The complete title of this book runs ‘Analyzing Linguistic Data: A Practical Introduction to Statistics using R’ and as such it very well reflects the purpose and spirit of the book. The author guides the reader in about 350 pages from descriptive and basic statistical methods over classification and clustering to (generalised) linear and mixed models. Each of the methods is introduced in the context of concrete linguistic problems and demonstrated on exciting datasets from current research in the language sciences. In line with its practical orientation, the book focuses primarily on using the methods and interpreting the results. This implies that the mathematical treatment of the techniques is held at a minimum if not absent from the book. In return, the reader is provided with very detailed explanations on how to conduct the analyses using R [1]. The first chapter sets the tone being a 20-page introduction to R. For this and all subsequent chapters, the R code is intertwined with the chapter text and the datasets and functions used are conveniently packaged in the languageR package that is available on the Comprehensive R Archive Network (CRAN). With this approach, the author has done an excellent job in enabling researchers and students alike to reproduce the analyses and learn by doing. Another quality as a textbook is the fact that every chapter ends with Workbook sections where the user is invited to exercise his or her analysis skills on supplemental datasets. Full solutions including code, results and comments are given in Appendix A (30 pages). Instructors are therefore very well served by this text, although they might want to balance the book with some more mathematical treatment depending on the target audience. After the introductory chapter on R, the book opens on graphical data exploration. Chapter 3 treats probability distributions and common sampling distributions. Under basic statistical methods (Chapter 4), distribution tests and tests on means and variances are covered. Chapter 5 deals with clustering and classification. Strangely enough, the clustering section has material on PCA, factor analysis, correspondence analysis and includes only one subsection on clustering, devoted notably to hierarchical partitioning methods. The classification part deals with decision trees, discriminant analysis and support vector machines. The regression chapter (Chapter 6) treats linear models, generalised linear models, piecewise linear models and a substantial section on models for lexical richness. The final chapter on mixed models is particularly interesting as it is one of the few text book accounts that introduce the reader to using the (innovative) lme4 package of Douglas Bates which implements linear mixed-effects models. Moreover, the case studies included in this

Analyzing linguistic data: a practical introduction to statistics using R

Summary


1. Methods to measure metabolites of steroid hormones from faeces have become very popular in wildlife conservation and ecology, because they allow gathering physiological data without the necessity to capture the animals. However, this advantage comes at costs that are particularly relevant when studying free-living animals in their natural environments. Previous methodological reviews have stressed the importance of validations to prove that real metabolites of the hormone in question are measured, but the research community has largely ignored further caveats relating to sex, diet, metabolic rate and individual differences in hormone metabolite formation.



2. Often the sexes differ in how they metabolize hormones. As a consequence, one may not be able to compare hormone metabolite concentrations between males and females of one species.



3. Diet can alter the way hormones are metabolized, and different diets can change the amount of faecal bulk. Both phenomena can result in measurement artefacts that may seriously distort the estimation of hormone metabolite concentrations. As a consequence, comparisons of hormone metabolite concentrations, for example, between seasons or populations, may become problematic.



4. Changes in ambient temperature and food availability may trigger large fluctuations in metabolic rate of free-living animals. These fluctuations may then result in major distortions of faecal hormone metabolite concentrations without any change in bioactive hormone levels.



5. Bacteria metabolize hormones in the gut. Individual differences in bacterial composition can cause differences in how hormones are decomposed. Thus, individuals may differ with regard to what kind of hormone metabolites they form and with regard to the relative composition of these hormone metabolites. As only specific metabolites are measured, differences in metabolism may distort the results.



6. In summary, non-invasive hormone research measures various end products of a hormone after its clearance from the circulation and extensive modification by bacteria. Not only does this increase random variance, it may also generate systematic noise, which may seriously distort the signal (i.e. the hormonal status of the individual) in a non-random manner. Thus, we still need to learn much more about whether this widely used technique reliably measures the physiological status of animals in uncontrolled environments.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.2041-210X.2012.00203.x

On the use of non-invasive hormone research in uncontrolled, natural environments: The problem with sex, diet, metabolic rate and the individual

Humans are an ultrasocial species. This sociality, however, cannot be fully explained by the canonical approaches found in evolutionary biology, psychology, or economics. Understanding our unique social psychology requires accounting not only for the breadth and intensity of human cooperation but also for the variation found across societies, over history, and among behavioral domains. Here, we introduce an expanded evolutionary approach that considers how genetic and cultural evolution, and their interaction, may have shaped both the reliably developing features of our minds and the well-documented differences in cultural psychologies around the globe. We review the major evolutionary mechanisms that have been proposed to explain human cooperation, including kinship, reciprocity, reputation, signaling, and punishment; we discuss key culture-gene coevolutionary hypotheses, such as those surrounding self-domestication and norm psychology; and we consider the role of religions and marriage systems. Empirically, we synthesize experimental and observational evidence from studies of children and adults from diverse societies with research among nonhuman primates.

