Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 1
Free will, determinism, and intuitive judgments about the heritability of behavior
Emily A. Willoughby1
Alan C. Love2
Matt McGue1
Jack Quigley1
James J. Lee1
Correspondence concerning this article should be addressed to Emily Willoughby
([email protected]), 75 East River Rd., Minneapolis, MN 55455, office phone 612-625-6881.
1 Department of Psychology, University of Minnesota Twin Cities, Minneapolis, MN, USA 2 Department of Philosophy, University of Minnesota Twin Cities, Minneapolis, MN, USA Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 2
Abstract
The fact that genes and environment contribute differentially to variation in human behaviors, traits and attitudes is central to the field of behavior genetics. To the public, perceptions about these differential contributions may affect ideas about human agency. We surveyed two independent samples (N = 301 and N = 740) to assess beliefs about free will, determinism, political orientation, and the relative contribution of genes and environment to 21 human traits.
We find that beliefs about the heritability of these traits cluster into four distinct groups, which differentially predict both beliefs about human agency and political orientation. Despite apparent ideological influences on these beliefs, the correspondence between lay judgments of heritabilities and published estimates is large (r = .77). Belief in genetic determinism emerges as a modest predictor of accuracy in these judgments. Additionally, educated mothers with multiple children emerge as particularly accurate in their judgments of the heritabilities of these traits.
Keywords
Heritability, free will, determinism, human agency, genetics, environment
Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 3
Introduction
The problem of free will is one of the oldest in philosophy. Our intuitive sense that we have the capacity to decide between two or more alternative courses of action has provoked a diversity of interpretations. This is unsurprising because of the connections between our intuitive sense of free will and concepts of moral responsibility, just reward, individual autonomy and dignity, and genuine love or friendship (O’Connor 2010). Enlightenment-era Scottish philosopher David
Hume is largely responsible for crystallizing the issue of free will into two primary positions: compatibilism, which posits that a deterministic universe is consistent with freedom of choice, and incompatibilism, which holds that determinism and free will are inherently at odds (Hume,
1975). Within this framing, the question of free will is concentrated on whether we have a particular kind of causal control of our decisions and actions.
Although Hume championed the compatibilist position, the psychologist William James famously called compatibilism “a quagmire of evasion” and struggled for much of his life to reconcile his understanding of a law-governed, deterministic universe with the subjective experience of having free will (James, 1884). Despite developments in our understanding of determinism (Earman 1986; Ismael 2016) and a rich set of distinctions that reveal a complex set of phenomena associated with our intuitive sense of free choice (Kane 2002; O’Connor 2010), a number of contemporary researchers have questioned whether we have free will at all
(compatibilist or incompatibilist). Empirical studies in neuroscience and cognitive psychology have been central to this line of questioning. Experiments designed to ascertain the relationship between our awareness of a conscious decision and the brain activity involved in carrying out an associated physical action seem to suggest that we do not have conscious control of the initiation of our actions and our impression that we do is illusory (Libet 1985). However, there is little Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 4 consensus on how to interpret these experiments (Mele 2009). Others have claimed that advances in our understanding of the biological basis of behavior eliminate any place for the requisite causal control we intuitively associate with a genuine sense of free will and that major public policy revisions are required as a consequence (Cashmore 2010).
In parallel with this trend of questioning whether we have free will, “experimental philosophy” has emerged as a field that uses empirical data to seek regularities in the attitudes and behaviors of everyday people to shed light on the psychological processes and experiential circumstances that predict belief in or specific interpretations of key concepts. Free will has been a salient topic in these endeavors. For example, experimental philosophers have asked questions about the relationship between moral responsibility and free will by surveying the general public on what they actually believe (Nahmias et al. 2005), or by seeing whether these beliefs show significant differences in culture (Weinberg, Nichols and Stitch, 2001) or even gender (Adleberg,
Thompson and Nahmias 2014). Although these studies have illuminated important patterns in how people conceptualize free will under certain conditions and change their conception in light of specific factors, no unifying perspective has been identified. The empirical evidence does not provide unqualified endorsement of whether most people adopt compatibilist or incompatibilist orientations to the concept of free will (Chan, Deutsch, and Nichols 2016).
While psychology and philosophy have been wrestling with the concept of free will for centuries, behavioral genetics is another approach that can address questions about how people perceive free will and what factors contribute to their perceptions. Implicit in traditional debates about “nature vs. nurture” is the empirical claim that features of our biological constitution and features of our social and developmental experience make differential contributions to human behavior (Tabery 2014). Studies of monozygotic twins—a standard methodology utilized in Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 5 behavior genetics—have shown for decades that twins can exhibit uncanny similarities as a direct consequence of genetic similarity. These studies also tend to engender a fascination with free will:
I am often asked, “Given your experience with identical twins reared apart, what
is your feeling about determinism and free will?” This is an interesting question.
Observation of similarity between twins reared apart constantly stimulated me to
ponder the problem of genetic determinism. I am not prepared to even attempt to
try to answer the question. (Bouchard 1991, p. 311)
Intriguingly, the relationship between our intuitions about free will and knowledge about the heritability of human traits has remained largely unstudied. For example, on the standard framing of compatibilism versus incompatibilism, a deterministic universe would have the same logical implications for free will regardless of whether genes or environment are more responsible for determining our behavior. And yet it seems plausible that people might have different intuitions about how these types of causal factors are related to the concept of free will. This suggests that a behavioral genetic approach is uniquely equipped to answer two important questions in this vein: (1) how do attitudes about free will and determinism relate to what people believe about the genetic and environmental contributions to behavior?, and, (2) what influences do genetic and environmental factors actually have on beliefs about free will and determinism?
The present study explores answers to (1) with a survey of two large American samples with the long-term aim of answering (2) by looking at beliefs about free will and determinism in a sample of adoptive and biological families with a genetically-sensitive design. Surveying over 1000 participants on Amazon Mechanical Turk, we examine the Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 6 clusters of attitudes formed by responses on several measures of belief in free will and determinism, political orientation, personality, and a suite of demographic questions. Of particular interest is whether and to what extent outcomes can be predicted from responses on novel subscales that discriminate between genetic and environmental determinism, as well as a measure of judgments on the relative amount of genetic and environmental contributions to 21 human traits. These include both physical and psychological traits in both abnormal and normal dimensions, such as height, schizophrenia, intelligence, and sexual orientation. The intention of surveying these judgments is to ascertain how individuals with little or no genetics education perceive the relative contribution of genes and environment to individual differences in these traits, and how these judgments relate to other measured variables. This positions us to probe questions about the formation and consequences of these beliefs, as well as whether and to what extent intuitive heritability judgments accurately reflect published estimates from meta-analyses and large-scale twin studies.
Methods
Sample
Amazon Mechanical Turk was used for recruiting subjects to complete an online questionnaire for monetary compensation in two pilot studies across several weeks. Mechanical Turk (MTurk) is an open online marketplace where participants (“workers”) can choose to complete a “human intelligence task” (HIT) using an online platform for compensation. MTurk has been found to produce social science data that are at least as reliable as those obtained through traditional methods (Buhrmester, Kwang and Gosling 2011). Data quality can be further enhanced by restricting and filtering on certain criteria. In the present study, response validity was maximized Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 7 by including several validity-check items, as well as by limiting participants to a HIT approval rate of 90% or greater and the number of lifetime HITs approved to 100 or greater. Participants had to be at least 18 years old and located in the United States.
