2.1.1 LH Traits, Trade-offs, and Fast-Slow Strategies

Hence, as more individuals are produced than can possibly survive, there must in every case be a struggle for existence, either one individual with another of the same species, or with the individuals of distinct species, or with the physical conditions of life. (Darwin, Reference Darwin1859/1975, p. 63)

In the chapter entitled “Struggle for Existence” of The Origin of Species, Darwin pointed out a critical assumption about evolution – that there will always be more lives than there are resources to support them, resulting in constant intra- and inter-species competition in which the fittest survive. The same competition or struggle for existence plays out in individual organisms as different life functions and life needs to support organism survival compete for the limited resources and the energy individual organisms can acquire in their constant struggle for existence. Survival of the fittest, therefore, means the optimal allocation of limited energy and resources for different life functions and needs. Species that have survived to the present day have, during evolution, optimized the allocation and distribution of limited resources for different species-specific developmental needs. Through adaptive phenotypic plasticity during ontogeny and across generations, individuals of a species have been able to adjust their individual allocations of resources within their species-typical range. As will be discussed in later sections, these population and individual differences develop through a combination of genetic variation and phenotypic plasticity in response to environmental contingencies.

The strategic allocation of limited resources or energy to support different life functions and needs is called life history strategy, where life history refers to the processes and time span during which organisms “struggle for existence” (i.e., labor to capture energy in the environment and turn it into survival and reproduction; Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009; Ellison, Reference Ellison2017; Stearns, Reference Stearns1992). Reproduction includes mating and parenting. Survival means living, which entails growth and development, and maintenance of the body and mind in preparation for reproduction. LH thus consists of reproduction and preparation for reproduction. Whereas mating represents direct efforts of reproduction, parenting that contributes to the growth and development of progeny also represents preparation for reproduction. Because preparation for reproduction concerns the body’s development and maintenance, this part of LH is referred to as somatic functions or somatic efforts and activities, in contrast to reproductive functions, efforts, and activities. LH strategies are resource allocation plans to optimize somatic and reproductive functions. Because resources and energy are limited in the struggle for existence, what goes to reproduction cannot be used for bodily development and maintenance, for example, and trade-offs have to be made between somatic and reproductive functions. LH strategies are therefore trade-off strategies that optimize trade-offs in distributing limited resources and energy between different life functions and needs along the demarcation between somatic and reproductive domains.

Developmental outcomes resulting from the trade-off expenditure of energy are LH traits. Because the trade-off is between growth and development on the one hand and reproduction on the other hand, LH traits are either growth- and development-relatedor reproduction-related life events and characteristics. Examples of LH traits include age at sexual maturity, size at maturity, gestation period, birth weight, number of offspring, postnatal growth rates, breastfeeding duration, birth spacing, length of childhood or juvenile dependence, level of parental investment per child, adult body size, and longevity (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009; Stearns, Reference Stearns1992). Each of these LH traits may take one of two values toward the opposite ends of the trait distribution as a result of the trade-off between reproduction versus growth and development or preparation for reproduction. For example, a later age and larger size at sexual maturity indicate more energy expenditure on somatic growth and development, whereas an earlier age and smaller size at sexual maturity suggest more allocation of resources trade in favor of reproduction over growth and development. Similarly, longer (vs. shorter) gestation, longer (vs. shorter) breastfeeding time, heavier (vs. lighter) birth weight, longer (vs. shorter) childhood, longer (vs. shorter) birth spacing, and a smaller (vs. larger) number of offspring represent more (vs. less) parental investment per child and trade-off energy allocations against reproduction (vs. growth and development). These and other LH traits constitute and serve as indicators of LH trade-off strategies (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). The LH trade-off strategy that puts more energy expenditure or resource investment on growth and development at the cost of delayed reproduction is called a slow LH strategy because it leads to slow or slower and more invested growth and development. The other end of trade-off that trades faster and less invested growth and development for earlier and more active reproduction is called a fast or faster LH strategy. Adopted from Del Giudice (Reference Del Giudice, Gendolla, Tops and Koole2015), Table 1 presents some of the biological LH traits of fast versus slow species or LH strategists.

Table 1 The fast–slow continuum of life history variation

Adopted from Del Giudice, M. (Reference Del Giudice, Gendolla, Tops and Koole2015). Self-regulation in an evolutionary perspective. In G. H. E. Gendolla, M. Tops, & S. L. Koole (Eds.), Handbook of biobehavioral approaches to self-regulation (pp. 25–41). Springer Science + Business Media.

2.1.2 Rushton’s Contribution and Psychological LH Research

Biologists investigate these LH traits mainly between species (Stearns, Reference Stearns1992), although also within species (Réale et al., Reference Réale, Garant, Humphries, Bergeron, Careau and Montiglio2010). Rushton (Reference Rushton1985) was the first to study LH trait variations within human populations and introduced LH research into psychology. An earlier presentation of fast-slow LH was r and K selection (Stearns, Reference Stearns1992), where r denotes maximum reproduction rate and thus fast LH, and where K represents the carrying capacity of the environment that limits population growth and slows LH. Similar to the fast-slow LH distinction, r-selected species mature early, have many small offspring, make higher reproductive efforts, and have lower parental investment, while K-selected species mature late, have a few large offspring, make smaller reproductive efforts, and exert larger parental investment (MacArthur & Wilson, Reference MacArthur and Wilson1967; Pianka, Reference Pianka1970). Rushton (Reference Rushton1988) used the r-K species-difference framework to explain individual differences as well as racial differences within the human populations. Most notable is his LH research on race. Based on birth weight, brain size, and IQ (slow LH traits), and birth rate, birth interval, postnatal motor development, and sexual behavior (fast LH traits), Rushton concluded that Africans are closer to r or fast LH strategists compared to Asians and Whites, whereas Whites pursue higher K or slower LH strategy than Africans, and Asians are the slowest LH strategists (Rushton, Reference Rushton1996; Rushton & Ankney, Reference Rushton and Ankney2000; Rushton & Bogaert, Reference Rushton and Bogaert1987; Rushton & Rushton, Reference Rushton and Rushton2003). Unfortunately, other than criticism (e.g., Cartmill, Reference Cartmill1998; Smedley & Smedley, Reference Smedley and Smedley2005), Rushton’s LH work on race has not been followed up by the literature.

2.1.3 Psychological Focus on LH Related Behaviors

However, Rushton’s approach to study within-species LH variations has taken root in psychology. Whereas biologists examine growth and reproduction related LH traits as outcomes as well as indicators of fast and slow LH trade-off strategies, evolutionary psychologists mainly examine behaviors that propel and lead to the biological LH traits, and they are referred to as LH related behaviors (Chang et al., Reference Chang, Lu and Lansford2019a; Del Giudice, Reference Del Giudice2020; Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009; Sear, Reference Sear2020). Corresponding to the LH trade-off between fast and slow pace of life and longer or shorter longevity is time management behavior, such as future orientation vs. future discounting (Wang et al., Reference Wang, Kruger and Wilke2009), conscientiousness and prudence vs. procrastination and risk taking (Chen & Chang, Reference Chen and Chang2016; Del Giudice, Reference Del Giudice2018; Lu & Chang, Reference Lu and Chang2019; Sear, Reference Sear2020), insight, control, and planning vs. impulsivity, emotionality, reactivity (Figueredo et al., Reference Figueredo, Jacobs and Gladden2018), and reduced executive function (Chang et al., Reference Chang, Liu and Lu2021; Del Giudice, Reference Del Giudice2014; Morgan & Lilienfeld, Reference Morgan and Lilienfeld2000). Two types of sociality are also associated with fast or faster and slow or slower LH strategies. A slow or slower LH strategy entails more affiliative, more mutualistic, and other-centered sociality that is mindful of future coexistence and long-term cooperation (Chang et al., Reference Chang, Liu and Lu2021; Del Giudice, Reference Del Giudice2014; Figueredo et al., Reference Figueredo, Jacobs and Gladden2018). Such sociality is adaptive in long-term group-living settings because it promotes mutual relationships and orderly competition and thus maximizes collective acquisition of resources (Chang et al., Reference Chang, Lu and Lansford2019b; Chen & Chang, Reference Chen and Chang2012; Figueredo et al., Reference Figueredo, Jacobs and Gladden2018; Figueredo & Jacobs, Reference Figueredo, Jacobs, Frias-Armenta and Corral-Verdugo2010; Zhu et al., Reference Zhu, Hawk and Chang2018, Reference Zhu, Hawk and Chang2019). By contrast, a fast or faster LH strategy is associated with more antagonistic, more exclusive, and self-centered sociality by which individuals attend to their immediate survival needs with reduced concern for future conspecific coexistence and diminished trust of outgroup members (Zhu et al., Reference Zhu, Lu and Chang2021). Disregard for social propriety, crime, and violence are likely to follow. These antisocial behaviors, in turn, contribute to short-sightedness and perpetuate the cycle of fast LH driving antagonistic sociality (Chang et al., Reference Chang, Liu and Lu2021). Compiled from the literature, Table 2 lists human LH-related behaviors representing faster versus slower LH strategies.

Amalgamating trade-offs operating within each specific LH-related behavior, as well as LH traits, in a similar direction, reveals the same overarching patterns of fast-slow LH trade-off strategies. These coherent constellations of interrelated LH-related behaviors and trade-offs offer insights into variations both between and within species (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). Species or individuals positioned toward the fast or faster end of the fast-slow LH spectrum exhibit characteristics like shorter gestation times, early or earlier reproduction, smaller body size, and a larger number of offspring, as well as future discounting, impulsivity, risk taking, and antagonistic sociality (Chang et al., Reference Chang, Lu and Lansford2019b). Conversely, those found at the slow or slower end of the continuum display precisely the opposite traits (Del Giudice et al., Reference Del Giudice, Gangestad, Kaplan and Buss2015), including future orientation and affiliative sociality (Chang et al., Reference Chang, Lu and Lansford2019b). These organized trade-offs capture the essence of how LH strategies vary across species and individuals.