The Origins and Psychology of Human Cooperation

An Introduction to Generalized Linear Models

https://onlinelibrary.wiley.com/doi/pdf/10.1002/wps.20729

What is resilience: an affiliative neuroscience approach.

Intergroup conflict is evident throughout the history of our species, ubiquitous across human societies, and considered crucial for the evolution of humans' large-scale cooperative nature. Like humans, chimpanzee societies exhibit intragroup coordination and coalitionary support during violent intergroup conflicts. In both species, cooperation among group members is essential for individuals to gain access to benefits from engaging in intergroup conflict. Studies suggest that a contributive mechanism regulating in-group cooperation during intergroup conflicts in humans involves the neuropeptide hormone oxytocin, known to influence trust, coordination, and social cognition, although evidence from natural settings is lacking. Here, applying a noninvasive method, we investigate oxytocinergic system involvement during natural intergroup conflicts in wild chimpanzees. We found that chimpanzees of both sexes had significantly higher urinary oxytocin levels immediately before and during intergroup conflict compared with controls. Also, elevated hormone levels were linked with greater cohesion during intergroup conflicts, rather than with the level of potential threat posed by rival groups, intragroup affiliative social interactions, or coordinated behavior alone. Thus, the oxytocinergic system, potentially engendering cohesion and cooperation when facing an out-group threat, may not be uniquely human but rather a mechanism with evolutionary roots shared by our last common ancestor with chimpanzees, likely expediting fitness gains during intergroup conflict.

https://www.pnas.org/content/pnas/114/2/268.full.pdf

Oxytocin reactivity during intergroup conflict in wild chimpanzees.

Why share when access to benefits is uncertain is crucial to our understanding of the evolution of humans' extensive cooperation. Here, we investigated some of the different human sharing hypotheses and potential neuroendocrine mechanisms, in one of our closest living relatives, chimpanzees. The strongest predictor of sharing across food types was the presence of enduring and mutually preferred grooming partners, more than harassment, direct signalling, or trade. Moreover, urinary oxytocin levels were higher after the sharing of both individually and jointly acquired resources compared with controls. We conclude that the emotional connection inherent in social bonds was a key factor determining sharing patterns, with the oxytocinergic system potentially facilitating long-term cooperative exchanges. Testing for the role of social bonds in increasing predictability of sharing behaviour, a feature frequently overlooked, may help us to identify the evolutionary drivers of resource sharing and mechanisms that sustain delayed reciprocity between non-kin.

https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2018.1643

Social bonds facilitate cooperative resource sharing in wild chimpanzees

Cooperative hunting and meat sharing are hypothesized as fundamental to human life history adaptations and biological success. Wild chimpanzees also hunt in groups, and despite the potential of inferring ancestral hominid adaptations, it remains unclear whether chimpanzee hunting is a cooperative act. Here we show support for cooperative acquisition in wild chimpanzees since hunters are more likely to receive meat than bystanders, independent of begging effort. Engagement in prey searches and higher hunt participation independently increase hunting success, suggesting that coordination may improve motivation in joint tasks. We also find higher levels of urinary oxytocin after hunts and prey searches compared with controls. We conclude that chimpanzee hunting is cooperative, likely facilitated by behavioral and neuroendocrine mechanisms of coordination and reward. If group hunting has shaped humans’ life history traits, perhaps similar pressures acted upon life history patterns in the last common ancestor of human and chimpanzee. Liran Samuni et al. examined meat sharing behavior and oxytocin in wild chimpanzees, elucidating the mechanism facilitating cooperation. They find that hunters are more likely to receive food independent of begging, suggesting that prosocial behaviors are rewarded in primates, likely facilitated by oxytocin.

/pdf/reward-of-labor-coordination-and-hunting-success-in-wild-2l99rcfxch.pdf

Reward of labor coordination and hunting success in wild chimpanzees

Grooming interactions benefit groomers, but may have negative consequences for bystanders. Grooming limits bystanders' grooming access and ensuing alliances could threaten the bystander's hierarchy...

https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.171296

Anna Preis

Papers

Oxytocin reactivity during intergroup conflict in wild chimpanzees.

Social bonds facilitate cooperative resource sharing in wild chimpanzees

Reward of labor coordination and hunting success in wild chimpanzees

Bystanders intervene to impede grooming in Western chimpanzees and sooty mangabeys

Cortisol and oxytocin show independent activity during chimpanzee intergroup conflict.