The final sample of the first study (sample 1) consisted of 301 participants (42.9% female) with 50.8% falling within the “25 to 34” age range. No participants were excluded on the basis of failed validity checks or incomplete answers. A total of 68 participants responded to both surveys, and their responses on the second survey were removed from the final analyses.
Participants in the second study (sample 2) consisted of 740 unique participants (48.2% female) with 46.2% falling within the “25 to 34” age range.
Measures
Samples 1 and 2 differed markedly in the range of measures employed across the surveys.
Sample 1 (N = 301) evaluated participants on a suite of 9 demographic questions and 184 questions assessing personality, religion, knowledge of basic genetics, and attitudes toward current affairs and the causes of human behavior. Sample 2 (N = 740) evaluated participants on a total of 11 demographic questions and 21 questions assessing attitudes about the contributions of genes and environment to various human traits.
Demographics
Both samples of participants responded to a demographic section that assessed age range, gender, marital status, educational degree, years of education, working status, hours worked in a week, and political views. The survey administered to sample 2 included additional questions on range of household income and number of adopted and biological male and female children for each participant. Demographic distributions were similar for most variables across the two samples. Demographic comparison of the two samples is summarized in Table 1. Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 8
Table 1
Free will and determinism scales
Participants in sample 1 answered a series of 69 questions on their opinions about the causes of human behavior. Questions were extracted from two well-known measures of human agency.
The Free Will and Determinism Scale-Plus (FAD+; Paulhus and Carey 2011) is one of the most widely used self-report measures of free will/determinism. It consists of 27 items on 4 subscales: (1) Free Will (FW; 7 items, alpha = .70), (2) Scientific Determinism (SD; 7 items, alpha = .69), (3) Fatalistic Determinism (FD; 5 items, alpha = .82), and (4) Unpredictability
(UNP; 8 items, alpha = .72). The scales are minimally intercorrelated (all r < .20 in absolute value) and related to the Big Five (largest r is between Neuroticism and Fatalistic Determinism, r
= .22). The Scientific Determinism scale includes two items that deal with genetic/biological determinism (SD-BIO), two items that deal with environmental/psychosocial determinism (SD-
ENV), and three items that deal with general determinism (SD-GEN). We have added 9 items (3 for SD-GEN, 3 for SD-ENV, and 3 for SD-GEN) to create three 5-item subscales. In generating the items, we have attempted to make the Genetic and Environmental subscales as parallel as possible by mirroring the wording of the questions closely. All items are keyed positively, and responses are given on a 5-point scale with each point anchored (Strongly disagree = 1, Disagree
= 2, Neither agree nor disagree = 3, Agree = 4, Strongly Agree = 5).
The Free Will Inventory (FWI; Nadelhoffer, 2014) consists of three 5-item scales: (1)
Free Will (FW), (2) Determinism (DET), and (3) Dualism/Anti-Reductionism, which was not used in the final survey. Nadelhoffer reports an alpha reliability of .80 and .77 for FW and DET, respectively. All items are keyed and anchored identically to the FAD+ items. Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 9
The Belief in Genetic Determinism scale (BGD; Keller 2005) consists of 18 items in its full scale; we have chosen to include the 10 with the highest factor loadings. Keller reports that the full version has an average alpha reliability of .84 across multiple studies. All items are scored on a 5-point “agreement/disagreement” scale.
The Belief in Social Determinism scale (BSD; Rangel and Keller 2011) consists of 12 items in its full scale; again, we have chosen to include the 10 with the highest factor loadings to make it as parallel as possible to the BGD scale. Rangel and Keller report that the full version has an average alpha reliability of .84 across multiple studies. All items are scored on a 5-point
“agreement/disagreement” scale.
Current affairs and religion
In addition to a single demographic question asking participants where they fall on the political spectrum on a 1 to 5 scale and a selection of General Social Survey (GSS) items, two scales are used as proxies of political orientation:
Authoritarianism (SL-A; Duckitt et al. 2010) consists of 18 items that represent three different facets; this is the "short form" of the 30-item measure. As presented, items 1–6 represent Authoritarian Submission; 7–12 represent Conventionalism; 13–18 represent
Authoritarian Aggression.
Egalitarianism (SL-EG; Feldman and Steenbergen 2001; Feldman 1988) consists of a combination of eight items from two similar scales. Five of these items were used in a Minnesota
Twin Registry survey from 2008. The additional three items were included to augment coverage of "equality of opportunity" rather than "equality of outcome," which the 2008 measure has been criticized for lacking. Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 10
Religiousness consists of 13 items drawn from assessments used for the Minnesota
Center for Twin and Family Research. The items are straightforward questions asking general religious affiliation and a variety of behaviors related to observance of religious holidays, reading religious text, and salience of religious activity in family and everyday life.
Genetics literacy and heritability judgments
Genetics literacy was assessed from a total of 10 items, 8 selected from the Public
Understanding and Attitudes towards Genetics and Genomics (PUGGS; Carver et al. 2017) questionnaire and two items used to assess public knowledge on genetics and genetic testing from Haga et al. (2013). The full-scale PUGGS questionnaire was constructed to assess the knowledge of college students about genetics and genomics, and consists of 45 items developed and reviewed by international experts from genetics, education, and other fields.
Heritability judgments were assessed from an adaptation of a section of the PUGGS questionnaire. This section consists of a table of 21 human traits, including representatives of normal and abnormal physical traits and normal and abnormal psychological traits, with the following instructions:
“People vary in traits (physical features, behaviors, diseases and disorders) such as
those shown in the table below. Both genetic factors and environmental factors
contribute to differences among people. Environmental factors can for example include
culture, upbringing, eating habits and exposure to pollution. For each of the
characteristics below, indicate to what extent you think genetic and environmental
factors contribute to differences among people.” Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 11
Responses are keyed on a 1 to 5 scale (Only environmental factors = 1, Mainly environmental factors = 2, Genetic and environmental factors contribute roughly the same = 3, Mainly genetic factors = 4, Only genetic factors = 5).
Analysis
All statistical analyses were conducted in SPSS and R. Reported subscale measures are all mean values of scores unless otherwise indicated. Due to the large number of variables and generally small effect sizes, significance is established at p < .01 unless otherwise indicated.
Results
Heritability judgments
Mean judgments of heritability for each trait and its variance were very similar between samples
1 and 2 for most of the surveyed traits; these are displayed for all traits in Table 2. Absolute differences in mean judgments were 0.11 or less and averaged 0.05 on the 1 to 5 scale. In both samples, participants rated differences in height, eye color, blood group, and colorblindness as the most heritable traits, with means falling above 4.0 in both studies and the highest estimates in genetic contribution for eye color (sample 1 M = 4.67, SD = 0.71; sample 2 M = 4.64, SD = 0.70) and blood group (sample 1 M = 4.61, SD = 0.79; sample 2 M = 4.64, SD = 0.73). Difference in political belief was judged to have the smallest genetic contribution in both studies, and was the only surveyed trait whose mean fell below 2.0 (sample 1 M = 1.67, SD = 0.89; sample 2 M =
1.72, SD = 0.85). Sexual orientation and political belief both had the largest spread in both samples, with standard deviations approaching or exceeding 1.0.
