Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics

Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics. Nicholas Judd, Bruno Sauce & Torkel Klingberg. npj Science of Learning volume 7, Article number: 33, Dec 15 2022. https://www.nature.com/articles/s41539-022-00148-5

Abstract: Schooling, socioeconomic status (SES), and genetics all impact intelligence. However, it is unclear to what extent their contributions are unique and if they interact. Here we used a multi-trait polygenic score for cognition (cogPGS) with a quasi-experimental regression discontinuity design to isolate how months of schooling relate to intelligence in 6567 children (aged 9–11). We found large, independent effects of schooling (β ~ 0.15), cogPGS (β ~ 0.10), and SES (β ~ 0.20) on working memory, crystallized (cIQ), and fluid intelligence (fIQ). Notably, two years of schooling had a larger effect on intelligence than the lifetime consequences, since birth, of SES or cogPGS-based inequalities. However, schooling showed no interaction with cogPGS or SES for the three intelligence domains tested. While schooling had strong main effects on intelligence, it did not lessen, nor widen the impact of these preexisting SES or genetic factors.

Discussion

Schooling showed substantial and independent effects for each intelligence domain tested: cIQ, fIQ, and WM. In line with previous research, we found the raw effect of schooling on cIQ to be larger than for fIQ, though this difference was not significant13. This was also true for the relative influence of schooling compared to the effect of chronological age, as that ratio for cIQ was almost double that of fIQ (1.1 vs 0.54), showing almost near equal influences per year of schooling and age. Surprisingly, WM had the highest ratio (2.2), with the effect of schooling being more than double that of age. While this is in agreement with a previous study in younger children21, it should be interpreted with caution as the WM measure comprised of only one task59.

As expected, SES and cogPGS were highly correlated with each other, highlighting the need to isolate the independent effects of each. Both had large, independent effects on cIQ, fIQ, and WM. In a follow-up analysis, we estimated the contribution of each SES component separately. Notably, all SES components were significant for each intelligence domain, with the effects from parental education and income being similar in size while neighborhood quality was roughly half.

A child’s SES is not independent of their cogPGS31, which makes it difficult to support causal inferences of these factors as well as interpret the interaction between them (i.e., the endogeneity problem)60. This gene-environment dependence can cause spurious gene-environment interactions41,61. Our sibling analysis sheds light on this issue. We found the within-family effect of cogPGS to be roughly half of the between-family effect for cIQ, in line with the literature51,52. This indicates the presence of passive genotype-environment correlations—whereby parents create family environments consistent with their genotypes, which in turn facilitate the development of their children’s intelligence. Since we only had data from 392 families, a lack of statistical power is most likely the reason for our null findings for fIQ and WM. While our cogPGS estimate in the full sample should be interpreted with caution, previous research has shown SES to be the major source of these between-family effects51,62.

Predominant theories of GE-interplay imply a positive sign—genetically endowed cognition influences one’s proximal environment, and that environment, in turn, influences one’s cognition in continuous, reciprocal interactions, such as the multiplier theory34, the transactional model35 and the bioecological model36. In line with this, a meta-analysis on twin research found the heritability of intelligence to increase with higher SES in the United States63. But this effect is far from consistent. The same meta-analysis did not replicate this finding on data collected outside the United States. Furthermore, a large twin study found negative and null results for heritability by SES interaction regarding mathematics and reading in Florida64. Two studies using a similar polygenic marker to ours did not find evidence of GE-interplay for EA65,66, yet a study with 130,000 adults in the UK found a very small negative interaction of SES with neighborhood quality for fIQ and EA40. Crucially, the standardized effect size of this interaction was (β < 0.02 SD)—translating to less than a third IQ point throughout one’s entire life—in turn having no practical effect for the individual.

We did not find any significant interaction between schooling and SES or cogPGS. One strength of our design is that schooling is, in principle, independent from cogPGS and SES. We are not aware of any other research looking at GE-interplay with schooling. A recent study did find low PGS children in high-SES schools to continue with mathematics much longer than genetically similar children in low-SES schools39. However, we see their result as more relevant to inform a cogPGS-SES effect rather than the gene-by-schooling interaction.

We expected our interaction terms to either compensate or accelerate preexisting differences. Schooling, for example, could increase (i.e., Mathew’s effect) economic/genetic inequality or lessen these differences between children (i.e., catch-up effect). The Coleman Report, a seminal study with more than half a million students and over 3000 schools in 1966, controversially concluded schools did not contribute to widening achievement gaps between children29. Conversely, there is some evidence that schooling might lessen socioeconomic disparities between children (i.e., a catch-up effect) for cognitive skills67.

Our study indicates schooling to not be a major driving force for either increasing or decreasing differences due to SES or cogPGS. Yet, we emphasize caution in interpreting these null effects as our range of schooling (3rd–5th grade) was limited, and Bayesian analysis showed that an effect of less than 0.02 SD could not be ruled out (Fig. 3). Since any interaction effect with schooling could accumulate—that is, continue to increase each year—a very small (<0.05 SD) effect size could be of practical relevance54,55. For example, an interaction with schooling as small as 0.02 SD could accumulate over five years to 0.1 SD or roughly 30% of the largest SES main effect (i.e., cIQ = 0.29). This is, of course, a simplistic scenario assuming no counteracting mechanisms, yet it illustrates how very small effect sizes can become consequential68,69. In contrast, cogPGS–SES’ interaction is a lifelong effect and does not have the potential to accumulate in the same way. However, our sample had a slightly lower SES (Cohen’s d = −0.23) than the average for the United States, therefore, we cannot rule out an interaction at the lower tail of the SES distribution.

