Language is a hallmark of the human species; the flexibility and unbounded expressivity of our linguistic abilities is unique in the biological world. In this book, Morten Christiansen and Nick Chater argue that to understand this astonishing phenomenon, we must consider how language is created: moment by moment, in the generation and understanding of individual utterances; year by year, as new language learners acquire language skills; and generation by generation, as languages change, split, and fuse through the processes of cultural evolution. Christiansen and Chater propose a revolutionary new framework for understanding the evolution, acquisition, and processing of language, offering an integrated theory of how language creation is intertwined across these multiple timescales.
Christiansen and Chater argue that mainstream generative approaches to language do not provide compelling accounts of language evolution, acquisition, and processing. Their own account draws on important developments from across the language sciences, including statistical natural language processing, learnability theory, computational modeling, and psycholinguistic experiments with children and adults. Christiansen and Chater also consider some of the major implications of their theoretical approach for our understanding of how language works, offering alternative accounts of specific aspects of language, including the structure of the vocabulary, the importance of experience in language processing, and the nature of recursive linguistic structure.

Statistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.

"The nervous system is made up of a large number of interacting elements. To understand how such a complex system functions requires the construction and analysis of computational models at many different levels. This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience." (Amazon)
"This is a wonderful, clear and compelling text on mathematically-minded computational modelling in neuroscience. It is beautifully aimed at those engaged in capturing quantitatively, and thus simulating, complex neural phenomena at multiple spatial and temporal scales, from intracellular calcium dynamics and stochastic ion channels, through compartmental modelling, all the way to aspects of development. It takes particular care to define the processes, potential outputs and even some pitfalls of modelling; and can be recommended for containing the key lessons and pointers for people seeking to build their own computational models. By eschewing issues of coding and information processing, it largely hews to concrete biological data, and it nicely avoids sacrificing depth for breadth. It is very suitably pitched as a Master's level text, and its two appendices, on mathematical methods and software resources, will rapidly become dog-eared."
Peter Dayan, University College London