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1 semantic-formal language
descriptive language — дексриптивный язык; описательный язык
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2 semantic-formal language
Большой англо-русский и русско-английский словарь > semantic-formal language
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3 semantic-formal language
1) Математика: семантически формальный язык2) Вычислительная техника: формально-семантический языкУниверсальный англо-русский словарь > semantic-formal language
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4 semantic-formal language
English-Russian dictionary of computer science and programming > semantic-formal language
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5 semantic-formal language
English-Russian scientific dictionary > semantic-formal language
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6 language
- absolutely homogeneous language - absolutely inadequate language - absolutely nonhomogeneous language - absolutely nonperfect language - absolutely nonsimple language - absolutely perfect language - absolutely simple language - completely formalized language - completely homogeneous language - left categorial language - locally perfectly adequate language - locally simple language - locally strongly language - locally well adequate language - noncompletely homogeneous language - right categorial language - scattered context languageindistinguishable in the language — лог. неразличимый на языке, эквивалентный
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7 language
язык || языковой- action description language
- actual machine language
- agent programming language
- AI language
- Algol-like language
- algorithmical language
- algorithmic language
- application-oriented language
- applicative language
- artificial language
- assembler language
- assembly language
- assembly-output language
- assignment-free language
- behavioral language
- bidirectional language
- block-structured language
- Boolean-based language
- business definition language
- business-oriented language
- calculus-type language
- C-based language
- client-side language
- code language
- command language
- compiled language
- compiler language
- component definition language
- composite language
- computer language
- computer-dependent language
- computer-independent language
- computer-oriented language
- computer-programming language
- computer-sensitive language
- consensus language
- context-free language
- control language
- conversational language
- core language
- data definition language
- data description language
- data language
- data manipulation language
- data storage description language
- database language
- data-entry language
- data-flow language
- data-query language
- declarative language
- defining language
- descriptive language
- descriptor language
- design language
- device media control language
- direct execution language
- directly interpretable language
- Dyck language
- end-user language
- escape language
- evolutive language
- executive-control language
- executive language
- explicit language
- extensible language
- fabricated language
- finite state language
- flow language
- foreign language
- formalized language
- frame-based language
- freestanding language
- functional language
- generated language
- graphics language
- graph-oriented language
- hardware-description language
- hardware language
- higher-level language
- higher-order language
- host language
- human language
- human-oriented language
- human-readable language
- indexed language
- information retrieval language
- informational language
- information language
- inherently ambiguous language
- input language
- input/output language
- instruction language
- integrated language
- interactive language
- interim language
- intermediate language
- internal language
- interpreted language
- job control language
- job-oriented language
- knowledge representation language
- language pair
- letter-equivalent languages
- linear language
- linear-programming language
- list-processing language
- logic-type language
- low-level language
- machine language
- machine-dependent language
- machine-independent language
- machine-oriented language
- macroassembly language
- macro language
- macroinstruction language
- macroprogramming language
- man-to-computer language
- mathematical formular language
- memory management language
- mnemonic language
- modeling language
- native language
- natural language
- NC programming language
- nested language
- network-oriented language
- nonprocedural language
- numder language
- object language
- object modeling language
- object-oriented language
- one-dimensional language
- operator-oriented language
- original language
- page description language
- parallel language
- phrase structure language
- predicate language
- predicate logic-based language
- predicate logic language
- privacy language
- problem statement language
- problem-oriented language
- procedural language
- procedure-oriented language
- process control language
- production language
- program language
- programming language
- pseudo language
- pseudomachine language
- query language
- readable specification language
- reference language
- regular language
- relational language
- relational-type language
- representation language - requirements modeling language
- restricted language
- rule-based language
- ruly language
- schema language
- science-oriented language
- script language
- self-contained language
- semantic-formal language
- semiformal language
- sentential language
- serial language
- simulation language
- single-assignment language
- source language
- specialized language
- specification language
- stream-based language
- strict language
- structured programming language
- structured query language
- super language
- super-high-level language
- symbolic language
- symbolic programming language
- syntax language
- synthetic language
- system input language
- system language
- system-oriented language
- tabular language
- target language
- TC language
- time sharing language
- type-free language
- unified modeling language
- update language
- user language
- user-oriented language
- very-high-level languageEnglish-Russian dictionary of computer science and programming > language
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8 Language
Philosophy is written in that great book, the universe, which is always open, right before our eyes. But one cannot understand this book without first learning to understand the language and to know the characters in which it is written. It is written in the language of mathematics, and the characters are triangles, circles, and other figures. Without these, one cannot understand a single word of it, and just wanders in a dark labyrinth. (Galileo, 1990, p. 232)It never happens that it [a nonhuman animal] arranges its speech in various ways in order to reply appropriately to everything that may be said in its presence, as even the lowest type of man can do. (Descartes, 1970a, p. 116)It is a very remarkable fact that there are none so depraved and stupid, without even excepting idiots, that they cannot arrange different words together, forming of them a statement by which they make known their thoughts; while, on the other hand, there is no other animal, however perfect and fortunately circumstanced it may be, which can do the same. (Descartes, 1967, p. 116)Human beings do not live in the object world alone, nor alone in the world of social activity as ordinarily understood, but are very much at the mercy of the particular language which has become the medium of expression for their society. It is quite an illusion to imagine that one adjusts to reality essentially without the use of language and that language is merely an incidental means of solving specific problems of communication or reflection. The fact of the matter is that the "real world" is to a large extent unconsciously built on the language habits of the group.... We see and hear and otherwise experience very largely as we do because the language habits of our community predispose certain choices of interpretation. (Sapir, 1921, p. 75)It powerfully conditions all our thinking about social problems and processes.... No two languages are ever sufficiently similar to be considered as representing the same social reality. The worlds in which different societies live are distinct worlds, not merely the same worlds with different labels attached. (Sapir, 1985, p. 162)[A list of language games, not meant to be exhaustive:]Giving orders, and obeying them- Describing the appearance of an object, or giving its measurements- Constructing an object from a description (a drawing)Reporting an eventSpeculating about an eventForming and testing a hypothesisPresenting the results of an experiment in tables and diagramsMaking up a story; and reading itPlay actingSinging catchesGuessing riddlesMaking a joke; and telling itSolving a problem in practical arithmeticTranslating from one language into anotherLANGUAGE Asking, thanking, cursing, greeting, and praying-. (Wittgenstein, 1953, Pt. I, No. 23, pp. 11 e-12 e)We dissect nature along lines laid down by our native languages.... The world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... No individual is free to describe nature with absolute impartiality but is constrained to certain modes of interpretation even while he thinks himself most free. (Whorf, 1956, pp. 153, 213-214)We dissect nature along the lines laid down by our native languages.The categories and types that we isolate from the world of phenomena we do not find there because they stare every observer in the face; on the contrary, the world is presented in a kaleidoscopic flux of impressions which has to be organized by our minds-and this means largely by the linguistic systems in our minds.... We are thus introduced to a new principle of relativity, which holds that all observers are not led by the same physical evidence to the same picture of the universe, unless their linguistic backgrounds are similar or can in some way be calibrated. (Whorf, 1956, pp. 213-214)9) The Forms of a Person's Thoughts Are Controlled by Unperceived Patterns of His Own LanguageThe forms of a person's thoughts are controlled by inexorable laws of pattern of which he is unconscious. These patterns are the unperceived intricate systematizations of his own language-shown readily enough by a candid comparison and contrast with other languages, especially those of a different linguistic family. (Whorf, 1956, p. 252)It has come to be commonly held that many utterances which look like statements are either not intended at all, or only intended in part, to record or impart straightforward information about the facts.... Many traditional philosophical perplexities have arisen through a mistake-the mistake of taking as straightforward statements of fact utterances which are either (in interesting non-grammatical ways) nonsensical or else intended as something quite different. (Austin, 1962, pp. 2-3)In general, one might define a complex of semantic components connected by logical constants as a concept. The dictionary of a language is then a system of concepts in which a phonological form and certain syntactic and morphological characteristics are assigned to each concept. This system of concepts is structured by several types of relations. It is supplemented, furthermore, by redundancy or implicational rules..., representing general properties of the whole system of concepts.... At least a relevant part of these general rules is not bound to particular languages, but represents presumably universal structures of natural languages. They are not learned, but are rather a part of the human ability to acquire an arbitrary natural language. (Bierwisch, 1970, pp. 171-172)In studying the evolution of mind, we cannot guess to what extent there are physically possible alternatives to, say, transformational generative grammar, for an organism meeting certain other physical conditions characteristic of humans. Conceivably, there are none-or very few-in which case talk about evolution of the language capacity is beside the point. (Chomsky, 1972, p. 98)[It is] truth value rather than syntactic well-formedness that chiefly governs explicit verbal reinforcement by parents-which renders mildly paradoxical the fact that the usual product of such a training schedule is an adult whose speech is highly grammatical but not notably truthful. (R. O. Brown, 1973, p. 330)he conceptual base is responsible for formally representing the concepts underlying an utterance.... A given word in a language may or may not have one or more concepts underlying it.... On the sentential level, the utterances of a given language are encoded within a syntactic structure of that language. The basic construction of the sentential level is the sentence.The next highest level... is the conceptual level. We call the basic construction of this level the conceptualization. A conceptualization consists of concepts and certain relations among those concepts. We can consider that both levels exist at the same point in time and that for any unit on one level, some corresponding realizate exists on the other level. This realizate may be null or extremely complex.... Conceptualizations may relate to other conceptualizations by nesting or other specified relationships. (Schank, 1973, pp. 191-192)The mathematics of multi-dimensional interactive spaces and lattices, the projection of "computer behavior" on to possible models of cerebral functions, the theoretical and mechanical investigation of artificial intelligence, are producing a stream of sophisticated, often suggestive ideas.But it is, I believe, fair to say that nothing put forward until now in either theoretic design or mechanical mimicry comes even remotely in reach of the most rudimentary linguistic realities. (Steiner, 1975, p. 284)The step from the simple tool