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Models of human memory

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    Historical dictionary of quotations in cognitive science > Bibliography

  • 2 Computer Metaphors

       Within the AI community there is a growing dissatisfaction concerning the adequacy of sequential models to simulate the cognitive processes....
       For an example of the dissimilarity between computers and nervous systems, consider that in conventional computers... each piece of data [is] located in its own special space in the memory bank [and] can be retrieved only by a central processor that knows the address in the memory bank for each datum. Human memory appears to be organized along entirely different lines. For one thing, from a partial or a degraded stimulus human memory can "reconstruct" the rest, and there are associative relationships among stored pieces of information based on considerations of context rather than on considerations of location.... t now appears doubtful that individual neurons are so specific that they are tuned to respond to a single item and nothing else. Thus, connectionist models tend to devise and use distributed principles, which means that elements may be selective to a range of stimuli and there are no "grandmother cells."...
       Information storage, it appears, is in some ill-defined sense a function of connectivity among sets of neurons. This implies that there is something fundamentally wrong in understanding the brain's memory on the model of individual symbols stored at unique addresses in a data bank....
       A further source of misgivings about the computer metaphor concerns real-time constraints. Although the signal velocities in nervous systems are quite slow in comparison to those in computers, brains are nonetheless far, far faster than electronic devices in the execution of their complex tasks. For example, human brains are incomparably faster than any computer in word-nonword recognition tasks. (P. S. Churchland, 1986, pp. 458-459)

    Historical dictionary of quotations in cognitive science > Computer Metaphors

  • 3 Cognition

       As used here, the term "cognition" refers to all processes by which the sensory input is transformed, reduced, elaborated, stored, recovered, and used. It is concerned with these processes even when they operate in the absence of relevant stimulation, as in images and hallucinations....
       [G]iven such a sweeping definition, it is apparent that cognition is involved in everything a human being might possibly do; that every psychological phenomenon is a cognitive phenomenon. (Neisser, 1976, p. 4)
       Man is describable as a dual processor, dual memory system with extensive input-output buffering within each system. The input-output system appears to have substantial peripheral computing power itself. But man is not modeled by a dual processor computer. The two processors of the brain are asymmetric. The semantic memory processor is a serial processor with a list structure memory. The image memory processor may very well be a sophisticated analog processor attached to an associative memory. When we propose models of cognition it would perhaps be advisable if we specified the relation of the model to this system architecture and its associated addressing system and data structure. (Hunt, 1973, pp. 370-371)

    Historical dictionary of quotations in cognitive science > Cognition

  • 4 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 Performance
       he 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

  • 5 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, Eventually
       Just 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)
       Many 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 Form
       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. 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 Formation
       It 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 Contexts
       Even 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)
        18) The Assumption That the Mind Is a Formal System
       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 Intelligence
       The 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 Propositions
       In 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

