Abstract
In the present research, we examined the role of intelligence in reproductive ecology with the hypothesis that intelligence has some characteristics of a slow life-history behavioral trait. We did this by analyzing the associations between intelligence, environmental harshness in childhood (parental investment, family dysfunction and economic poverty), and three fertility-related outcomes: planned and observed age at first reproduction and expected total fertility. Data was collected on a large sample of adults (N = 1475). Nonlinear, quadratic relations between harsh environment and intelligence were detected: the highest intellectual abilities were found in participants who lived in conditions of intermediate harshness. Furthermore, intelligence was positively associated with both planned and observed age at first reproduction and negatively with expected total fertility. Finally, the interactions between environment and intelligence in the prediction of these outcomes were found: individuals with a lower intellectual capacity who grew up in families with decreased maternal investment plan to have their first child earlier in their lifetime; on the other hand, lower intellectual capacities interacted with elevated paternal investment to predict higher expected number of children. Obtained results largely support the hypothesis of intelligence as a trait which contributes to a slow life-history dynamics. However, they imply that the relations between intelligence and childhood environment, especially family relations, may be complex. Study findings reveal the fruitfulness of an evolutionary ecological approach to intelligence in contemporary humans.
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Notes
The validity of the present intelligence measure is previously established by showing positive correlations with the education and the SEEKING system (personality trait which encompasses characteristics like curiosity, exploration and problem-solving: Davis et al. 2003), in the subsample of these same participants.
References
Arden, R., Gottfredson, L. S., & Miller, G. (2009). Does a fitness factor contribute to the association between intelligence and health outcomes? Evidence from medical abnormality counts among 3654 US veterans. Intelligence, 37(6), 581–591. https://doi.org/10.1016/j.intell.2009.03.008.
Ayoub, C., O’Connor, E., Rappolt-Schlictmann, G., Vallotton, C., Raikes, H., & Chazan-Cohen, R. (2009). Cognitive skill performance among young children living in poverty: Risk, change, and the promotive effects of early head start. Early Childhood Research Quarterly, 24, 289–305. https://doi.org/10.1016/j.ecresq.2009.04.001.
Belsky, J. (2012). The development of human reproductive strategies: Progress and prospects. Current Directions in Psychological Science, 21, 310–316. https://doi.org/10.1177/0963721412453588.
Belsky, J., Schlomer, G. L., & Ellis, B. J. (2012). Beyond cumulative risk: Distinguishing harshness and unpredictability as determinants of parenting and early life history strategy. Developmental Psychology, 48, 662–673. https://doi.org/10.1037/a0024454.
Beri, S., Patton, B. W., & Braithwaite, V. A. (2014). How ecology shapes prey fish cognition. Behavioural Processes, 109, 190–194. https://doi.org/10.1016/j.beproc.2014.09.020.
Blom, G. (1958). Statistical estimates and transformed beta-variables. NY: John Wiley & sons.
Brown, C., & Braithwaite, V. A. (2004). Effects of predation pressure on the cognitive ability of the poeciliid Brachyraphis episcopi. Behavioral Ecology, 16, 482–487. https://doi.org/10.1093/beheco/ari016.
Calvin, C. M., Deary, I. J., Fenton, C., Roberts, B. A., Der, G., Leckenby, N., & Batty, G. D. (2011). Intelligence in youth and all cause-mortality: Systematic review with meta-analysis. International Journal of Epidemiology, 40, 626–644. https://doi.org/10.1093/ije/dyq190.
Chisholm, J. S., Quinlivan, J. A., Petersen, R. W., & Coall, D. A. (2005). Early stress predicts age at menarche and first birth, adult attachment, and expected lifespan. Human Nature, 16, 233–265. https://doi.org/10.1007/s12110-005-1009-0.
Copping, L. T., Campbell, A., & Muncer, S. (2014). Psychometrics and life history strategy: The structure and validity of the high K strategy scale. Evolutionary Psychology, 12, 200–222. https://doi.org/10.1177/147470491401200115.
Copping, L. T., Campbell, A., Muncer, S., & Richardson, G. B. (2017). The psychometric evaluation of human life histories: A reply to Figueredo, Cabeza de Baca, black, Garcia, Fernandes, wolf, and Woodley (2015). Evolutionary Psychology, 15, 1–14. https://doi.org/10.1177/1474704916663727.
