Volume 40, Issue 1

Based on a qualitative, discourse-analytic and a quantitative, sociolinguistic analysis, this article investigates four sets of linguistic features and their occurrence in recordings of 36 lectures and interactional classes collected at a university in Germany. It examines how structural markers, questions, question tags, and turn-initial response tokens contribute to variations of style in response to academic division, speech mode, communicative role in academic discourse and gender. Of these four factors, the latter appears to be the least influential in the use of the structures investigated, due to, as is argued, global discourse restrictions in academic speech. Qualitative analysis shows that global restrictions can be overridden locally as certain discourse contexts are amenable to the appearance of features that contribute to more interactional and cooperative speech styles, frequently linked to females. The article concludes that a foundational understanding of relevant discourse genres and their constraints and a combination of qualitative and quantitative methods can make an important contribution to a better understanding of the dynamics of language and gender.

This paper provides an overview of central aspects of statistical modeling of linguistic data. Starting from a general definition of model , the paper discusses the goals of modeling as well as a variety of issues bearing upon the formulation/definition of statistical models. It then surveys model selection, the choice of ‘the best model’ and three fundamental notions affecting the interpretation of models. Finally, the paper turns to validation and replicability and addresses a variety of challenges researchers face during modeling processes.

In this article we give a short introduction to the online method of event-related (brain) potentials (ERPs) and their importance for our understanding of language structure and grammar. This methodology places high demands on (technical) requirements for laboratory equipment as well as on the skills of the investigator. However, the high costs are relatively balanced compared to the advantages of this experimental method. By using ERPs, it becomes possible to monitor the electrophysiological brain activity associated with speech processing in real time (millisecond by millisecond) and to draw conclusions on human language processing and the human parser. First, we present briefly how this method works and how ERPs can be classified (Section 1 and 2). In the following, we show that the ERP method can be used to study the processing of e. g. semantic, pragmatic and syntactic information (Section 3). Crucial for our discussion will be the interpretation of the so-called ERP components and their connection and importance for psycholinguistics and theoretical linguistics. In our presentation, we emphasize, that the electrophysiological brain activity in relation to specific (e. g. linguistic) stimuli can be used to identify distinct processes, which give a deeper insight into the different processing steps of language. At the end of this article (Section 4), we present some results from ERP studies of German negative-polar elements. Additionally, we highlight the advantage and benefits of an alternative method to analyze ERP data compared to the more ‘classical’ average technique.

Computational neurolinguistics integrates methods from computational (psycho-)linguistics and computational neuroscience in order to model neural correlates of linguistic behavior. We illustrate these techniques using an example of the language processing of German negative polarity items (NPI) in the event-related brain potential (ERP) paradigm. To that aim, we first describe the syntactic and semantic licensing conditions of NPIs by means of slightly modified minimalist grammars. In a second step we use dynamic cognitive modeling (DCM) to map the state descriptions of a minimalist parser onto activation patterns of a neural network. Thirdly, the network’s synaptic weights are trained with the correct parse of NPI constructions. Using these weights we calculate neural harmony measures for correct and for ungrammatical NPI constructions. In a final step we correlate the harmonies of the dynamical model with experimentally obtained ERP amplitudes by means of a simple statistical model.