Wednesday, September 22, 2021

If childcare was more affordable would women do less of it, or are they so keen to secure the best for their children would they still maximise hours? Across Europe, women with money to spend on outsourcing do not spend less time on childcare

“Women’s Work”: Welfare State Spending and the Gendered and Classed Dimensions of Unpaid Care. Naomi Lightman, Anthony Kevins. Gender & Society, August 16, 2021. https://doi.org/10.1177/08912432211038695

Abstract: This study is the first to explicitly assess the connections between welfare state spending and the gendered and classed dimensions of unpaid care work across 29 European nations. Our research uses multi-level model analysis of European Quality of Life Survey data, examining childcare and housework burdens for people living with at least one child under the age of 18. Two key findings emerge: First, by disaggregating different types of unpaid care work, we find that childcare provision is more gendered than classed—reflecting trends toward “intensive mothering”. Housework and cooking, on the contrary, demonstrate both gender and class effects, likely because they are more readily outsourced by wealthier individuals to the paid care sector. Second, while overall social expenditure has no effect on hours spent on childcare and housework, results suggest that family policy may shape the relationship between gender, income, and housework (but not childcare). Specifically, family policy expenditure is associated with a considerably smaller gender gap vis-à-vis the time dedicated to housework: This effect is present across the income spectrum, but is particularly substantial in the case of lower income women.

Keywords: care work, inequality, gender, social policy, comparative/cross-national


Discussion and Conclusion

In this article, we examined how gender, class, and national social expenditure (both overall and specifically on family policy) may shape the time individuals spend on unpaid childcare and housework. Building on existing large-scale and cross-national studies analyzing unpaid care (e.g., Altintas and Sullivan 2016; Esping-Andersen and Schmitt 2020; Hook 2010), we used England’s (2005) Devaluation Framework to assess the gendered and classed dimensions of unpaid care, as well as the role welfare states might play in diminishing care inequalities.

Our analysis relied on multi-level models using the 2007–2008 and 2016–2017 waves of the European Quality of Life Survey. Examining respondents living with at least one child below the age of 18 years across 29 European nations, two major findings emerged. First, we demonstrated the importance of disaggregating different types of unpaid care work, supporting prior arguments that not all unpaid care work is perceived equally. In the case of childcare provision, we found that the divides were more gendered than classed. This result supports scholarship about “intensive mothering” (e.g., England and Srivastava 2013; Schneider and Hastings 2017), which suggests that although time spent on childcare has been increasing among all parents, it has increased disproportionately for mothers within higher income households, since it serves as an important means of class reproduction (Ennis 2014; Lareau 1987). Our findings thus reinforce prior research suggesting that after-work-hours childcare is not being readily outsourced among highly educated and high-income mothers (even though many could easily afford to do so). This is typically attributed to shifting perceptions about the needs of children, cultural orientations toward mothering, and growing demands to provide children with a competitive advantage (Altintas and Sullivan 2016; Dotti Sani and Treas 2016; Nelson 2010). As Faircloth (2014) notes, this has led to mothers taking on a “God-like” role, and investing ever more time, energy, and material resources to ensure that the future opportunities of their offspring are maximized.

Conversely, in the case of housework and cooking, we found that time use varied according to both gender and household income—likely because such tasks are often considered “mundane” and are more readily outsourced to the paid care sector by wealthier women, through services such as housekeeping, dry cleaning, and prepared meals (Coltrane 2000; Dotti Sani and Treas 2016; Williams 2001). We thus found substantive differences in the time spent on indirect forms of care work, with low-income women spending the most time on these activities. This finding provides strong support for the need to disaggregate measures of unpaid care work when examining intersectional dynamics.

A major contribution of our analysis is that we provide pertinent nuance regarding the role of social policy vis-à-vis the interplay between gender, class, and unpaid care work across countries. Overall, social expenditure was found to have no clear relationship to hours spent on childcare or housework. However, when the focus was narrowed to analyze the effects of social policies specifically targeted on families (i.e., child allowances and credits, childcare supports, parental leave supports, and single-parent payments), we found a clear structuring effect on the relationship between gender, income, and housework (but not childcare). Notably, greater family policy expenditure was associated with a considerably smaller gap between the time women and men dedicated to housework; but while this relationship was present at all income levels, it was strongest at the lower end of the income spectrum—suggesting both a gendered and classed dynamic.

