Wednesday, August 3, 2022

Environmental factors play a greater role in the etiology of psychotic experiences than genetic factors: some groups of individuals with seemingly low genetic risk for psychotic experience may develop them if exposed to high levels of environmental risk

Taylor MJ, Freeman D, Lundström S, Larsson H, Ronald A. Heritability of Psychotic Experiences in Adolescents and Interaction With Environmental Risk. JAMA Psychiatry. Published online August 03, 2022. doi:10.1001/jamapsychiatry.2022.1947

Key Points

Question  Are psychotic experiences less heritable for adolescents who experience more environmental risk factors?

Findings  In a twin study of 4855 twin pairs aged 16 years from the UK, the relative importance of genetic influences on certain psychotic experiences diminished with more exposure to environmental risk factors. A similar pattern of results was observed in an independent sample from Sweden (N = 8568 pairs).

Meaning  These results suggest gene-by-environment interactions in relation to psychotic experiences; some groups of individuals with seemingly low genetic risk for psychotic experience may develop them if exposed to high levels of environmental risk, in a similar manner to clinical observations in relation to schizophrenia.


Importance  Genetic risk factors are known to play a role in the etiology of psychotic experiences in the general population. Little is known about whether these risk factors interact with environmental risks for psychotic experiences.

Objective  To assess etiological heterogeneity and exposure to environmental risks associated with psychotic experiences in adolescence using the twin design.

Design, Setting, and Participants  This twin study, conducted from December 1, 2014, to August 31, 2020, included a UK-based sample of twin pairs aged 16 years. This investigation evaluated the extent to which the genetic variance underlying psychotic experiences and the magnitude of the heritability of psychotic experiences was moderated by exposure to 5 environmental risk factors (bullying, dependent life events, cannabis use, tobacco use, and low birth weight). Psychotic experiences were assessed by 5 self-reported measures and 1 parent-reported measure. Participants’ exposure to environmental risks was assessed at birth and age 12 to 16 years. Structural equation models were used to assess differences in the variance in and heritability of psychotic experiences across these exposures, while controlling for gene-environment correlation effects. Analyses were repeated in an independent Swedish sample. Data analyses were performed from September 1, 2018, to August 31, 2020.

Main Outcomes and Measures  Primary outcome measures were exposure to environmental factors, as measured by a composite score, and psychotic experiences.

Results  A total of 4855 twin pairs (1926 female same-sex pairs, 1397 male same-sex pairs, and 1532 opposite-sex pairs) were included from the Twins Early Development Study (TEDS), and 6435 twin pairs (2358 female same-sex pairs, 1861 male same-sex pairs, and 2216 opposite-sex pairs) were included from the Child and Adolescent Twin Study in Sweden (CATSS). Mean age of twins from TEDS was 16.5 years. Mean age of twins from CATSS was 18.6 years. More exposure to environmental risk factors was associated with having more psychotic experiences. The relative contribution of genetic influences to psychotic experiences was lower with increasing environmental exposure for paranoia (44%; 95% CI, 33%-53% to 38%; 95% CI, 14%-58%), cognitive disorganization (47%; 95% CI, 38%-51% to 32%; 95% CI, 11%-45%), grandiosity (41%; 95% CI, 29%-52% to 32%; 95% CI, 9%-48%), and anhedonia (49%; 95% CI, 42%-53% to 37%; 95% CI, 15%-54%). This pattern was replicated for the measure of psychotic experiences in the independent Swedish replication sample. The heritability of hallucinations and parent-rated negative symptoms remained relatively constant.

Conclusions and Relevance  Findings of this twin study suggest that environmental factors play a greater role in the etiology of psychotic experiences than genetic factors. The relative importance of environmental factors is even higher among individuals exposed to environmental risks for psychotic experiences, highlighting the importance of a diathesis-stress or bioecological framework for understanding adolescent psychotic experiences.


In this cohort study, we tested whether the heritability of adolescent psychotic experiences changes with exposure to environmental risks associated with psychotic experiences. Results suggest a gene-by-environment interaction for paranoia, hallucinations, cognitive disorganization, grandiosity, and anhedonia. The findings were replicated in an independent Swedish sample, thus lending robustness to our results. Our study thus suggests differences in heritability of certain psychotic experiences that may be associated with environmental exposures.

