William J. Rapaport

Philosophy has been characterized (e.g., by Benson Mates) as a field whose problems are unsolvable. This has often been taken to mean that there can be no progress in philosophy as there is in mathematics or science. The nature of problems and solutions is considered, and it is argued that solutions are always parts of theories, hence that acceptance of a solution requires commitment to a theory (as suggested by William Perry's scheme of cognitive development). Progress can be had in philosophy in the same way as in mathematics and science by knowing what commitments are needed for solutions. Similar views of Rescher and Castañeda are discussed.

Hauser argues that his pocket calculator (Cal) has certain arithmetical abilities: it seems Cal calculates. That calculating is thinking seems equally untendentious. Yet these two claims together provide premises for a seemingly valid syllogism whose conclusion - Cal thinks - most would deny. He considers several ways to avoid this conclusion, and finds them mostly wanting. Either we ourselves can't be said to think or calculate if our calculation-like performances are judged by the standards proposed to rule out Cal; or the standards- e.g., autonomy and self-consciousness- make it impossible to verify whether anything or anyone (save oneself) meets them. While appeals to the intentionality of thought or the unity of minds provide more credible lines of resistance, available accounts of intentionality and mental unity are insufficiently clear and warranted to provide very substantial arguments against Cal's title to be called a thinking thing. Indeed, considerations favoring granting that title are more formidable than is generally appreciated. Rapaport's comments suggest that, on a strong view of thinking, mere calculating is not thinking (and pocket calculators don't think), but on a weak, but unexciting, sense of thinking, pocket calculators do think. He closes with some observations on the implications of this conclusion.

Deliberate contextual vocabulary acquisition (CVA) is a reader’s ability to figure out a (not the) meaning for an unknown word from its “context”, without external sources of help such as dictionaries or people. The appropriate context for such CVA is the “belief-revised integration” of the reader’s prior knowledge with the reader’s “internalization” of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new classroom curriculum designed to help students use CVA to improve their reading comprehension.

A computer can come to understand natural language the same way Helen Keller did: by using “syntactic semantics”—a theory of how syntax can suffice for semantics, i.e., how semantics for natural language can be provided by means of computational symbol manipulation. This essay considers real-life approximations of Chinese Rooms, focusing on Helen Keller’s experiences growing up deaf and blind, locked in a sort of Chinese Room yet learning how to communicate with the outside world. Using the SNePS computational knowledge-representation system, the essay analyzes Keller’s belief that learning that “everything has a name” was the key to her success, enabling her to “partition” her mental concepts into mental representations of: words, objects, and the naming relations between them. It next looks at Herbert Terrace’s theory of naming, which is akin to Keller’s, and which only humans are supposed to be capable of. The essay suggests that computers at least, and perhaps non-human primates, are also capable of this kind of naming.

Everyone has a different "learning style". (A good introduction to the topic of learning styles is Claxton & Murrell 1987. For more on different learning styles, see Keirsey Temperament and Character Web Site, William Perry's Scheme of Intellectual and Ethical Development, Holland 1966, Kolb 1984, Sternberg 1999. For an interesting discussion of some limitations of learning styles from the perspective of teaching styles, see Glenn 2009/2010.) For some online tools targeted at different learning styles, see "100 Helpful Web Tools for Every Kind of Learner".

We discuss a research project that develops and applies algorithms for computational contextual vocabulary acquisition (CVA): learning the meaning of unknown words from context. We try to unify a disparate literature on the topic of CVA from psychology, first- and secondlanguage acquisition, and reading science, in order to help develop these algorithms: We use the knowledge gained from the computational CVA system to build an educational curriculum for enhancing students’ abilities to use CVA strategies in their reading of science texts at the middle-school and college undergraduate levels. The knowledge gained from case studies of students using our CVA techniques feeds back into further development of our computational theory. Keywords: artificial intelligence, knowledge representation, reading, reasoning, science education, vocabulary acquisition.