We attribute this finding to several factors. On one hand, family policy expenditure often results in more cash-in-hand for families, but likely does not provide enough additional funds to pay for substantive amounts of childcare for lower income households. On the other hand, cultural and social expectations surrounding childrearing may discourage many mothers from reducing time spent on childcare—but there is likely less concern about using any increased funds to reduce “menial” burdens tied to cleaning, cooking, or laundry. In this scenario, cash benefits can make a more substantial difference in the ability to outsource or reduce time spent on housework, especially for low-income women.

Taken together, our findings thus provide evidence of the role family policy expenditure can play in addressing gendered inequities in unpaid care work for all women—but especially those at the lower end of the income spectrum. In turn, these intersectional dynamics provide a powerful argument against welfare state retrenchment and suggest that investing in family policy can have meaningful impacts on reducing inequalities in unpaid care, particularly for lower income mothers.

There are nevertheless several limitations to our study, which in turn point out valuable avenues for future research. First, the data and methodological approach used here only allow us to highlight correlations; we are thus unable to say anything about the potential causal relationships and mechanisms that might lie behind our findings. Second, it is unlikely that we have revealed the full effects of family policy expenditure. Research suggests that family policies can increase women’s labor market attachment, thereby potentially shrinking the gender care gap by reducing the amount of time women can dedicate to unpaid care work (Ferragina 2020); yet these (mediated) effects are necessarily ignored in our models, since employment status and hours worked are considered to be key control variables (see, for example, Esping-Andersen and Schmitt 2020). As a consequence, we are likely missing a portion of family policy expenditure’s total effect on unpaid care work. Third, and notwithstanding our attempt to factor unobserved cross-country variation into our analysis, it may well be the case that cultural differences, for example, play an important role driving patterns of unpaid care work. Indeed, this point seems especially crucial given research suggesting that policies that are not aligned with cultural norms may only marginally affect the gendered division of unpaid care (Nakazato 2019). Fourth, because our analysis is limited to parents, the results cannot be extended to housework in general, as trends within childless households may differ substantively. Finally, our focus on disaggregated social expenditure is limited to a relatively broad group of programs—namely, family policy expenditure. Future work investigating specific types of family policies would therefore be especially valuable for further disentangling the relationships identified in this study.

To conclude, we note that our findings take on particularly acute significance in the context of the COVID-19 pandemic, which has fostered increased attention to unpaid care work. Early research suggests that this health emergency has amplified overall unpaid caring burdens within households, due to factors such as remote working conditions, a lack of childcare, increased home schooling, and heightened risks to aging populations (Oleschuk 2020). Although the precise gendered and classed dynamics of this heightened care burden are yet to be determined, emerging evidence suggests that it is women who are disproportionately undertaking this increase in labor, spurring suggestions that “the coronavirus is a disaster for feminism” (Lewis 2020). The findings in this study suggest that reinvesting in social policies that target families may be one concrete way to address this growth in unpaid care work, serving as an important step in post-pandemic recovery efforts. This, in turn, invites future research on the intersectional dynamics of unpaid care work, with particular attention to the caring burdens of Indigenous and racialized women, as a means toward reimagining a more equitable use of “human infrastructure” in the social organization of unpaid care.


Found weak support for a positive association of cognitive ability with economic conservatism that is mediated through income

Do Smarter People Have More Conservative Economic Attitudes? Assessing the Relationship Between Cognitive Ability and Economic Ideology. Alexander Jedinger, Axel M. Burger. Personality and Social Psychology Bulletin, September 22, 2021. https://doi.org/10.1177/01461672211046808

Abstract: Evidence on the association of cognitive ability with economic attitudes is mixed. We conducted a meta-analysis (k = 20, N = 46,426) to examine the relationship between objective measures of cognitive ability and economic ideology and analyzed survey data (N = 3,375) to test theoretical explanations for the association. The meta-analysis provided evidence for a small positive association with a weighted mean effect size of r = .07 (95% CI = [0.02, 0.12]), suggesting that higher cognitive ability is associated with conservative views on economic issues, but effect sizes were extremely heterogeneous. Tests using representative survey data provided support for both a positive association of cognitive ability with economic conservatism that is mediated through income as well as for a negative association that is mediated through a higher need for certainty. Hence, multiple causal mechanisms with countervailing effects might explain the low overall association of cognitive ability with economic political attitudes.

