Whither dominance? An enduring evolutionary legacy of primate sociality

This article discusses dominance personality dimensions found in primates, particularly in the great apes, and how they compare to dominance in humans. Dominance traits are seen in virtually all primate species, and these dimensions reflect how adept an individual is at ascending within a social hierarchy. Among great apes, dominance is one of the most prominent personality factors but, in humans, dominance is usually modeled as a facet of extraversion. Social, cultural, and cognitive differences between humans and our closest ape relatives are explored, alongside humanity’s hierarchical and egalitarian heritage. The basic characteristics of dominance in humans and nonhuman great apes are then described, alongside the similarities and differences between great apes. African apes live in societies each with its own hierarchical organization. Humans were a possible exception for some of our history, but more recently, hierarchies have dominated. The general characteristics of high-dominance humans, particularly those living in industrialized nations, are described. Dominance itself can be subdivided into correlated subfactors: domineering, prestige, and leadership. Various explanations have been posed for why dominance has declined in prominence within human personality factor structures, and several possibilities are evaluated. The value of dominance in personality research is discussed: dominance has links to, for instance, age, sex, aggression, self-esteem, locus of control, stress, health, and multiple socioeconomic status indicators. The piece concludes with recommendations for researchers who wish to assess dominance in personality

Data reduction analyses of animal behaviour:Avoiding Kaiser’s criterion and adopting more robust automated methods

Data reduction analyses such as principal components and exploratory factor analyses identify relationships within a set of potentially correlated variables, and cluster correlated variables into a smaller overall quantity of groupings. Because of their relative objectivity, these analyses are popular throughout the animal literature to study a wide variety of topics. Numerous authors have highlighted ‘best practice’ guidelines for component/factor ‘extraction’, i.e. determining how many components/factors to extract from a data reduction analysis, because this can greatly impact the interpretation, comparability and replicability of one’s results. Statisticians agree that Kaiser’s criterion, i.e. extracting components/factors with eigenvectors >1.0, should never be used, yet, within the animal literature, a considerable number of authors still use it, even as recently as 2018 and across a wide range of taxa (e.g. insects, birds, fish, mammals) and topics (e.g. personality, cognition, health, morphology, reproduction). It is therefore clear that further awareness is needed to target the animal sciences to ensure that results optimize structural stability and, thus, comparability and reproducibility. In this commentary, we first clarify the distinction between principal components and exploratory factor analyses in terms of analysing simple versus complex structures, and how this relates to component/factor extraction. Second, we highlight empirical evidence from simulation studies to explain why certain extraction methods are more reliable than others, including why automated methods are better, and why Kaiser’s criterion is inappropriate and should therefore never be used. Third, we provide recommendations on what to do if multiple automated extraction methods ‘disagree’ which can arise when dealing with complex structures. Finally, we explain how to perform and interpret more robust and automated extraction tests using R

Latent Variable Structural Equation Modeling: A Flexible Tool for Establishing Validity and More

Petrinovich highlighted many salient issues in the behavioral and social sciences that are of concern to this day, such as insufficient attention to construct validity. Structural equation modeling, particularly with regard to latent variables, is introduced and discussed in this context. Though conceptual issues remain, analytic and statistical techniques have made immense strides in the past three decades since the article was written, and properly used, offer solutions to many problems Petrinovich identified

Blood pressure and cognitive function across the eighth decade:A prospective study of the Lothian Birth Cohort 1936

