summary of Hierarchical Regression Analysis for Age, N = 100

Step	Variable	ΔR²	β	b
1	r Factor	.08**	.38	2.22***
2	K Factor	.19***	.48	3.02***
3	Interaction	.06***	.25	1.92**
	Intercept			21.93

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps.The overall model was significant, R² = .33, F(3, 96) = 15.77, p < .001. ** p < .01; ***p <.001

Delay of sexual intercourse. To examine the contribution of the r Factor, the K Factor, and their interaction to explain the time lapsed from age at first sexual intercourse and age at first menstruation, a hierarchical multiple regression analysis was performed. Delay of Sex was computed by subtracting Age at First Menstruation from Age at First Sexual Intercourse, M = 4.22, SD = 2.49. For sexually active participants (N = 92), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the variables entered equaled .05 (adjusted R² = .02), which was not significantly different from zero, F(3, 88) = 1.67, p > .05. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 16. A graph illustrating the interaction of the two factors is presented in Figure 29.

Table 16

Summary of Hierarchical Regression Analysis for Delay of Sex, Not Sexually Active Participants Removed, N = 92

Step	Variable	ΔR²	β	b
1	r Factor	.01	-.05	-.16
2	K Factor	.04^✚	.20	.60^✚
3	Interaction	.00	-.07	-.27
	Intercept			4.18

Religiosity. To examine the contribution of the r Factor, the K Factor, and their interaction to explain religiosity, a hierarchical multiple regression analysis was performed. For all participants (N = 100), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the three variables entered equaled .24 (adjusted R² = .22), which was significantly different from zero, F(3,96) = 10.41, p < .01. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 17. A graph illustrating the interaction of the two factors is presented in Figure 30.

Table 17

Summary of Hierarchical Regression Analysis for Religiosity, N = 100

Step	Variable	ΔR²	β	b
1	r Factor	.10**	-.21	-.36*
2	K Factor	.12***	.35	.65***
3	Interaction	.02^✚	-.15	-.33^✚
	Intercept			2.18

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps. The overall model was significant, R² = .24, F(3, 96) = 10.41, p < .001. ** p < .01; * p < .05; ^✚ p = .10

Possibility of being infertile. To examine the contribution of the r Factor, the K Factor, and their interaction to explain the possibility of being infertile, a hierarchical multiple regression analysis was performed. For all participants (N = 100), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the variables entered equaled .06 (adjusted R² = .04), which was nearly significantly different from zero, F(3, 96) = 2.22, p = .09. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 18. A graph illustrating the interaction of the two factors is presented in Figure 31.

Table 18

Summary of Hierarchical Regression Analysis for Possibility of Being Infertile, N = 100

Step	Variable	ΔR²	β	b
1	r Factor	.03^✚	.10	.13
2	K Factor	.04*	-.20	-.28*
3	Interaction	.00	.01	.01
	Intercept			2.08

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps. The second overall model was significant, R² = .07, F(2,97) = 3.36, p < .05. * p < .05; ^✚ p = .10

Sex frequency in the past year. To examine the contribution of the r Factor, the K Factor, and their interaction to explain frequency of sex in the past year, a hierarchical multiple regression analysis was performed. For sexually active participants (N = 86), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the variables entered equaled .10 (adjusted R² = .06), which was significantly different from zero, F(3, 82) = 2.96, p = .04. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 19. A graph illustrating the interaction of the two factors is presented in Figure 32.

Table 19

Summary of Hierarchical Regression Analysis for Sex Frequency in the Past Year, Not Active Participants Removed, N = 86

Step	Variable	ΔR²	β	b
1	r Factor	.07**	.29	18.96**
2	K Factor	.01	.07	4.99
3	Interaction	.02^✚	.14	12.585^✚
	Intercept			53.49

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps. The overall model was significant, R² = .10, F(3,82) = 2.96, p < .05. ** p < .01; ;^✚ p = .18

For sexually active dual method users (N = 25), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the r Factor, the K Factor, and their interaction entered equaled .40 (adjusted R² = .32), which was significantly different from zero, F(3, 21) = 4.68, p < .01. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 20. A graph illustrating the interaction of the two Factors is presented in Figure 33.

Table 20

Summary of Hierarchical Regression Analysis for Sex Frequency in the Past Year from Dual Method Users, N = 28

Step	Variable	ΔR²	β	b
1	r Factor	.20*	.63	20.77***
2	K Factor	.15*	.38	14.98*
3	Interaction	.10^✚	.29	11.45^✚
	Intercept			37.59

*** p < .001; *p < .05;^✚ p = .08

Current health. To examine the contribution of the r Factor, the K Factor, and their interaction to explain reported current health, a hierarchical multiple regression analysis was performed. For all participants (N = 100), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the r Factor and K Factor entered equaled .09 (adjusted R² = .06), which was significantly different from zero, F(3, 96) = 3.16, p = .03. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 21. A graph illustrating the interaction of the two factors is presented in Figure 34.

Table 21

Summary of Hierarchical Regression Analysis for Current Health, N = 100

Step	Variable	ΔR²	β	b
1	r Factor	.04*	-.14	-.14
2	K Factor	.04*	.22	.22*
3	Interaction	.00	-.04	-.05
	Intercept			3.98

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps. The overall model was significant, R² = .09, F(3,96) = 3.16, p < .05. *p < .05

Age at first sexual intercourse. To examine the contribution of the r Factor, the K Factor, and their interaction to explain age at first sexual intercourse, a hierarchical multiple regression analysis was performed. For sexually active participants (N = 92), the r Factor was entered in the first step, the K Factor was entered in the second step, and step three included the Interaction variable (r Factor X K Factor). The results of step three indicated that the variance accounted for (R²) with the variables entered equaled .10 (adjusted R² = .07), which was significantly different from zero, F(3, 88) = 3.40, p = .02. The unstandardized regression coefficients (b) and the intercept, and the standardized regression coefficients (β) are reported in Table 22. A graph illustrating the interaction of the two factors is presented in Figure 35.

Table 22

Summary of Hierarchical Regression Analysis for Age at First Sexual Intercourse, Not Active Participants Removed, N = 92

Step	Variable	ΔR²	β	b
1	r Factor	.01	-.02	-.04
2	K Factor	.10**	.31	.79**
3	Interaction	.00	-.04	-.12
	Intercept			16.72

Note: Standardized (β) and unstandardized (b) coefficients are from the final model (i.e., after Step 3). ΔR2 values are provided for each predictor as they were entered in consecutive steps. The overall model was significant, R² = .10, F(3,88) = 3.40, p < .05. ** p < .01

Discussion

Life History Strategies

It was proposed that the traditional view of the K strategy be maintained with respect to future time perspective, and long-term mating strategy. Concurrently, sexual drive, sexual desire, sexual flexibility, and lack of sexual restraint (Figueredo et al., 2006) should be removed from K and placed on a separate factor; this second factor could be described as r. It was predicted that sexual clusters of behavior are sex drive, attitudes towards sex, sexual excitability, and sexual sensation seeking; those behaviors also lie on a continuum. Individuals can be low in sexual restraint (r) or high, just as much as they can be high or low on the K spectrum. These sexual clusters of behavior should load on one factor.

Results of a two-factor solution on life history strategies scales supported this hypothesis. As observed in the factor-loading matrix presented in Tables Four and Five, the scales assessing sexual behaviors (r) loaded strongly on the first factor (Sociosexuality, Brief Sexual Attitudes Permissiveness, Sexual Sensation Seeking, Sexual Desire Inventory Solitary, and Sexual Desire Inventory Dyadic) and subsequently loaded weakly or negatively on the second factor, which is comprised of scales assessing non-sexual behaviors (K; Mini-K, HKSS, and Future Orientation). Likewise, the scales assessing non-sexual behaviors (K) loaded strongly on the second factor and also loaded weakly or negatively on the first factor; this pattern of loadings illustrates the distinct and separate nature of these two factors. In addition, results from the factor correlation matrix also revealed these two factors are distinct, r = -.18.

This two-factor solution does account for individuals that exhibit high K strategies in non-sexual clusters of behavior while exhibiting traditional r (low K) strategies in sexual clusters of behavior. To measure life history strategy on one continuum, as done previously, with sexual and non-sexual clusters of behavior within the single continuum does not reveal the whole picture of human life. First, to evaluate a two-factor solution of life history strategies, I attempted to predict contraceptive behaviors.

Effectiveness Score

Dual method users and single method users. Results did reveal that participants reporting the use of more than one method (dual users) had higher effectiveness scores, on all three variations of measurement, than participants reporting the use of only one method (see Tables 8 and 9). As a check of validity, it appears that dual users have increased contraceptive skills, contraceptive knowledge, and use more effective methods overall. Participants could also denote if they were dual method users (e.g., pill and condom, vaginal ring and condom, etc.).

The majority of participants were single method users (N = 208), while 68 participants reported being dual method users. Fascinatingly, many participants reported using the withdrawal method in addition to other methods listed in the Current or Recent Methods on the CMQ (N = 68). Future research might look to explore this result, since 25 percent of participants were reporting this variation of “dual” methods. Future research might also look to explore the particular cognitions behind this pattern of behavior: are individuals withdrawing because they do not trust their primary method? Do they not understand how their method works? Or are they a method they believe to be reliable and effective? Furthermore, future research might tease apart individuals who self-identify as dual method users, when in fact, they are using the withdrawal method instead of the traditionally accepted condom method in conjunction with another method.

Contraceptive Effectiveness

It was hypothesized that an interaction between r and K strategies will be found to predict contraceptive method effectiveness, such that high r and high K women utilized the most effective methods. The results revealed a trend in the hypothesized direction (see Table 1); although underpowered, high r and high K women utilized the most effective methods (see Figure 1). With continued collection of data, this interaction between r and K will be supported. The methodology used in the current study was brand new; no previous literature has examined a composite of individuals’ contraceptive use skills, contraceptive knowledge, and their contraceptive method as an index of their contraceptive effectiveness. Prior research has examined various demographic variables to explain why women won’t use contraception (Nettleman et al., 2007), their level of commitment to preventing pregnancy (Bartz et al., 2007), or inconsistent use of contraception (Davies et al., 2006); however, no previous research has examined the weight of three variables that might help explain an individual’s contraceptive behaviors. The results, although in the hypothesized direction, lacked significance. This could be due to a myriad of issues caused by lack of power and the new measurement procedure. Future research will hopefully include a more comprehensive review of each user and their contraceptive behaviors. Hopefully future research will aid in progressing this avenue of measurement forward, specifically in addressing a contracepting individual wholly: by examining their use skills, knowledge, and choice of method in order to fully understand their contraceptive behaviors. More comprehensive information in this manner might lend to contraceptive education reform and expand what we know about contraception overall.

Contraceptive Knowledge

It was hypothesized that an interaction between r and K strategies would be found to predict contraceptive knowledge, such that high r and high K women would be most knowledgeable about contraception. The results revealed a trend in the hypothesized direction; although lacking significance due to being underpowered, high K, high r participants were the most knowledgeable of contraception.

However, participants in this sample did not score well on the 21-point multiple-choice examination assessing contraceptive knowledge; the mean score was 12.40 (SD = 2.66). This lack of statistical support could also be due to the nature of these questions; the presence of a multiple choice test embedded within self-report measures might be responsible for error, since this method of measurement was in contrast with what individuals expect when participating in survey research. However, this result does highlight the necessity for more comprehensive sex education.

This is not to say that individuals’ contraceptive knowledge should not be taken into account when assessing their contraceptive behaviors. Questions on the General Knowledge of Contraception Scale included items that were general in nature: “a woman can get pregnant” with the options: “(a) a few minutes after sexual intercourse; (b) a few hours after sexual intercourse; (c) a few days after sexual intercourse, (d) all of the above.” These questions are not specific to any method, but to the consequences of sexual intercourse in general; thus, irrespective of the participant’s method, they should be able to answer these questions to the best of their knowledge. In addition, the Contraceptive Knowledge Measure included items for each of the four main methods (pill, IUD, ring, and condom); those individuals using these specific methods might more easily answer these questions. In conclusion, this specific population might not have the contraceptive knowledge to replicate previous findings (e.g., Frost et al., 2012).

General Contraceptive Attitudes

Contraceptive Morality Attitudes

It was hypothesized that an interaction between r and K strategies would be found to predict morality attitudes towards contraceptives, such that high r, high K women would be the most morally accepting of contraceptives. The results revealed a trend in the hypothesized direction; although underpowered, high r and high K women were the most morally approving of contraception. Contraceptives are methods that prevent pregnancy, consequences of the behavior high r, high K individuals engage in more freely than their counterparts.

