Papers by William Rapaport
Advances in Multimedia and Interactive Technologies
Computationalism should not be the view that (human) cognition is computation; it should be the v... more Computationalism should not be the view that (human) cognition is computation; it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. If semiotic systems are systems that interpret signs, then both humans and computers are semiotic systems. Finally, minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers.
Springer eBooks, 1998
We present a computational analysis of de re, de ditto, and de se belief and knowledge reports. O... more We present a computational analysis of de re, de ditto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Castaiteda, namely, that the simple rule '(A knows that P) implies P' apparently does not hold If P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P, including those containing quasi-indexicals. In so doing, we explore the difference between reasoning in a public communication language and in a knowledge-representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (e.g., written narrative) in order to fully capture certain features of their semantics.
Advances in multimedia and interactive technologies book series, 2018
Computationalism should not be the view that (human) cognition is computation; it should be the v... more Computationalism should not be the view that (human) cognition is computation; it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. If semiotic systems are systems that interpret signs, then both humans and computers are semiotic systems. Finally, minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers.
Cognitive Science, 1997
We present a computational analysis of de re, de ditto, and de se belief and knowledge reports. O... more We present a computational analysis of de re, de ditto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Castaiteda, namely, that the simple rule '(A knows that P) implies P' apparently does not hold If P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P, including those containing quasi-indexicals. In so doing, we explore the difference between reasoning in a public communication language and in a knowledge-representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (e.g., written narrative) in order to fully capture certain features of their semantics.
This paper discusses the theoretical background and some of the results of an interdisciplinary, ... more This paper discusses the theoretical background and some of the results of an interdisciplinary, cognitive-science research project on the comprehension of narrative text. The unifying theme of our work has been the notion of a deictic center: a mental model of spatial, temporal, and character information contributed by the reader of the narrative and used by the reader in understanding the narrative. We examine the deictic center in the light of our investigations from the viewpoints of linguistics, cognitive psychology, individual differences (language pathology), literary theory of narrative, and artificial intelligence.
Minds Mach., 1995
Galbraith, M., Rapaport, W.J. Preface. Mind Mach 5, 513–515 (1995).
https://cse.buffalo.edu/~rapaport/perry.positions.html, 2010
William Perry's Scheme of Intellectual and Ethical Development. A journey along the 9 "... more William Perry's Scheme of Intellectual and Ethical Development. A journey along the 9 "Perry" positions (as modified by Belenky et al. 1986). William J. Rapaport. ...
Minds and Machines, 2004
1. Steven Pinker should be well-known to readers of this journal, not only for his research in li... more 1. Steven Pinker should be well-known to readers of this journal, not only for his research in linguistics and cognitive science, but for his semi-popular book, The Language Instinct (1994; for theM & M review, see Kemmerer 1995). For those of us who likedThe Language Instinct (as I did), Pinker now offers us the rest of the story: How the Mind Works , despite its inclusive-sounding title, literally complements its predecessor (cf. p. x), since it omits all discussion of language; thus, readers hoping for a user manual for the mind (and instructors hoping for a single-volume cognitive-science text) may be disappointed. One might have hoped for at least a chapter-length précis of the earlier book, though that would no doubt have made this 660-page tome even heftier. As it is, the book covers a wide range of topics: vision; computationalism; evolutionary psychology and natural selection; consciousness and qualia; intelligence; concepts and categories; the problem of other minds; reaso...
There are many branches of philosophy called “the philosophy of X”, where X = disciplines ranging... more There are many branches of philosophy called “the philosophy of X”, where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments. 1
Is the brain a digital computer? Searle says that this is meaningless; I say that it is an empiri... more Is the brain a digital computer? Searle says that this is meaningless; I say that it is an empirical question. Is the mind a computer program? Searle says no; I say: properly understood, yes. Can the operations of the brain be simulated on a digital computer? Searle says: trivially yes; I say yes, but that it is not trivial.