One limitation of this study is that the 1.1 million individuals used to estimate the cogPGS are heavily biased towards those of European descent and from higher SES areas30,70. This means our results regarding genetics should only be generalized to white populations. Furthermore, GWAS methods cannot detect certain types and sizes of GE interactions since they are intended to detect additive effects71. Another consideration is how to interpret findings with multi-trait GWAS’s—in our case cognitive ability, mathematics, and educational attainment—since one of the supplementary phenotypes could be driving the results72. The extent of this issue depends on (1) the relative sample size differences between the GWAS included and (2) the genetic correlation of these traits. In our case, there are sample size differences between educational attainment and cognitive ability, yet the very high (r ~ 0.75) genetic correlation between these traits most likely mitigates this issue73. Lastly, a strength of our study is that we controlled for ancestry-based genetic PCs in the full model, rather than just correcting cogPGS. While this means SES’s relationship with cognition is controlled for population stratification, it also brought the limitation that we had to exclude subjects without DNA, resulting in the average level of SES increasing.

We found that schooling causes relatively large increases in children’s intelligence. The two years of schooling (3rd to 5th grade) caused a larger difference in intelligence than either SES or cogPGS. However, schooling did not change the rank order of individuals’ intelligence. This was shown by the lack of significant two-way interactions between Schooling, SES, and cogPGS, although our power to detect potentially meaningful small effects for schooling was limited. Intriguingly, we did not find any interaction between SES and cogPGS, this means that children’s genetic differences do not matter more, or less, for intelligence dependent upon their SES background.

Masturbation Prevalence, Frequency, Reasons, and Associations with Partnered Sex in the Midst of the COVID-19 Pandemic: Findings from a U.S. Nationally Representative Survey

Masturbation Prevalence, Frequency, Reasons, and Associations with Partnered Sex in the Midst of the COVID-19 Pandemic: Findings from a U.S. Nationally Representative Survey. Debby Herbenick, Tsung-chieh Fu, Ruhun Wasata & Eli Coleman. Archives of Sexual Behavior, Dec 27 2022. https://link.springer.com/article/10.1007/s10508-022-02505-2

Abstract: Despite well-documented individual, relational, and health benefits, masturbation has been stigmatized and is understudied compared to partnered sex. In a US nationally representative survey of adults, we aimed to: (1) assess the prevalence and frequency of participants’ prior-year masturbation, (2) describe reasons people give for not masturbating, (3) describe reasons people give for masturbating, and (4) examine the association between masturbation frequency and actual/desired partnered sex frequency in the prior year. Significantly more men than women reported lifetime masturbation, past month masturbation, and greater masturbation frequency. The most frequently endorsed reasons for masturbating related to pleasure, feeling “horny,” stress relief, and relaxation. The most frequently endorsed reasons for not masturbating were lack of interest, being in a committed relationship, conflict with morals or values, or being against one’s religion. Among women, those who desired partnered sex much more often and a little more often were 3.89 times (95% CI: 2.98, 5.08) and 2.07 times (95% CI: 1.63, 2.62), respectively, more likely to report higher frequencies of past-year masturbation than those who desired no change in their partnered sex frequency. Among men, those who desired partnered sex much more often and a little more often were 4.40 times (95% CI: 3.41, 5.68) and 2.37 times (95% CI: 1.84, 3.06), respectively, more likely to report higher frequencies of past-year masturbation activity than those who reported that they desired no change in their current partnered sex frequency. Findings provide contemporary U.S. population-level data on patterns of adult masturbation.

Discussion

Our study provides contemporary U.S. population estimates on past-year masturbation prevalence and frequency, given that the last published estimates of masturbation were more than a decade old. We extend the literature by providing U.S. nationally representative data on reasons for, and reasons for not, masturbating, as previous studies that addressed masturbation reasons were limited to convenience samples. Consistent with prior nationally representative surveys in various countries (Das et al., 2009; Gerressu et al., 2008; Herbenick et al., 2010; Richters et al., 2014), significantly more men than women in our study reported ever having masturbated, having masturbated recently (in this case, in the past month), and masturbating more often. Given the variation between studies in terms of ages surveyed and how data are presented, we are unable to make direct comparisons in terms of proportions or rates. However, the overall pattern of men reporting greater prevalence, frequency, and recency of masturbation remains. Researchers have speculated that such gender differences may be explained by differences in sex drive or may be due to gender differences in traditional sexual scripts that normalize masturbation among boys and men while repressing or stigmatizing it among girls and women (Baumeister et al., 2001; Fischer & Traeen, 2022; Haus & Thompson, 2020). Certainly, this study confirms how common masturbation or not masturbating is among men and women despite the differences between the genders. This helps demystify the myths and misinformation about this stigmatized sexual behavior which is so often a source of guilt and shame (Coleman, 2003; Das, 2007).

A unique contribution of our study is that, in a US nationally representative survey, we examined reasons for, and reasons for not, masturbating in the prior year. In terms of reasons for not masturbating, the most common reasons endorsed were that participants were just not interested (significantly more women than men), they were in a committed relationship (significantly more men than women), or that it was against their morals, values, or religion. Another reason selected by more women than men pertained to feeling uncomfortable with one’s body, though even this was selected by relatively few women. Although our study did not examine the specific contributors to discomfort with one’s body, prior research has found that poor body image may interfere with both solo masturbation and partnered sexual expression (Dosch et al., 2016). Conversely, a recent survey of German women found that having masturbated was associated with body acceptance (Burri & Carvalheira, 2019). In the U.S., women—and especially older women, women of size, women of color, and women living with disabling conditions—may be particularly vulnerable to poorer body image due to misogyny, sexual harassment, racism, ableism, ageism as well as the self-objectification, sexual self-monitoring, and self-embarrassment these may contribute to (Gruber & Fineran, 2016; Koch et al., 2005; Leath et al., 2020; Moin et al., 2009; Salcedo, 2022; Taskin Yilmaz et al., 2019; Thompson, 2018; Thorpe et al., 2021). Masturbation can be an important way to learn about one’s body as well as to direct joy, appreciation, and pleasure toward one’s own body, sexuality, and sense of self (Dodson, 1987; Fahs & Frank, 2014; Meiller & Hargons, 2019).