Davis, K. L., Panksepp, J., & Normansell, L. (2003). The affective neuroscience personality scales: Normative data and implications. Neuropsychoanalysis, 5, 57–69.
de Wit, H., Flory, J. D., Acheson, A., McCloskey, M., & Manuck, S. B. (2007). IQ and nonplanning impulsivity are independently associated with delay discounting in middle-aged adults. Personality and Individual Differences, 42, 111–121. https://doi.org/10.1016/j.paid.2006.06.026.
Del Giudice, M. (2019). “Rethinking the fast-slow continuum of individual differences.” PsyArXiv. August 29. doi:https://doi.org/10.31234/osf.io/4uhz8.
Del Giudice, M., Gangestad, S. W., & Kaplan, H. S. (2015). Life history theory and evolutionary psychology. In D. M. Buss (Ed.), The handbook of evolutionary psychology—Vol. 1: Foundations (2nd ed., pp. 88–114). New York, NY: John Wiley.
Dunkel, C. S., Mathes, E. W., Kesselring, S. N., Decker, M. L., & Kelts, D. J. (2015). Parenting influence on the development of life history strategy. Evolution and Human Behavior, 36, 374–378. https://doi.org/10.1016/j.evolhumbehav.2015.02.006.
Ellis, B. J., Figueredo, A. J., Brumbach, B. H., & Schlomer, G. L. (2009). Fundamental dimensions of environmental risk. The impact of harsh versus unpredictable environments on the evolution and development of life history strategies. Human Nature, 20, 204–268. https://doi.org/10.1007/s12110-009-9063-7.
Figueredo, A. J. (2007). The Arizona Life History Battery [Electronic Version]. http://www.u.arizona.edu/~ajf/alhb.html.
Figueredo, A. J., Vásquez, G., Brumbach, B. H., & Schneider, S. M. R. (2004). The heritability of life history strategy: The K-factor, covitality, and personality. Social Biology, 51, 121–134. https://doi.org/10.1080/19485565.2004.9989090.
Figueredo, A. J., Vásquez, G., Brumbach, B. H., & Schneider, S. M. (2007). The K-factor, covitality, and personality. Human Nature, 18(1), 47–73. https://doi.org/10.1007/BF02820846.
Figueredo, A. J., Cabeza de Baca, T., Black, C. J., Garcia, R. A., Fernandes, H. B. F., Wolf, P. S. A., & Woodley, A. (2015). Methodologically sound: Evaluating the psychometric approach to the assessment of human life history. Evolutionary Psychology, 13, 299–338. https://doi.org/10.1177/147470491501300202.
Frankenhuis, W. E., & de Weerth, C. (2013). Does early-life exposure to stress shape or impair cognition? Current Directions in Psychological Science, 22, 407–412. https://doi.org/10.1177/0963721413484324.
Frankenhuis, W. E., Panchanathan, K., & Nettle, D. (2016). Cognition in harsh and unpredictable environments. Current Opinion in Psychology, 7, 76–80. https://doi.org/10.1016/j.copsyc.2015.08.011.
Giosan, C. (2006). High-K strategy scale: A measure of the high-K independent criterion of fitness. Evolutionary Psychology, 4(1), 394–405.
Goodman, G. S., Quas, J. A., & Ogle, C. M. (2010). Child maltreatment and memory. Annual Review of Psychology, 61, 325–351. https://doi.org/10.1146/annurev.psych.093008.100403.
Gottfredson, L. S., & Deary, I. J. (2004). Intelligence predicts health and longevity, but why? Current Directions in Psychological Science, 13, 1–4. https://doi.org/10.1111/j.0963-7214.2004.01301001.x.
Griskevicius, V., Delton, A. W., Robertson, T. E., & Tybur, J. M. (2011). Environmental contingency in life history strategies: The influence of mortality and socioeconomic status on reproductive timing. Journal of Personality and Social Psychology, 100, 241–254. https://doi.org/10.1037/a0021082.
Gruijters, S. L., & Fleuren, B. P. (2018). Measuring the unmeasurable: The psychometrics of life history strategy. Human Nature, 29, 33–44. https://doi.org/10.1007/s12110-017-9307-x.
Knežević, G. (2003). Koreni amoralnosti [the roots of amorality]. Beograd: Institut za kriminološka i sociološka istraživanja, Institut za psihologiju.
McDaniel, M. A. (2005). Big-brained people are smarter: A meta-analysis of the relationship between in vivo brain volume and intelligence. Intelligence, 33, 337–346. https://doi.org/10.1016/j.intell.2004.11.005.
Međedović, J. (2017). Intelligence and fitness: The mediating role of educational level. Evolutionary Psychology, 15, 1–8. https://doi.org/10.1177/1474704917706936.
