The two-volume set LNCS 3561 and LNCS 3562 constitute the refereed proceedings of the First International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2005, held in Las Palmas, Canary Islands, Spain in June 2005. The 118 revised papers presented are thematically divided into two volumes; the first includes all the contributions mainly related with the methodological, conceptual, formal, and experimental developments in the fields of Neurophysiology and cognitive science. The second volume collects the papers related with bioinspired programming strategies and all the contributions related with the computational solutions to engineering problems in different application domains.

This new Companion traces the development of cognitive anthropology from its beginnings in the late 1950s to the present, and evaluates future directions of research in the field. In 29 contributions from leading anthropologists, there is an overview of cognitive and cultural structures, insights into how cognition works in everyday life and interacts with culture, and examples of contemporary research. A Companion to Cognitive Anthropology is essential for anyone interested in the questions of how culture shapes cognitive processes.

Human speech and music share a number of similarities and differences. One of the closest similarities is their temporal nature as both (i) develop over time, (ii) form sequences of temporal intervals, possibly differing in duration and acoustical marking by different spectral properties, which are perceived as a rhythm, and (iii) generate metrical expectations. Human brains are particularly efficient in perceiving, producing, and processing fine rhythmic information in music and speech. However a number of critical questions remain to be answered: Where does this human sensitivity for rhythm arise? How did rhythm cognition develop in human evolution? How did environmental rhythms affect the evolution of brain rhythms? Which rhythm-specific neural circuits are shared between speech and music, or even with other domains? Evolutionary processes’ long time scales often prevent direct observation: understanding the psychology of rhythm and its evolution requires a close-fitting integration of different perspectives. First, empirical observations of music and speech in the field are contrasted and generate testable hypotheses. Experiments exploring linguistic and musical rhythm are performed across sensory modalities, ages, and animal species to address questions about domain-specificity, development, and an evolutionary path of rhythm. Finally, experimental insights are integrated via synthetic modeling, generating testable predictions about brain oscillations underlying rhythm cognition and its evolution. Our understanding of the cognitive, neurobiological, and evolutionary bases of rhythm is rapidly increasing. However, researchers in different fields often work on parallel, potentially converging strands with little mutual awareness. This research topic builds a bridge across several disciplines, focusing on the cognitive neuroscience of rhythm as an evolutionary process. It includes contributions encompassing, although not limited to: (1) developmental and comparative studies of rhythm (e.g. critical acquisition periods, innateness); (2) evidence of rhythmic behavior in other species, both spontaneous and in controlled experiments; (3) comparisons of rhythm processing in music and speech (e.g. behavioral experiments, systems neuroscience perspectives on music-speech networks); (4) evidence on rhythm processing across modalities and domains; (5) studies on rhythm in interaction and context (social, affective, etc.); (6) mathematical and computational (e.g. connectionist, symbolic) models of “rhythmicity” as an evolved behavior.

Two recent innovations, the emergence of formal cognitive models and the addition of cognitive neuroscience data to the traditional behavioral data, have resulted in the birth of a new, interdisciplinary field of study: model-based cognitive neuroscience. Despite the increasing scientific interest in model-based cognitive neuroscience, few active researchers and even fewer students have a good knowledge of the two constituent disciplines. The main goal of this edited collection is to promote the integration of cognitive modeling and cognitive neuroscience. Experts in the field will provide tutorial-style chapters that explain particular techniques and highlight their usefulness through concrete examples and numerous case studies. The book will also include a thorough list of references pointing the reader towards additional literature and online resources.

Although many archaeologists have a good understanding of the basics in computer science, statistics, geostatistics, modeling, and data mining, more literature is needed about the advanced analysis in these areas. This book aids archaeologists in learning more advanced tools and methods while also helping mathematicians, statisticians, and computer

Connections and Symbols provides the first systematic analysis of the explosive new field of Connectionism that is challenging the basic tenets of cognitive science. Does intelligence result from the manipulation of structured symbolic expressions? Or is it the result of the activation of large networks of densely interconnected simple units? Connections and Symbols provides the first systematic analysis of the explosive new field of Connectionism that is challenging the basic tenets of cognitive science. These lively discussions by Jerry A. Fodor, Zenon W. Pylyshyn, Steven Pinker, Alan Prince, Joel Lechter, and Thomas G. Bever raise issues that lie at the core of our understanding of how the mind works: Does connectionism offer it truly new scientific model or does it merely cloak the old notion of associationism as a central doctrine of learning and mental functioning? Which of the new empirical generalizations are sound and which are false? And which of the many ideas such as massively parallel processing, distributed representation, constraint satisfaction, and subsymbolic or microfeatural analyses belong together, and which are logically independent? Now that connectionism has arrived with full-blown models of psychological processes as diverse as Pavlovian conditioning, visual recognition, and language acquisition, the debate is on. Common themes emerge from all the contributors to Connections and Symbols: criticism of connectionist models applied to language or the parts of cognition employing language like operations; and a focus on what it is about human cognition that supports the traditional physical symbol system hypothesis. While criticizing many aspects of connectionist models, the authors also identify aspects of cognition that could he explained by the connectionist models. Connections and Symbols is included in the Cognition Special Issue series, edited by Jacques Mehler.

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientifi c and engineering sectors.