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IJP - International Journal of Psycho-analysis

JAPA - Journal of the American Psychoanalytic Association

SE - Standard Edition of the Complete Psychological Works of Sigmund Freud, ed. James Strachey (London: Hogarth Press and the Institute of Psycho-Analysis, 1953—74.)

PSOC - Psychoanalytic Study of the Child (New Haven: Yale University Press)

PQ - Psychoanalytic Quarterly

WAF - The Writings of Anna Freud, ed. Anna Freud (New York: International Universities Press, 1966—74)

PMC - Psychoanalysis The Major Concepts ed. Burness E. Moore and Bernard D. Fine (New Haven: Yale University Press)

\

О словаре: _about - Psychoanalytic Terms and Concepts

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1. Abend, S. M. Identity. PMC. Forthcoming.

2. Abend, S. M. (1974) Problems of identity. PQ, 43.

3. Abend, S. M., Porder, M. S. & Willick, M. S. (1983) Borderline Patients. New York: Int. Univ. Press.

4. Abraham, K. (1916) The first pregenital stage of libido. Selected Papers. London, Hogarth Press, 1948.

5. Abraham, K. (1917) Ejaculatio praecox. In: selected Papers. New York Basic Books.

6. Abraham, K. (1921) Contributions to the theory of the anal character. Selected Papers. New York: Basic Books, 1953.

7. Abraham, K. (1924) A Short study of the development of the libido, viewed in the light of mental disorders. In: Selected Papers. London: Hogarth Press, 1927.

8. Abraham, K. (1924) Manic-depressive states and the pre-genital levels of the libido. In: Selected Papers. London: Hogarth Press, 1949.

9. Abraham, K. (1924) Selected Papers. London: Hogarth Press, 1948.

10. Abraham, K. (1924) The influence of oral erotism on character formation. Ibid.

11. Abraham, K. (1925) The history of an impostor in the light of psychoanalytic knowledge. In: Clinical Papers and Essays on Psychoanalysis. New York: Basic Books, 1955, vol. 2.

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804. Spitz, R. A. (1959) A Genetic Field Theory of Ego Formation. New York: Int. Univ. Press.

805. Spitz, R. A. (1965) The First Year of Life. New York:Int. Univ. Press.

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817. Stoller, R. J. (1972) The "bedrock" of masculinity and femininity: bisexuality. Arch. Gen. Psychiat., 26.

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    The Problem of the Causal Efficacy of Intentionality

   Mental states are both caused by the operations of the brain and realized in the structure of the brain (and the rest of the central nervous system). Once the possibility of mental and physical phenomena standing in both these relations is understood we have removed at least one major obstacle to seeing how mental states which are caused by brain states can also cause further brain states and mental states.

   But this model of "caused by" and "realized in" only raises the next question, how can Intentionality function causally? Granted that Intentional states can themselves be caused by and realized in the structure of the brain, how can Intentionality itself have any causal efficacy? When I raise my arm my intention in action causes my arm to go up. This is a case of a mental event causing a physical event. But, one might ask, how could such a thing occur? My arm going up is caused entirely by a series of neuron firings. We do not know where in the brain these firings originate, but they go at some point through the motor cortex and control a series of arm muscles which contract when the appropriate neurons fire. Now what has any mental event got to do with all of this? As with our previous questions, I want to answer this one by appealing to different levels of description of a substance, where the phenomena at each of the different levels function causally. (Searle, 1983, pp. 265, 268)

1 to affect.

las medidas afectan a los pensionistas the measures affect pensioners

La conversación afecta sus ideas The conversation affects his ideas.

La tensión nerviosa afecta a María Stress affects Mary.

2 to upset, to affect badly.

le afectó mucho la muerte de su hermano his brother's death hit him hard

3 to damage.

a esta madera le afecta mucho la humedad this wood is easily damaged by damp

4 to affect, to feign.

afectó enfado he feigned o affected anger

María afecta interés pero no es así Mary feigns interest but it is not so.

5 to pretend to.

El chico afecta saber mucho The boy pretends to know a lot.