Contraceptive Self-Efficacy

It was hypothesized that an interaction between r and K strategies would be found to predict contraceptive self-efficacy, such that high r, high K women would have the highest contraceptive self-efficacy. Results from single users did not fully support my fifth hypothesis, but were trending in the hypothesized direction. High r, high K participants had the highest contraceptive self-efficacy. Along with more data to reveal the relationship of the two factors with contraceptive self-efficacy, it might be more pertinent to examine the individual’s reported behaviors (as seen the CUQ) as an index of their contraceptive self-efficacy, rather than the measure at hand. With that, future research might also look to examine if participants’ contraceptive effectiveness scores can predict their contraceptive self-efficacy, or vice versa.

Life History Outcomes

In order to examine the notion and efficacy of a two-factor solution of life history strategies, I also attempted to predict relevant life history outcomes.

Age. For age, a significant interaction was found between the r Factor and the K Factor. There was a greater gap in age between high r individuals than low r individuals; furthermore, high r, high K individuals were the oldest in the sample. Furthermore, it seems in this sample, high K females endorsed sexual clusters of behavior more as they mature.

It is also interesting to examine that individuals low on the K Factor were the youngest; perhaps the K strategy is indicative of overall maturity. Individuals low on the K Factor can be aggressive, rarely cooperate, rarely engage in altruistic behaviors, disregard of social rules, manipulative, exploitative, short term oriented; all of which can be viewed as individuals lacking maturity.

Religiosity. Results of a nearly significant interaction revealed low K, high r participants were the least religious, while high K, low r participants were the most religious. Figueredo and others (2006), as traditionally measured, high K individuals are more likely to be high on religiosity, and these results confirm that notion. Interestingly, Figueredo and others (2006) also note that indicators of sexual restrictedness include increased religiosity; the negative direction of the r Factor in predicting religiosity supports this notion directly, the more a participant endorses sexual unrestrictedness, the lower they report their religiosity. Although the interaction was not nearly significant, these results highlight the interplay between the two-factor solution to help explain relevant life history outcomes such that high r, high K participants were less religious than low r, low K participants.

Sex frequency in past year.

Single method users. Results, although almost significant, were trending in the hypothesized direction; high r, high K participants had the highest sex frequency in the past year. This result acts as a validity check for using the two-factor solution in measuring life history strategies. It is apparent that individuals endorsing sexual clusters behaviors would also engage in sexual intercourse more frequently. Interestingly, for all participants and single users, the K factor was not a significant predictor in sexual intercourse frequency in the past year. This lack of predictive ability with respect to a pivotal and relevant life history outcome highlights the need for two-factor solution measuring life history strategies. Simply being low on K factor does not reveal an individual engages in sexual variety, sexual promiscuity, and unrestrained sexuality (as viewed in the traditional model of measurement); instead, their scores on the r Factor significantly predicted this relevant life history outcome.

Dual method users. Results from sexually active dual users revealed stronger support for a two-factor solution compared to that of single method users, in spite of the fact that this group had a low sample size (n = 25). A nearly significant interaction revealed high r, high K participants had the most sex in the past year. These results still support the two-factor solution for measuring life history strategies. Furthermore, sexual frequency was highest for high r, high K dual method users, suggesting that the combination of high sex drive from the r Factor and safer sex from the K Factor makes for a high frequency of sex!

Age at first sexual intercourse. Results from sexually active participants, lacking significance, were in the predicted direction, such that high r participants were younger at age of first sex. However, it might be more pertinent to examine the time elapsed between menstruation and age at first sexual intercourse as a more relevant life history outcome, rather than these two events in isolation.

Delay of sexual intercourse. Delay of Sex was computed by subtracting age at first menstruation from age at first sexual intercourse. Age at first menstruation did not yield significant results upon analysis, but delay of sexual intercourse did show support for the two-factor solution. Results from sexually active participants, although not significant, revealed participants higher on the r Factor did not delay sex long after first menstruation; however, individuals lower on the K Factor delayed sex longer than their high K counterparts. These results are fascinating in that they support the notion of utilizing a two-factor solution to measure relevant life history outcomes; with more data, the interaction between the two factors should become clearer.

Possibility of being infertile. Results revealed a main effect for the K factor. Individuals high on the K factor were less likely to hold the belief that they were infertile. This is an interesting result in that it highlights the nature of the K factor. The K factor is concerned with extensive parental investment and intensive offspring investment, both of which are indicative of an individual being able to successfully bear children. This is not to say that they are engaging in the proximate act of sexual intercourse, but they hold the belief that they are not likely to have issues with conception. Interestingly, as measured traditionally, individuals low on K are seen to have increased fecundity; this might be true in reality, however, the results of the current study report that individuals high on K perceive themselves as having higher fecundity than their low K counterparts.

Current health. Results revealed a main effect for the K factor. Individuals high on the K factor reported being more healthy than their low K counterparts. This result supports the traditional view of measuring life history strategies. The K strategy involves clusters of behavior that are indicative of increased health: extensive parental investment, consideration of risks, long lifespan, increased inclusive fitness, extensive somatic investment, and long-term thinking.

Limitations

In the current study, the lack of power was attributed to the response rate, which was well below 50%; this also contributed to the ever-fluctuating sample sizes for each of the scales utilized. Due to using both convenience sampling, in which participants were not rewarded for their participation, and a human subjects pool, in which participants were rewarded for participation, the attrition from participants not receiving compensation was high. Furthermore, the sheer number of items required of the participants also led to a higher attrition rate for individuals participating without compensation. Furthermore, oversight in measurement construction lead to condom users responding to five questions about their method specific use skills, pill and ring users responding to two questions, IUD users responding to one, and individuals not utilizing these methods answered none.

Future Directions

More data will be collected to not only flesh out the interaction between the r Factor and K Factor for the hypothesized contraceptive results that were not significant, but also to reveal their relationship with the relevant LH outcomes explored as well. The next step in improving the overall experimental design at hand is to modify, shorten, and clarify the scales used to assess the participant’s life history strategy and contraceptive behaviors. However, this study lays foundational groundwork on what dimensions comprise an individuals sexual clusters of behavior (r Factor) and non-sexual clusters of behavior (K Factor); future psychometric work can improve and streamline the measurement of these two-factors solution for life history strategies. Factor analyses and reliability analyses should be ran item level to determine what items are the best indicators of each strategy; these items could be used to create a short form for assessing these two strategies. Furthermore, refinement of measurement of contraceptive effectiveness can be made, due to the initial work of the current study. Future studies will hopefully fine-tune the assessment of an individual’s actual contraceptive behavior, either by utilizing an observational method (e.g., Lindemann & Brigham, 2003) or more detailed self-reports specific to their contraceptive method, either in Likert-scale or free-response fashion. Likewise, questions should be constructed for methods outside the primary four, as utilized in the current study.

Conclusion

Seventy seven percent of participants in this sample reported using a contraceptive method, while only 23 percent reported no method. These results shed initial light on how individuals can pursue both an r and K strategy. The resource that allows individuals to pursue both traditional r and traditional K strategies is contraception. Life history theory, in its purest form, is concerned predominantly with the timing of reproduction, since that is the final stage organisms can allocate resources to in their lifetime (Griskevicius et al., 2011). The question of how individuals time their reproduction is raised: they use contraception.

Current measures of life history strategies are not comprehensive and problematic: to ask someone if they endorse long-term mating (e.g., the Mini-K, Figueredo et al., 2006) doesn’t provide a representative summary of the clusters of behaviors indicative of their life history strategy, specifically strategies signifying the timing of their reproduction. Instead, it would be much more pertinent to ask individuals more specifically about their sexual restraint, or lack thereof, and their contraceptive behaviors. These two facets might reveal a more realistic index of what life history strategy they are employing. Because life history, biologically and psychologically, really is about reproductive timing (Griskevicius et al., 2011): it takes sex to reproduce and it takes contraception to prevent it. The questions individuals should be asked are more along the lines of: do you want to reproduce, and when? Do you want to prevent pregnancy, and what length do you go to prevent pregnancy? How well do you prevent reproduction, and how often do you engage in intercourse (the act of reproduction)? These behaviors are indicative of a life history strategy, as it relates to the theory it was originally distilled from (Promislow & Harvey, 1990, 1991).

It is through the use of contraception females can pursue a mixed strategy of combining both r and K strategies. Although women have been practicing contraception since they could keep written records, it was only recently that highly effective hormonal methods allowed women to virtually perfect their contracepting behaviors (Szarewski & Guillebaud, 1991). Since sex with contraception lowers the risk of pregnancy, women can combine r and K strategies. High K specific strategies such as high personal investment (attending a university, pursuing a career, etc.), delay of reproduction, and increased investment in offspring quality with birth spacing can be achieved by utilizing an effective form of contraception. However, these women can also engage in a high frequency of intercourse both inside and outside the context of relationships, a strategy often displayed by high r individuals.

Contraception makes it possible for an individual to begin sexual activity at an early age while investing in self-development (e.g., pursuing a college degree), to have sex with multiple partners without conception, choose one long-term partner (with or without non-conception extra pair copulation), and invest heavily in a limited number of children (due to contraception’s child spacing and limiting benefits). Through contraception, females can pursue promiscuous or committed sex; females can have as much sex without conception as desired; and consequently, enjoy sex proximately without the ultimate consequence of pregnancy. The results of the study support this notion. Specifically, the majority of participants were employing a contraceptive method (80%), the majority of participants are sexually active and the average age at first sex was 15.54, 43.2 percent of participants in the current study were dating exclusively, and no participants (in analysis) were pregnant. Furthermore, 96.8% of participants in analysis did not have children and 96.8% have not had an abortion; the combining of the r and K strategies seems to be successful in terms of reproductive consequences.

To conceptualize life history strategies in this manner before might have been difficult. Similarly, we know knowledge is a predictor of contraceptive use; so if individuals could gain more knowledge about contraception, they could effectively change their strategy under this theory and might actually be successful (Frost et al., 2012). However, according to Kaplan and Lancaster (2003), “anthropological research among small-scale societies reveals that with increasing resources and increasing investment in embodied capital, people choose to have fewer children, and they choose to have them later.” The resources could not only indicate shifting from low K to high K, but those resources could be access to and implementation of safe and effective contraception. Through contraception, these individuals can be successful in having fewer children and having them later in life, other indicators of high K strategies. This is not to say that one strategy is better than others, even while some say one is more “socially desirable” (Figureredo et al., 2006). Nonetheless, when it comes to global overpopulation and carrying capacity (Ehrlich & Ehrlich, 2013), maternal mortality (Esscher, Högberg, Haglund, & Essën, 2013; Storeng, Drabo, Filippi, 2013), AIDS/HIV (Kanki, 2013), limited access to contraception (Dennis et al., 2012), threats to access (Horga, Gerdts, & Potts, 2013), in terms of reproductive timing: the right combination of each strategy is better.

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Challenging "Resource Holding Potential"

2013-06-10T13:21:00.001-07:00

Cesario et al. 2010

Study 1 revealed an individual’s ecology drives their cognitions when primed with an image they perceive as aggressive; if an individual has an ability to flee the situation, they will endorse flight action semantics, while an individual that does not have this ability will endorse fight action semantics. However, the following studies indicate that individuals might be prone to endorsing fight action semantics even when they have the option to endorse the opposite. The article though, does not provide a satisfactory operational definition of RHP, resource holding potential, which was implicated in studies 2a and 2b. What actually comprises an individual’s RHP? The article assesses RHP by asking the participant if they would engage in a confrontation, indicating on a Likert-type scale, with a picture of a black man (other races were presented, but were not interested). The authors then deem this satisfactory evidence of an individual that has a high RHP (willing to confront) or low RHP (unwilling to confront). This however, does not seem satisfactory, especially if they want to establish distancing behavior or closing in behavior as a source of individual differences as a result of subliminal priming. Hopefully, future research in this area would expand upon RHP; specifically, what information is accessed to determine an individual’s RHP. Does the individual’s immediate ecology contribute to this assessment, or is it the environment they grew up or reside in? Is this a self-assessment of perceived strength by the individual and their potential enemy? Or does RHP depend mostly on the size and strength of potential enemy? This would be an interesting avenue to explore, as it provides information to when an individual might be the exception: when a person would choose to fight instead of flight.

