The Sexual Mind: Exploring the Origins of Arousal

The Sexual Mind: Exploring the Origins of Arousal. Osmo Kontula, May 2021 (Finnish 2017). https://www.vaestoliitto.fi/uploads/2021/05/ccafc96b-sexual-mind_final.pdf

The sexual mind

The sexual mind is always active during the course of our daily lives – if we allow this for ourselves. A substantial portion of the processing of sexually evocative situations takes place in the subconscious. Our awareness of them depends partially on whether we are prepared in the given circumstances and moment to allow ourselves to have sexually charged thoughts. The mind may block this awareness because it is fastened onto something else – perhaps a grave or serious problem that immerses us.

New things are constantly being introduced in our sexual lives, for us to ponder in our various life situations and seek novel ways to implement. Many of us would like to discover ways to increase the pleasure we feel. Others wonder how they might preserve even the smallest spark of passion in their long-term relationship. Many others crave confirmation that they are sexually normal – whatever that means for each individual. Some want solutions to sexual problems, while others would like to understand why their minds and bodies do not travel in tandem with their own expectations of their sexual desire, or with their partner’s desire. The sexual mind presents a major challenge and an enormous opportunity.

The mind is the conduit to the awakening of sexual interest and desire, and launches our individual processes of sexual arousal. The mind comprises both our conscious and unconscious interest in sexual matters. Exploring our own sexual mind helps to open new pathways to sexuality that often remain unknown even to ourselves. The exploration also gives us a deeper understanding of our sexual motives. 

Sexuality is present in our lives from the moment of birth until death. Each of us is an expert in our own sexuality. It is therefore strange that we know the least and have the least awareness of the very things that are most important to us in terms of sexuality – for example, why we are especially captivated by certain sexual phenomena and not others, and why some of them are nearly irresistible to us. [...]

Substantial percentages of people do not want to receive information even when it bears on health, sustainability, & consumer welfare; , substantial percentages of people also do want to receive that information

Sunstein, Cass R. and Reisch, Lucia and Kaiser, Micha, What Do People Want to Know? Information Avoidance and Food Policy Implications (May 4, 2021). SSRN: https://ssrn.com/abstract=3839513

Abstract: What information would people like to have? What information would they prefer to avoid? How does the provision of information bear on welfare? And what does this mean for food policy? Representative surveys in eleven nations find that substantial percentages of people do not want to receive information even when it bears on health, sustainability, and consumer welfare. Nonetheless, substantial percentages of people also do want to receive that information, and people’s willingness to pay for information, contingent on their wanting it, is mostly higher than people’s willingness to pay not to receive information, contingent on their not wanting it. We develop a model and estimate the welfare effects of information provision. We find substantial benefits and costs, with the former outweighing the latter. The results suggest that in principle, policymakers should take both instrumental and hedonic effects into account when deciding whether to impose disclosure requirements for food, whether the domain involves health, safety, or moral considerations. If policymakers fail to consider either instrumental or hedonic effects, and if they fail to consider the magnitude of those effects, they will not capture the welfare consequences of disclosure requirements. Our evidence has concrete implications for how to think about, and capture, the welfare consequences of such requirements with respect to food.

Keywords: Information avoidance, information seeking, willingness to pay, belief-based utility

JEL Classification: D00, D9, D11, D90, D91

The uses and abuses of tree thinking in cultural evolution

The uses and abuses of tree thinking in cultural evolution. Cara L. Evans, Simon J. Greenhill, Joseph Watts, Johann-Mattis List, Carlos A. Botero, Russell D. Gray and Kathryn R. Kirby. Philosophical Transactions of the Royal Society B: Biological Sciences, July 5 2021, Volume 376Issue 1828, online May 17 2021, https://doi.org/10.1098/rstb.2020.0056

