Communicative Efficiency In The Lexicon

In this dissertation, I argue that a variety of probabilistic patterns in natural language phonology derive from communicative efficiency. I present evidence from phonetically transcribed dictionaries of 60 languages from 25 major language families showing that both probability distributions over phonological structures licensed by the categorical grammar, and the global organization of the phonological lexicon as a whole facilitate the efficient communication of intended messages from speaker to listener.Specifically, I show that the occurrence probabilities of different grammatical structures render natural language phonology an efficient code for communication given the effort involved in producing different categories and the specific kinds of noise introduced by the human language channel. I also present evidence that co-occurrence restrictions on consonants sharing place features serve a communicative purpose in that they facilitate the identification of words with respect to each other. Furthermore, I show that the organization of the phonological lexicon as a whole is subject to communicative efficiency. Concretely, I show that words in human language preferentially rely on highly perceptible contrasts for distinctness, beyond what is expected from the probabilistic patterning of the individual sounds that distinguish them. This shows that redundancy in the phonological code is not randomly distributed, but exists to supplement imperceptibile distinctions between larger units as needed.I argue that cross-linguistic biases in the distributions of individual sounds arise from humans using their language in ways that accommodate anticipated mistransmission (Jurafsky et al. 2001, van Son and Pols 2003, Aylett and Turk 2004) thus presenting a serious challenge to theories relegating the emergence of communicative efficiency in phonology to properties of the human language channel only (Ohala 1981, Blevins 2004, 2006). Furthermore, I present preliminary computational and experimental evidence that the optimization of the lexicon as a whole could have arisen from the aggregate effects of speakers' biases to use globally distinct word forms over the course of a language's history (cf Martin, 2007).

All living beings try to save effort, and humans are no exception. This groundbreaking book shows how we save time and energy during communication by unconsciously making efficient choices in grammar, lexicon and phonology. It presents a new theory of 'communicative efficiency', the idea that language is designed to be as efficient as possible, as a system of communication. The new framework accounts for the diverse manifestations of communicative efficiency across a typologically broad range of languages, using various corpus-based and statistical approaches to explain speakers' bias towards efficiency. The author's unique interdisciplinary expertise allows her to provide rich evidence from a broad range of language sciences. She integrates diverse insights from over a hundred years of research into this comprehensible new theory, which she presents step-by-step in clear and accessible language. It is essential reading for language scientists, cognitive scientists and anyone interested in language use and communication.

Illustrated with rich examples, this book shows how language users can save effort by choosing efficient structures and word order.

This volume brings together the latest research from leading scholars on the mental lexicon - the representation of language in the mind/brain at the level of individual words and meaningful sub-word units. In recent years, the study of words as mental objects has grown rapidly across several fields, including linguistics, psychology, philosophy, neuroscience, education, and cognitive science. This comprehensive collection spans multiple disciplines, topics, theories, and methods to highlight important advances in the study of the mental lexicon, identify areas of debate, and inspire innovation in the field from present and future generations of scholars. The book is divided into three parts. Part I presents modern linguistic and cognitive theories of how the mind/brain represents words at the phonological, morphological, syntactic, semantic, and pragmatic levels. This part also discusses broad architectural issues pertaining to the internal organization of the lexicon, the relation between words and concepts, and the role of compositionality. Part II examines how children learn the form and meaning of words in their native language, bridging learner- and environment-driven contributions and taking into account variability across both individual learners and communities. Chapters in the final part explore how the mental lexicon contributes to language use during listening, speaking, and conversation, and includes perspectives from bilingualism, sign languages, and disorders of lexical access and production.

A conversation between two people can only take place if the words intended by each speaker are successfully recognized. Spoken word recognition is at the heart of language comprehension. This automatic and smooth process remains a challenge for models of spoken word recognition. Both the process of mapping the speech signal onto stored representations for words, and the format of the representation themselves are subject to debate. So far, existing research on the nature of spoken word representations has focused mainly on native speakers. The picture becomes even more complex when looking at spoken word recognition in a second language. Given that most of the world’s speakers know and use more than one language, it is crucial to reach a more precise understanding of how bilingual and multilingual individuals encode spoken words in the mental lexicon, and why spoken word recognition is more difficult in a second language than in the native language. Current models of native spoken word recognition operate under two assumptions: (i) that listeners’ perception of the incoming speech signal is optimal; and (ii) that listeners’ lexical representations are accurate. As a result, lexical representations are easily activated, and intended words are successfully recognized. However, these assumptions are compromised when applied to a later-learned second language. For a variety of reasons (e.g., phonetic/phonological, orthographic), second language users may not perceive the speech signal optimally, and they may still be refining the motor routines needed for articulation. Accordingly, their lexical representations may differ from those of native speakers, which may in turn inhibit their selection of the intended word forms. Second language users also have to solve a larger selection challenge—having words in more than one language to choose from. Thus, for second language users, the links between perception, lexical representations, orthography, and production are all but clear. Even for simultaneous bilinguals, important questions remain about the specificity and interdependence of their lexical representations and the factors influencing cross-language word activation. This Frontiers Research Topic seeks to further our understanding of the factors that determine how multilinguals recognize and encode spoken words in the mental lexicon, with a focus on the mapping between the input and lexical representations, and on the quality of lexical representations.

th The 5 International Conference on Hybrid Artificial Intelligence Systems (HAIS 2010) has become a unique, established and broad interdisciplinary forum for researchers and practitioners who are involved in developing and applying symbolic and sub-symbolic techniques aimed at the construction of highly robust and reliable problem-solving techniques, and bringing the most relevant achievements in this field. Overcoming the rigid encasing imposed by the arising orthodoxy in the field of arti- cial intelligence, which has led to the partition of researchers into so-called areas or fields, interest in hybrid intelligent systems is growing because they give freedom to design innovative solutions to the ever-increasing complexities of real-world pr- lems. Noise and uncertainty call for probabilistic (often Bayesian) methods, while the huge amount of data in some cases asks for fast heuristic (in the sense of suboptimal and ad-hoc) algorithms able to give answers in acceptable time frames. High dim- sionality demands linear and non-linear dimensionality reduction and feature extr- tion algorithms, while the imprecision and vagueness call for fuzzy reasoning and linguistic variable formalization. Nothing impedes real-life problems to mix diffic- ties, presenting huge quantities of noisy, vague and high-dimensional data; therefore, the design of solutions must be able to resort to any tool of the trade to attack the problem. Combining diverse paradigms poses challenging problems of computational and methodological interfacing of several previously incompatible approaches. This is, thus, the setting of HAIS conference series, and its increasing success is the proof of the vitality of this exciting field.

This book constitutes the proceedings of the 5th International Conference on Hybrid Artificial Intelligent Systems, held in San Sebastian, Spain, in June 2010.

The two volumes, LNCS 6686 resp. LNCS 6687, constitute the refereed proceedings of the 4th International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2011, held in La Palma, Canary Islands, Spain, in May/June 2011. The 108 revised full papers presented in LNCS 6686 resp. LNCS 6687 were carefully reviewed and selected from numerous submissions. The first part, LNCS 6686, entitled "Foundations on Natural and Artificial Computation", includes all the contributions mainly related to the methodological, conceptual, formal, and experimental developments in the fields of neurophysiology and cognitive science. The second part, LNCS 6687, entitled "New Challenges on Bioinspired Applications", contains the papers related to bioinspired programming strategies and all the contributions related to the computational solutions to engineering problems in different application domains, specially Health applications, including the CYTED ``Artificial and Natural Computation for Health'' (CANS) research network papers.