2.1.4 Possibilities of Mixed LH Strategies

The existence of LH continua does not necessarily indicate that all LH traits are neatly lined along a single slow-fast continuum (Stearns, Reference Stearns1992). In reality, some species exhibit a blend of slow and fast LH traits (e.g., Kraus et al., Reference Kraus, Thomson, Künkele and Trillmich2005). For example, humans fall toward the extremely slow end of the slow-fast continuum. This positioning is characterized by an extended period of childhood, later onset of reproduction, and greater longevity compared to any other land-dwelling mammals (Hawkes, Reference Hawkes, Hawkes and Paine2006). However, certain aspects of human LH deviate from this overall slow pattern. Notably, humans exhibit relatively early weaning, short interbirth intervals, and continuous sexuality. Similarly, individuals might display a blend of strategies at the behavioral level. For instance, one could be exceptionally hardworking, conscientious, and consistently avoid procrastination, traits typically associated with a future-oriented, slower LH strategy. Yet, simultaneously, this same individual might exhibit present-oriented, fast LH characteristics by neglecting health, failing to conserve bodily energy, and discounting greater future prospects. Understanding why humans display this mix of slow and fast LH traits has been a focal point of LH analysis that is beyond the scope of this Element. More relevant and pertinent to both biological and psychological LH research are the causes of fast-slow LH trade-off strategies.

2.2.1 Population Density and Intraspecific Competition

Initially, LH strategies were believed to be chiefly influenced by population density and the ensuing intraspecific competition for resources required to support population growth (MacArthur & Wilson, Reference MacArthur and Wilson1967). This phenomenon was attributed to what is known as density-dependent selection. When population density is relatively low, natural selection favors a high reproductive rate to quickly fill the environment with offspring who exploit the abundant food supply freely. In this context, parental investment and offspring competitive abilities are less critical because even parentally uninvested or less invested offspring can survive and thrive in resource-rich competition-free environments. This outcome, driven by natural selection, mirrors a fast life history strategy and is referred to as r selection defined earlier.

As population density increases due to a high reproductive rate, the focus of natural selection shifts toward promoting parental investment and offspring competitive abilities to secure limited resources in densely populated environments. The result is higher offspring quality, lower offspring quantity, and a lower reproductive rate. This outcome represents K selection. The environmental conditions resulting from r and K selection are linked to the evolution of LH strategies (Pianka, Reference Pianka1970). Specifically, exposure to low-density and resource-rich r environments would favor fast LH traits that maximize reproduction speed and offspring quantity, resulting in a large number of low-quality offspring receiving little parenting, whereas high-density and resource-limited K environments would select for slow LH traits that enable the production of and parental investment in a small number of highly fit offspring. As mentioned earlier, Rushton (Reference Rushton1985) further applied this r-K framework to explain human LH variations. Today, animals, including humans, exhibiting traits indicative of r selection are generally referred to as displaying a fast LH strategy, and those with K selection characteristics are regarded as displaying a slow LH strategy (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). Within the r-K framework, the determining factors are population density and intraspecific competition which are positively inter-correlated and both positively predict slow LH strategies especially emphasizing parental investment and offspring quality.

Whereas population density and intraspecific competition represent driving forces shaping LH strategies, other selective pressures, such as age-specific mortality and morbidity influenced by environmental harshness and unpredictability, are increasingly recognized as more fundamental in shaping the evolution and development of LH strategies (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). Consequently, density-dependent effects have diminished over time and the mortality-causing environmental harshness and unpredictability have become the primary foci of the species evolution and individual development of LH strategies.

2.2.2 Environmental Harshness and Unpredictability

Environmental harshness is defined as the rates, frequencies, or mean levels at which extrinsic risks cause mortality and morbidity at a specific age in a population, and environmental unpredictability refers to stochastic variations or variance of environmental harshness across time or space (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). Additional definitions follow. First, age is mainly distinguished between prime-age adults and dependent juveniles or children, and mortality-morbidity affecting the adult versus child population shapes LH strategies differently. Second, originally, LH theory focused only on mortality but did not discuss morbidity or disability that includes serious injuries, illnesses, and other physical or mental handicaps. Ellis et al. (Reference Ellis, Figueredo, Brumbach and Schlomer2009) introduced mobility and used it together with mortality to define human LH because serious physical and mental disabilities in people affect their trade-off allocation of resources between reproduction and growth and development or preparation for reproduction (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). Finally, LH theory distinguishes between intrinsic and extrinsic components of morbidity and mortality (Williams, Reference Williams1957). The intrinsic component refers to functional degradation in an organism’s internal system that stems mainly from aging-related wear and tear of the body and mind (Carnes et al., Reference Carnes, Holden, Olshansky, Witten and Siegel2006). Extrinsic morbidity and mortality refer to disability and death that befall an organism due to external and mostly uncontrollable factors such as predation, accidents, and infectious diseases. These two components of mortality interact in bringing about an animal’s eventual demise (Carnes et al., Reference Carnes, Holden, Olshansky, Witten and Siegel2006; Koopman et al., Reference Koopman, Wensink, Rozing, Van Bodegom and Westendorp2015), and the internal state (discussed in later sections) and the external environment (e.g., harshness and unpredictability) together determine LH strategies (Chang et al., Reference Chang, Lu and Lansford2019a). Extrinsic mortality and morbidity stem from external or environmental threats (e.g., infectious diseases) that are relatively independent of age-specific or adult animals’ own survival efforts and abilities. For example, when environmental harshness or the extrinsic mortality-morbidity rate is high as amid a severe infectious disease pandemic, all prime-age adults in a population will suffer the same casualties even though some members are healthier or follow more disease-control measures than others. By contrast, when levels of extrinsic morbidity-mortality are low (i.e., harshness is low), external threats of death and disability will affect members of a population differently depending on their age (e.g., children and older adults) as well as their survival abilities (e.g., immune functioning) and efforts (e.g., disinfecting nests by birds and physical exercise in humans).

Environmental harshness and unpredictability are conceptually distinct and predictively additive in formulating fast or faster LH strategies (Belsky et al., Reference Belsky, Schlomer and Ellis2012; Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009, Reference Ellis, Sheridan, Belsky and McLaughlin2022; Lu et al., Reference Lu, Liu and Chang2022a; Usacheva et al., Reference Usacheva, Choe, Liu, Timmer and Belsky2022). “Both high absolute levels of adult mortality (harshness) and high variation in adult mortality (unpredictability), therefore, select for fast LH strategies. This equivalency makes logical sense: both harshness and unpredictability present adult organisms with morbidity-mortality risks that are largely insensitive to their adaptive decisions or strategies” (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009, p. 229). More detailed logic of equivalency follows. Harshness and unpredictability are distinct because they represent frequencies or mean (harshness) versus stochastic variance (unpredictability) of extrinsic mortality risks that determine environmental safety. High extrinsic mortality and morbidity result from either high frequencies of extrinsic mortality risks (harshness) or high uncontrollability or stochastic variation of such risks (unpredictability). Thus, harshness and unpredictability are predictively additive as they entail the same extrinsic effect that inflicts age-specific mortality and morbidity independent of individuals’ intrinsic life conditions (e.g., good health) or survival efforts (e.g., hard work). A safe environment entailing and resulting from low frequency and low stochastic variance of extrinsic mortality risks avails itself when both harshness (rates or mean of extrinsic mortality) and unpredictability (stochastic variance) are low, whereas an unsafe environment results from high extrinsic mortality when either harshness or unpredictability is high.

When these two dimensions are both low as in a safe and controllable environment, natural selection favors a slow LH strategy to maximize physical and mental development by organisms’ acquiring energy and resources and accumulating knowledge and skills to enhance future resource-capturing abilities and reproductive competitiveness. Everything else being constant, a safe environment fosters population density and increased intraspecific competition (MacArthur & Wilson, Reference MacArthur and Wilson1967). In response, organisms must develop their physical and mental capacities and must invest more in their offspring to keep up with increased competition. Environmental safety or low harshness and unpredictability also ensure a more predictable future, which, in turn, makes certain that investments in one’s own as well as one’s offspring’s physical and mental enhancement and development will pay off. Considered together, these interrelated factors stemming from safe environments of low harshness and unpredictability predicate that slow or slower LH is the winning strategy favored by natural selection (Lu et al., Reference Lu, Yang, Liu, Zhu and Chang2023). By contrast, in an unsafe environment either due to harshness or unpredictability or both causing casualties (mortality-morbidity) beyond an organism’s survival efforts and abilities, the winning strategy favored by nature is not to attempt to invest in future resource-garnering capabilities or future reproductive potentials but to outpace extrinsic mortality and morbidity risks by growing fast and reproducing early. Thus, an ever slightly increased probability of escaping uncontrollable mortality and morbidity post-reproductively means that fast or faster LH strategists out-survive slow or slower strategists in an unsafe environment high in harshness, unpredictability, or both. Evolution therefore tends to couple safe and stable living environments low in harshness and unpredictability with slow or slower LH strategies and couples unsafe environments high in harshness or unpredictability with fast or faster LH strategies. This species-general environment-LH contingency drives evolution, as well as development (discussed in the next section), of LH strategies.

