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Information and the Human Brain

  • 1 Information

       Information is carried by physical entities, such as books or sound waves or brains, but it is not itself material. Information in a living system is a feature of the order and arrangement of its parts, which arrangement provides the signs that constitute a "code" or "language."... The organization of the brain can be considered as the written script of the programs of our lives. So the important feature of brains is not the material that they are made of but the information that they carry.
       What neuroscience can do is to translate the language in which the brain programs are written into ordinary language. Since these are the programs that produce the phenomena of human language we are not really escaping it. We are using the analogies of language and of writing to understand the entities that produce them. As so often in the past, man, having invented an artifact (in this case writing) to help him with his life (by carrying information), is now trying to describe himself in terms of his artifact. (Young, 1978, p. 2)

    Historical dictionary of quotations in cognitive science > Information

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    Historical dictionary of quotations in cognitive science > Bibliography

  • 3 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

  • 4 visual processing

    обработка графических, визуальных и/или видеоданных; визуальное восприятие
    к этой области относятся различные компьютерные технологии распознавания (опознавания).

    This information probably will be presented in graphical form, to exploit more fully the large fraction of the human brain devoted to visual processing and the visual system's pattern recognition ability. — Эта информация будет, по-видимому, представляться в графической форме, чтобы наиболее полно использовать большую часть человеческого мозга, отвечающую за визуальное восприятие, и возможности распознавания образов, присущие компьютерной системе визуализации (обработки визуальных данных) см. тж. graphical form, recognition, visual system

    Англо-русский толковый словарь терминов и сокращений по ВТ, Интернету и программированию. > visual processing

  • 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 Computers

       The brain has been compared to a digital computer because the neuron, like a switch or valve, either does or does not complete a circuit. But at that point the similarity ends. The switch in the digital computer is constant in its effect, and its effect is large in proportion to the total output of the machine. The effect produced by the neuron varies with its recovery from [the] refractory phase and with its metabolic state. The number of neurons involved in any action runs into millions so that the influence of any one is negligible.... Any cell in the system can be dispensed with.... The brain is an analogical machine, not digital. Analysis of the integrative activities will probably have to be in statistical terms. (Lashley, quoted in Beach, Hebb, Morgan & Nissen, 1960, p. 539)
       It is essential to realize that a computer is not a mere "number cruncher," or supercalculating arithmetic machine, although this is how computers are commonly regarded by people having no familiarity with artificial intelligence. Computers do not crunch numbers; they manipulate symbols.... Digital computers originally developed with mathematical problems in mind, are in fact general purpose symbol manipulating machines....
       The terms "computer" and "computation" are themselves unfortunate, in view of their misleading arithmetical connotations. The definition of artificial intelligence previously cited-"the study of intelligence as computation"-does not imply that intelligence is really counting. Intelligence may be defined as the ability creatively to manipulate symbols, or process information, given the requirements of the task in hand. (Boden, 1981, pp. 15, 16-17)
       The task is to get computers to explain things to themselves, to ask questions about their experiences so as to cause those explanations to be forthcoming, and to be creative in coming up with explanations that have not been previously available. (Schank, 1986, p. 19)
       In What Computers Can't Do, written in 1969 (2nd edition, 1972), the main objection to AI was the impossibility of using rules to select only those facts about the real world that were relevant in a given situation. The "Introduction" to the paperback edition of the book, published by Harper & Row in 1979, pointed out further that no one had the slightest idea how to represent the common sense understanding possessed even by a four-year-old. (Dreyfus & Dreyfus, 1986, p. 102)
       A popular myth says that the invention of the computer diminishes our sense of ourselves, because it shows that rational thought is not special to human beings, but can be carried on by a mere machine. It is a short stop from there to the conclusion that intelligence is mechanical, which many people find to be an affront to all that is most precious and singular about their humanness.
       In fact, the computer, early in its career, was not an instrument of the philistines, but a humanizing influence. It helped to revive an idea that had fallen into disrepute: the idea that the mind is real, that it has an inner structure and a complex organization, and can be understood in scientific terms. For some three decades, until the 1940s, American psychology had lain in the grip of the ice age of behaviorism, which was antimental through and through. During these years, extreme behaviorists banished the study of thought from their agenda. Mind and consciousness, thinking, imagining, planning, solving problems, were dismissed as worthless for anything except speculation. Only the external aspects of behavior, the surface manifestations, were grist for the scientist's mill, because only they could be observed and measured....
       It is one of the surprising gifts of the computer in the history of ideas that it played a part in giving back to psychology what it had lost, which was nothing less than the mind itself. In particular, there was a revival of interest in how the mind represents the world internally to itself, by means of knowledge structures such as ideas, symbols, images, and inner narratives, all of which had been consigned to the realm of mysticism. (Campbell, 1989, p. 10)
       [Our artifacts] only have meaning because we give it to them; their intentionality, like that of smoke signals and writing, is essentially borrowed, hence derivative. To put it bluntly: computers themselves don't mean anything by their tokens (any more than books do)-they only mean what we say they do. Genuine understanding, on the other hand, is intentional "in its own right" and not derivatively from something else. (Haugeland, 1981a, pp. 32-33)
       he debate over the possibility of computer thought will never be won or lost; it will simply cease to be of interest, like the previous debate over man as a clockwork mechanism. (Bolter, 1984, p. 190)
       t takes us a long time to emotionally digest a new idea. The computer is too big a step, and too recently made, for us to quickly recover our balance and gauge its potential. It's an enormous accelerator, perhaps the greatest one since the plow, twelve thousand years ago. As an intelligence amplifier, it speeds up everything-including itself-and it continually improves because its heart is information or, more plainly, ideas. We can no more calculate its consequences than Babbage could have foreseen antibiotics, the Pill, or space stations.
       Further, the effects of those ideas are rapidly compounding, because a computer design is itself just a set of ideas. As we get better at manipulating ideas by building ever better computers, we get better at building even better computers-it's an ever-escalating upward spiral. The early nineteenth century, when the computer's story began, is already so far back that it may as well be the Stone Age. (Rawlins, 1997, p. 19)
       According to weak AI, the principle value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion than before. But according to strong AI the computer is not merely a tool in the study of the mind; rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states. And according to strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations. (Searle, 1981b, p. 353)
       What makes people smarter than machines? They certainly are not quicker or more precise. Yet people are far better at perceiving objects in natural scenes and noting their relations, at understanding language and retrieving contextually appropriate information from memory, at making plans and carrying out contextually appropriate actions, and at a wide range of other natural cognitive tasks. People are also far better at learning to do these things more accurately and fluently through processing experience.
       What is the basis for these differences? One answer, perhaps the classic one we might expect from artificial intelligence, is "software." If we only had the right computer program, the argument goes, we might be able to capture the fluidity and adaptability of human information processing. Certainly this answer is partially correct. There have been great breakthroughs in our understanding of cognition as a result of the development of expressive high-level computer languages and powerful algorithms. However, we do not think that software is the whole story.
       In our view, people are smarter than today's computers because the brain employs a basic computational architecture that is more suited to deal with a central aspect of the natural information processing tasks that people are so good at.... hese tasks generally require the simultaneous consideration of many pieces of information or constraints. Each constraint may be imperfectly specified and ambiguous, yet each can play a potentially decisive role in determining the outcome of processing. (McClelland, Rumelhart & Hinton, 1986, pp. 3-4)