  • 6 Creativity

       Put in this bald way, these aims sound utopian. How utopian they areor rather, how imminent their realization-depends on how broadly or narrowly we interpret the term "creative." If we are willing to regard all human complex problem solving as creative, then-as we will point out-successful programs for problem solving mechanisms that simulate human problem solvers already exist, and a number of their general characteristics are known. If we reserve the term "creative" for activities like discovery of the special theory of relativity or the composition of Beethoven's Seventh Symphony, then no example of a creative mechanism exists at the present time. (Simon, 1979, pp. 144-145)
       Among the questions that can now be given preliminary answers in computational terms are the following: how can ideas from very different sources be spontaneously thought of together? how can two ideas be merged to produce a new structure, which shows the influence of both ancestor ideas without being a mere "cut-and-paste" combination? how can the mind be "primed," so that one will more easily notice serendipitous ideas? why may someone notice-and remember-something fairly uninteresting, if it occurs in an interesting context? how can a brief phrase conjure up an entire melody from memory? and how can we accept two ideas as similar ("love" and "prove" as rhyming, for instance) in respect of a feature not identical in both? The features of connectionist AI models that suggest answers to these questions are their powers of pattern completion, graceful degradation, sensitization, multiple constraint satisfaction, and "best-fit" equilibration.... Here, the important point is that the unconscious, "insightful," associative aspects of creativity can be explained-in outline, at least-by AI methods. (Boden, 1996, p. 273)
       There thus appears to be an underlying similarity in the process involved in creative innovation and social independence, with common traits and postures required for expression of both behaviors. The difference is one of product-literary, musical, artistic, theoretical products on the one hand, opinions on the other-rather than one of process. In both instances the individual must believe that his perceptions are meaningful and valid and be willing to rely upon his own interpretations. He must trust himself sufficiently that even when persons express opinions counter to his own he can proceed on the basis of his own perceptions and convictions. (Coopersmith, 1967, p. 58)
       he average level of ego strength and emotional stability is noticeably higher among creative geniuses than among the general population, though it is possibly lower than among men of comparable intelligence and education who go into administrative and similar positions. High anxiety and excitability appear common (e.g. Priestley, Darwin, Kepler) but full-blown neurosis is quite rare. (Cattell & Butcher, 1970, p. 315)
       he insight that is supposed to be required for such work as discovery turns out to be synonymous with the familiar process of recognition; and other terms commonly used in the discussion of creative work-such terms as "judgment," "creativity," or even "genius"-appear to be wholly dispensable or to be definable, as insight is, in terms of mundane and well-understood concepts. (Simon, 1989, p. 376)
       From the sketch material still in existence, from the condition of the fragments, and from the autographs themselves we can draw definite conclusions about Mozart's creative process. To invent musical ideas he did not need any stimulation; they came to his mind "ready-made" and in polished form. In contrast to Beethoven, who made numerous attempts at shaping his musical ideas until he found the definitive formulation of a theme, Mozart's first inspiration has the stamp of finality. Any Mozart theme has completeness and unity; as a phenomenon it is a Gestalt. (Herzmann, 1964, p. 28)
       Great artists enlarge the limits of one's perception. Looking at the world through the eyes of Rembrandt or Tolstoy makes one able to perceive aspects of truth about the world which one could not have achieved without their aid. Freud believed that science was adaptive because it facilitated mastery of the external world; but was it not the case that many scientific theories, like works of art, also originated in phantasy? Certainly, reading accounts of scientific discovery by men of the calibre of Einstein compelled me to conclude that phantasy was not merely escapist, but a way of reaching new insights concerning the nature of reality. Scientific hypotheses require proof; works of art do not. Both are concerned with creating order, with making sense out of the world and our experience of it. (Storr, 1993, p. xii)
       The importance of self-esteem for creative expression appears to be almost beyond disproof. Without a high regard for himself the individual who is working in the frontiers of his field cannot trust himself to discriminate between the trivial and the significant. Without trust in his own powers the person seeking improved solutions or alternative theories has no basis for distinguishing the significant and profound innovation from the one that is merely different.... An essential component of the creative process, whether it be analysis, synthesis, or the development of a new perspective or more comprehensive theory, is the conviction that one's judgment in interpreting the events is to be trusted. (Coopersmith, 1967, p. 59)
       In the daily stream of thought these four different stages [preparation; incubation; illumination or inspiration; and verification] constantly overlap each other as we explore different problems. An economist reading a Blue Book, a physiologist watching an experiment, or a business man going through his morning's letters, may at the same time be "incubating" on a problem which he proposed to himself a few days ago, be accumulating knowledge in "preparation" for a second problem, and be "verifying" his conclusions to a third problem. Even in exploring the same problem, the mind may be unconsciously incubating on one aspect of it, while it is consciously employed in preparing for or verifying another aspect. (Wallas, 1926, p. 81)
       he basic, bisociative pattern of the creative synthesis [is] the sudden interlocking of two previously unrelated skills, or matrices of thought. (Koestler, 1964, p. 121)
        11) The Earliest Stages in the Creative Process Involve a Commerce with Disorder
       Even to the creator himself, the earliest effort may seem to involve a commerce with disorder. For the creative order, which is an extension of life, is not an elaboration of the established, but a movement beyond the established, or at least a reorganization of it and often of elements not included in it. The first need is therefore to transcend the old order. Before any new order can be defined, the absolute power of the established, the hold upon us of what we know and are, must be broken. New life comes always from outside our world, as we commonly conceive that world. This is the reason why, in order to invent, one must yield to the indeterminate within him, or, more precisely, to certain illdefined impulses which seem to be of the very texture of the ungoverned fullness which John Livingston Lowes calls "the surging chaos of the unexpressed." (Ghiselin, 1985, p. 4)
       New life comes always from outside our world, as we commonly conceive our world. This is the reason why, in order to invent, one must yield to the indeterminate within him, or, more precisely, to certain illdefined impulses which seem to be of the very texture of the ungoverned fullness which John Livingston Lowes calls "the surging chaos of the unexpressed." Chaos and disorder are perhaps the wrong terms for that indeterminate fullness and activity of the inner life. For it is organic, dynamic, full of tension and tendency. What is absent from it, except in the decisive act of creation, is determination, fixity, and commitment to one resolution or another of the whole complex of its tensions. (Ghiselin, 1952, p. 13)
       [P]sychoanalysts have principally been concerned with the content of creative products, and with explaining content in terms of the artist's infantile past. They have paid less attention to examining why the artist chooses his particular activity to express, abreact or sublimate his emotions. In short, they have not made much distinction between art and neurosis; and, since the former is one of the blessings of mankind, whereas the latter is one of the curses, it seems a pity that they should not be better differentiated....
       Psychoanalysis, being fundamentally concerned with drive and motive, might have been expected to throw more light upon what impels the creative person that in fact it has. (Storr, 1993, pp. xvii, 3)
       A number of theoretical approaches were considered. Associative theory, as developed by Mednick (1962), gained some empirical support from the apparent validity of the Remote Associates Test, which was constructed on the basis of the theory.... Koestler's (1964) bisociative theory allows more complexity to mental organization than Mednick's associative theory, and postulates "associative contexts" or "frames of reference." He proposed that normal, non-creative, thought proceeds within particular contexts or frames and that the creative act involves linking together previously unconnected frames.... Simonton (1988) has developed associative notions further and explored the mathematical consequences of chance permutation of ideas....
       Like Koestler, Gruber (1980; Gruber and Davis, 1988) has based his analysis on case studies. He has focused especially on Darwin's development of the theory of evolution. Using piagetian notions, such as assimilation and accommodation, Gruber shows how Darwin's system of ideas changed very slowly over a period of many years. "Moments of insight," in Gruber's analysis, were the culminations of slow long-term processes.... Finally, the information-processing approach, as represented by Simon (1966) and Langley et al. (1987), was considered.... [Simon] points out the importance of good problem representations, both to ensure search is in an appropriate problem space and to aid in developing heuristic evaluations of possible research directions.... The work of Langley et al. (1987) demonstrates how such search processes, realized in computer programs, can indeed discover many basic laws of science from tables of raw data.... Boden (1990a, 1994) has stressed the importance of restructuring the problem space in creative work to develop new genres and paradigms in the arts and sciences. (Gilhooly, 1996, pp. 243-244; emphasis in original)

    Historical dictionary of quotations in cognitive science > Creativity

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