Specifically, there was an observed reduction in heritability of psychotic experiences in the presence of environmental exposures. These results are consistent with a bioecological framework, which would predict that more favorable environments would lead to higher heritability.20Our results run contrary to a diathesis-stress pathway to psychotic experiences, which would predict that environmental risks trigger a genetic susceptibility to a given disorder and would thus lead to higher heritability of a phenotype in the presence of environmental risks.20

It is also important to put our results in the context of prior studies of gene-by-environment interaction in relation to psychotic experiences. One study33 reported that the association between environmental risks and psychotic experiences increased among individuals with a family history of psychosis. Although this finding also supports gene-by-environment interaction, the results are somewhat different than what would be expected from our analyses because our analyses suggest that genetic factors were less salient in the presence of environmental exposures. Moreover, other studies34,35 have found no evidence of gene-by-environment interaction for psychotic experiences. Methodological differences may underlie these discrepancies. First, family history is not the same as genetic influence because family history includes a combination of genetic and shared environmental factors. Second, we focused on a composite exposure score comprising 5 environmental factors; prior studies have focused on more specific factors, including childhood physical abuse35 and trauma.34 Third, we focused on 6 specific psychotic experiences here, whereas prior studies used single measures. Finally, it is important to note that prior studies focus specifically on the interaction between genetic risk for psychotic experiences based on family history, by contrast with the current study that used the twin design. In our study, we tested whether heritability differed dependent on exposure to environmental risks. Heritability is a population statistic, and as such, an approach based on calculating heritability is somewhat different from an approach based on using family history as a proxy for individual genetic risk.

On a clinical level, it is first important to clarify that we focused on a young sample, who were aged 16 or 18 years. Many of these individuals will be too young to have been diagnosed with a psychotic disorder, but it is known that psychotic experiences in this age group can lead to severe clinical conditions in some individuals. Nonetheless, clinically it is often noted that many individuals with schizophrenia do not have a family history of schizophrenia. Indeed, although the relative risk for schizophrenia is increased among relatives of individuals diagnosed with schizophrenia, most relatives of individuals with schizophrenia do not develop schizophrenia.36 Our results extend these observations to adolescent psychotic experiences and indicate that they may develop in a variety of contexts. Specifically, our results suggest that psychotic experiences may be prevalent in populations with a high degree of exposure to environmental risks associated with psychotic experiences. Indeed, there is substantial conjecture that psychotic experiences can be reached through multiple pathways, such as pathways that are more based on genetic propensity and others that are more down to environmental risks; however, to our knowledge, this is one of the first studies to provide replicable empirical findings on this topic.

The previously mentioned arguments should be tempered, however, for certain types of psychotic experiences. Although we observed that the heritability differed according to environmental exposure for some psychotic experiences, the heritability was more consistent for hallucinations and negative symptoms. This is important from a clinical perspective, given that negative symptoms are thought to be particularly predictive of subsequent mental illness. As such, these results lend further weight to the argument that the etiology of psychotic experiences may differ according to specific subtypes of psychotic experience.8

Strengths and Limitations

Strengths of our study included the use of 2 representative, population-based samples in different countries. In 1 sample, we measured 6 different specific psychotic experiences, enabling us to consider gene-by-environment interactions for different psychotic experiences. There were several limitations, however. Although we employed data from both the UK and Sweden, we still only focused on 2 European countries. Many of our exposures may differ in prevalence across the world, and it would therefore be useful to assess whether similar results emerge in countries with more environmental variability than the UK and Sweden. Our measures of tobacco and cannabis use were more brief than our other measures. Future studies should use more detailed measures. We also created a composite score that involved counting the number of exposures each participant had undergone, which included summing exposures that may have different etiologies or mechanisms underlying their association with psychotic experiences. Further, although our model has controlled for gene environment correlation, we recognize that birth weight is a complex phenotype influenced by parent and child genetics and prenatal environment.

