These original contributions by philosophers and historians of science discuss a range of issues pertaining to the testing of hypotheses in modern physics by observation and experiment. Chapters by Lawrence Sklar, Dudley Shapere, Richard Boyd, R. C. Jeffrey, Peter Achinstein, and Ronald Laymon explore general philosophical themes with applications to modern physics and astrophysics. The themes include the nature of the hypothetico-deductive method, the concept of observation and the validity of the theoretical-observation distinction, the probabilistic basis of confirmation, and the testing of idealizations and approximations. The remaining four chapters focus on the history of particular twentieth-century experiments, the instruments and techniques utilized, and the hypotheses they were designed to test. Peter Galison reviews the development of the bubble chamber; Roger Stuewer recounts a sharp dispute between physicists in Cambridge and Vienna over the interpretation of artificial disintegration experiments; John Rigden provides a history of the magnetic resonance method; and Geoffrey Joseph suggests a statistical interpretation of quantum mechanics that can be used to interpret the Stern-Gerlach and double-slit experiments. This book inaugurates the series, Studies from the Johns Hopkins Center for the History and Philosophy of Science, directed by Peter Achinstein and Owen Hannaway. A Bradford Book.

Observation, Experiment, and Hypothesis in Modern Physical Science

Science & Math

Achinstein, Peter

Hannaway, Owen

History & Philosophy

Bradford Books

Neurocomputing: Foundations of Research

Researchers will find Neurocomputing an essential guide to the concepts employed in this field that have been taken from disciplines as varied as neuroscience, psychology, cognitive science, engineering, and physics.

Anderson, James A.

Computers & Technology

The Gricean account of language is at the center of much current work in the philosophy of language and the philosophy of mind. Anita Avramides maintains that Grice's paradigm can be used to defend very different conceptions of mind and of meaning. In this clearly argued book she describes Grice's analysis of meaning and proposes two interpretations of it, one reductive and one nonreductive.  Much current work in cognitive science assumes that the content of words and thoughts can be explained in naturalistic terms. A leading idea (advanced mainly by Stephen Schiffer and Brian Loar) is that the naturalistic account of content will proceed in two stages: a reduction of the semantic features of word; to the contents of propositional attitudes and then a reduction of the latter to physical or functional notions. The appeal of Grice's work on meaning to this two stage reductionist strategy is that Gricean definitions seem to provide the only way of reducing the semantic to the psychological, a reduction that is required for a naturalistic account of intentionality.  While Avramides defends the method of analysis as one suited to the concept of meaning, she rejects a reductive interpretation of the analysis. She argues that any attempt to reduce the semantic to the psychological leads to a mistaken conception of mind. She exposes the assumptions behind the reductive interpretation and offers fresh and original arguments for the antireductionist position. Against the reductive Gricean she defends the idea that there is a deep epistemological symmetry between semantic and propositional attitude notions, a result of which is that semantically characterized linguistic behavior is central to our concept of belief.  Anita Avramides is Lecturer in Philosophy at The Queens College, The University of Oxfor. A Bradford Book

Meaning and Mind: An Examination of a Gricean Account of Language

Health, Fitness & Dieting

Avramides, Anita

Psychology & Counseling

Modeling Legal Argument provides a comprehensive treatment of case-based reasoning and a detailed description of a computer program called Hypo, that models the way attorneys argue with cases, real and hypothetical. The program offers significant advantages over "keyword" case retrieval systems in the legal field and demonstrates how to design expert systems that assist the user by presenting reasonable alternative answers on all sides of an issue and by citing case examples to explain their advice. Hypo analyzes problem situations dealing with trade secrets disputes, retrieves relevant legal cases from its database and fashions them into reasonable legal arguments about who should win. The arguments demonstrate the program's ability to reason symbolically with past cases, to draw factual analogies between cases, to cite them in arguments, to distinguish them, and to pose counter-examples and hypotheticals based on past cases. Modeling Legal Argument discusses the law as a paradigm of case-based argument, introduces Hypo and its adversarial reasoning process, provides an overview of the Hypo program, and gives extended examples of the model's reasoning capabilities. It describes the case knowledge base, a dimensional index, basic mechanisms of case-based reasoning, and offers a theory of case-based argument in Hypo. Ashley evaluates Hypo's performance and takes up adversarial case-based reasoning beyond the law and extensions of the Hypo model. Kevin D. Ashley is a Research Scientist at the Learning Research an Development Center and Assistant Professor of Law at the University of Pittsburgh. Modeling Legal Argument is included in the Artificial Intelligence and Legal Reasoning series, edited by L. Thorne McCarty and Edwina L. Rissland.

Modeling Legal Argument: Reasoning with Cases and Hypotheticals

Ashley, Kevin D.

Computer Science

The Core Language Engine presents the theoretical and engineering advances embodied in one of the most comprehensive natural language processing systems designed to date. Recent research results from different areas of computational linguistics are integrated into a single elegant design with potential for application to tasks ranging from machine translation to information system interfaces.
Bridging the gap between theoretical and implementation oriented literature, The Core Language Engine describes novel analyses and techniques developed by the contributors at SRI International's Cambridge Computer Science Research Centre. It spans topics that include a wide-coverage unification grammar for English syntax and semantics, context-dependent and contextually disambiguated logical form representations, interactive translation, efficient algorithms for parsing and generation, and mechanisms for quantifier scoping, reference resolution, and lexical acquisition.
Contents Introduction to the CLE • Logical Forms • Categories and Rules • Unification Based Syntactic Analysis • Semantic Rules for English • Lexical Analysis • Syntactic and Semantic Processing • Quantifier Scopin • Sortal Restrictions • Resolving Quasi Logical Forms • Lexical Acquisition • The CLE in Application Development • Ellipsis, Comparatives, and Generation • Swedish-English QLF Translation

