Esther Herrmann

Bio: Esther Herrmann is an academic researcher from Max Planck Society. The author has contributed to research in topics: Cognition & Prosocial behavior. The author has an hindex of 24, co-authored 58 publications receiving 3867 citations. Previous affiliations of Esther Herrmann include University of Portsmouth.

Humans Have Evolved Specialized Skills of Social Cognition: The Cultural Intelligence Hypothesis

Abstract: Humans have many cognitive skills not possessed by their nearest primate relatives. The cultural intelligence hypothesis argues that this is mainly due to a species-specific set of social-cognitive skills, emerging early in ontogeny, for participating and exchanging knowledge in cultural groups. We tested this hypothesis by giving a comprehensive battery of cognitive tests to large numbers of two of humans' closest primate relatives, chimpanzees and orangutans, as well as to 2.5-year-old human children before literacy and schooling. Supporting the cultural intelligence hypothesis and contradicting the hypothesis that humans simply have more "general intelligence," we found that the children and chimpanzees had very similar cognitive skills for dealing with the physical world but that the children had more sophisticated cognitive skills than either of the ape species for dealing with the social world.

The evolution of self-control

Abstract: Cognition presents evolutionary research with one of its greatest challenges. Cognitive evolution has been explained at the proximate level by shifts in absolute and relative brain volume and at the ultimate level by differences in social and dietary complexity. However, no study has integrated the experimental and phylogenetic approach at the scale required to rigorously test these explanations. Instead, previous research has largely relied on various measures of brain size as proxies for cognitive abilities. We experimentally evaluated these major evolutionary explanations by quantitatively comparing the cognitive performance of 567 individuals representing 36 species on two problem-solving tasks measuring self-control. Phylogenetic analysis revealed that absolute brain volume best predicted performance across species and accounted for considerably more variance than brain volume controlling for body mass. This result corroborates recent advances in evolutionary neurobiology and illustrates the cognitive consequences of cortical reorganization through increases in brain volume. Within primates, dietary breadth but not social group size was a strong predictor of species differences in self-control. Our results implicate robust evolutionary relationships between dietary breadth, absolute brain volume, and self-control. These findings provide a significant first step toward quantifying the primate cognitive phenome and explaining the process of cognitive evolution.

Two Key Steps in the Evolution of Human Cooperation The Interdependence Hypothesis

Abstract: Modern theories of the evolution of human cooperation focus mainly on altruism. In contrast, we propose that humans’ species-unique forms of cooperation—as well as their species-unique forms of cognition, communication, and social life—all derive from mutualistic collaboration (with social selection against cheaters). In a first step, humans became obligate collaborative foragers such that individuals were interdependent with one another and so had a direct interest in the well-being of their partners. In this context, they evolved new skills and motivations for collaboration not possessed by other great apes (joint intentionality), and they helped their potential partners (and avoided cheaters). In a second step, these new collaborative skills and motivations were scaled up to group life in general, as modern humans faced competition from other groups. As part of this new group-mindedness, they created cultural conventions, norms, and institutions (all characterized by collective intentionality), with kn...

How does cognition evolve? Phylogenetic comparative psychology

Abstract: Now more than ever animal studies have the potential to test hypotheses regarding how cognition evolves. Comparative psychologists have developed new techniques to probe the cognitive mechanisms underlying animal behavior, and they have become increasingly skillful at adapting methodologies to test multiple species. Meanwhile, evolutionary biologists have generated quantitative approaches to investigate the phylogenetic distribution and function of phenotypic traits, including cognition. In particular, phylogenetic methods can quantitatively (1) test whether specific cognitive abilities are correlated with life history (e.g., lifespan), morphology (e.g., brain size), or socio-ecological variables (e.g., social system), (2) measure how strongly phylogenetic relatedness predicts the distribution of cognitive skills across species, and (3) estimate the ancestral state of a given cognitive trait using measures of cognitive performance from extant species. Phylogenetic methods can also be used to guide the selection of species comparisons that offer the strongest tests of a priori predictions of cognitive evolutionary hypotheses (i.e., phylogenetic targeting). Here, we explain how an integration of comparative psychology and evolutionary biology will answer a host of questions regarding the phylogenetic distribution and history of cognitive traits, as well as the evolutionary processes that drove their evolution.

Five-year olds, but not chimpanzees, attempt to manage their reputations.

Abstract: Virtually all theories of the evolution of cooperation require that cooperators find ways to interact with one another selectively, to the exclusion of cheaters This means that individuals must make reputational judgments about others as cooperators, based on either direct or indirect evidence Humans, and possibly other species, add another component to the process: they know that they are being judged by others, and so they adjust their behavior in order to affect those judgments – so-called impression management Here, we show for the first time that already preschool children engage in such behavior In an experimental study, 5-year-old human children share more and steal less when they are being watched by a peer than when they are alone In contrast, chimpanzees behave the same whether they are being watched by a groupmate or not This species difference suggests that humans' concern for their own self-reputation, and their tendency to manage the impression they are making on others, may be unique to humans among primates

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Origins of Human Communication

Abstract: Winner, 2009 Eleanor Maccoby Book Award in Developmental Psychology, presented by the American Psychological Association. and Honorable Mention, Literature, Language & Linguistics category, 2008 PROSE Awards presented by the Professional/Scholarly Publishing Division of the Association of American Publishers. Human communication is grounded in fundamentally cooperative, even shared, intentions. In this original and provocative account of the evolutionary origins of human communication, Michael Tomasello connects the fundamentally cooperative structure of human communication (initially discovered by Paul Grice) to the especially cooperative structure of human (as opposed to other primate) social interaction. Tomasello argues that human cooperative communication rests on a psychological infrastructure of shared intentionality (joint attention, common ground), evolved originally for collaboration and culture more generally. The basic motives of the infrastructure are helping and sharing: humans communicate to request help, inform others of things helpfully, and share attitudes as a way of bonding within the cultural group. These cooperative motives each created different functional pressures for conventionalizing grammatical constructions. Requesting help in the immediate you-and-me and here-and-now, for example, required very little grammar, but informing and sharing required increasingly complex grammatical devices. Drawing on empirical research into gestural and vocal communication by great apes and human infants (much of it conducted by his own research team), Tomasello argues further that humans' cooperative communication emerged first in the natural gestures of pointing and pantomiming. Conventional communication, first gestural and then vocal, evolved only after humans already possessed these natural gestures and their shared intentionality infrastructure along with skills of cultural learning for creating and passing along jointly understood communicative conventions. Challenging the Chomskian view that linguistic knowledge is innate, Tomasello proposes instead that the most fundamental aspects of uniquely human communication are biological adaptations for cooperative social interaction in general and that the purely linguistic dimensions of human communication are cultural conventions and constructions created by and passed along within particular cultural groups. Jean Nicod Lectures A Bradford Book

打磨Using Language,倡导新理念

Abstract: 人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

Analyzing linguistic data: a practical introduction to statistics using R

Abstract: 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