Keywords: cognitive ability, intelligence, economic attitudes, economic ideology, meta-analysis

In the present research, we investigated the association of cognitive abilities with economic attitudes by synthesizing the extant empirical evidence in a meta-analysis (Study 1) and by testing hypotheses concerning possible mechanisms underlying this association that follow from different theoretical perspectives (Study 2). Our meta-analysis provided evidence for a small positive association (r = .07) of cognitive abilities with economic conservatism, on average. However, the effect sizes and directions of the associations were very heterogeneous. The strength of the association was moderated by several methodological features of the extant studies: It tended to be more pronounced in studies that used measures of operational rather than symbolic economic ideology (or mixed scales), in studies that used probability samples of the population rather than self-selected samples, and in studies that used Turkish, British, or Scandinavian rather than North-American samples. However, it was not moderated through the type or number of items of the cognitive ability measure that was used.

In the light of the heterogeneity of the size and sign of the association of mental abilities with economic attitudes observed in Study 1, Study 2 aimed at investigating different hypotheses that have been proposed to explain the association. Here, we found support for a mediation of a positive effect of mental abilities on economic conservatism through income. This supports the self-interest hypothesis according to which higher cognitive abilities facilitate higher social status and high-status individuals are less supportive of governmental regulations of markets, and redistributive social policies because they have more to lose from these measures than low-status individuals (Johnston, 2018). We found no support for the economic sophistication hypothesis according to which a positive association of cognitive abilities with economic conservatism is mediated through economic knowledge. However, we found support for a negative effect of cognitive abilities on economic conservatism that is mediated through need for certainty. Importantly, the fact that we found support for two hypotheses proposing countervailing effects of mental abilities on economic political attitudes through different causal mechanism offers an explanation for the weak average association and the heterogeneity of the empirical evidence we observe in Study 1.

Some points concerning our investigation of causal mechanisms in the present research need to be highlighted: First, we used correlational data to test hypotheses about causal mechanisms. The mediation analyses we conducted allow for conclusions about whether the empirical data are compatible with and support specific hypotheses about causal mechanisms. However, these analyses cannot provide strong evidence for causal effects or detect unique causal mechanisms (see Fiedler et al., 2011). Second, the theoretical perspectives and hypotheses we described and tested are far from exhaustive. The central conclusion from the pattern of results of Study 2 is that there is evidence for multiple mechanisms with sometimes countervailing effects. However, other theoretical perspectives and mechanisms than the ones we focused on might also play a role in explaining the association of mental abilities with economic political attitudes.

Third, we derived very abstract hypotheses from the theoretical perspectives we introduced to test them empirically. The formulation and empirical test of abstract hypotheses served the purpose of the current research well. However, each of the theoretical perspectives entails more precise predictions concerning the causal mechanism that links cognitive abilities to economic political attitudes. For example, the epistemic needs hypothesis holds that individuals with high epistemic needs feel attracted to economic conservatism because core elements of economic conservatism are functional for satisfying these needs. While the fact that we find evidence for a negative link between cognitive abilities and economic conservatism that is mediated through epistemic needs supports this view, it is not clear whether a functional fit indeed explains the association of epistemic needs with economic conservatism. In this respect, it has been argued that a functional link between psychological needs and political attitudes exists primarily for sociocultural but not for economic attitudes (e.g., Federico & Malka, 2018Johnston & Wronski, 2015Malka & Soto, 2015). From this perspective, in contexts where social and economic conservatism are communicated as a coherent package in the political discourse, individuals with high epistemic needs who are familiar with the discourse and perceive politics as personally relevant tend to endorse economic conservatism to express their identity as conservatives rather than because economic conservatism is particularly suitable to satisfy their needs (for empirical evidence, see Jedinger & Burger, 20192020Johnston et al., 2017Malka et al., 2014).

There is much room for future research to test different theoretical assumption on specific causal links between cognitive abilities empirically. A further promising avenue for future research on the link between cognitive abilities and political attitudes lies in focusing on specific combinations of economic and sociocultural attitudes along with corresponding symbolic self-categorizations of individuals. For example, findings by Yilmaz et al. (2020) indicate that self-identified libertarians, who combine economic conservatism with liberal sociocultural views, play a crucial role in driving the association of cognitive style with economic conservatism.

Our findings should also be considered in the light of the fact that the data of the present investigation mainly encompass samples from Western, industrialized, rich, and democratic countries while cultural and national differences may have implications for the intelligence-ideology nexus. Hence, an important avenue for future research is to extend the investigation of the link of cognitive abilities with economic policy preferences to a broader set of cultural contexts.