Table 2
Since both MTurk samples included the same 21 heritability judgment questions (total unique N = 1041) and these judgments were stable, the samples were combined for most Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 12 subsequent analyses. A detailed breakdown of percentage of all respondents on each possible response across all 21 traits is shown in Figure 1.
Fig. 1
Ratings of heritability on these 21 traits were moderately intercorrelated across the full sample of 1041 participants. Exploratory principal components analysis (PCA) was performed on the combined dataset to evaluate the possibility of meaningful trait clusters. A heat-map correlation matrix of heritability judgments for these 21 traits is shown in Figure 2.
Fig. 2
A scree plot suggests that only the first four components of the PCA are most likely to have meaningful explanatory power for the variance in heritability judgments in the combined sample (Supplementary Figure 1).
Varimax rotation was used on the first four components to bring the differences between heritability judgments into focus. This produces four intuitively-related groups of traits. Height, eye color, blood group and colorblindness clustered together into a group conceptually represented by purely physical traits of the human body. Intelligence, personality, musical ability, violent behavior, and athleticism clustered together into a psychological attribute group.
Diabetes, alcoholism, obesity, blood pressure, and heart disease formed its own distinct cluster that is perhaps best described as lifestyle attributes. Bipolar disorder, schizophrenia, depression and attention deficit/hyperactivity disorder (ADHD) formed a clear group of psychiatric disorders. Curiously, sexual orientation clustered unequivocally with this group (Table 3).
Table 3
Two traits did not cluster definitively into one of these groups: breast cancer seems almost equally split between the component representing psychiatric traits and that representing Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 13 lifestyle traits, and political orientation, which positively loads most strongly onto the component representing psychological traits, but negatively loads even more strongly onto the physical traits component. The reason for both splits is unclear. Factor scores for these four components, estimated via regression method, were assigned to each participant for use as predictor variables.
Heritability judgments for both political beliefs and breast cancer were not included in the estimation of these scores due to the lack of an interpretation of their associations.
Heritability judgments, free will and determinism, and political orientation
Descriptive statistics for all measures of free will and determinism used in sample 1 (N = 301) are shown in Table 4, along with comparisons of alpha reliability between published scales and the altered or truncated scales in the current sample. Mean scores for each scale are constructed from the same 5-point Likert scale. Alpha reliabilities for all scales were robust, generally meeting or exceeding published alphas where available.
Table 4
Most scale and subscale measures of free will and determinism were significantly intercorrelated. The two measures of free will (FWI: FW, FAD+: FW) were significantly correlated (r = .83, p < .01), and all subscale measures of determinism (BGD, BSD, FWI: determinism, FAD+: scientific determinism, FAD+: fatalistic determinism) were generally all positively related to one another and negatively related to the measures of free will (Table 5).
Some of the largest observed correlations include the relationship between BGD (genetic determinism) and the FAD+’s biological determinism scale at r = .63 (p < .01); between BGD and FAD+: scientific determinism, at r = .58 (p < .01), and FAD+: fatalistic determinism with
FWI: determinism at r = .59 (p < .01).
Table 5 Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 14
In both the FAD+ and the FWI, the free will subscale is weakly negatively correlated with most of the determinism subscales. For the FWI, the free will subscale correlates with the determinism subscale at –.18 (p < .01). The free will subscale of the FAD+ correlates with scientific, environmental, and biological determinism at r = –.15 (p < .01), –.14 (p < .05), and –
.18 (p < .01), respectively. These weak negative correlations offer support for the conclusion of
Chan, Deutsch, and Nichols (2016) that there is little empirical support for the existence of a
“compatibilist” or “incompatibilist” orientation of individuals towards questions of free will.
Measures of free will and determinism relate modestly but significantly to judgments of the heritability of human traits (Table 6). Both measures of free will (from the FAD+ and the
FWI) are positively related to high heritability judgments on the physical trait factor relative to the others, and both measures of determinism are negatively related to the physical trait factor.
The psychological trait factor, by contrast, relates positively and significantly, albeit modestly, with the determinism subscales of the FAD+ and the FWI, as well as strongly with the BGD, but did not relate significantly to the measures of free will. Both the psychiatric and lifestyle heritability factors did not relate significantly to any subscale measures of free will or determinism.
Table 6
Political viewpoint, assessed on a 1–5 scale from “very conservative” (1) to “very liberal”
(5), is a common variable between both datasets and therefore permits a higher-powered exploration of its relationship with other variables. Overall, participants in the aggregate sample
(N = 1041) were more likely to identify as liberal than conservative (M = 3.34, SD = 1.14).
Between participants without children (N = 606) and with children (N = 435), childless individuals were significantly more likely to identify as liberal (mean difference across the 1–5 Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 15 political spectrum = 0.40, t(1039) = 5.70, p < .01), consistent with existing research (Weeden &
Kurzban, 2014; Hawley, 2016).
Scores on authoritarianism and egalitarianism correlated with one another at r = –.64 (p <
.01) and with political orientation (authoritarianism r = –.65, egalitarianism r = .64, both p <
.01). Authoritarianism also correlates significantly with both the FAD+ (r = .27) and the FWI (r
= .18) measures of free will, with higher authoritarianism scores predicting stronger belief in free will (both p < .01). For unclear reasons, authoritarianism was positively associated with belief in determinism on the FWI (r = .20, p < .01), but not the FAD+ (r = .00, p = .99). Belief in genetic and social determinism (BGD and BSD) correlated with authoritarianism in opposite directions, with higher authoritarianism predicting higher BGD scores (r = .16, p = .01) and lower BSD scores (r = –.18, p < .01). The relationships of political ideology and egalitarianism with measures of free will and determinism were generally weaker than the comparable correlations for authoritarianism (Table 7).
Table 7
For the 21 human traits surveyed for opinions on their relative contributions from genes and environment, 9 of these correlated significantly (p < .01) but generally modestly with liberalism: schizophrenia (r = .12), bipolar disorder (r = .08), intelligence (r = –.10), personality
(r = –.13), political beliefs (r = –.12), depression (r = .08), attention deficit/hyperactivity disorder
(r = .13), musical talent (r = –.14), and sexual orientation (r = .29). Factor scores for the psychiatric trait cluster correlated moderately with political ideology (r = .22, p < .01), with higher ratings of heritability for psychiatric traits associated with a more liberal orientation
(Table 7). Factor scores for the psychological trait cluster also correlated significantly with political orientation (r = –.15, p < .01). In this case, however, the higher rating of heritability was Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 16 by conservatives rather than liberals. This contrasting trend for both the psychiatric trait factor and the psychological trait factor is reiterated in their associations with authoritarianism
(psychiatric: r = –.17, p < .01; psychological: r = .19, p < .01) and with egalitarianism
(psychiatric: r = .11, p = .05; psychological: r = –.15, p < .01). See Supplementary Table 3 for a detailed breakdown of each factor and its relationship to political ideology, authoritarianism and egalitarianism. If the trait factors are considered as meaningful clusters, conservative and authoritarian attitudes tend to be more strongly associated with higher judgments of the genetic contribution to psychological traits and lower judgments of the genetic contribution to psychiatric traits, and liberal and egalitarian attitudes are typically associated with the opposite.