to the master tool, a tool to make tools (what we would now call a machine tool), seems to me indeed to parallel the final step to human language, which I call reconstitution. It expresses in a practical and social context the same understanding of hierarchy, and shows the same analysis by function as a basis for synthesis. (Bronowski, 1977, pp. 127-128)t is the language donn eґ in which we conduct our lives.... We have no other. And the danger is that formal linguistic models, in their loosely argued analogy with the axiomatic structure of the mathematical sciences, may block perception.... It is quite conceivable that, in language, continuous induction from simple, elemental units to more complex, realistic forms is not justified. The extent and formal "undecidability" of context-and every linguistic particle above the level of the phoneme is context-bound-may make it impossible, except in the most abstract, meta-linguistic sense, to pass from "pro-verbs," "kernals," or "deep deep structures" to actual speech. (Steiner, 1975, pp. 111-113)A higher-level formal language is an abstract machine. (Weizenbaum, 1976, p. 113)Jakobson sees metaphor and metonymy as the characteristic modes of binarily opposed polarities which between them underpin the two-fold process of selection and combination by which linguistic signs are formed.... Thus messages are constructed, as Saussure said, by a combination of a "horizontal" movement, which combines words together, and a "vertical" movement, which selects the particular words from the available inventory or "inner storehouse" of the language. The combinative (or syntagmatic) process manifests itself in contiguity (one word being placed next to another) and its mode is metonymic. The selective (or associative) process manifests itself in similarity (one word or concept being "like" another) and its mode is metaphoric. The "opposition" of metaphor and metonymy therefore may be said to represent in effect the essence of the total opposition between the synchronic mode of language (its immediate, coexistent, "vertical" relationships) and its diachronic mode (its sequential, successive, lineal progressive relationships). (Hawkes, 1977, pp. 77-78)It is striking that the layered structure that man has given to language constantly reappears in his analyses of nature. (Bronowski, 1977, p. 121)First, [an ideal intertheoretic reduction] provides us with a set of rules"correspondence rules" or "bridge laws," as the standard vernacular has it-which effect a mapping of the terms of the old theory (T o) onto a subset of the expressions of the new or reducing theory (T n). These rules guide the application of those selected expressions of T n in the following way: we are free to make singular applications of their correspondencerule doppelgangers in T o....Second, and equally important, a successful reduction ideally has the outcome that, under the term mapping effected by the correspondence rules, the central principles of T o (those of semantic and systematic importance) are mapped onto general sentences of T n that are theorems of Tn. (P. Churchland, 1979, p. 81)If non-linguistic factors must be included in grammar: beliefs, attitudes, etc. [this would] amount to a rejection of the initial idealization of language as an object of study. A priori such a move cannot be ruled out, but it must be empirically motivated. If it proves to be correct, I would conclude that language is a chaos that is not worth studying.... Note that the question is not whether beliefs or attitudes, and so on, play a role in linguistic behavior and linguistic judgments... [but rather] whether distinct cognitive structures can be identified, which interact in the real use of language and linguistic judgments, the grammatical system being one of these. (Chomsky, 1979, pp. 140, 152-153)23) Language Is Inevitably Influenced by Specific Contexts of Human InteractionLanguage cannot be studied in isolation from the investigation of "rationality." It cannot afford to neglect our everyday assumptions concerning the total behavior of a reasonable person.... An integrational linguistics must recognize that human beings inhabit a communicational space which is not neatly compartmentalized into language and nonlanguage.... It renounces in advance the possibility of setting up systems of forms and meanings which will "account for" a central core of linguistic behavior irrespective of the situation and communicational purposes involved. (Harris, 1981, p. 165)By innate [linguistic knowledge], Chomsky simply means "genetically programmed." He does not literally think that children are born with language in their heads ready to be spoken. He merely claims that a "blueprint is there, which is brought into use when the child reaches a certain point in her general development. With the help of this blueprint, she analyzes the language she hears around her more readily than she would if she were totally unprepared for the strange gabbling sounds which emerge from human mouths. (Aitchison, 1987, p. 31)Looking at ourselves from the computer viewpoint, we cannot avoid seeing that natural language is our most important "programming language." This means that a vast portion of our knowledge and activity is, for us, best communicated and understood in our natural language.... One could say that natural language was our first great original artifact and, since, as we increasingly realize, languages are machines, so natural language, with our brains to run it, was our primal invention of the universal computer. One could say this except for the sneaking suspicion that language isn't something we invented but something we became, not something we constructed but something in which we created, and recreated, ourselves. (Leiber, 1991, p. 8)Historical dictionary of quotations in cognitive science > Language
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9 language
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absolute language
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algorithmical language
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algorithmic language
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applicative language
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artificial language
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assembler language
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block-structured language
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Boolean algebra-based language
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Boolean based language
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command language
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compilative language
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compiler language
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computer language
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computer-dependent language
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computer-independent language
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computer-oriented language
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computer-sensitive language
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context-free language
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control language
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conversational language
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core language
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data language
- data manipulation language -
data-base language
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data-definition language
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data-query language
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declarative language
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deduction-oriented language
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design language
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explicit language
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expression-oriented language
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extensible language
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FG-kernel language
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finite state language
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formal specification language
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function language
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functional language
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graphics-oriented language
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graphics language
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hardware-based language
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high-level language
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host language
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human language
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human-oriented language
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hybrid language
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imperative language
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input language
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instruction language
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interactive language
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interface language
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intermediate language
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interpretive language
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job control language
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kernel language
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knowledge representation language
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list-processing language
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low-level language
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machine language
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machine-dependent language
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machine-independent language
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machine-oriented language
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macro language
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meta language
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mnemonic language
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narrative language
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native language
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native-mode language
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natural language
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NC-AM language
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network control language
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nonprocedural language
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nucleus language
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object language
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object-oriented language
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original language
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parallel language
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plain language
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privacy language
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problem solving language
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problem-oriented language
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procedural language
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program development language
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program language
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programming language
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pseudo language
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query language
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real-time language
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reference language
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regular language
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relational language
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retrieval language
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robot language
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rule language
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semantic language
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sentential language
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simulation language
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source language
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specification description language
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specification language
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stratified language
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structured language
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symbolic language
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system language
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system-oriented language
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target language
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typed language
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unstratified language
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untyped language
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user-oriented language
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world-modeling language -
10 formal semantic language
Техника: формальный семантический языкУниверсальный англо-русский словарь > formal semantic language