Open-ended responses related to reasons for not masturbating highlighted how erectile difficulties can interfere with men’s masturbation (not just partnered sex), as well as how masturbating may be inhibited by feeling too old or tired. Participants’ comments about erectile function highlight the importance of facilitating access to educational, therapeutic, and pharmaceutical treatments for erectile difficulties, especially given the well-established benefits of masturbation. Participants’ write-in responses also demonstrated that some people simply prefer partnered sex or feel satisfied by sex with their partner and consequently don’t choose solitary forms of sexual stimulation.

In terms of reasons participants selected for having masturbated in the prior year, the reasons most often endorsed were the same for both women and men, with no significant differences. Our findings for women were largely consistent with prior research involving convenience samples of women living in the U.S., Portugal, and Hungary. These studies have found that sexual pleasure has been the most common reason given for masturbating, with fewer women in these studies endorsing reasons such as stress relief, relaxation, or to help them fall asleep (Carvalheira & Leah, 2013; Rowland et al., 2020). However, we also found support for people masturbating due to lack of a partner and having less partnered sex than they want, offering at least some support for the compensatory model of masturbation in relation to partnered sex. Other reasons related to having levels of arousal that interfere with other activities, self-exploration, and viewing masturbation as a part of overall health and well-being.

We also found that far more men than women reported that they desired greater frequency of sex. Few participants indicated wanting less frequent sex (7% women, 1% men). However, more men wanted more frequent sex (71% men, 47% women) and more women reported being generally satisfied with their sexual frequency (45% women, 27% men). In terms of the interplay between masturbation and partnered sex, we found support for the complementary model, at least among women. That is, women’s greater frequency of partner sex was associated with greater frequency of masturbation in the prior year. Yet, we also found some support for the compensatory model of masturbation and partnered sex; both women and men who desired more frequent partnered sex were more likely to masturbate more often. These findings may also simply reflect overall levels of sexual desire. Subsequent research might examine this relationship in light of participants’ sexual desire for both partnered sex and solo masturbation, their enjoyment of each, and/or their overall sexual satisfaction.

The present study was conducted in the USA during spring 2021, a time when SARS-CoV-2 vaccines were available to adults but unevenly taken up (Levenson, 2021; Salomon et al., 2021). Although there has long been a need for greater scientific attention to masturbation, this has been particularly true during the ongoing COVID-19 pandemic. During this time, public health professionals have recommended that people choose masturbation over sex with people outside their household (Government of the District of Columbia, 2021; The NYC Health Department, 2021) and, globally, people have moved in and out of COVID-related stay-at-home guidance (Huang et al., 2022; Phillips et al., 2021). Masturbation and the COVID-19 pandemic may be related in other ways, other than just risk avoidance; for example, some people (due to working from home and staying at home more often) may have masturbated less often due either to lack of privacy or because they had more opportunities for partnered sex (at least if they had a household partner). Our finding that more young adult men, compared with older men, selected COVID-19 risk reduction as a reason for masturbating aligns with a recent convenience survey of Canadian college students, showing that more than half of students had used masturbation as a risk reduction strategy during the early months of the pandemic (Gilbert et al., 2021). A U.S. national study also found small but significant self-reported increases in masturbation among men during the COVID-19 pandemic (Gleason et al., 2021) whereas a U.S. nationally representative survey conducted during the initial April 2020 lockdown found that conflict between romantic/sexual behaviors was associated with decreases in several sexual behaviors, including masturbation (Luetke et al., 2020).

Strengths and Limitations

The present study used U.S. nationally representative probability sampling which enhances the ability to generalize findings to U.S. non-institutionalized adults able to read and complete surveys in the English language. The study’s survey completion rate was good especially given declining survey completion rates in recent years (Mindell et al., 2015). Where possible, we used items from prior research and from established measures. Prior studies related to reasons for and for not masturbating have often included unique items developed for their own surveys (Bowman, 2014; Carvalheira & Leah, 2013; Rowland et al., 2020), limiting the ability for direct comparisons between studies. However, the items are close enough that the general meanings hold (e.g., related to pleasure, to help fall asleep). We were also limited in our ability to make comparisons for reasons for masturbating (or not masturbating) given that prior research had been limited to college, community, or online convenience samples and the present study collected US nationally representative data from adults ages 18 and over.

Due to budget considerations and to be attentive to participant burden, we could not use as many of the items assessing reasons for, and reasons for not, masturbating as were included in the original 62- item and 72-item scales (Young & Muehlenhard, 2011). However, by including 25 of these 134 reasons we still assessed a greater number of masturbation-related reasons than most prior studies and our study findings can be generalized to U.S. adults. We selected these items by choosing both those that were more commonly addressed in prior convenience samples as well as some reasons that reflected timely issues such as privacy (e.g., due to the COVID19 pandemic and many people moving home or living with others) as well as contemporary interests in the broader sexuality field, such as related to trying to stop watching pornography or feeling unable to stop masturbating.