Figure 3 illustrates the mean standardized factor scores on each of the four factors of heritability judgments of the surveyed human traits.
Fig. 3
Heritability judgments and published estimates
One feature of participants’ judgments of heritability for these traits may be the most interesting: their correspondence with empirical estimates of heritability. Thanks to a rich history of twin, adoption and family studies that have culminated in some recent meta-analyses, the correlation between these judgments in an MTurk sample of over a thousand participants and actual measured heritability estimates can be calculated.
Table 8
Table 8 shows the mean heritability judgment for each trait in the combined sample of
1041 participants, on a 1–5 scale, compared to the best available estimate of heritability for the same or similar traits in published literature. Most estimates come from the 2015 meta-analysis by Polderman et. al, which documents the results of fifty years of twin studies on over 17,000 Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 17 separate traits. The name of the comparable trait used in the published literature is displayed alongside the name used in the current study. In several cases including political beliefs, violent behavior and obesity, the trait whose meta-analytic heritability estimate was used is a collection of similar traits that form a related grouping, which has been chosen on the basis of greatest similarity and inclusion of the surveyed trait. The robust prediction enabled by these collections is deemed superior over citing individual studies that might suffer from smaller independent samples. For example, since few twin studies on political orientation have been conducted, the category was chosen that included this trait within “societal attitudes.” Similarly, “violent behavior” resides comfortably within the Polderman et al. (2015) grouping of “conduct disorders.” Among the traits that were absent or too nonspecific from the Polderman et al. (2015) meta-analysis, estimates of athletic and musical ability were taken from the Vinkhuyzen et al.
(2009) meta-analysis on the heritability of talents and abilities.
Fig. 4
The correlation between heritability judgments and published estimates is .77 (Figure 4).
Sexual orientation represents the largest discrepancy between lay opinion and published estimates, with a sizable majority of participants overestimating its heritability. Removing this trait from the correlation boosts it to .84. This correspondence between everyday judgments of heritability and the empirical estimates cannot be explained by education or even genetic knowledge alone. The correlation between heritability judgments and published estimates differed very little between participants with an associate’s degree or lower (N = 505, r = .77) and those with a bachelor’s degree or higher (N = 536, r = .76), and was even slightly lower in the more-educated group. Likewise, participants’ score on the genetic knowledge component of the first sample survey similarly failed to be associated with accuracy in heritability judgment. Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 18
The group scoring above the mean (N = 138) was only slightly better at correlating with the published estimates (r = .77) than the group scoring below the mean (N = 163, r = .74). The physical component of the heritability judgments is the only one that correlated significantly with genetic knowledge scores in the MTurk sample (r = .30, p < .01), and highest degree earned did not even approach significance in its association with any of the four heritability factors. Age and having had children similarly did not make a significant difference to accuracy in predictions of heritability of these 20 traits (see Supplementary Tables 1 and 2).
The mean comparisons across Supplementary Table 1 indicate that the association between heritability judgments and published heritability estimates was not moderated by education level, score on genetics literacy items, having had children, and age group. Political orientation, on the other hand, produces a more substantial difference. The mean heritability judgments across the 20 surveyed traits of the 262 sampled conservatives (those who chose “very conservative” or “conservative”) correlated with the published estimates at r = .81, while the mean judgments of those 512 participants identifying as “liberal” or “very liberal” correlated with the published estimates at r = .73. The mean judgments of the 267 sampled moderates fell in between at r = .79. The relevance of this difference, larger than that produced by any other categorical demographic variable surveyed, is bolstered by breaking down those who identified as “very” liberal or conservative: the 172 “very liberal” participants had mean heritability judgments that correlated with the published estimates at r = .70, whereas the “very conservative” participants showed r = .80.
Fig. 5
However, a close look at Fig. 5 reveals why this happens. For both ends of the spectrum—and, indeed, for the majority of all 1041 participants in the sample—the heritability Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 19 judgment for sexual orientation is the single biggest outlier, with most participants believing that the relative contribution of genes to its prevalence is far greater than it is. Though liberals and conservatives differ significantly (p < .01) in their mean judgments of heritability for schizophrenia, personality, political beliefs, attention deficit/hyperactivity disorder, and musical talent, the biggest difference by far is for sexual orientation with a mean difference of –.76 on the
1 to 5 scale. The magnitude of this difference is greater than twice that of the next-largest (for a full list of the traits and their difference in heritability judgments between liberals and conservatives, see Supplementary Table 4). When the heritability judgment for sexual orientation is removed across all participants, the relative accuracy of estimations changes for both conservatives and liberals, and the correlation no longer declines from “very conservative” to
“very liberal” (Supplementary Table 5). For a full comparison of published heritability estimates to the standardized mean judgments of conservatives, liberals and moderates on all surveyed human traits, see Supplementary Fig. 2.
Heritability judgment difference scores, determinism, and motherhood
Heritability judgments also can be used to derive a rough individual-level measure of accuracy.
Rating scores of each participant on each trait are transformed to the same scale as the published estimates, where 0 for heritability variance is equivalent to “only environmental factors” (a 1 on the survey) and 1 is equivalent to “only genetic factors” (a 5 on the survey). “Genetic and environmental factors contribute roughly equally” (a 3 on the survey), a functional equivalent to stating that 50% of the variance in a trait is due to genetic factors, is converted to a heritability of
.50. Once converted, a difference score is calculated for each participant on each trait by subtracting the published estimate from the participant’s converted judgment and taking its absolute value. This result represents a measure of the difference between a participant’s Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 20 judgment in the relative contribution of genes and environment in differences among people on a trait of interest, and that trait’s empirical heritability estimate. Moreover, averaging the distance across all 20 traits for each participant functions as a single variable to capture the accuracy of a participant’s view of the heritability of human traits across the surveyed domains. (For a full comparison of difference scores with their directionality preserved, thus indicating over- and underestimation of each trait’s heritability, see Supplementary Figure 3).
These mean accuracy difference scores end up having surprising predictive validity. The mean, standard deviation and range of difference in accuracy for each trait is shown in Table 9.
The mean absolute difference score of all surveyed traits together is small, at .18 (SD = .05).
These scores do not correlate significantly with any FAD+ or FWI subscale of free will or determinism, but they do correlate modestly with both essentialism scales: genetic (BGD) and social (BSD) determinism at r = –.12 (p = .03) and r = –.13 (p = .02), respectively, indicating that stronger genetic and social deterministic beliefs are associated with less distance from the mean empirical heritability for these traits. Table 9 shows the association of BGD and BSD with each heritability accuracy score across traits as well as with the overall mean accuracy score.
While the significant association of mean accuracy with BSD is due to an overall pattern of weakly negative correlations, the association with BGD is due to a handful of stronger ones, especially for obesity (r = –.19), intelligence (r = –.17), personality (r = –.21) and violent behavior (r = –.28), all at p < .01 (Table 9). In this case, inaccurate estimates of the heritability of sexual orientation cannot explain the pattern.
Table 9
Accuracy scores for heritability estimates also do not correlate significantly with political orientation or ideology, nor with most surveyed demographic variables, including highest Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 21 obtained degree. They do, however, correlate significantly with genetic knowledge scores (r = –
.17, p < .01), years of education (r = –.12, p < .01), number of children (r = –.13, p < .01) and age (r = –.11, p < .01). The accuracy score only correlates significantly with number of biological children (r = –.13, p < .01), and not at all with adopted children (r = –.04, p = .31).