Objectives We investigated the associations among blood pressure and cognitive functions across the eighth decade, while accounting for antihypertensive medication and lifetime stability in cognitive function.Design Prospective cohort study.Setting This study used data from the Lothian Birth Cohort 1936 (LBC1936) study, which recruited participants living in the Lothian region of Scotland when aged 70 years, most of whom had completed an intelligence test at age 11 years.Participants 1091 members of the LBC1936 with assessments of cognitive ability in childhood and older adulthood, and blood pressure measurements in older adulthood.Primary and secondary outcome measures Participants were followed up at ages 70, 73, 76 and 79, and latent growth curve models and linear mixed models were used to analyse both cognitive functions and blood pressure as primary outcomes.Results Blood pressure followed a quadratic trajectory in the eighth decade: on average blood pressure rose in the first waves and subsequently fell. Intercepts and trajectories were not associated between blood pressure and cognitive functions. Women with higher early-life cognitive function generally had lower blood pressure during the eighth decade. Being prescribed antihypertensive medication was associated with lower blood pressure, but not with better cognitive function.Conclusions Our findings indicate that women with higher early-life cognitive function had lower later-life blood pressure. However, we did not find support for the hypothesis that rises in blood pressure and worse cognitive decline are associated with one another in the eighth decade

Generational differences in loneliness and its psychological and sociodemographic predictors:An exploratory and confirmatory machine learning study

BACKGROUND: Loneliness is a growing public health issue in the developed world. Among older adults, loneliness is a particular challenge, as the older segment of the population is growing and loneliness is comorbid with many mental as well as physical health issues. Comorbidity and common cause factors make identifying the antecedents of loneliness difficult, however, contemporary machine learning techniques are positioned to tackle this problem. METHODS: This study analyzed four cohorts of older individuals, split into two age groups – 45–69 and 70–79 – to examine which common psychological and sociodemographic are associated with loneliness at different ages. Gradient boosted modeling, a machine learning technique, and regression models were used to identify and replicate associations with loneliness. RESULTS: In all cohorts, higher emotional stability was associated with lower loneliness. In the older group, social circumstances such as living alone were also associated with higher loneliness. In the younger group, extraversion's association with lower loneliness was the only other confirmed relationship. CONCLUSIONS: Different individual and social factors might underlie loneliness differences in distinct age groups. Machine learning methods have the potential to unveil novel associations between psychological and social variables, particularly interactions, and mental health outcomes

Do childhood socioeconomic circumstances moderate the association between childhood cognitive ability and all-cause mortality across the life course? Prospective observational study of the 36-day sample of the Scottish Mental Survey 1947

Background There is growing evidence that higher childhood cognitive ability predicts lower all-cause mortality risk across the life course. Whereas this association does not appear to be mediated by childhood socioeconomic circumstances, it is unclear whether socioeconomic circumstances moderate this association.Methods The moderating role of childhood socioeconomic circumstances was assessed in 5318 members of the 36-day sample of the Scottish Mental Survey 1947. Univariate, sex-adjusted and age-adjusted, and mutually adjusted Cox models predicting all-cause mortality risk up to age 79 years were created using childhood IQ scores and childhood social class as predictors. Moderation was assessed by adding an interaction term between IQ scores and social class and comparing model fit.Results An SD advantage in childhood IQ scores (HR=0.83, 95% CI 0.79 to 0.86, p<0.001) and a single-class advantage in childhood social class (HR=0.92, 95% CI 0.88 to 0.97, p<0.001) independently predicted lower mortality risk. Adding the IQ–social class interaction effect did not improve model fit (χ2Δ=1.36, p=0.24), and the interaction effect did not predict mortality risk (HR=1.03, 95% CI 0.98 to 1.07, p=0.25).Conclusions The present study demonstrated that the association between higher childhood cognitive ability and lower all-cause mortality risk is not conditional on childhood social class. Whereas other measures of socioeconomic circumstances may play a moderating role, these findings suggest that the benefits of higher childhood cognitive ability for longevity apply regardless of the material socioeconomic circumstances experienced in childhood

Chimpanzee personality and its relations with cognition and health: a comparative perspective