* * *

► verbo transitivo

1 (aparentar) to affect

■ afectar la voz to talk in an affected way

2 (impresionar) to move

3 (dañar) to damage

4 (concernir) to concern

► verbo pronominal afectarse

1 (impresionarse) to be affected, be moved

* * *

1) to affect

2) feign

* * *

1. VT

1) (=repercutir sobre) to affect

2) (=entristecer) to sadden; (=conmover) to move

3) frm (=fingir) to affect, feign

4) (Jur) to tie up, encumber

5) LAm [+ forma] to take, assume

6) LAm (=destinar) to allocate

afectarse

* * *

verbo transitivo

1)

a) ( tener efecto en) to affect

b) ( afligir) to affect (frml)

2) ( fingir) <admiración/indiferencia> to affect, feign

* * *

= affect, colour [color, -USA], cut into, disturb, hit, impair, mar, plague, take + Posesivo + toll (on), beset (with/by), concern, afflict, disrupt, bias, prejudice, cross over, bedevil, dog, dent, make + a dent in, ail, strike, spill over into, take + a toll on, hobble, cast + an impact.

Ex. Errors such as indexers assigning unsuitable terms to concepts, or relationships being omitted, will affect precision.

Ex. Lastly, the style, length and contents of an abstract should and will be coloured by the resources of the abstracting agency.

Ex. The paperback has cut sharply into fiction circulation, and Ennis is right in questioning this type of library.

Ex. Transcribe the data as found, however, if case endings are affected, if the grammatical construction of the data would be disturbed, or if one element is inseparably linked to another.

Ex. Flooding, fire, earthquake, collapsed buildings and landslides are the most frequent kinds of disasters to hit libraries: nearly all will lead to wet books.

Ex. It is difficult to neglect either entirely, without impairing the effectiveness in fulfilling the other objective.

Ex. Unfortunately, much of Metcalfe's writing is marred by what appears to be a deep-rooted prejudice against the classified approach, particularly as exemplified by Ranganathan.

Ex. Title indexes have always been plagued by the absence of terminology control.

Ex. The pressures which modern society puts on all its members are great and those pressures take their toll.

Ex. Since 1963 they have produced their own bibliographic listings with various degrees of efficiency and comprehensiveness but usually with the same depressing tardiness in recording new publications which has so beset the UNDEX listings.

Ex. The first issue concerns the consistent description of subjects.

Ex. There will also be those who have in fact decided what information they need but are afflicted by the paralysis of 'unverbalised thought'.

Ex. Essentially, problem patrons can be considered in three groups: (1) the dangerous or apparently dangerous; (2) the patron who disrupts readers; and (3) the nuisance whose focus is the librarian.

Ex. A sample would be biased if some elements in the population have no chance of selection.

Ex. The very requirements for success in one area may prejudice success in another.

Ex. Conversely, indirect costs are those factors that are difficult to assign to individual products because they cross over several products.

Ex. The article has the title 'Piracy, crooked printers, inflation bedevil Russian publishing'.

Ex. The title of the article is 'Sweeping away the problems that dog the industry?'.

Ex. Perhaps by the year 2010 newspaper circulations might be seriously dented by online services.

Ex. Office automation products and techniques will be able to make a sizeable dent in the growing number of office workers.

Ex. The federal government has been once again defined as something broken and part of the problem ailing America.

Ex. The collections of the National Library of the Czech Republic have suffered from the floods that recently struck a large part of the country.

Ex. The artificiality of institutional concepts has spilled over into the structure of the publishing services on which the user depends for Community information.

Ex. Agoraphobia can take a toll on sufferers' families as well as the sufferers themselves, as some agoraphobics may become housebound or cling to certain people for safety.

Ex. With Florida's no-fault auto insurance law set to expire in October, there are fears that that medical services could be hobbled.

Ex. An interest-rate increase is a weapon to fight inflation which will cast an impact on all industries.

----

* afectar a = cut across, have + impact (on), have + effect on, have + implication for, impinge on/upon, operate on, carry over to.

* afectar a la eficacia de Algo = prejudice + effectiveness.

* afectar al mundo = span + the globe.

* afectar a todo = run through.

* afectar a todo el país = sweep + the country.

* afectar a una decisión = colour + decision, affect + decision.

* afectar completamente = engulf.

* afectar directamente = cut to + the quick.

* afectar directamente a = cut to + the heart of.

* afectar fuertemente = hit + hard.

* afectar mucho = hit + hard.

* dificultad + afectar = difficulty + dog.

* no afectar = be immune against, leave + unaffected.

* no ser afectado = leave + unaffected.

* problema + afectar = problem + afflict, problem + plague.

* problemática que afecta a = issues + surrounding.

* que afecta a = surrounding.

* que afecta a toda la sociedad = culture-wide.

* que afecta a todas las culturas = culture-wide.

* que afecta a varias edades = cross-age [cross age].

* que afecta a varias generaciones = cross-generational.

* ser afectado por = have + a high stake in.

* sin ser afectado = untouched.

* verse muy afectado por = have + a high stake in.