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11 формально-семантический язык
Большой англо-русский и русско-английский словарь > формально-семантический язык
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12 семантически формальный язык
Большой англо-русский и русско-английский словарь > семантически формальный язык
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13 mathematical formula
A mathematical statement in a formal language that can be given a semantic meaning. -
14 Artificial Intelligence
In my opinion, none of [these programs] does even remote justice to the complexity of human mental processes. Unlike men, "artificially intelligent" programs tend to be single minded, undistractable, and unemotional. (Neisser, 1967, p. 9)Future progress in [artificial intelligence] will depend on the development of both practical and theoretical knowledge.... As regards theoretical knowledge, some have sought a unified theory of artificial intelligence. My view is that artificial intelligence is (or soon will be) an engineering discipline since its primary goal is to build things. (Nilsson, 1971, pp. vii-viii)Most workers in AI [artificial intelligence] research and in related fields confess to a pronounced feeling of disappointment in what has been achieved in the last 25 years. Workers entered the field around 1950, and even around 1960, with high hopes that are very far from being realized in 1972. In no part of the field have the discoveries made so far produced the major impact that was then promised.... In the meantime, claims and predictions regarding the potential results of AI research had been publicized which went even farther than the expectations of the majority of workers in the field, whose embarrassments have been added to by the lamentable failure of such inflated predictions....When able and respected scientists write in letters to the present author that AI, the major goal of computing science, represents "another step in the general process of evolution"; that possibilities in the 1980s include an all-purpose intelligence on a human-scale knowledge base; that awe-inspiring possibilities suggest themselves based on machine intelligence exceeding human intelligence by the year 2000 [one has the right to be skeptical]. (Lighthill, 1972, p. 17)4) Just as Astronomy Succeeded Astrology, the Discovery of Intellectual Processes in Machines Should Lead to a Science, EventuallyJust as astronomy succeeded astrology, following Kepler's discovery of planetary regularities, the discoveries of these many principles in empirical explorations on intellectual processes in machines should lead to a science, eventually. (Minsky & Papert, 1973, p. 11)5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the ProgramMany problems arise in experiments on machine intelligence because things obvious to any person are not represented in any program. One can pull with a string, but one cannot push with one.... Simple facts like these caused serious problems when Charniak attempted to extend Bobrow's "Student" program to more realistic applications, and they have not been faced up to until now. (Minsky & Papert, 1973, p. 77)What do we mean by [a symbolic] "description"? We do not mean to suggest that our descriptions must be made of strings of ordinary language words (although they might be). The simplest kind of description is a structure in which some features of a situation are represented by single ("primitive") symbols, and relations between those features are represented by other symbols-or by other features of the way the description is put together. (Minsky & Papert, 1973, p. 11)[AI is] the use of computer programs and programming techniques to cast light on the principles of intelligence in general and human thought in particular. (Boden, 1977, p. 5)The word you look for and hardly ever see in the early AI literature is the word knowledge. They didn't believe you have to know anything, you could always rework it all.... In fact 1967 is the turning point in my mind when there was enough feeling that the old ideas of general principles had to go.... I came up with an argument for what I called the primacy of expertise, and at the time I called the other guys the generalists. (Moses, quoted in McCorduck, 1979, pp. 228-229)9) Artificial Intelligence Is Psychology in a Particularly Pure and Abstract FormThe basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense. We can now see why this includes psychology and artificial intelligence on a more or less equal footing: people and intelligent computers (if and when there are any) turn out to be merely different manifestations of the same underlying phenomenon. Moreover, with universal hardware, any semantic engine can in principle be formally imitated by a computer if only the right program can be found. And that will guarantee semantic imitation as well, since (given the appropriate formal behavior) the semantics is "taking care of itself" anyway. Thus we also see why, from this perspective, artificial intelligence can be regarded as psychology in a particularly pure and abstract form. The same fundamental structures are under investigation, but in AI, all the relevant parameters are under direct experimental control (in the programming), without any messy physiology or ethics to get in the way. (Haugeland, 1981b, p. 31)There are many different kinds of reasoning one might imagine:Formal reasoning involves the syntactic manipulation of data structures to deduce new ones following prespecified rules of inference. Mathematical logic is the archetypical formal representation. Procedural reasoning uses simulation to answer questions and solve problems. When we use a program to answer What is the sum of 3 and 4? it uses, or "runs," a procedural model of arithmetic. Reasoning by analogy seems to be a very natural mode of thought for humans but, so far, difficult to accomplish in AI programs. The idea is that when you ask the question Can robins fly? the system might reason that "robins are like sparrows, and I know that sparrows can fly, so robins probably can fly."Generalization and abstraction are also natural reasoning process for humans that are difficult to pin down well enough to implement in a program. If one knows that Robins have wings, that Sparrows have wings, and that Blue jays have wings, eventually one will believe that All birds have wings. This capability may be at the core of most human learning, but it has not yet become a useful technique in AI.... Meta- level reasoning is demonstrated by the way one answers the question What is Paul Newman's telephone number? You might reason that "if I knew Paul Newman's number, I would know that I knew it, because it is a notable fact." This involves using "knowledge about what you know," in particular, about the extent of your knowledge and about the importance of certain facts. Recent research in psychology and AI indicates that meta-level reasoning may play a central role in human cognitive processing. (Barr & Feigenbaum, 1981, pp. 146-147)Suffice it to say that programs already exist that can do things-or, at the very least, appear to be beginning to do things-which ill-informed critics have asserted a priori to be impossible. Examples include: perceiving in a holistic as opposed to an atomistic way; using language creatively; translating sensibly from one language to another by way of a language-neutral semantic representation; planning acts in a broad and sketchy fashion, the details being decided only in execution; distinguishing between different species of emotional reaction according to the psychological context of the subject. (Boden, 1981, p. 33)Can the synthesis of Man and Machine ever be stable, or will the purely organic component become such a hindrance that it has to be discarded? If this eventually happens-and I have... good reasons for thinking that it must-we have nothing to regret and certainly nothing to fear. (Clarke, 1984, p. 243)The thesis of GOFAI... is not that the processes underlying intelligence can be described symbolically... but that they are symbolic. (Haugeland, 1985, p. 113)14) Artificial Intelligence Provides a Useful Approach to Psychological and Psychiatric Theory FormationIt is all very well formulating psychological and psychiatric theories verbally but, when using natural language (even technical jargon), it is difficult to recognise when a theory is complete; oversights are all too easily made, gaps too readily left. This is a point which is generally recognised to be true and it is for precisely this reason that the behavioural sciences attempt to follow the natural sciences in using "classical" mathematics as a more rigorous descriptive language. However, it is an unfortunate fact that, with a few notable exceptions, there has been a marked lack of success in this application. It is my belief that a different approach-a different mathematics-is needed, and that AI provides just this approach. (Hand, quoted in Hand, 1985, pp. 6-7)We might distinguish among four kinds of AI.Research of this kind involves building and programming computers to perform tasks which, to paraphrase Marvin Minsky, would require intelligence if they were done by us. Researchers in nonpsychological AI make no claims whatsoever about the psychological realism of their programs or the devices they build, that is, about whether or not computers perform tasks as humans do.Research here is guided by the view that the computer is a useful tool in the study of mind. In particular, we can write computer programs or build devices that simulate alleged psychological processes in humans and then test our predictions about how the alleged processes work. We can weave these programs and devices together with other programs and devices that simulate different alleged mental processes and thereby test the degree to which the AI system as a whole simulates human mentality. According to weak psychological AI, working with computer models is a way of refining and testing hypotheses about processes that are allegedly realized in human minds.... According to this view, our minds are computers and therefore can be duplicated by other computers. Sherry Turkle writes that the "real ambition is of mythic proportions, making a general purpose intelligence, a mind." (Turkle, 1984, p. 240) The authors of a major text announce that "the ultimate goal of AI research is to build a person or, more humbly, an animal." (Charniak & McDermott, 1985, p. 7)Research in this field, like strong psychological AI, takes seriously the functionalist view that mentality can be realized in many different types of physical devices. Suprapsychological AI, however, accuses strong psychological AI of being chauvinisticof being only interested in human intelligence! Suprapsychological AI claims to be interested in all the conceivable ways intelligence can be realized. (Flanagan, 1991, pp. 241-242)16) Determination of Relevance of Rules in Particular ContextsEven if the [rules] were stored in a context-free form the computer still couldn't use them. To do that the computer requires rules enabling it to draw on just those [ rules] which are relevant in each particular context. Determination of relevance will have to be based on further facts and rules, but the question will again arise as to which facts and rules are relevant for making each particular determination. One could always invoke further facts and rules to answer this question, but of course these must be only the relevant ones. And so it goes. It seems that AI workers will never be able to get started here unless they can settle the problem of relevance beforehand by cataloguing types of context and listing just those facts which are relevant in each. (Dreyfus & Dreyfus, 1986, p. 80)Perhaps the single most important idea to artificial intelligence is that there is no fundamental difference between form and content, that meaning can be captured in a set of symbols such as a semantic net. (G. Johnson, 1986, p. 250)Artificial intelligence is based on the assumption that the mind can be described as some kind of formal system manipulating symbols that stand for things in the world. Thus it doesn't matter what the brain is made of, or what it uses for tokens in the great game of thinking. Using an equivalent set of tokens and rules, we can do thinking with a digital computer, just as we can play chess using cups, salt and pepper shakers, knives, forks, and spoons. Using the right software, one system (the mind) can be mapped into the other (the computer). (G. Johnson, 1986, p. 250)19) A Statement of the Primary and Secondary Purposes of Artificial IntelligenceThe primary goal of Artificial Intelligence is to make machines smarter.The secondary goals of Artificial Intelligence are to understand what intelligence is (the Nobel laureate purpose) and to make machines more useful (the entrepreneurial purpose). (Winston, 1987, p. 1)The theoretical ideas of older branches of engineering are captured in the language of mathematics. We contend that mathematical logic provides the basis for theory in AI. Although many computer scientists already count logic as fundamental to computer science in general, we put forward an even stronger form of the logic-is-important argument....AI deals mainly with the problem of representing and using declarative (as opposed to procedural) knowledge. Declarative knowledge is the kind that is expressed as sentences, and AI needs a language in which to state these sentences. Because the languages in which this knowledge usually is originally captured (natural languages such as English) are not suitable for computer representations, some other language with the appropriate properties must be used. It turns out, we think, that the appropriate properties include at least those that have been uppermost in the minds of logicians in their development of logical languages such as the predicate calculus. Thus, we think that any language for expressing knowledge in AI systems