It is further important to be aware that the environmental composite is not identical across TEDS and CATSS. The life events scale, for example, includes items about increasing numbers of quarrels with parents in CATSS, which are not covered in TEDS. Percentile-based cutoffs were used to define low birth weight; birth weight was lower in TEDS than CATSS on average, and therefore, this led to heavier twins being captured in CATSS. Exclusion criteria also differed between samples; individuals with extreme obstetric complications were excluded from TEDS but not CATSS. The fact that we observed similar results between CATSS and TEDS gives us confidence that these differences did not strongly influence our results; however, they should nevertheless be interpreted with these differences in measure in mind. Finally, twins are generally born lighter than singletons. We included birth weight as an exposure here, and hence, individuals with very low birth weight may have been overrepresented in our sample. However, studying birth weight in twins here is unlikely to create any issues for generalizability for 2 reasons. First, our modeling analyses were focused on variance rather than mean differences. Second, twins were compared with twins in the design (not singletons); as such, modest mean differences between singletons and twins in birth weight did not affect the findings.

Widely cited psychology study, suggesting that winners of competitions are more likely to cheat subsequently, fails to replicate

Does competitive winning increase subsequent cheating? Andrew M. Colman, Briony D. Pulford, Caren A. Frosch, Marta Mangiarulo and Jeremy N. V. Miles. Royal Society Open Science, Volume 9, Issue 8, August 3 2022.

Abstract: In this preregistered study, we attempted to replicate and substantially extend a frequently cited experiment by Schurr and Ritov, published in 2016, suggesting that winners of pairwise competitions are more likely than others to steal money in subsequent games of chance against different opponents, possibly because of an enhanced sense of entitlement among competition winners. A replication seemed desirable because of the relevance of the effect to dishonesty in everyday life, the apparent counterintuitivity of the effect, possible problems and anomalies in the original study, and above all the fact that the researchers investigated only one potential explanation for the effect. Our results failed to replicate Schurr and Ritov's basic finding: we found no evidence to support the hypotheses that either winning or losing is associated with subsequent cheating. A second online study also failed to replicate Schurr and Ritov's basic finding. We used structural equation modelling to test four possible explanations for cheating—sense of entitlement, self-confidence, feeling lucky and inequality aversion. Only inequality aversion turned out to be significantly associated with cheating.

3. Discussion

Schurr & Ritov's [1] experiments were severely underpowered and vitiated by other design and methodological problems. In particular, their basic finding that competitive winning is associated with subsequent cheating was based on a study in which participants were not assigned randomly to experimental and control treatment conditions. Our study 1 replicated Schurr and Ritov's study as closely as possible with adequate power and random assignment to experimental and control conditions. We observed significant levels of cheating in both experimental and control conditions but failed to replicate Schurr and Ritov's basic finding of higher cheating by winners, although the experimental manipulation of winning or losing in both of our experiments was identical to Schurr and Ritov's. We also found no evidence for any significant effect of competitive losing on cheating in the subsequent game of chance.

In study 2, we tested the hypotheses that competitive winning or losing is associated with subsequent cheating in an even larger experiment, conducted online, with participants assigned randomly to winning, losing, paired control, and unpaired control treatment conditions. Once again, we observed significant levels of cheating in all treatment conditions but found no evidence to support the hypotheses that either winning or losing is associated with subsequent cheating. There was no significant difference in cheating between our paired and unpaired control conditions—whether cheating was associated with money being taken from another participant or from the experimenter.

This study also included an investigation, using SEM, to test the hypotheses that winning is associated with a latent variable that we labelled 'pride', indicated by self-confidence, a feeling of luckiness, and a sense of entitlement, and that pride is associated with subsequent cheating, or that losing is associated with a latent variable of 'shame', indicated by a sense of entitlement and inequality aversion, and that shame is associated with subsequent cheating. We measured all the indicator variables with psychometric scales that showed high reliability in our study, and the only significant association that emerged was between inequality aversion and cheating. This suggests that participants who were least inequality-averse were most likely to cheat in the coin-flip game, whether they had won or lost the previous competitive perceptual task. The association of inequality aversion with cheating was not strong, but it is worth investigating experimentally. It may reflect a more general sense of fairness among participants who are inequality averse. If those who value fairness strongly tend to be inequality averse and also construe cheating as a form of unfairness, the association would be explained, but that explanation requires further experimental evidence.