Međedović, J. (2018a). Exploring the links between psychopathy and life history in a sample of college females: A behavioral ecological approach. Evolutionary Psychological Science, 4, 466–473. https://doi.org/10.1007/s40806-018-0157-5.
Međedović, J. (2018b). Testing the state-dependent behavior models in humans: Environmental harshness moderates the link between personality and mating. Personality and Individual Differences, 125, 68–73. https://doi.org/10.1016/j.paid.2017.12.035.
Međedović, J. (2019). Life history in a postconflict society: Violent intergroup conflict facilitates fast life-history strategy. Human Nature, 30, 59–70. https://doi.org/10.1007/s12110-018-09336-y.
Međedović, J., Petrović, B., Želeskov-Đorić, J., & Savić, M. (2017). Interpersonal and affective psychopathy traits can enhance human fitness. Evolutionary Psychological Science, 3, 306–315. https://doi.org/10.1007/s40806-017-0097-5.
Meisenberg, G. (2010). The reproduction of intelligence. Intelligence, 38, 220–230. https://doi.org/10.1016/j.intell.2010.01.003.
Meisenberg, G., & Woodley, M. A. (2013). Global behavioral variation: A test of differential-K. Personality and Individual Differences, 55, 273–278. https://doi.org/10.1016/j.paid.2012.04.016.
Mettke-Hofmann, C. (2014). Cognitive ecology: Ecological factors, life-styles, and cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 5, 345–360. https://doi.org/10.1002/wcs.1289.
Mittal, C., Griskevicius, V., Simpson, J. A., Sung, S., & Young, E. S. (2015). Cognitive adaptations to stressful environments: When childhood adversity enhances adult executive function. Journal of Personality and Social Psychology, 109, 604. https://doi.org/10.1037/pspi0000028.
Morand-Ferron, J., Cole, E. F., & Quinn, J. L. (2016). Studying the evolutionary ecology of cognition in the wild: A review of practical and conceptual challenges. Biological Reviews, 91, 367–389. https://doi.org/10.1111/brv.12174.
Nettle, D. (2011). Flexibility in reproductive timing in human females: Integrating ultimate and proximate explanations. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 366, 357–365. https://doi.org/10.1098/rstb.2010.0073.
Nettle, D., Coall, D. A., & Dickins, T. E. (2010). Birthweight and paternal involvement predict early reproduction in British women: Evidence from the national child development study. American Journal of Human Biology, 22, 172–179. https://doi.org/10.1002/ajhb.20970.
Niemelä, P. T., Vainikka, A., Forsman, J. T., Loukola, O. J., & Kortet, R. (2013). How does variation in the environment and individual cognition explain the existence of consistent behavioral differences? Ecology and Evolution, 3, 457–464. https://doi.org/10.1002/ece3.451.
Pallier, G., Wilkinson, R., Danthiir, V., Kleitman, S., Knezevic, G., Stankov, L., & Roberts, R. D. (2002). The role of individual differences in the accuracy of confidence judgments. The Journal of General Psychology, 129, 257–299. https://doi.org/10.1080/00221300209602099.
Raven, J., Raven, J. C., & Court, J. H. (1993). Raven manual section 1: General overview. Oxford, England: Oxford Psychologists Press.
Reeve, C. L., Lyerly, J. E., & Peach, H. (2013). Adolescent intelligence and socio-economic wealth independently predict adult marital and reproductive behavior. Intelligence, 41, 358–365. https://doi.org/10.1016/j.intell.2013.05.010.
Reeve, C. L., Heeney, M. D., & Woodley of Menie, M. A. (2018). A systematic review of the state of literature relating parental general cognitive ability and number of offspring. Personality and Individual Differences, 134, 107–118. https://doi.org/10.1016/j.paid.2018.05.036.
Richardson, G. B., Sanning, B. K., Lai, M. H., Copping, L. T., Hardesty, P. H., & Kruger, D. J. (2017). On the psychometric study of human life history strategies: State of the science and evidence of two independent dimensions. Evolutionary Psychology, 15, 1474704916666840. https://doi.org/10.1177/1474704916666840.
Rodgers, J. L., Kohler, H. P., McGue, M., Behrman, J. R., Petersen, I., Bingley, P., & Christensen, K. (2008). Education and cognitive ability as direct, mediating, or spurious influences on female age at first birth: Behavior genetic models fit to Danish twin data. American Journal of Sociology, 114(S1), S202–S232. https://doi.org/10.1086/592205
Rushton, J. P. (2004). Placing intelligence into an evolutionary framework or how g fits into the r–K matrix of life-history traits including longevity. Intelligence, 32, 321–328. https://doi.org/10.1016/j.intell.2004.06.003.