must be at least as expressive as the first-order predicate calculus. (Genesereth & Nilsson, 1987, p. viii)21) Perceptual Structures Can Be Represented as Lists of Elementary PropositionsIn artificial intelligence studies, perceptual structures are represented as assemblages of description lists, the elementary components of which are propositions asserting that certain relations hold among elements. (Chase & Simon, 1988, p. 490)Artificial intelligence (AI) is sometimes defined as the study of how to build and/or program computers to enable them to do the sorts of things that minds can do. Some of these things are commonly regarded as requiring intelligence: offering a medical diagnosis and/or prescription, giving legal or scientific advice, proving theorems in logic or mathematics. Others are not, because they can be done by all normal adults irrespective of educational background (and sometimes by non-human animals too), and typically involve no conscious control: seeing things in sunlight and shadows, finding a path through cluttered terrain, fitting pegs into holes, speaking one's own native tongue, and using one's common sense. Because it covers AI research dealing with both these classes of mental capacity, this definition is preferable to one describing AI as making computers do "things that would require intelligence if done by people." However, it presupposes that computers could do what minds can do, that they might really diagnose, advise, infer, and understand. One could avoid this problematic assumption (and also side-step questions about whether computers do things in the same way as we do) by defining AI instead as "the development of computers whose observable performance has features which in humans we would attribute to mental processes." This bland characterization would be acceptable to some AI workers, especially amongst those focusing on the production of technological tools for commercial purposes. But many others would favour a more controversial definition, seeing AI as the science of intelligence in general-or, more accurately, as the intellectual core of cognitive science. As such, its goal is to provide a systematic theory that can explain (and perhaps enable us to replicate) both the general categories of intentionality and the diverse psychological capacities grounded in them. (Boden, 1990b, pp. 1-2)Because the ability to store data somewhat corresponds to what we call memory in human beings, and because the ability to follow logical procedures somewhat corresponds to what we call reasoning in human beings, many members of the cult have concluded that what computers do somewhat corresponds to what we call thinking. It is no great difficulty to persuade the general public of that conclusion since computers process data very fast in small spaces well below the level of visibility; they do not look like other machines when they are at work. They seem to be running along as smoothly and silently as the brain does when it remembers and reasons and thinks. On the other hand, those who design and build computers know exactly how the machines are working down in the hidden depths of their semiconductors. Computers can be taken apart, scrutinized, and put back together. Their activities can be tracked, analyzed, measured, and thus clearly understood-which is far from possible with the brain. This gives rise to the tempting assumption on the part of the builders and designers that computers can tell us something about brains, indeed, that the computer can serve as a model of the mind, which then comes to be seen as some manner of information processing machine, and possibly not as good at the job as the machine. (Roszak, 1994, pp. xiv-xv)The inner workings of the human mind are far more intricate than the most complicated systems of modern technology. Researchers in the field of artificial intelligence have been attempting to develop programs that will enable computers to display intelligent behavior. Although this field has been an active one for more than thirty-five years and has had many notable successes, AI researchers still do not know how to create a program that matches human intelligence. No existing program can recall facts, solve problems, reason, learn, and process language with human facility. This lack of success has occurred not because computers are inferior to human brains but rather because we do not yet know in sufficient detail how intelligence is organized in the brain. (Anderson, 1995, p. 2)Historical dictionary of quotations in cognitive science > Artificial Intelligence
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15 Bibliography
■ Aitchison, J. (1987). Noam Chomsky: Consensus and controversy. New York: Falmer Press.■ Anderson, J. R. (1980). Cognitive psychology and its implications. San Francisco: W. H. Freeman.■ Anderson, J. R. (1983). The architecture of cognition. Cambridge, MA: Harvard University Press.■ Anderson, J. R. (1995). Cognitive psychology and its implications (4th ed.). New York: W. H. Freeman.■ Archilochus (1971). In M. L. West (Ed.), Iambi et elegi graeci (Vol. 1). Oxford: Oxford University Press.■ Armstrong, D. M. (1990). The causal theory of the mind. In W. G. Lycan (Ed.), Mind and cognition: A reader (pp. 37-47). Cambridge, MA: Basil Blackwell. (Originally published in 1981 in The nature of mind and other essays, Ithaca, NY: University Press).■ Atkins, P. W. (1992). Creation revisited. Oxford: W. H. Freeman & Company.■ Austin, J. L. (1962). How to do things with words. Cambridge, MA: Harvard University Press.■ Bacon, F. (1878). Of the proficience and advancement of learning divine and human. In The works of Francis Bacon (Vol. 1). Cambridge, MA: Hurd & Houghton.■ Bacon, R. (1928). Opus majus (Vol. 2). R. B. Burke (Trans.). Philadelphia, PA: University of Pennsylvania Press.■ Bar-Hillel, Y. (1960). The present status of automatic translation of languages. In F. L. Alt (Ed.), Advances in computers (Vol. 1). New York: Academic Press.■ Barr, A., & E. A. Feigenbaum (Eds.) (1981). The handbook of artificial intelligence (Vol. 1). Reading, MA: Addison-Wesley.■ Barr, A., & E. A. Feigenbaum (Eds.) (1982). The handbook of artificial intelligence (Vol. 2). Los Altos, CA: William Kaufman.■ Barron, F. X. (1963). The needs for order and for disorder as motives in creative activity. In C. W. Taylor & F. X. Barron (Eds.), Scientific creativity: Its rec ognition and development (pp. 153-160). New York: Wiley.■ Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge: Cambridge University Press.■ Bartley, S. H. (1969). Principles of perception. London: Harper & Row.■ Barzun, J. (1959). The house of intellect. New York: Harper & Row.■ Beach, F. A., D. O. Hebb, C. T. Morgan & H. W. Nissen (Eds.) (1960). The neu ropsychology of Lashley. New York: McGraw-Hill.■ Berkeley, G. (1996). Principles of human knowledge: Three Dialogues. Oxford: Oxford University Press. (Originally published in 1710.)■ Berlin, I. (1953). The hedgehog and the fox: An essay on Tolstoy's view of history. NY: Simon & Schuster.■ Bierwisch, J. (1970). Semantics. In J. Lyons (Ed.), New horizons in linguistics. Baltimore: Penguin Books.■ Black, H. C. (1951). Black's law dictionary. St. Paul, MN: West Publishing.■ Bloom, A. (1981). The linguistic shaping of thought: A study in the impact of language on thinking in China and the West. Hillsdale, NJ: Erlbaum.■ Bobrow, D. G., & D. A. Norman (1975). Some principles of memory schemata. In D. G. Bobrow & A. Collins (Eds.), Representation and understanding: Stud ies in Cognitive Science (pp. 131-149). New York: Academic Press.■ Boden, M. A. (1977). Artificial intelligence and natural man. New York: Basic Books.■ Boden, M. A. (1981). Minds and mechanisms. Ithaca, NY: Cornell University Press.■ Boden, M. A. (1990a). The creative mind: Myths and mechanisms. London: Cardinal.■ Boden, M. A. (1990b). The philosophy of artificial intelligence. Oxford: Oxford University Press.■ Boden, M. A. (1994). Precis of The creative mind: Myths and mechanisms. Behavioral and brain sciences 17, 519-570.■ Boden, M. (1996). Creativity. In M. Boden (Ed.), Artificial Intelligence (2nd ed.). San Diego: Academic Press.■ Bolter, J. D. (1984). Turing's man: Western culture in the computer age. Chapel Hill, NC: University of North Carolina Press.■ Bolton, N. (1972). The psychology of thinking. London: Methuen.■ Bourne, L. E. (1973). Some forms of cognition: A critical analysis of several papers. In R. Solso (Ed.), Contemporary issues in cognitive psychology (pp. 313324). Loyola Symposium on Cognitive Psychology (Chicago 1972). Washington, DC: Winston.■ Bransford, J. D., N. S. McCarrell, J. J. Franks & K. E. Nitsch (1977). Toward unexplaining memory. In R. Shaw & J. D. Bransford (Eds.), Perceiving, acting, and knowing (pp. 431-466). Hillsdale, NJ: Lawrence Erlbaum Associates.■ Breger, L. (1981). Freud's unfinished journey. London: Routledge & Kegan Paul.■ Brehmer, B. (1986). In one word: Not from experience. In H. R. Arkes & K. Hammond (Eds.), Judgment and decision making: An interdisciplinary reader (pp. 705-719). Cambridge: Cambridge University Press.■ Bresnan, J. (1978). A realistic transformational grammar. In M. Halle, J. Bresnan & G. A. Miller (Eds.), Linguistic theory and psychological reality (pp. 1-59). Cambridge, MA: MIT Press.■ Brislin, R. W., W. J. Lonner & R. M. Thorndike (Eds.) (1973). Cross- cultural research methods. New York: Wiley.■ Bronowski, J. (1977). A sense of the future: Essays in natural philosophy. P. E. Ariotti with R. Bronowski (Eds.). Cambridge, MA: MIT Press.■ Bronowski, J. (1978). The origins of knowledge and imagination. New Haven, CT: Yale University Press.■ Brown, R. O. (1973). A first language: The early stages. Cambridge, MA: Harvard University Press.■ Brown, T. (1970). Lectures on the philosophy of the human mind. In R. Brown (Ed.), Between Hume and Mill: An anthology of British philosophy- 1749- 1843 (pp. 330-387). New York: Random House/Modern Library.■ Bruner, J. S., J. Goodnow & G. Austin (1956). A study of thinking. New York: Wiley.■ Calvin, W. H. (1990). The cerebral symphony: Seashore reflections on the structure of consciousness. New York: Bantam.■ Campbell, J. (1982). Grammatical man: Information, entropy, language, and life. New York: Simon & Schuster.■ Campbell, J. (1989). The improbable machine. New York: Simon & Schuster.■ Carlyle, T. (1966). On heroes, hero- worship and the heroic in history. Lincoln: University of Nebraska Press. (Originally published in 1841.)■ Carnap, R. (1959). The elimination of metaphysics through logical analysis of language [Ueberwindung der Metaphysik durch logische Analyse der Sprache]. In A. J. Ayer (Ed.), Logical positivism (pp. 60-81) A. Pap (Trans). New York: Free Press. (Originally published in 1932.)■ Cassirer, E. (1946). Language and myth. New York: Harper and Brothers. Reprinted. New York: Dover Publications, 1953.■ Cattell, R. B., & H. J. Butcher (1970). Creativity and personality. In P. E. Vernon (Ed.), Creativity. Harmondsworth, England: Penguin Books.■ Caudill, M., & C. Butler (1990). Naturally intelligent systems. Cambridge, MA: MIT Press/Bradford Books.■ Chandrasekaran, B. (1990). What kind of information processing is intelligence? A perspective on AI paradigms and a proposal. In D. Partridge & R. Wilks (Eds.), The foundations of artificial intelligence: A sourcebook (pp. 14-46). Cambridge: Cambridge University Press.■ Charniak, E., & McDermott, D. (1985). Introduction to artificial intelligence. Reading, MA: Addison-Wesley.■ Chase, W. G., & H. A. Simon (1988). The mind's eye in chess. In A. Collins & E. E. Smith (Eds.), Readings in cognitive science: A perspective from psychology and artificial intelligence (pp. 461-493). San Mateo, CA: Kaufmann.■ Cheney, D. L., & R. M. Seyfarth (1990). How monkeys see the world: Inside the mind of another species. Chicago: University of Chicago Press.■ Chi, M.T.H., R. Glaser & E. Rees (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (pp. 7-73). Hillsdale, NJ: Lawrence Erlbaum Associates.■ Chomsky, N. (1957). Syntactic structures. The Hague: Mouton. Janua Linguarum.■ Chomsky, N. (1964). A transformational approach to syntax. In J. A. Fodor & J. J. Katz (Eds.), The structure of language: Readings in the philosophy of lan guage (pp. 211-245). Englewood Cliffs, NJ: Prentice-Hall.■ Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press.■ Chomsky, N. (1972). Language and mind (enlarged ed.). New York: Harcourt Brace Jovanovich.■ Chomsky, N. (1979). Language and responsibility. New York: Pantheon.■ Chomsky, N. (1986). Knowledge of language: Its nature, origin and use. New York: Praeger Special Studies.■ Churchland, P. (1979). Scientific realism and the plasticity of mind. New York: Cambridge University Press.■ Churchland, P. M. (1989). A neurocomputational perspective: The nature of mind and the structure of science. Cambridge, MA: MIT Press.■ Churchland, P. S. (1986). Neurophilosophy. Cambridge, MA: MIT Press/Bradford Books.■ Clark, A. (1996). Philosophical Foundations. In M. A. Boden (Ed.), Artificial in telligence (2nd ed.). San Diego: Academic Press.■ Clark, H. H., & T. B. Carlson (1981). Context for comprehension. In J. Long & A. Baddeley (Eds.), Attention and performance (Vol. 9, pp. 313-330). Hillsdale, NJ: Lawrence Erlbaum Associates.■ Clarke, A. C. (1984). Profiles of the future: An inquiry into the limits of the possible. New York: Holt, Rinehart & Winston.■ Claxton, G. (1980). Cognitive psychology: A suitable case for what sort of treatment? In G. Claxton (Ed.), Cognitive psychology: New directions (pp. 1-25). London: Routledge & Kegan Paul.■ Code, M. (1985). Order and organism. Albany, NY: State University of New York Press.■ Collingwood, R. G. (1972). The idea of history. New York: Oxford University Press.■ Coopersmith, S. (1967). The antecedents of self- esteem. San Francisco: W. H. Freeman.■ Copland, A. (1952). Music and imagination. London: Oxford University Press.■ Coren, S. (1994). The intelligence of dogs. New York: Bantam Books.