It is well known that people from other disciplines have made significant contributions to philosophy and have influenced philosophers. It is also true (though perhaps not often realized, since philosophers are not on the receiving end, so to speak) that philosophers have made significant contributions to other disciplines and have influenced researchers in these other disciplines, sometimes more so than they have influenced philosophy itself. But what is perhaps not as well known as it ought to be is that researchers in other disciplines, writing in those other disciplines' journals and conference proceedings, are doing philosophically sophisticated work, work that we in philosophy ignore at our peril. Work in cognitive science and artificial intelligence (AI) often overlaps such paradigmatic philosophical specialties as logic, the philosophy of mind, the philosophy of language, and the philosophy of action. This special issue offers a sampling of research in cognitive science and AI that is philosophically relevant and philosophically sophisticated.

We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Castañeda, 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 (for example, written narrative) in order to fully capture certain features of their semantics.

Alexius Meinong developed a notion of defective objects in order to account for various logical and psychological paradoxes. The notion is of historical interest, since it presages recent work on the logical paradoxes by Herzberger and Kripke. But it fails to do the job it was designed for. However, a technique implicit in Meinong's investigation is more successful and can be adapted to resolve a similar paradox discovered by Romane Clark in a revised version of Meinong's Theory of Objects due to Rapaport. One family of paradoxes remains, but it is argued that they are unavoidable and relatively harmless.

This essay continues my investigation of `syntactic semantics': the theory that, pace Searle's Chinese-Room Argument, syntax does suffice for semantics (in particular, for the semantics needed for a computational cognitive theory of natural-language understanding). Here, I argue that syntactic semantics (which is internal and first-person) is what has been called a conceptual-role semantics: The meaning of any expression is the role that it plays in the complete system of expressions. Such a `narrow', conceptual-role semantics is the appropriate sort of semantics to account (from an `internal', or first-person perspective) for how a cognitive agent understands language. Some have argued for the primacy of external, or `wide', semantics, while others have argued for a two-factor analysis. But, although two factors can be specified–-one internal and first-person, the other only specifiable in an external, third-person way–-only the internal, first-person one is needed for understanding how someone understands. A truth-conditional semantics can still be provided, but only from a third-person perspective

Narrative passages told from a character's perspective convey the character's thoughts and perceptions. We present a discourse process that recognizes characters' thoughts and perceptions in third-person narrative. An effect of perspective on reference In narrative is addressed: references in passages told from the perspective of a character reflect the character's beliefs. An algorithm that uses the results of our discourse process to understand references with respect to an appropriate set of beliefs is presented.

John Searle once said: "The Chinese room shows what we knew all along: syntax by itself is not sufficient for semantics. (Does anyone actually deny this point, I mean straight out? Is anyone actually willing to say, straight out, that they think that syntax, in the sense of formal symbols, is really the same as semantic content, in the sense of meanings, thought contents, understanding, etc.?)." I say: "Yes". Stuart C. Shapiro has said: "Does that make any sense? Yes: Everything makes sense. The question is: What sense does it make?" This essay explores what sense it makes to say that syntax by itself is sufficient for semantics.

This project continues our interdisciplinary research into computational and cognitive aspects of narrative comprehension. Our ultimate goal is the development of a computational theory of how humans understand narrative texts. The theory will be informed by joint research from the viewpoints of linguistics, cognitive psychology, the study of language acquisition, literary theory, geography, philosophy, and artificial intelligence. The linguists, literary theorists, and geographers in our group are developing theories of narrative language and spatial understanding that are being tested by the cognitive psychologists and language researchers in our group, and a computational model of a reader of narrative text is being developed by the AI researchers, based in part on these theories and results and in part on research on knowledge representation and reasoning. This proposal describes the knowledge-representation and natural-language-processing issues involved in the computational implementation of the theory; discusses a contrast between communicative and narrative uses of language and of the relation of the narrative text to the story world it describes; investigates linguistic, literary, and hermeneutic dimensions of our research; presents a computational investigation of subjective sentences and reference in narrative; studies children’s acquisition of the ability to take third-person perspective in their own storytelling; describes the psychological validation of various linguistic devices; and examines how readers develop an understanding of the geographical space of a story. This report is a longer version of a project description submitted to NSF. This document, produced in May 2007, is a L ATEX version of Technical Report 89-07 (Buffalo: SUNY Buffalo Department of Computer Science, August 1989), with slightly..

