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

Perceptual category learning of photographic and painterly stimuli in rhesus macaques (Macaca mulatta) and humans

Humans are highly adept at categorizing visual stimuli, but studies of human categorization are typically validated by verbal reports. This makes it difficult to perform comparative studies of categorization using non-human animals. Interpretation of comparative studies is further complicated by the possibility that animal performance may merely reflect reinforcement learning, whereby discrete features act as discriminative cues for categorization. To assess and compare how humans and monkeys classified visual stimuli, we trained 7 rhesus macaques and 41 human volunteers to respond, in a specific order, to four simultaneously presented stimuli at a time, each belonging to a different perceptual category. These exemplars were drawn at random from large banks of images, such that the stimuli presented changed on every trial. Subjects nevertheless identified and ordered these changing stimuli correctly. Three monkeys learned to order naturalistic photographs; four others, close-up sections of paintings with distinctive styles. Humans learned to order both types of stimuli. All subjects classified stimuli at levels substantially greater than that predicted by chance or by feature-driven learning alone, even when stimuli changed on every trial. However, humans more closely resembled monkeys when classifying the more abstract painting stimuli than the photographic stimuli. This points to a common classification strategy in both species, one that humans can rely on in the absence of linguistic labels for categories

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

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

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