Anxiety about the robot workforce reduces prejudice toward outgroups, makes people more accepting of outgroup members as leaders & family members, & increases wage equality across ingroup & outgroup members

Jackson, J. C., Castelo, N., & Gray, K. (2020). Could a rising robot workforce make humans less prejudiced? American Psychologist. Jan 2020,  https://doi.org/10.1037/amp0000582

Abstract: Automation is becoming ever more prevalent, with robot workers replacing many human employees. Many perspectives have examined the economic impact of a robot workforce, but here we consider its social impact: How will the rise of robot workers affect intergroup relations? Whereas some past research has suggested that more robots will lead to more intergroup prejudice, we suggest that robots could also reduce prejudice by highlighting commonalities between all humans. As robot workers become more salient, intergroup differences—including racial and religious differences—may seem less important, fostering a perception of common human identity (i.e., panhumanism). Six studies (ΣN = 3,312) support this hypothesis. Anxiety about the rising robot workforce predicts less anxiety about human outgroups (Study 1), and priming the salience of a robot workforce reduces prejudice toward outgroups (Study 2), makes people more accepting of outgroup members as leaders and family members (Study 3), and increases wage equality across ingroup and outgroup members in an economic simulation (Study 4). This effect is mediated by panhumanism (Studies 5–6), suggesting that the perception of a common human ingroup explains why robot salience reduces prejudice. We discuss why automation may sometimes exacerbate intergroup tensions and other times reduce them.

When Robots are Salient, Human Groups Don’t Seem So Different

Psychologists have long recognized the importance of categorization in social judgments. We are much kinder to someone categorized as a member of our “in-group” than to someone categorized as part of an “out-group” even if these people are indistinguishable from each other (Tajfel, 1970; Tajfel et al., 1971). For example, when strangers are split into two groups based on random coin-flips or the color of their nametag, people evaluate those in their own group more positively (and give them more money) compared with those in the other group (Billig & Tajfel, 1973; Jackson et al., 2018).

In daily life, features such as race, religion, gender, sexual orientation, and nationality provide markers with which we can assign in-group and out-group identities. However, events can sometimes prompt social “recategorizations” that override these salient markers, making someone from a different race or nationality seem like part of one’s in-group (Hornsey & Hogg, 2000; Gaertner et al., 1989; Gaertner et al., 1993). Allport (1954) first noted that a person’s ingroups can vary hierarchically, ranging from one’s family and friends, to one’s country or race, to one’s status as human. Allport proposed that increasing the salience of common superordinate memberships can lead people to be more inclusive in terms of who they identify with and who they will cooperate with. Someone of a different race will be viewed less favorably when identity is classified in terms of one’s race, but more favorably when the perceiver adopts a broader panhuman identity, in which all humans are viewed as part of the in-group.

This perspective is now termed the Common In-Group Identity (CII) model (Gaertner et al. 1993), and it can be an effective way of reducing intergroup prejudice. For example, leading people to replace subordinate categories (“us and them”) with superordinate categories (“we”) can decrease prejudice and discrimination (Dovidio et al., 1997; Gaertner et al. 1989; Guerra et al., 2010; Riek et al., 2006), and inter-ethnicity roommates who defined themselves as part of a common human identity were more likely to develop friendships than roommates who defined themselves as part of their ethnic identity (West et al., 2009). Other research has shown that people’s tendency to identify with humanity as a whole (rather than subordinate groups such as one’s community or race) predicts less ethnocentrism and more out-group prosociality and concern with global humanitarian issues (McFarland, Brown, & Webb, 2013; McFarland, Webb, & Brown, 2012). These studies each suggest that prompting people to adopt a panhuman identity can decrease prejudice and discrimination towards out-groups.

We suggest that the salience of robot workers may increase panhumanism and reduce prejudice by highlighting the existence of a group (robots) that is not human. The large differences between humans and robots may make the differences between humans seem smaller than they normally appear. Christians and Muslims have different beliefs, but at least both are made from flesh and blood; Latinos and Asians may eat different foods, but at least they actually eat food. We therefore predict that, to the extent that the salience of robot workers increases people’s panhumanism, it may decrease prejudice and discrimination against human out-groups.