The Role of Genetics for Survival After 80: Heritability is estimated at about 12%, lower than previously reported in older adults

What Matters and What Matters Most for Survival After age 80? A Multidisciplinary Exploration Based on Twin Data. Boo Johansson and Valgeir Thorvaldsson. Front. Psychol., Sep 22 2021. https://doi.org/10.3389/fpsyg.2021.723027

Abstract: Given research and public interest for conditions related to an extended lifespan, we addressed the questions of what matters and what matters most for subsequent survival past age 80. The data was drawn from the population-based and multidisciplinary Swedish OCTO Twin Study, in which a sample (N = 699) consisting of identical and same-sex fraternal twin pairs, followed from age 80 until death, provided detailed data on health, physical functioning, life style, personality, and sociodemographic conditions. Information concerning date of birth and death were obtained from population census register. We estimated heritability using an ACE model and evaluated the role of multiple predictors for the mortality-related hazard rate using Cox regression. Our findings confirmed a low heritability of 12%. As expected, longer survival was associated with being a female, an apolipoprotein E (APOE) e4 allele non-carrier, and a non-smoker. Several diseases were found to be associated with shorter survival (cerebrovascular, dementia, Parkinson’s, and diabetes) as well as certain health conditions (high diastolic blood pressure, low body mass index, and hip fracture). Stronger grip and better lung function, as well as better vision (but not hearing), and better cognitive function (self-evaluated and measured) was related to longer survival. Social embeddedness, better self-evaluated health, and life-satisfaction were also significantly associated with longer survival. After controlling for the impact of comorbidity, functional markers, and personality-related predictors, we found that sex, cerebrovascular diseases, compromised cognitive functioning, self-related health, and life-satisfaction remained as strong predictors. Cancer was only associated with the mortality hazard when accounting for other co-morbidities. The survival estimates were mostly in anticipated directions and contained effect sizes within the expected range. Noteworthy, we found that some of the so-called “soft-markers” remained strong predictors, despite a control for other factors. For example, self-evaluation of health and ratings of life-satisfaction provide additional and valuable information.

Discussion

In this study, we addressed the questions of what matters and what matters most for survival after age 80. We based our analyses on data from a population-based twin sample of monozygotic (identical) and same-sex fraternal (dizygotic) twins followed from age 80, until death. The fact that we conducted our analyses using a select sample of hardy survivors, born more than 100 years ago, should be considered when comparing our findings of predictions and for their relevance at younger ages. The observed median life expectancy (age at which 50% of a birth cohort is alive) for those born in Sweden during the period 1893–1913 was in the range of 65–72 years for males and for 70–79 years for females. The expectancy for the individuals in our birth cohort to be alive at age 80 and beyond was only in between 2.5–6% for males and 8.5–9.2% for females (see SCB, 2020). This remark, concerning generation and cohort differences, is important to consider in efforts to identify and determine the relative impact of various mortality-related predictors. In this respect, we may find that longevity predictors can vary in type or differ in magnitude considerably across birth cohorts, which needs to be considered when comparing findings from a sample born more than 100 years ago with data from more recent birth cohorts. Furthermore, predictors of longevity, which are informative and relevant from an early age, are not necessarily valid to predict subsequent survival for those who have survived into a later stage of life. This was evident in our study by the fact that SES and financial status no longer acted as predictors for survival, as would be expected in younger samples.

The Role of Sociodemographic for Survival

Studies typically find that SES and education act as relatively strong predictors for longevity (e.g., Stringhini et al., 2017Steptoe and Zaninotto, 2020). However, we could not replicate these findings, which likely reflect a restricted education range in our sample as well as greater homogeneity in overall socioeconomic status. Later born cohorts of late life survivors may therefore show other associations with these two common survival markers. Age at baseline was positively associated with subsequent survival. This infers that, given comparison of the hazard rate at a specific age (e.g., age 91) those that accepted study participation at later ages showed a lower expected hazard rate. This finding inform us that those who entered the study at a higher age in fact represent “the even more hardy ones” who will survive even longer than their counterparts who accepted participation at younger ages. Less surprising was our finding that women tend to live longer. For marital status, we only found that our small sample of divorced individuals showed a higher mortality risk. However, this finding needs to be replicated in samples with a higher frequency of divorced individuals, although our finding is in line with previous reports on the lethal consequences of divorce (e.g., Norgård Berntsen and Kravdal, 2012).

The Role of Genetics for Survival

The analysis revealed a heritability estimate of about 12%, which is a lower estimate than previously reported in older adults (e.g., Christensen et al., 2006). This corresponds to claims that the heritability for subsequent survival is likely to be higher in the younger age range. However, Ruby et al. (2018) reported that the heritability for birth cohorts across the 1800s and early 1900s is rather well below 10%. As expected, we could confirm the significant role of APOE status. Thus, the association with the APOE e4 allele remained in late life, as those with a e4 allele had a shorter remaining life span, compared with non e4 carriers (e.g., Wolters et al., 2019). Notably, in complementary analyses (not reported), the APOE effect was reduced (β = 0.048, SE = 0.122; exp(β) = 1.05, 95% CI [0.83, 1.33]) to non-significance when we accounted for cognitive status.