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that 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. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary.

For centuries, philosophers studying the great mysteries of human subjectivity have focused on the mind/body problem and the difference between human beings and animals. Now a new ontological question takes center stage: to what extent can a manufactured object (a computer) exhibit qualities of mind? There have been passionate exchanges between those who believe that a "manufactured mind" is possible and those who believe that mind cannot exist except as a living, socially situated, embodied person. As with earlier arguments, this one shows no sign of being resolved. But the fight over computationalism (the belief that all mental processes can be generated by computer programs) has immediate, "hard" consequences for technological research and development, social- and cognitive-science methodology, and for our everyday experience of the world and ourselves. This special issue consists of papers presented at a conference of the same title held at the Center for Cognitive Science at the University at Buffalo, 22-23 May 1990. The authors come from a variety of disciplinary backgrounds (Computer Science, Linguistics, Philosophy, Psychology, Sociology, etc.), and bring a wide variety of perspectives to the topic.

This paper describes the SNePS knowledge-representation and reasoning system. SNePS is an intensional, propositional, semantic-network processing system used for research in AI. We look at how predication is represented in such a system when it is used for cognitive modeling and natural-language understanding and generation. In particular, we discuss issues in the representation of fictional entities and the representation of propositions from fiction, using SNePS. We briefly survey four philosophical ontological theories of fiction and sketch an epistemological theory of fiction using a story operator and rules for allowing propositions to migrate into and out of story spaces

Contextual vocabulary acquisition (CVA) is the deliberate acquisition of a meaning for a word in a text by reasoning from context, where “context” includes: (1) the reader’s “internalization” of the surrounding text, i.e., the reader’s “mental model” of the word’s “textual context” (hereafter, “co-text” [3]) integrated with (2) the reader’s prior knowledge (PK), but it excludes (3) external sources such as dictionaries or people. CVA is what you do when you come across an unfamiliar word in your reading, realize that you don’t know what it means, decide that you need to know what it means in order to understand the passage, but there is no one around to ask, and it is not in the dictionary (or you are too lazy to look it up). In such a case, you can try to figure out its meaning “from context”, i.e., from clues in the co-text together with your prior knowledge. Our computational theory of CVA—implemented in a the SNePS knowledge representation and reasoning system [28]—begins with a stored knowledge base containing SNePS representations of relevant PK, inputs SNePS representations of a passage containing an unfamiliar word, and draws inferences from these two (integrated) information sources. When asked to define the word, definition algorithms deductively search the resulting network for information of the sort that might be found in a dictionary definition, outputting a definition frame whose slots are the kinds of features that a definition might contain (e.g., class membership, properties, actions, spatio-temporal information, etc.) and whose slot-fillers contain information gleaned from the network [6–8,20,23,24]. We are investigating ways to make our system more robust, to embed it in a naturallanguage-processing system, and to incorporate morphological information. Our research group, including reading educators, is also applying our methods to the develop-

Cognitive agents, whether human or computer, that engage in natural-language discourse and that have beliefs about the beliefs of other cognitive agents must be able to represent objects the way they believe them to be and the way they believe others believe them to be. They must be able to represent other cognitive agents both as objects of beliefs and as agents of beliefs. They must be able to represent their own beliefs, and they must be able to represent beliefs as objects of beliefs. These requirements raise questions about the number of tokens of the belief representation language needed to represent believers and propositions in their normal roles and in their roles as objects of beliefs. In this paper, we explicate the relations among nodes, mental tokens, concepts, actual objects, concepts in the belief spaces of an agent and the agent's model of other agents, concepts of other cognitive agents, and propositions. We extend, deepen, and clarify our theory of intensional knowledge representation for natural-language processing, as presented in previous papers and in light of objections raised by others. The essential claim is that tokens in a knowledge-representation system represent only intensions and not extensions. We are pursuing this investigation by building CASSIE, a computer model of a cognitive agent and, to the extent she works, a cognitive agent herself. CASSIE's mind is implemented in the SNePS knowledge-representation and reasoning system.