Motivated Directed Attention and Person Perception

2013-05-15T15:57:00.000-07:00

It can be argued that humans have evolved sophisticated cognitive skills that facilitate complex interactions between individuals in our distinctly ultra-social environments. One goal of social cognitive psychology is to examine the cognitive mechanisms that have evolved that allow humans to uniquely and extensively socialize. The goal of this paper is to address a few cognitive mechanisms that allow humans to pursue and achieve functional goals, specifically related to reproduction. The adaptive allocation of attention in social environments, by both men and women, to stimuli that enables successful mating to occur will be discussed. Furthermore, the impact of females’ ovulatory status on selective attention to and bias towards mating-relevant stimuli will be discussed. The paper will address evolved motivations that directly influence cognition, which impacts social interactions with predominately opposite-sex members of the species. Hopefully this paper will highlight the necessity of examining the cognitive mechanisms that underlie the social consequences of the downstream behaviors we are most interested in as researchers.

The first steps in creating a mental representation are attention and encoding. Whether it’s the sight of a double-scoop mint chocolate chip ice cream cone on a hot day, or noticing the beautiful face of a passerby who just might be your ideal romantic partner: one must first attend to the stimuli and in doing so, the sensory and perceptual information becomes encoded internally, creating a mental representation that can be further processed and manipulated cognitively. Fiske and Taylor note that there are several stages of encoding that are practical to social cognition: first, we analyze our environment pre-attentively, this process drives what we focus our attention on, and we then apply meaning to this stimuli by drawing upon our stores of knowledge to further engage in complex interactions with the stimuli. Attention, also known as the extent of selective cognitive work you engage in, is especially focused to stimuli that are social in nature; this includes the people we encounter in our lives, the self we never part with, and the interactions between the two. Alongside the preference for social stimuli, information processing is even more selective, in that some information or categories are inherently more fascinating and captivating than others (Fiske & Taylor, 2008). Deciding what information is naturally more interesting and relevant relies on examining the goals that affect social cognition. Ecological and evolutionary approaches to motivation and cognition have stressed that goals with a proximate impact on the perception of the social environment should be goals that have had the most impact on ultimate adaptive outcome. It is practical to conclude that the upstream processes in social situations are persistently attuned to motivations linked to survival and reproduction. The purpose of this paper is to highlight how functional motivations can influence upstream cognition, specifically attention and person perception.

Perception of, and memory for, events or behaviors are strongly influenced by the motivations of the perceiver. Fiske and Taylor (2008) noted five primary motivations that hold sway in our social cognitive processes: belonging, understanding, controlling, enhancing self, and trusting in-group members. Our goals and motivations seem to have power over lower-level cognitions and perceptions. For example, the threat of social exclusion, a threat that impinges on the belonging motivation, leads individuals to selectively attend to smiling faces: these threatened individuals identified smiling faces much quicker, fixated on those smiling faces, and disengaged from those faces much slower (DeWall, Maner, & Rouby, 2009). The results reveal the impact of motivation on downstream processes, specifically how social exclusion increases attention towards stimuli displaying social acceptance. This enhanced, selective attention towards smiling faces highlights the individual’s need to seek out and garner social acceptance following the exclusion event, subsequently satisfying the belongingness motivation. The influence of the motivation to belong on early-stage perceptual processing can help expand our understanding of how and why more downstream processes, pro-social or aggressive behaviors occur. In addition to Fiske and Taylor’s five motivations, it is pertinent to examine the two motivations that serve a well-defined functional purpose, survival and reproduction, which can further shed light on how upstream cognitive processes of attention and perception lead to downstream, adaptive behaviors (Neuberg, Kenrick, Maner, & Schaller, 2004).

Humans tend to be unceasingly attuned to features of others that are telling of qualities indicating desirability as potential mating partners. Physical attractiveness is one such feature that plays a pivotal role in reproductive relationships. It is predicted that individuals will have their attention directed selectively to individuals that are physically attractive, and this selectiveness should be exaggerated in individuals with mating goals activated. Maner et al. (2003) conducted studies in which participants viewed arrays of both male and female faces of varying levels of attractiveness. Both sexes estimated high proportions of attractive women under conditions in which the arrays were only present for a very short period of time. Nevertheless, when arrays were presented longer, this overestimation for attractive females disappeared; together these first two studies imply early stage visual processing is captured by female attractiveness for both men and women. Likewise, women did not show this bias towards attractive men in their estimations. Taken together, these results support the notion that men utilize level of physical attractiveness as in indicator of fertility, a desirable trait in a potential romantic partner, and women gauge other women to evaluate their potential as competitors; both are reproductive motivations that selectively direct the perceivers’ attention.

As noted in the studies by Maner et al. (2003), the presence of an attractive opposite sex member in the environment cues reproductive goals for both men and women, which in turn directs upstream processing. Maner et al. (2003) did not further explore the consequences of this attentional bias towards attractive female faces, what downstream processes were impacted, merely the studies highlight the existence of such a bias: the attention of the perceiver, male or female, was directed and held by attractive female faces. It can be assumed, however, that this attentional bias should lead to greater chances of successfully completing the motivation. This attention bias facilitates downstream behavioral responses that are altered in a way in which options most readily activated are behaviors that were associated with an increase in reproductive success in our ancestral past (Neuberg et al., 2004). The mating goal not only directs the attention towards the target, but the perceiver has an activated repertoire of behaviors specific to those used in mating that can lead to a greater likelihood of reproductive success. For example, once a mating goal is active, the perceiver can quickly and effortlessly access and engage in behaviors that could lead to success with the target mate; for example, conveying intelligence through a joke or kindness through a smile (see Buss, 2002).

Examining research regarding the impact of ovulatory status on cognition might help to explore this notion of upstream attentional biases directing downstream behaviors to the successful completion of a reproductive motivation. Hormonal influences may act as a temporary motivational factor that can regulate our downstream behaviors, which in turn can lead to the successful goal attainment. Depending on where a woman is in her cycle, her sensitivity to perceptual cues of maleness can be described as a behavior that offers a myriad of further reproductive benefits (Macrae, Alnwick, Milne, & Schloerscheidt, 2002). At peak fertility, an ovulating woman does not need to engage in effortful processing to determine whether a potential mate is fit to reproduce with, since masculinity is used as a proxy for immunological competence, or how well the body can fight disease, which is an aptly desired mating characteristic (Thornhill & Gangestad, 1999). Results of study one revealed women who were at high risk for conception categorized male faces much quicker than those women who were at low risk; here, participants that were ovulating could attend to facial features that were indicative of masculinity, and their heightened attention to these features allowed for downstream advantage when categorizing the face as either male or female. Further research might examine how skilled an ovulating female is at delineating between varying degrees of masculinity, and subsequently actual testosterone levels. If ovulating women had increased abilities in discriminating between which males were in fact more masculine, signaling the increased immunocompetence, and thus the optimum mate would highlight the adaptive motivational function of this attentional bias and stimuli discriminatory advantage as allotted during peak fertility. In the second study, participants were evaluated on how quickly they could classify words that were either stereotypically matched or mismatched after primed by male or female faces; overall, stereotype matching was quickest when primed by male faces overall, but participants with a high risk of conception classified stereotype-matching words more rapidly than low risk individuals. Females with high conception risk had increased attentional biases towards physical indicators of maleness in faces, and this increased biases led to downstream advantages: superior access to categorical information about men as well. When females are at the highest risk of conception, their lower level perceptual processes are altered in a way that allows for more skillful appraisal of potential mating partners via the activation of information that is masculine. Furthermore, this study implicates the influence of hormones on directing upstream perceptual processes to achieve functional goals efficiently (Macrae et al., 2002).

Once a motivation is activated, it directs attention selectively to features of the social environment that have qualities related to attaining the activated goal. Reproductively relevant goals would most likely direct attention towards attaining a good mate: sex of the individual, level of physical attraction, and level of social dominance. The activation of mating goals would influence lower level processes in a way that is biased towards successful completion of the goal through downstream processes. The alteration of person perception processes during ovulation provides evidence for upstream processes that facilitate successful mating. Results from Macrae et al. (2002) support the notion that women can have reproductive motivations chronically, yet temporarily, activated. Since an ovulating female is at the highest risk for conception, it makes sense that she might have reproduction on the mind, implicitly or explicitly. However, it is unclear whether the mating goal comes first and subsequent perception of the environment is altered accordingly, or the presence of a potential sexual partner cues the mating goal, which subsequently alters further processing of stimuli in the environment. Results from Maner et al.’s (2003) study four also supports the notion that individuals can be dispositionally inclined to reproduce, or to have the motivation of reproduction habitually activated. Further examining of individuals that are dispositionally attuned to reproduction might shed light on the order of events.

Individuals can differ along the sociosexuality continuum, that is, there are individual differences in what mating strategies are preferred. Sexually restricted individuals prefer long-term mating strategies, commitment, and emotional intimacy prior to sexual intercourse. Sexually unrestricted individuals prefer short-term mating strategies, casual sex, sexual variety, and one-night stands (Simpson & Gangestad, 1991). Duncan, Park, Faulkner, Schaller, Neuberg, & Kenrick’s (2007) study utilized a task that presented participants with both attractive and unattractive faces at the same time, and had participants detect a feature change in a face (disappearing eye or nose) as a source of attentional constraint. Sexually unrestricted men, those who dispositionally prefer short-term mating strategies, selectively allocated attention to attractive females: they were quicker to detect changes in attractive female faces and slower to detect changes in unattractive female faces. The fourth study by Maner et al. (2003) utilized eye-tracking technology to measure fixation; results revealed both sexes were biased in attention towards the physically attractive female targets. In this fourth study, women did exhibit a bias towards physically attractive male faces, yet, this fourth study also measured sexual restrictedness; sexually unrestricted men and women selectively attended to attractive opposite-sex faces, supporting the notion that motivation to mate steered their attention. Finally, women’s attention to attractive men and attention to attractive women was strongly positively related; women who were dispositionally motivated to search out possible mates are also interested in assessing their potential competitors for these mates. Results of both Maner et al.’s (2003) fourth study and Duncan et al.’s (2007) study highlight the impact of mating goals on attention, specifically dispositional motivations: individuals with reproduction motivations chronically activated perceive their environment accordingly, to attain their goal. Males that were sexually restricted did not display an attractive opposite sex preference; perhaps indicating the motivation must be activated first in order for the upstream processing to be altered.

The adaptive nature of this low level attention biasing process suggests evolved perceptual processes that are elastic in their response to contextual cues, and are subject to influence by the perceiver’s disposition (Duncan et al., 2007). The men with personalities more inclined to engage in mating behaviors exhibited a stronger attentional bias towards attractive opposite-sex faces, faces that reveal indicators of increased fertility and increased reproductive value. This attentional bias serves to increase the likelihood of a mating event occurring, fulfilling the functional motivation of reproduction. It is worthy to note, however, that the most attractive female faces are ones that are symmetrical; symmetry has been found to connote increased genetic quality (Thornhill & Gangestad, 1999). Most research on facial attractiveness employ faces that are symmetrical; as faces become less symmetrical, they become less attractive. Duncan et al.’s (2007) study can support the assumption that men have a bias for symmetry since it serves as an indicator of mate quality. Challenging this however, is results indicating a preference for visual symmetry is only found in men: men, not women, exhibit a preference for visual symmetry across multiple domains, including abstract and real world objects (Shepherd & Bar, 2011). Since this preference generalizes to stimuli unrelated to mating, it can be questioned as to whether or not the preferences satisfy the motive for reproduction or due to enhanced perceptual processing; unusually, this preference is not exhibited by women, so future studies would need to examine this male dominant preference.

The effects of goal activation on attention and perception consequently influence the downstream processes as well. The aforementioned effects of attention direction and perception should influence memory. In three experiments, Becker, Kenrick, Guerin, and Maner (2005) utilized the matching game Concentration to determine if early perceptual processing enhanced encoding and retrieval; specifically they examined whether a target’s sex and level of physical attractiveness impacted how well their spatial location is remembered. Study one utilized same sex arrays of faces to be matched, and results indicated biases in memory for the location of attractive female faces and subsequently both sexes exhibited this spatial location memory enhancement. Study two utilized mixed sex arrays for the matching task, and results supported the first study in which attractive female faces were matched in fewer turns than attractive men. Study three supported the previous studies and revealed there were a lower proportion of mismatches for attractive women, opposed to average-looking women. Intriguingly, they also found that attractive male faces were mismatched at a higher proportion, and attractive female faces were matched first. This strong tendency to recall the location of attractive female faces for both men and women reveals the selective direction of attention towards the location of cues relevant to reproductive motivations, either in pursuit of a mate or evaluation of a potential competitor. Also, this physically attractive female face permeates early processing at both stages, first, it selectively directs perceivers’ attention, and second, it possesses features distinct enough to garner extended encoding into memory. From study one to study three, the task became more and more difficult with the addition of faces; nevertheless, the advantage in spatial location memory for attractive female faces remained. It seems as though this ability to capture attention, as displayed by attractive female faces is immune to distraction; however, further studies should examine to what extent this bias remains, if other motivations are activated (e.g., survival) how salient will the attractive female faces remain? Still, the enhanced spatial memory for attractive faces supports the notion that motivations can impact upstream processing. It would be of great importance to remember where you saw an attractive face, not so much the actual face itself, in order to engage with that individual further. This enhanced spatial location memory for attractive faces occurs automatically, whether the perceiver wishes to engage with the face further in pursuit of their reproduction motivation should recruit more effortful processing of the face, and subsequent facial recognition. Challenging the assumption that females attend to attractive same-sex faces to serve intra-sexual competition is the notion that females are prone to seek affiliation when stressed (Taylor, 2006). Perhaps the matching task induced a level of stress that would direct attention towards individuals that would fulfill the motivation to affiliate; future research will need to tease apart which motivation more strongly directs female attention towards attractive same-sex faces, perhaps through explicit activation of either goal prior to completing the matching task.