One key question that needs to be addressed is why the results of both of our studies failed to replicate Schurr & Ritov's [1] basic finding that competitive winning is associated with subsequent cheating in a game of chance. One possibility is that Schurr and Ritov's finding, based as it was on a severely underpowered study without proper random assignment to experimental and control groups, cannot validly be inferred from their results. A second possibility relates to their unusual methodology, in which half the participants in every testing session were randomly assigned as passive participants, whose only role was to receive the money that the other half—the active participants—left behind after taking what they claimed was owing to them after the dice game. In our studies, the participants were told that the money that they left behind would go to ‘the other person you are paired with’, with the implication that it was one of the other participants, and in that sense, from the participants' point of view, it was similar to what Schurr and Ritov's participants believed. However, the active participants in Schurr and Ritov's experiment believed that the money they left would go to others who had done absolutely nothing in the experiment, whereas the participants in our replications could have believed that the money would go to others who were fully participating. All of Schurr and Ritov's participants who rolled the dice were told that ‘The rest of the money will go to one of the participants sitting in the lab who did not play the two-dice-under-a-cup game’ [1, p. 1757]. This might possibly explain the failure of our experiments to replicate Schurr and Ritov's basic finding if their winners, in contrast to ours, believed that the recipients of money left behind were more deserving of being cheated, but that would suggest that the basic finding applies only in the artificial context of their experimental setup or in very limited and unusual circumstances. In everyday life, people who cheat rarely, if ever, know that their victims have done nothing to earn the money out of which they are being cheated. A third possibility is that the discrepancy between Schurr and Ritov's findings and ours arises from a cross-cultural difference between students in Israel and the UK; but we are unaware of any evidence that might support that interpretation, it is very unlikely given that Israel and the UK are both WEIRD (western, educated, industrialized, rich, democratic) cultures [36], and if correct it would severely limit the generality of Schurr and Ritov's basic finding (and also, by symmetry, our own basic finding).

Given the published evidence that more cheating tends to occur in online than laboratory studies [26,27], it is worth noting that we found no such difference. In study 1, incentives were lower than in study 2 and participants cheated, on average, by 31p out of a possible maximum of £3.00 (10.3%), while in study 2 they cheated by £3.25 out of a maximum of £50.00 (6.5%). The incentives were much greater in study 2, therefore participants' cheating translated into greater monetary terms in study 2. In our laboratory-based study 1, using Cohen's [11] index of effect size, the overall effect size of cheating was d = 0.46 and in our online study 2 it was d = 0.42. The finding of such a negligible difference can perhaps be explained by the fact that the dice-under-a-cup game that we used in the laboratory in study 1 provides an opportunity for cheating that seems almost entirely ‘safe’, in the sense that it would be impossible to detect a particular instance of cheating. If that interpretation is right, then our online experiment, in which the corresponding task was a coin-flip task, may not have provided a significantly greater sense of security, and participants may have felt equally disinhibited from cheating in both experiments. Thus the general cheating that we found across all conditions would be in line with recent evidence that cheating tends to occur particularly when it is unobservable by the experimenters [37].

Our studies have not provided much enlightenment as to what leads some people to cheat. In both studies, cheating occurred at a low but significant level in all treatment conditions, and the only psychometric variable that correlated significantly with cheating was inequality aversion. Our SEM revealed only one path that reached statistical significance, from shame to number of heads claimed and hence cheating. One of the indicators of shame was inequality aversion. Further research is clearly required to determine whether inequality aversion is indeed causally related to cheating and if so why. One possibility is that inequality aversion is associated with a more general concern for fairness and that people who value fairness are less likely to cheat because they perceive cheating as a form of unfairness, but without further evidence this interpretation remains speculative.

The aim of study 2 was to discover variables, possibly but not necessarily including competitive winning or losing, that might explain cheating in a subsequent game of chance. The SEM should reveal whether, and if so how, winning or losing is implicated. We hypothesized that sense of entitlement, self-confidence, personal luckiness and inequality aversion might help to explain cheating. For example, if Schurr & Ritov [1] were right, then winning should be associated with sense of entitlement and sense of entitlement should be associated with cheating. Sense of entitlement is interpreted by the authors of the scale that we used to measure it [31] as a personality trait, and we should perhaps expect a personality trait to be largely unaffected by an experimental manipulation such as winning or losing. However, the SEM does not require winning or losing to play any part in the potential relationship of any of the other variables to cheating. For example, we might have found that trait sense of entitlement is associated with cheating, irrespective of any association with winning or losing, just as we did, in fact, find that inequality aversion is associated with cheating without any significant association with winning or losing.


Contra Saez, Piketty et al., in a world in which ideas drive GDP, maximizing the welfare of the middle class and below requires a lower, not higher top tax rate

Taxing Top Incomes in a World of Ideas. Charles I. Jones. Journal of Political Economy, Aug 2022.