    Historical dictionary of quotations in cognitive science > Computers

  • 7 Cognitive Psychology

       The basic reason for studying cognitive processes has become as clear as the reason for studying anything else: because they are there. Our knowledge of the world must be somehow developed from stimulus input.... Cognitive processes surely exist, so it can hardly be unscientific to study them. (Neisser, 1967, p. 5).
       The task of the cognitive psychologist is a highly inferential one. The cognitive psychologist must proceed from observations of the behavior of humans performing intellectual tasks to conclusions about the abstract mechanisms underlying the behavior. Developing a theory in cognitive psychology is much like developing a model for the working of the engine of a strange new vehicle by driving the vehicle, being unable to open it up to inspect the engine itself....
       It is well understood from the automata theory... that many different mechanisms can generate the same external behavior. (Anderson, 1980, pp. 12, 17)
       [Cognitive psychology does not] deal with whole people but with a very special and bizarre-almost Frankensteinian-preparation, which consists of a brain attached to two eyes, two ears, and two index fingers. This preparation is only to be found inside small, gloomy cubicles, outside which red lights burn to warn ordinary people away.... It does not feel hungry or tired or inquisitive; it does not think extraneous thoughts or try to understand what is going on. It is, in short, a computer, made in the image of the larger electronic organism that sends it stimuli and records its responses. (Claxton, 1980, p. 13)
       4) Cognitive Psychology Has Not Succeeded in Making a Significant Contribution to the Understanding of the Human Mind
       Cognitive psychology is not getting anywhere; that in spite of our sophisticated methodology, we have not succeeded in making a substantial contribution toward the understanding of the human mind.... A short time ago, the information processing approach to cognition was just beginning. Hopes were high that the analysis of information processing into a series of discrete stages would offer profound insights into human cognition. But in only a few short years the vigor of this approach was spent. It was only natural that hopes that had been so high should sink low. (Glass, Holyoak & Santa, 1979, p. ix)
       Cognitive psychology attempts to understand the nature of human intelligence and how people think. (Anderson, 1980, p. 3)
       The past few years have witnessed a noticeable increase in interest in an investigation of the cognitive processes.... It has resulted from a recognition of the complex processes that mediate between the classical "stimuli" and "responses" out of which stimulus-response learning theories hoped to fashion a psychology that would by-pass anything smacking of the "mental." The impeccable peripheralism of such theories could not last. One might do well to have a closer look at these intervening "cognitive maps." (Bruner, Goodnow & Austin, 1956, p. vii)