The Role of Diseases and Health Related Factors for Survival

Among the many analyzed diseases, we confirm strong expected associations for dementia, cerebrovascular disease, diabetes, Parkinson’s disease, and history of hip fracture. The effect sizes for dementia, CVD, diastolic BP, and BMI remained relatively unaffected when we controlled for comorbidities. The hip fracture effect replicates previous findings of an excess mortality risk after a hip fracture that last over many years (e.g., von Friesendorff et al., 2016). This frailty may be associated with immobility preventing a physically active and healthier lifestyle. The effects sizes for hip fracture, as well as for diabetes and Parkinson’s disease, were substantially reduced when we controlled for comorbidity (see Table 4).

More surprisingly, we found that the presence of thyroid disease predicted longer survival in our sample, which awaits further investigations, as both subclinical hypothyroidism and hyperthyroidism previously have been associated with an increased mortality risk (e.g., Ochs et al., 2008). A similar positive survival effect was found for cataract. These paradoxical findings may be explained as selection effects. We can speculate whether individuals receiving diagnosis for these conditions are more vital and more demanding for an appropriate treatment. Interestingly, the predictive value of both thyroid disease and cataract remained relatively unaffected even after controlling for all other diseases (see Table 4), which means that these unexpected results are not accounted for by comorbidities. Also, given that we accounted for cognitive status, the thyroid disease effect size remained similar (β = −0.250, SE = 0.126; exp(β) = 0.78, 95% CI [0.61, 0.99]). The effect size for cataract, however, was reduced somewhat (β = −0.092, SE = 0.091; exp(β) = 0.91, 95% CI [0.76, 1.09]).

Depression was not related to subsequent survival, which was an unexpected finding given that many studies show that depression substantially increases the mortality risk (e.g., Wulsin et al., 1999), and that late-life depression is associated with higher risk of both all-cause and cardiovascular mortality (Wei et al., 2019). A possible explanation for our finding is that our depression diagnosis is likely to reflect compromised mental health at earlier ages, rather than in later life.

Further, we found that higher diastolic blood pressure, but not systolic, was associated with a shorter survival. This is in line with previous studies showing that higher systolic blood pressure in older ages can be compensatory and in fact associated with better survival, while diastolic pressure is negatively related to all-cause mortality (e.g., Satish et al., 2001). We also found that higher BMI in fact was protective and associated with longer survival. Notably, few individuals were overweight in our sample. Our finding corresponds to previous reports of a U-shaped association between BMI and all-cause mortality (e.g., Cheng et al., 2016). In fact, when we modeled the hazard rate as a conditional function of an additional quadratic BMI component, we received the following estimate, β = 0.005, SE = 0.002; exp(β) = 1.005, 95% CI [1.003, 1.010], and a linear component, β = −0.296, SE = 0.128; exp(β) = 0.74, 95% CI [0.58, 0.96], implying a non-linear U-shaped association. A low BMI is typically found to be accompanied with an increased mortality risk which in our sample indicate compromised overall health.

Notably, cancer was not a significant predictor when we only controlled for baseline age, sex and education (shown in Table 3, with an effect size of 1.16). However, when we controlled for other health-related variables and diseases, the effect size became substantially larger, i.e., 1.38 and 1.33, respectively (see Tables 45). This finding implies a suppression effect, which may reflect the broad malignancy category with several cancer types among our cancer survivors (26%), offered life-promoting treatments. Another explanation relates to comorbidities (e.g., dementia, CVD) that initially hid the effect of cancer.

Our findings largely correspond to previous studies demonstrating differential survival related to various disease conditions in later life. The results also confirm numerous studies showing that self-rated health is an informative marker for subsequent survival. Those who evaluate and self-diagnose their health as better also tend to live longer (e.g., Lyyra et al., 2006aFeenstra et al., 2020). We may perhaps find it remarkably that self-rated health remains a relatively strong predictor of mortality (e.g., Jylhä, 2009), even when we control for multiple health related variables (seen in a comparison of effect sizes in Tables 34 where the effect size only dropped from 1,82 to 1.69). The association between self-rated health and mortality cannot be fully accounted for by individual differences in cognitive status or personality-related variable like life-satisfaction (as shown in Table 5, were the effect size dropped to 1.39). As previously emphasized, self-rated health reflects a broader assessment of own health and functioning with reference to age-fellows, rather than experiences of a disease burden (Strawbridge and Wallhagen, 1999).