Early cognitive processes are guided by evolutionarily relevant motivations, which in turn impacts downstream processes. Interestingly, the fifth study by Maner et al (2003) revealed women’s memory for attractive men is poor. The initial selective attention to attractive men is strong, but the processing might end there since women are not inclined to mate with male strangers and physical attractiveness isn’t a feature that women (during the majority of their cycle) are interested in. A study by Anderson et al. (2010) supports this notion that ovulation status redirects attention towards attractive men, but the subsequent memory for these attractive male faces is not improved. It is assumed that increased visual attention to stimuli should increase memory for that target; however, results revealed women near ovulation did not have better memory for the male faces they selectively attended to. This lack of memory for attractive male faces could indicate these women were not truly cognitively processing these faces, but instead engaging in a different reproductively relevant strategic behavior, such as increased eye contact to convey romantic interest. Furthermore, the ovulating females might not engage in effortful encoding of these attractive male faces, since they are strangers, and the cost of mating with this unknown male is too high, thus shutting down the continued processing. Either way, the implicit redirection of ovulating women’s attention towards attractive male faces serves as support of a reproductive motivation being activated. Determining what mechanism would enhance memory for attractive male faces is not essential though, since females evaluate males on different reproductive-relevant cues, such as social dominance or intelligence (Li & Kenrick, 2006).

The activation of a motivational system leads to social cognitive consequences that facilitate adaptive behavioral responses; the functional motives perspective is vital in revealing this relationship, above and beyond the associative priming perspective (Neuberg et al., 2004). We perceive and think in ways that serve our goals most effectively and efficiently. The motivational states of reproduction and survival cannot be condensed purely to the dichotomy of approach and avoid proclivities, instead, each motivation has a distinct cascade of perceptual, cognitive, and behavioral consequences (Kenrick, Neuberg, Griskevicius, Becker, & Schaller, 2010). However, much of early social cognitive research focuses on pure epistemic goals (Neuberg et al., 2004). There arises a mutual benefit between the fields of Social Cognitive Psychology and Evolutionary Psychology: the evolutionary perspective benefits greatly from learning of basic cognitive processing to study these human social goals, and the social cognitive perspective can benefit from examining these processes as they are influenced by functional motives. This framework can lead to more precise predictions of human motivations: by ascertaining functionally distinct motivational states that have repercussions for reproduction and survival, when activated, is predicted “to exert a specific set of consequences for human attention, perception, cognition, and behavior in relation to specific kinds of fitness-relevant stimuli in the social environment” (Kenrick et al., 2010, p.64).

In conclusion, upstream processes such as attention and perception are directed in functional ways by fundamental goals of survival and reproduction. The importance of studying these lower level cognitions and perceptual processes, as they are directed our motivations, will enlighten individuals researching potential nuances in downstream judgments, cognitions, and behaviors. Furthermore, these results, results from Maner et al.’s (2003) fourth study, and results Macrae et al. (2002) highlight the notion that the reproduction motivation can be activated dispositionally, and then subsequently, attention and person perception processes are directed accordingly. Future studies will need to empirically assess if the motivation of reproduction is triggered after encountering a potential sexual partner, of if the motivation of reproduction, being essentially fundamental in nature, is chronically and implicitly activated at all times. Further research will hopefully expand upon and branch out to examine proximate goals that serve the ultimate motivations of survival and reproduction.

References

Anderson, U.S., Perea, E.F., Becker, D.V., Ackerman, J.M., Shapiro, J.R., Neuberg, S.L., & Kenrick, D.T. (2010). I only have eyes for you: Ovulation redirects attention (but not memory) to attractive men. Journal of Experimental Social Psychology, 46(5), 804-808. doi: 10.1016/j.jesp.2010.04.015

Becker, D.V., Kenrick, D.T., Guerin, S., & Maner, J.K. (2005). Concentrating on beauty: Sexual selection and sociospatial memory. Personality and Social Psychology Bulletin, 31(12), 1643-1652. DOI: 10.1177/0146167205279583

Buss, D.M. (2002). Human Mating Strategies. Samfundsokonomen, 4, 47-58.

DeWall, C.N., Maner, J.K., & Rouby, D.A. (2009). Social exclusion and early-stage interpersonal perception: Selective attention to signs of acceptance. Journal of Personality and Social Psychology, 96(4), 729-741. doi: 10.1037/a0014634.

Duncan, L.A., Park, J.H., Faulkner, J., Schaller, M., Neuberg, S.L., & Kenrick, D.T. (2007). Adaptive allocation of attention: Effects of sex and sociosexuality on visual attention to attractive opposite-sex faces. Evolution and Human Behavior, 28(5), 259-364. doi: 10.1016/j.evolhumbehav.2007.05.001

Fiske, S.T. & Taylor, S.E. (2008). Social cognition: From brains to culture. New York: McGraw-Hill

Kenrick, D.T., Neuberg, S.L., Griskevicius, V., Becker, D.V., & Schaller, M. (2010). Goal-driven cognition and functional behavior: The fundamental-motives framework. Current Directions in Psychological Science, 19(1), 63-67. DOI: 10.1177/0963721409359281

Li, N. P., & Kenrick, D. T. (2006). Sex similarities and differences in preferences for short-term mates: What, whether, and why. Journal of Personality and Social Psychology, 90(3), 468-489. DOI: 10.1037/0022-3514.90.3.468

Macrae, C.N., Alnwick, K.M., Milne, A.B., & Schloerscheidt, A.M. (2002). Person perception across the menstrual cycle: Hormonal influences on social-cognitive functioning. Psychological Science, 13(6), 532-536.

Maner, J. K., Kenrick, D. T., Becker, D. V., Delton, A. W., Hofer, B.,Wilbur, C., & Neuberg, S. L. (2003). Sexually selective cognition: Beauty captures the mind of the beholder. Journal of Personality and Social Psychology, 85, 1107–1120.

Neuberg, S.L., Kenrick, D.T., Maner, J.K., & Schaller, M. (2004). From evolved motives to everyday mentation: Evolution, goals, and cognition. In J.P. Forgas & K.D. Williams (Eds.), Social motivation: Conscious and unconscious processes (pp. 133–152). New York: Cambridge University Press.

Shepherd, K., & Bar, M. (2011). Preference for symmetry: Only on Mars? Perception, 40, 1254-1256. doi:10.1068/p7057

Simpson, J.A. & Gangestad, S.W. (1991). Individual differences in sociosexuality: Evidence for discriminant and convergent validity. Journal of Personality and Social Psychology, 60, 870-883.

Taylor, S. E. (2006) Tend and befriend: Biobehavioral bases of afﬁliation under stress. Current Directions in Psychological Science 15(6), 273-77.

Thornhill, R., & Gangestad, S. W. (1999). The scent of symmetry: A human sex pheromone that signals fitness? Evolution and Human Behavior, 20, 175–201.

How does affect affect our cognitions, specifically the judgments we make?: Embodied cognition and embodied emotion

2013-05-05T16:19:00.001-07:00

With this semester's Social Cognition seminar coming to a close, I will be posting some musings I had. There will be a few. Comments, criticism, inquiries, and academic discussion on these topics is undoubtedly encouraged.

Embodied cognition refers to the phenomenon in which our sensorimotor sensations and perceptions impact our cognitive processes (Fiske & Taylor). This implies that bottom-up processing of information can impact our judgments.

Morality, in one function, can serve to prevent people from doing something that will hurt themselves or others.

Many morality judgments respond to disgust (Haidt). It seems as though there is an inherent embodiment of certain behaviors that are disgusting, which in turn become moralized. That is, when stimuli invoke feelings of disgust, we feel the need to make a morality judgment towards that stimulus.

For example, disgust can function to keep pathogens out of our bodies (rotting flesh of animals or decomposing fruits and vegetables),

just as it functions to keep from incurring the genetic costs of inbreeding (incest avoidance; Lieberman).

However, it seems that we don’t even need to fully embody the disgusting act to make a morality judgment; we can see someone engaging in a disgusting behavior and deem it immoral (e.g., bestiality).

It’s interesting that this link between disgust and morality is so strong; do political conservatives not gag when they think of homosexual sex, just as they would gag when biting down on a rotten apple? (here's a softball of a study...)

any second now...they're gonna....do it.

Perhaps this gets at the root of embodied cognition at its purest.

Our ability to feel empathy, to distort the divide between another person and our self, allows us to possess morality. With that same ability to empathize, we can actually sense, in our own body, the emotions being experienced by another, and disgust is just as vital of an emotion as pain in our moral compass.

Embodied cognition, therefore embodied emotion, is sensorimotor input that affects our judgments, might be responsible for the morality that is unique to humans.

inspired in part by: deWaal

Part 4 Consilience Conference 2012: A lowly graduate student's notes

2012-05-03T08:30:00.004-07:00

These posts will not be in any discernible order; nor will they resemble the order of presentations during the conference. They will merely reflect what I found profoundly interesting and what presentations sparked future research endeavors. These thoughts will be poorly cited and eventually as time permits, I will fill in citations as I move along.

With that being said, if it seems as though I think my thoughts are original or of my own, when in fact someone already went there...it's not plagiarism. Its my daily life. Someone. Already. Did. It. In that case, comment with a citation or two.

Patricia Churchland

What are social instincts?

What genes take us from a reptilian brain to a mammalian brain?

Attachment and trust are our moral value center.

Social Problem Solving:
Our brains regulate, repress, calculate, plan, track reputations...
This problem solving is located in the PFC. There are deep connections to the deep (reptilian/Darwinian) brain.

"Deep value" is to ensure one's own survival; this is akin to the processing that occurs in the reptilian brain. "Mammalian value" is to ensure not only one's own survival, but the survival of others (especially offspring.)

In mammals, especially humans, a trade-off exists: a mother has to deliver a baby through a bony pelvis, so the brain needs to be relatively small at birth to accomplish this. This creates a large newborn dependence on its mother (sometimes even through graduate school....) in order to have brain mature to adult size. This immature brain of warm-blooded loco-motors must be able to adapt to a multitude of environments. So the benefits of having newborn dependence outweighs the costs.

Oxytocin promotes bonding, cooperation, mutual grooming, sex, and related reproductive behaviors. It also lowers cortisol and provides a calming effect. There is a decrease in defensive postures, increased level of trust, and autonomic arousal decreases. Dopamine also plays a key role in learning, if there is a block of dopamine, organisms fail to learn.

This is when the magic happened:

Lack of parental investment --> underdeveloped PFC --> low K lifestyle (FAST) --> low investment in offspring --> underdeveloped PFC --> low K lifestyle (FAST)

This cycle is perpetuated since individuals with an underdeveloped PFC do not feel the rewards that come from investing in their offspring: they find no pleasure in helping; consequently, they do not feel the pain when their offspring are in distress. They can't learn to parent. Their reward systems are not hardwired to invest in their offspring.

Do low K women have low endogenous oxytocin and/or dopamine, especially following childbirth?

There are many more unanswered questions, I will definitely be coming back to this post and updating.

Current Research in Psychological Science: Part 2

2012-05-01T18:20:00.000-07:00

"Psychometric Properties of the HEXACO Personality Inventory"
Kibeom Lee and Micheal C. Ashton

The authors believe honesty and humility are the basis of individual differences in altruistic behavior, specifically reciprocal altruism. High levels of honesty/humility are expected to cooperate even when they could defect on someone who is not likely to retaliate; they wouldn’t take advantage of another individual. Likewise high levels of Agreeableness would cooperate, even with someone who is not going to reciprocate. This is very interesting that the authors propose an evolutionary framework for the individual differences for adaptive problems. It seems as though the emotionality framework drives our kin altruism and inclusive fitness behaviors.