    Historical dictionary of quotations in cognitive science > Cognitive Psychology

  • 8 Intelligence

       There is no mystery about it: the child who is familiar with books, ideas, conversation-the ways and means of the intellectual life-before he begins school, indeed, before he begins consciously to think, has a marked advantage. He is at home in the House of intellect just as the stableboy is at home among horses, or the child of actors on the stage. (Barzun, 1959, p. 142)
       It is... no exaggeration to say that sensory-motor intelligence is limited to desiring success or practical adaptation, whereas the function of verbal or conceptual thought is to know and state truth. (Piaget, 1954, p. 359)
       ntelligence has two parts, which we shall call the epistemological and the heuristic. The epistemological part is the representation of the world in such a form that the solution of problems follows from the facts expressed in the representation. The heuristic part is the mechanism that on the basis of the information solves the problem and decides what to do. (McCarthy & Hayes, 1969, p. 466)
       Many scientists implicitly assume that, among all animals, the behavior and intelligence of nonhuman primates are most like our own. Nonhuman primates have relatively larger brains and proportionally more neocortex than other species... and it now seems likely that humans, chimpanzees, and gorillas shared a common ancestor as recently as 5 to 7 million years ago.... This assumption about the unique status of primate intelligence is, however, just that: an assumption. The relations between intelligence and measures of brain size is poorly understood, and evolutionary affinity does not always ensure behavioral similarity. Moreover, the view that nonhuman primates are the animals most like ourselves coexists uneasily in our minds with the equally pervasive view that primates differ fundamentally from us because they lack language; lacking language, they also lack many of the capacities necessary for reasoning and abstract thought. (Cheney & Seyfarth, 1990, p. 4)
       Few constructs are asked to serve as many functions in psychology as is the construct of human intelligence.... Consider four of the main functions addressed in theory and research on intelligence, and how they differ from one another.
       1. Biological. This type of account looks at biological processes. To qualify as a useful biological construct, intelligence should be a biochemical or biophysical process or at least somehow a resultant of biochemical or biophysical processes.
       2. Cognitive approaches. This type of account looks at molar cognitive representations and processes. To qualify as a useful mental construct, intelligence should be specifiable as a set of mental representations and processes that are identifiable through experimental, mathematical, or computational means.
       3. Contextual approaches. To qualify as a useful contextual construct, intelligence should be a source of individual differences in accomplishments in "real-world" performances. It is not enough just to account for performance in the laboratory. On [sic] the contextual view, what a person does in the lab may not even remotely resemble what the person would do outside it. Moreover, different cultures may have different conceptions of intelligence, which affect what would count as intelligent in one cultural context versus another.
       4. Systems approaches. Systems approaches attempt to understand intelligence through the interaction of cognition with context. They attempt to establish a link between the two levels of analysis, and to analyze what forms this link takes. (Sternberg, 1994, pp. 263-264)
       High but not the highest intelligence, combined with the greatest degrees of persistence, will achieve greater eminence than the highest degree of intelligence with somewhat less persistence. (Cox, 1926, p. 187)
       There are no definitive criteria of intelligence, just as there are none for chairness; it is a fuzzy-edged concept to which many features are relevant. Two people may both be quite intelligent and yet have very few traits in common-they resemble the prototype along different dimensions.... [Intelligence] is a resemblance between two individuals, one real and the other prototypical. (Neisser, 1979, p. 185)
       Given the complementary strengths and weaknesses of the differential and information-processing approaches, it should be possible, at least in theory, to synthesise an approach that would capitalise upon the strength of each approach, and thereby share the weakness of neither. (Sternberg, 1977, p. 65)

    Historical dictionary of quotations in cognitive science > Intelligence

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