Further, U.S. nationally representative surveys are limited by the small proportion of participants who identify outside the gender binary, leaving the present study focused largely on people identifying as women and men. We presented descriptive data for gender nonbinary, transgender women, and transgender men participants with the hopes that these data may still be useful to the field. To be attentive to space, we did not present masturbation rates by age, sexual orientation identity, race/ethnicity, relationship type, or other demographic characteristics; in subsequent manuscripts, we hope to examine at least some of these. Finally, our sexual behavior frequency measures were limited to behaviors within the past year. While we acknowledge that sexual behavior frequencies may ebb and flow over the course of a year, we also wanted to capture an overall frequency over a longer period of time in order for a more stable estimate rather than a shorter period time which may be prone to life circumstances (e.g., illness, traveling away from partner, childbirth).

It was also interesting to note that respondents thought it essentially as easy to change sexual preferences as it was the body mass index

Beliefs about personal change. Adrian Furnham, Ryne A. Sherman. Acta Psychologica, Volume 232, February 2023, 103821. https://doi.org/10.1016/j.actpsy.2022.103821

Abstract: In all, 510 Europeans completed an online questionnaire rating their beliefs about personal change, including the established Dweck Mindset measure. Their ratings of 27 characteristics from BMI to sexual preference factored into 5 interpretable factors labelled Personality, Beliefs and Habits, Health, Social Status and Physical. Correlation indicated beliefs about change were most related to religious beliefs but also sex and age. Dweck ratings of ability and personality growth were logically related to beliefs about change on the five factors and also to religious beliefs and self-rated optimism. Regressions indicated that being religious was the most consistent predictor about change, as well as age and education. Many beliefs about change were in direct contraction to the academic literature on the topic. Implications and limitations are acknowledged.

Keywords: AbilityChangePersonalityGrowthMindset

4. Discussion

The issue concerning the possibility of (positive) change over a life-time in personal characteristics could be dichotomised as an optimistic vs pessimistic, idealist vs realist or essentialists vs non essentialist difference (Haslam et al., 2004). Our question is why some people favour one approach over another and their correlates; what personal factors predict whether individuals believe in change? Dweck has addressed this but focusing on just two characteristics.

Probably academics are just as divided as lay-people on this issue, possibly because of the difficulty of doing research. To answer the question means getting very high quality, longitudinal data over long periods of time (up to 50 years) where a wide variety of possibly confounding, mediating and moderating factors that influence changes in behaviour at different points in time are also assessed. While some researchers have been able to tap into various existent data banks (in education, medical and military) environments, each has problems associated with it making it difficult to answer some of the fundamental questions of change (Furnham and Cheng, 2015a, Furnham and Cheng, 2015b, Furnham and Cheng, 2016, Furnham and Cheng, 2017).

In this study we looked at people's beliefs about change about a wide range of characteristics including those variables often examined by differential psychologists, namely personality and intelligence. It appears that overall they believe Neuroticism and Conscientiousness were more likely to change compared to Openness and Extraversion. They also believed both EQ and IQ were equally likely to change, while there is extensive evidence of the stability of IQ and the many and extensive failure of efforts to improve it (Deary et al., 2000). The four features they thought least likely to change were height, religious beliefs, punctuality and trait Openness while those most likely to change were physical health, wealth, EQ and looks. It was also interesting to note that respondents thought it essentially as easy to change sexual preferences as it was BMI. Again, the academic literature would suggest the opposite (Seligman, 2007). One question is where people get their ideas about change, and indeed how easy it is the change their beliefs about change. Further there is the question of how much change (fundamental vs trivial) and whether the change is long lasting. Thus diets can lead to change in BMI but often there is a clear return to the original BMI.

As may be expected, people who were more likely to believe that they had changed were more likely to believe change possible. This makes it all the more desirable to have observer data on change. Indeed, when people meet at reunions (school, university, military) after long periods they appear to be surprised how little people had changed in their personality, beliefs and behaviour compared to their physical appearance. This suggests a classic attribution error.

The factor analysis of 27 characteristics made sense and reasonably confirmed the a-priori classification of the items. The positive correlations between the five factors (0.20 < r < 0.63) with half being greater than r > 0.40 suggests a Mindset type factor: Chango-philes and Chango-phobes.

Correlations with the two Dweck Mindset factors showed an interesting difference. It was the ability growth mindset that seemed most related to the change factors, which makes sense. Some would see this as a naïve optimism that ability, and many more human characteristics are susceptible to change, rather than the concept growth which is not as clear.

Age was not strongly related to beliefs about change but two of the five correlations were significant in the expected direction proving some support for H1. No doubt religious people endorse the concept of change more than non-religious people as most religions focus on personal change and consequent redemption. This confirmed H2. Equally it was interesting to observe that political beliefs were unrelated to beliefs about change which did not confirm H3. There was strong evidence for H4 and H5 that optimistic people with high self-esteem believed most in the opportunity for change.

Lay beliefs about change is certainly relevant to all those attempting to help people change their behaviour like clinicians, coaches and counsellors. Presumably people would not seek out help if they did not believe they could undergo some sort of beneficial change though understanding their beliefs about how the process works and their part in it, as well as how much they can change are important. Thus being naively optimistic may be as much as predictor of failure as cynical skepticism about change. Indeed it is not clear whether many “self-help” change books and programmes promise much more than they can possibly deliver.

Like all studies this had limitations. It would have been desirable to know more about the participants, particularly their personal attempts at changing any aspect of their lifestyle or themselves. Similarly it would have been desirable to have actual measures of their IQ, health and personality to determine whether these are related to change beliefs.