Subjective ethical standards of generosity: Most feel strapped for cash, but believe higher earners are flush with spare money; higher earners report having much less spare money than what lower earners expect

Passing the buck to the wealthier: Reference-dependent standards of generosity. Jonathan Z.Berman et al. Organizational Behavior and Human Decision Processes, Volume 157, March 2020, Pages 46-56. https://doi.org/10.1016/j.obhdp.2019.12.005

Highlights
•    Subjective ethical standards of giving are reference-dependent with respect to the self.
•    Most feel strapped for cash, but believe higher earners are flush with spare money.
•    Higher earners report having much less spare money than what lower earners expect.
•    Higher earners are thus expected to donate much more than what they self-assess.
•    People are more accurate at assessing the spare money of lower earners than higher earners.

Abstract: Who is expected to donate to charity, and how much should they give? Intuitively, the less financially constrained someone is the more they should give. How then do people evaluate who is constrained and who has money to spare? We argue that perceptions of spare money are reference-dependent with respect to one’s current self: those who earn more than oneself are perceived as having an abundance of spare money and thus as ethically obligated to donate. However, those higher earners themselves report having little to spare, and thus apply lower donation standards to themselves. Moreover, a meta-analysis of our file-drawer reveals an asymmetry: individuals overestimate the spare money of higher earners but estimate the scant spare money of lower earners more accurately. Across all incomes assessed, people “pass the buck” to wealthier others (or to their future wealthier selves), who in turn, “pass the buck” to even wealthier others.

From 2017... Approaches to Measuring Creativity: A Systematic Literature Review

Approaches to Measuring Creativity: A Systematic Literature Review. Sameh Said-Metwaly, Eva Kyndt, Wim van den Noortgate. Creativity, Vol. 4, Issue 2, 2017. https://www.degruyter.com/downloadpdf/j/ctra.2017.4.issue-2/ctra-2017-0013/ctra-2017-0013.pdf

Abstract: This paper presents a review of the literature on the measurement of creativity. Creativity definitions are discussed as a starting point for understanding the nature of this construct. The four major approaches to measuring creativity (process, person, product and press) are reviewed, pointing out commonly used instruments as well as the advantages and weaknesses of each approach. This review reveals that the measurement of creativity is an unsettled issue, and that the existing instruments purporting to measure creativity suffer from serious conceptual and psychometric shortcomings. Research gaps and suggestions for future research are discussed.

Results

From the 2,064 papers identified by the search process, 221 papers were selected based on screening titles and abstracts. Among these, 152 papers met the inclusion criteria. The 152 included papers addressed the measurement of creativity and significant issues related to this measurement. Four distinct approaches to measuring creativity (process, person, product and press), in addition to the most commonly used instruments in each approach were identified. In the following, we first discuss creativity definitions, pointing to the different categories of these definitions. Then, we describe the approaches to measuring creativity and the advantages and weaknesses of each of these approaches, with an emphasis on the psychometric properties of the most common instruments used in each approach.

Defining creativity

Creativity has proven, over the years, to be difficult to define and measure due to its complex and multidimensional nature (Barbot et al., 2011; Batey & Furnham, 2006; Cropley, 2000; Runco, 2004, 2007; Treffinger et al., 2002). Treffinger (1996) reviewed the creativity literature and presented more than 100 different definitions for this concept. Despite these different definitions, the majority of creativity studies tend to employ only a few of these definitions, whereas other studies avoid providing a definition of this construct at all (Kaufman, Plucker, & Russell, 2012; Plucker & Makel, 2010). Furthermore, researchers and educators may use the term creativity to refer to entirely different aspects, including cognitive processes, personal characteristics and past experiences (Treffinger et al., 2002). In addition, researchers sometimes use terms such as innovation, invention, imagination, talent, giftedness and intelligence interchangeably with creativity.

In general, definitions of creativity typically reflect at least one of four different perspectives: cognitive processes associated with creativity (later in this paper referred to as ‘process’), personal characteristics of creative individuals (‘person’), creative products or outcomes (‘product’) and the interaction between the creative individual and the context or environment (‘press’) (Couger, Higgins, & McIntyre, 1993; Horn & Salvendy, 2006; Rhodes, 1961; Thompson & Lordan, 1999; Zeng et al., 2011).

