This chapter first considers the functional and semantic overlap between discourse connectives and discourse markers, where the latter is presented as including the former. Since the two categories share most protypical features, the fuzzy boundaries between the two categories are explained in terms of partial overlap.

We then show that the description of connectives’ meanings and functions can proceed following an onomasiological as well as a semasiological approach. The latter has given rise to numerous case studies in a variety of languages aiming to come to a fine-grained semantic description of specific connectives. Strenghts and weaknesses of such studies are presented. Onomasiological approaches focus on a set connectives that are categorized together on the basis of shared semantic properties, trying to disentangle their similarities and differences, within and across languages. In the final section, we turn to one of the most described features of discourse connectives namely its polysemy and polyfunctionality, and how contextual cues may help solve this ambiguity, and how polysemy is a key explaining factor in the (frequency) distribution of connectives.

This chapter provides an overview of studies that call on the syntactic features of connectives as a means to disambiguate their function and meaning. These syntactic features cover the morphosyntactic nature of discourse connectives as well as their syntagmatic distribution. On the basis of existing lexicons of discourse connectives, we first give an overview of the morphosyntactic distribution of discourse connectives in several European and non-European languages. We then address a number of studies that focus on the (semi-automatic) identification and annotation of discourse connectives in context. This is of particular interest in the field of natural language processing, but also in the field of contrastive linguistics, where it has been shown that syntactic categories, including those underlying the description of discourse connective uses, are not always cross-linguistically valid. The final section is devoted to the relationship between the syntagmatic position of discourse connectives and their meaning, which has given rise to numerous studies at the grammar-discourse interface highlighting the fuzzy boundary between discourse connectives and discourse markers.

Text and pictures serve different purposes in multimedia comprehension. Conceptual processing of texts and pictures results in propositions, whereby text-based propositions and picture-based propositions specialize in different kinds of information. These propositions are merged into an overarching conceptual semantic network guiding mental model construction. The construction process receives descriptive guidance by text-based and picture-based propositional representations as well as depictive guidance by perceptual representation of pictures through structure mapping. Because texts are more constrained in terms of processing order, they can provide more conceptual guidance through a subject matter than pictures. A distinction can be made between initial model construction and adaptive model specification. Initial model construction aims at general coherence formation; adaptive model specification aims at selective processing of task-relevant information. Initial mental model construction is more likely to be text-driven than adaptive mental model specification, while adaptive mental model specification is more likely to be picture-driven than initial mental model construction.

In this chapter, we discuss how the various assumptions and principles that underlie the relevance-theoretic pragmatic framework can be applied to the pragmatic processes and inferential tasks. We begin by introducing relevance as an analytical framework that is based on key assumptions about human cognition and communication. These assumptions have consequences, and they allow us to explain and predict how utterances are interpreted. We see how these consequences play out in a range of examples that are discussed in the rest of the chapter. We start by looking at implicitly communicated meaning, before considering where we might draw the line between implicitly and explicitly communicated meaning. According to relevance theory, inference plays a role, not only in working out what a speaker is implicating, but also in working out what she is explicitly communicating. We then look in more detail at the various inferential processes that contribute to a speaker’s explicit meaning (reference assignment, disambiguation, and pragmatic enrichment), and we think about how a hearer reaches a hypothesis about the speaker’s overall intended meaning.

In this chapter, we take a closer look at the components that make up a speaker’s intended meaning. The aim of this chapter is to give an overview of the breadth and depth of pragmatic work that is involved in everyday communication. Working out what a speaker intends to communicate on any given occasion involves more than just decoding the words that she has uttered. In this chapter, we introduce the pragmatic processes that are involved in deriving a speaker’s overall intended meaning. We start by considering the processes that are involved in working out what the speaker intends to explicitly communicate. This section will include discussion of reference assignment, disambiguation, and pragmatic enrichment. We then look at the contribution that speech acts and communication of speaker emotion play. Finally, we consider examples of implicitly communicated meaning. In short, this chapter lays out the gaps between what is said and what is communicated and demonstrates how these need to be filled to derive a speaker’s intended meaning.

This chapter discusses diverse typological, corpus-based and experimental evidence related to differential argument marking of subject and object. The different types of evidence converge, supporting the efficiency-based interpretation of differential marking based on the principle of negative correlation between accessibility and costs. This chapter provides a novel contribution to this well-explored topic because the intuitions about the causes that lead to the emergence of cross-linguistic patterns are captured in the form of measurable probabilities that can be found in corpus data. Of particular importance here are conditional probabilities of the role given the referential features of arguments, which determine the accessibility of the intended role interpretation. This chapter also demonstrates that we can disentangle some competing explanations of differential marking using experiments with artificial language learning and communication.