New well-being measure considers egative affect (pain, sadness, anger and worry) & positive affect (life satisfaction, enjoyment, smiling and being well-rested)

Wellbeing Rankings. David G. Blanchflower & Alex Bryson. NBER Working Paper 30759, December 2022. DOI 10.3386/w30759.

Abstract: Combining data on around four million respondents from the Gallup World Poll and the US Daily Tracker Poll we rank 164 countries, the 50 states of the United States and the District of Colombia on eight wellbeing measures. These are four positive affect measures - life satisfaction, enjoyment, smiling and being well-rested – and four negative affect variables – pain, sadness, anger and worry. Pooling the data for 2008-2017 we find country and state rankings differ markedly depending on whether they are ranked using positive or negative affect measures. The United States ranks lower on negative than positive affect, that is, its country wellbeing ranking looks worse using negative affect than it does when using positive affect. Combining rankings on all eight measures into a summary ranking index for 215 geographical locations we find that nine of the top ten and 16 of the top 20 ranked are US states. Only one US state ranks outside the top 100 – West Virginia (101). Iraq ranks lowest - just below South Sudan. Country-level rankings on the summary wellbeing index differ sharply from those reported in the World Happiness Index and are more comparable to those obtained with the Human Development Index.

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Americans Have Found Their Happy Place. Tyler Cowen, Bloomberg. https://www.bloomberg.com/opinion/articles/2022-12-23/the-happiest-place-on-earth-hawaii-and-the-rest-of-america-too

Two economists, David G. Blanchflower of Dartmouth and Alex Bryson of University College London, have come up with a new and more intuitive way to measure well-being. The results are striking. If you consider US states as comparable to countries, 16 of the top 20 political units in the world for well-being are in the US — including the top seven.

Many happiness surveys ask individuals how satisfied they are with their lives. That is one way of phrasing the happiness question, but it has its biases. It tends to favor nations where people have a strong sense of self-satisfaction — or, if you want to put a more negative gloss on it, where the people are somewhat smug. Those are some of the studies in which Finland and Denmark come in first.

The genius of this most recent study is that it considers both positive and negative affect, and gives countries (and US states) separate ratings for the two. In other words, it recognizes there is more than one dimension to well-being. It lists four variables as part of negative affect: pain, sadness, anger and worry. Positive affect consists of four measures: life satisfaction, enjoyment, smiling and being well-rested. So life satisfaction is only one part of the measure.

One interesting result is that nations that avoid negative affect are not necessarily the same as those which enjoy the highest positive affect. Some countries — including the US — have a lot of extremes. Americans tend to go to the limit on both the upside and the downside.

Bhutan is an extreme contrast along these same lines. Measured only by positive affect, the Bhutanese are No. 9 in the world, an impressive showing. But for negative affect they rank No. 149 — in other words, they experience a great deal of negative emotion, perhaps due to the extreme hardships in their lives. Considering both positive and negative affect, they come in at No. 99, not a bad showing for such a poor country (better, in fact, than the UK’s 111.)

Denmark’s positive affect puts it only at No. 71, befitting the popular image of a country where not everyone is jumping for joy. Arkansas has a better positive affect, coming in at No. 67. But Denmark rates higher overall (38, to Arkansas’s 72) because Arkansas shows higher negative affect (87, to Denmark’s 66).

Measuring both positive and negative affect, the 10 happiest political units in the world are, in order: Hawaii, Minnesota, North Dakota, South Dakota, Iowa, Nebraska, Kansas, Taiwan, Alaska and Wisconsin. Of the top 50 places, 36 are US states (I include the District of Columbia, No. 16). China is No. 30.

Zero-sum thinking is associated with preferences for progressive economic policies in general (redistribution, affirmative action inter alia)

Zero-Sum Thinking and the Roots of U.S. Political Divides. Shahil Chinoy, Nathan Nunn, Sandra Sequeira, and Stefanie Stantcheva. Dec 2022. https://nathannunn.sites.olt.ubc.ca/files/2022/12/Zero_Sum_US_Political_Divides.pdf

Abstract: We examine the causes and consequences of an important cultural and psychological trait: the extent to which one views the world in zero-sum terms – i.e., that benefits to one person or group tend to come at the cost of others. We implement a survey among approximately 15,000 individuals living in the United States that measures zero-sum thinking, political and policy views, and a rich set of characteristics about their ancestry. We find that a more zero-sum view is strongly correlated with several policy views about the importance of government, the value of redistributive policies, the impact of immigration, and one’s political orientation. We find that zero-sum thinking can be explained by experiences of an individual’s ancestors (parents and grandparents), including the amount of intergenerational upward mobility they experienced, the degree of economic hardship they suffered, whether they immigrated to the United States or were exposed to more immigrants, and whether they had experiences with enslavement. These findings underscore the importance of psychological traits, and how they are transmitted inter-generationally, in explaining current political divides in the United States.

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We then study the potential implications of a zero-sum mindset for attitudes and views in the United States. We find that individuals who view the world in more zero-sum terms tend to support policies that redistribute income from the rich to the poor or redistribute access to resources towards disadvantaged groups. This includes redistributive policies like taxation, universal healthcare, and affirmative action for women and African-Americans. Consistent with these specific views, we also find that zero-sum thinking is associated with preferences for liberal economic policies in general and with stronger political alignment with the Democratic Party (and weaker alignment with the Republican Party).

[...]