We present a computational analysis of de re, de ditto, and de se belief and knowledge reports. O... more We present a computational analysis of de re, de ditto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Castaiteda, namely, that the simple rule ‘(A knows that P) implies P ’ apparently does not hold If P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P, including those containing quasi-indexicals. In so doing, we explore the difference between reasoning in a public communication language and in a knowledge-representation language, we demonstrate the importance of representing proper names explicitly, and we provide support for the necessity of considering sentences in the context of extended discourse (e.g., written narrative) in order to fully capture certain features of their semantics. 1
The Mathematics Teacher
Paper folding can help in understanding some infinite sequences and in finding their limits. A si... more Paper folding can help in understanding some infinite sequences and in finding their limits. A simple physical model useful at all levels of ability is presented and infinite sequences of interest to senior high school students are explored.
Grazer Philosophische Studien
Journal of Symbolic Logic, Jun 1, 1988
Ford's "Helen Keller Was Never in a Chinese Room" claims that my argument in "How Helen Keller Us... more Ford's "Helen Keller Was Never in a Chinese Room" claims that my argument in "How Helen Keller Used Syntactic Semantics to Escape from a Chinese Room" fails because Searle and I use the terms 'syntax' and 'semantics' differently, hence are at cross purposes. Ford has misunderstood me; this reply clarifies my theory. Jason Michael Ford's "Helen Keller Was Never in a Chinese Room" (2010) claims that my argument in "How Helen Keller Used Syntactic Semantics to Escape from a Chinese Room" (Rapaport 2006) fails because Searle and I use the terms 'syntax' and 'semantics' differently, hence are at cross purposes. I think Ford has misunderstood me, so I am grateful for this opportunity to clarify my theory. The theory of syntactic semantics (Rapaport 1988) underlies computationalism: the claim that cognition is computable, i.e., that there is an algorithm (or a family of algorithms) that compute cognitive functions (Rapaport 1998). The theory has three parts: First, cognitive agents have direct access only to internal representatives of external objects. As Ray Jackendoff (2002, §10.4) says, a cognitive agent understands the world by "pushing the world into the mind". Therefore, both words and their meanings (including external objects serving as their referents) are represented internally in a single language of thought (LOT). For humans, this LOT is a biological neural network; for computers, it might be some kind of knowledge-representation and reasoning system (such as SNePS; see Shapiro & Rapaport 1987). 1 Second, it follows that words, their meanings, and semantic relations between them are all syntactic, where syntax is the study of relations among members of a single set (of signs, or marks, or neurons, etc.), and semantics is the study of relations between two sets (of signs, marks, neurons, etc., on the one hand, and their meanings, on the other) (cf. Morris 1938). "Pushing" meanings into the same set as symbols for them allows semantics to be done syntactically: It turns semantic relations between two sets (a set of internal marks and a set of (external) meanings) into syntactic relations among the marks of a single (internal) LOT. For example, truth tables and formal semantics are both syntactic enterprises, as are the relations between neuron firings representing signs and neuron firings representing external meanings. Consequently, symbol-manipulating computers can do semantics by doing syntax. Finally, understanding is recursive: We understand a syntactic domain (call it 'SYN 1 ') indirectly by interpreting it in terms of a semantic domain (call it 'SEM 1 '). But SEM 1 must be antecedently understood by considering it as a syntactic domain (rename it 'SYN 2 ') interpreted in terms of yet another semantic domain, which also must be antecedently understood. And so on. But, in order not to make it go on ad infinitum, there must be a base case: a domain that is understood directly, i.e., in terms of itself (i.e., not "antecedently"). Such direct understanding is syntactic understanding (Rapaport 1986b). (And perhaps it is holistic understanding; cf. Rapaport 2002.) Thus, the theory of syntactic semantics asserts that syntax suffices for semantic cognition, that cognition is therefore computable, and that computers are hence capable of thinking.
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Papers by William Rapaport