* * *

verbo transitivo

1)

a) ( tener efecto en) to affect

b) ( afligir) to affect (frml)

2) ( fingir) <admiración/indiferencia> to affect, feign

* * *

* * *

afectar [A1 ]

1 (tener efecto en) to affect

la nueva ley no afecta al pequeño empresario the new law doesn't affect the small businessman

está afectado de una grave enfermedad pulmonar ( frml); he is suffering from a serious lung disease

la enfermedad le afectó el cerebro the illness affected her brain

las zonas afectadas por las inundaciones the areas hit o affected by the floods

2 (afligir) to affect ( frml)

lo que dijiste lo afectó mucho what you said upset him terribly

3 ( Der) ‹bienes› to encumber

B (fingir) ‹admiración/indiferencia› to affect, feign afectar + INF to pretend to + INF

* * *

a) ( tener efecto en) to affect;

◊ esto nos afecta a todos this affects us all

b) ( afligir) to affect (frml);

◊ la noticia lo afectó mucho the news upset him terribly

* * *

afectar vt

1. [incumbir] to affect;

las medidas afectan a los pensionistas the measures affect pensioners

2. [afligir] to upset, to affect badly;

todo lo afecta he's very sensitive;

lo afectó mucho la muerte de su hermano his brother's death hit him hard

3. [producir perjuicios en] to damage;

la sequía que afectó a la región the drought which hit the region;

a esta madera le afecta mucho la humedad this wood is easily damaged by damp

4. [simular] to affect, to feign;

afectó enfado he feigned o affected anger

5. RP [destinar, asignar] to assign

* * *

1 ( producir efecto en) affect

2 ( conmover) upset, affect

3 ( fingir) feign

* * *

afectar vt

1) : to affect

2) : to upset

3) : to feign, to pretend

* * *

afectar vb

1. to affect

2. (conmover) to affect / to upset [pt. & pp. upset]

   1) To Know the Different Operations of the Mind

   It becomes, therefore, no inconsiderable part of science... to know the different operations of the mind, to separate them from each other, to class them under their proper heads, and to correct all that seeming disorder in which they lie involved when made the object of reflection and inquiry.... It cannot be doubted that the mind is endowed with several powers and faculties, that these powers are distinct from one another, and that what is really distinct to the immediate perception may be distinguished by reflection and, consequently, that there is a truth and falsehood which lie not beyond the compass of human understanding. (Hume, 1955, p. 22)

   2) The Mind Is Furnished by Experience

   Let us then suppose the mind to be, as we say, white Paper, void of all Characters, without any Ideas: How comes it to be furnished? Whence comes it by that vast store, which the busy and boundless Fancy of Man has painted on it, with an almost endless variety? Whence has it all the materials of Reason and Knowledge? To this I answer, in one word, from Experience. (Locke, quoted in Herrnstein & Boring, 1965, p. 584)

   3) Mythical Thought Is as Rigorous as That of Modern Science

   The kind of logic in mythical thought is as rigorous as that of modern science, and... the difference lies, not in the quality of the intellectual process, but in the nature of things to which it is applied.... Man has always been thinking equally well; the improvement lies, not in an alleged progress of man's mind, but in the discovery of new areas to which it may apply its unchanged and unchanging powers. (Leґvi-Strauss, 1963, p. 230)

   4) The Mind Has Nothing But Itself to Know Itself

   MIND. A mysterious form of matter secreted by the brain. Its chief activity consists in the endeavor to ascertain its own nature, the futility of the attempt being due to the fact that it has nothing but itself to know itself with. (Bierce, quoted in Minsky, 1986, p. 55)

   5) To Know Is to Represent Accurately

   [Philosophy] understands the foundations of knowledge and it finds these foundations in a study of man-as-knower, of the "mental processes" or the "activity of representation" which make knowledge possible. To know is to represent accurately what is outside the mind, so to understand the possibility and nature of knowledge is to understand the way in which the mind is able to construct such representation.... We owe the notion of a "theory of knowledge" based on an understanding of "mental processes" to the seventeenth century, and especially to Locke. We owe the notion of "the mind" as a separate entity in which "processes" occur to the same period, and especially to Descartes. We owe the notion of philosophy as a tribunal of pure reason, upholding or denying the claims of the rest of culture, to the eighteenth century and especially to Kant, but this Kantian notion presupposed general assent to Lockean notions of mental processes and Cartesian notions of mental substance. (Rorty, 1979, pp. 3-4)

   6) The Question of Mind in Relation to Machine

   Under pressure from the computer, the question of mind in relation to machine is becoming a central cultural preoccupation. It is becoming for us what sex was to Victorians-threat, obsession, taboo, and fascination. (Turkle, 1984, p. 313)

   7) Understanding the Mind Remains as Resistant to Neurological as to Cognitive Analyses

   Recent years have been exciting for researchers in the brain and cognitive sciences. Both fields have flourished, each spurred on by methodological and conceptual developments, and although understanding the mechanisms of mind is an objective shared by many workers in these areas, their theories and approaches to the problem are vastly different....