Zero-sum thinking has also been studied in the context of in-groups and out-groups. An#alyzing which factors increase the likelihood of hosting refugees, Piotrowski et al. (2019) find that zero-sum thinking is positively correlated with patriotism (a view in which out-groups are perceived as cooperators) and a willingness to host refugees, and negatively correlated with nationalism (a view in which out-groups are perceived as competitors). On racial attitudes, Norton and Sommers (2011) document that white respondents seem to consider racism a zero#sum game in which decreases in perceived bias against Black people over time translate into higher “reverse racism” against white people. Wilkins et al. (2015) show that high-status groups (white people and men) are more likely to espouse zero-sum beliefs than low-status groups (Black people and women), especially when they feel that their own group is being discriminated against. Stefaniak et al. (2020) also show that zero-sum beliefs are more common among white respondents (the advantaged group) than among Black respondents (the disadvantaged group) and are positively correlated with supporting the status quo, i.e., negatively correlated with their willingness to become “allies” of disadvantaged groups. Our evidence on how historical exposure to enslavement in the U.S. shapes zero-sum thinking among white individuals today is in line with these findings.

Was GPT-3 a Psychopath? Evaluating Large Language Models from a Psychological Perspective

Is GPT-3 a Psychopath? Evaluating Large Language Models from a Psychological Perspective. Xingxuan Li, Yutong Li, Linlin Liu, Lidong Bing, Shafiq Joty. Dec 20 2022. https://arxiv.org/abs/2212.10529v1

Abstract: Are large language models (LLMs) like GPT-3 psychologically safe? In this work, we design unbiased prompts to evaluate LLMs systematically from a psychological perspective. Firstly, we test the personality traits of three different LLMs with Short Dark Triad (SD-3) and Big Five Inventory (BFI). We find all of them show higher scores on SD-3 than the human average, indicating a relatively darker personality. Furthermore, LLMs like InstructGPT and FLAN-T5, which are fine-tuned with safety metrics, do not necessarily have more positive personalities. They score higher on Machiavellianism and Narcissism than GPT-3. Secondly, we test the LLMs in GPT-3 series on well-being tests to study the impact of fine-tuning with more training data. Interestingly, we observe a continuous increase in well-being scores from GPT-3 to InstructGPT. Following the observations, we show that instruction-finetune FLAN-T5 with positive answers in BFI can effectively improve the model from a psychological perspective. Finally, we call on the community to evaluate and improve LLMs' safety systematically instead of at the sentence level only.

Do you remember the hype around the gut microbiome, when it was widely believed that depletion of gut bacteria in rodents fuels anxiety and affects social behavior? Ideas left in the dust.

A Systematic Review of the Effects of Gut Microbiota Depletion on Social and Anxiety-related Behaviours in Adult Rodents: Implications for Translational Research. Loreto Olavarría-Ramírez et al. Neuroscience & Biobehavioral Reviews, December 22 2022, 105013. https://doi.org/10.1016/j.neubiorev.2022.105013

Abstract: The microbiota-gut-brain axis is associated with several behaviours, including those relevant to anxiety or sociability in rodents, however, no conceptual framework has yet been available. Summary of the effects of antibiotic-mediated gut microbiota depletion on anxiety and sociability is essential to both inform further preclinical investigations and to guide translational research into human studies. The main objective is to examine the role of gut microbiota depletion on anxiety and sociability in rodents, and to consider how the findings can be translated to inform the design of research in humans. We reviewed 13 research articles, indicating significant changes in gut microbiota composition and diversity have been found in animals treated with a mix or a single antibiotic. Nonetheless, there is no consensus regarding the impact of gut microbiota depletion on anxiety-like or social behaviour. Gut microbiota depletion may be a useful strategy to examine the role of gut microbes in anxiety and sociability, but the lack of data from rigorous animal investigations precludes any definitive interpretations for a translational impact on human health.

Introduction

Anxiety patterns represent a well-known mental health issue in humans (Terlizzi and Norris 2021). Anxiety is a behavioural and physiological condition in humans and animals characterised by stress-associated feelings of tension and expectancy as well as physiological changes (Steimer 2002). In extreme cases, anxiety can be a component of severe neuropsychiatric disorders, including Generalised Anxiety Disorder (Hidalgo and Sheehan, 2012, DeMartini et al., 2019) and Major Depressive Disorder (Trivedi 2020).Another important component of human well-being and mental health is sociability. Social skills are essential to build resilience to social stress from childhood (Fenwick-Smith et al. 2018), and deficits in this ability represent a risk factor for a range of psychosocial problems and mental health issues (Uzunian and Vitalle, 2015, Turner et al., 2018, Fusar-Poli et al., 2020). In rodents, social skills are crucial in supporting life in social groups, and rodent studies have provided valuable information into social behaviour (Lee and Beery 2019). Social recognition and social memory are closely related abilities with important implications in the social structure of rodents, as they may need to recognize and remember specific individuals in order to assess how to behave toward these individuals (Lee and Beery 2019). These social elements have been useful for the development of rodent models of impaired social skills, i.e., Autism Spectre Disorder and Social Anxiety Disorder (Toth et al., 2012, Kazdoba et al., 2016, Qi et al., 2021).

The gut microbiome refers to the trillions of microorganisms including bacteria, archaea, fungi, and viruses interacting with each other within the gut (Cryan et al. 2019), and is capable of significantly participating in the bidirectional communication between the gut and the brain, suggesting the term “microbiota-gut-brain axis” (Cryan et al. 2019). In the context of the microbiome, more specific microbial communities are further defined, including the mycobiome (the collective of fungi within the microbiome (Seed 2014)), and the virome (the collective of viruses in found in the host (Liang and Bushman 2021)).

