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Not All Risks Are Created Equal
Author: Dr.Shu Li's research group      Update time: 2016/11/16
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Risks of different types (natural, social, moral, recreational etc.) have been a driving force for both human evolution and development. Working with international collaborator, Prof. Wang Xiaotian from the University of South Dakota, Li Shu research group from the Key Laboratory of Behavioral Sciences of the Institute of Psychology explored the genetic and environmental influences on human risk taking in different task domains. The researchers used the twin research paradigm and meta analysis method to support the field of risk-taking propensity, namely, whether it is in the behavior of expression, or in the genetic mechanism, the correlation between the risk-taking propensity of various fields are low.

In order to develop a valid tool for measuring individual differences in risk-taking propensity involving both evolutionarily typical and modern risks, the researchers integrated several domain-specific risk-taking and developed a synthetic scale based on the results of factor analyses and validly tests. The end product is a Domain-Specific Risk-Taking Scale across Seven Domains (DOSPERT-7): cooperation/competition, safety, reproduction, natural/physical risk, moral risk, financial risk, and gambling.

Then there was a twin study with a total of 240 same-sex twin pairs (108 female pairs and 132 male pairs) sampled from the Beijing Twin Study (BeTwiSt) registry. Using the DOSPERT-7 the researchers estimated genetic and environmental influences on individual differences in risk-taking propensity over the seven domains. The effects on risk propensity were partitioned into four components: additive genetic (A), dominant genetic (D), shared environmental (C), and non-shared environmental (E) effects.  AE (additive genetic plus non-shared environmental effects) models had the best fit for most of the domains, except for gambling and safety domains where CE models had the best fit, suggesting strong shared and non-shared environmental influences. Supporting the notion of risk-domain specificity, both the behavioral and genetic correlations among the 7 domains were generally low. Among the relatively few correlations between pairs of risk domains, the analysis revealed a common genetic factor that regulates moral, financial, and natural/physical risk taking.

After a series of meta-analyses of extant twin studies across the 7 risk domains, the results showed that individual differences in risk-taking propensity and its consistency across domains were mainly regulated by additive genetic influences and individually unique environmental experiences. The heritability estimates from the meta-analyses ranged from 29% in financial risk taking to 55% in safety.


Figure 1. Comparison of heritability estimates from Study 2 and the meta-analyses of 100 twin studies across seven risk domains.

In summary, this is the first effort to separate genetic and environmental influences on risk taking across multiple domains in a single study and integrate the findings of extant twin studies via a series of meta-analyses conducted in different task domains.

This research was partially supported by the National Natural Science Foundation of China (Nos. 31170976, 31471005, 71201163, 31671166); the U.S. National Science Foundation Grant SES-1123341 to XT Wang.; Chinese Academy of Sciences Cross-Disciplinary Research Grant to XT Wang, sponsored by the K.C. Wong Foundation; the Foundation for the Supervisor of Beijing Excellent Doctoral Dissertation (No. 20138012501); and the Open Research Fund of the Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences.

The paper is now available online in Journal of Experimental Psychology: General.

Wang, X.T. (Xiao-Tian), Zheng, R., Xuan, Y-H., Chen, J. & Li, S. (2016). Not all risks are created equal: a twin study and meta-analyses of risk-taking across seven domains. Journal of Experimental Psychology: General. 145(11), 1548-1560. http://dx.doi.org/10.1037/xge0000225.

Website: http://psycnet.apa.org/journals/xge/145/11/1548/



Shu Li

Institute of Psychology, Chinese Academy of Sciences


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