Despite the above argument for equivalency of harshness and unpredictability effects (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009), environmental unpredictability is conceptually more complex (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009; Frankenhuis et al., Reference Frankenhuis, Panchanathan and Nettle2016) and methodologically more difficult to measure (Young et al., Reference Young, Frankenhuis and Ellis2020) than harshness. Its conceptual complexity entails the formulation of alternative and opposing LH strategies, such as conservative and diversified bet-hedging (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009) where to reduce or increase reproductive efforts to better prepare for unpredictable environments. Its methodological difficulty in part casts some doubt on empirical verifications of these alternative strategies. For example, bet-hedging strategies have not been empirically supported with human populations. (We discuss some of these issues in the last part of the Element as considerations and possible directions for future psychological LH research.) In summing up the current state of human LH research, both empirically and theoretically, human LH researchers unequivocally conclude that, similar to harshness, environmental unpredictability predicts fast or faster LH.

“Some people experience environments that are both harsh and unpredictable, such that mortality and morbidity are high, threats appear without warning, and opportunities are fleeting” (Frankenhuis et al., Reference Frankenhuis, Panchanathan and Nettle2016, p. 76).

“LH strategies should detect and respond to proximal cues to environmental unpredictability (e.g., stochastic changes in ecological context, geography, economic conditions, family composition, parental behavior) by entraining faster LH strategies” (Belsky et al., Reference Belsky, Schlomer and Ellis2012, p. 664).

“In response, humans may have developed conditional adaptions that enabled accelerated life history development if exposed to environmental unpredictability” (Young et al., 2022, p. 552).

“Because harshness and unpredictability are conceptually distinct, developmental exposures to each of these environmental factors should uniquely and thus additively contribute to variation in LH strategy (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009)” (Belsky et al., Reference Belsky, Schlomer and Ellis2012, p. 664).

“ … distinct and additive effects of harshness and unpredictability have been chronicled. Of note, results prove generally consistent with LH expectations. Thus, greater harshness and unpredictability predicted faster LH-relevant traits in adolescence and young adulthood … ” (Zhang et al., Reference Zhang, Schlomer, Ellis and Belsky2022, p. 668).

“This is because unpredictable environments, over both evolutionary and developmental timeframes, bias developmental systems toward discounting future costs and benefits relative to current ones (Hill et al., Reference Hill, Ross and Low1997; Wilson & Daly, Reference Wilson and Daly1997). Accordingly, individuals should respond to signals of environmental unpredictability by adopting faster life history strategies (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009)” (Ellis et al., Reference Ellis, Sheridan, Belsky and McLaughlin2022, p. 451).

“Accordingly, exposure to unpredictability is thought to favor the fast life history, characterized by prioritization of immediate gratification over long-term planning and an emphasis on mating over parental investment, all at the cost of physical or mental tenacity (Belsky et al., Reference Belsky, Schlomer and Ellis2012; Brumbach et al., Reference Brumbach, Figueredo and Ellis2009; Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009; Simpson et al., Reference Simpson, Griskevicius, Kuo, Sung and Collins2012)” (Usacheva et al., Reference Usacheva, Choe, Liu, Timmer and Belsky2022, p. 516).

2.3.1 Phenotypic Plasticity

Phenotypic plasticity, also referred to as reaction norms, involves a single genotype (genetic makeup) expressing diverse phenotypes (observable traits or characteristics) due to organisms reacting to varying external and internal life experiences (Stearns & Koella, Reference Stearns and Koella1986). Similarly, a reaction norm is defined as a range of phenotypes that a genotype will produce in response to ecological conditions that consistently influenced fitness during a species’ evolutionary history (Schlichting & Pigliucci, Reference Schlichting and Pigliucci1998). From the gene’s perspective, the genotypic capacity to support various phenotypes allows a genotype to spread and maintain a strong presence across a wide environmental gradient (Ghalambor et al., Reference Ghalambor, Mckay, Carroll and Reznick2007; West-Eberhard, Reference West-Eberhard2003). From the individual organism’s standpoint, phenotypic plasticity enables adjustments of physical and behavioral traits based on specific environmental conditions encountered throughout an organism’s lifetime. The genotype sets the developmental limits for what an individual organism can realize, whereas the phenotype is what the organism actually realizes in its ontogenetic interaction with the environment. The variations in structure, physiology, and especially behavior among individuals arise because of phenotypic plasticity shaped by a multitude of environmental influences (West-Eberhard, Reference West-Eberhard2003).

From single-celled organisms to complex multicellular beings, including plants and animals, phenotypic plasticity manifests across various levels of biological organization. For instance, plants can adjust their growth patterns, leaf size, root development, and flowering times in response to environmental factors like light, soil, and water availability. When a plant is in a shaded environment, it may elongate its stem and develop larger, thinner leaves to maximize light capture. Conversely, in a well-lit environment, the same plant may exhibit a more compact growth form and smaller, thicker leaves to prevent excessive water loss and sun damage (Sultan, Reference Sultan2000). In animals, for instance, certain fish species exhibit different color patterns and behaviors depending on the surrounding habitat, allowing them to blend in and avoid predation (Endler, Reference Endler1980). Some insects can adjust their wing size and metabolic rates in response to temperature variations, enabling them to survive in different climatic conditions (Huey & Berrigan, Reference Huey and Berrigan2001). Changes in skin pigmentation in response to sun exposure and the development of larger muscles in individuals engaged in physical activities are good human examples of phenotypic plasticity. Some marine fish can even change sex as a form of phenotypic plasticity in response to the environment’s changing sex ratio. For example, Clark’s anemonefish live in groups with all males and one female matriarch who is transitioned usually from a largest and most aggressive male fish. Similarly, species with males as the dominant members of a hierarchy display the opposite sexual phenotypic plasticity. For example, a large female goby fish may develop male gonads and reproductive functions to replace a dominant patriarch in the event of his disappearance due to death or injury (Frankenhuis & Nettle, Reference Frankenhuis and Nettle2020).

2.3.2 Adaptive Developmental Plasticity and LH Development

However, more relevant to LH research and developmental psychology is the phenotypic plasticity of ontogenetic development and LH strategies, termed adaptive developmental plasticity (Nettle & Bateson, Reference Nettle and Bateson2015), and also known as conditional adaptation (Boyce & Ellis, Reference Boyce and Ellis2005). This process involves integrating environmental information into an organism’s long-term development. It allows organisms to adapt their LH phenotypes during ontogeny to better match prevailing environmental conditions (Bateson et al., Reference Bateson, Barker and Clutton-Brock2004; Nettle & Bateson, Reference Nettle and Bateson2015). Unlike phenotypic plasticity, which focuses on short-term responses to immediate environmental conditions, adaptive developmental plasticity involves longer-term changes that occur during one’s growth and development. Also known as conditional adaptation, the mechanism responds to specific features of childhood environments, and entrain LH pathways that reliably match these environmental features forecasted to characterize the future social and physical world into which children will mature (Bateson et al., Reference Bateson, Barker and Clutton-Brock2004; Boyce & Ellis, Reference Boyce and Ellis2005). Humans have evolved developmental mechanisms that detect and internally encode information about the harshness and unpredictability of early childhood environments, using it to calibrate LH strategies for a similarly predicted adult world (Nettle & Bateson, Reference Nettle and Bateson2015). The LH calibration posits that early childhood experiences in adverse or benign environments hold predictive information about the future adult environment that is expected to share similar features. During early childhood, individuals are particularly sensitive to environmental conditions, which serve as information to predict potential challenges and opportunities individuals might encounter later in life. Because of the evolutionarily selected contingency between environmental conditions and LH strategies, children respond to environments from safe to dangerous with biobehavioral LH phenotypes that represent the slower or faster ends of the species’ spectrum.

This adaptive calibration of LH requires consistency between child and adult environments. This assumption is needed for the evolution of developmental phenotypic plasticity or otherwise, natural selection might favor either a generalist type or a chameleon-like type, which adapts to a constantly shifting environment (Frankenhuis & Panchanathan, Reference Frankenhuis and Panchanathan2011). Most other animals generally spend their mature years in environments similar to the ones they grew up in. Evidence suggests that human ancestral environments remained relatively stable over generations (Tooby & Cosmides, Reference Tooby and Cosmides1990) or within a single generation (Richerson et al., Reference Richerson, Boyd and Bettinger2001). Even in modern day living, children do not often migrate to a vastly different environment within their lifetime and adaptive developmental plasticity would result in well-matched LH strategies (Frankenhuis & Panchanathan, Reference Frankenhuis and Panchanathan2011). However, when a mismatch does arise often as later environments significantly improve over earlier harsh living conditions in today’s rapid social economic progression, the discrepancy may render the induced phenotype no longer providing the anticipated fitness advantage. Psychological LH research, illustrating this non-adaptive developmental plasticity as well as mostly adaptive phenotypic plasticity, is presented next.

4.1.1 Mixed Findings Unsupportive of Species-General LH Principle

Partly derived from Williams (Reference Williams1957), a fundamental principle in LH research is the correlation between the nature of the environment – whether adverse or benign – and the adoption of fast or faster versus slow or slower LH strategies. A substantial body of LH research in psychology supports this principle, but there are instances of unsupportive findings. Two prominent LH investigations, the Study of Early Child Care and Youth Development and the Minnesota Longitudinal Study of Risk and Adaptation, have contributed significantly to the human LH research literature, and both show a few mixed results. In the first study, no predictive relation emerged between childhood environmental harshness (income-to-needs ratio) and fast LH strategy indicators, such as the number of sex partners (Hartman et al., Reference Hartman, Sung, Simpson, Schlomer and Belsky2018). Similarly, unpredictability indicators like household moves and parental job transitions did not forecast traits associated with fast LH, such as the age of first sexual activity and externalizing behavior (Hartman et al., Reference Hartman, Sung, Simpson, Schlomer and Belsky2018). In the second study, environmental harshness and unpredictability did not predict fast LH indicators at age 23, such as aggression and number of sexual partners, and unpredictability even exhibited an opposite effect, predicting a reduction in delinquent or criminal behavior at age 23 (Simpson et al., Reference Simpson, Griskevicius, Kuo, Sung and Collins2012).

