語用論(Pragmatics)を考える

Traditional mechanistic accounts of language processing derive almost entirely from the study of monologue. Yet, the most natural and basic form of language use is dialogue. As a result, these accounts may only offer limited theories of the mechanisms that un- derlie language processing in general. We propose a mechanistic account of dialogue, the interactive alignment account, and use it to de- rive a number of predictions about basic language processes. The account assumes that, in dialogue, the linguistic representations em- ployed by the interlocutors become aligned at many levels, as a result of a largely automatic process. This process greatly simplifies production and comprehension in dialogue. After considering the evidence for the interactive alignment model, we concentrate on three aspects of processing that follow from it. It makes use of a simple interactive inference mechanism, enables the development of local di- alogue routines that greatly simplify language processing, and explains the origins of self-monitoring in production. We consider the need for a grammatical framework that is designed to deal with language in dialogue rather than monologue, and discuss a range of implica- tions of the account.

/pdf/toward-a-mechanistic-psychology-of-dialogue-2jquck2ahu.pdf

Toward a mechanistic psychology of dialogue

Eye movements are now widely used to investigate cognitive processes during reading, scene perception, and visual search. In this article, research on the following topics is reviewed with respect to reading: (a) the perceptual span (or span of effective vision), (b) preview benefit, (c) eye movement control, and (d) models of eye movements. Related issues with respect to eye movements during scene perception and visual search are also reviewed. It is argued that research on eye movements during reading has been somewhat advanced over research on eye movements in scene perception and visual search and that some of the paradigms developed to study reading should be more widely adopted in the study of scene perception and visual search. Research dealing with "real-world" tasks and research utilizing the visual-world paradigm are also briefly discussed.

https://www.cogsci.ucsd.edu/~bkbergen/cogs200/Rayner-Bartlett.pdf

Eye movements and attention in reading, scene perception, and visual search.

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

A grammar of spoken Chinese = 中國話的文法

https://www.sfs.uni-tuebingen.de/~cebert/teaching/10Unterspezifikation/papers/Depth%20of%20Processing%20in%20Language%20Comprehension%20-%20Not%20Noticing%20the%20Evidence%20(Sanford%20+%20Sturt).pdf

Depth of processing in language comprehension: not noticing the evidence

https://web.stanford.edu/~jurafsky/sturtjml.pdf

The time-course of the application of binding constraints in reference resolution

A number of lines of study suggest that word meanings are not always fully exploited in comprehension. In two experiments, we used a text-change paradigm to study depth of semantic processing during reading. Participants were instructed to detect words that changed across two consecutive presentations of short texts. The results suggest that the full details of word meanings are not always incorporated into the interpretation and that the degree of semantic detail in the representation is a function of linguistic focus. The results provide evidence for the idea that representations are only good enough for the purpose at hand (Ferreira, Bailey, & Ferraro, 2002).

/pdf/linguistic-focus-and-good-enough-representations-an-3tw8r4p8d9.pdf

Linguistic Focus and Good-Enough Representations: An Application of the Change-Detection Paradigm

Structural change and reanalysis difficulty in language comprehension

https://faculty.wcas.northwestern.edu/~myo507/Papers/SlatteryEtAL_GoodEnough_Published.pdf

Patrick Sturt

Papers

Depth of processing in language comprehension: not noticing the evidence

The time-course of the application of binding constraints in reference resolution

Linguistic Focus and Good-Enough Representations: An Application of the Change-Detection Paradigm

Structural change and reanalysis difficulty in language comprehension

Lingering misinterpretations of garden path sentences arise from competing syntactic representations