■ Cottingham, J. (Ed.) (1996). Western philosophy: An anthology. Oxford: Blackwell Publishers.■ Cox, C. (1926). The early mental traits of three hundred geniuses. Stanford, CA: Stanford University Press.■ Craik, K.J.W. (1943). The nature of explanation. Cambridge: Cambridge University Press.■ Cronbach, L. J. (1990). Essentials of psychological testing (5th ed.). New York: HarperCollins.■ Cronbach, L. J., & R. E. Snow (1977). Aptitudes and instructional methods. New York: Irvington. Paperback edition, 1981.■ Csikszentmihalyi, M. (1993). The evolving self. New York: Harper Perennial.■ Culler, J. (1976). Ferdinand de Saussure. New York: Penguin Books.■ Curtius, E. R. (1973). European literature and the Latin Middle Ages. W. R. Trask (Trans.). Princeton, NJ: Princeton University Press.■ D'Alembert, J.L.R. (1963). Preliminary discourse to the encyclopedia of Diderot. R. N. Schwab (Trans.). Indianapolis: Bobbs-Merrill.■ Dampier, W. C. (1966). A history of modern science. Cambridge: Cambridge University Press.■ Darwin, C. (1911). The life and letters of Charles Darwin (Vol. 1). Francis Darwin (Ed.). New York: Appleton.■ Davidson, D. (1970) Mental events. In L. Foster & J. W. Swanson (Eds.), Experience and theory (pp. 79-101). Amherst: University of Massachussetts Press.■ Davies, P. (1995). About time: Einstein's unfinished revolution. New York: Simon & Schuster/Touchstone.■ Davis, R., & J. J. King (1977). An overview of production systems. In E. Elcock & D. Michie (Eds.), Machine intelligence 8. Chichester, England: Ellis Horwood.■ Davis, R., & D. B. Lenat (1982). Knowledge- based systems in artificial intelligence. New York: McGraw-Hill.■ Dawkins, R. (1982). The extended phenotype: The gene as the unit of selection. Oxford: W. H. Freeman.■ deKleer, J., & J. S. Brown (1983). Assumptions and ambiguities in mechanistic mental models (1983). In D. Gentner & A. L. Stevens (Eds.), Mental modes (pp. 155-190). Hillsdale, NJ: Lawrence Erlbaum Associates.■ Dennett, D. C. (1978a). Brainstorms: Philosophical essays on mind and psychology. Montgomery, VT: Bradford Books.■ Dennett, D. C. (1978b). Toward a cognitive theory of consciousness. In D. C. Dennett, Brainstorms: Philosophical Essays on Mind and Psychology. Montgomery, VT: Bradford Books.■ Dennett, D. C. (1995). Darwin's dangerous idea: Evolution and the meanings of life. New York: Simon & Schuster/Touchstone.■ Descartes, R. (1897-1910). Traite de l'homme. In Oeuvres de Descartes (Vol. 11, pp. 119-215). Paris: Charles Adam & Paul Tannery. (Originally published in 1634.)■ Descartes, R. (1950). Discourse on method. L. J. Lafleur (Trans.). New York: Liberal Arts Press. (Originally published in 1637.)■ Descartes, R. (1951). Meditation on first philosophy. L. J. Lafleur (Trans.). New York: Liberal Arts Press. (Originally published in 1641.)■ Descartes, R. (1955). The philosophical works of Descartes. E. S. Haldane and G.R.T. Ross (Trans.). New York: Dover. (Originally published in 1911 by Cambridge University Press.)■ Descartes, R. (1967). Discourse on method (Pt. V). In E. S. Haldane and G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 1, pp. 106-118). Cambridge: Cambridge University Press. (Originally published in 1637.)■ Descartes, R. (1970a). Discourse on method. In E. S. Haldane & G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 1, pp. 181-200). Cambridge: Cambridge University Press. (Originally published in 1637.)■ Descartes, R. (1970b). Principles of philosophy. In E. S. Haldane & G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 1, pp. 178-291). Cambridge: Cambridge University Press. (Originally published in 1644.)■ Descartes, R. (1984). Meditations on first philosophy. In J. Cottingham, R. Stoothoff & D. Murduch (Trans.), The philosophical works of Descartes (Vol. 2). Cambridge: Cambridge University Press. (Originally published in 1641.)■ Descartes, R. (1986). Meditations on first philosophy. J. Cottingham (Trans.). Cambridge: Cambridge University Press. (Originally published in 1641 as Med itationes de prima philosophia.)■ deWulf, M. (1956). An introduction to scholastic philosophy. Mineola, NY: Dover Books.■ Dixon, N. F. (1981). Preconscious processing. London: Wiley.■ Doyle, A. C. (1986). The Boscombe Valley mystery. In Sherlock Holmes: The com plete novels and stories (Vol. 1). New York: Bantam.■ Dreyfus, H., & S. Dreyfus (1986). Mind over machine. New York: Free Press.■ Dreyfus, H. L. (1972). What computers can't do: The limits of artificial intelligence (revised ed.). New York: Harper & Row.■ Dreyfus, H. L., & S. E. Dreyfus (1986). Mind over machine: The power of human intuition and expertise in the era of the computer. New York: Free Press.■ Edelman, G. M. (1992). Bright air, brilliant fire: On the matter of the mind. New York: Basic Books.■ Ehrenzweig, A. (1967). The hidden order of art. London: Weidenfeld & Nicolson.■ Einstein, A., & L. Infeld (1938). The evolution of physics. New York: Simon & Schuster.■ Eisenstein, S. (1947). Film sense. New York: Harcourt, Brace & World.■ Everdell, W. R. (1997). The first moderns. Chicago: University of Chicago Press.■ Eysenck, M. W. (1977). Human memory: Theory, research and individual difference. Oxford: Pergamon.■ Eysenck, M. W. (1982). Attention and arousal: Cognition and performance. Berlin: Springer.■ Eysenck, M. W. (1984). A handbook of cognitive psychology. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Fancher, R. E. (1979). Pioneers of psychology. New York: W. W. Norton.■ Farrell, B. A. (1981). The standing of psychoanalysis. New York: Oxford University Press.■ Feldman, D. H. (1980). Beyond universals in cognitive development. Norwood, NJ: Ablex.■ Fetzer, J. H. (1996). Philosophy and cognitive science (2nd ed.). New York: Paragon House.■ Finke, R. A. (1990). Creative imagery: Discoveries and inventions in visualization. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Flanagan, O. (1991). The science of the mind. Cambridge MA: MIT Press/Bradford Books.■ Fodor, J. (1983). The modularity of mind. Cambridge, MA: MIT Press/Bradford Books.■ Frege, G. (1972). Conceptual notation. T. W. Bynum (Trans.). Oxford: Clarendon Press. (Originally published in 1879.)■ Frege, G. (1979). Logic. In H. Hermes, F. Kambartel & F. Kaulbach (Eds.), Gottlob Frege: Posthumous writings. Chicago: University of Chicago Press. (Originally published in 1879-1891.)■ Freud, S. (1959). Creative writers and day-dreaming. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (Vol. 9, pp. 143-153). London: Hogarth Press.■ Freud, S. (1966). Project for a scientific psychology. In J. Strachey (Ed.), The stan dard edition of the complete psychological works of Sigmund Freud (Vol. 1, pp. 295-398). London: Hogarth Press. (Originally published in 1950 as Aus den AnfaЁngen der Psychoanalyse, in London by Imago Publishing.)■ Freud, S. (1976). Lecture 18-Fixation to traumas-the unconscious. In J. Strachey (Ed.), The standard edition of the complete psychological works of Sigmund Freud (Vol. 16, p. 285). London: Hogarth Press.■ Galileo, G. (1990). Il saggiatore [The assayer]. In S. Drake (Ed.), Discoveries and opinions of Galileo. New York: Anchor Books. (Originally published in 1623.)■ Gassendi, P. (1970). Letter to Descartes. In "Objections and replies." In E. S. Haldane & G.R.T. Ross (Eds.), The philosophical works of Descartes (Vol. 2, pp. 179-240). Cambridge: Cambridge University Press. (Originally published in 1641.)■ Gazzaniga, M. S. (1988). Mind matters: How mind and brain interact to create our conscious lives. Boston: Houghton Mifflin in association with MIT Press/Bradford Books.■ Genesereth, M. R., & N. J. Nilsson (1987). Logical foundations of artificial intelligence. Palo Alto, CA: Morgan Kaufmann.■ Ghiselin, B. (1952). The creative process. New York: Mentor.■ Ghiselin, B. (1985). The creative process. Berkeley, CA: University of California Press. (Originally published in 1952.)■ Gilhooly, K. J. (1996). Thinking: Directed, undirected and creative (3rd ed.). London: Academic Press.■ Glass, A. L., K. J. Holyoak & J. L. Santa (1979). Cognition. Reading, MA: AddisonWesley.■ Goody, J. (1977). The domestication of the savage mind. Cambridge: Cambridge University Press.■ Gruber, H. E. (1980). Darwin on man: A psychological study of scientific creativity (2nd ed.). Chicago: University of Chicago Press.■ Gruber, H. E., & S. Davis (1988). Inching our way up Mount Olympus: The evolving systems approach to creative thinking. In R. J. Sternberg (Ed.), The nature of creativity: Contemporary psychological perspectives. Cambridge: Cambridge University Press.■ Guthrie, E. R. (1972). The psychology of learning. New York: Harper. (Originally published in 1935.)■ Habermas, J. (1972). Knowledge and human interests. Boston: Beacon Press.■ Hadamard, J. (1945). The psychology of invention in the mathematical field. Princeton, NJ: Princeton University Press.■ Hand, D. J. (1985). Artificial intelligence and psychiatry. Cambridge: Cambridge University Press.■ Harris, M. (1981). The language myth. London: Duckworth.■ Haugeland, J. (Ed.) (1981). Mind design: Philosophy, psychology, artificial intelligence. Cambridge, MA: MIT Press/Bradford Books.■ Haugeland, J. (1981a). The nature and plausibility of cognitivism. In J. Haugeland (Ed.), Mind design: Philosophy, psychology, artificial intelligence (pp. 243-281). Cambridge, MA: MIT Press.■ Haugeland, J. (1981b). Semantic engines: An introduction to mind design. In J. Haugeland (Ed.), Mind design: Philosophy, psychology, artificial intelligence (pp. 1-34). Cambridge, MA: MIT Press/Bradford Books.■ Haugeland, J. (1985). Artificial intelligence: The very idea. Cambridge, MA: MIT Press.■ Hawkes, T. (1977). Structuralism and semiotics. Berkeley: University of California Press.■ Hebb, D. O. (1949). The organisation of behaviour. New York: Wiley.■ Hebb, D. O. (1958). A textbook of psychology. Philadelphia: Saunders.■ Hegel, G.W.F. (1910). The phenomenology of mind. J. B. Baille (Trans.). London: Sonnenschein. (Originally published as Phaenomenologie des Geistes, 1807.)■ Heisenberg, W. (1958). Physics and philosophy. New York: Harper & Row.■ Hempel, C. G. (1966). Philosophy of natural science. Englewood Cliffs, NJ: PrenticeHall.■ Herman, A. (1997). The idea of decline in Western history. New York: Free Press.■ Herrnstein, R. J., & E. G. Boring (Eds.) (1965). A source book in the history of psy chology. Cambridge, MA: Harvard University Press.■ Herzmann, E. (1964). Mozart's creative process. In P. H. Lang (Ed.), The creative world of Mozart (pp. 17-30). London: Oldbourne Press.■ Hilgard, E. R. (1957). Introduction to psychology. London: Methuen.■ Hobbes, T. (1651). Leviathan. London: Crooke.■ Holliday, S. G., & M. J. Chandler (1986). Wisdom: Explorations in adult competence. Basel, Switzerland: Karger.■ Horn, J. L. (1986). In R. J. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 3). Hillsdale, NJ: Erlbaum.■ Hull, C. (1943). Principles of behavior. New York: Appleton-Century-Crofts.■ Hume, D. (1955). An inquiry concerning human understanding. New York: Liberal Arts Press. (Originally published in 1748.)■ Hume, D. (1975). An enquiry concerning human understanding. In L. A. SelbyBigge (Ed.), Hume's enquiries (3rd. ed., revised P. H. Nidditch). Oxford: Clarendon. (Spelling and punctuation revised.) (Originally published in 1748.)■ Hume, D. (1978). A treatise of human nature. L. A. Selby-Bigge (Ed.), Hume's enquiries (3rd. ed., revised P. H. Nidditch). Oxford: Clarendon. (With some modifications of spelling and punctuation.) (Originally published in 1690.)■ Hunt, E. (1973). The memory we must have. In R. C. Schank & K. M. Colby (Eds.), Computer models of thought and language. (pp. 343-371) San Francisco: W. H. Freeman.■ Husserl, E. (1960). Cartesian meditations. The Hague: Martinus Nijhoff.■ Inhelder, B., & J. Piaget (1958). The growth of logical thinking from childhood to adolescence. New York: Basic Books. (Originally published in 1955 as De la logique de l'enfant a` la logique de l'adolescent. [Paris: Presses Universitaire de France])■ James, W. (1890a). The principles of psychology (Vol. 1). New York: Dover Books.■ James, W. (1890b). The principles of psychology. New York: Henry Holt.■ Jevons, W. S. (1900). The principles of science (2nd ed.). London: Macmillan.■ Johnson, G. (1986). Machinery of the mind: Inside the new science of artificial intelli gence. New York: Random House.■ Johnson-Laird, P. N. (1983). Mental models: Toward a cognitive science of language, inference, and consciousness. Cambridge, MA: Harvard University Press.■ Johnson-Laird, P. N. (1988). The computer and the mind: An introduction to cognitive science. Cambridge, MA: Harvard University Press.■ Jones, E. (1961). The life and work of Sigmund Freud. L. Trilling & S. Marcus (Eds.). London: Hogarth.■ Jones, R. V. (1985). Complementarity as a way of life. In A. P. French & P. J. Kennedy (Eds.), Niels Bohr: A centenary volume. Cambridge, MA: Harvard University Press.■ Kant, I. (1933). Critique of Pure Reason (2nd ed.). N. K. Smith (Trans.). London: Macmillan. (Originally published in 1781 as Kritik der reinen Vernunft.)■ Kant, I. (1891). Solution of the general problems of the Prolegomena. In E. Belfort (Trans.), Kant's Prolegomena. London: Bell. (With minor modifications.) (Originally published in 1783.)■ Katona, G. (1940). Organizing and memorizing: Studies in the psychology of learning and teaching. New York: Columbia University Press.■ Kaufman, A. S. (1979). Intelligent testing with the WISC-R. New York: Wiley.■ Koestler, A. (1964). The act of creation. New York: Arkana (Penguin).■ Kohlberg, L. (1971). From is to ought. In T. Mischel (Ed.), Cognitive development and epistemology. (pp. 151-235) New York: Academic Press.■ KoЁhler, W. (1925). The mentality of apes. New York: Liveright.■ KoЁhler, W. (1927). The mentality of apes (2nd ed.). Ella Winter (Trans.). London: Routledge & Kegan Paul.■ KoЁhler, W. (1930). Gestalt psychology. London: G. Bell.■ KoЁhler, W. (1947). Gestalt psychology. New York: Liveright.