Abstract: Modern phylogenetic methods are increasingly being used to address questions about macro-level patterns in cultural evolution. These methods can illuminate the unobservable histories of cultural traits and identify the evolutionary drivers of trait change over time, but their application is not without pitfalls. Here, we outline the current scope of research in cultural tree thinking, highlighting a toolkit of best practices to navigate and avoid the pitfalls and ‘abuses' associated with their application. We emphasize two principles that support the appropriate application of phylogenetic methodologies in cross-cultural research: researchers should (1) draw on multiple lines of evidence when deciding if and which types of phylogenetic methods and models are suitable for their cross-cultural data, and (2) carefully consider how different cultural traits might have different evolutionary histories across space and time. When used appropriately phylogenetic methods can provide powerful insights into the processes of evolutionary change that have shaped the broad patterns of human history.

1. Introduction

Theories of cultural evolution are built on the observation that cultural features undergo innovation, modification and transmission. Over time, these processes have generated remarkable variation in human cultures. Humans speak around 7000 distinct languages, affiliate with hundreds of religions, employ a range of kinship systems, engage in an array of subsistence practices and adhere to a bewildering number of social conventions [1]. Phylogenetic methods provide a powerful approach to studying macro-evolutionary patterns of innovation, modification and transmission [2–4]. Their application to human culture has helped reinvigorate cross-cultural comparative research but has also been subject to criticism—both valid and misguided.

Phylogenies, also known as evolutionary trees, represent the common ancestry of populations and the splitting events that have occurred over the course of their history. Phylogenetic methods encompass a broad family of mathematical approaches that can be used to construct, analyse and incorporate phylogenies (figure 1). Originally developed to study the evolution of biological organisms, these methods offer a general toolkit with the potential to provide answers to a range of cultural evolutionary questions.

Figure 1. Phylogenetic methods that can be used to study cultural macro-evolution. Black arrows indicate that the preceding methodological steps are directly incorporated in later methods: (a) tree construction [5] is required for all subsequent steps; (b) testing for phylogenetic signal (e.g. [6–8]) forms an integral part of phylogenetic regression (e.g. [9–11]), which in turn forms the basis of phylogenetic path analysis which can identify causal relationships; (c) ancestral state reconstruction (e.g. [12]), estimated in conjunction with rates of trait change and transformation (e.g. [13,14]), is required for models of trait correlation [15–17] and diversification ([18,19]; but see [20]). Red arrows indicate that suitable tests of phylogenetic signal (i.e. that the trait data fit sufficiently to the history inferred by the tree) should be conducted by the researcher before using methods detailed in (c); (see also §2). Shading: grey shading indicates methods that both assume and require inferred historical relationships between the cultural units (tree taxa) to sufficiently reflect the history of the trait; green shading denotes methods that detect and quantify tree-like structure in cross-cultural data; blue shading denotes methods that detect and control for tree-like data structure among societies, but do not require it.

An important distinction in cultural phylogenetics research is between methods of building trees (i.e. reconstructing the histories of cultural units based on assumptions of vertical transmission of cultural features (traits); figure 1a) and methods that use previously constructed trees in models that investigate the evolution and distribution of other cultural traits (figure 1b-c). A further important division in tree thinking occurs between those methods and questions that simply detect and control for tree-like structure when examining variation in cross-cultural data (e.g. What does the distribution of traits among societies tell us about the history of those societies and/or traits? Does horizontal or vertical transmission better explain the observed distribution of traits?figure 1b), and those methods that require that the modelled data are tree-like (i.e. methods that ask: What was the ancestral form of a cultural feature?figure 1c).

Phylogenetic methods offer exciting possibilities for a wide range of questions, only some of which explicitly require tree-like data. For data that are sufficiently tree-like, one of the strongest appeals of phylogenetic methods is that they offer the possibility to illuminate the unobservable past. Phylogenetic methods can reconstruct the ancestry of a vertically transmitted trait from the evolutionary signatures detected in its present-day distribution, even when archaeological records are entirely unavailable. However, despite this exciting potential, debate continues over how best to integrate cultural heterogeneity, disentangle the signatures of vertical transmission, horizontal diffusion and local socio-ecological drivers, and demonstrate that a cultural trait exhibits enough tree-like structure to justify using methods that reconstruct its evolutionary past.