This could be empirically tested in a very simple way. Participants could play the Prisoner’s Dilemma game, a staple of studying altruistic and inclusive fitness behaviors. The participants’ personality could be measured using the HEXACO and then could play a few rounds of the game. Different conditions could be explored to determine the strength of the HEXACO’s ability to predict, likewise, to determine if situational variables influence these traits. The participant could play any number of rounds of the game and that could influence whether they adopt a certain strategy, such as tit-for-tat. The participant could also play with various individuals; participants of the same sex, same race, same age, or different sex, age, or race. Would a person with high honesty/humility identify with a wider range of individuals as “kin” and thus extend altruism?

Evolutionary biology presupposes individuals that identify (select) as their kin should share genetic material and identification of kin hinges upon indicators of genetic material. However, reciprocal altruism is based upon identifying individuals, who might not necessarily be kin, but likely to reciprocate in the future; the “you scratch my back, I’ll scratch yours” understanding between two people drives reciprocal altruism. Individuals must rely on social cues that are indicative of individuals that are more likely to reciprocate. It would be interesting if honesty/humility individuals were actually just poor at determining if individuals would reciprocate or not; likewise, poor at recognizing individuals that would manipulate them. This might explain why they are more likely to cooperate with individuals that are not likely to reciprocate and even less likely to take advantage of individuals that would not reciprocate. Empirically testing this would be much more difficult. It has already been shown individuals that are more attractive are more likely to be recipients of reciprocal altruism, if there are other indicators that could be found that others rely on, those could be used to test the honesty/humility individuals’ judgments.

Current Research in Psychologial Science: Part 1

2012-05-01T18:18:00.001-07:00

The Dark Triad: Facilitating a Short-Term Mating Strategy in Men

PETER K. JONASON, NORMAN P. LI, GREGORY D. WEBSTER and DAVID P. SCHMITT

Abstract

This survey (N=224) found that characteristics collectively known as the Dark Triad (i.e. narcissism, psychopathy and Machiavellianism) were correlated with various dimensions of short-term mating but not long-term mating. The link between the Dark Triad and shortterm mating was stronger for men than for women. The Dark Triad partially mediated the sex difference in short-term mating behaviour. Findings are consistent with a view that the Dark Triad facilitates an exploitative, short-term mating strategy in men. Possible implications, including that Dark Triad traits represent a bundle of individual differences that promote a reproductively adaptive strategy are discussed. Findings are discussed in the broad context of how an evolutionary approach to personality psychology can enhance our understanding of individual differences.

These men are successful at having multiple sexual partners and then profit from not having to stay and raise the offspring. But again, why would women want to consciously mate with these individuals, fully knowing they don’t possess any traits that a good father has. These women might be using these characteristics to ensure the men they sleep with outside of their monogamous relationship won’t stay around; they guarantee to have their cake and eat it too. These Dark Triad individuals might be great candidates for extra pair copulation; however, empirically, this must be tested. Individuals with Dark Triad traits should be tested on their mate quality, especially qualities females seek in extra pair partners or short-term partners. They might be found to have high testosterone, high sperm quality, highly attractive, etc. The authors illustrate that Dark Triad men prefer quantity rather than quality in their mates; this also is found cross-culturally. No research has yet examined why women prefer these men when engaging in short-term mating, or what qualities they are assessing outside of their personality characteristics.

Interestingly, is the question of why this strategy, if short-term mating is all men want to do, why it hasn’t been mastered by all males? This can be explained from traditional game theory. Dark Triad males can be seen as hawks; only at their strongest when doves surround them. Since these Dark Triad individuals are rare and only successful when they are rare, females might not have evolved mechanisms for detection. Other questions come to mind though, are there genetic components to the Dark Triad? Are these components heritable?

Moving back to the short-term mating orientation, are these men successful; that is, do they successfully impregnate these women they mate with? If they are having many offspring then obviously this strategy is vilified. But in the age of contraception, they might not be as “successful” in evolutionary terms as they would have been back on the savannah.

Hopefully future research can answer these questions. Are these Dark Triad men, the perfect extra pair/short term mate? Do they have characteristics such as high testosterone or quality immune systems, which outweighs their (probable) heritable personalities? Are these men having more offspring than non-Dark Triad individuals? What are women assessing when they mate with these Dark Triad men? Are these women merely subjects of forced copulation? Does the use of birth control moderate these women’s preferences for Dark Triad males? A very interesting area of study, but much empirical work is needed.

Part 3 Consilience Conference 2012: A lowly graduate student's notes

2012-05-01T18:11:00.000-07:00

Michael Rose

Why did we become so smart?

One hypothesis is man as tool-user.
However, there is a huge diversity in tool use across the globe, and with each different environment, there should be varying degrees of intelligence.
Another hypothesis is man as a social being.
Our brain is lethal compared to built-in tools. This destabilized the game...And from it began a mental arms race. A free mental arms race.

So our brains serve as generalized calculators. However, fitness calculations are implicit. We don't consciously process our fitness on a day to day basis. This Darwinian brain, if you will, is hidden from our conscious awareness.

This is probably for our benefit, I mean, its hard enough to stop thinking about sex for 20 seconds....but to have it constantly on your brain, and not in the pleasurable way, but more the "baby factory" way....

So our brains have a system that regulates these Darwinian drives.

Now, I will not concur with Dr. Rose, in that this is "god" living inside our skulls. That is so unnecessary. I am completely convinced our cortex, specifically the prefrontal cortex is there to regulate. Its already been shown to regulate our behavior, our emotions, and it makes complete sense to say it regulates our Darwinian drives. Individuals with damage to certain prefrontal areas have an array of problems; from inability to plan ahead to engaging in odd sexual behaviors.

My first thought is, that no wonder anyone under 20-25 has extreme difficulties turning off their Darwinian drives...their regulators are not fully developed. This isn't to say they are consciously aware of their fitness, but rather, they are more vulnerable to the urges to survive and reproduce and reproduce and reproduce.

Dr. Rose continues to tell the age-old yarn about poor, old Phineas Gage. Iron shot straight through his brain, severing the link between his limbic system and regulating cortex. He soon was plagued with a terrible demeanor, inability to control his emotions, and (new to me) exhibited pseudopsychopathology. Gage seemed to lose control over his Darwinian brain and drives. So are adolescents just tiny Darwinian Psychopaths? (that would be quite the interesting study....)

So, without further ado, to Life History we go....

Do your life history strategies reside in your prefrontal cortex?!?

Someone who exhibits a fast, low K lifestyle, might have a smaller prefrontal cortex...
They lack the cognitive resources to regulate their Darwinian drives.
Someone who exhibits a slow, high K lifestyle might have a larger (normal) prefrontal cortex...
They have the cognitive resources to regulate these same drives.

Of course, correlational studies will be necessary to determine if individuals that have r/K strategies do indeed have lower PFC volume. But also, examining if damage to certain areas produce one strategy or the other, or if we are lucky enough, to have someone previously slow incur damage that sped up their strategy.

Now this will be revisited shortly. Please anxiously await my post based on Patricia Churchland's presentation.

Part 2 Consilience Conference 2012: A lowly graduate student's notes

2012-04-30T18:38:00.003-07:00

Christopher Boehm

Altruism Problem: Free-riders are cheaters that preclude altruism.

The BULLY: New free rider, found in any social dominance hierarchy. However, close groups of individuals don't need cheater detection. Early in hominin evolution, subordinate band members hold down bullies, through egalitarian methods; consensus groups, social pressure, and deviant reform are oft chosen methods.

Gossip is the key to moralistic social control.

Not only does it permit private evaluation of others (deviants), it allows the entire group to form an opinion about the deviant, and decide on a collective action.

Executions and their impact on the gene pool:

Often times, capital punishment within tribes is considered murder by outsiders, and often hidden from anthropologists. Capital punishment would be delivered to group intimidation (bullies) first, cunning deviants second, sexual transgressors third, and miscellaneous transgressors last.

50% of all capital punishment occurrences were to curb bullies (lying, theft, adultery: free rider suppression). Likewise, what offenses are reported as deviant are dominated by intimidators, including murder and sorcery.

Irreversible sanctioning is delivered about 9% of the time, where executions are delegated to kin, the group executes together, or permanent expulsion. However, 91% of the time, they deliver reversible sanctioning, spatial distancing, corporeal punishment, gossip, criticism, ostracism, and shaming. Through this reversible sanctioning, they are allowing lesser deviants to reform. With that, the reversible sanctioned individual might take a hit to their overall fitness, but their genes are maintained in the pool. (As opposed to the permanently sanctioned individual, also depended on when he is executed....a topic I'll revisit.)

Bullies can't be dealt with individually; this could result in revenge killing by other kin, no matter how diplomatic the capital punishment was. Social dynamics might be a dominant force in free rider suppression.

6 Theories of Altruism:

Mutualism
Short term, one-shot transactions between two people. Both benefit instantly; immune to free rider problems
Reciprocal Altruism
Long term transactions between people. Cheaters and free riders are a big deal. Possible explanation for marriage (interesting study idea...)
Group Selection
Between group > Within group. No individual compensation necessary. Highly vulnerable to free rider problem.
Misplaced Nepotism
People ACT like kin; bonding is transferred to non-kin along with the benefits. Some deceptive free riding can result
Simon's Docility Model
Good cultural learner; culture-taught altruism. Selfish free riders and bullies are going to ignore culture and fake altruistic behaviors.
Social Selection
Positive partner choice by reputation; others know you are an altruist, so you will be chosen as a mate and as cooperative partner
Negative partner choice by reputation; others know you're selfish and subsequently avoid you.
Collective social sanctions; divest a bully of their profits and split between band members.

Altruism is 100% culturally attractive; feeds positive selection by reputation.

Good reputation:
Marriage and subsistence partners benefit. Compensation from net gains in fitness and assortation: altruist + altruist = 2X mutual gains. Likewise, it is hard to be a cheater in a small hunting band. Too many gossips monitoring behavior.
Negative reputation:
Avoid cheaters easy, bullies are hard to avoid however.
Punitive social selection:
Group punishes free riders. Intimidators are the most potent and object of target.

Bullies will overpower other altruists and weak or less powerful non-altruists.

Punitive social selection can rely on the ultimate sanctioning of free riders or can reform these free riders. Punitive social selection can also deter would be free riders from engaging in these behaviors. With that being said, free riding genes do not doom you to an early death by firing squad. The trait can be useful, with self control, in approved social competition arenas; could even confer large advantages.

Free-riders and altruists can coexist at fixation and humans are superbly skilled at free rider suppression.

With this social control aspect of punitive social selection or via reputation, no cheater detection mechanisms are needed.

I couldn't imagine getting to the point or
to future research ideas without giving you the mightily condensed version of the presentation.

One developmental psychologist's hand shot straight into the air after the presentation, with a question that was resonating in my head immediately after I heard the same bolded phrase above:
humans are superbly skilled at free rider suppression.

But the bullying problem today, in our adolescents and children, is rampant. How can what Dr. Boehm conveyed be applied in our schools today?

Obviously juvenille bullying and adult bullying are two completely distinct transactions. School-yard bullying is built upon one solid rule: don't tattle. It is obvious throughout the presentation (and through my scanty notes) that adult bullying can be suppressed through punitive social selection. This hinges upon, wait for it, GOSSIP.

In order to halt bullying, it seems as though we must enlist gossip. Even more so, sex differences in free riders/bullying might also hold the key. Female juveniles (sounds way fancier than young girls) engage in passive aggressive bullying. However, females are skilled with gossip. This might be why traditional "male" bullying is not found in females.

It could be said that if male juveniles are taught to enlist punitive social selection, it might slow the rampant bullying plaguing our schools today.

One final point I must revisit, is that of LH (of course.) The LH strategies of bullies would be a very interesting area of future research.
First, by examining how these free rider genes are maintained in populations. If these individuals are not being permanently sanctioned until after reproduction has occurred, the result is obvious. In order to have an effect, it must impact reproductive output.

If a bully resides in a society with stringent social punishment, they might exhibit a low K, high r strategy; a fast lifestyle. They are aware (implicitly or explicitly) that their behaviors will lead to their demise.

If a bully resides in a society that lacks stringent social punishment or even permits reform, they might exhibit a high K, low r strategy; a slow lifestyle.

These are barren hypotheses, but still might be interesting to investigate. It could also provide empirical support for the the need for social selection to suppress free riders/bullies.

