In Concepts, Kinds, and Cognitive Development, Frank C. Keil provides a coherent account of how concepts and word meanings develop in children, adding to our understanding of the representational nature of concepts and word meanings at all ages. Keil argues that it is impossible to adequately understand the nature of conceptual representation without also considering the issue of learning. Weaving together issues in cognitive development, philosophy, and cognitive psychology, he reconciles numerous theories, backed by empirical evidence from nominal kinds studies, (...) natural-kinds studies, and studies of fundamental categorical distinctions. He shows that all this evidence, when put together, leads to a better understanding of semantic and conceptual development. The book opens with an analysis of the problems of modeling qualitative changes in conceptual development, investigating how concepts of natural kinds, nominal kinds, and artifacts evolve. The studies on nominal kinds document a powerful and unambiguous developmental pattern indicating a shift from a reliance on global tabulations of characteristic features to what appears to be a small set of defining ones. The studies on natural kinds document an analogous shift toward a core theory instead of simple definition. Both sets of studies are strongly supported by cross cultural data. While these patterns seem to suggest that the young child organizes concepts according to characteristic features, Keil argues that there is a framework of conceptual categories and causal beliefs that enables even very young children to understand kinds at a deeper, theoretically guided, level. This account suggests a new way of understanding qualitative change and carries strong implications for how concepts are represented at any point in development. A Bradford Book. (shrink)

Essentialism is the idea that certain categories, such as "dog," "man," or "intelligence," have an underlying reality or true nature that gives objects their identity. Where does this idea come from? In this book, Susan Gelman argues that essentialism is an early cognitive bias. Young children's concepts reflect a deep commitment to essentialism, and this commitment leads children to look beyond the obvious in many converging ways: when learning words, generalizing knowledge to new category members, reasoning about the insides of (...) things, contemplating the role of nature versus nurture, and constructing causal explanations. Gelman argues against the standard view of children as concrete or focused on the obvious, instead claiming that children have an early, powerful tendency to search for hidden, non-obvious features of things. She also attacks claims that children build up their knowledge of the world based on simple, associative learning strategies, arguing that children's concepts are embedded in rich folk theories. Parents don't explicitly teach children to essentialize; instead, during the preschool years, children spontaneously construct concepts and beliefs that reflect an essentialist bias.Essentialist accounts have been offered, in one form or another, for thousands of years, extending back at least to Aristotle and Plato. Yet this book is the first to address the issues surrounding essentialism from a psychological perspective. Gelman synthesizes over 15 years of empirical research on essentialism into a unified framework and explores the broader lessons that the research imparts concerning, among other things, human concepts, children's thinking, and the ways in which language influences thought. This volume will appeal to developmental, cognitive, and social psychologists, as well as to scholars in cognitive science and philosophy. (shrink)

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is (...) “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. (shrink)

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to a (...) single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

Frege was the grandfather of analytical philosophy, Husserl the founder of the phenomenological school, two radically different philosophical movements. In 1903, say, how would they have appeared to any German student of philosophy who knew the work of both? Not, certainly, as two deeply opposed thinkers: rather as remarkably close in orientation, despite some divergence of interests. They may be compared with the Rhine and the Danube, which rise quite close to one another and for a time pursue roughly parallel (...) courses, only to diverge in utterly different directions and flow into different seas. Why, then, did this happen? What small ingredient into the thought of each was eventually magnified into so great an effect? (shrink)

When contrasted with "Continental" philosophy, analytical philosophy is often called "Anglo-American." Dummett argues that "Anglo-Austrian" would be a more accurate label. By re-examining the similar origins of the two traditions, we can come to understand why they later diverged so widely, and thus take the first step toward reconciliation.