The Role of Lifestyle Factors for Survival

Smoking was, as expected related to shorter survival. More interestingly, we found that self-reported intellectual engagement and social embeddedness also predicted subsequent survival, pointing toward the importance of maintaining social life and acquiring as well as preserving knowledge for making life worth living. An interesting study in this context, focusing on the valuation of life and more specifically on active attachment showed that old and very old individuals differ in terms of endorsement and with respect to what makes a life worth living. Whereas health factors were more important among the young-old, social factors were more important in the old-old group (Jopp et al., 2008). Our findings support and extend this interpretation in the context of survival.

The Role of Cognitive Health for Survival

Our cognitive status indicator revealed a clear pattern showing that those with better cognition also tended to live longer, which partly was accounted for by the fact that individuals categorized as 3–5 met the dementia criterion. Noteworthy, better self-rated memory was also positively associated with survival. It is by now repeatedly shown that cognitive impairment and decline is indicative for a shorter life span, specifically demonstrated in terminal cognitive decline trajectories for various cognitive abilities (e.g., Thorvaldsson et al., 2008).

The Role of Functional Markers for Survival

Among the functional markers, we found that the measures of grip strength and lung function were associated with subsequent survival; those with better performance on these two measures lived longer. This confirm previous findings, for example, McGrath et al. (2018), who showed that decreased handgrip strength was associated with ADL limitations and higher hazard for mortality. Our finding that better self-evaluated visual acuity was positively associated with survival is also in line with studies showing that worse visual acuity is indicative of a higher mortality rate (e.g., Freeman et al., 2005). Hearing was not a significant marker for mortality in our study, which may reflect that relatively few individuals were afflicted with serious hearing loss, preventing everyday coping and interactions in social life. Notably, when we included all the functional markers into the same model the effect size dropped for all variables. This may reflect that similar underlying neurophysiological mechanism can be responsible for the mortality-related associations across these markers, which is in line with the common cause assumption (e.g., Christensen et al., 2001) of aging degeneration.

The Role of Personality Characteristics and Life Satisfaction for Survival

Among the examined markers in this category of potential predictors, we only found that life-satisfaction to be positively associated with a longer subsequent survival. This result is in line with several studies (e.g., Sadler et al., 2012Hülür et al., 2017). However, compared with findings reported by Hülür et al. (2017), we found no associations with our measures of personal control (general or health related locus of control) and survival, which partly may reflect that those scales were only taken by a select portion of individuals, able to comprehend and return the inventories.

Multiple Predictors in Concert and Survival

A strength in the present study is that it allowed a simultaneous examination of the potential role among multiple predictors. Following the first step of identifying potential predictors, “what matters,” we then turned to the question of “what matters most”? In doing so, it is important to remember that human functioning is highly inter-related, which make it unlikely to find isolated health conditions and other markers associated with late life survival. Interestingly, we could anyhow identify that some diseases categories, for example cerebrovascular disease and dementia, remained strong predictors in preventing a more extended life span after age 80. In the same manner, we found that self-rated health to be a strong survival indicator and that life satisfaction acted as positive marker for subsequent survival in advanced ages.

Although it would seem attractive to present a ranking list in response to the question of “what matters most,” it is also important to realize that many of the candidate variables evaluated in this study were inter-correlated. Therefore, the specific effect sizes were often substantially affected by a simultaneous inclusion of several variables into the same model. In addition, scale characteristics and metric properties (such as reliability and validity), differ across measures, rendering the comparison even more difficult. We therefore hesitate to provide a detailed weight for what matters most. However, as seen in Table 5, our analyses provide strong support for a shortlist that encompasses cerebrovascular disease, cognitive status, self-rated health, and life-satisfaction, in addition to the expected survival advantage among women, non-smokers, and non-carriers of the APOE-e4 allele. Our finding of an overall heritability estimate of 12% also emphasize the importance of multiple non-genetic influences for late life survival.