Part 1 Consilience Conference 2012: A lowly graduate student's notes

2012-04-30T17:38:00.000-07:00

Ellen Dissanayake

I must admit, I didn't grasp the gravity of her talk at first. Partially because I was upstairs getting as many snacks as possible into my purse (grad school scavenging 101) but once I became enthralled with her presentation on Rock Art, I immediately had some research studies come to mind.

She mentioned drawing and doodling expends resources, such as time and energy. It can be said that these behaviors are costly. I immediately gravitated towards Life History (LH) theory. (Well, again full disclosure, I can't get this paradigm out of my mind.)

Thought I would update: click here for Life History Theory overview

Individuals engaging in these drawing and doodling behaviors might be high K individuals, slow individuals, individuals who are not being bombarded by cues from their environment that their life could go up in smoke in a few seconds. Low K individuals shouldn't have time for drawing or doodling, they spend a good amount of time reproducing. High K individuals should consider these drawing and doodling times methods to increase their somatic existence.

However, one theory Dr. Dissanayake purported was individuals that engaged in dangerous cave drawing (which would draw attention from the noise and/or take away from daily activities) were adolescents or juveniles. There was a significant amount of risk in entering a dark scary cave and drawing dirty pictures of everyone else's Mom....

These individuals had to be fast, low K, high r. Previous research has linked fast individuals with engaging in risk taking behaviors, and graffiti-ing a cave would definitely fall under that list.

Naturally, my mind when to tattooing, graffiti-ing, and all other risk taking artification processes.

At the outset, it would seem as though individuals that had the time and the resources to engage in drawing, doodling, and rock art would be slow, high K individuals. However, by the end of the presentation, it might be the case that fast, low K individuals are the likely candidates to be engaging in these rock art behaviors.

Future studies might look to correlate risky artification processes with low K individuals, especially adolescent or juveniles (not implying the penal sentence they would serve...).

Evolutionary Explanations of Affect Regulation

2012-04-25T13:21:00.001-07:00

Evolutionary Explanations of Affect Regulation

Evolutionary Psychology

The theory of evolution by natural selection was recognized over 150 years ago (Darwin, 1859). It has only been in the past 20 years that psychological, social, and behavior sciences have began to integrate the principles already deeply incorporated in biology and ecology. The goal of evolutionary psychology is not only to incorporate evolutionary biology, anthropology, neuroscience, and cognitive psychology to map human nature but also to rewrite existing literature, framing previous efforts into this perspective (Tooby & Cosmides, 1992.) It is worth mentioning that evolutionary psychology is not to be acknowledged as a subset of psychology, like social psychology or cognitive psychology, but rather “a way of thinking about psychology” available as a lens for viewing any topic therein (Cosmides & Tooby, 2000. Pg 1.)

Emotions

Emotions, and the theory surrounding them, were some of the first constructs Tooby and Cosmides examined when looking to apply their concepts and methods to cognitive science (Tooby, 1985; Tooby & Cosmides, 1990.) The acceptance and prevalent use of the evolutionary explanation of emotions has been long withstanding. Emotions are super-ordinate programs, when activated by a recurrent adaptive problem, activate and deactivate a multitude of cognitive components, which guide the organism to an outcome promoting reproduction, thus increasing fitness (Cosmides & Tooby, 2000.) Emotions influence goals, prioritize motivation, prompt information gathering, impose a conceptual framework, alter perception, affect memory, direct attention, mobilize physiology, and facilitate communication (Cosmides & Tooby, 2000.) Emotions can influence behavior, specialize inferential thinking, stimulate reflexes, promote learning, color events, determine effort allocation, recalibrate current experience, alter infinite outcomes of behaviors, and propel culture (Cosmides & Tooby, 2000.)

Affect Regulation

Affect regulation is defined as the things we do to influence which emotions we experience, when we experience them, and how we experience and express them (Gross, 1998.) Affect regulation is primarily referred to as a diverse set of processes by which emotions are regulated (Gross, 1999.) Important to this discussion is the other function of affect regulation: how individuals influence their own emotions and how these influence other’s emotions (Gross, 1999.) It is through this mechanism of regulation that emotions co-evolved with to convey information about an individual’s state to others (Cosmides & Tooby, 2000.) Being able to effectively regulate your current emotions in order to communicate your internal state would be necessary in order to successfully survive and reproduce. In particular, evolutionary psychology helps clarify the goals of affective regulation, especially when the current environment is dramatically different from the environment to which these emotions and mechanisms to regulate them evolved (Gross, 1999.) These evolutionarily relevant goals range from evading a predator to selecting a successful mate. It is commonly understood that these archaic emotions prompt responses that are useless, appraisals that are a waste of time, or responses that are conflicting with important goals (Gross, 1999.) However, this is a common misunderstanding. Painful states or overzealous responses to stress are evolved defense patterns that proved to be invaluable in our evolutionary past, just as much as the cough of a pneumatic (Nesse, 1990.) Defective regulation of affect causes suffering, more than just anxiety, sadness, or anger. It is this evolutionary perspective that allows an understanding of these regulatory mechanisms in an ultimate sense, independent of their misunderstood proximate reasons. When looking to understand the mechanisms of an emotion, even one that seems not adaptive such as anxiety, it is best to look at the cognitive and physiological mechanisms that regulate that emotion (Nesse, 1990.) Emotion, the cues that elicit them, and the resulting regulatory behaviors are different in cultures and individuals, but in no way does this undercut an evolutionary approach (Nesse, 1990.) The tool-box hypothesis was formed by natural selection: “far more useful than fixed patterns of response are patterns and regulatory mechanisms that adjust to the needs of the current environment;” it is natural selection that makes this flexibility possible (Nesse, 1990, pg. 280.) For example, our current environment would establish that guns, cars, and electrical outlets are dangerous and should elicit an appropriate amount of fear, while in the EEA heights, darkness, and spiders posed dangerous threats and induced fear; conditioning the former requires great effort (Nesse, 1990.) Consequently, natural selection doesn’t necessitate rigid patterns of response: instead it shaped a tendency to establish certain avoidance reactions and the capacity to modify these responses to our current environment (Nesse, 1990.)

The ability to regulate affect voluntarily arises from several cortical regions, most developed within the human species. The anterior cingulated cortex (ACC), dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), and prefrontal cortex (PFC) are thought to be the neural correlates of affect regulation (for review, Beer & Lombardo, 2005.) In regards to affect generation, detection, and attention direction the amygdala has been found to be responsible (Lieberman et al., 2007.)

Effortful Control and Affect Regulation

Evidence in the field of cognitive neuroscience has established that there are two distinct forms of cognitive processing: implicit and explicit. Implicit cognitive systems are outside consciousness, automatic, swift, effortless, and an adaptation from evolution; explicit cognitive systems are conscious, controllable, slow, limited, effortful, and acquired through culture (MacDonald, 2008). Implicit processing is recruited reflexively when encountering evolutionarily relevant stimuli; explicit processing is recruited when confronted with a situation that is novel, requiring flexibility and planning of response (MacDonald, 2008). Conflict arises when one must exert control over implicit processes; this can occur in two domains: cognitive (deemed executive function) and socioaffective (deemed effortful control) (MacDonald, 2008). Through time, however, implicit affective adaptations are subject to being controlled by the explicit processing mechanisms (MacDonald, 2008.) This arose as a consequence of our newfound ability to think ahead, examine information that is not evolutionarily recurrent, and weigh costs and benefits of our behavioral outcomes (MacDonald, 2008).

This effortful control is what we know as affect regulation. Whether or not a man utilizes aggression (successfully down-regulation of aggression) toward his romantic rival rests upon his ability to evaluate the potential costs and benefits of his action; the conflict lies within his ability to override the implicit processing (desire for revenge) with explicit processing (fear of prison) (MacDonald, 2008.) Not only are these behavioral examples indicative of the overlap between effortful control and affect regulation, but research on human brain evolution also indicates this overlap. Affective states, consequence of implicit processing, like fear in a dangerous situation, arise through pathways directly from the amygdala (LeDoux, 2000) while explicit processing is seated in the prefrontal cortex (PFC) (MacDonald, 2008). Neuropsychological research has also placed affective regulation in the PFC (Beer & Lombardo, 2007; Davison, Fox, & Kalin, 2007; Lieberman et al., 2007; and Phan et al., 2005.) The overlap of these prefrontal cortex regions are indicative of how the ability to weigh costs and benefits of behavioral outcomes would lead to an increase in fitness. In socioaffective control literature the VMFPC, OFC, and ventral ACC are implicated (MacDonald, 2008). Damage to the OFC, DLPFC, VMPFC, and ACC in humans is indicative of regulatory and effortful control dysfunction (for review, Beer & Lombardo, 2005.) Activation in these cortical areas serves to inhibit activation in other areas, specifically the OFC (Beer & Lombardo, 2007; and MacDonald, 2008.) Individuals that exhibit low effortful control also have lower ability at regulation of negative affect. This alludes to the consistent overlap between effortful control and affect regulation; giving rise to the necessity of consolidation of investigative efforts.

This significant overlap in literature signals the necessity for evolutionary psychology to be then lens to which affective regulation researchers look through. For example, the often held notion as disruptive and unnecessary reflexive leap backward at the sight of a snake is merely an installment of evolutionary design; in a situation where to err is fatal, this reflex is to prevent the organism from potential harm, while the slow, explicit processing of the actual curved stick reveals its true self. Research on implicit and explicit methods of affect regulation is relatively recent (Gyurak, Gross, & Etkin, 2011.) This explicit processing is critical to adaptive function and thus adaptive affect regulation. MacDonald (2008) proposed that the prefrontal mechanisms of effortful control are generally to inhibit short-term gains in return for long-term payoffs. This exact statement can be rephrased in terminology seen in affect regulation literature: the prefrontal mechanisms act non-hedonically, instead operating in favor of utilitarian regulation motives (see Tamir, Chiu, & Gross, 2007.) This captures the necessity for collaboration of evolutionary psychologists and affect psychologists to provide a better and comprehensive understanding of the emotions and the regulatory strategies utilized to govern them. However, why we evolved this affect regulation ability has been answered, but to serve what purpose? Humans are not only emotional animals, but social animals as well.

The Social Emotions and Affect Regulation

Interactions between people are the result of two mechanisms: kin selection and reciprocal altruism (Nesse, 1990.) Kin selection operates upon Hamilton’s rule: where the genetic relatedness of the recipient is multiplied by the reproductive benefit gained by the recipient, this being greater than the reproductive cost incurred on the giver (Nesse, 1990.) The more genetic material you share with someone, the more likely you are to help him or her. Reciprocal altruism operates upon cooperation with individuals that probably don’t share genetic material with you (Nesse, 1990.) The prisoner’s dilemma game is utilized when trying to explain the benefits of cooperation between individuals: constant cooperation yields the maximum long-term benefits for both players and thus is the optimal result of the game. Reciprocal relationships are human pattern interactions that occur daily: from giving a friend a ride to school to lending them a substantial amount of money. Social emotions function within these reciprocal relationships: individuals that constantly cooperate experience friendship, love, obligation, and pride; individuals that constantly defect experience rejection and hatred; and individuals that defect when one cooperates experience anger, anxiety, and guilt (Nesse, 1990.)

The social emotions serve adaptive functions through a multitude of avenues. The positive emotions elicited by constant cooperation act as mediators of commitment (Nesse, 1990.) The negative emotions elicited when the other person defects when you cooperate, such as anger, serve adaptive purposes (Nesse, 1990.) Affect regulation research has revealed that negative affect, such as anger, is preferred when preparing for confrontation (Tamir & Ford, 2009.) This can also be said of someone that was recently betrayed in a reciprocal relationship: anger is a signal that the defection has been detected and is not tolerated in the future by that individual (Nesse, 1990.) Any potential defector will be less likely to defect against the individual that effectively displays anger and more likely to cooperate upon future interactions. Anxiety also serves an adaptive purpose, as it motivates cooperation even when the interaction is least desirable (Nesse, 1990.) This anxiety is reflected in the non-hedonic preferences of individuals when addressing self-relevant goals: such as confronting an individual that has compromised your reciprocal relationship (Tamir and Ford, 2009.) Although helping a younger sibling study for an exam on a Friday night seems unpleasant, the anxiety that the impending exam elicits motivates the individual away from immediate pleasure (inebriation) and instead towards the long-term payoff (future cooperation.) It is this ability to cooperate between two individuals that lead to the formation of small groups over the course of human evolution. The necessity for survival and reproduction within small groups serve as the purpose for effortful control and for adaptive social emotions.