This thesis aimed to address two main questions. First, considering that personality is frequently associated with cognitive abilities in humans, do chimpanzees’ personalities and cognitive capacities covary in ways similar to what is observed in humans, as well as older evolutionary cousins, rhesus macaques? Second, given that human and animal personality have previously been shown to relate to health and longevity, does personality in chimpanzees also relate to various measures of health? Chapter 1 provides an introduction to and brief history of comparative personality psychology, particularly in the context of intelligence research and psychosomatic medicine. Chapter 2 describes three studies with a group of 19 zoo-housed chimpanzees who interacted with touchscreen tasks over the course of 3 years of research. We found that high Conscientious chimpanzees were more likely to stick with the tasks, and performed better as a results, but once their extra experience was taken into account, their performance advantage disappeared. However, we also found associations between better interest and performance with high Openness, high Extraversion, and low Agreeableness. In Chapter 3 we examine performance in conjunction with personality, with 9 rhesus macaques. The macaques also engaged with touchscreen tasks, but were expert subjects and displayed plateau performance. We found consistent associations between many measures of performance and both high Openness and high Friendliness (which is similar to Extraversion). With Chapter 4 we transition to our studies of personality and health. Chapter 4 examines personality and longevity in a sample of 538 personality rated, captive chimpanzees. These chimpanzees were followed for between 6 and 23 years after being rated. We found that high Agreeableness chimpanzees were more likely to live longer, but no other personality traits had a significant impact on longevity. In Chapter 5, we compared biomarkers from samples of 177 chimpanzees housed at the Yerkes National Primate Research Centre, and 29,314 humans from the National Health and Nutrition Examination Survey. Both samples had been tested for the most common haematological and metabolic blood biomarkers, and we used these to assess stress in the form of allostatic load, between species. We found a similar structure of biomarkers in across humans and chimpanzees. In Chapter 6, we took our allostatic load measure from chapter 5 and looked at how it was associated with personality, in the same chimpanzee sample from the Yerkes Primate Centre, and in the longitudinal Midlife in the United States and Midlife in Japan biomarker study samples, which consisted of 993 and 382 individuals, respectively. We found that Agreeableness was associated with allostatic load in both human samples, whereas Dominance was associated with allostatic load in chimpanzees. Finally, Chapter 7 summarises the results presented in these five empirical chapters, and places our findings in the context of the existing literature. We discuss the limitations of the research, and offer some suggestions for future study

How youth cognitive and sociodemographic factors relate to the development of overweight and obesity in the UK and the USA: a prospective cross-cohort study of the National Child Development Study and National Longitudinal Study of Youth 1979

Objectives We investigated how youth cognitive and sociodemographic factors are associated with the aetiology of overweight and obesity. We examined both onset (who is at early risk for overweight and obesity) and development (who gains weight and when).Design Prospective cohort study.Setting We used data from the US National Longitudinal Study of Youth 1979 (NLSY) and the UK National Child Development Study (NCDS); most of both studies completed a cognitive function test in youth.Participants 12 686 and 18 558 members of the NLSY and NCDS, respectively, with data on validated measures of youth cognitive function, youth socioeconomic disadvantage (eg, parental occupational class and time spent in school) and educational attainment. Height, weight and income data were available from across adulthood, from individuals’ 20s into their 50s.Primary and secondary outcome measures Body mass index (BMI) for four time points in adulthood. We modelled gain in BMI using latent growth curve models to capture linear and quadratic components of change in BMI over time.Results Across cohorts, higher cognitive function was associated with lower overall BMI. In the UK, 1 SD higher score in cognitive function was associated with lower BMI (β=−0.20, 95% CI −0.33 to −0.06 kg/m²). In America, this was true only for women (β=−0.53, 95% CI −0.90 to −0.15 kg/m²), for whom higher cognitive function was associated with lower BMI. In British participants only, we found limited evidence for negative and positive associations, respectively, between education (β=−0.15, 95% CI −0.26 to −0.04 kg/m²) and socioeconomic disadvantage (β=0.33, 95% CI 0.23 to 0.43 kg/m²) and higher BMI. Overall, no cognitive or socioeconomic factors in youth were associated with longitudinal changes in BMI.Conclusions While sociodemographic and particularly cognitive factors can explain some patterns in individuals’ overall weight levels, differences in who gains weight in adulthood could not be explained by any of these factors