Other LH studies have also shown mixed findings, such as that economic harshness is not linked with earlier onset of sexual activities (Nolin & Ziker, Reference Nolin and Ziker2016). Additionally, reduced maternal capital, representing environmental harshness, was associated with delayed rather than accelerated menarche in daughters (Wells et al., Reference Wells, Cole and Cortina-Borja2019). In non-LH studies, the pervasive measure of environmental harshness represented by poverty or low SES was correlated with financial risk aversion rather than risk-taking, as would be predicted by LH theory (see Haushofer & Fehr, Reference Haushofer and Fehr2014, for review). Individuals exposed to violence were reported to exhibit higher levels of altruism (Voors et al., Reference Voors, Nillesen and Verwimp2012). Moreover, those experiencing poverty scored higher on empathy, prosociality, altruism, and ethical behavior – characteristics representing a slow rather than fast LH strategy (Amir et al., Reference Amir, Jordan and Rand2018; Miller et al., Reference Miller, Kahle and Hastings2015; Piff et al., Reference Piff, Kraus, Côté, Cheng and Keltner2010, Reference 83Piff, Stancato, Côté, Mendoza-Denton and Keltner2012; Stellar et al., Reference Stellar, Manzo, Kraus and Keltner2012).

4.1.2 Human Capitalized Mortality Reduction Effort

The mixed findings about human LH development indicate deviations from the species-general pathway, part of which typically involves accelerating growth to maximize the chance of reproducing before encountering extrinsic mortality. Rather than solely adopting fast LH strategies disregarding mortality risks, humans have consistently made efforts to reduce mortality, leading to a delay in reproduction and a slowing of LH (Lu et al., Reference Lu, Wang, Liu and Chang2022b). Hence, over the past two million years, almost every aspect of human LH has slowed by more than tenfold (Smith & Tompkins, Reference Smith and Tompkins1995). If humans had exclusively responded to mortality conditions with fast LH strategies without making efforts to mitigate extrinsic mortality risks and enhance living conditions, today’s human living environment would likely resemble that of Australopithecus from two million years ago. Instead, humans have effectively conquered nature (Alexander, Reference Alexander1990; Flinn et al., Reference Flinn, Geary and Ward2005), significantly reducing most extrinsic risks that drive fast LH in non-human animals by causing indiscriminate and uncontrollable casualties within age-specific populations. According to Alexander (Reference Alexander1990), the primary selection pressure for humans arises from intraspecific competition, generating substantial within-species variation, much of which is linked to mortality reduction efforts and capabilities. Extrinsic mortality risks become manageable based on individuals’ efforts and abilities in overcoming environmental challenges. The successful conquest of otherwise insurmountable extrinsic challenges contributes to and is facilitated by delayed maturity and development, extended lifespan, and an enlarged brain – distinctive characteristics of the slow LH traits observed in humans (Chen & Maklakov, Reference Chen and Maklakov2012; Hill & Kaplan, Reference Hill and Kaplan1999).

The foregoing COVID-19 pandemic serves as a compelling illustration that human LH development may not rigidly adhere to species-general principles. One such general prediction is that pathogen load drives fast LH. However, especially during the early phases of the pandemic, countries historically high in pathogen prevalence, such as those in Africa and Asia (with China as a notable example), swiftly implemented behavioral control measures. The populations in these regions generally complied and cooperated without significant controversy. In contrast, Western nations, particularly the United States, where both contemporary and historical pathogen loads are comparatively low, did not demonstrate the same level of promptness or restrictiveness in their preventive measures, and individuals in these countries exhibited resistance or reluctance to adhere to disease control arrangements. Data based on 150 countries show a positive correlation between country-level disease control effort and vigilance and a slow LH inclination, aggregated from individual responses to questionnaire items (Lu et al., Reference Lu, Yang, Liu, Zhu and Chang2023). The slow LH inclination seemingly arises from and, in turn, reinforces disease control efforts and successes. This observation challenges the species-general LH prediction that pathogens drive faster LH strategies. It also highlights potential human uniqueness in LH development, with such human-specific factors as culture (Lu et al., Reference Lu, Jin and English2021; Zhu et al., Reference Zhu, Lu and Chang2020) and governance (Lupu & Zechmeister, Reference Lupu and Zechmeister2021) possibly modifying the species-general LH prediction.

4.1.3 Parenting Adjustment of Species-General Principles

A promising avenue for future psychological LH research involves identifying human-specific or more specific LH-related features as moderating variables. These variables can provide insights into human-specific adjustments to species-general LH processes. For example, humans possess species-distinct characteristics, such as an extended childhood and extensive parental care, socialization, and education (Geary, Reference Geary2002). These aspects, often transmitted across generations in the form of culture, are all linked specifically to slow LH traits. Future investigation may focus on these relatively human-specific or human-capitalized and slow LH-related features in engendering auxiliary processes that represent but also adjust or moderate the primary LH systems. Parenting provides an example of such human-capitalized process in adjusting the species-general contingency between environmental adversities and fast LH strategies and forming an additional pathway for the development of human slow LH.

The links between parenting and LH strategies are severalfold. First, parenting is shaped by and serves as a conduit for transmitting a child’s environment (Bornstein, Reference Bornstein, Bornstein and Leventhal2015). In safe and stable environments, parents are not only capable but also inclined to invest more in their offspring (Chisholm et al., Reference Chisholm, Ellison and Evans1993). This heightened parental investment is selected because a safe and stable environment promises parental investment returns in terms of increased offspring survival and reproductive success. Conversely, in harsh and unpredictable environments fraught with high mortality risks, parents allocate fewer resources to offspring, prioritizing mating over parenting due to environmental uncertainty diminishing returns on parental investment. Consequently, parenting emerges as a mediator of childhood environments, sustaining the link between a favorable or adverse childhood environment and the development of slower or faster life history strategies. Actual environments and experiences mediated through parenting should exert comparable effects on a child’s LH calibrations (Belsky et al., Reference Belsky, Steinberg and Draper1991). Unsupportive parenting as well as parental absence are perceived and experienced by a child as environmental harshness (Belsky et al., Reference Belsky, Steinberg and Draper1991; Warren & Barnett, Reference Warren and Barnett2020). Similarly, parental behavioral inconsistency or significant parental transitions and changes signify environmental unpredictability, influencing faster LH development accordingly. Parenting, therefore, operates as an intermediary that conveys external information and plays a pivotal role in influencing the calibration of LH strategies.

Moreover, parenting, mating, and their corresponding behavioral manifestations constitute intrinsic components of slow and fast LH, with parenting emerging as a LH trait most decisively characterizing the slow LH strategy (Kaplan, Reference Kaplan1996). In the human context, children initially interface with the external environment through caregiving provided by their parents (Bornstein, Reference Bornstein2019). Maternal and parental care have evolved as mechanisms to shield offspring from mortality threats, including predation (Bowlby, Reference Bowlby1982). Parenting contributes to slowing LH by actively working to mitigate extrinsic mortality risks, while mating acts to accelerate LH by overlooking and downplaying mortality threats and perpetuating the adverse environmental contingency on fast LH. Parenting, being an integral facet of slow LH, gains additional prominence in the human scenario through other interconnected slow LH events. These events include premature birth, an adaptation linked to the enlarged cranium, an extended childhood (unparalleled among animals), and postnatal brain development, which fulfills three-fourths of adult brain size. This interplay of LH events affords human parenting the temporal space (i.e., a prolonged childhood) and the adaptability of brain plasticity, allowing the socialization of offspring toward a slower LH strategy. This redirection of an offspring’s LH trajectory toward the slower end of the species’ spectrum effectively attenuates the causal connection between childhood environmental adversities and the development of fast LH strategies.

With the provision of parenting, and considering that humans engage in the most extensive parental investment (Geary & Flinn, Reference Geary and Flinn2001), even when a child inherits a harsh or unpredictable living environment from a caregiver, and irrespective of how closely the caregiving reflects environmental adversities, the child is not expected to be at greater risk or develop a faster LH compared to a scenario of not receiving protection and care from a caregiver. Given the evolutionarily selected function of parenting to promote slow LH, it becomes more probable that the child experiences a more protected environment in the form of more consistent, responsive, and supportive parenting than would be predicted based on the adverse environment alone. These successive parenting and child interactive events lead to the slowing of LH, effectively disrupting, moderating, and downregulating the species-general link between extrinsic mortality and fast LH.

Furthermore, parenting functions to transfer culture and knowledge across generations (Bornstein, Reference Bornstein2012). Hence, the slowing of human LH is a continuous transgenerational process initiated by parents and continued by successive generations. What is of importance is a mindset about extrinsic mortality that is being cross-generationally transferred and persistently sustained. Formed by parenting and taking the form of attachment and an internal working model (Bowlby, Reference Bowlby1982; Cassidy, Reference Cassidy1986; Collins & Read, Reference Collins and Read1990; Main, Reference Main, Parkes, Stevenson-Hinde and Marris1991), this mindset about self-efficacy and the controllability of the external environment emerges early in the developmental stages of each new generation, persists throughout adulthood, and continues to influence succeeding generations. Parenting or non-dysfunctional parenting, by fostering a secure internal working model, equips a child with a sense of confidence in evaluating extrinsic mortality conditions and the child’s own capacity to navigate external threats (Bowlby, Reference Bowlby1982; Chen & Chang, Reference Chen and Chang2012; Chisholm, Reference Chisholm1996; Lu et al., Reference Lu, Liu and Chang2022a; Zimmermann, Reference Zimmermann1999). The internalization of a secure mental representation of the world in relation to oneself can potentially shift species expectations about extrinsic mortality threats. From perceiving these threats as uncontrollable and inescapable, the parentally socialized mindset may view them as controllable and reducible. Parenting reduces extrinsic mortality and slows human LH by nurturing a pervasive belief in the dependability, controllability, and predictability of the world that is associated with such deliberate cognition as insight, planning, and control (Figueredo et al., Reference Figueredo, Jacobs and Gladden2018). When confronted with environmental adversity, well-socialized human offspring are poised to manage external challenges, effectively redirecting their LH behavioral trajectory. Instead of following the externally predicted fast track of disregarding mortality and accelerating reproduction, they pivot onto a slower pathway aimed at mitigating mortality risks and consequently delaying reproduction. Fostered and strengthened across generations, the legacy of a mortality-reducing mindset contributes continuously to incrementally altering species-general LH developmental pathways, increasing human-capitalized processes beyond the sole influence of environmental adversities, and embarking on ever slowing human LH development.