Affect Regulation in Groups

Early in the course of human evolution, in order to successfully survive and reproduce, one must be within a small group of cooperating individuals (Buss & Kenrick, 1998.) This strategy leads to the greatest benefits in reproduction and thus favored by natural selection. The interactions among these smalls groups, along with the moods and emotions experienced in these groups, are essential to human life; studying the cause and effect of affect within a group is becoming increasingly interesting to researchers (Spoor & Kelly, 2004.) Spoor and Kelly (2004) proposed “the experience of shared affective states within groups, as well as specific mechanisms to regulate and maintain certain affective states in groups, developed because of their adaptive value within the context of group living” (Spoor & Kelly, 2004, p. 398.) Since the early environment of evolutionary adaptedness forced humans to living in small groups, and the structure within these groups limited by kin selection and reciprocal altruism, it is important to study the affect experience of these individuals in order to better understand emotions and the regulation of them today. The problem of cooperation would have been the primary adaptive problem for humans, especially since groups often included members that might not share genetic material with each other directly (Brewer, 1997.) It is postulated that the development of moods and emotions in groups serves as the driving force that coordinates group activity through the communication of information among the group and facilitation of group bonds (Spoor & Kelly, 2004.)

By definition, group mood is conceived of as the composition of individual moods of the members within the group; group mood occurs across the aggregate and affects the individuals within the group (Spoor & Kelly, 2004.) Researchers interested in group affect must make a distinction between homogenous affect or heterogeneous affect; homogenous affect occurring when individuals’ affect within the group is similar and heterogeneous affect occurring when individuals’ affect within the group is distinctly different (Spoor & Kelly, 2004.) However, no preference is given to either scenario, both combinations are equally important in an evolutionary perspective. It is widely accepted that emotions serve functions (Cosmides & Tooby, 2000), specifically social functions (Nesse, 1990) in order to express information to others (Ekman, 1993.) Spoor and Kelly (2004) propose that emotions and moods provide a quick and effective method to coordinate group members’ behavior and goals. Within a group, a system that swiftly transmits affective information, especially information about the environment, would have provided a great advantage in survival (Spoor & Kelly, 2004.) The authors propose explicit affect regulation is the mechanism that successfully accomplishes this goal. Emotional contagion, such as mimicry and synchrony, serve to converge affect across group members, creating a homogenous affective group state (Spoor & Kelly, 2004.) Just as you imitate the body language of a person you are romantically attracted to in order to advertise this attraction, mimicry in groups can serve as a way to transmit your current affective states to others, even automatically, with minimal effort. The transmission of negative affect would be very important in the EEA, and thus would occur more rapidly in order for survival (Joiner, 1994.) Communicating your noticing of a lion lurking in the nearby brush without explicit vocalization or dramatic motor movements would be quite vital to survival. This rapid transmission of negative affect can also have deleterious effects in today’s society as well: one individual’s experience of dissent or dissatisfaction can transmit across the entire group, sometimes unconsciously, bringing about dissent or dissatisfaction across the aggregate. Albeit, this explicit regulation of negative affect served more benefits to survival in the EEA.

Another benefit of explicit affect regulation within a group would be affect’s ability to communicate status and rank within a group: low moods signaling low status, high moods signaling high status (Nesse, 1991.) Houser and Lovaglia (2002) proposed that affect and status interactions occur in a specific pattern: high status group members will exhibit positive affect frequently, while low status members exhibit negative affect frequently. The group as a whole would profit from “developing affect regulation strategies that maintain appropriate levels of affect for certain group members,” particularly the affect of low status members (Spoor & Kelly, 2004, p. 404.) This status hierarchy is maintained via heterogeneous affect within the group. Spoor and Kelly (2004) hypothesized that once status hierarchies are formed, they are quite persistent, resulting from the hard work of high status individuals to maintain the group’s bonds and survival benefits; remaining in this successful group, even as a low status individual, would outweigh the selfish interests to dissent and leave. This function of heterogeneous affect within a group is supported by empirical evidence. Tidens et al. (2000) discovered that high status individuals would experience pride in response to a positive outcome and anger in response to a negative outcome; likewise, low status individuals would experience gratitude in response to positive outcomes and sadness in response to negative outcomes.

This research regarding status hierarchies and group affect has serious implications for self-esteem and affect regulation research. As Wood, Heimpel, and Michela (2003) revealed that individuals with low self-esteem are likely to dampen positive affect, individuals with high self-esteem are likely to savor positive affect. This theory could have direct correlations to the utility of group affect on status hierarchies, suggesting there are more than just hedonic regulation preferences being exhibited by low self-esteem individuals. Those with low self-esteem might not wish to threaten their position of low rank by gloating about their positive event, which could anger individuals higher in status, and potentially result in their dismissal from the successful group. Even more so, this dampening of positive affect by low-self esteem individuals could be the result of implicit processing resulting from a potential genetic disposition to be low in status rank. Consistent with Tiedens et al.’s (2000) findings, Heimpel, Wood, Marshall, and Brown (2002) also found that individuals low in self-esteem following a negative life event are not likely to repair their mood, and instead express sadness. The statement made by Heimpel et al. (2002): “in some cases, [low self-esteem] people respond in ways that seem particularly maladaptive” could be refuted through examination of the utility of group affect on status hierarchies. Like any great inspirational speech before a championship game, effectively regulating affect within a group prior to collective activities helps communicate a desired unified group outcome (Spoor & Kelly, 2004.) In total, explicit affect regulation within a group functions to alert positive and negative environmental cues and maintain status hierarchies; a successful adaptation to an unyielding EEA.

The second function of shared affect within groups proposed by Spoor and Kelly (2004) is the facilitation of bonds between members of the group. It is this shared affect that is thought to attract individuals to the group at the beginning and what keeps them loyal throughout (Brewer, 1997.) Since negative group affect regulation is closely related to survival situations and communication, positive group affect regulation serves a bonding function (Spoor & Kelly, 2004.) Both of these two functions of group affect lead to the most successful surviving and reproducing humans. “Our current affective experiences in groups were likely shaped by an evolutionary history that favored a functional role for affect and affect regulation mechanisms in groups” (Spoor & Kelly, 2004, p. 409.) It is this research within evolutionary psychology that provides answers to questions regarding the ultimate purpose of human function, even if the proximate purpose of that function seems disorderly, dysfunctional, or lacking any genuine purpose. Looking at affect regulation research through the evolutionary psychology lens might provide insights otherwise missed previously. The above definitely highlights the importance of collaboration across these fronts, since a more cohesive structured explanation of affect regulation could arise. Finally, an important pillar of evolutionary psychology is the examination of sex differences within human psychological functioning. Since sexual dimorphism is a product of natural selection, it is natural to believe sex differences also exist in the realm of affective regulation, even if current methodology within the field have not yet tapped into these differences.

Sex differences and Affect Regulation

It has only been recently accepted that males and females evolved specific expressive methods for communicating and interacting with same-sex individuals (Vigil, 2007.) It could also be postulated that men and women evolved separate affect regulation and expression behaviors (Vigil, 2008.) A Socio-Relational Framework of Expressive Behaviors (SRFB; Vigil, 2008) has been proposed to explain the variation in the expression of affect behaviors by two primary dimensions: basic and reciprocity potential. Basic motivations of expression in behavior come from our reptilian brain: approach or avoid environmental stimuli (Vigil, 2008.) Humans, with the cognitive capacity to evaluate the cost and benefits of each situation encountered, display affiliative responses and emotions when encountering a situation perceived to be reciprocated or display avoidant responses and emotions when a situation has the potential to not be reciprocated (Vigil, 2008.) For example, if you come across an attractive other of the opposite sex, joy and happiness is expressed (emotions that promote bonding); if you come across a hostile individual, anger and fear are expressed (emotions that promote distancing.) The reciprocity potential conveys information regarding capacity or trustworthiness of the interaction with another individual; capacity being either to help or hurt others, while trustworthiness being the probability of actually reciprocating altruism (Vigil, 2008.) With that being said, humans should evolved sensitivity to cues of capacity and trustworthiness and convey this same information accurately to others in order to form fitness improving relationships with other humans (Vigil, 2008.)

Sex differences in affect expression behavior is due to the sex differences in relationship dynamics, formed throughout human’s evolutionary history (Vigil, 2008.) The male-male coalitional competition dictates that males tend to remain in close proximity to their male-kin, while females are forced to migrate into the social networks formed by these related males coalition (Geary et al., 2002.) From this system, males would interact with kin regularly while females interact with non-kin. As previously stated, human interactions between non-kin is driven by reciprocal altruism; females must seek and secure relationships among non-kin and do this by displaying submissiveness or weakness and pacification or compassion (Vigil, 2008.) This signaling by females conveys their trustworthiness, a facet of this reciprocity potential, non-verbally and effectively, in turn allowing for greater allocation of investment in these relationships (Vigil, 2008.) In contrast, since kin surrounds males regularly, this expression of weakness and compassion in order to allocate resources is unnecessary. Instead, males engage in expressive behaviors that advertise the capacity component of this reciprocal potential; advertising dominance, aggression, and elaborate motor movements are examples of dominant behavior (Vigil, 2008.) Empirically this has been supported as well, Vigil (2008) found higher displays of physical aggression in males and crying behaviors in females indicative of the aforementioned selective advantage in affective signaling behaviors. Males express elevated levels of aggression to influence peers to distance themselves and display their still present ability to protect themselves during stressful events (Vigil, 2008.) It could be said that this dominant display of aggression can also lead others to affiliate with the male in the future while deterring other dangerous males from interacting with the male (Vigil, 2008.) Furthermore, males are more capable of perceiving the desired social outcomes of other males, while females are more perceptive to the desired social outcomes of female expressions than males (Vigil, 2008.) That is, when a man sees a female crying, his immediate, implicit response is to distance himself from her rather than provide comfort; likewise, a female that saw this display of distress would want to affiliate with and provide comfort to her. From this, it can be concluded that nonverbal affect behaviors evolved predominantly to communicate with same-sex peers (Vigil, 2008.)

This knowledge could be tremendously useful in research of affect expressive behaviors: having males evaluate distress behaviors of the opposite sex might not be as fruitful as having males evaluate behaviors of the same sex. This also has implications for our understanding of emotional intelligence and individual differences in affect regulation. For example, Wilkowski and Robinson (2008) declared that emotional clarity resulted in reductions of anger: individuals more capable of identifying an undesirable affective state would down regulate this state quickly and effectively. Females who, as determined above, do not express aggression in response to distress but instead express affiliative behaviors dominate their sample. The majority of females within the sample would prefer to down regulate anger quickly, since it promotes distancing. This emotional clarity might intrinsically possess a sex bias: anger, as used by males in stressful situations to promote the adaptive strategy of distancing and protection, might not be down regulated by males, or seen as undesirable, which incorrectly implies they lack emotional clarity. This also could explain why men seem to suppress emotions that would convey submissiveness, vulnerability, or weakness (see Gross & John, 2003.) Men might be more inclined to not express their current emotion state, dependent upon who they are surrounded by (kin or non-kin), what current state they are in (positive or negative), and their current social goal (affiliate or avoidant.) Examining these sex differences is crucial in painting the whole picture of affect regulation strategies. This seemingly deleterious act of social avoidance today might have posed a selective advantage in our evolutionary past; understanding these mechanisms might provide insight and allow for clinical implementation. Incorporation of sex differences into our already fervent study of individual differences in affect regulation would be fruitful.

Lastly, these sex differences in affect expression could be rooted in neuroendocrine mechanisms that are distinct in males and females (Vigil, 2008.) While males might operate on the Fight or Flight mechanism of the sympathetic nervous system, females might operate on the Tend and Befriend mechanism (Taylor, 2006.) An affiliative neurocircuitry has been found that promotes affiliation in response to stress (Taylor, 2006); it would be obvious that this system is dominant in females, whose evolutionary past surrounded by non-kin promoted this behavior to secure resources. This Tend and Befriend response to stress is heavily dependent on oxytocin, a hormone that is endogenously high in females (Taylor, 2002.) This expression of sadness resulted in increased assistance from affiliates throughout our evolutionary past, thus increasing the utility and occurrence of this affect display in females still today. In a study on the value of tears, as the result of crying, tears communicate sadness and sincerity; these tears in adulthood serve to mobilize emotional support (Ziefman & Brown, 2011.) These visual cues of distress are reliable and not easily suppressed (Ziefman & Brown, 2011), especially when we know that this outward expression of distress can be voluntarily suppressed or exaggerated (Fridlund, 1994.) As Hackenbracht and Tamir (2010) established, sadness was found to be useful when looking to elicit help; however, they failed to include sex differences in these preferences within their analyses. It would be expected that females preferred to elicit sadness more so than males and consequently, females would be more adept at doing so. Future research in regard to the utility of sadness, or aggression, should include how males and females might differ on the emotion they prefer to elicit and within what context.

