Böcker utgivna av MIT Press

The computer analogy of the mind has been as widely adopted in contemporary cognitive neuroscience as was the analogy of the brain as a collection of organs in phrenology. Just as the phrenologist would insist that each organ must have its particular function, so contemporary cognitive neuroscience is committed to the notion that each brain region must have its fundamental computation. In After Phrenology, Michael Anderson argues that to achieve a fully post-phrenological science of the brain, we need to reassess this commitment and devise an alternate, neuroscientifically grounded taxonomy of mental function. Anderson contends that the cognitive roles played by each region of the brain are highly various, reflecting different neural partnerships established under different circumstances. He proposes quantifying the functional properties of neural assemblies in terms of their dispositional tendencies rather than their computational or information-processing operations. Exploring larger-scale issues, and drawing on evidence from embodied cognition, Anderson develops a picture of thinking rooted in the exploitation and extension of our early-evolving capacity for iterated interaction with the world. He argues that the multidimensional approach to the brain he describes offers a much better fit for these findings, and a more promising road toward a unified science of minded organisms.

Because 'The Structure of the Japanese Language' is both descriptive and analytical, it will prove useful both as a basic handbook of supplementary reading for second-year or more advanced courses in Japanese.

How Brazilian postwar avant-garde artists updated modernism in a way that was radically at odds with European and North American art historical narratives.

The transformations of the Strip--from the fake Wild West to neon signs twenty stories high to "starchitecture"--and how they mirror America itself.

A study of the philosophical implications of evolutionary psychology, suggesting that knowledge is a set of natural kinds housed in the modules of a massively modular mind.In Reconstructing Reason and Representation, Murray Clarke offers a detailed study of the philosophical implications of evolutionary psychology. In doing so, he offers new solutions to key problems in epistemology and philosophy of mind, including misrepresentation and rationality. He proposes a naturalistic approach to reason and representation that is informed by evolutionary psychology, and, expanding on the massive modularity thesis advanced in work by Leda Cosmides and John Tooby, argues for a modular, adapticist account of misrepresentation and knowledge. Just as the reliability of representation can be defended on the basis of an account of the proper function of cognitive modularity, misrepresentation can be explained through an appeal to the "gap theory," by noting the divergence between the proper and actual domains of cognitive modules in a massively modular mind. Clarke argues for an externalist, modular reliabilism by suggesting that evolution has equipped us with generally reliable inferential systems even if they do not always produce true beliefs. He argues that reliable deductive and inductive inference occurs only when cognitive modules deal with actual domains that are sufficiently similar to their proper domains. This psychologically informed, naturalized adapticism leads to the suggestion that knowledge is a set of natural kinds housed in the modules of a massively modular mind. Typically, the proper function of these cognitive modules is to provide us with truths that enable us to satisfy our basic biological needs. Beyond reasoning modules, other cognitive modules discussed include the ability to orient ourselves in space, and our abilities with language, numbers, object reasoning, and social understanding. Clarke also defends Cosmides and Tooby's massive modularity hypothesis against such critics as Jerry Fodor by demonstrating that these critics consistently misrepresent Cosmides and Tooby's position.

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications.Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.

This new anthology will serve as an ideal introduction to phenomenology for analytic philosophers, both as a text and as the single most useful source book on Husserl for cognitive scientists.

The task of archiving was once entrusted only to museums, libraries, and other institutions that acted as repositories of culture in material form. But with the rise of digital networked media, a multitude of self-designated archivists -- fans, pirates, hackers -- have become practitioners of cultural preservation on the Internet. These nonprofessional archivists have democratized cultural memory, building freely accessible online archives of whatever content they consider suitable for digital preservation. In Rogue Archives, Abigail De Kosnik examines the practice of archiving in the transition from print to digital media, looking in particular at Internet fan fiction archives.De Kosnik explains that media users today regard all of mass culture as an archive, from which they can redeploy content for their own creations. Hence, "remix culture" and fan fiction are core genres of digital cultural production. De Kosnik explores, among other things, the anticanonical archiving styles of Internet preservationists; the volunteer labor of online archiving; how fan archives serve women and queer users as cultural resources; archivists' efforts to attract racially and sexually diverse content; and how digital archives adhere to the logics of performance more than the logics of print. She also considers the similarities and differences among free culture, free software, and fan communities, and uses digital humanities tools to quantify and visualize the size, user base, and rate of growth of several online fan archives.

This comprehensive collection of twenty-nine readings covers artificial intelligence from its historical roots to current research directions and practice. With its helpful critique of the selections, extensive bibliography, and clear presentation of the material, Computation and Intelligence will be a useful adjunct to any course in AI as well as a handy reference for professionals in the field. The book is divided into five parts. The first part contains papers that present or discuss foundational ideas linking computation and intelligence, typified by A. M. Turing's "Computing Machinery and Intelligence". The second part, Knowledge Representation, presents a sampling of the numerous representational schemes - by Newell, Minsky, Collins and Quillian, Winograd, Schank, Hayes, Holland, McClelland, Rumelhart, Hinton, and Brooks. The third part, Weak Method Problem Solving, focuses on the research and design of syntax based problem solvers, including the most famous of these, the Logic Theorist and GPS. The fourth part, Reasoning in Complex and Dynamic Environments, presents a broad spectrum of the AI communities' research in knowledge-intensive problem solving, from McCarthy's early design of systems with "common sense" to model based reasoning. The two concluding selections, by Marvin Minsky and by Herbert Simon, respectively, present the recent thoughts of two of AI's pioneers who revisit the concepts and controversies that have developed during the evolution of the tools and techniques that make up the current practice of artificial intelligence.