This essay re-examines Meinong's "Über Gegenstandstheorie" and undertakes a clarification and revision of it that is faithful to Meinong, overcomes the various objections to his theory, and is capable of offering solutions to various problems in philosophy of mind and philosophy of language. I then turn to a discussion of a historically and technically interesting Russell-style paradox (now known as "Clark's Paradox") that arises in the modified theory. I also examine the alternative Meinong-inspired theories of Hector-Neri Castañeda and Terence Parsons.

Natural languages differ from most formal languages in having a partial, rather than a total, semantic interpretation function; e.g., some noun phrases don't refer. The usual semantics for handling such noun phrases (e.g., Russell, Quine) require syntactic reform. The alternative presented here is semantic expansion, viz., enlarging the range of the interpretaion function to make it total. A specific ontology based on Meinong's Theory of Objects, which can serve as domain on interpretation, is suggested, and related to the work of (...) Castaneda, Frege, Katz and Fodor, Parsons, and Scott. (shrink)

This major publication is a history of the semantic tradition in philosophy from the early nineteenth century through its incarnation in the work of the Vienna Circle, the group of logical positivists that emerged in the years 1925–1935 in Vienna who were characterised by a strong commitment to empiricism, a high regard for science, and a conviction that modern logic is the primary tool of analytic philosophy. In the first part of the book, Alberto Coffa traces the roots of logical (...) positivism in a semantic tradition that arose in opposition to Kant's theory that a priori knowledge is based on pure intuition and the constitutive powers of the mind. In Part II, Coffa chronicles the development of this tradition by members and associates of the Vienna Circle. Much of Coffa's analysis draws on the unpublished notes and correspondence of many philosophers. The book, however, is not merely a history of the semantic tradition from Kant 'to the Vienna Station'. Coffa also critically reassesses the role of semantic notions in understanding the ground of a priori knowledge and its relation to empirical knowledge and questions the turn the tradition has taken since Vienna. (shrink)

A proof of ‘correctness’ for a mathematical algorithm cannot be relevant to executions of a program based on that algorithm because both the algorithm and the proof are based on assumptions that do not hold for computations carried out by real-world computers. Thus, proving the ‘correctness’ of an algorithm cannot establish the trustworthiness of programs based on that algorithm. Despite the (deceptive) sameness of the notations used to represent them, the transformation of an algorithm into an executable program is a (...) wrenching metamorphosis that changes a mathematical abstraction into a prescription for concrete actions to be taken by real computers. Therefore, it is verification of program executions (processes) that is needed, not of program texts that are merely the scripts for those processes. In this view, verification is the empirical investigation of: (a) the behavior that programs invoke in a computer system and (b) the larger context in which that behavior occurs. Here, deduction can play no more, and no less, a role than it does in the empirical sciences. (shrink)

What might a theory of mental imagery look like, and how might one begin formulating such a theory? These are the central questions addressed in the present paper. The first section outlines the general research direction taken here and provides an overview of the empirical foundations of our theory of image representation and processing. Four issues are considered in succession, and the relevant results of experiments are presented and discussed. The second section begins with a discussion of the proper form (...) for a cognitive theory, and the distinction between a theory and a model is developed. Following this, the present theory and computer simulation model are introduced. This theory specifies the nature of the internal representations (data structures) and the processes that operate on them when one generates, inspects, or transforms mental images. In the third, concluding, section we consider three very different kinds of objections to the present research program, one hinging on the possibility of experimental artifacts in the data, and the others turning on metatheoretical commitments about the form of a cognitive theory. Finally, we discuss how one ought best to evaluate theories and models of the sort developed here. (shrink)

The objective of research in the foundations of Al is to explore such basic questions as: What is a theory in Al? What are the most abstract assumptions underlying the competing visions of intelligence? What are the basic arguments for and against each assumption? In this essay I discuss five foundational issues: (1) Core Al is the study of conceptualization and should begin with knowledge level theories. (2) Cognition can be studied as a disembodied process without solving the symbol grounding (...) problem. (3) Cognition is nicely described in propositional terms. (4) We can study cognition separately from learning. (5) There is a single architecture underlying virtually all cognition. I explain what each of these implies and present arguments from both outside and inside Al why each has been seen as right or wrong. (shrink)

Most recent philosophical thought about the scientific representation of the world has focused on dyadic relationships between language-like entities and the world, particularly the semantic relationships of reference and truth. Drawing inspiration from diverse sources, I argue that we should focus on the pragmatic activity of representing, so that the basic representational relationship has the form: Scientists use models to represent aspects of the world for specific purposes. Leaving aside the terms "law" and "theory," I distinguish principles, specific conditions, models, (...) hypotheses, and generalizations. I argue that scientists use designated similarities between models and aspects of the world to form both hypotheses and generalizations. (shrink)

A debate over the theoretical capabilities of formal methods in computer science has raged for more than two years now. The function of this paper is to summarize the key elements of this debate and to respond to important criticisms others have advanced by placing these issues within a broader context of philosophical considerations about the nature of hardware and of software and about the kinds of knowledge that we have the capacity to acquire concerning their performance.

The notion of program verification appears to trade upon an equivocation. Algorithms, as logical structures, are appropriate subjects for deductive verification. Programs, as causal models of those structures, are not. The success of program verification as a generally applicable and completely reliable method for guaranteeing program performance is not even a theoretical possibility.