■ KoЁhler, W. (1969). The task of Gestalt psychology. Princeton, NJ: Princeton University Press.■ Kuhn, T. (1970). The structure of scientific revolutions (2nd ed.). Chicago: University of Chicago Press.■ Langer, E. J. (1989). Mindfulness. Reading, MA: Addison-Wesley.■ Langer, S. (1962). Philosophical sketches. Baltimore: Johns Hopkins University Press.■ Langley, P., H. A. Simon, G. L. Bradshaw & J. M. Zytkow (1987). Scientific dis covery: Computational explorations of the creative process. Cambridge, MA: MIT Press.■ Lashley, K. S. (1951). The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in behavior, the Hixon Symposium (pp. 112-146) New York: Wiley.■ LeDoux, J. E., & W. Hirst (1986). Mind and brain: Dialogues in cognitive neuroscience. Cambridge: Cambridge University Press.■ Lehnert, W. (1978). The process of question answering. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Leiber, J. (1991). Invitation to cognitive science. Oxford: Blackwell.■ Lenat, D. B., & G. Harris (1978). Designing a rule system that searches for scientific discoveries. In D. A. Waterman & F. Hayes-Roth (Eds.), Pattern directed inference systems (pp. 25-52) New York: Academic Press.■ Levenson, T. (1995). Measure for measure: A musical history of science. New York: Touchstone. (Originally published in 1994.)■ Leґvi-Strauss, C. (1963). Structural anthropology. C. Jacobson & B. Grundfest Schoepf (Trans.). New York: Basic Books. (Originally published in 1958.)■ Levine, M. W., & J. M. Schefner (1981). Fundamentals of sensation and perception. London: Addison-Wesley.■ Lewis, C. I. (1946). An analysis of knowledge and valuation. LaSalle, IL: Open Court.■ Lighthill, J. (1972). A report on artificial intelligence. Unpublished manuscript, Science Research Council.■ Lipman, M., A. M. Sharp & F. S. Oscanyan (1980). Philosophy in the classroom. Philadelphia: Temple University Press.■ Lippmann, W. (1965). Public opinion. New York: Free Press. (Originally published in 1922.)■ Locke, J. (1956). An essay concerning human understanding. Chicago: Henry Regnery Co. (Originally published in 1690.)■ Locke, J. (1975). An essay concerning human understanding. P. H. Nidditch (Ed.). Oxford: Clarendon. (Originally published in 1690.) (With spelling and punctuation modernized and some minor modifications of phrasing.)■ Lopate, P. (1994). The art of the personal essay. New York: Doubleday/Anchor Books.■ Lorimer, F. (1929). The growth of reason. London: Kegan Paul. Machlup, F., & U. Mansfield (Eds.) (1983). The study of information. New York: Wiley.■ Manguel, A. (1996). A history of reading. New York: Viking.■ Markey, J. F. (1928). The symbolic process. London: Kegan Paul.■ Martin, R. M. (1969). On Ziff's "Natural and formal languages." In S. Hook (Ed.), Language and philosophy: A symposium (pp. 249-263). New York: New York University Press.■ Mazlish, B. (1993). The fourth discontinuity: the co- evolution of humans and machines. New Haven, CT: Yale University Press.■ McCarthy, J., & P. J. Hayes (1969). Some philosophical problems from the standpoint of artificial intelligence. In B. Meltzer & D. Michie (Eds.), Machine intelligence 4. Edinburgh: Edinburgh University Press.■ McClelland, J. L., D. E. Rumelhart & G. E. Hinton (1986). The appeal of parallel distributed processing. In D. E. Rumelhart, J. L. McClelland & the PDP Research Group (Eds.), Parallel distributed processing: Explorations in the mi crostructure of cognition (Vol. 1, pp. 3-40). Cambridge, MA: MIT Press/ Bradford Books.■ McCorduck, P. (1979). Machines who think. San Francisco: W. H. Freeman.■ McLaughlin, T. (1970). Music and communication. London: Faber & Faber.■ Mednick, S. A. (1962). The associative basis of the creative process. Psychological Review 69, 431-436.■ Meehl, P. E., & C. J. Golden (1982). Taxometric methods. In Kendall, P. C., & Butcher, J. N. (Eds.), Handbook of research methods in clinical psychology (pp. 127-182). New York: Wiley.■ Mehler, J., E.C.T. Walker & M. Garrett (Eds.) (1982). Perspectives on mental rep resentation: Experimental and theoretical studies of cognitive processes and ca pacities. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Mill, J. S. (1900). A system of logic, ratiocinative and inductive: Being a connected view of the principles of evidence and the methods of scientific investigation. London: Longmans, Green.■ Miller, G. A. (1979, June). A very personal history. Talk to the Cognitive Science Workshop, Cambridge, MA.■ Miller, J. (1983). States of mind. New York: Pantheon Books.■ Minsky, M. (1975). A framework for representing knowledge. In P. H. Winston (Ed.), The psychology of computer vision (pp. 211-277). New York: McGrawHill.■ Minsky, M., & S. Papert (1973). Artificial intelligence. Condon Lectures, Oregon State System of Higher Education, Eugene, Oregon.■ Minsky, M. L. (1986). The society of mind. New York: Simon & Schuster.■ Mischel, T. (1976). Psychological explanations and their vicissitudes. In J. K. Cole & W. J. Arnold (Eds.), Nebraska Symposium on motivation (Vol. 23). Lincoln, NB: University of Nebraska Press.■ Morford, M.P.O., & R. J. Lenardon (1995). Classical mythology (5th ed.). New York: Longman.■ Murdoch, I. (1954). Under the net. New York: Penguin.■ Nagel, E. (1959). Methodological issues in psychoanalytic theory. In S. Hook (Ed.), Psychoanalysis, scientific method, and philosophy: A symposium. New York: New York University Press.■ Nagel, T. (1979). Mortal questions. London: Cambridge University Press.■ Nagel, T. (1986). The view from nowhere. Oxford: Oxford University Press.■ Neisser, U. (1967). Cognitive psychology. New York: Appleton-Century-Crofts.■ Neisser, U. (1972). Changing conceptions of imagery. In P. W. Sheehan (Ed.), The function and nature of imagery (pp. 233-251). London: Academic Press.■ Neisser, U. (1976). Cognition and reality. San Francisco: W. H. Freeman.■ Neisser, U. (1978). Memory: What are the important questions? In M. M. Gruneberg, P. E. Morris & R. N. Sykes (Eds.), Practical aspects of memory (pp. 3-24). London: Academic Press.■ Neisser, U. (1979). The concept of intelligence. In R. J. Sternberg & D. K. Detterman (Eds.), Human intelligence: Perspectives on its theory and measurement (pp. 179-190). Norwood, NJ: Ablex.■ Nersessian, N. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. N. Giere (Ed.), Cognitive models of science (pp. 3-44). Minneapolis: University of Minnesota Press.■ Newell, A. (1973a). Artificial intelligence and the concept of mind. In R. C. Schank & K. M. Colby (Eds.), Computer models of thought and language (pp. 1-60). San Francisco: W. H. Freeman.■ Newell, A. (1973b). You can't play 20 questions with nature and win. In W. G. Chase (Ed.), Visual information processing (pp. 283-310). New York: Academic Press.■ Newell, A., & H. A. Simon (1963). GPS: A program that simulates human thought. In E. A. Feigenbaum & J. Feldman (Eds.), Computers and thought (pp. 279-293). New York & McGraw-Hill.■ Newell, A., & H. A. Simon (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.■ Nietzsche, F. (1966). Beyond good and evil. W. Kaufmann (Trans.). New York: Vintage. (Originally published in 1885.)■ Nilsson, N. J. (1971). Problem- solving methods in artificial intelligence. New York: McGraw-Hill.■ Nussbaum, M. C. (1978). Aristotle's Princeton University Press. De Motu Anamalium. Princeton, NJ:■ Oersted, H. C. (1920). Thermo-electricity. In Kirstine Meyer (Ed.), H. C. Oersted, Natuurvidenskabelige Skrifter (Vol. 2). Copenhagen: n.p. (Originally published in 1830 in The Edinburgh encyclopaedia.)■ Ong, W. J. (1982). Orality and literacy: The technologizing of the word. London: Methuen.■ Onians, R. B. (1954). The origins of European thought. Cambridge, MA: Cambridge University Press.■ Osgood, C. E. (1960). Method and theory in experimental psychology. New York: Oxford University Press. (Originally published in 1953.)■ Osgood, C. E. (1966). Language universals and psycholinguistics. In J. H. Greenberg (Ed.), Universals of language (2nd ed., pp. 299-322). Cambridge, MA: MIT Press.■ Palmer, R. E. (1969). Hermeneutics. Evanston, IL: Northwestern University Press.■ Peirce, C. S. (1934). Some consequences of four incapacities-Man, a sign. In C. Hartsborne & P. Weiss (Eds.), Collected papers of Charles Saunders Peirce (Vol. 5, pp. 185-189). Cambridge, MA: Harvard University Press.■ Penfield, W. (1959). In W. Penfield & L. Roberts, Speech and brain mechanisms. Princeton, NJ: Princeton University Press.■ Penrose, R. (1994). Shadows of the mind: A search for the missing science of conscious ness. Oxford: Oxford University Press.■ Perkins, D. N. (1981). The mind's best work. Cambridge, MA: Harvard University Press.■ Peterfreund, E. (1986). The heuristic approach to psychoanalytic therapy. In■ J. Reppen (Ed.), Analysts at work, (pp. 127-144). Hillsdale, NJ: Analytic Press.■ Piaget, J. (1952). The origin of intelligence in children. New York: International Universities Press. (Originally published in 1936.)■ Piaget, J. (1954). Le langage et les opeґrations intellectuelles. Proble` mes de psycho linguistique. Symposium de l'Association de Psychologie Scientifique de Langue Francёaise. Paris: Presses Universitaires de France.■ Piaget, J. (1977). Problems of equilibration. In H. E. Gruber & J. J. Voneche (Eds.), The essential Piaget (pp. 838-841). London: Routlege & Kegan Paul. (Originally published in 1975 as L'eґquilibration des structures cognitives [Paris: Presses Universitaires de France].)■ Piaget, J., & B. Inhelder. (1973). Memory and intelligence. New York: Basic Books.■ Pinker, S. (1994). The language instinct. New York: Morrow.■ Pinker, S. (1996). Facts about human language relevant to its evolution. In J.-P. Changeux & J. Chavaillon (Eds.), Origins of the human brain. A symposium of the Fyssen foundation (pp. 262-283). Oxford: Clarendon Press. Planck, M. (1949). Scientific autobiography and other papers. F. Gaynor (Trans.). New York: Philosophical Library.■ Planck, M. (1990). Wissenschaftliche Selbstbiographie. W. Berg (Ed.). Halle, Germany: Deutsche Akademie der Naturforscher Leopoldina.■ Plato (1892). Meno. In The Dialogues of Plato (B. Jowett, Trans.; Vol. 2). New York: Clarendon. (Originally published circa 380 B.C.)■ Poincareґ, H. (1913). Mathematical creation. In The foundations of science. G. B. Halsted (Trans.). New York: Science Press.■ Poincareґ, H. (1921). The foundations of science: Science and hypothesis, the value of science, science and method. G. B. Halstead (Trans.). New York: Science Press.■ Poincareґ, H. (1929). The foundations of science: Science and hypothesis, the value of science, science and method. New York: Science Press.■ Poincareґ, H. (1952). Science and method. F. Maitland (Trans.) New York: Dover.■ Polya, G. (1945). How to solve it. Princeton, NJ: Princeton University Press.■ Polanyi, M. (1958). Personal knowledge. London: Routledge & Kegan Paul.■ Popper, K. (1968). Conjectures and refutations: The growth of scientific knowledge. New York: Harper & Row/Basic Books.■ Popper, K., & J. Eccles (1977). The self and its brain. New York: Springer-Verlag.■ Popper, K. R. (1959). The logic of scientific discovery. London: Hutchinson.■ Putnam, H. (1975). Mind, language and reality: Philosophical papers (Vol. 2). Cambridge: Cambridge University Press.■ Putnam, H. (1987). The faces of realism. LaSalle, IL: Open Court.■ Pylyshyn, Z. W. (1981). The imagery debate: Analog media versus tacit knowledge. In N. Block (Ed.), Imagery (pp. 151-206). Cambridge, MA: MIT Press.■ Pylyshyn, Z. W. (1984). Computation and cognition: Towards a foundation for cog nitive science. Cambridge, MA: MIT Press/Bradford Books.■ Quillian, M. R. (1968). Semantic memory. In M. Minsky (Ed.), Semantic information processing (pp. 216-260). Cambridge, MA: MIT Press.■ Quine, W.V.O. (1960). Word and object. Cambridge, MA: Harvard University Press.■ Rabbitt, P.M.A., & S. Dornic (Eds.). Attention and performance (Vol. 5). London: Academic Press.■ Rawlins, G.J.E. (1997). Slaves of the Machine: The quickening of computer technology. Cambridge, MA: MIT Press/Bradford Books.■ Reid, T. (1970). An inquiry into the human mind on the principles of common sense. In R. Brown (Ed.), Between Hume and Mill: An anthology of British philosophy- 1749- 1843 (pp. 151-178). New York: Random House/Modern Library.■ Reitman, W. (1970). What does it take to remember? In D. A. Norman (Ed.), Models of human memory (pp. 470-510). London: Academic Press.■ Ricoeur, P. (1974). Structure and hermeneutics. In D. I. Ihde (Ed.), The conflict of interpretations: Essays in hermeneutics (pp. 27-61). Evanston, IL: Northwestern University Press.■ Robinson, D. N. (1986). An intellectual history of psychology. Madison: University of Wisconsin Press.■ Rorty, R. (1979). Philosophy and the mirror of nature. Princeton, NJ: Princeton University Press.■ Rosch, E. (1977). Human categorization. In N. Warren (Ed.), Studies in cross cultural psychology (Vol. 1, pp. 1-49) London: Academic Press.■ Rosch, E. (1978). Principles of categorization. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization (pp. 27-48). Hillsdale, NJ: Lawrence Erlbaum Associates.■ Rosch, E., & B. B. Lloyd (1978). Principles of categorization. In E. Rosch & B. B. Lloyd (Eds.), Cognition and categorization. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Rose, S. (1970). The chemistry of life. Baltimore: Penguin Books.■ Rose, S. (1976). The conscious brain (updated ed.). New York: Random House.■ Rose, S. (1993). The making of memory: From molecules to mind. New York: Anchor Books. (Originally published in 1992)■ Roszak, T. (1994). The cult of information: A neo- Luddite treatise on high- tech, artificial intelligence, and the true art of thinking (2nd ed.). Berkeley: University of California Press.■ Royce, J. R., & W. W. Rozeboom (Eds.) (1972). The psychology of knowing. New York: Gordon & Breach.■ Rumelhart, D. E. (1977). Introduction to human information processing. New York: Wiley.■ Rumelhart, D. E. (1980). Schemata: The building blocks of cognition. In R. J. Spiro, B. Bruce & W. F. Brewer (Eds.), Theoretical issues in reading comprehension. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Rumelhart, D. E., & J. L. McClelland (1986). On learning the past tenses of English verbs. In J. L. McClelland & D. E. Rumelhart (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 2). Cambridge, MA: MIT Press.■ Rumelhart, D. E., P. Smolensky, J. L. McClelland & G. E. Hinton (1986). Schemata and sequential thought processes in PDP models. In J. L. McClelland, D. E. Rumelhart & the PDP Research Group (Eds.), Parallel Distributed Processing (Vol. 2, pp. 7-57). Cambridge, MA: MIT Press.■ Russell, B. (1927). An outline of philosophy. London: G. Allen & Unwin.■ Russell, B. (1961). History of Western philosophy. London: George Allen & Unwin.■ Russell, B. (1965). How I write. In Portraits from memory and other essays. London: Allen & Unwin.■ Russell, B. (1992). In N. Griffin (Ed.), The selected letters of Bertrand Russell (Vol. 1), The private years, 1884- 1914. Boston: Houghton Mifflin. Ryecroft, C. (1966). Psychoanalysis observed. London: Constable.■ Sagan, C. (1978). The dragons of Eden: Speculations on the evolution of human intel ligence. New York: Ballantine Books.■ Salthouse, T. A. (1992). Expertise as the circumvention of human processing limitations. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: Prospects and limits (pp. 172-194). Cambridge: Cambridge University Press.■ Sanford, A. J. (1987). The mind of man: Models of human understanding. New Haven, CT: Yale University Press.■ Sapir, E. (1921). Language. New York: Harcourt, Brace, and World.■ Sapir, E. (1964). Culture, language, and personality. Berkeley: University of California Press. (Originally published in 1941.)■ Sapir, E. (1985). The status of linguistics as a science. In D. G. Mandelbaum (Ed.), Selected writings of Edward Sapir in language, culture and personality (pp. 160166). Berkeley: University of California Press. (Originally published in 1929).■ Scardmalia, M., & C. Bereiter (1992). Literate expertise. In K. A. Ericsson & J. Smith (Eds.), Toward a general theory of expertise: Prospects and limits (pp. 172-194). Cambridge: Cambridge University Press.■ Schafer, R. (1954). Psychoanalytic interpretation in Rorschach testing. New York: Grune & Stratten.■ Schank, R. C. (1973). Identification of conceptualizations underlying natural language. In R. C. Schank & K. M. Colby (Eds.), Computer models of thought and language (pp. 187-248). San Francisco: W. H. Freeman.■ Schank, R. C. (1976). The role of memory in language processing. In C. N. Cofer (Ed.), The structure of human memory. (pp. 162-189) San Francisco: W. H. Freeman.■ Schank, R. C. (1986). Explanation patterns: Understanding mechanically and creatively. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Schank, R. C., & R. P. Abelson (1977). Scripts, plans, goals, and understanding. Hillsdale, NJ: Lawrence Erlbaum Associates.■ SchroЁdinger, E. (1951). Science and humanism. Cambridge: Cambridge University Press.■ Searle, J. R. (1981a). Minds, brains, and programs. In J. Haugeland (Ed.), Mind design: Philosophy, psychology, artificial intelligence (pp. 282-306). Cambridge, MA: MIT Press.■ Searle, J. R. (1981b). Minds, brains and programs. In D. Hofstadter & D. Dennett (Eds.), The mind's I (pp. 353-373). New York: Basic Books.■ Searle, J. R. (1983). Intentionality. New York: Cambridge University Press.■ Serres, M. (1982). The origin of language: Biology, information theory, and thermodynamics. M. Anderson (Trans.). In J. V. Harari & D. F. Bell (Eds.), Hermes: Literature, science, philosophy (pp. 71-83). Baltimore: Johns Hopkins University Press.■ Simon, H. A. (1966). Scientific discovery and the psychology of problem solving. In R. G. Colodny (Ed.), Mind and cosmos: Essays in contemporary science and philosophy (pp. 22-40). Pittsburgh: University of Pittsburgh Press.■ Simon, H. A. (1979). Models of thought. New Haven, CT: Yale University Press.■ Simon, H. A. (1989). The scientist as a problem solver. In D. Klahr & K. Kotovsky (Eds.), Complex information processing: The impact of Herbert Simon. Hillsdale, N.J.: Lawrence Erlbaum Associates.■ Simon, H. A., & C. Kaplan (1989). Foundations of cognitive science. In M. Posner (Ed.), Foundations of cognitive science (pp. 1-47). Cambridge, MA: MIT Press.■ Simonton, D. K. (1988). Creativity, leadership and chance. In R. J. Sternberg (Ed.), The nature of creativity. Cambridge: Cambridge University Press.■ Skinner, B. F. (1974). About behaviorism. New York: Knopf.■ Smith, E. E. (1988). Concepts and thought. In J. Sternberg & E. E. Smith (Eds.), The psychology of human thought (pp. 19-49). Cambridge: Cambridge University Press.■ Smith, E. E. (1990). Thinking: Introduction. In D. N. Osherson & E. E. Smith (Eds.), Thinking. An invitation to cognitive science. (Vol. 3, pp. 1-2). Cambridge, MA: MIT Press.■ Socrates. (1958). Meno. In E. H. Warmington & P. O. Rouse (Eds.), Great dialogues of Plato W.H.D. Rouse (Trans.). New York: New American Library. (Original publication date unknown.)■ Solso, R. L. (1974). Theories of retrieval. In R. L. Solso (Ed.), Theories in cognitive psychology. Potomac, MD: Lawrence Erlbaum Associates.■ Spencer, H. (1896). The principles of psychology. New York: Appleton-CenturyCrofts.■ Steiner, G. (1975). After Babel: Aspects of language and translation. New York: Oxford University Press.■ Sternberg, R. J. (1977). Intelligence, information processing, and analogical reasoning. Hillsdale, NJ: Lawrence Erlbaum Associates.■ Sternberg, R. J. (1994). Intelligence. In R. J. Sternberg, Thinking and problem solving. San Diego: Academic Press.■ Sternberg, R. J., & J. E. Davidson (1985). Cognitive development in gifted and talented. In F. D. Horowitz & M. O'Brien (Eds.), The gifted and talented (pp. 103-135). Washington, DC: American Psychological Association.■ Storr, A. (1993). The dynamics of creation. New York: Ballantine Books. (Originally published in 1972.)■ Stumpf, S. E. (1994). Philosophy: History and problems (5th ed.). New York: McGraw-Hill.■ Sulloway, F. J. (1996). Born to rebel: Birth order, family dynamics, and creative lives. New York: Random House/Vintage Books.■ Thorndike, E. L. (1906). Principles of teaching. New York: A. G. Seiler.■ Thorndike, E. L. (1970). Animal intelligence: Experimental studies. Darien, CT: Hafner Publishing Co. (Originally published in 1911.)■ Titchener, E. B. (1910). A textbook of psychology. New York: Macmillan.■ Titchener, E. B. (1914). A primer of psychology. New York: Macmillan.■ Toulmin, S. (1957). The philosophy of science. London: Hutchinson.■ Tulving, E. (1972). Episodic and semantic memory. In E. Tulving & W. Donaldson (Eds.), Organisation of memory. London: Academic Press.■ Turing, A. (1946). In B. E. Carpenter & R. W. Doran (Eds.), ACE reports of 1946 and other papers. Cambridge, MA: MIT Press.■ Turkle, S. (1984). Computers and the second self: Computers and the human spirit. New York: Simon & Schuster.■ Tyler, S. A. (1978). The said and the unsaid: Mind, meaning, and culture. New York: Academic Press.■ van Heijenoort (Ed.) (1967). From Frege to Goedel. Cambridge: Harvard University Press.■ Varela, F. J. (1984). The creative circle: Sketches on the natural history of circularity. In P. Watzlawick (Ed.), The invented reality (pp. 309-324). New York: W. W. Norton.■ Voltaire (1961). On the Penseґs of M. Pascal. In Philosophical letters (pp. 119-146). E. Dilworth (Trans.). Indianapolis: Bobbs-Merrill.■ Wagman, M. (1991a). Artificial intelligence and human cognition: A theoretical inter comparison of two realms of intellect. Westport, CT: Praeger.■ Wagman, M. (1991b). Cognitive science and concepts of mind: Toward a general theory of human and artificial intelligence. Westport, CT: Praeger.■ Wagman, M. (1993). Cognitive psychology and artificial intelligence: Theory and re search in cognitive science. Westport, CT: Praeger.■ Wagman, M. (1995). The sciences of cognition: Theory and research in psychology and artificial intelligence. Westport, CT: Praeger.■ Wagman, M. (1996). Human intellect and cognitive science: Toward a general unified theory of intelligence. Westport, CT: Praeger.■ Wagman, M. (1997a). Cognitive science and the symbolic operations of human and artificial intelligence: Theory and research into the intellective processes. Westport, CT: Praeger.■ Wagman, M. (1997b). The general unified theory of intelligence: Central conceptions and specific application to domains of cognitive science. Westport, CT: Praeger.■ Wagman, M. (1998a). Cognitive science and the mind- body problem: From philosophy to psychology to artificial intelligence to imaging of the brain. Westport, CT: Praeger.■ Wagman, M. (1998b). Language and thought in humans and computers: Theory and research in psychology, artificial intelligence, and neural science. Westport, CT: Praeger.■ Wagman, M. (1998c). The ultimate objectives of artificial intelligence: Theoretical and research foundations, philosophical and psychological implications. Westport, CT: Praeger.■ Wagman, M. (1999). The human mind according to artificial intelligence: Theory, re search, and implications. Westport, CT: Praeger.■ Wagman, M. (2000). Scientific discovery processes in humans and computers: Theory and research in psychology and artificial intelligence. Westport, CT: Praeger.■ Wall, R. (1972). Introduction to mathematical linguistics. Englewood Cliffs, NJ: Prentice-Hall.■ Wallas, G. (1926). The Art of Thought. New York: Harcourt, Brace & Co.■ Wason, P. (1977). Self contradictions. In P. Johnson-Laird & P. Wason (Eds.), Thinking: Readings in cognitive science. Cambridge: Cambridge University Press.■ Wason, P. C., & P. N. Johnson-Laird. (1972). Psychology of reasoning: Structure and content. Cambridge, MA: Harvard University Press.■ Watson, J. (1930). Behaviorism. New York: W. W. Norton.■ Watzlawick, P. (1984). Epilogue. In P. Watzlawick (Ed.), The invented reality. New York: W. W. Norton, 1984.■ Weinberg, S. (1977). The first three minutes: A modern view of the origin of the uni verse. New York: Basic Books.■ Weisberg, R. W. (1986). Creativity: Genius and other myths. New York: W. H. Freeman.■ Weizenbaum, J. (1976). Computer power and human reason: From judgment to cal culation. San Francisco: W. H. Freeman.■ Wertheimer, M. (1945). Productive thinking. New York: Harper & Bros.■ Whitehead, A. N. (1925). Science and the modern world. New York: Macmillan.■ Whorf, B. L. (1956). In J. B. Carroll (Ed.), Language, thought and reality: Selected writings of Benjamin Lee Whorf. Cambridge, MA: MIT Press.■ Whyte, L. L. (1962). The unconscious before Freud. New York: Anchor Books.■ Wiener, N. (1954). The human use of human beings. Boston: Houghton Mifflin.■ Wiener, N. (1964). God & Golem, Inc.: A comment on certain points where cybernetics impinges on religion. Cambridge, MA: MIT Press.■ Winograd, T. (1972). Understanding natural language. New York: Academic Press.■ Winston, P. H. (1987). Artificial intelligence: A perspective. In E. L. Grimson & R. S. Patil (Eds.), AI in the 1980s and beyond (pp. 1-12). Cambridge, MA: MIT Press.■ Winston, P. H. (Ed.) (1975). The psychology of computer vision. New York: McGrawHill.■ Wittgenstein, L. (1953). Philosophical investigations. Oxford: Basil Blackwell.■ Wittgenstein, L. (1958). The blue and brown books. New York: Harper Colophon.■ Woods, W. A. (1975). What's in a link: Foundations for semantic networks. In D. G. Bobrow & A. Collins (Eds.), Representations and understanding: Studies in cognitive science (pp. 35-84). New York: Academic Press.■ Woodworth, R. S. (1938). Experimental psychology. New York: Holt; London: Methuen (1939).■ Wundt, W. (1904). Principles of physiological psychology (Vol. 1). E. B. Titchener (Trans.). New York: Macmillan.■ Wundt, W. (1907). Lectures on human and animal psychology. J. E. Creighton & E. B. Titchener (Trans.). New York: Macmillan.■ Young, J. Z. (1978). Programs of the brain. New York: Oxford University Press.■ Ziman, J. (1978). Reliable knowledge: An exploration of the grounds for belief in science. Cambridge: Cambridge University Press.Historical dictionary of quotations in cognitive science > Bibliography
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16 Grammar