The gut microbiome has been shown to be involved in brain function and behaviour, with specific relevance to anxiety (Cryan and Dinan, 2012, Cryan et al., 2019). Varying gut microbiota composition, function, and relative abundance of specific taxa have been associated with diverse health conditions including autoimmune diseases, metabolic disorders, cancer, anxiety and sociability (Duvallet et al., 2017, Nishida et al., 2018, Sherwin et al., 2019, Simpson et al., 2021). In contrast, changes in the microbiome have also been linked to potential beneficial effects, including promotion of mental health-boosting and anti-stress actions (Dinan and Cryan, 2017, van de Wouw et al., 2018). For example, differences in the gut microbiome have been associated with improvements in depression, anxiety (Simpson et al. 2021), autism (Kang et al., 2017, Fattorusso et al., 2019), and neurological disorders like Alzheimer’s disease (Jiang et al. 2017), Huntington’s disease (Konjevod et al. 2021), and Parkinson’s disease (Sampson et al., 2016, Sun and Shen, 2018). However, despite the correlations between psychiatric disorders and the microbiome that have been highlighted, the causal role of gut-brain interactions in the pathophysiology of these disorders remains unclear.

In preclinical research, the microbiota-gut-brain axis has been experimentally addressed by using specific animal paradigms, such as germ-free (GF) animals, antibiotics (ABX), pre/probiotic supplementation (Luczynski et al., 2016, Kennedy et al., 2018), and faecal microbiota transplantation (FMT) (Gheorghe et al. 2021) to manipulate the gut microbiome and observe the consequences for brain function and behaviour. Each paradigm has particular benefits in research. For example, GF animals are born and raised in aseptic conditions to ensure the complete absence of microbes, which has facilitated understanding of the effects of gut microbiota specifically during development (Bhattarai and Kashyap 2016). Prebiotics, compounds that can induce growth of beneficial microorganisms in the gastrointestinal tract (Holscher 2017), or probiotics, live bacteria with beneficial effects to health (Azad et al. 2018), can be administered at different life stages to study their effects in the host. The ABX approach (which can involve individual antibiotics or their combination in a cocktail) is used to either significantly decrease the prevalence of specific bacteria or to induce depletion of the whole gut bacterial microbiome, without interfering with other communities, such as the mycobiome and the virome (Angelucci et al. 2019). This technique has particular translational utility given the ubiquitous global use of antibiotics (Browne et al. 2021), and may provide insight into possible consequences of antibiotic consumption on the brain. Researchers may be advised to consider this advantage of ABX studies over the germ-free or FMT approach (the latter requiring a pre-transplant antibiotic treatment), while these alternative techniques offer more consistent and complete microbiome changes.

A plethora of studies have investigated the association of gut microbiome changes in composition and diversity with anxiety (Bear et al., 2021, Foster, 2021, Simpson et al., 2021) and sociability (Sherwin et al., 2019, Vuong and Hsiao, 2019, Bellone and Luscher, 2021). Most of these investigations have been carried out in rodent models and using antibiotics to deplete the gut microbiota (Kennedy et al. 2018), and have produced variable behavioural and physiological outcomes. Because of these variable outcomes and the relative novelty of the field, it has been challenging to interpret the potential role of the microbiota-gut-brain axis in anxiety and sociability, as well as the applied implications for human mental health. Thus, it is necessary to compile and summarize the current data to discuss and interpret the consequences of microbiota depletion in anxiety-like behaviour and sociability in rodent models, and to determine the research that is yet to be done to facilitate future translatability.

Changes in gut microbiome composition and diversity tend to be measured using a few common parameters: alpha-diversity (variation within a microbiome), beta-diversity (variation between microbiomes), and relative abundances of phyla (groups with a defined similarity in 16 S rRNA genes). While these parameters are well-conceived and informative, an intestinal microbiome is a complex high-dimensional structure with many other properties, with the potential for causal relationships with the brain. For instance, the degree of disruption of a microbiome (independent of its pre- and post-intervention states) may determine its effects on the nervous system; this is supported by some apparently paradoxical effects of microbiome products on neural activity (Darch and McCafferty 2022). Equally, the pre-intervention state of a microbiome may determine whether an intervention can influence behaviour. Finally, the characteristics of a microbiome as it pertains to behaviour may depend upon the absolute abundance of a particular genus, or even species, of bacteria rather than the relative abundance of phyla and genera (Rinninella et al. 2019). These parameters are less frequently used in existing studies, perhaps due to the ease of inter-study comparison afforded by relative abundance, and the challenges of testing all 100+ species for significant differences in absolute abundance.

ABX utilise different mechanisms to either kill or prevent the growth and spread of bacteria (Hutchings et al. 2019). For instance, some ABX like ampicillin, β-lactams that inhibit the biosynthesis of the cell wall of bacteria impacting a broad spectrum of species (Peechakara and Gupta 2021). Others, like vancomycin, specifically inhibit cell wall biosynthesis of Gram-positive bacteria (Levine 2006). Depending on the hypothesis being tested in a given study, specific bacterial communities or a wider spectrum of microbial species/genera can be depleted in the gastrointestinal tract by using single ABX or a more complex cocktail. The use of ABX to investigate the role of the gut microbiome carries advantages in terms of cost, time, and specificity in comparison to the other prominent microbiota-depleted murine model, germ-free animals. First, state-of-the-art facilities are necessary to breed rodents under GF conditions for multiple generations (Bhattarai and Kashyap 2016), while the exposure to ABXs can be applied in most animal facilities with minimal infrastructure (Kennedy et al. 2018). Second, GF models are limited as translational models due to the difficulties in assessing rodent behaviour in a germ-free environment, and the substantive difference between a pre-birth through development abolition of the entire microbiome on one hand, and the types of microbiome perturbations likely to occur in humans on the other (Uzbay 2019). These are important considerations which are reflected in the higher number of studies using ABX administration compared with GF animals, supporting the aim of this review in focusing on ABX-induced microbiota depletion.