Sheppard, P., Pearce, M. S., & Sear, R. (2016). How does childhood socioeconomic hardship affect reproductive strategy? Pathways of development. American Journal of Human Biology, 28, 356–363. https://doi.org/10.1002/ajhb.22793.
Simpson, J. A., Griskevicius, V., Kuo, S. I., Sung, S., & Collins, W. A. (2012). Evolution, stress, and sensitive periods: The influence of unpredictability in early versus late childhood on sex and risky behavior. Developmental Psychology, 48, 674–686. https://doi.org/10.1037/a0027293.
Spence, R., Magurran, A. E., & Smith, C. (2011). Spatial cognition in zebrafish: The role of strain and rearing environment. Animal Cognition, 14, 607–612. https://doi.org/10.1007/s10071-011-0391-8.
Stahlschmidt, Z., O’Leary, M. E., & Adamo, S. (2013). Food limitation leads to risky decision making and to tradeoffs with oviposition. Behavioral Ecology, 25(1), 223–227. https://doi.org/10.1093/beheco/art110.
Steinberg, L., Graham, S., O’Brien, L., Woolard, J., Cauffman, E., & Banich, M. (2009). Age differences in future orientation and delay discounting. Child Development, 80(1), 28–44. https://doi.org/10.1111/j.1467-8624.2008.01244.x.
Sternberg, R. J., & Detterman, D. K. (Eds.). (1986). What is intelligence? Contemporary viewpoints on its nature and definition. Norwood, NJ: Ablex.
Tello-Ramos, M. C., Branch, C. L., Kozlovsky, D. Y., Pitera, A. M., & Pravosudov, V. V. (2019). Spatial memory and cognitive flexibility trade-offs: To be or not to be flexible, that is the question. Animal Behaviour, 147, 129–136. https://doi.org/10.1016/j.anbehav.2018.02.019.
Teovanović, P., Knežević, G., & Stankov, L. (2015). Individual differences in cognitive biases: Evidence against one-factor theory of rationality. Intelligence, 50, 75–86doi:https://doi.org/10.1016/j.intell.2015. 02.008..
Wang, M. C., Kim, S., Gonzalez, A. A., MacLeod, K. E., & Winkleby, M. A. (2007). Socioeconomic and food-related physical characteristics of the neighbourhood environment are associated with body mass index. Journal of Epidemiology & Community Health, 61, 491–498. https://doi.org/10.1136/jech.2006.051680.
Webster, G. D., Graber, J. A., Gesselman, A. N., Crosier, B. S., & Schember, T. O. (2014). A life history theory of father absence and menarche: A meta-analysis. Evolutionary Psychology, 12, 147470491401200202. https://doi.org/10.1177/147470491401200202.
Woodley, M. A. (2011). The cognitive differentiation-integration effort hypothesis: A synthesis between the fitness indicator and life history models of human intelligence. Review of General Psychology, 15, 228–245. https://doi.org/10.1037/a0024348.
Woodley, M. A., Fernandes, H. B., & Madison, G. (2014). Strategic differentiation–integration effort amongst the 47 prefectures of Japan. Personality and Individual Differences, 63, 64–68. https://doi.org/10.1016/j.paid.2014.01.043.
Yeo, R. A., Gangestad, S. W., Liu, J., Calhoun, V. D., & Hutchison, K. E. (2011). Rare copy number deletions predict individual variation in intelligence. PLoS One, 6(1), e16339. https://doi.org/10.1371/journal.pone.0016339.
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Authors would like to express their gratitude to anonymous reviewers whose helpful comments helped in improving this manuscript.
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The work on this manuscript was financed by the Serbian Ministry of Education, Science and Technological Development via the project 47011, realized by the Institute of Criminological and Sociological Research.
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Boban Petrović is deceased. This paper is dedicated to his memory.
- Boban Petrović
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The plots of standardized residuals of intelligence predicted by environmental characteristics. Residuals of intelligence with only a linear term of environmental variable as a predictor are shown on the left; residuals of intelligence with linear and quadratic terms of environment as predictors are shown on right. It can be seen that the graphs on the right have lesser deviation from normal distribution (all except for Poverty as a predictor).
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Međedović, J., Petrović, B. Cognitive Ecology in Humans: The Role of Intelligence in Reproductive Ecology. Evolutionary Psychological Science 6, 216–228 (2020). https://doi.org/10.1007/s40806-019-00228-7
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DOI: https://doi.org/10.1007/s40806-019-00228-7