In summary, future developmental psychological LH research could identify human-unique or human-capitalized features and mechanisms, such as parenting, that are linked to LH and play auxiliary roles alongside primary and species-general LH processes. Beyond parenting, longer-term and broader-based socialization, education, and acculturation processes (Bornstein, Reference Bornstein2017), all human-capitalized, aim to rein in nature, thereby reducing and eliminating extrinsic mortality risks and consequently slowing human LH. Over the course of evolution, these human-capitalized auxiliary processes have become increasingly potent, eventually eclipsing species-general evolutionary forces. Subsequent research could leverage variables like parenting, education, and culture (e.g., the individualism-collectivism continuum) to directly predict aspects of slow LH, in addition to their serving as mediators and moderators of the species-general relation between environmental conditions and LH strategies.

4.2.1 The Measurement of Harshness and Unpredictability

Environmental harshness and unpredictability are conceptualized as the frequency and variance of aspects of the living environment that cause extrinsic mortality and morbidity (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). The measurement of these constructs relies on complex assumptions (Frankenhuis et al., Reference Frankenhuis, Nettle and Dall2019; Young et al., Reference Young, Frankenhuis and Ellis2020). Natural selection has honed the brain’s ability to distinguish aspects of the environment where adaptations have either frequently occurred or have been less relevant over evolution. Natural selection treats the former but not the latter as privileged information, registering implicit responses and learning and thus molding phenotypic plasticity (Ellis et al., Reference Ellis, Sheridan, Belsky and McLaughlin2022; Tooby & Cosmides, Reference Tooby and Cosmides1990). Mortality and morbidity risks, represented by environmental harshness and unpredictability, are adaptively relevant and are readily accessed by organisms with relative ease and efficiency (Ellis et al., Reference Ellis, Sheridan, Belsky and McLaughlin2022). Learning about and responding to this information are ultimately integrated into the developing organism’s phenotypic plasticity (Ellis et al., Reference Ellis, Sheridan, Belsky and McLaughlin2022). Learning and acquiring information about environmental unpredictability, as well as harshness, are conceptualized in a measurement framework called the ancestral cuing framework. The framework assumes that humans evolved to detect, encode, and respond to discrete events that serve as cues to ancestral environmental conditions with sufficient reliability (Young et al., Reference Young, Frankenhuis and Ellis2020). For example, experienced threat may serve as an ancestral cue to morbidity-mortality from harm imposed by other agents (Ellis et al., Reference Ellis, Sheridan, Belsky and McLaughlin2022). Parental transition and residential relocation or, on a more micro, day-to-day, moment-to-moment level, family routine change, chaos in the home, and inconsistent parenting are considered examples of ancestral cues of environmental unpredictability. In all these examples, the cues are assumed to be reliably associated with mortality conditions in ancestral environments so that the mind has already been shaped by natural selection to respond to these cues in formulating adaptive phenotypic responses (e.g., fast vs. slow LH strategies). Psychological LH research predominantly employs the ancestral cuing approach. For example, harshness or threat has been cued by such threats as dangerous neighborhoods (e.g., Hampson et al., Reference Hampson, Andrews, Barckley, Gerrard and Gibbons2016; Yang et al., Reference Yang, Lu and Chang2023), exposure to violence, drug, and alcohol use (Brumbach et al., Reference Brumbach, Figueredo and Ellis2009), and encounters with illness, injury, and death (Szepsenwol et al., Reference Szepsenwol, Shai, Zamir and Simpson2021; Zhu et al., Reference Zhu, Hawk and Chang2018). Unpredictability has been cued by various proxies of uncertainty, including changes in employment (e.g., Simpson et al., Reference Simpson, Griskevicius, Kuo, Sung and Collins2012; Zuo et al., Reference Zuo, Huang, Cai and Wang2018) or residence (Chang & Lu, Reference Chang and Lu2018; Hartman et al., Reference Hartman, Sung, Simpson, Schlomer and Belsky2018; Simpson et al., Reference Simpson, Griskevicius, Kuo, Sung and Collins2012), fluctuations in economic conditions (Chang & Lu, Reference Chang and Lu2018; Ross & McDuff, Reference Ross and McDuff2008; Usacheva et al., Reference Usacheva, Choe, Liu, Timmer and Belsky2022), and disruptions in family membership composition (Belsky et al., Reference Belsky, Steinberg and Draper1991; Ellis et al., Reference Ellis, Shakiba, Adkins and Lester2021; Mell et al., Reference Mell, Safra, Algan, Baumard and Chevallier2018)

4.2.2 Less than Satisfactory Statistical Results

Despite the well-defined conceptual and measurement frameworks related to harshness and unpredictability, statistical results from many published studies are not entirely satisfactory. Two potential issues are discussed here. First, the results obtained from these measures are not robust, yielding only moderate effect sizes. The Minnesota Longitudinal Study of Risk and Adaptation (MLSRA; Sroufe et al., Reference Sroufe, Egeland, Carlson and Collins2005) and the NICHD Study of Early Child Care and Youth Development (SECCYD, NICHD Early Child Care Research Network, 2005) are two extensive datasets that have generated LH publications widely cited in the literature. From papers based on these datasets that employed harshness and unpredictability to predict LH-related criterion variables, the average predictive coefficients for all criterion variables reported in these publications, as indicated in Table 3, are moderate and exhibit high similarity between harshness and unpredictability. The grand mean averaged across all publications is 0.13 for harshness and 0.14 for unpredictability. As shown in Table 3, both the grand means and the individual values are moderate and are undifferentiating between the two constructs.

The undifferentiated or less than differentiating correlations shown in Table 3 point to the second potential issue which is a lack of discriminant validity between these two constructs, harshness and unpredictability. The lack of discriminant validity is further evidenced by comparing the hetero-trait correlation between these two constructs and the mono-trait correlations of the indicators within each construct. In general, the mono-trait correlations should be substantially higher than the hetero-trait correlations to warrant discriminant validity. However, in many published studies involving these two constructs, the two sets of correlations are almost reversed. For example, in Lu et al. (Reference Lu, Liu and Chang2022a), harshness is gauged by negative life events, harsh economic conditions, and perceived financial difficulty, with an average correlation of 0.33 among these three mono-trait indicators. In this study, unpredictability is measured through unsafe neighborhood, chaos in the home, and life routine irregularity, showing an average mono-trait correlation of 0.24. However, the hetero-trait correlation between harshness and unpredictability in this study is 0.47, which, paradoxically, is substantially larger than the two mono-trait correlations. Similarly, in Belsky et al. (Reference Belsky, Schlomer and Ellis2012), the hetero-trait correlation between harshness (measured by income-to-need ratio, with a lower ratio indicating higher harshness) and unpredictability (indicated by paternal transitions, household moves, and parental employment changes) is −0.43, paradoxically surpassing in absolute value the mono-trait correlation (r = 0.28) averaged over the three unpredictability indicators. In Simpson et al. (Reference Simpson, Griskevicius, Kuo, Sung and Collins2012), the hetero-trait correlation between harshness and unpredictability (r = 0.42) is again greater than the mono-trait correlation averaged over three unpredictability indicators (r = 0.28).

The lack of discrimination between harshness and unpredictability is further demonstrated by the high similarity between the two constructs in predictive coefficient estimates when predicting a third criterion variable. This indiscrimination is evident in various studies. For example, Feng and Zhang (Reference Feng and Zhang2023) found positive predictions of fast LH strategy (measured by partial items of Mini-K and reverse coded) by harshness and unpredictability to be 0.28 and 0.21, respectively. Harshness (mortality-morbidity) and economic unpredictability similarly predict parental stress (0.15 and 0.20, respectively) and father-reported parental distress (0.22 and 0.21, respectively) (Szepsenwol et al., Reference Szepsenwol, Shai, Zamir and Simpson2021). In another example, heightened childhood unpredictability and current harshness exhibit similar predictions for increased difficulties in emotional control (0.11 and 0.08 in Study 1, and 0.18 and 0.13 in Study 2, respectively) (Szepsenwol et al., Reference Szepsenwol, Simpson and Griskevicius2022). The correlations of these two constructs are 0.09 and 0.07, respectively, with a composite variable consisting of early sexual activity and sexual risk-taking (Maranges & Strickhouser, Reference Maranges and Strickhouser2022). Harshness and unpredictability also exhibit similar correlations with maternal sensitivity at 0.29 and −0.27, respectively (Belsky et al., Reference Belsky, Schlomer and Ellis2012), and with intimate partner violence at 0.19 and −0.19 (Szepsenwol et al., Reference Szepsenwol, Zamir and Simpson2019); negative signs represent reverse coding of variables

4.2.3 Ways to Improve the Measurement of Harshness and Unpredictability

These less than robust statistical results raise questions about measurement validity and reliability of the two constructs, namely to what extent do the ancestral cue indicators truly measure and differentiate between harshness and unpredictability, and to what extent do the two measured constructs uniquely predict LH related variables? There are several ways to improve the validity of the operationalization of these two important constructs. One proposed solution involves abandoning the current measurement strategy and adopt experimental methods to induce the mental states of uncertainty and harshness, as implemented by LH researchers (e.g., Mittal et al., Reference Mittal, Griskevicius, Simpson, Sung and Young2015; Young et al., Reference Young, Griskevicius, Simpson, Waters and Mittal2018). Another approach to improve the measurement of unpredictability is referred to as the statistical learning method (Frankenhuis et al., Reference Frankenhuis, Nettle and Dall2019; Young et al., Reference Young, Frankenhuis and Ellis2020), where individuals are assumed to use their lived experiences as raw data to develop a model of predictability and then compare an ongoing experience with the model to detect discrepancy between the model and the current experience. Li and Belsky (Reference Li and Belsky2022) pioneered a third approach. Given that harshness and unpredictability are defined as the frequency and variance of the same mortality-causing factor, a straightforward approach is to consider the sample observation and its variance, respectively, as measurements of the two corresponding constructs.