Although the specific mechanisms of how the gut microbiota communicates with the brain are just starting to be deciphered, in the last decade extensive research has demonstrated that this bidirectional communication can occur via inflammatory pathways (Rooks and Garrett 2016), vagus nerve signalling (Bravo et al. 2011), and microbiota-derived metabolites (Dalile et al. 2019). For example, an investigation comparing GF mice with specific pathogen free (SPF) mice revealed that the GF group display less anxiety-like behaviour (Neufeld et al. 2011). Another study demonstrated that the anxiety-like behaviour can be transferred through the gut microbiota via FMT (Li et al. 2019). In terms of sociability, pre-clinical studies using GF mice showed deficits in social recognition and social cognition (Buffington et al., 2016, Sgritta et al., 2019). These insights suggest that a perturbed or totally absent gut microbiome may result in altered anxiety-associated behaviours and social behaviours.

The most used behavioural tests to measure anxiety-related behaviours in rodents include the light-dark box test, the elevated plus maze test and the open field test (Lezak et al. 2017), which are based on measuring the natural avoidance behaviour of rodents towards open and illuminated areas (Holter et al. 2015). Since rodents are social beings, social recognition is critical for the structure and stability of their environment (Lacey and Solomon 2003). The three-chamber social interaction test assesses the interaction of a rodent with a conspecific and with an object, where increased preference for the former is interpreted as increased sociability (Kaidanovich-Beilin et al. 2011).

Understanding the effects of gut microbiota depletion in rodent models and their consequences for anxiety and sociability may provide valuable information about the microbiome-gut-brain axis in general, and guide translational research on the potential for microbiome interventions to modulate human anxiety and/or sociability. The aim of the present review is therefore to examine the effects of gut microbiota depletion with ABX on anxiety and sociability in rodents.

Why do Black households live in neighborhoods with much lower socioeconomic status than the neighborhoods of white households with similar incomes?

What explains neighborhood sorting by income and race? Dionissi Aliprantis, Daniel R.Carroll, Eric R.Young. Journal of Urban Economics, December 20 2022, 103508. https://doi.org/10.1016/j.jue.2022.103508

Abstract: Why do Black households live in neighborhoods with much lower socioeconomic status (SES) than the neighborhoods of white households with similar incomes? The explanation is not wealth. High-income, high-wealth Black households live in neighborhoods with similar SES as low-income, low-wealth white households. Instead, we provide evidence that many Black households prefer low-SES neighborhoods with Black residents to high-SES neighborhoods without Black residents. The variety of neighborhood SES available in a metro’s Black neighborhoods, which is typically low, drives the neighborhood SES of Black households.

Keywords: NeighborhoodIncomeWealthRaceHomophily

JEL J15J18R11R23

5 Conclusion

This paper documented new facts about neighborhood sorting in the US. It was previously known that Black and white households of similar incomes live in neighborhoods with different levels of socioeconomic status (SES). It was also previously known that the racial composition of neighborhoods affects location choices. What was not known before this paper was whether wealth or the price of neighborhood SES were omitted variables that could explain racial differences in neighborhood SES, and the extent to which racial composition affects African Americans’ neighborhood SES. We have shown that financial constraints related to wealth or the price of housing do not explain neighborhood sorting by income and race, and that race is a central determinant of the neighborhood externalities experienced by African Americans. Future research will be needed to quantify the relative importance of psychological costs and benefits, white flight, and racial discrimination. Our results draw attention to what we consider to be an under-appreciated phenomenon, the psychological costs of being “Black in white space” (Anderson (2020)). The psychological costs of living in predominantly-white neighborhoods are large enough for many African Americans to outweigh any educational, labor market, or safety benefits they might experience due to living in a higher-SES neighborhood. Interpreted in terms of this mechanism, our results provide one way of quantifying how costly it is for Black people to interact with white people. As suggested here at the level of neighborhoods, and in other studies at the levels of schools and workplaces (Fletcher et al. (2020), Ananat et al. (2020), Hellerstein and Neumark (2008)), making “white spaces” more welcoming for Black people appears to be an important step in achieving racial equality. By showing that race outweighs economic factors for neighborhood sorting in the US, this paper highlights that public policy should not be focused entirely on access and economics, but should also be designed with attention to race. In the case of generating integrated neighborhoods, the success or failure of policies will hinge on understanding precisely which factors matter the most in determining neighborhood choices. The preferred policy might be very different depending on whether neighborhood choices are driven more by discrimination in the housing market (Turner et al. (2013), Ross and Yinger (2002)); the related inertia of past practices (Courchane and Ross (2019), Nowak and Smith (2018)); information (Bergman et al. (2020)); family and social networks (B¨uchel et al. (2019), van der Klaauw et al. (2019)); racial hostility (Harriot (2019)); white flight (Shertzer and Walsh (2019), Derenoncourt (2018), Card et al. (2008), Ellen (2000)); amenities (Caetano and Maheshri (2019)); preferences for same-race neighbors or communities (Bayer and Blair (2019), Wong (2013)); or the supply of new housing (Monkkonen et al. (2020)); and the extent to which these mechanisms have changed over time (Blair (2019), Mallach (2019)).