Here, we review the application of phylogenetic methods in cross-cultural research. We focus specifically on the questions researchers should ask in order to avoid common methodological pitfalls when (i) deciding about the units of the underlying cultural data, (ii) constructing trees and (iii) assuming tree-like transmission of other cultural features. Throughout, we outline a series of best practices and highlight emerging methods that promise to advance our understanding of macro-evolutionary patterns of mechanism and causation in culture.

Girls know how to choose: Fathers lived in larger cities, had higher education, were heavier and taller , more attractive & masculine, had lighter eyes, darker hair, & were more agreeable, conscientious, & emotionally stable than non-fathers

She Always Steps in the Same River: Similarity Among Long-Term Partners in Their Demographic, Physical, and Personality Characteristics. Zuzana Štěrbová, Petr Tureček and Karel Kleisner. Front. Psychol., February 5 2019. https://doi.org/10.3389/fpsyg.2019.00052

Abstract: In mate choice, individuals consider a wide pool of potential partners. It has been found that people have certain preferences, but intraindividual stability of mate choice over time remains little explored. We tested individual consistency of mate choice with respect to a number of demographic, physical, and personality characteristics. Only mothers were recruited for this study, because we wanted to find out not only whether women choose long-term partners with certain characteristics but also whether the father of their child(ren) differs from their other long-term (ex-)partners. Women (N = 537) of 19–45 years of age indicated the demographic, physical (by using image stimuli), and personality characteristics of all of their long-term partners (partners per respondent: mean = 2.98, SD = 1.32). Then we compared the average difference between an individual’s long-term partners with the expected average difference using a permutation test. We also evaluated differences between partners who had children with the participants (fathers) and other long-term partners (non-fathers) using permutation tests and mixed-effect models. Our results revealed that women choose long-term partners consistently with respect to all types of characteristics. Although effect sizes for the individual characteristics were rather weak, maximal cumulative effect size for all characteristics together was high, which suggests that relatively low effect sizes were caused by high variability with low correlations between characteristics, and not by inconsistent mate choice. Furthermore, we found that despite some differences between partners, fathers of participants’ child(ren) do fit their ‘type’. These results suggest that mate choice may be guided by relatively stable but to some degree flexible preferences, which makes mate choice cognitively less demanding and less time-consuming. Further longitudinal studies are needed to confirm this conclusion.

Results

Mate choice consistency was higher than expected in all assessed qualities except for facial masculinity and beardedness. Difference between observed and expected consistency was statistically significant in most qualities, but effect sizes differed substantially. While consistency of mate choice in residence or weight was substantial, it was only medium-sized or small with respect to hair or eye color. Complete results are summarized in Table 1 and Figure 1.

Table 1. Mate choice consistency: complete results.

Figure 1. Visualization of permutation tests of mate choice consistency centered around observed image and normalized along the SD of expected image distribution. Difference between the observed and expected value is expressed in standard deviations from the expected value distribution. The higher the bell curve above the Observed image value, the higher the actual mate choice consistency. Bell curve below Observed image value indicates a trait where the observed mate choice was less consistent than expected.

The average effect size was highest in demographic variables, but none of the pairwise comparisons between groups of variables (demographic, physical, and psychological) was statistically significant (p > 0.1). Permutation test results are visualized in Figure 1. All sample sizes and descriptive statistics of all variables are listed in the Appendix. The different estimates of effect size were highly correlated. The proportion of males who had to be relocated between respondents correlated with the variance accounted for by the respondent at 0.93, whereby a linear model of relationship between these two measures supports the idea that the latter is approximately double of the former. The slope in the model where respondent-attributable variance regressed on the proportion of partners to relocate was 2.08 (95% CI = 1.72–2.45) with minimal (not significantly different from 0) intercept of -0.18 (95% CI = -3.19–2.83). Results yielded by the simple Pearson correlation correlated at 0.91 with the percentage of partners to relocate and at 0.98 with respondent-attributable variance. All of these measures can be thus treated as functionally equivalent.