Abstract: This paper considers top income taxation when (i) new ideas drive economic growth, (ii) the reward for successful innovation is a top income, and (iii) innovation cannot be perfectly targeted by a research subsidy—think about the business methods of Walmart, the creation of Uber, or the “idea” of Amazon. These conditions lead to a new force affecting the optimal top tax rate: by slowing the creation of new ideas that drive aggregate GDP, top income taxation reduces everyone’s income, not just income at the top. This force sharply constrains both revenue-maximizing and welfare-maximizing top tax rates.

Abstract of a 2019 version, which refers to Saez by name: This paper considers the taxation of top incomes when the following conditions apply: (i) new ideas drive economic growth, (ii) the reward for creating a successful innovation is a top income, and (iii) innovation cannot be perfectly targeted by a separate research subsidy --- think about the business methods of Walmart, the creation of Uber, or the "idea" of These conditions lead to a new force affecting the optimal top tax rate: by slowing the creation of the new ideas that drive aggregate GDP, top income taxation reduces everyone's income, not just the income at the top. When the creation of ideas is the ultimate source of economic growth, this force sharply constrains both revenue-maximizing and welfare-maximizing top tax rates. For example, for extreme parameter values, maximizing the welfare of the middle class requires a negative top tax rate: the higher income that results from the subsidy to innovation more than makes up for the lost redistribution. More generally, the calibrated model suggests that incorporating ideas as a driver of economic growth cuts the optimal top marginal tax rate substantially relative to the basic Saez calculation.

[Some can admit to the unavoidability of going downwards, to a less rich world, if we want to redistribute and have no upper class...: Revolutionaries in societies that used 1/4 as much energy as we do thought communism right around the corner; let's get the abundance that matters (everyone be free to pursue learning, play, sport, amusement, companionship, & travel)]

1. Introduction

This paper considers the taxation of top incomes when the following conditions apply: (i) new ideas drive economic growth, (ii) the reward for creating a successful new idea is a top income, and (iii) innovation is broad-based and cannot be perfectly targeted by a separate research subsidy.

The classic tradeoff in the optimal taxation literature is between redistribution and the incentive effects that determine the “size of the pie.” But in most of that literature — starting with Mirrlees (1971), Diamond (1998), Saez (2001) and Diamond and Saez (2011) — the “size of the pie” effects are relatively limited. In particular, when a top earner reduces her effort because of a tax, that reduces her income but may have no or only modest effects on the incomes of everyone else in the economy.

In constrast, I embed the optimal tax literature in the idea-based growth theory of Romer (1990), Aghion and Howitt (1992), and Grossman and Helpman (1991). According to this literature, the enormous increase in living standards over the last century is the result of the discovery of new ideas — perhaps by a relatively small number of people. To the extent that top income taxation distorts this innovation, it can impact not only the income of the innovator but also the incomes of everyone else in the economy.

The nonrivalry of ideas is key to this result and illustrates why incorporating physical capital or human capital into the top tax calculation is insufficient. If you add one unit of human capital or one unit of physical capital to an economy — think of adding a computer or an extra year of education for one person — you make one worker more productive, because these goods are rival. But if you add a new idea — think of the computer code for the original spreadsheet or the blueprint for the electric generator — you can make any number of workers more productive. Because ideas are nonrival, each person’s wage is an increasing function of the entire stock of ideas. A distortion that reduces the production of new ideas therefore impacts everyone’s income, not just the income of the inventor herself.

A standard policy implication in this literature is that it may be optimal to subsidize formal R&D, and one could imagine subsidizing research but taxing top incomes as a way to simultaneously achieve both efficient research and socially-desirable redistribution. Instead, we consider a world with both basic and applied research. Basic research uncovers fundamental truths about the way the world works and is readily subsidized with government funding. Applied research then turns these fundamental truths into consumer products or firm-level process innovations. This is the task of entrepreneurs and may not be readily subsidized as formal R&D. Think about the creation of Walmart or, organizational innovations in the health care and education industries, the latest software underlying the Google search engine, or even the creation of nonrival goods like a best-selling novel or the most recent hit song. Formal R&D is a small part of what economists would like to measure as efforts to innovate. For example, around 70% of measured R&D occurs in the manufacturing industry. In 2012, only 18 million workers (out of US employment that exceeds 130 million) were employed by firms that conducted any official R&D. 1 According to their 2018 corporate filings, Walmart and Goldman-Sachs report doing zero R&D.