These expressions of aggression and crying in males and females, respectively, in response to distress are effective at causing others to avoid or affiliate (Vigil, 2008.) Supporting the notion that effective affect regulation is adaptive, especially when used to optimize an individual’s social network in order to promote healing and recovery from an adverse experience (Vigil, 2008.) In conclusion, it should be noted that nonverbal affect behaviors are specialized across the sexes and effective, thus consequently adaptive (Vigil, 2008); this reasoning should be attended to when researching differences in affect regulation strategies.

Limitations and Future Directions

As with all endeavors, limitations infiltrate great intentions. As humans evolved the ability to control evolved preferences through effortful control and the evaluation of infinite cost and benefits, however, our expansive culture bombards our human brains with information that might compete with our ability to control automatic processing (MacDonald, 2008.) The constant exposure to the media expands our immediate existence to encompass the existence of people that we otherwise would never meet or see. The mere exposure to supermodel level attractive females might not have occurred early in our evolutionary past, which in turn clouds our ability to rate prospective female mates that are encountered daily. Suddenly, the average male finds no mate sufficient enough in attractiveness (in comparison to these supermodels) and thus does not mate; this comes as a consequence of evolution not directly programming humans with a proximal mechanism that increases fitness (MacDonald, 2008.) As a result, uncertainty lies between what is deemed as success from an evolutionary standpoint and human psychology (MacDonald, 2008.)

The human emotions being deemed useful and shaped by natural selection is something that has taken quite some time to become accepted, especially when human psychological adaptations are not as clear-cut and obvious like a turtle’s shell or a tiger’s stripes. The limitation to seeing affective regulation as an adaptation is the lack of evidence directly pinning affective regulation as an output of natural selection. These inferences are made inherently, primarily through speculative efforts; however, this gap is slowly being closed and it could be proposed that future work in affect regulation will incorporate evolutionary theory in order to understand the proximate and ultimate purposes behind effective and adaptive regulation.

As Spoor and Kelly (2004) eloquently stated, “evolutionary psychology has the potential to provide a unifying theory for understanding many of the disparate phenomena in psychology” (pg. 409.) Just as research in emotions benefited from an evolutionary perspective, affect regulation will benefit wholly from incorporating the theory of evolution into future research endeavors. Hopefully soon, the use of the term adaptive within affect regulation literature will be clearly defined and universally understood. The distinctions between effective and adaptive regulation will be a thing of the past; future researchers will no longer err in their classification (see Campos, Walle, Dahl, & Main, 2011.) With that being said, the instrumental model of emotion regulation might be most accepting of incorporating evolutionary theory; as Tamir (2011) alluded to, an emotion that is adaptive in one context might not be adaptive in another and “social context in which regulation takes place” must be acknowledged (pg. 4.)

Furthermore, research into group affect and affect regulation through an evolutionary lens might prove to be beneficial, even to others outside the immediate realm of evolutionary psychology and affect regulation. For example, it has been shown that emotional contagion of negative affect might prove to be quite useful, as it brings awareness to negative aspects of a work environment, which in turn motivates change (Spoor & Kelly, 2004.) This research into group affect regulation might help shed light on individuals’ affective regulation, since group affect is comprised of each individual’s current affective state. When a regulatory strategy seems not adaptive at the singular level, perhaps examining this strategy at a group level might reveal intentions. Being aware of the utility of emotion to communicate hierarchy status, this might shed light on the previously perceived non-adaptive regulation as an individual with low self-esteem not celebrating alone at home after receiving an A on a final exam. The emotion regulation properties of the status hierarchy have been previously identified: an individual is influenced by affective signals from others and consequently, these individuals seek out environments that facilitate regulation (Sloman, Atkinson, Milligan, & Liotti, 2002.) This could explain why low self-esteem individuals seek to maintain this low status, not only as self-verification but since they regulate most effectively within their current status hierarchy; moving social groups might result in a disruption of their current status, either positively or negatively, and would result in dysregulation. This evolutionary perspective can consequently reframe low self-esteem as being a positive adaptive function to avoid conflict (Sloman, Atkinson, Milligan, & Liotti, 2002.)

Moreover, understanding how psychological mechanisms evolved can provide clinical insights and improvements. Since happiness is not the normal state of functioning but rather, a goal to which we all strive, provides inspiration to alleviate our current discomfort, and thus consequently continue to improve our fitness (Nesse, 1990.) An evolutionary perspective provides much needed insight to the realm of emotional disorders. Recognizing that we have negative affective experiences for good reasons (to defend against situations that might decrease fitness), clinicians should look at the current life situation of the individual experiencing this negative affect (Nesse, 1990.) Understanding the goals of the individual, what strategies are being used to accomplish those goals, and their ability to achieve that goal might contribute to the possibility of an emotional disorder being present (Nesse, 1990.) Although these defensive responses to events seem to be over responsive, exaggerated, and unbearable, understanding the evolutionary functions of the emotion will allow us to lessen our once normal and superiorly adaptive, but superfluous emotional afflictions (Nesse, 1990.) Concluding, future research should aim to establish what kinds of suffering can or can not be relieved without compromising an adaptation, and this can only be accomplished through scrutinization of the evolutionary functions of emotions (Nesse, 1990.) “Knowledge of the underlying biology [of psychological mechanisms] informs the integration of art and science” (Sloman, Atkinson, Milligan, & Liotti, 2002, pg. 324.)

References

Gross, J.J. (1998). The emerging field of emotion regulation: Divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74, 224-237.

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Digit Ratio and Intelligence

2011-04-18T12:36:00.000-07:00

So, I have been struggling to think of original things to post. Partly because my semester is coming to a close and partly because all I do is test subjects day in and day out, for reasons still cloudy to me. So I thought I would post what I have been working on: my undergrad thesis if you will. It definitely needs work (19 subjects is quite the joke.) However, it is still in the making and this beauty scored me second place here at our S&T undergrad research conference. Feel free to read up and comment, just be productive. I could use the help :)

Second to fourth digit ratio: A measure related to Intelligence

The ratio of index finger (2D) to ring finger length (4D) is an index of prenatal exposure to testosterone and estrogen (Lutchmaya, Baron-Cohen, Raggatt, Knickmeyer, & Manning, 2004). Direct measurements of these prenatal androgen levels are unavailable to researchers who wish to determine the affects of those androgens on adult behavior. Instead, researchers are looking to an alternate tactic to correlate behaviors with a accepted measure of prenatal androgen action, such as the 2D:4D ratio. Research suggests that the ratio of second-to-fourth digit length may be sexually differentiated (Kuepper & Hennig, 2007) with males, on average, having a lower 2D:4D ratio than females. This translates into males having a shorter second digit relative to the fourth digit (Manning, 2002). Prenatal testosterone masculinises the brain, resulting in higher spatial ability (Csathao et al., 2003), higher musicality (Sluming & Manning, 2000), depression and autism in men (Manning, Baron-Cohen, Wheelwright, & Sanders, 2001), and sexual orientation (Csatho et al., 2003; Lippa, Manning & Robinson, 2003). Puts, Gaulin, Sporter, and McBurney (2004) criticizes the use of 2D:4D as an index of prenatal influence of testosterone. Puts noted that most positive results were from one lab, the exception being studies on sexual orientation, and the results can vary from equivalent to incongruous, prone to illegitimate results, the samples are exclusively one sex or the other, and most often undergraduates. Ultimately, Puts proposed that 2D:4D may be an index of prenatal testosterone exposure, but that exposure itself has different effects on the two sexes, traits, and individuals.

The fluctuating nature of these androgens during development also contributes to the dimorphic traits differentiating at indistinctive times. Broad dispositions like Woodcock-Johnson’s Cognitive Abilities or the Big Five personality dimensions, of which the development is most likely not limited to short periods of time during pregnancy, might show reliable relations with 2D:4D. With regard to intelligence, one would expect that high prenatal testosterone (low 2D:4D) causes a masculine pattern: high numerical skills, but lower verbal skills. Luxen & Buunk (2004) performed a study utilizing the relationship between verbal and numerical intelligence with 2D:4D. They found right-handed 2D:4D, Verbal and Numerical intelligence, and Agreeableness is displayed in a typical masculine pattern: low 2D:4D, low verbal intelligence, high numerical intelligence, and low agreeableness. Digit ratio might more capable of shedding light on which cognitive abilities are more masculine or feminine. However, no previous research has examined digit ratio and Woodcock Johnson III Test of Cognitive Abilities. The WCJ-III is a valuable instrument for examining intelligence because it is based on the Cattell-Horn-Carroll (CHC) theory of cognitive abilities, the only instrument that can boast this feature. The WCJ-III looks at intellectual ability, but also seven CHC factors including comprehension-knowledge, long-term retrieval, visual-spatial thinking, auditory processing, fluid reasoning, processing speed, and short-term memory. Clusters of these seven tests are utilized for test interpretation, which minimizes the danger of generalizing from the score for a single, narrow ability to a broad, multifaceted ability. This ultimately results in higher validity. One could expect high digit ratio to correlate positively with long-term retrieval, a typical female pattern. The prediction I tested for the relationship between digit ratio and the WCJ-III are as follows.

Predictions

H1: 2D:4D ratio will be positively correlated with long-term retrieval.

Method

Participants

Participants were recruited through a sign up sheet on the Research Sign up Bulletin Board in the Humanities and Social Sciences’ building. The students were offered class credit in return for their participation. 19 subjects have been recruited at the completion of this report (13 male, 6 female). Average age is 21.2 years (SD = 5.8); all 19 participants are students and 73.7% completed some college; and 94.7% of participants are Caucasian.

Materials

Ratios. The second digit and fourth digit length was measured on the subjects’ right hands in inches. The subjects placed their right hands on a clean sheet of paper. A tick mark was placed at the tip of the second and fourth digit, but careful not to mark above the nail. A second tick mark will be placed at the base of the second and fourth digit. The distance between the two tick marks was measured in millimeters. The actual digit ratio was computed by dividing the second digit length by the fourth digit length. (Mean digit ratio = 1.02, SD = .056).

Personality. The Big Five Inventory Version-44 was utilized to assess personality. The 44-item survey was chosen for the five dimensions, Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. A second questionnaire, intended to probe further on the anxiety scale will be administered. It is a nine-question length survey.

Cognitive Abilities. The Woodcock-Johnson was used to assess cognitive abilities. Seven tests will be administered, each assessing a different area of cognition. It will be scored, and the results will be given to the subject to keep.

Procedure

Participants were asked to complete a consent form prior to participation. The measures were counterbalanced to ensure the comprehensive batteries assessing personality and cognition, known to cause fatigue, have no lingering effects on the health measures. Each participant had a different order of tests administered. Upon completion of all the measures, they were scored and the results were provided to each participant.

Results

All analyses were performed using the statistical package SPSS 18.0. Analysis focused on the scores of each subtest of the WCJ-III and the digit ratio measurements. A significant relationship exists between 2D:4D and visual-auditory learning r(19) = .532, p = .019 (see Figure 1). 28% of variance in visual-auditory learning is accounted for by digit ratio, R² = .0283.

Discussion

I found evidence of a relation between prenatal testosterone exposure (as indexed by 2D:4D) and visual-auditory learning. Visual-auditory learning is a test of long-term storage and retrieval. On this test of associative and meaningful memory, the subject learns and recalls pictographic representations of words. This thinking ability test requires participants to learn, store, and retrieve a series of visual-auditory associations. We found a positive correlation with visual auditory learning and digit ratio. The results make sense in the light of effects of prenatal testosterone that masculinises the brain: low testosterone exposure (high 2D:4D) makes individuals perform better with regard to long-term storage and retrieval, a typical female pattern. Low testosterone exposure results in more female typical patterns and characteristics, including the necessity of a fast, efficient, and reliable ability to store information. Evolutionarily speaking, females are delegated tasks pertaining to gathering of food for the group. This requires an ability to store and retrieve locations of the best food, the best water, etc. The efficient retrieval of this information would result in a payoff proximately in fitness, ultimately in survival.

The lack of the number of participants restrained the power of this project. In the future, more participants will be needed to determine if any other facet of the WCJ-III revealed any significant relationships with digit ratio. For example, it could be predicted that low digit ratio would exhibit a negative relationship with visual spatial abilities. I was hoping to determine if digit ratio was a stronger predictor of the variance in visual spatial than sex alone. This could be said of the seven other subtests of the WCJ-III. Studies using larger sample sizes may be able to detect these relationships between intelligence and digit ratios.