Links between pairs of partners’ qualities are summarized in Table 2. In total, 103 out of 210 correlations were significant even after Benjamini–Hochberg correction for multiple comparisons. Maximal cumulative effect size was 50.95% (expressed in the proportion of partners to switch between individuals). The first 10 variables ordered according to their unique contribution starting with the highest (residence, weight, relative height, age difference, attractiveness, hair color, openness, BMI, height, agreeableness, in this order) explained 48.30% of partner assignment. The other 11 variables contributed little (their unique contributions were less than 1%) or not at all (after the inclusion of all other variables, facial masculinity and beardedness failed to show any positive numbers). Full results are visualized in Figure 2.

Table 2. Relations between investigated qualities of romantic partners expressed in shared effect sizes and Pearson correlations.

Figure 2. Visualization of maximal cumulative effect size. Variables are added in order given by maximal unique contribution to overall consistency.

Reaching maximal possible effect size suggests that adding yet other variables to a similar model of cumulative consistency would add little to our current sum. On the other hand, it is conceivable that one might select precisely those variables which are not intercorrelated and explain a majority of mate choice consistency in just a handful independent dimensions. In theory, complex interaction patterns may lead to an even higher cumulative effect size since 50% of partners to relocate as an effect size limit applies to a single variable with two levels and represents the difference between maximal and minimal consistency (i.e., not maximal and expected). The high proportion of significantly correlated pairs of variables (49%), does, however, fit well within the impression of a substantial redundancy in our model.

Permutation test of changes in mate choice consistency revealed that fathers are significantly exceptional amongst participants’ long-term partners in beardedness, muscularity, hirsuteness, extraversion, and openness. The average image without these individuals was lower than the image in permutation runs where an equivalent proportion of random partners (i.e., fathers and non-fathers) was excluded. Fathers were not significantly typical long-term partners in any of the assessed qualities. Complete results of these tests are summarized in Table 3 and visualization is provided in Figure 3.

Table 3. Permutation test of father exceptionality, complete results.

Figure 3. Visualization of permutation tests of father exceptionality centered around the observed image when fathers were excluded from the sample of partners and normalized along the SD of expected image distribution in such a situation. Difference between observed and expected values is expressed in standard deviations of expected value distribution. The higher the bell curve above the observed image value, the more exceptional were the fathers among the long-term partners of an individual. Bell curve below the observed image value indicates a trait where fathers were more typical representatives of an individual’s long-term partners.

In qualities where fathers were indicated as exceptional individuals (except for extraversion), mean trait values differed between fathers and non-fathers, while variances differed in beardedness, muscularity, and hirsuteness. Fathers were more bearded, hairier, more muscular, and showed a higher openness to experience. These differences might explain the overall exceptionality of fathers except for extraversion. It seems that fathers are outliers within partner sets even where the group means and variances of father and non-father sets do not differ. Moreover, fathers lived in larger cities, had higher education, were heavier and taller (although relatively, their height was closer to the height of respondents), more attractive and masculine, had lighter eyes, darker hair, more masculine faces, and were more agreeable, conscientious, and emotionally stable than non-fathers.

Group variances differed in several qualities. Fathers were significantly more variable than non-fathers with respect to age difference from the respondent and less variable in attractiveness, masculinity (general and facial), BMI, conscientiousness, and agreeableness. It seems that along these variables, either or both of the extremes are not the right for the ‘father material’. A graphic overview which compares densities that indicate differences between group means and variances is presented in Figure 4. Complete results in a textual form are listed in Table 4.

Figure 4. Visualization of differences between fathers and non-fathers. Significance of difference between group means and variances is estimated from mixed effect models with respondent ID treated as a random factor. Significance levels are indicated as follows: ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001.

Table 4. Results of Mixed effect models comparing father/non-father means and variances.