With regard to the process perspective, Torrance (1977), as a pioneer in creativity research, defined creativity as the process of perceiving problems or gaps in knowledge, developing hypotheses or propositions, testing and validating hypotheses and finally sharing the results. Similarly, Mednick (1962) proposed that creativity involves the process of bringing associative elements together into new combinations to meet the task requirements. Guilford (1950) suggested some factors for interpreting variations in creativity including sensitivity to problems, fluency, flexibility, originality, synthesizing, analyzing, reorganizing or redefining, complexity and evaluating. In his Structure-of-Intellect (SOI) Model, Guilford (1975) considered creativity as a form of problem solving and distinguished between two types of cognitive operations: divergent production and convergent production. Divergent production is a broad search used in open problems to generate logical answers or alternatives, whereas convergent production is a focused search that leads to the generation of a specific logical imperative for a problem, in which a particular answer is required. Guilford (1975) considered divergent production process to be more relevant to successful creative thinking.

Focusing on the person perspective, a wide array of personal characteristics and traits have been suggested as being associated with creativity including attraction to complexity, high energy, behavioural flexibility, intuition, emotional variability, self-esteem, risk taking, perseverance, independence, introversion, social poise and tolerance to ambiguity (Barron & Harrington, 1981; Feist, 1998; James & Asmus, 2000-2001; Runco, 2007). However, having such traits does not actually guarantee the occurrence of creative achievement, the effect of intrinsic motivation still remains (Amabile, 1983). In other words, personality may be seen as related to the motivation to be creative rather than to creativity itself, with both of these being necessary for creative achievement (James & Asmus, 2000-2001). Task motivation is one of three key components in Amabile’s (1983, 1988, 1996) componential model of creativity that are necessary for creative performance, together with domain-relevant skills (including knowledge about the domain, technical skills and domain-related talent) and creativity-relevant skills (including personality characteristics and cognitive styles).

By turning the focus of defining creativity towards the creative products, Khatena and Torrance (1973) defined creativity as constructing or organizing ideas, thoughts and feelings into unusual and associative bonds using imagination power. Gardner (1993) stated that creative individuals are able to solve problems, model products, or define new questions in a novel but acceptable way in a particular cultural context. Creativity is also seen as the ability to produce or design something that is original, adaptive with regard to task constraints, of high quality (Kaufman & Sternberg, 2007; Lubart & Guignard, 2004; Sternberg & Lubart, 1999), useful, beautiful and novel (Feist, 1998; Mumford, 2003; Ursyn, 2014).

Finally, regarding the press perspective, that is, the interaction between the creative person and the environment or climate, McLaren (1993) stated that creativity could not be fully understood through human endeavour without taking into account its socio-moral context and intent (James, Clark, & Cropanzano, 1999). Investigating the environment for creativity therefore requires that all the factors that promote or inhibit creativity should be taken into consideration (Thompson & Lordan, 1999). In the componential model of organizational innovation and creativity, Amabile (1988) proposed three broad environmental factors related to creativity: organizational motivation or orientation to innovate, available resources and management practices. Geis (1988) identified five factors to ensure a creative environment: a secure environment with minimum administrative or financial intervention, an organizational culture that makes it easy for people to create and discover independently, rewards for performance to support intrinsic motivation, managerial willingness to take risks in the targeted areas of creativity and providing training to enhance creativity. Several studies have indicated the impact of climate or environment variables on creative achievement (e.g. Couger et al., 1993; Paramithaa & Indarti, 2014), particularly with respect to the initial exploratory stages of creative endeavours in which individuals’ need for approval and support plays an important role in motivating their further efforts (Abbey & Dickson, 1983).

Despite these different perspectives in defining creativity, some aspects are shared by many researchers. Researchers generally agree that creativity involves the production of novel and useful responses (Batey, 2012; Mayer, 1999; Mumford, 2003; Runco & Jaeger, 2012). These two characteristics, novelty and usefulness, are widely mentioned in most definitions of creativity (Zeng, Proctor, & Salvendy, 2009), although there is still some debate about the definitions of these two terms (Batey, 2012; Batey & Furnham, 2006; Runco & Jaeger, 2012). Another area of consensus is that creativity is regarded as a multifaceted phenomenon that involves cognitive, personality and environmental components (Batey & Furnham, 2006; Lemons, 2011; Runco, 2004). As Harrington (1990, p.150) asserted “Creativity does not “reside” in any single cognitive or personality process, does not occur at any single point in time, does not “happen” at any particular place, and is not the product of any single individual”.