The idea creation and implementation that occurs beyond formal R&D may be distorted by the tax system. In particular, high incomes are the prize that motivates entrepreneurs to turn a basic research insight that results from formal R&D into a product or process that ultimately benefits consumers. High marginal tax rates reduce this effort and therefore reduce innovation and the incomes of everyone in the economy. Taking this force into account is important quantitatively. For example, consider raising the top marginal tax rate from 50% to 75%. As we discuss below, in the United States, the share of income that this top marginal rate applies to is around 10%, so the change raises about 2.5% of GDP in revenue before the behavioral response. In the baseline calibration, such an increase in the top marginal rate reduces innovation and lowers GDP per person in the long run by around 6 percent. With a utilitarian welfare criterion, this obviously reduces welfare. But even redistributing the 2.5% of GDP to the bottom half of the population would leave them worse off on average: the 6% decline in their incomes is not offset by the 5% increase from redistribution. In other words, unless the social welfare function puts disproportionate weight on the poorest people in society, raising the top marginal rate from 50% to 75% reduces social welfare.

We consider various revenue- and welfare-maximizing top tax rate calculations, first ignoring the effect on innovation and then taking it into account. For a broad range of parameter values, the effects are large. For example, in a baseline calculation, the revenue-maximizing top tax rate that ignores the innovation spillover is 92%. In contrast, the rate that incorporates innovation and maximizes a utilitarian social welfare function is just 22%. Moreover, if ideas play an even more significant role than assumed in this baseline, it is possible for the optimal top income tax rate to turn negative: the increase in everyone’s income associated with subsidizing innovation exceeds the gains associated with redistribution.

Importantly, however, the point of this paper is not to estimate “the” optimal top income tax rate. Such a calculation involves many additional considerations documented in the existing literature (reviewed below) that are omitted from the analysis here. Instead, the point is that future work aimed at calculating such a number will certainly want to explicitly consider the effects of top income taxation on the creation of new ideas. They appear to be quantitatively important.

The remainder of the paper is organized as follows. After a brief literature review, Section 2 lays out the steady-state of a rich dynamic growth model and considers the top tax rate that maximizes revenue, along the lines of Diamond and Saez (2011) and others. Section 3 then considers the tax scheme that maximizes the welfare of the “bottom 90%” or the median voter (they are the same people here). This turns out to matter quantitatively: the planner cares about distorting the creation of ideas not merely because it affects the revenue that can be earned by regular workers, but because it affects their consumption and economic growth directly. This setup is especially convenient for two reasons. First, it yields a nice closed-form solution. Second, it allows us to remain agnostic about the source of the behavioral tax elasticity for top earners: whether this comes from an effort choice or an occupational choice or from something else is irrelevant; we just need to know the elasticity.

Section 4 goes further and finds the tax system that maximizes a utilitarian social welfare function. While this objective function is obviously of interest, the solution does not admit a closed-form expression. In addition, we must be explicit about the nature of the behavioral tax elasticity for top earners, which makes the model less general. Section 5 discusses additional results and extensions, including empirical evidence on growth and top income taxation. Finally, Section 6 builds the full dynamic growth model that nests the simple model as a special case in the steady state. 

Research indicates that people will behave in ways that are consistent with the genes they believe they possess; it is applicable too to the context of risk-taking

Genetic Risk Information Influences Risk-Taking Behavior. Ryan Wheat, Matthew Vess and Patricia Holte. Social Cognition, Vol. 40, No. 4. August 2022.

Abstract: Research indicates that people will behave in ways that are consistent with the genes they believe they possess. We examined this tendency in the context of risk-taking. We predicted that bogus genetic testing results indicating a propensity for risk-taking would cause participants to demonstrate riskier behavior. Participants submitted saliva tests and were randomly assigned to receive bogus genetic feedback indicating high propensity or low propensity for risk-taking. They then completed a standardized measure of risk-taking behavior. Results showed that those who received feedback indicating they were genetically disposed to risky behavior demonstrated higher risk-taking behavior than those who received feedback indicating that they were genetically disposed to risk aversion. These findings extend work on genetic feedback effects to a new domain and further reveal the ways that genetic feedback shapes behavior independent of one's actual genetic propensities.