Evolutionary psychology is one of many biologically informed approaches to the study of human behavior. Along with cognitive psychologists, evolutionary psychologists propose that much, if not all, of our behavior can be explained by appeal to internal psychological mechanisms. What distinguishes evolutionary psychologists from many cognitive psychologists is the proposal that the relevant internal mechanisms are adaptations—products of natural selection—that helped our ancestors get around the world, survive and reproduce. To understand the central claims of evolutionary psychology we require an understanding of some key concepts in evolutionary biology, cognitive psychology, philosophy of science and philosophy of mind. Philosophers are interested in evolutionary psychology for a number of reasons. For philosophers of science —mostly philosophers of biology—evolutionary psychology provides a critical target. There is a broad consensus among philosophers of science that evolutionary psychology is a deeply flawed enterprise. For philosophers of mind and cognitive science evolutionary psychology has been a source of empirical hypotheses about cognitive architecture and specific components of that architecture. Philosophers of mind are also critical of evolutionary psychology but their criticisms are not as all-encompassing as those presented by philosophers of biology. Evolutionary psychology is also invoked by philosophers interested in moral psychology both as a source of empirical hypotheses and as a critical target.

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Stanford Encyclopedia of Philosophy

The measurement of meaning

I. the origin of species by means of natural selection

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.

打磨Using Language,倡导新理念

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

Recent studies of dyadic interaction have examined phenomena of synchronization, entrainment, alignment, and convergence. All these forms of behavioral matching have been hypothesized to play a supportive role in establishing coordination and common ground between interlocutors. In the present study, evidence is found for a new kind of coordination termed complexity matching. Temporal dynamics in conversational speech signals were analyzed through time series of acoustic onset events. Timing in periods of acoustic energy was found to exhibit behavioral matching that reflects complementary timing in turn-taking. In addition, acoustic onset times were found to exhibit power law clustering across a range of timescales, and these power law functions were found to exhibit complexity matching that is distinct from behavioral matching. Complexity matching is discussed in terms of interactive alignment and other theoretical principles that lead to new hypotheses about information exchange in dyadic conversation and interaction in general.

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Complexity matching in dyadic conversation.

The study of interpersonal synchrony examines how interacting individuals grow to have similar behavior, cognition, and emotion in time. Many of the established methods of analyzing interpersonal synchrony are costly and time-consuming; the study of bodily synchrony has been especially laborious, traditionally requiring researchers to hand-code movement frame by frame. Because of this, researchers have been searching for more efficient alternatives for decades. Recently, some researchers (e.g., Nagaoka & Komori (IEICE Transactions on Information and Systems, 91(6), 1634–1640, 2008); Ramseyer & Tschacher, 2008) have applied computer science and computer vision techniques to create frame-differencing methods (FDMs) to simplify analyses. In this article, we provide a detailed presentation of one such FDM, created by modifying and adding to existing FDMs. The FDM that we present requires little programming experience or specialized equipment: Only a few lines of MATLAB code are required to execute an automated analysis of interpersonal synchrony. We provide sample code and demonstrate its use with an analysis of brief, friendly conversations; using linear mixed-effects models, the measure of interpersonal synchrony was found to be significantly predicted by time lag (p < .001) and by the interaction between time lag and measures of interpersonal liking (p < .001). This pattern of results fits with existing literature on synchrony. We discuss the current limitations and future directions for FDMs, including their use as part of a larger methodology for capturing and analyzing multimodal interaction.

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Frame-differencing methods for measuring bodily synchrony in conversation.

Research on interpersonal convergence and synchrony characterizes the way in which interacting individuals come to have more similar affect, behaviour, and cognition over time. Although its dynamics have been explored in many settings, convergence during conflict has been almost entirely overlooked. We present a simple but ecologically valid study comparing how different situational contexts that highlight affiliation and argument impact interpersonal convergence of body movement and to what degree emotional states affect convergence in both conversational settings. Using linear mixed-effect models, we found that in-phase bodily synchrony decreases significantly during argument. However, affective changes did not significantly predict changes in levels of interpersonal synchrony, suggesting that differences in affect valences between affiliation and argument cannot solely explain our results.

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Argument disrupts interpersonal synchrony

Successful interaction requires complex coordination of body movements. Previous research has suggested a functional role for coordination and especially synchronization (i.e., time-locked movement across individuals) in different types of human interaction contexts. Although such coordination has been shown to be nearly ubiquitous in human interaction, less is known about its function. One proposal is that synchrony supports and facilitates communication (Topics Cogn Sci 1:305–319, 2009). However, questions still remain about what the properties of coordination for optimizing communication might look like. In the present study, dyads worked together to construct towers from uncooked spaghetti and marshmallows. Using cross-recurrence quantification analysis, we found that dyads with loosely coupled gross body movements performed better, supporting recent work suggesting that simple synchrony may not be the key to effective performance (Riley et al. 2011). We also found evidence that leader–follower dynamics—when sensitive to the specific role structure of the interaction—impact task performance. We discuss our results with respect to the functional role of coordination in human interaction.

Movement dynamics reflect a functional role for weak coupling and role structure in dyadic problem solving

Much work on communication and joint action conceptualizes interaction as a dynamical system. Under this view, dynamic properties of interaction should be shaped by the context in which the interaction is taking place. Here we explore interpersonal movement coordination or synchrony---the degree to which individuals move in similar ways over time---as one such context-sensitive property. Studies of coordination have typically investigated how these dynamics are influenced by either high-level constraints (i.e., slow-changing factors) or low-level constraints (i.e., fast-changing factors like movement). Focusing on nonverbal communication behaviors during naturalistic conversation, we analyzed how interacting participants' head movement dynamics were shaped simultaneously by high-level constraints (i.e., conversation type; friendly conversations or arguments) and low-level constraints (i.e., perceptual stimuli; non-informative visual stimuli vs. informative visual stimuli). We found that high- and low-level constraints interacted non-additively to affect interpersonal movement dynamics, highlighting the context sensitivity of interaction and supporting the view of joint action as a complex adaptive system.

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Alexandra Paxton

Papers

Complexity matching in dyadic conversation.

Frame-differencing methods for measuring bodily synchrony in conversation.

Argument disrupts interpersonal synchrony

Movement dynamics reflect a functional role for weak coupling and role structure in dyadic problem solving

Interpersonal Movement Synchrony Responds to High- and Low-Level Conversational Constraints.