Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this "hierarchical prediction machine" approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

/pdf/whatever-next-predictive-brains-situated-agents-and-the-epj56cs868.pdf

Whatever next? Predictive brains, situated agents, and the future of cognitive science

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation. With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication. Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

https://www.foo.be/cours/dess-20122013/b/Natural%20Language%20Processing%20with%20Python%20-%20O'Reilly2009.pdf

Natural Language Processing with Python

Discourse in the Novel

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 usage-based view takes grammar to be the cognitive organization of one's experience with language. Aspects of that experience, for instance, the frequency of use of certain constructions or particular instances of constructions, have an impact on representation that is evidenced in speaker knowledge of conventionalized phrases and in language variation and change. It is shown that particular instances of constructions can acquire their own pragmatic, semantic, and phonological characteristics. In addition, it is argued that high-frequency instances of constructions undergo grammaticization processes (which produce further change), function as the central members of categories formed by constructions, and retain their old forms longer than lower-frequency instances under the pressure of newer formations. An exemplar model that accommodates both phonological and semantic representation is elaborated to describe the data considered.

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From Usage to Grammar: The Mind's Response to Repetition

So-called “degree achievements” (DAs), such as widen, lengthen, cool, dry, and straighten, have long caused problems for theories of aspectual classification, because they constitute one area in which the traditional Vendler/Dowty system breaks down.1 As first observed in Dowty 1979, these verbs display both telic and atelic properties according to standard diagnostics (see also Abusch 1986). This paper explores and analyzes the specific problems associated with DAs in an effort to better understand the underpinnings of telicity. The aspectual inconsistency of DAs can be illustrated by examining their behavior with respect to several standard tests for (a)telicity. For example, atelic predicates are known to be entailed by their progressive forms, while telic predicates are not (Vendler 1957, Dowty 1979):

/pdf/scalar-structure-underlies-telicity-in-degree-achievements-35h7xx728m.pdf

Scalar Structure Underlies Telicity in "Degree Achievements"

http://linguistics.berkeley.edu/~kjohnson/ling210/sociophonetics/Hay_Warren_Drager_06.pdf

Factors influencing speech perception in the context of a merger-in-progress

Functions of humor in the conversations of men and women

While it is widely assumed that high-frequency morphologically complex forms tend to display characteristics of noncompositionality, models of morphological processing do not predict a direct relationship between absolute frequency and decomposition. Rather, they predict a relationship between decomposition and the relative frequency of the derived form and the base. This paper argues that such a relative frequency effect does, indeed, exist. First, the results of a simple experiment demonstrate that subjects perceive derived forms that are more frequent than their bases to be significantly less complex than matched counterparts that are less frequent than their bases. And second, dictionary calculations reveal that derived forms that are more frequent than their bases are significantly more likely to display symptoms of semantic drift than derived forms containing higher-frequency bases. High-frequency forms, however, are no more prone to semantic drift than low-frequency forms. These results provide evidence that it is relative frequency, rather than absolute frequency, that affects the decomposability of morphologically complex words A low-frequency form is likely to be nontransparent if it is composed of even-lower-frequency parts. And a high-frequency form may be highly decomposable if the base word it contains is higher frequency still.

Jennifer Hay

Papers

Scalar Structure Underlies Telicity in "Degree Achievements"

Factors influencing speech perception in the context of a merger-in-progress

Functions of humor in the conversations of men and women

Lexical frequency in morphology: Is everything relative?

Multimodal markers of irony and sarcasm