Controlling for age in a partial correlation does not affect the significance of years of education or number of children as a predictor, but controlling for number of children renders the relationship with age nonsignificant (p = .06).
Fig. 6
Gender is also associated with accuracy: women are significantly more accurate in their predictions of heritability than are men, with a mean distance from the published estimates of
0.17 for women (SD = 0.05) and 0.19 (SD = 0.05) for men (t(1036) = –4.8, p < .01)). Together, number of children and gender are the best predictors of accuracy of any variables measured in the sample (Fig. 6). One-way ANOVA across four groups (women with children, women without children, men with children, and men without children) supports a significant difference
(F(3, 1034) = 9.01, p < .01), as does one-way ANOVA across six groups (men without children, men with one child, men with two or more children, women without children, women with one child, and women with two or more children): F(5, 732) = 7.21, p < .01). These group comparisons strongly support these two variables as being especially salient in predictive accuracy of heritability judgments across multiple traits.
Discussion
The aim of the present study was to add a novel assessment of beliefs about trait heritability to the ongoing debate about free will and determinism from a uniquely behavioral genetic perspective. In doing so, we have collated a corpus of beliefs and assumptions of individual Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 22
Americans about genetic and environmental contributions to a variety of human traits, and have discovered that beliefs about the heritability of these traits tend to cluster into four factors comprising physical, psychological, psychiatric, and “lifestyle”-oriented traits. That these categories are significantly associated with measures of free will and determinism, as well as with outcomes such as political ideology, is informative as to how the general public forms opinions about empirically established genetic relationships. While educational attainment is modestly associated with accuracy of heritability judgments, it is unrelated to magnitude of mean heritability judgment for most of the surveyed traits, with the exceptions being restricted to physical qualities (height, eye color, etc.). Genetic knowledge scores developed from materials by Carver 2017 and Haga et al. 2013 were even less related to accuracy on heritability judgments than educational level.
Multiple surveyed measures of free will and determinism are significantly related to the magnitude of scores comprising the physical trait factor of heritability judgments. Both free will subscales (FAD+ and FWI) correlated positively with physical trait factor scores (r = .32 and r =
.25, respectively; both p < .01), while the determinism subscales tended to correlate in the negative direction with roughly equal magnitude (Table 6). The only other significant association between the FAD+ and FWI scales with the trait factors is that of the determinism subscales
(FAD-SD, FWI-DET) with the psychological trait factor, at r = .22 and r = .17, respectively (p <
.01). The key pattern to emerge is that belief in the existence of free will is related significantly to beliefs about high genetic contributions to purely physical traits, such as eye color and height, relative to other categories of traits, but belief in a deterministic universe is associated with stronger beliefs about the genetic contribution to psychological traits, such as intelligence and personality. A significant correlation between the FAD+ determinism scale and beliefs about the Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 23 heritability of behavior might be expected given that some of the FAD+ items reflect belief in the biological determinism of behavior (e.g., “Parents’ character will determine the character of their children”). The FWI determinism scale, however, deals only with philosophical notion of determinism (e.g., “Given the way things were at the Big Bang, there is only one way for everything to happen in the universe after that”). It seems that individuals who endorse the existence of a deterministic universe have a tendency to view biological factors as a way to account for behavior consistently with this worldview, although admittedly the correlations here are modest in magnitude.
J. Keller’s genetic and social determinism scales are particularly interesting, especially in relation to the accuracy of heritability judgments. It is unsurprising that the Belief in Genetic
Determinism scale (BGD) is a strong predictor of high ratings of the genetic contribution to psychological traits (r = .54, p < .01), largely because many of the items in the scale are directly assessing attitudes about the heritability of psychological traits; e.g., “Intelligence is a trait that is strongly determined by genetic predispositions” and “Many talents that individuals possess can be attributed to genetic causes” (Keller 2005). It is more surprising that the BGD is one of the only free will/determinism-related measures that significantly predicts accuracy as well (the other being BSD), with the strongest associations with accuracy (operationalized as distance from the published estimates from meta-analyses) on violent behavior (r = –.28) and personality
(r = –.21). Our data suggest that individuals who score low on the BGD scale, rather than holding a more enlightened view about the role of genetics in behavior, tend to discount empirical evidence on the heritability of behavioral traits. While Keller characterizes high scores on genetic determinism as being associated with “prejudice and in-group bias”, it is worth noting that no measure of free will or determinism predicted accuracy as well as the BGD, which was Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 24 only weakly associated (positively) with authoritarianism and (negatively) with egalitarianism in our sample.
The association of political ideology with the magnitude of heritability estimates is also deserving of attention. That political ideology is associated with beliefs about and trust in scientific topics has been documented for climate change (McCright and Dunlap 2011), evolution (Nadelson and Hardy 2015), and vaccinations, nuclear power, and genetically modified organisms (Hamilton 2015). While the present study is likely the first to document the relationship between political orientation and opinions on the heritability of specific human traits, many of the significant outcomes seem to be in line with the results of related research.
Conservatives are significantly more likely to believe that correlates of success and achievement, like intelligence and musical ability, have a greater genetic component, while liberals are more likely to think the same for traits that have been historically stigmatized, like psychiatric disorders and sexual orientation. These associations are consistent with research suggesting that moral judgments are a hallmark of the political split in the United States, and that liberals tend to endorse moral foundations built around care, compassion and reciprocity, while conservatives are equally observant of issues related to loyalty, authority, sanctity, and orderliness (Haidt and
Graham 2007; Graham, Haidt and Nosek 2009; Hirsh, DeYoung, Xu and Peterson 2010).
Despite these ideological differences in heritability rating, there is no association between overall mean accuracy (distance from published estimates) and political ideology in what amounts to a surprising sort of “balancing out.” Conservatives, moderates and liberals together produce a correlation between intuited and published heritability estimates that is among the strongest of any relationship found in the dataset (r = .77), indicating that even in the absence of genetic Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 25 knowledge, and even if social attitudes bias individual assumptions, people’s observations and intuitions about the genetic contributions to human traits are relatively informed.
That motherhood is the single strongest demographic predictor of accuracy in heritability judgments is a strong indicator that many people form their attitudes about the heritability of human traits from observations and experience, rather than from biases about social and political issues. Parents, after all, have the ability to observe firsthand the results of an empirical experiment on the heritability of human traits in their own home. They can see that their children resemble them along multiple dimensions; furthermore, a parent of multiple children can see how the shared environment does not necessarily make them alike. Mothers may be uniquely observant of their children’s abilities, needs and attributes. While it is clear that social and political biases do inform the magnitude of heritability judgments, the best predictors of these judgments are education and parenthood—an encouraging prospect indeed for the public understanding of findings from behavior genetics. Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 26
Compliance with Ethical Standards
Research was supported in part by a grant from the John Templeton Foundation as part of their
Genetics and Human Agency initiative (grant number 60780). All authors declare that they have no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 27
References
Adleberg T, Thompson M, Nahmias, E (2014) Do men and women have different philosophical
intuitions? Further data. Philosophical Psychology, 28(5):615–641
Bouchard Jr., TJ (1991) A twice-told tale: Twins reared apart. Pages 188–215 in Grove WM,
Cicchetti D, eds. Thinking Clearly About Psychology: Personality and Psychopathology,
vol. 2. University of Minnesota Press.