However, a simpler, data-driven approach is able to directly enhance the statistical outcomes. Given that both harshness and unpredictability measures exhibit similar predictive pattern and magnitude when predicting a third variable, are highly correlated, and the hetero-trait correlation surpasses mono-trait correlations based on within-trait indicators, a straightforward solution is to amalgamate the two sets of indicators into a single composite measure that will yield more robust statistical results. For example, in Lu et al. (Reference Lu, Liu and Chang2022a) discussed earlier, if the two constructs are merged into one composite, called, say, “environmental adversity,” its predictive capability for a fast LH behavioral profile increases to 0.38 from the current 0.23 (harshness) and 0.21 (unpredictability). Moreover, the reliability of the combined adversity construct is higher (0.92) than the two separate estimates (0.80 for harshness and 0.73 for unpredictability). These results are post hoc and data-driven but offer insights into how individuals respond to harshness and unpredictability.

Ellis et al. (Reference Ellis, Sheridan, Belsky and McLaughlin2022) broke down harshness into two components: morbidity-mortality stemming from inflicted harm by external sources (threat) and morbidity-mortality due to inadequate environmental resources (deprivation). This breakdown underscores the unique developmental impacts arising from the presence of unexpected experiences in the threat-based form of harshness versus the absence of expected experiences in the deprivation-based form of harshness (McLaughlin et al., Reference McLaughlin, Sheridan, Humphreys, Belsky and Ellis2021). Especially after decomposition, the “unexpected” harshness or threat is inherently more strongly correlated with unpredictability, on several grounds. First, despite the distinct statistical structure of unpredictability, characterized by high variance and low autocorrelation over time or space (Frankenhuis et al., Reference Frankenhuis, Nettle and Dall2019; Young et al., Reference Young, Frankenhuis and Ellis2020), the actual experience induced by an ancestral cuing represents one episode of threat, much like the decomposed harshness. Second, because both harshness (frequency) and unpredictability (variance) are presented to the respondents as frequency questions (e.g., how many times did your family move?), respondents typically respond based on frequency alone, inadvertently conflating the two constructs. In fact, in this measurement framework, a higher score on measures of unpredictability may paradoxically indicate lower underlying autocorrelation and therefore a lower rather than higher representation of unpredictability. Third, similarly, the sensation of unpredictability is inherently embedded within experiences of threat or harshness, with the LH impact of harshness already encapsulating that of unpredictability. Since ancestral examples of harshness, such as predatory attacks, disease outbreaks, or intraspecific violence, are inherently unpredictable, indicators commonly used by LH researchers may elicit a blended experience of threat or harshness and surprise or unpredictability. Finally, the numerical results regarding discriminant and predictive validity discussed earlier do not seem to support the definition that the two constructs are conceptually distinct and predictively additive (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). Therefore, from both a measurement standpoint and the perspective of respondents’ experiences, harshness and unpredictability may appear indistinguishable, justifying their combination both logically and numerically.

4.3.1 Unleashing Statistical Power

A suppressor variable effect represents a phenomenon where the inclusion of a third variable, known as the suppressor variable, enhances the predictive power of the outcome variable by another predictor or set of predictors (Conger, Reference Conger1974). Contrary to the usual trend where the inclusion of additional predictor variables diminishes the predictive validity of existing predictors, the inclusion of a suppressor variable enhances the predictive validity of at least one existing predictor because a suppressor variable shares information with predictor variables that is irrelevant to the outcome variable (Maassan & Bakker, Reference Maassan and Bakker2001). A robust correlational study should include all relevant variables, either as predictor, including mediators, or control variables, to reveal genuine patterns of relations free from suppressor variable effects and other misspecification fallacies. In LH research, the combination of high levels of extrinsic morbidity-mortality and abundant resource availability may favor faster LH strategies, whereas the combination of low mortality risks, high population density, and limited resources may favor slower LH strategies (Ellis et al., Reference Ellis, Sheridan, Belsky and McLaughlin2022). Failing to include density-dependent variables in a mortality-based model could obscure potential relations among existing variables. Similarly, the effect of harsh environment on fast LH and senescence also depends on other factors such as population density (Abrams, Reference Abrams1993; Baldini, Reference Baldini2015). Across different species, population density is correlated with both fast (Sibly & Hone, Reference Sibly and Hone2002) and slow LH (Sng et al., Reference Sng, Neuberg, Varnum and Kenrick2017). The association with fast LH may be mediated by stress due to overcrowding, whereas the slow LH association may be driven by increased competition over limited resources by more individuals per total land area (Lutz et al., Reference Lutz, Testa and Penn2006). Depending on which of the two processes is more dominant, population density, when included in a mortality-based LH study, may function as a suppressor variable that may unleash the predictive power of existing mortality conditions.

Competition is another robust density-dependent predictor of LH (Andrea & Rousset, Reference André and Rousset2020; Dańko et al., Reference Dańko, Burger, Argasiński and Kozłowski2018; Lutz et al., Reference Lutz, Testa and Penn2006). Integrated into the mortality-based LH framework, competition represents a mortality reduction effort that is opposite to mortality-disregard embedded in fast LH (Andrea & Rousset, Reference André and Rousset2020). Thus, some shared elements between mortality conditions and competition should be different from elements shared between mortality conditions and fast LH strategy, creating a potential suppressor variable effect. Similarly, resource or SES as a density-dependent variable is often perceived to have minimal impact on behavioral outcomes related to human LH. This perception stems from the notion that, in modern living, even individuals with lower economic status are generally above the poverty threshold, only below which resource scarcity and deprivation make robust evolutionary and LH predictions (Chang & Lu, Reference Chang and Lu2018). However, inclusion of SES in studies may enhance the predictive power of mortality-based variables, such as environmental unpredictability. In Chang and Lu (Reference Chang and Lu2018) focusing on Chinese left-behind children, where parents had migrated to urban areas for work, both resource (measured by family income, perceived family resource, and child-perceived provisioning) and extrinsic risk (exposure to mortality and morbidity, child-perceived stress, chaos in the home, and parental absence) predicted mini-K and fast LH behavioral outcomes. In a re-analysis excluding resource, the prediction of mini-K and fast LH behavioral outcomes by extrinsic risk diminished from slightly to substantially, although their statistical significance remained unchanged. Notably, when both variables, resource and extrinsic risk, were included in the model, the regression coefficient associated with extrinsic risk was almost twice as strong compared to that of resource. Many published studies use SES as a mortality variable representing environmental harshness. Similar to the findings of Chang and Lu (Reference Chang and Lu2018), three of these studies reported much stronger effects associated with unpredictability compared to harshness or SES (Hartman et al., Reference Hartman, Sung, Simpson, Schlomer and Belsky2018; Lu et al., Reference Lu, Liu and Chang2022a; Szepsenwol et al., Reference Szepsenwol, Simpson, Griskevicius and Raby2015), potentially indicating model misspecification. In summary, a robust LH study may try to include both density- and mortality-based factors, even if only one set of variables is the primary focus.

4.3.2 Theoretical Grounds

More than statistical improvement, density-dependent variables provide theoretical insight especially into human LH development. The initial understanding of variation in LH strategy pointed to density-dependent selection (MacArthur & Wilson, Reference MacArthur and Wilson1967), which involves concepts like resource availability and intraspecific competition. Known as r-K selection theory, this initial perspective suggests that population density impacts resource availability, and intraspecific competition is related to both resource and mortality or safety conditions. In this literature and focusing on humans, population density induces intraspecific competition without necessarily causing resource depletion. In general, nutritional and cognitive resources, viewed as investments in embodied capital (Kaplan, Reference Kaplan1996), represent slow LH components. Competition, as a representation of resource-increasing and mortality-reducing efforts and abilities, involves both resource and safety conditions and slows human LH.