I think that the failure to offer a precise account of the notion "grammar" is not just a superficial defect in linguistic theory that can be remedied by adding one more definition. It seems to me that until this notion is clarified, no part of linguistic theory can achieve anything like a satisfactory development.... I have been discussing a grammar of a particular language here as analogous to a particular scientific theory, dealing with its subject matter (the set of sentences of this language) much as embryology or physics deals with its subject matter. (Chomsky, 1964, p. 213)Obviously, every speaker of a language has mastered and internalized a generative grammar that expresses his knowledge of his language. This is not to say that he is aware of the rules of grammar or even that he can become aware of them, or that his statements about his intuitive knowledge of his language are necessarily accurate. (Chomsky, 1965, p. 8)Much effort has been devoted to showing that the class of possible transformations can be substantially reduced without loss of descriptive power through the discovery of quite general conditions that all such rules and the representations they operate on and form must meet.... [The] transformational rules, at least for a substantial core grammar, can be reduced to the single rule, "Move alpha" (that is, "move any category anywhere"). (Mehler, Walker & Garrett, 1982, p. 21)4) The Relationship of Transformational Grammar to Semantics and to Human Performancehe implications of assuming a semantic memory for what we might call "generative psycholinguistics" are: that dichotomous judgments of semantic well-formedness versus anomaly are not essential or inherent to language performance; that the transformational component of a grammar is the part most relevant to performance models; that a generative grammar's role should be viewed as restricted to language production, whereas sentence understanding should be treated as a problem of extracting a cognitive representation of a text's message; that until some theoretical notion of cognitive representation is incorporated into linguistic conceptions, they are unlikely to provide either powerful language-processing programs or psychologically relevant theories.Although these implications conflict with the way others have viewed the relationship of transformational grammars to semantics and to human performance, they do not eliminate the importance of such grammars to psychologists, an importance stressed in, and indeed largely created by, the work of Chomsky. It is precisely because of a growing interdependence between such linguistic theory and psychological performance models that their relationship needs to be clarified. (Quillian, 1968, p. 260)here are some terminological distinctions that are crucial to explain, or else confusions can easily arise. In the formal study of grammar, a language is defined as a set of sentences, possibly infinite, where each sentence is a string of symbols or words. One can think of each sentence as having several representations linked together: one for its sound pattern, one for its meaning, one for the string of words constituting it, possibly others for other data structures such as the "surface structure" and "deep structure" that are held to mediate the mapping between sound and meaning. Because no finite system can store an infinite number of sentences, and because humans in particular are clearly not pullstring dolls that emit sentences from a finite stored list, one must explain human language abilities by imputing to them a grammar, which in the technical sense is a finite rule system, or programme, or circuit design, capable of generating and recognizing the sentences of a particular language. This "mental grammar" or "psychogrammar" is the neural system that allows us to speak and understand the possible word sequences of our native tongue. A grammar for a specific language is obviously acquired by a human during childhood, but there must be neural circuitry that actually carries out the acquisition process in the child, and this circuitry may be called the language faculty or language acquisition device. An important part of the language faculty is universal grammar, an implementation of a set of principles or constraints that govern the possible form of any human grammar. (Pinker, 1996, p. 263)A grammar of language L is essentially a theory of L. Any scientific theory is based on a finite number of observations, and it seeks to relate the observed phenomena and to predict new phenomena by constructing general laws in terms of hypothetical constructs.... Similarly a grammar of English is based on a finite corpus of utterances (observations), and it will contain certain grammatical rules (laws) stated in terms of the particular phonemes, phrases, etc., of English (hypothetical constructs). These rules express structural relations among the sentences of the corpus and the infinite number of sentences generated by the grammar beyond the corpus (predictions). (Chomsky, 1957, p. 49)Historical dictionary of quotations in cognitive science > Grammar
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17 Logic
My initial step... was to attempt to reduce the concept of ordering in a sequence to that of logical consequence, so as to proceed from there to the concept of number. To prevent anything intuitive from penetrating here unnoticed, I had to bend every effort to keep the chain of inference free of gaps. In attempting to comply with this requirement in the strictest possible way, I found the inadequacy of language to be an obstacle. (Frege, 1972, p. 104)I believe I can make the relation of my 'conceptual notation' to ordinary language clearest if I compare it to the relation of the microscope to the eye. The latter, because of the range of its applicability and because of the ease with which it can adapt itself to the most varied circumstances, has a great superiority over the microscope. Of course, viewed as an optical instrument it reveals many imperfections, which usually remain unnoticed only because of its intimate connection with mental life. But as soon as scientific purposes place strong requirements upon sharpness of resolution, the eye proves to be inadequate.... Similarly, this 'conceptual notation' is devised for particular scientific purposes; and therefore one may not condemn it because it is useless for other purposes. (Frege, 1972, pp. 104-105)To sum up briefly, it is the business of the logician to conduct an unceasing struggle against psychology and those parts of language and grammar which fail to give untrammeled expression to what is logical. He does not have to answer the question: How does thinking normally take place in human beings? What course does it naturally follow in the human mind? What is natural to one person may well be unnatural to another. (Frege, 1979, pp. 6-7)We are very dependent on external aids in our thinking, and there is no doubt that the language of everyday life-so far, at least, as a certain area of discourse is concerned-had first to be replaced by a more sophisticated instrument, before certain distinctions could be noticed. But so far the academic world has, for the most part, disdained to master this instrument. (Frege, 1979, pp. 6-7)There is no reproach the logician need fear less than the reproach that his way of formulating things is unnatural.... If we were to heed those who object that logic is unnatural, we would run the risk of becoming embroiled in interminable disputes about what is natural, disputes which are quite incapable of being resolved within the province of logic. (Frege, 1979, p. 128)[L]inguists will be forced, internally as it were, to come to grips with the results of modern logic. Indeed, this is apparently already happening to some extent. By "logic" is not meant here recursive function-theory, California model-theory, constructive proof-theory, or even axiomatic settheory. Such areas may or may not be useful for linguistics. Rather under "logic" are included our good old friends, the homely locutions "and," "or," "if-then," "if and only if," "not," "for all x," "for some x," and "is identical with," plus the calculus of individuals, event-logic, syntax, denotational semantics, and... various parts of pragmatics.... It is to these that the linguist can most profitably turn for help. These are his tools. And they are "clean tools," to borrow a phrase of the late J. L. Austin in another context, in fact, the only really clean ones we have, so that we might as well use them as much as we can. But they constitute only what may be called "baby logic." Baby logic is to the linguist what "baby mathematics" (in the phrase of Murray Gell-Mann) is to the theoretical physicist-very elementary but indispensable domains of theory in both cases. (Martin, 1969, pp. 261-262)There appears to be no branch of deductive inference that requires us to assume the existence of a mental logic in order to do justice to the psychological phenomena. To be logical, an individual requires, not formal rules of inference, but a tacit knowledge of the fundamental semantic principle governing any inference; a deduction is valid provided that there is no way of interpreting the premises correctly that is inconsistent with the conclusion. Logic provides a systematic method for searching for such counter-examples. The empirical evidence suggests that ordinary individuals possess no such methods. (Johnson-Laird, quoted in Mehler, Walker & Garrett, 1982, p. 130)The fundamental paradox of logic [that "there is no class (as a totality) of those classes which, each taken as a totality, do not belong to themselves" (Russell to Frege, 16 June 1902, in van Heijenoort, 1967, p. 125)] is with us still, bequeathed by Russell-by way of philosophy, mathematics, and even computer science-to the whole of twentieth-century thought. Twentieth-century philosophy would begin not with a foundation for logic, as Russell had hoped in 1900, but with the discovery in 1901 that no such foundation can be laid. (Everdell, 1997, p. 184)Historical dictionary of quotations in cognitive science > Logic
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18 Cognitive Science
The basic idea of cognitive science is that intelligent beings are semantic engines-in other words, automatic formal systems with interpretations under which they consistently make sense.... [P]eople and intelligent computers turn out to be merely different manifestations of the same underlying phenomenon. (Haugeland, 1981b, p. 31)2) Experimental Psychology, Theoretical Linguistics, and Computational Simulation of Cognitive Processes Are All Components of Cognitive ScienceI went away from the Symposium with a strong conviction, more intuitive than rational, that human experimental psychology, theoretical linguistics, and computer simulation of cognitive processes were all pieces of a larger whole, and that the future would see progressive elaboration and coordination of their shared concerns.... I have been working toward a cognitive science for about twenty years beginning before I knew what to call it. (G. A. Miller, 1979, p. 9)Cognitive Science studies the nature of cognition in human beings, other animals, and inanimate machines (if such a thing is possible). While computers are helpful within cognitive science, they are not essential to its being. A science of cognition could still be pursued even without these machines.Computer Science studies various kinds of problems and the use of computers to solve them, without concern for the means by which we humans might otherwise resolve them. There could be no computer science if there were no machines of this kind, because they are indispensable to its being. Artificial Intelligence is a special branch of computer science that investigates the extent to which the mental powers of human beings can be captured by means of machines.There could be cognitive science without artificial intelligence but there could be no artificial intelligence without cognitive science. One final caveat: In the case of an emerging new discipline such as cognitive science there is an almost irresistible temptation to identify the discipline itself (as a field of inquiry) with one of the theories that inspired it (such as the computational conception...). This, however, is a mistake. The field of inquiry (or "domain") stands to specific theories as questions stand to possible answers. The computational conception should properly be viewed as a research program in cognitive science, where "research programs" are answers that continue to attract followers. (Fetzer, 1996, pp. xvi-xvii)What is the nature of knowledge and how is this knowledge used? These questions lie at the core of both psychology and artificial intelligence.The psychologist who studies "knowledge systems" wants to know how concepts are structured in the human mind, how such concepts develop, and how they are used in understanding and behavior. The artificial intelligence researcher wants to know how to program a computer so that it can understand and interact with the outside world. The two orientations intersect when the psychologist and the computer scientist agree that the best way to approach the problem of building an intelligent machine is to emulate the human conceptual mechanisms that deal with language.... The name "cognitive science" has been used to refer to this convergence of interests in psychology and artificial intelligence....This working partnership in "cognitive science" does not mean that psychologists and computer scientists are developing a single comprehensive theory in which people are no different from machines. Psychology and artificial intelligence have many points of difference in methods and goals.... We simply want to work on an important area of overlapping interest, namely a theory of knowledge systems. As it turns out, this overlap is substantial. For both people and machines, each in their own way, there is a serious problem in common of making sense out of what they hear, see, or are told about the world. The conceptual apparatus necessary to perform even a partial feat of understanding is formidable and fascinating. (Schank & Abelson, 1977, pp. 1-2)Within the last dozen years a general change in scientific outlook has occurred, consonant with the point of view represented here. One can date the change roughly from 1956: in psychology, by the appearance of Bruner, Goodnow, and Austin's Study of Thinking and George Miller's "The Magical Number Seven"; in linguistics, by Noam Chomsky's "Three Models of Language"; and in computer science, by our own paper on the Logic Theory Machine. (Newell & Simon, 1972, p. 4)Historical dictionary of quotations in cognitive science > Cognitive Science
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19 FSL
1) Компьютерная техника: free-space loss2) Авиация: Fuel System Limitations3) Спорт: Florida State League4) Военный термин: federal stock listings, field storage location5) Техника: formal semantic language, frequency-selective limiter6) Шутливое выражение: Fix Silvia Later7) Грубое выражение: Fight Stupid Laws8) Гидрография: объём водохранилища при полном обеспечении водопотребителей (Full Supply Level capacity)9) Сокращение: Fleet Support Ltd (UK)10) Нефть: from the south line11) Пищевая промышленность: forward shear lock = прямоточный затвор (в экструдере)12) Бурение: от южной линии (from south line)13) Океанография: Forecast Systems Laboratory14) Расширение файла: Form (Paradox for Windows)15) Аэропорты: Fossil Downs, Western Australia, Australia -
20 FSL
1. formal semantic language - формальный семантический язык;2. free-space loss - потери в свободном пространстве;3. frequency-selective limiter - частотно-избирательный ограничитель
См. также в других словарях:
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