Buhrmester M, Kwang T, Gosling SD (2011) Amazon’s Mechanical Turk: A new source of
inexpensive, yet high-quality, data? Perspectives on Psychological Science 6(1):3–5
Carver RB, et al. (2017) Young adults’ belief in genetic determinism, and knowledge and
attitudes towards modern genetics and genomics: The PUGGS questionnaire. PLOS One
12(1):e0169808
Cashmore AR (2010) The Lucretian swerve: The biological basis of human behavior and the
criminal justice system. Proceedings of the National Academy of Sciences 107(10):4499–
4504
Chan H-Y, Deutsch M, Nichols S (2016) Free will and experimental philosophy. In Sytsma J,
Buckwalter W, eds. A companion to experimental philosophy. Wiley-Blackwell
Duckitt J, Bizumic B, Krauss SW, Heled E (2010) A tripartite approach to right-wing
authoritarianism: The authoritarianism-conservatism-traditionalism model. Political
Psychology 31(5):685–715
Duffy, D (2015) Genetics of eye color. eLS:1–9
Earman J (1986) A primer on determinism. Springer.
Feldman S (1988) Structure and consistency in public opinion: The role of core beliefs and
values. American Journal of Political Science 32(2):416–440 Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 28
Feldman S, Steenbergen MR (2001) The humanitarian foundation of public support for social
welfare. American Journal of Political Science 45(3):658–677
Gosling SD, Vazire S, Srivastava S, John OP (2004) Should we trust Web-based studies? A
comparative analysis of six preconceptions about Internet questionnaires. American
Psychologist 59:93–104
Graham J, Haidt J, Nosek BA (2009) Liberals and conservatives rely on different sets of moral
foundations. Journal of Personality and Social Psychology 96(5):1029–1046
Haga SB, et al. (2013) Public knowledge of and attitudes towards genetics and genetic testing.
Genetic Testing and Molecular Biomarkers 17(4):327–335
Haidt J, Graham J (2007) When morality opposes justice: conservatives have moral intuitions
that liberals may not recognize. Social Justice Research, 20(1):98–116
Hamilton LC (2015) Conservative and liberal views of science, does trust depend on topic? The
Carsey School of Public Policy at the Scholars’ Repository, 252.
Hawley G (2016) Right-Wing Critics of American Conservatism. University Press of Kansas.
Hirsh JB, DeYoung CG, Xu X, Peterson JB (2010) Compassionate liberals and polite
conservatives: associations of agreeableness with political ideology and moral values.
Personality and Social Psychology Bulletin 36(5):655–663
Hume D (1975/1738) A treatise of human nature. In Selby-Bigge LA, ed. 2nd ed. revised by P.H.
Nidditch. Clarendon Press.
Ismael JT (2016) How physics makes us free. Oxford University Press.
James W (1956/1884) The dilemma of determinism. In The Will to Believe: And Other Essays in
Popular Philosophy and Human Immortality. Dover. Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 29
Kan C et al. (2016) Genetic overlap between type 2 diabetes and depression in Swedish and
Danish twin registries. Molecular Psychiatry 21:903–909
Kane R, ed. (2002) Oxford handbook on free will. Oxford University Press.
Keller J (2005) In genes we trust: The biological component of psychological essentialism and
its relationship to mechanisms of motivated social cognition. Journal of Personality and
Social Psychology 88(4):686–702.
Långström N, et al (2010) Genetic and environmental effects on same-sex sexual behavior: A
population study of twins in Sweden. Archives of Sexual Behavior 39(1):75–80
Libet B (1985) Unconscious cerebral initiative and the role of conscious will in voluntary action.
Behavioral and Brain Sciences 8:529–566
McCright AM, Dunlap RE (2011) The politicization of climate change and polarization in the
American public’s views of global warming, 2001–2010. The Sociological Quarterly
52(2):155–194
Mele A (2009) Effective intentions: The Power of Conscious Will. Oxford University Press.
Mucci LA, et al. (2016) Familial risk and heritability of cancer among twins in Nordic countries.
JAMA 315(1):68–76
Nadelhoffer T, et al. (2014) The free will inventory: Measuring beliefs about agency and
responsibility. Consciousness and Cognition 25:27–41
Nadelson LS, Hardy KK (2015) Trust in science and scientists and the acceptance of evolution.
Evolution: Education and Outreach 8(9):1–9
Nahmias E, Morris S, Nadelhoffer T, Turner J (2005) Surveying freedom: Folk intuitions about
free will and moral responsibility. Philosophical Psychology 18(5): 561–584 Running head: LAY BELIEFS ABOUT HERITABILITY AND HUMAN AGENCY 30
O'Connor T (2016) Free Will. In Zalta EN, ed. The Stanford Encyclopedia of Philosophy.
Metaphysics Research Lab.
Paulhus DL, Carey JM (2011) The FAD-plus: Measuring lay beliefs regarding free will and
related constructs. Journal of Personality Assessment 93(1):96–104
Rangel U, Keller J (2011) Essentialism goes social: belief in social determinism as a component
of psychological essentialism. Journal of Personality and Social Psychology
100(6):1056–78
Tabery J (2014) Beyond Versus: The Struggle to Understand the Interaction of Nature and
Nurture. MIT Press.
Vinkhuyzen, AAE, et al. (2009) The heritability of aptitude and exceptional talent across
different domains in adolescents and young adults. Behavior Genetics 39:380–392
Weeden J, Kurzban R (2014) The Hidden Agenda of the Political Mind: How Self-Interest
Shapes Our Opinions and Why We Won’t Admit It. Princeton University Press.