   Early experimental psychologists, such as Wundt and James, were as interested in and knowledgeable about the anatomy and physiology of the nervous system as about the young science of the mind. However, the experimental study of mental processes was short-lived, being eclipsed by the rise of behaviorism early in this century. It was not until the late 1950s that the signs of a new mentalism first appeared in scattered writings of linguists, philosophers, computer enthusiasts, and psychologists.

   In this new incarnation, the science of mind had a specific mission: to challenge and replace behaviorism. In the meantime, brain science had in many ways become allied with a behaviorist approach.... While behaviorism sought to reduce the mind to statements about bodily action, brain science seeks to explain the mind in terms of physiochemical events occurring in the nervous system. These approaches contrast with contemporary cognitive science, which tries to understand the mind as it is, without any reduction, a view sometimes described as functionalism.

   The cognitive revolution is now in place. Cognition is the subject of contemporary psychology. This was achieved with little or no talk of neurons, action potentials, and neurotransmitters. Similarly, neuroscience has risen to an esteemed position among the biological sciences without much talk of cognitive processes. Do the fields need each other?... [Y]es because the problem of understanding the mind, unlike the wouldbe problem solvers, respects no disciplinary boundaries. It remains as resistant to neurological as to cognitive analyses. (LeDoux & Hirst, 1986, pp. 1-2)

   8) Approaches to the Study of the Mind

   Since the Second World War scientists from different disciplines have turned to the study of the human mind. Computer scientists have tried to emulate its capacity for visual perception. Linguists have struggled with the puzzle of how children acquire language. Ethologists have sought the innate roots of social behaviour. Neurophysiologists have begun to relate the function of nerve cells to complex perceptual and motor processes. Neurologists and neuropsychologists have used the pattern of competence and incompetence of their brain-damaged patients to elucidate the normal workings of the brain. Anthropologists have examined the conceptual structure of cultural practices to advance hypotheses about the basic principles of the mind. These days one meets engineers who work on speech perception, biologists who investigate the mental representation of spatial relations, and physicists who want to understand consciousness. And, of course, psychologists continue to study perception, memory, thought and action.

... [W]orkers in many disciplines have converged on a number of central problems and explanatory ideas. They have realized that no single approach is likely to unravel the workings of the mind: it will not give up its secrets to psychology alone; nor is any other isolated discipline-artificial intelligence, linguistics, anthropology, neurophysiology, philosophy-going to have any greater success. (Johnson-Laird, 1988, p. 7)

   1) Programs and the Complexity of Human Mental Processes

   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)

   2) Artificial Intelligence Is an Engineering Discipline

   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)

   3) A Sceptical View of Artificial Intelligence

   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)

   5) Problems in Machine Intelligence Arise Because Things Obvious to Any Person Are Not Represented in the Program

   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)

   6) The Meaning of a Symbolic Description

   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)

   7) The Principle of Artificial Intelligence

   [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)

   8) Artificial Intelligence Recognizes the Need for Knowledge in Its Systems

   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)

    10) There Are Many Types of Reasoning

   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)

    11) Programs Are Beginning to Do Things That Critics Have Asserted to Be Impossible

   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)

    12) The Synthesis of Man and Machine

   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)

    13) The Thesis of Good Old-Fashioned Artificial Intelligence (GOFAI)

   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)

    15) Four Kinds of Artificial Intelligence

   We might distinguish among four kinds of AI.

   ♦ Nonpsychological 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.

   ♦ Weak Psychological AI

   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.

   ♦ Strong Psychological AI

... 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)

   ♦ Suprapsychological AI

   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)

    17) Form and Content Are Not Fundamentally Different

   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)

    20) Mathematical Logic Provides the Basis for Theory in AI

   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)

    22) Definitions of Artificial Intelligence

   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)

    23) The Computer Can Not Be a Model of the Mind

   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)

    24) Computers Will Not Always Be Inferior to Human Brains

   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)

   1) A Comparison of the Digital Computer and the Brain

   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)

   2) Computers Do Not Crunch Numbers, They Manipulate Symbols

   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)

   3) Getting Computers to Explain Things to Themselves

   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)

   4) Some Limits of Artificial Intelligence

   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)

   5) The Computer as a Humanizing Influence

   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)

   6) The Intentionality of Computers Is Essentially Borrowed, Hence Derivative

   [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)

   7) The Possibility of Computer Thought

   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)

   8) We Are Getting Better at Building Even Better Computers, an Ever- Escalating Upward Spiral

   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.

