Presenting a landmark in linguistics and cognitive science, Ray Jackendoff proposes a new holistic theory of the relation between the sounds, structure, and meaning of language and their relation to mind and brain. Foundations of Language exhibits the most fundamental new thinking in linguistics since Noam Chomsky's Aspects of the Theory of Syntax in 1965—yet is readable, stylish, and accessible to a wide readership. Along the way it provides new insights on the evolution of language, thought, and communication.

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Time underlies many interesting human behaviors. Thus, the question of how to represent time in connectionist models is very important. One approach is to represent time implicitly by its effects on processing rather than explicitly (as in a spatial representation). The current report develops a proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory. In this approach, hidden unit patterns are fed back to themselves: (...) the internal representations which develop thus reflect task demands in the context of prior internal states. A set of simulations is reported which range from relatively simple problems (temporal version of XOR) to discovering syntactic/semantic features for words. The networks are able to learn interesting internal representations which incorporate task demands with memory demands: indeed, in this approach the notion of memory is inextricably bound up with task processing. These representations reveal a rich structure, which allows them to be highly context‐dependent, while also expressing generalizations across classes of items. These representations suggest a method for representing lexical categories and the type/token distinction. (shrink)

Chomsky proposes a reformulation of the theory of transformational generative grammar that takes recent developments in the descriptive analysis of particular ...

Noam Chomsky's book on syntactic structures is a serious attempts on the part of a linguist to construct within the tradition of scientific theory-construction ...

In these essays the minimalist approach to linguistic theory is formulated and progressively developed.

The Chomskian revolution in linguistics gave rise to a new orthodoxy about mind and language. Michael Devitt throws down a provocative challenge to that orthodoxy. What is linguistics about? What role should linguistic intuitions play in constructing grammars? What is innate about language? Is there a 'language faculty'? These questions are crucial to our developing understanding of ourselves; Michael Devitt offers refreshingly original answers. He argues that linguistics is about linguistic reality and is not part of psychology; that linguistic rules (...) are not represented in the mind; that speakers are largely ignorant of their language; that speakers' intuitions do not reflect information supplied by the language faculty and are not the main evidence for grammars; that the rules of 'Universal Grammar' are largely, if not entirely, innate structure rules of thought; indeed, that there is little or nothing to the language faculty. Devitt's controversial theses will prove highly stimulating to anyone working on language and the mind. (shrink)

Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities in their underlying capacities, vs. when such failures (...) are only superficial or peripheral? This article draws on a foundational insight from cognitive science—the distinction between performance and competence—to encourage “species-fair” comparisons between humans and machines. The performance/competence distinction urges us to consider whether the failure of a system to behave as ideally hypothesized, or the failure of one creature to behave like another, arises not because the system lacks the relevant knowledge or internal capacities (“competence”), but instead because of superficial constraints on demonstrating that knowledge (“performance”). I argue that this distinction has been neglected by research comparing human and machine behavior, and that it should be essential to any such comparison. Focusing on the domain of image classification, I identify three factors contributing to the species-fairness of human–machine comparisons, extracted from recent work that equates such constraints. Species-fair comparisons level the playing field between natural and artificial intelligence, so that we can separate more superficial differences from those that may be deep and enduring. (shrink)

Traditional arguments against the identification of the language of thought with natural language assume a picture of natural language which is largely inconsistent with that suggested by contemporary linguistic theory. This has led certain philosophers and linguists to suggest that this identification is not as implausible as it once seemed. In this paper, I discuss the prospects for such an identification in light of these developments in linguistic theory. I raise a new challenge against the identification thesis: the existence of (...) ungrammatical but acceptable expressions seems to require a gap between thought and language. I consider what must be the case in order for this objection to be dealt with. (shrink)

This state-of-the-art guide to some of the most exciting work in current linguistics explores how the core components of the language faculty interact. It examines how these interactions are reflected in linguistic and cognitive theory, considers what they reveal about the operations of language within the mind, and looks at their reflections in expression and communication. Leading international scholars present cutting-edge accounts of developments in the interfaces between phonetics, phonology, morphology, syntax, semantics, and pragmatics. They bring to bear a rich (...) variety of methods and theoretical perspectives, focus on a broad array of issues and problems, and illustrate their arguments from a wide range of the world's languages. After the editors' introduction to its structure, scope, and content, the book is divided into four parts. The first, Sound, is concerned with the interfaces between phonetics and phonology, phonology and morphology, and phonology and syntax. Part II, Structure, considers the interactions of syntax with morphology, semantics, and the lexicon, and explores the status of the word and its representional status in the mind. Part III, Meaning, revisits the syntax-semantics interface from the perspective of compositionality, and looks at issues concerned with intonation, discourse, and context. The authors in the final part of the book, General Architectural Concerns, examine work on Universal Grammar, the overall model of language, and linguistic and associated theories of language and cognition. All scholars and advanced students of language will value this book, whether they are in linguistics, cognitive science, philosophy, artificial intelligence, computational science, or informatics. (shrink)

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, (...) and other applications. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where a physiological structure becomes applicable for a (...) function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

ABSTRACTWhile over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” that can help to unify existing approaches and provide new avenues for research. I first outline (...) three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the “generative methodology,” and Section 3 develops a version of this methodology, the “Competence Framework for AI research”. (shrink)

This note briefly responds to Devitt’s (2008) riposte to Collins’s (2008a) argument that linguistic realism prima facie fails to accommodate unvoiced elements within syntax. It is argued that such elements remain problematic. For it remains unclear how conventions might target the distribution of PRO and how they might explain hierarchical structure that is presupposed by such distribution and which is not witnessed in concrete strings.