The Core Language Engine (ACL-MIT Series in Natural Language Processing) (ACL-MIT PRESS SERIES IN NATURAL LANGUAGE PROCESSING)

Reference

Words, Language & Grammar

An Introduction to Neural Networks

An Introduction to Neural Networks falls into a new ecological niche for texts. Based on notes that have been class-tested for more than a decade, it is aimed at cognitive science and neuroscience students who need to understand brain function in terms of computational modeling, and at engineers who want to go beyond formal algorithms to applications and computing strategies. It is the only current text to approach networks from a broad neuroscience and cognitive science perspective, with an emphasis on the biology and psychology behind the assumptions of the models, as well as on what the models might be used for. It describes the mathematical and computational tools needed and provides an account of the author's own ideas.
Students learn how to teach arithmetic to a neural network and get a short course on linear associative memory and adaptive maps. They are introduced to the author's brain-state-in-a-box (BSB) model and are provided with some of the neurobiological background necessary for a firm grasp of the general subject.
The field now known as neural networks has split in recent years into two major groups, mirrored in the texts that are currently available: the engineers who are primarily interested in practical applications of the new adaptive, parallel computing technology, and the cognitive scientists and neuroscientists who are interested in scientific applications. As the gap between these two groups widens, Anderson notes that the academics have tended to drift off into irrelevant, often excessively abstract research while the engineers have lost contact with the source of ideas in the field. Neuroscience, he points out, provides a rich and valuable source of ideas about data representation and setting up the data representation is the major part of neural network programming. Both cognitive science and neuroscience give insights into how this can be done effectively: cognitive science suggests what to compute and neuroscience suggests how to compute it.

Certification

Foreword by Michael Arbib
This introduction to the principles, design, and practice of intelligent behavior-based autonomous robotic systems is the first true survey of this robotics field. The author presents the tools and techniques central to the development of this class of systems in a clear and thorough manner. Following a discussion of the relevant biological and psychological models of behavior, he covers the use of knowledge and learning in autonomous robots, behavior-based and hybrid robot architectures, modular perception, robot colonies, and future trends in robot intelligence.
The text throughout refers to actual implemented robots and includes many pictures and descriptions of hardware, making it clear that these are not abstract simulations, but real machines capable of perception, cognition, and action.

Behavior-Based Robotics (Intelligent Robotics and Autonomous Agents)

Arkin, Ronald C.

Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future.
The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book.
ContributorsJames A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow

Talking Nets: An Oral History of Neural Networks

Sunny Auyang tackles what she calls "the large pictures of the human mind," exploring the relevance of cognitive science findings to everyday mental life. Auyang proposes a model of an "open mind emerging from the self-organization of infrastructures," which she opposes to prevalent models that treat mind as a disembodied brain or computer, subject to the control of external agents such as neuroscientists and programmers.
Although cognitive science has obtained abundant data on neural and computational processes, it barely explains such ordinary experiences as recognizing faces, feeling pain, or remembering the past. In this book Sunny Auyang tackles what she calls "the large pictures of the human mind," exploring the relevance of cognitive science findings to everyday mental life. Auyang proposes a model of an "open mind emerging from the self-organization of infrastructures," which she opposes to prevalent models that treat mind as a disembodied brain or computer, subject to the control of external agents such as neuroscientists and programmers. Her model consists of three parts: (1) the open mind of our conscious life; (2) mind's infrastructure, the unconscious processes studied by cognitive science; and (3) emergence, the relation between the open mind and its infrastructure.
At the heart of Auyang's model is the mind that opens to the world and makes it intelligible. A person with an open mind feels, thinks, recognizes, believes, doubts, anticipates, fears, speaks, and listens, and is aware of I, together with it and thou. Cognitive scientists refer to the "binding problem," the question of how myriad unconscious processes combine into the unity of consciousness. Auyang approaches the problem from the other end -- by starting with everyday experience rather than with the mental infrastructure. In so doing, she shows both how analyses of experiences can help to advance cognitive science and how cognitive science can help us to understand ourselves as autonomous subjects.

Mind in Everyday Life and Cognitive Science

Auyang, Sunny Y.

The fundamental question of the ethics of belief is "What ought one to believe?" According to the traditional view of evidentialism, the strength of one’s beliefs should be proportionate to the evidence. Conventional ways of defending and challenging evidentialism rely on the idea that what one ought to believe is a matter of what it is rational, prudent, ethical, or personally fulfilling to believe. Common to all these approaches is that they look outside of belief itself to determine what one ought to believe.  In this book Jonathan Adler offers a strengthened version of evidentialism, arguing that the ethics of belief should be rooted in the concept of belief—that evidentialism is belief's own ethics. A key observation is that it is not merely that one ought not, but that one cannot, believe, for example, that the number of stars is even. The "cannot" represents a conceptual barrier, not just an inability. Therefore belief in defiance of one's evidence (or evidentialism) is impossible. Adler addresses such questions as irrational beliefs, reasonableness, control over beliefs, and whether justifying beliefs requires a foundation. Although he treats the ethics of belief as a central topic in epistemology, his ideas also bear on rationality, argument and pragmatics, philosophy of religion, ethics, and social cognitive psychology.

Belief's Own Ethics

Politics & Social Sciences

Adler, Jonathan Eric

Philosophy

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