Weinberg J, Nichols S, Stich, S (2001) Normativity and epistemic intuitions. Philosophical
Topics 29:429–460
Zdravkovic S. et al. (2002) Heritability of death from coronary heart disease: a 36-year follow-up
of 20,966 Swedish twins. J Intern Med 252(2):247–254
Zhu G, et al. (2004) A genome scan for eye color in 502 twin families: Most variation is due to a
QTL on chromosome 15q. Twin Research 7(2):197–210 Table 1 Percentages for both samples on a selection of demographic variables
Sample 1 Sample 2 (N = 301) (N = 740) Age range (%) 18 to 24 10.6 9.2 25 to 34 50.8 46.2 35 to 44 25.2 25.1 45 to 54 8.3 10.8 55 to 64 3.3 5.9 65+ 1.7 2.7 Sex (%) Female 42.9 48.2 Male 56.8 51.5 Other 0.3 0.3 Marital status (%) Married 37.2 41.2 Separated 1.7 0.9 Never married 54.8 49.7 Divorced 6 7.2 Widowed 0.3 0.9 Have children (%) Yes 35.5 44.3 No 64.5 55.7 Highest educational degree (%) Never completed high school 0.7 0.3 High school/GED 26.2 24.5 Vocational/technical 4.7 7.7 Community college/associate 15.6 16.6 Four-year college/university 47.5 42.3 Masters 3.7 6.4 Doctoral 1.7 2.3 Political orientation (%) Very conservative 4.7 6.6 Conservative 18.9 19.2 Moderate 24.9 25.9 Liberal 34.6 31.9 Very liberal 16.9 16.4
Table 2 Mean and standard deviation for each sample's judgments of heritability on 21 human traits
Sample 1 Sample 2 (N = 301) (N = 740) Trait M SD M SD Height 4.15 0.74 4.17 .73 Eye color 4.67 0.71 4.64 .70 Color blindness 4.51 0.81 4.41 .82 Blood group (ABO) 4.61 0.79 4.64 .73 Bipolar disorder 3.64 0.86 3.57 .84 Schizophrenia 3.61 0.88 3.55 .85 Depression 2.90 0.82 2.96 .77 Attention deficit/hyperactivity 3.54 0.91 3.45 .92 disorder (ADHD) Sexual orientation 3.35 1.16 3.41 1.20 Diabetes 2.92 0.70 3.02 .77 Alcoholism 2.72 0.81 2.71 .84 Blood pressure 3.04 0.78 3.02 .71 Obesity 2.65 0.78 2.68 .77 Heart disease 3.12 0.75 3.16 .73 Intelligence 3.37 0.86 3.32 .82 Personality 2.66 0.78 2.77 .76 Violent behavior 2.47 0.75 2.52 .74 Athleticism 3.19 0.86 3.20 .87 Musical ability 2.84 0.93 2.93 1.03 Political belief 1.67 0.89 1.72 .85 Breast cancer 3.38 0.70 3.27 .75
Note: The contribution of genetic and environmental factors to each trait was rated on a 1 (only environmental) to 5 (only genetic) scale. Higher scores indicate greater judgment of genetic contributions to variation in the trait relative to environmental contributions.
Table 3 Varimax-rotated component matrix showing each of the 21 traits' association with four extracted components
Component Trait 1 2 3 4 Height 0.64 0.02 0.02 0.17 Bipolar disorder 0.13 0.75 0.01 0.09 Eye color 0.80 0.11 0.01 –0.03 Schizophrenia 0.18 0.78 0.03 0.06 Diabetes –0.02 0.10 –0.02 0.69 Alcoholism –0.24 0.29 0.23 0.42 Obesity –0.25 0.10 0.14 0.61 Intelligence 0.11 0.09 0.71 0.01 Blood group (ABO) 0.77 0.14 0.03 –0.04 Personality –0.17 0.04 0.62 0.12 Breast cancer a 0.19 0.32 –0.10 0.35 Violent behavior –0.32 0.21 0.54 0.20 Blood pressure 0.07 0.03 0.14 0.71 Political beliefs a –0.58 –0.06 0.37 0.20 Athleticism 0.21 0.02 0.63 0.03 Depression –0.12 0.55 0.19 0.25 Color blindness 0.66 0.26 0.06 0.01 Attention deficit/hyperactivity 0.21 0.61 0.06 0.21 disorder (ADHD) Heart disease 0.17 0.03 0.10 0.68 Musical talent –0.05 0.12 0.72 0.07 Sexual orientation 0.07 0.52 0.17 –0.09
Note: Extraction method: Principal component analysis. Rotation method: Varimax with Kaiser normalization. Rotation converged in 5 iterations. Bolded values indicate predominant loading for each trait. ª Political beliefs and breast cancer did not have a single predominant loading.
Table 4 Descriptive statistics and alpha reliabilities for each scale and subscale used for measuring attitudes on the causes of human behavior
Number Alpha Alpha of items M SD (published) (sample)
Free Will and Determinism Scale-Plus (FAD+) Free will (FAD-FW) 7 3.78 0.72 .70 .83 Scientific determinism (FAD-SD) 19 2.83 0.60 .69 .88 General (SD-GEN) 5 2.85 0.79 NA .78 Environmental (SD-ENV) 5 3.00 0.71 NA .74 Biological (SD-BIO) 5 2.67 0.80 NA .79 Fatalistic determinism (FAD-FD) 5 2.48 0.73 .82 .85 Unpredictability (FAD-UNP) 8 3.38 0.64 .72 .73 Free Will Inventory (FWI) Free will (FWI-FW) 5 3.72 0.84 .80 .87 Determinism (FWI-DET) 5 2.41 0.83 .77 .82 Belief in Genetic Determinism Scale (BGD)a 10 3.05 0.69 .84 .85 Belief in Social Determinism Scale (BSD)a 10 3.56 0.54 .85 .80
Note: Alpha reliability in each published scale is compared to that of the current sample (N = 301) where available. a Published alphas are for longer scales Table 5 Correlation matrix showing the pattern of relationships among multiple scales and subscales of free will and determinism
FAD+ FWI Essentialism 1 2 3 4 5 6 7 8 9 Free Will and Determinism-plus (FAD+) 1. Free will 2. Scientific determinism –.15** 3. Environmental determinism –.14* .78** 4. Biological determinism –.18** .74** .52** 5. Fatalistic determinism .06 .39** .38** .42** 6. Unpredictability .03 .04 .15* .13* .20** Free Will Inventory (FWI) 7. Free will .83** –.17** –.16** –.19** –.04 –.03 8. Determinism –.11* .60** .49** .44** .59** .03 –.18** Essentialism 9. Belief in genetic determinism (BGD) –.08 .58** .37** .63** .33** –.01 –.09 .37** 10. Belief in social determinism (BSD) .00 .34** .51** .15** .07 .17** .00 .12* .13*