Computability theory originated with the seminal work of Gödel, Church, Turing, Kleene and Post in the 1930s. This theory includes a wide spectrum of topics, such as the theory of reducibilities and their degree structures, computably enumerable sets and their automorphisms, and subrecursive hierarchy classifications. Recent work in computability theory has focused on Turing definability and promises to have far-reaching mathematical, scientific, and philosophical consequences. Written by a leading researcher, Computability Theory provides a concise, comprehensive, and authoritative introduction to contemporary (...) computability theory, techniques, and results. The basic concepts and techniques of computability theory are placed in their historical, philosophical and logical context. This presentation is characterized by an unusual breadth of coverage and the inclusion of advanced topics not to be found elsewhere in the literature at this level. The book includes both the standard material for a first course in computability and more advanced looks at degree structures, forcing, priority methods, and determinacy. The final chapter explores a variety of computability applications to mathematics and science. Computability Theory is an invaluable text, reference, and guide to the direction of current research in the field. Nowhere else will you find the techniques and results of this beautiful and basic subject brought alive in such an approachable and lively way. (shrink)

In the era of “big data,” science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is of (...) particular relevance to biomedicine, covering theoretical components of ontologies, best practices for ontology design, and examples of biomedical ontologies in use. After defining an ontology as a representation of the types of entities in a given domain, the book distinguishes between different kinds of ontologies and taxonomies, and shows how applied ontology draws on more traditional ideas from metaphysics. It presents the core features of the Basic Formal Ontology (BFO), now used by over one hundred ontology projects around the world, and offers examples of domain ontologies that utilize BFO. The book also describes Web Ontology Language (OWL), a common framework for Semantic Web technologies. Throughout, the book provides concrete recommendations for the design and construction of domain ontologies. (shrink)

The state of the art of the bioengineering aspects of the morphology of microorganisms and their relationship to process performance are described in this volume. Materials and methods of the digital image analysis and mathematical modeling of hyphal elongation, branching and pellet formation as well as their application to various fungi and actinomycetes during the production of antibiotics and enzymes are presented.

Although the two volumes of _Logic, Language, and Meaning _can be used independently of one another, together they provide a comprehensive overview of modern logic as it is used as a tool in the analysis of natural language. Both volumes provide exercises and their solutions.

From an early age, humans know a surprising amount about basic physical principles, such as gravity, force, mass, and shape. We can see this in the way that young children play, and manipulate objects around them. The same behaviour has long been observed in primates - chimpanzees have been shown to possess a remarkable ability to make and use simple tools. But what does this tell us about their inner mental state - do they therefore share the same understanding to (...) that of a young child? Do they understand the simple, underlying physical principles involved? Though some people would say that they do, this book reports groundbreaking research that questions whether this really is the case. -/- Folk Physics for Apes challenges the assumptions so often made about apes. It offers us a rare glimpse into the workings of another mind, examining how apes perceive and understand the physical world - an understanding that appears to be both similar to, and yet profoundly different from our own. The book will have broad appeal to evolutionary psychologists, developmental psychologists, and those interested in the sub-disciplines of cognitive science (philosophy, anthropology). The book additionally offers for developmental psychologists some valuable new non-verbal techniques for assessing causal understanding in young children. (shrink)

Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ in this sense is often used by philosophers as a synonym of ‘metaphysics’ (a label meaning literally: ‘what comes after the Physics’), a term used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’. But in recent years, in a development hardly noticed by philosophers, the (...) term ‘ontology’ has gained currency in the field of computer and information science, and in information-driven research in bioinformatics and related areas. We examine these new developments in applied ontology, and show what lessons they might have for both philosophers and information scientists. (shrink)

Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynamic programming parsers have restricted training and evaluation to artificially short sentences, we present the first general, featurerich discriminative parser, based on a conditional random field model, which has been successfully scaled to the full WSJ parsing data. Our efficiency is primarily due to the use of stochastic optimization techniques, as well as parallelization and chart prefiltering. On WSJ15, (...) we attain a state-of-the-art F-score of 90.9%, a 14% relative reduction in error over previous models, while being two orders of magnitude faster. On sentences of length 40, our system achieves an F-score of 89.0%, a 36% relative reduction in error over a generative baseline. (shrink)