The famous discussion of nature vs. nurture was somewhat interrupted by the realization that both aspects, genetics and environment, have important input in individuals. By combining the studies of genetics and psychology it is possible to correlate results from the fields and search for possible, and hopefully specific, genes involved in the expression of certain traits. For instance, Poulin et al (2011) explored the relationship between receptor genes of oxytocin and vasopressin and prediction of prosocial behavior. In this particular study, Poulin et al (2011) intended to “examine whether oxytocin and vasopressin interact with threat to predict prosocial behavior outside the contexts of the laboratory and close relationships” by examining
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nurture resolution opened the doors to new perspectives of study that would include both factors and their subsets, such as the past one, their reliability is still debatable in the context of their replicability. Dick et al (2015), for instance, argument that there should a concern and special attention to candidate gene studies. One of the reasons they pose this concern is because there are different conceptualizations of the results depending on the field. According to Dick et al (2015), for psychologists and members of other social sciences the genetics-environment effects are interpreted “as a genotype moderating the association between an environmental factor and an outcome” (p. 41), in other words, it is the environmental factors the ones that are associated with increased risks. On the other hand, for geneticists the genetics-environment is interpreted “as an environment moderating the association between a genotype and the outcome” (p, 41), the opposite of the previous conceptualization. In addition, Dick et al (2015) say that “widely studied candidate genes were not found to be significant when studied systematically across the backdrop of the genome in well-powered studies” (p. 42) with few exceptions. Dick et al (2015) also say that there is probable publication bias, where the “interest in positive findings is clearly misplaced if they are false, as the can (mis)guide research efforts and funding priorities” (p. 42). Moreover, Dick et al (2015) say that there is a high incidence of false-positive- rates, and that this is due to a variety of factors including “smaller effect sizes, … greater flexibility in design, definitions, outcomes, and analytic models … [and] in data collection” (p. 43). Relatedly, Dick et al (2015) comment that “many behavioral measures have no ‘true scale’ … no meaningful zero points (p. 44-5) as well as the selection of models, varying from a risk-difference and a risk-ration framework and the
nurture resolution opened the doors to new perspectives of study that would include both factors and their subsets, such as the past one, their reliability is still debatable in the context of their replicability. Dick et al (2015), for instance, argument that there should a concern and special attention to candidate gene studies. One of the reasons they pose this concern is because there are different conceptualizations of the results depending on the field. According to Dick et al (2015), for psychologists and members of other social sciences the genetics-environment effects are interpreted “as a genotype moderating the association between an environmental factor and an outcome” (p. 41), in other words, it is the environmental factors the ones that are associated with increased risks. On the other hand, for geneticists the genetics-environment is interpreted “as an environment moderating the association between a genotype and the outcome” (p, 41), the opposite of the previous conceptualization. In addition, Dick et al (2015) say that “widely studied candidate genes were not found to be significant when studied systematically across the backdrop of the genome in well-powered studies” (p. 42) with few exceptions. Dick et al (2015) also say that there is probable publication bias, where the “interest in positive findings is clearly misplaced if they are false, as the can (mis)guide research efforts and funding priorities” (p. 42). Moreover, Dick et al (2015) say that there is a high incidence of false-positive- rates, and that this is due to a variety of factors including “smaller effect sizes, … greater flexibility in design, definitions, outcomes, and analytic models … [and] in data collection” (p. 43). Relatedly, Dick et al (2015) comment that “many behavioral measures have no ‘true scale’ … no meaningful zero points (p. 44-5) as well as the selection of models, varying from a risk-difference and a risk-ration framework and the