Strengths and Limitations

Certain limitations and strengths merit comments. First, our sample was comprised of late life twin survivors born in the late 1800 and at the beginning of the 19th century. To test for potential selection effects due to twin ship, we compared our twin sample with a population-based community sample of non-twins largely in the same age range for health and overall functioning (Simmons et al., 1997). In this analysis, one member of each twin dyad was randomly selected. Adjustments for age, sex, and type of housing reveled significant differences only in three out of 20 comparisons, in which the twins were more advantaged in health and bio-behavioral functioning. The conclusion from this comparison was that twin pairs surviving into very late life are largely similar to a representative sample of non-twins of the same age (Simmons et al., 1997). Furthermore, the unique experiences and exposures in our select cohort born more than hundred years ago are unlikely to be similar to that of later cohorts in which the likelihood for survival have increased considerable over the years. Despite this important remark, the predictors identified in our sample are likely to be valid also for later born individuals, although this claim needs clarification in empirical studies. Second, the validity and reliability of our predictors varied, with some relatively brief indices (e.g., a medical history of having or not meeting a certain diagnostic category, without severity accounted for) while others reflected more detailed measurements (e.g., grip strength, lung function, blood pressure, and BMI). Third, our predictors do not cover all potential markers, although we originally selected them based on gerontological relevance for a broad population-based longitudinal study. Fourth, we did not examine additive or multiplicative effects of having multiple diseases (i.e., multimorbidity) which was beyond the scope of the present study.

Despite these potential shortcomings, the strength of our study refers to the fact that we were able to use a rich and comprehensive data set gathered in a population-based sample of twins examined in–person for a whole day over a broad range of variables. This allowed analyses of the overall research question of what matters for subsequent survival past age 80 as well as analysis of heritability. Of special importance is the fact that our study encompasses detailed and valid information drawn from official register data on exact date of birth, as well as date of death.

Experimental evidence for the gaze-signaling hypothesis: White sclera enhances the visibility of eye gaze direction in humans and chimpanzees

Experimental evidence for the gaze-signaling hypothesis: White sclera enhances the visibility of eye gaze direction in humans and chimpanzees. Fumihiro Kano, Yuri Kawaguchi, Hanling Yeow. bioRxiv, Sep 21 2021. https://doi.org/10.1101/2021.09.21.461201

Abstract: Hallmark social activities of humans, such as cooperation and cultural learning, involve eye-gaze signaling through joint attentional interaction and ostensive communication. The gaze-signaling and related cooperative-eye hypotheses posit that humans evolved unique external eye morphology, including exposed white sclera (the white of the eye), to enhance the visibility of eye-gaze for conspecifics. However, experimental evidence is still lacking. This study tested the ability of human and chimpanzee participants to detect the eye-gaze directions of human and chimpanzee images in computerized tasks. We varied the level of brightness and size in the stimulus images to examine the robustness of the eye-gaze directional signal against visually challenging conditions. We found that both humans and chimpanzees detected gaze directions of the human eye better than that of the chimpanzee eye, particularly when eye stimuli were darker and smaller. Also, participants of both species detected gaze direction of the chimpanzee eye better when its color was inverted compared to when its color was normal; namely, when the chimpanzee eye has artificial white sclera. White sclera thus enhances the visibility of eye-gaze direction even across species, particularly in visually challenging conditions. Our findings supported but also critically updated the central premises of the gaze-signaling hypothesis.


Specific factors and methodological decisions influencing brain responses to sexual stimuli in women

Specific factors and methodological decisions influencing brain responses to sexual stimuli in women. Sophie Rosa van’t Hof, Nicoletta Cera. Neuroscience & Biobehavioral Reviews, September 21 2021. https://doi.org/10.1016/j.neubiorev.2021.09.013

Highlights

• Several female-specific factors important for sexual arousal neuroimaging research

• Stress and mood could be assessed when analyzing on individual level

• Methodologies should focus on optimizing sexual arousal

• Sexual stimuli should be selected by women and optimal duration should be piloted

• Brain models of sexual arousal should be updated with data of women

Abstract: Most of the neuroimaging studies on sexual behavior have been conducted with male participants, leading to men-based models of sexual arousal. Here, possible factors and methodological decisions that might influence brain responses to sexual stimuli, specifically for the inclusion of women, will be reviewed. Based on this review, we suggest that future studies consider the following factors: menstrual phase, hormonal contraception use, history of sexual or psychiatric disorders or diseases, and medication use. Moreover, when researching sexual arousal, we suggest future studies assess sexual orientation and preferences, that women should select visual sexual stimuli, and a longer duration than commonly used. This review is thought to represent a useful guideline for future research in sexual arousal, which hopefully will lead to a higher inclusion of women and therefore more accurate neurobiological models of sexual arousal.