Especially in the case of humans, but applicable to other animals as well, increased competition has been associated with higher per capita resources and thus population density that drives competition is not necessarily negatively correlated with resource levels. For example, in sticklebacks, higher population density resulted in lower prey availability but increased food diversity as fish explored alternative prey types (Svanbäck & Bolnick, Reference Svanbäck and Bolnick2006). Similarly, limpets, a type of marine snail, and female striped mice expanded their feeding methods and hunting ranges, respectively, with increased populations (Creese & Underwood, Reference Creese and Underwood1982; Schradin et al., Reference Schradin, Schmohl and Rödel2010). In the case of sticklebacks, phenotypically different individuals introduced various alternative prey, thereby increasing the total or per capita food resources under high-density conditions compared to low-density conditions (Svanbäck & Bolnick, Reference Svanbäck and Bolnick2006). Furthermore, humans, in response to increased population density, have demonstrated the capacity to innovate and multiply existing resource production. This added productivity is evident in the development of large-scale agriculture and husbandry and subsequently manufactory and industry. These efforts to increase resources, coupled with the ability to explore various other means of resource acquisition like other animals, contribute to the delay of reproduction and a slower LH strategy in humans. A straightforward observation supporting this argument is the tracking of human population increase from earlier ancestral times (e.g., Australopithecine) to later periods (e.g., post-agricultural times). Over this time frame, all aspects of human LH have slowed by tenfold (Smith & Tompkins, Reference Smith and Tompkins1995). Mathematical models show increased competition relates to slow LH (Andrea & Rousset, Reference André and Rousset2020). Empirically, Sng et al. (Reference Sng, Neuberg, Varnum and Kenrick2017) demonstrated positive correlations by plotting population density for 55 world regions (Figure 7) and 50 U.S. states (Figure 8) against slow LH strategy, a composite comprising variables such as life expectancy and average births per woman.

Figure 7 Nation level population density and composite life history strategy score (excluding sociosexuality and future orientation), identified by world region. Higher composite scores mean slower life history strategy.

Adopted from Sng, O., Neuberg, S. L., Varnum, M. E., & Kenrick, D. T. (Reference Sng, Neuberg, Varnum and Kenrick2017). The crowded life is a slow life: Population density and life history strategy. Journal of Personality and Social Psychology, 112, 736–754.

Figure 7 Long description

The x-axis represents the logarithm of population density (ranging from 0.0 to 3.0), while the y-axis displays a composite slow life history score (spanning negative 7.5 to 5.0) derived from open datasets measuring life history traits. Data points are coded by geographic region: circles for West Eurasia, squares for East Eurasia, crosses for the Insular Pacific, triangles for Africa, plus signs for North America, and ovals for South America. A central regression line traversing the plot confirms the overall trend, higher population density correlates with slower life history strategies, characterized by delayed reproduction and reduced risk-taking. Regional clustering is evident, with West Eurasian (circles) and East Eurasian (squares) nations concentrated at higher population densities and elevated life history scores, while African (triangles) and South American (ovals) regions show greater variability at lower densities. The graph underscores ecological density-dependence in shaping life history trade-offs, with tightly packed populations favoring long-term investment strategies.

Figure 8 U.S. state level population density and composite life history strategy score, identified by census region. Higher composite scores mean slower life history strategy.

Adopted from Sng, O., Neuberg, S. L., Varnum, M. E., & Kenrick, D. T. (Reference Sng, Neuberg, Varnum and Kenrick2017). The crowded life is a slow life: Population density and life history strategy. Journal of Personality and Social Psychology, 112, 736–754.

Figure 8 Long description

The x-axis shows logarithm population density values (ranging from 0.0 to 4.0), while the y-axis represents a composite slow life history score derived from open data (spanning negative 10.0 to 15.0). Census regions are differentiated by distinct marker shapes: circles (New England), squares (Mid-Atlantic), crosses (East North Central), triangles (West North Central), crosses (South Atlantic), ovals (East South Central), diamonds (West South Central), asterisks (Mountain), and bow-shapes (Pacific). A prominent regression line traverses the plot, confirming that higher population density strongly predicts slower life history traits, such as delayed reproduction and reduced risk-taking. Regional clustering is evident, with densely populated regions like the West North Central (triangles) and Mid-Atlantic (squares) scoring highest on slow life history, while sparser regions like the Mountain (asterisks) and Pacific (bow-shapes) show greater variability. This visualization underscores ecological density-dependence in shaping behavioral strategies within the United States.

Increased resources through competition, as opposed to windfall opportunities, play a significant role in fostering a slow LH strategy. A slow LH is supported by the provision of necessary nutritional or energetic supply critical for the development of embodied capital (Kaplan et al., Reference Kaplan, Hill, Lancaster and Hurtado2000), including augmented physical size and heightened social and cognitive competencies (Ellis et al., Reference Ellis, Reid and Kramerin press). In environments characterized by consistent enrichment or support during early and middle childhood, individuals receive signals that investments in the body and brain are sustainable in the long term, thereby making a long-term developmental plan and calibrating a slow LH strategy (Yang et al., Reference Yang, Lu and Changin press). Conversely, conditions marked by chronic resource scarcity may prompt individuals to develop an energy-sparing phenotype, steering individuals toward uninvested physical and mental development and accelerated reproduction. Although abundant resources acquired without competition may also drive a fast LH strategy (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009, Reference Ellis, Sheridan, Belsky and McLaughlin2022), the overall trend indicates a positive correlation between resource levels and slow LH components. These components, rooted in nutritionally based development of the body and mind, are embodied and capitalized through competitive processes (Yang et al., Reference Yang, Lu and Changin press).

Two types of intraspecific competition have been identified in the literature (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009; Parker, Reference Parker2000; Weir & Grant, Reference Weir and Grant2004). The first type is known as scramble or exploitation competition. In this type, competition occurs incidentally when conspecifics unintentionally encounter and consume the same resources without direct interaction. This competition depletes resources but does not contribute to slow LH. The second type is contest or interference competition, wherein conspecifics do not possess equal access to resources due to active interference, often involving aggression or the establishment of a dominance hierarchy (Ellis et al., Reference Ellis, Figueredo, Brumbach and Schlomer2009). This type of competition approximates capitalist economic competition and test-based educational advancement, driving human evolution and socioeconomic and educational development. Unlike scramble competition, contest competition is intentional, planned, and not the result of happenstance, constituting a slow LH strategy. In the context of today’s economic competition on various levels – individual, corporate, and national – contest competition involves the deliberate production, hoarding, and accumulation of resources for the purpose of acquisition and monopolization. The competitive process results in the acquisition of nutritional and cognitive resources, which are then converted into embodied physical and mental capital (Kaplan et al., Reference Kaplan, Hill, Lancaster and Hurtado2000). This embodied capital manifests as a larger and stronger body, a more structurally connected brain, an improved immune system, and hormonally mediated gender-specific adaptations like enhanced childbearing ability in women and increased skeletal muscles and social dominance in men (Bribiescas, Reference Bribiescas2001, Reference Bribiescas2010; Ellison, Reference Ellison2003). The embodied capital also includes attainment of educational and economical substance and social status. The combination of energetic conversion and knowledge accumulation both constituting embodied capital represent a slow LH strategy, as time and energy are diverted away from immediate mating and reproduction. Consequently, the end product of competition contributes to the slowing of LH. The dynamic process of competition involves utilizing the body and brain for acquiring and producing material resources, learning knowledge and skills, ascending social hierarchy, and training and grooming offspring to do the same. These costly investments in embodied capital are integral to the development of a slower life history strategy, anticipating to live long enough to reap future benefits. This adaptation is particularly relevant in safe and stable environments obtained from mortality reduction efforts representing the same slower LH strategies

Similar to the increase in resources, competition is also involved in reducing mortality. Just as animals compete for more abundant food sources (Pusceddu et al., Reference Pusceddu, Mura, Floris and Satta2018), they also vie for safer food sources (Halliday & Morris, Reference Halliday and Morris2013), secure foraging patches (Brown, Reference Brown1988), and protected nesting sites (Forstmeier & Weiss, Reference Forstmeier and Weiss2004). Rather than completely disregarding mortality concerns and solely focusing on reproduction, animals actively engage in various forms of mortality reduction activities. Notably, competition extends to disease control, a prevalent form of mortality reduction. Most nonhuman animals actively manage diseases through both prophylactic measures, such as nest cleaning and fumigation, and therapeutic actions, such as using antibacterial plants (Hart & Hart, Reference Hart and Hart2018). These preventive measures are often implemented competitively. Many animals, in a prophylactic effort against gastrointestinal endoparasites, engage in self-medication. For example, Mediterranean goats consume tannin-rich Quercus coccifera to reduce the incidence of nematodes (Villalba et al., Reference Villalba, Miller, Ungar, Landau and Glendinning2014). It has been observed that, compared to lower-ranked, less competitive counterparts, the most dominant goats consume this medicinal foliage more frequently. Thus, competition that can be directed toward a therapeutic diet to protect the animals from parasites (Barroso et al., Reference Barroso, Alados and Boza2000) slows LH by reducing mortality.

Human disease control, characterized by more advanced and competitive practices, results in greater intraspecific variations than has been observed in other animals. Humans, akin to other animals but in a more sophisticated manner, actively participate in disease control through the adoption of external preventive and interventional strategies such as traditional herbal medicine, a practice present in almost all ancestral human groups (e.g., Petrovska, Reference Petrovska2012; Sneader, Reference Sneader2005). Additionally, humans have developed an elaborate behavioral immune system, involving attitudes, beliefs about disease control, and relevant temperaments and personalities, to facilitate implementation of disease control measures (Chang et al., Reference Chang, Mak and Li2011). Consequently, most extrinsic risks that induce fast LH in other animals, causing indiscriminate and uncontrollable casualties on the adult population, may have a diminished sweeping effect on humans. Intraspecific competition, which has become the primary selection pressure for human LH evolution, creates significant individual differences unlike those observed in other animals (Flinn et al., Reference Flinn, Geary and Ward2005; Nettle, Reference Nettle2006).