Women, but not men, are seen as more attractive with longer eyelashes; perceptions of health and femininity also increase with eyelash length; older women, rather than younger women, benefit the most from enhanced eyelashes

Adam, A. (2020). Beauty is in the eye of the beautiful: Enhanced eyelashes increase perceived health and attractiveness. Evolutionary Behavioral Sciences, Jan 2020. https://doi.org/10.1037/ebs0000192

Abstract: Although some aspects of physical attractiveness are specific to time and culture, other characteristics act as external cues to youth, health, and fertility. Like head hair, eyelashes change with age, and as such, they may also serve as external mating cues. In three experiments, I manipulated eyelash length in photographs of men and women and had participants rate them on attractiveness (Studies 1–3), perceived age (Studies 1–3), perceived health (Studies 2 and 3), and femininity (Study 3). The results indicate that women, but not men, are seen as more attractive with longer eyelashes; that perceptions of health and femininity also increase with eyelash length; and that older women, rather than younger women, benefit the most from enhanced eyelashes—but that longer eyelashes did not reduce perceptions of age.

How much common genetic factors account for the association between general risk-taking preferences and risk taking preferences, and choices in financial investments, stock market participation and business formation

Common genetic effects on risk-taking preferences and choices. Nicos Nicolaou & Scott Shane. Journal of Risk and Uncertainty, Jan 9 2020. https://link.springer.com/article/10.1007/s11166-019-09316-2

Abstract: Although prior research has shown that risk-taking preferences and choices are correlated across many domains, there is a dearth of research investigating whether these correlations are primarily the result of genetic or environmental factors. We examine the extent to which common genetic factors account for the association between general risk-taking preferences and domain-specific risk-taking preferences, and between general risk-taking preferences and risk taking choices in financial investments, stock market participation and business formation. Using data from 1898 monozygotic (MZ) and 1344 same-sex dizygotic (DZ) twins, we find that general risk-taking shares a common genetic component with domain-specific risk-taking preferences and risk-taking choices.