The late Hector-Neri Castañeda, the Mahlon Powell Professor of Philosophy at Indiana University, and founding editor of Noûs, has deeply influenced current analytic philosophy with diverse contributions, including guise theory, the theory of indicators and quasi-indicators, and the proposition/practition theory. This volume collects 15 papers--for the most part previously unpublished--in ontology, philosophy of language, cognitive science, and related areas by ex-students of Professor Castañeda, most of whom are now well-known researchers or even distinguished scholars. The authors share the conviction that Castañeda's work must continue to be explored and that his philosophical methodology must continue to be applied in an effort to further illuminate all the issues that he so deeply investigated. The topics covered by the contributions include intensional contexts, possible worlds, quasi-indicators, guise theory, property theory, Russell's substitutional theory of propositions, event theory, the adverbial theory of mental attitudes, existentialist ontology, and Plato's, Leibniz's, Kant's, and Peirce's ontologies. An introduction by the editors relates all these themes to Castañeda's philosophical interests and methodology.

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.

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test± passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably be persons

Syntactic semantics is a holistic, conceptual-role-semantic theory of how computers can think. But Fodor and Lepore have mounted a sustained attack on holistic semantic theories. However, their major problem with holism (that, if holism is true, then no two people can understand each other) can be fixed by means of negotiating meanings. Syntactic semantics and Fodor and Lepore’s objections to holism are outlined; the nature of communication, miscommunication, and negotiation is discussed; Bruner’s ideas about the negotiation of meaning are explored; and some observations on a problem for knowledge representation in AI raised by Winston are presented

H´ector-Neri Casta˜neda-Calder´on (December 13, 1924–September 7, 1991) was born in San Vicente Zacapa, Guatemala. He attended the Normal School for Boys in Guatemala City, later called the Military Normal School for Boys, from which he was expelled for refusing to fight a bully; the dramatic story, worthy of being filmed, is told in the “De Re” section of his autobiography, “Self-Profile” (1986). He then attended a normal school in Costa Rica, followed by studies in philosophy at the University of San Carlos, Guatemala. He won a scholarship to the University of Minnesota, where he received his B.A. (1950), M.A. (1952), and Ph.D. (1954), all in philosophy. His dissertation, “The Logical Structure of Moral Reasoning”, was written under the direction of Wilfrid Sellars. He returned to teach in Guatemala, and then received a scholarship to study at Oxford University (1955–1956), after which he took a sabbatical-replacement position in philosophy at Duke University (1956). His first full-time academic appointment was at Wayne State University (1957– 1969), where he founded the philosophy journal Noˆus (1967, a counter-offer made to him by Wayne State to encourage him to stay there rather than to take the chairmanship of philosophy at the University of Pennsylvania). In 1969, he moved (along with several of his Wayne colleagues) to Indiana University, where he eventually became the Mahlon Powell Professor of Philosophy and, later, its first Dean of Latino Affairs (1978–1981). He remained at Indiana until his death. He was also a visiting professor of philosophy at the University of Texas at Austin (1962–1963) and a fellow at the Center for Advanced Study in the Behavioral Sciences (1981–1982). He received grants and fellowships from the Guggenheim Foundation (1967–1968), the T. Andrew Mellon Foundation, the National Endowment for the Humanities, and the National Science Foundation. He was elected President of the American Philosophical Association Central Division (1979– 1980), named to the American Academy of Arts and Sciences (1990), and received the Presidential Medal of Honor from the Government of Guatemala (1991). Casta˜neda’s philosophical interests spanned virtually the entire spectrum of philosophy, and his theories form a highly interconnected whole..

This essay considers what it means to understand natural language and whether a computer running an artificial-intelligence program designed to understand natural language does in fact do so. It is argued that a certain kind of semantics is needed to understand natural language, that this kind of semantics is mere symbol manipulation (i.e., syntax), and that, hence, it is available to AI systems. Recent arguments by Searle and Dretske to the effect that computers cannot understand natural language are discussed, and a prototype natural-language-understanding system is presented as an illustration.