“ … the real challenge in the human environment throughout history that affected the evolution of the intellect was not climate, weather, food shortages, or parasites—not even predators. Rather, it was the necessity of dealing continually with our fellow humans in social circumstances that became ever more complex and unpredictable as the human line evolved. Social cleverness, especially through success in competition achieved by cooperation, becomes paramount … nothing would select more potently for increased social intelligence … than a within-species co-evolutionary arms race in which success depended on effectiveness in social competition.” (Alexander, Reference Alexander1990, pp. 4–7)

By extensively cataloging extrinsic mortality factors, Alexander (Reference Alexander1990) stated that human competition has effectively made extrinsic mortality risks inherently controllable through the implementation of mortality-reduction slow LH strategies (Lu et al., Reference Lu, Wang, Liu and Chang2022). In essence, competition, as a dual force including resource augmentation and mortality alleviation, contributes to the deceleration of human LH (Zhu et al., Reference Zhu, Hawk and Chang2018, Reference Zhu, Hawk and Chang2019). Originally rooted in density-dependent selection, human competition has evolved into an autonomous and distinct selection force, operating independently of resource and mortality conditions. Despite humanity’s long-standing dominance over nature with almost full control over the resource (density-dependent) and safety (mortality-based) components of the environment, competition persists, intensifies, and advances to new levels. Social competition and the mastering of competition-related social skills become a driving force by itself (Alexander, Reference Alexander1990; Flinn et al., Reference Flinn, Geary and Ward2005) that stems from and leads to the slowing of human LH. Recognizing that both food and safety are indispensable for sustaining life (Chang et al., Reference Chang, Lu and Lansford2019b), LH research ought to consider both density- and mortality-based environmental conditions. Both sets of factors, and not mortality factors alone, exert an influence on LH. Even when a specific study may emphasize one set of factors over the other, it is imperative to incorporate both to accurately construct the theoretical model and avoid falling into statistical fallacies.

4.4.1 Pragmatic Reasoning about the Psychometric Approach

The psychometric approach to unidimensional LH assumes the coordination of multiple LH traits or LH-related behaviors, coalesced together by an underlying strategy ranging from fast to slow LH. Psychologists employ this approach to identify a set of LH traits and behaviors from which to extract a single fast-slow LH factor that best fits the data. In contrast, biologists typically focus on a few biological LH traits, such as age at first reproduction, rather than a comprehensive LH strategy, and investigate single rather than multiple traits (Stearn, Reference Stearns1992). This seeming incongruence between the biometric and psychometric approaches mainly reflects the difficulty or impossibility of biological LH research in identifying a set of similarly coordinated traits that also hold significance and comparability across species. However, this difference should not imply that LH research excludes the investigation of LH strategies or multiple LH traits. In fact, Stearn (Reference Stearns1992) expressed the desire of biologists to take the multi-trait LH strategic approach. Even though only a few LH traits and trade-offs are studied, “at least 45 trade-offs are readily defined between life history traits” (Stearn, Reference Stearns1992, p. 194). “The word ‘strategy’ simply means ‘complex adaptation’; it refers to the coordinated evolution of all the life history traits together … It was first necessary to understand the evolution of one trait at a time … This does not mean that life history strategies are unimportant or uninteresting. They have great potential importance” (Stearn, Reference Stearns1992, p. 492). Thus, biological LH research has the aspiration to explore LH strategies. The added psychometric approach in psychological LH research can effectively realize this research potential and is therefore not incongruent with but is complementary to biometric endeavors.

Similarly congruent and complementary are LH-related behaviors as psychometric substitutes for biometric representations of LH. LH-related behaviors are indicators of the functional process by which to achieve the fitness outcomes represented by biological LH traits (Del Giudice, Reference Del Giudice2020; Figueredo et al., Reference Figueredo, de Baca and Black2015). Ultimately, the psychometric approach measures the process of resource allocation that produces LH traits as outcomes of the trade-off efforts. The LH strategy of prioritizing physical and mental development over early reproduction involves a suite of LH-related behaviors to allocate resources between these two domains. Similarly, the LH strategy of parental investment involves behaviors to allocate more energetic and material resources for raising the young compared to pursuing additional mates. Somatic effort, mating effort, and parental effort are all behavioral processes that produce LH traits such as age of first sexual intercourse, number of sexual partners, and number of children produced at any point in time (Figueredo et al., Reference Figueredo, de Baca and Black2015).

By focusing on behavioral processes rather than biological outcomes, the psychometric approach provides a comprehensive understanding of LH processes and causal relations. LH strategies may be treated as outcomes, influenced by the environment, and as inputs, exerting influences on subsequent behaviors. By contrast, biological LH research focuses mainly on the singular relation between the environment as the cause and LH traits as outcomes. Psychological LH research could, and future LH research in psychology should, employ the psychometric approach to explore comprehensive causal relations as illustrated in Figure 9a. In other words, early childhood experiences shape LH strategies, which are best assessed through a questionnaire, and subsequent behaviors are influenced by the devised LH strategy, in addition to responding directly to childhood experiences. By contrast, biological LH research examines LH traits as outcomes of environmental forces (Figure 9b). The psychological LH research framework has also been reflected in the POLS research (Réale, Reference Réale, Garant, Humphries, Bergeron, Careau and Montiglio2010), an area of biological research concentrating on within-species personality syndromes, where LH traits also function as input variables shaping the personalities of fast versus slow strategists.

Figure 9

(a) Process-based psychological LH research: LH strategy assessed possibly by a questionnaire mediates environmental influence on behavior.

Figure 9 Long description

A. depicts a process-based psychological model, where environmental factors shape human behavior indirectly through life-history strategies, operationalized via standardized questionnaires. B. illustrates an outcome-based biological model, where environmental conditions directly shape life-history traits (for example, pubertal timing, metabolic rates) quantified through biometric measures such as hormonal assays or physiological biomarkers. The figure underscores the complementary yet distinct approaches to studying life-history trade-offs across disciplines.

(b) Outcome-based biological LH research: Environment shapes LH traits as outcomes assessed by biometric or biodemographic indicators.

There are additional, practical reasons for advocating the use of the psychometric approach and LH-related behaviors. In general, psychological traits are less genetically canalized than physical traits and are thus more suitable for human LH exploration of finer phenotypic plasticity. Many LH traits derived from other animals may not adequately capture human LH due to confounding by numerous extraneous factors related to the modern human living circumstances. Consider the timing of the first birth. Contraception, abortion, cultural and religious influences, and individual ideocracies such as space availability for copulation and childcare, as well as other material or spiritual considerations – all these extraneous factors exert a more profound influence on the timing of the first birth than the fast or slow pace of life this LH trait is purported to represent. By contrast, a straightforward social-sexual orientation questionnaire (e.g., preference for casual sex vs. committed relationships) is more indicative of the true LH positioning. Numerous other physical LH traits are either confounded by extraneous factors of modern human living (e.g., weaning time or breastfeeding duration) or are so strongly canalized either genetically (e.g., clutch size or number of newborn babies) or socially (length of juvenile dependence or length of primary and secondary schooling) that they no longer significantly contribute to LH phenotypic variations.

4.4.2 Practical Suggestions about Additional LH Questionnaires

Psychological LH research need not adhere to a paradigm of “psychology imitating biology” but should forge its own methodological approach within the larger LH research framework. Measuring latent trait variations from observed variables such as questionnaires is a time-tested method that has proven effective over two centuries. In conducting human LH research, where participants articulate their thoughts and experiences unlike other animals, direct questioning becomes a logical and valuable method. The Mini-K instrument (Figueredo et al., Reference Figueredo, Vasquez, Brumbach, Schneider, Sefcek, Tal, Hill, Wenner and Jacobs2006) has been widely used for this purpose. Additional instruments are necessary not only to improve upon existing ones but also to provide necessary triangulation in construct validation as a collective and discipline-wide endeavor.

The fast-slow LH construct can be primarily defined as a regulatory system that regulates energy trade-off allocations imposed by environmental constraints. This focus is represented in existing instruments such as the mini-K, which defines LH strategies’ behavioral and cognitive aspects on a single continuum (Figueredo et al., Reference Figueredo, Vasquez, Brumbach, Schneider, Sefcek, Tal, Hill, Wenner and Jacobs2006). This definition highlights the role of LH strategies in mediating the relationship between environmental conditions and behavioral response phenotypes. It is crucial to avoid tapping into both ends of this relationship. For example, items such as “While growing up, I had a close and warm relationship with my biological mother” (Mini-K, Figueredo et al., Reference Figueredo, Vasquez, Brumbach, Schneider, Sefcek, Tal, Hill, Wenner and Jacobs2006) and “The neighborhood where I live is safe” (HKSS; Giosan, Reference Giosan2006) pertain to environmental conditions, serving as causes but not as aspects of LH strategies or behavioral phenotypes.

Another psychometric implication of this theoretical framework is that measures of LH strategies should be unidimensional. With a sufficiently large number of items, nonmeaningful variations might cluster into trivial factors. While it is acceptable to have more lower-level or secondary factors, they should coalesce into a single higher-order factor representing the unidimensional concept of fast-slow LH strategies. The 199-item Arizona Life History Battery (ALHB; Figueredo et al., Reference Figueredo, Vásquez, Brumbach and Schneider2007) serves as a good example. It comprises seven lower-level factors that converge onto a single higher-order factor. Similarly, the Mini-K, derived from the ALHB with similar but not identical items, includes six second-order factors converging onto a single higher-order factor.

Efforts should also made to design LH indicators so as to extract a single inclusive and encapsulating factor representing a general, broad tendency or strategy. Similar efforts should be made to avoid tapping into other psychological constructs. Finally, a LH questionnaire should be phrased or formatted in a way that reflects the all-encompassing conceptualization of LH strategies as a regulatory system governing specific behaviors. The wording should also be connotatively neutral regarding the faster vs. slower ends of the trait continuum. The use of a Harter-style scale (i.e., some people do … while others do …) can help neutralize endorsement of fast vs. slow strategies.