This book combines studies on referential as well as relational coherence and includes approaches to written and to spoken language, to production and to comprehension, to language specific and to cross-linguistic issues, to monolingual, bilingual and L2-acquisition. The theoretical issues and empirical findings discussed are of importance not only for theoretical linguistics, but also have a broad potential of practical implication.

All languages of the world provide their speakers with linguistic means to express causal relations in discourse. Causal connectives and causative auxiliaries are among the salient markers of causal construals. Cognitive scientists and linguists are interested in how much of this causal modeling is specific to a given culture and language, and how much is characteristic of general human cognition. Speakers of English, for example, can choose between because and since or between therefore and so. How different are these from the choices made by Dutch speakers, who speak a closely related language, but (unlike English speakers) have a dedicated marker for non-volitional causality (daardoor)? The central question in this volume is: What parameters of categorization shape the use of causal connectives and auxiliary verbs across languages? The book discusses how differences between even quite closely related languages (English, Dutch, Polish) can help us to elaborate the typology of levels and categories of causation represented in language. In addition, the volume demonstrates convergence of linguistic, corpus-linguistic and psycholinguistic methodologies in determining cognitive categories of causality. The basic notion of causality appears to be an ideal linguistic phenomenon to provide an overview of methods and, perhaps more importantly, invoke a discussion on the most adequate methodological approaches to study fundamental issues in language and cognition.

Over the past four decades, discourse coherence has been studied from linguistic, psycholinguistic, computational, and applied perspectives. This volume identifies current issues and under-researched topics in the pragmatics of discourse coherence. Nine studies from various disciplines address the realization and signalling of coherence relations in various genres and languages, their acquisition and use by first- and second-language learners and university students, the relationship between coherence relations and genre-specific discourse structure, and extensions of the coherence paradigm to multimodal discourse and visual art. This collection will be of interest to researchers from linguistics, applied linguistics, psychology, communication, and multimodal semiotics.

The aim of this monograph is to give impetus to research into one of the central questions in discourse studies: what makes a sequence of sentences or utterances a discourse? The theoretical framework for describing the possibilities of discourse continuation is delineated by two principles: the discursive and the dialogic principle. The chord of discourse is unfolded in a tripartite wire: Conjunction, Adjunction and Interjunction, each containing three aspects, leading to a Connectivity Model. This new three-by-three taxonomy of discourse relations incorporates findings from several theories and approaches that have evolved over the last three decades, including Systemic Functional Linguistics and Rhetorical Structure Theory. In comparing this model to other models, this book presents a state-of-the-art of discourse relation analysis combined with detailed accounts of many examples. This monograph furthermore proposes a new way of presenting discourse structures in connectivity graphs followed by eleven commandments for the segmentation and labeling of discourse, and three procedures for disambiguation if more labels are applicable. This study can provide a base for corpus linguistic analysis on discourse structures, computational approaches to discourse generation and cognitive experimental research of discourse competence."

Discourse, of Course comes after Jan Renkema’s Introduction to Discourse Studies (2004) for undergraduates. The new book is a collection of twenty short papers. It is a capita selecta course and meant for graduate programs. The aim of this book is threefold: • to present material for advanced courses in discourse studies; • to unfold a stimulating display of research projects to future PhD students; • to give an overview of new developments after the 2004 Introduction to Discourse Studies. This publication fulfills both the teacher's need for a state-of-the-art overview of the main topics in discourse, and the student's need to acquire standards for developing research plans in theses and dissertations. It gives a combination of approaches from very different schools in discourse studies, ranging from argumentation theory to genre theory, from the study of multimodal metaphors to cognitive approaches to coherence analysis. This book is not only meant to serve as a textbook, but also as a reference book for researchers who want an update for various main topics in the field.

Statistical models attempt to describe and quantify relationships between variables. In the models presented in this chapter, there is a response variable (sometimes called dependent variable) and at least one predictor variable (sometimes called independent or explanatory variable). When investigating a possible cause-and-effect type of relationship, the response variable is the putative effect and the predictors are the hypothesized causes. Typically, there is a main predictor variable of interest; other predictors in the model are called covariates. Unknown covariates or other independent variables not controlled in an experiment or analysis can affect the dependent or outcome variable and mislead the conclusions made from the inquiry (Bock, Velleman, & De Veaux, 2009). A p value (p) measures the statistical significance of the observed relationship; given the model, p is the probability that a relationship is seen by mere chance. The smaller the p value, the more confident we can be that the pattern seen in the data 2 is not random. In the type of models examined here, the R measures the prop- tion of the variation in the response variable that is explained by the predictors 2 specified in the model; if R is close to 1, then almost all the variation in the response variable has been explained. This measure is also known as the multiple correlation coefficient. Statistical studies can be grouped into two types: experimental and observational.