** Correlation is significant at the .01 level (2-tailed). * Correlation is significant at the .05 level (2-tailed).
Table 6 Correlations and p-values of select measures of free will and determinism with the four components of heritability judgments
Free will Determinism FAD+ FWI FAD+ (SD) FWI BGD BSD r p r p r p r p r p r p Physical trait .32 < .01 .25 < .01 –.22 < .01 –.32 < .01 –.16 .01 .21 < .01 factor Psychiatric trait –.03 .56 .00 .97 .12 .05 .01 .86 .15 .01 –.04 .45 factor Lifestyle trait .12 .04 .10 .08 .10 .09 .09 .11 .13 .03 .03 .56 factor Psychological trait –.11 .06 –.10 .07 .22 < .01 .17 < .01 .54 < .01 –.03 .61 factor
Note: Positive correlations indicate higher scores on free will/determinism scales relating positively to higher heritability ratings for the factor composites. Correlations are bolded at the p < .01 level. Abbreviations: FAD+ (SD): Free will and determinism scale (scientific determinism); FWI: Free will inventory; BGD: belief in genetic determinism scale; BSD: belief in social determinism scale
Table 7 Correlations and p-values of political ideology, authoritarianism, and egalitarianism with free will and determinism subscales and components of heritability judgments
Political ideology Authoritarianism Egalitarianism r p r p r p Free will FAD+ –.13 .03 .27 < .01 –.12 .04 FWI –.04 .49 .18 < .01 –.05 .40 Determinism FAD+ (SD) .01 .87 .00 0.99 –.02 .76 FWI –.10 .07 .20 < .01 –.06 .28 Genetic (BGD) –.09 .10 .16 .01 –.12 .04 Social (BSD) .14 .01 –.18 < .01 .11 .07 Physical trait factor .02 .46 –.16 .01 .19 < .01 Psychiatric trait factor .22 < .01 –.17 < .01 .11 .05 Lifestyle trait factor .01 .79 .09 .11 –.12 .04 Psychological trait factor –.15 < .01 .19 < .01 –.15 .01
Note: Positive correlations indicate higher scores on political ideology (liberal = higher score), authoritarianism, or egalitarianism scales relating positively to higher heritability ratings for the factor composites. Abbreviations: FAD+ (SD): Free will and determinism scale (scientific determinism); FWI: Free will inventory; BGD: belief in genetic determinism scale; BSD: belief in social determinism scale
Table 8 Mean values for heritability judgments of each of the 21 traits surveyed in a sample of 1041 participants and their associated published heritability estimates
Name of trait in Name of trait in Sample Published MTurk survey publications Study mean h2 mean h2
Height Height Polderman et al. (2015) 4.16 .80 Bipolar disorder Bipolar affective disorder Polderman et al. (2015) 3.59 .76 Eye color Eye color Zhu et al. (2004) 4.65 .92 Schizophrenia Schizophrenia Polderman et al. (2015) 3.57 .77 Diabetes Type 1 diabetes, Type 2 Polderman et al. (2015), 2.99 .64 diabetes Kan et al. (2016) Alcoholism Mental and behavioural Polderman et al. (2015) 2.71 .42 disorders due to use of alcohol Obesity Weight maintenance Polderman et al. (2015) 2.67 .63 functions Intelligence Higher-level cognitive Polderman et al. (2015) 3.34 .63 functions Blood group (ABO) a NA NA 4.63 1.00 Personality Temperament and Polderman et al. (2015) 2.74 .45 personality functions Breast cancer Breast cancer Mucci et al. (2016) 3.30 .31 Violent behavior Conduct disorders Polderman et al. (2015) 2.51 .49 Blood pressure Blood pressure functions Polderman et al. (2015) 3.03 .47 Political beliefs Societal attitudes Polderman et al. (2015) 1.70 .31 Athleticism Athletic ability b Vinkhuyzen et al. (2009) 3.20 .47 Depression Recurrent depressive Polderman et al. (2015) 2.94 .45 disorder Color blindness c NA NA 4.44 NA Attention deficit/ Hyperkinetic disorders Polderman et al. (2015) 3.48 .58 hyperactivity disorder (ADHD) Heart disease Death from coronary heart Zdravkovic et al. (2002) 3.15 .48 disease b Musical talent Musical ability b Vinkhuyzen et al. (2009) 2.91 .48 Sexual orientation Sexual orientation b Långström et al. (2010) 3.39 .28
Meta-analyses were used when possible for the most recent and robust estimate, with Polderman et al. (2015) providing over half of the estimates. Mean heritability for all participants was used when possible, though same-sex heritability was used where this was not available. a No recent large-scale twin study of blood group was found, so a value of 1.00 was assigned on the basis that ABO blood group is known to be essentially mediated entirely by genetic factors. b Mean of heritability estimates for men and women. c Color blindness was left out of accuracy analyses as no recent large-scale twin study was found.
Table 9 Range, mean, and standard deviations for absolute difference scores on each heritability judgment (N = 1041) and correlations both essentialism scales with these difference scores (N = 301)
Name of trait BGD BSD Min Max M SD r r Height .05 .80 .14 .12 –.07 –.07 * Bipolar disorder .01 .76 .18 .16 –.12 –.06 ** Eye color .08 .92 .13 .12 .07 –.19 * Schizophrenia .02 .77 .19 .16 –.15 –.08 Diabetes .11 .64 .20 .12 .00 –.06 Alcoholism .08 .58 .17 .12 .03 –.06 ** Obesity .12 .63 .24 .15 –.19 –.04 ** Intelligence .12 .63 .18 .11 –.17 –.03 ** Blood group (ABO) .00 1.00 .09 .19 .07 –.18 ** Personality .05 .55 .14 .13 –.21 –.02 Breast cancer .06 .69 .28 .16 .01 –.02 ** Violent behavior .01 .51 .16 .15 –.28 .10 Blood pressure .03 .53 .13 .13 –.09 –.06 * Political beliefs .06 .69 .22 .12 –.13 –.06 Athleticism .03 .53 .18 .15 .06 –.08 Depression .05 .55 .15 .13 .04 –.06 ADHD .08 .58 .19 .13 –.08 –.02 Heart disease .02 .52 .13 .14 .01 –.10 Musical talent .02 .52 .19 .16 –.01 .08 Sexual orientation .03 .72 .37 .22 .10 –.03
* * All traits .08 .46 .18 .05 –.12 –.13
Note: A negative correlation indicates greater accuracy; i.e., less distance from the accurate estimate. Abbreviations: BGD: belief in genetic determinism scale; BSD: belief in social determinism scale ** Correlation is significant at the .01 level (2-tailed). * Correlation is significant at the .05 level (2-tailed).
Fig. 1
Fig. 1 (caption) Likert bar plot representing heritability judgments for 21 traits. Colors of each bar represent the percentage of respondents on each possible response, from “mostly genetic” to “mostly environment”, for the surveyed 21 human traits in combined sample of 1041 participants. Percentages listed along the left represent “only environment” and “mostly environment” categories together, and percentages listed along the right represent “mostly genetic” and “only genetic” categories together. The center (gray) category represents percentage choosing “genes and environment contribute roughly the same”. Traits are ordered from those with largest combined percentage of “mostly” or “only genetic” at the top to smallest at the bottom. Fig. 2
Fig. 2 (caption) Heat-map correlation matrix showing the patterns of intercorrelation that emerged among heritability judgments of 21 human traits. Traits that clustered together in a PCA were placed adjacent to one another, appearing as four darker “squares” along the diagonal of the heat-map (see Supplementary Fig. 1 and Table 3). Fig. 3
Fig. 3 (caption) Bar graph showing standardized factor score means on each of the four trait factors (lifestyle, physical, psychiatric, and psychological), broken out by political orientation. The relatively small bars across the spectrum for the lifestyle and physical trait factors indicate their relatively small correlations with political orientation and of the individual traits that comprise them. The psychiatric and psychological trait factors, by contrast, show distinct and opposite patterns across the political spectrum that are consistent with the significant opposite-magnitude correlations between political orientation and heritability judgments for the traits that comprise these factors.
Fig. 4
Fig. 4 (caption) Scatterplot and associated regression line of mean heritability judgments for 20 human traits in an MTurk sample of 1041 participants and the published estimates of heritability for these traits from meta-analyses and large-scale twin studies. Points are color-coded to their factor membership, and selected traits of interest are labeled.
Fig. 5
Fig. 5 (caption) Scatterplot and regression lines for the mean heritability estimates across 20 human traits for both liberals (N = 512) and conservatives (N = 262) in the MTurk sample. The slope difference is represented by an r2 of .53 for liberals and .66 for conservatives.
Fig. 6
Fig. 6 (caption) Comparison of absolute mean difference scores in accuracy of heritability judgments across surveyed human traits for men, women, and full sample for those without children, one child, and two or more children. These estimates represent the magnitude of distance between each participant’s judgment of heritability and the published estimate on a 0 (only environmental factors) to 1 (only genetic factors) scale. Lower mean distance (y-axis) therefore represents better judgment of heritability across all traits. Error bars represent +/- standard error of the mean.