Discussion

Although prior research has shown that general risk preferences, domain-specific risk preferences and choices that involve risk are correlated, very little work has investigated whether these correlations were primarily the result of genetic or environmental factors. This study showed that the correlations between general risk preferences, domain-specific risk preferences, financial investment choices, stock market participation, and business formation choices are partially the result of genetic factors.
Human beings may have evolved into different types: Those whose genetic composition predisposes them to high-risk-high-return choices and those whose genetic composition predisposes them to low-risk-low-reward choices. Just as our ancestors chose between hunting and gathering in part because they had innate predispositions toward risk tolerance or risk aversion, today’s humans might choose between low-risk-low-return and high-risk-high-return occupations and investment strategies.
We posit that the common genetic component to these preferences leads to correlated behaviors among people. Genetic factors account for part of the covariance between general risk preferences and domain-specific risk preferences, between general risk preferences and financial investment choices, between general risk preferences and stock market participation, and between general risk preferences and the choice of entrepreneurship as an occupation. People that are more risk tolerant are more likely to invest in stocks, make riskier financial choices and choose risky occupations, in part, because of the biological processes underlying their behavior.
Our study contributes to a biosocial perspective on risk taking. Domain-specific risk preferences have a non-trivial genetic component. In addition, financial investment choices, the choice to become an entrepreneur, and stock market participation have a sizeable genetic component. These patterns suggest that cross-sectional differences in the preference for risk and risk-taking behavior emerge naturally in a society as a function of the distribution of genetic composition (Karlsson Linnèr et al. 2019).
These results have interesting implications for those who examine risk taking. Parent-child similarity in risk taking, a commonly found correlation, may not result from cultural transmission as much as from genetic factors. While our findings do not negate the significance of environmental factors, they show that genetic influences cannot be ignored.
In addition, our results show that all of the environmental influences in risk taking were of the non-shared variety. This suggests that differential experiences outside the family, such as work environment and work colleagues, are more important for risk taking than shared environmental factors such as parental education and shared family rules and upbringing.
Our analysis suggests that a non-trivial fraction of the correlation between risk-taking behaviors results from innate factors. Because the ways to enhance those behaviors vary depending on the levels of genetic and environmental correlations, our results suggest that researchers need to think more carefully about the ways in which interventions might be used to increase the level of risk-taking behavior. Even if variables display a phenotypic correlation, interventions to increase one variable will not be likely to increase the other unless the correlations are largely environmental. Our results showed that a greater fraction of the correlation between general risk-taking preference and stock market participation was genetic than the fraction of the correlation between general risk taking preference and the tendency to be an entrepreneur. Therefore, efforts to increase entry into entrepreneurship by changing risk preferences through education may prove more effective than efforts to increase stock market participation through educationally-induced shifts in general risk preferences.
In addition, our study has implications for molecular genetics research in risk-taking behavior. Because common genetic factors account for a sizeable portion of the correlation between risk-taking preferences and choices in different domains, genes associated with those preferences and choices in one domain are plausible candidate genes for molecular genetics studies of risk-taking preferences and choices in other domains. These genes may also be influential for identifying gene-environment interactions in risk-taking.
It is crucial to stress that our study does not contend that genes determine risk taking behavior. As Johnson et al. (2009) argue, “even highly heritable traits can be strongly manipulated by the environment, so heritability has little if anything to do with controllability” (p. 218). Genes may only predispose some people and not others to develop risk taking preferences and choices. Thus, it is imperative for future research to understand the role that genes play in concert with contextual and environmental factors.
Our study has several limitations. Approximately 92% of the sample is female, hindering our ability to generalize our results to males. If women are less risk-taking than men, the range of our findings might be restricted when applied to males. While we have no reason to believe that genetic factors would only influence the correlation between risk-taking behavior in women and not men, we cannot show the generalizability of our findings across gender either.
Moreover, as in all twin studies, our analysis assumes that there is no assortative mating. Assortative mating—which can arise when individuals have children with individuals who are genetically similar to them—increases the probability that children of similar parents receive more similar gene variants for some attributes than children of “non-similar” parents. Because assortative mating increases the genetic similarity between fraternal twins, but not between identical twins (Guo 2005), it biases the results of twin studies by underestimating the heritability estimates (Plomin et al. 2008). Because we do not know if there is parental assortative mating with respect to risk-taking preferences and choices, we must caution that our findings could be biased downward, and underrepresent the common genetic component to risk-taking.
Furthermore, any violation of the equal environments assumption (EEA) may also affect the robustness of our findings. If environmental factors behave towards identical twins more similarly than towards fraternal twins with respect to risk-taking preferences or choices, the validity of the EEA would be challenged. While we have no reason to believe that this would be the case, we do not have the evidence to empirically verify the validity of the EEA in our study.
In addition, our results may be affected by measurement error. Beauchamp et al. (2017) found that measurement-error-adjusted estimates of heritability were considerably higher than the non-adjusted estimates. They conjecture that “once measurement error is controlled for, the heritability of most economic attitudes will approach that of the ‘Big Five’ in personality research” (Beauchamp et al. 2017: 231). This suggests that the heritability estimates for our risk taking variables may be conservative.
Finally, our analysis says nothing about the specific genetic mechanism involved in risk taking preferences and choices. Our results are consistent with the proposition that people with different genotypes select into different environments for risk-taking, as well as the proposition that genes themselves have a proximal effect on risk-taking preferences and choices. Moreover, we cannot know from these results what genes are involved in risk-taking preferences and choices or how many genes influence the observed outcomes.
We conclude by strongly encouraging additional research on the genetics of risk-taking. Considering the complementary role that biology plays in accounting for risk-taking is important lest we limit our ability to explain this important phenomenon. While most social scientists are comfortable exploring the role of environmental factors, they are less comfortable looking at the part that genetics plays. But, as Song, Li, & Wang (2015) have stressed, the need to account for more of the variance in work-related behaviour suggests that the role of genetics should be more carefully considered.

Women: Socioeconomic status negatively correlated with subjective orgasm experience

Factors Associated with Subjective Orgasm Experience in Heterosexual Relationships. Ana Isabel Arcos-Romero & Juan Carlos Sierra. Journal of Sex & Marital Therapy, Jan 9 2020. https://doi.org/10.1080/0092623X.2019.1711273

Abstract: The main objective of this study was to determine the predictive capacity of different variables, organized based on Ecological theory (i.e., personal, interpersonal, social, and ideological), in the intensity of the subjective orgasm experience within the context of heterosexual relationships. The sample was composed of 1,300 adults (547 men, 753 women). The proposed model for men showed that more intense subjective orgasm experience was predicted by age, sexual sensations seeking, sexual satisfaction, and partner-focused sexual desire. The model for women showed that more intense subjective orgasm experience was predicted by age, erotophilia, sexual sensation seeking, partner-focused sexual desire, and sexual satisfaction.