Keywords: sexual arousalwomenbrainneuroimagingfunctional magnetic resonance imagingpositron emission transmission

1. INTRODUCTION

During the last twenty years, several studies investigated the cerebral correlates of human sexual behavior, with the majority using external sexual stimuli to evoke sexual arousal (for meta-analyses and reviews, see: Stoléru et al., 2012Georgiadis & Kringelbach, 2012Poeppl et al., 2016Mitricheva et al., 2019). Human sexual arousal refers to a complex set of social, psychological, and biological processes and therefore investigation of sexual arousal requires a multi-method and an interdisciplinary approach (Woodard & Diamond, 2008).

Sexual arousal can be induced by both internal cues, represented by sexual interest, autobiographical memories, fantasies, or, simply thoughts, and external sexual stimuli. External sexual stimuli, of different sensory modalities, have been considered a reliable tool to study the brain underpinnings of sexual arousal in both men and women. Sexual arousal is usually operationalized through the measurement of genital responses and self-reported (i.e., subjective) sexual arousal. Since both genital responses and subjective sexual arousal are activated, and regulated, by brain circuits responding to internal and external stimuli, sexual arousal has been measured by functional neuroimaging as well. Modalities of functional brain imaging include functional magnetic resonance imaging (fMRI), positron emission tomography (PET), electroencephalography (EEG), and magnetoencephalography (MEG). EEG and MEG have a considerably lower spatial resolution than fMRI and PET. Since this review will focus on the brain response patterns to sexual stimuli, results of EEG and MEG will not be discussed.

A wide array of brain regions is involved in processing and experiencing sexual arousal, not surprising for a complex task involving multiple sensory modalities and several cognitive functions as focused attention, working and long-term memory, and emotional appraisal (Stoléru et al., 2012Georgiadis & Kringelbach 2012). Two recent meta-analyses showed different results regarding the brain regions involved during visual sexual stimulation (VSS) in women and men. A meta-analysis by Poeppl et al. (2016) showed small between-gender differences in brain response in subcortical areas to sexual stimuli, whereas Mitricheva et al. (2019) did not find any differences in brain response to sexual stimuli between men and women. According to Mitricheva et al. (2019), this discrepancy in the meta-analyses results could depend on the inclusion of studies using different sensory modalities sexual stimulation (visual, olfactory, and tactile stimuli).

Although there is a common assumption of large sex differences in brain responses to sexual stimuli, and the evoked sexual arousal, these meta-analyses show small or null between genders differences. However, previous behavioral and psychophysiological studies found a significantly higher level of agreement between self-reported sexual arousal and genital response in men than in women (Chivers et al., 2010). Methodological issues, such as differences in devices and procedures used, or fundamental differences, might modulate this. An fMRI study by Parada et al. (20162018) examined both self-reported sexual arousal and genital responses in relation to brain responses in both men and women. Various subregions of the parietal cortex show significant changes in brain responses corresponding to the degree of self-reported sexual arousal, with no gender differences. The strength of the correlation between brain activation and genital response shows that women had a stronger brain-genital relation than men in the insula, amygdala, posterior cingulate cortex, lateral occipital cortex, and bilateral cerebellum. Conversely, in men, no brain regions showed a strong brain-genital correlation. This study presents that fMRI studies can be an important addition to psychophysiological and behavioral research in understanding complex questions, such as the gender differences in concordance between genital response and self-reported sexual arousal.

Previous neuroimaging studies on sexual arousal have predominantly included heterosexual male participants. The recent meta-analysis by Mitricheva et al. (2019) demonstrated the inclusion of 1184 male participants in contrast to 636 female participants. Of these 1184 male participants, 1054 were heterosexual, making it the largest group to be included in neuroimaging studies to sexual arousal. Due to the large inclusion of men, one of the most recent and influential models of brain responses to sexual stimuli is based on data of male participants (Stoléru et al., 2012). The overrepresentation of male participants and overgeneralization of theories and models based on male data is not limited to neurosexology but for instance also present in animal studies (Coiro & Pollak, 2019) or clinical trials (Feldman et al., 2019Holdcroft, 2007). By including more women, but also more non-heterosexual and non-cis participants, the specificity and clinical utility of future theoretical models could be improved. Besides theoretical reasons, the larger inclusion of women could lead to a better understanding of female-specific sexual disorders and diseases (e.g., female sexual arousal disorder, genito-pelvic pain/penetration disorder).

It is not clear why there is an overrepresentation of men in previous studies. A potential reason might be female-specific factors and methodological decisions, which could be seen as an obstacle. Hence, the present review will examine factors and methodological decisions that could potentially influence brain responses to sexual stimuli when women are included in neuroimaging studies to sexual arousal and genital response. Moreover, we will assess whether previous neuroimaging studies considered these factors.