   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)

    10) Why People Are Smarter Than Computers

   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)

1 to roll up (papel, alfombra).

María arrolló la lMaría al terminar Mary rolled the yarn when she finished.

2 to wrap up.

Silvia enrolló al bebé con una cobija Silvia wrapped the baby up in a blanket.

* * *

► verbo transitivo

1 (papel) to roll up; (hilo) to wind up; (cable) to coil

2 (a alguien) to involve, mix up

■ su familia lo enrolló en el negocio his family got him involved in the business

► verbo pronominal enrollarse

1 familiar figurado (hablar) to go on and on ( con, to), chatter ( con, to)

2 familiar figurado (tener relaciones) to have an affair ( con, with)

3 familiar figurado (liarse) to get involved ( con, with)

\

enrollarse bien argot to get on well with people

enrollarse como una persiana familiar to rabbit on and on

enrollarse mal argot to be difficult to get on with

* * *

* * *

1. VT

1) (=liar) [+ papel, persiana, filete] to roll (up); [+ cuerda, cable] [en sí mismo] to coil (up); [alrededor de algo] to wind (up)

2) Esp

3) Esp

enrollarse

* * *

verbo transitivo

1) <papel/persiana> to roll up; <cable/manguera> to coil

2) (Esp arg) ( confundir) to confuse, get... confused; ( en asunto) to involve, get... involved

enrollarsev pron

1) papel to roll up; cuerda/cable to coil up

2) (Esp)

a) (fam) ( hablar mucho)

b) (fam) ( tener relaciones amorosas)

c) (arg) ( con una actividad)

d) (arg) ( animarse) to get into the swing (colloq)

* * *

= roll up, wind, coil.

Ex. Occasionally charts or maps are rolled up and stored in cardboard rolls housed in a structure like an umbrella stand.

Ex. Bring the kite down by slowly winding the kite string around a kite spool.

Ex. This booklet is intended to provide general information on coiling of brain aneurysms.

----

* enrollarse = be a sport.

* que se enrolla = roll-up [rollup].

* * *

verbo transitivo

1) <papel/persiana> to roll up; <cable/manguera> to coil

2) (Esp arg) ( confundir) to confuse, get... confused; ( en asunto) to involve, get... involved

enrollarsev pron

1) papel to roll up; cuerda/cable to coil up

2) (Esp)

a) (fam) ( hablar mucho)

b) (fam) ( tener relaciones amorosas)

c) (arg) ( con una actividad)

d) (arg) ( animarse) to get into the swing (colloq)

* * *

* * *

enrollar [A1 ]

1 ‹papel/persiana› to roll up

2 ‹cable/manguera› to coil

enrollar el hilo en el carrete wind the thread onto the spool

3 ‹papel/carne› to roll up

B ( Esp fam)

1 (confundir) to confuse, get … confused

2 (en un asunto) to involve, get … involved

a mí no me enrolles en esto leave me out of this o don't get me involved in this

A «papel» to roll up; «cuerda/cable» to coil up

la cadena se enrolló en la rueda the chain wound o wrapped itself around the wheel

B ( Esp fam)

(hablar mucho): no te enrolles y ve al grano stop jabbering on o waffling and get to the point ( colloq)

no te enrolles hablando por teléfono don't stay on the phone too long

se enrolla como una persiana she really goes on ( colloq), she can talk the hind leg off a donkey

2 «pareja» to make out together ( AmE colloq), to get off together ( BrE colloq) enrollarse CON algn to make out WITH sb ( AmE colloq), to get off WITH sb ( BrE colloq)

se enrolló con mi prima pero no duró mucho he had a thing (going) with my cousin but it didn't last long

3 (con una actividad) enrollarse CON algo to get into sth ( colloq)

se enrollaron hablando de política they got deep into conversation about politics

4 (animarse) to get into the swing ( colloq), get with it ( BrE sl)

enrollarse bien ( Esp arg): se enrolla muy bien con la gente he gets on very well with o he has a way with people

ese pinchadiscos se enrolla muy bien that disc jockey is really cool ( colloq)

C ( Ven arg) (ponerse nervioso) to freak out (sl)

* * *

◊ enrollar el hilo en el carrete wind the thread onto the spool

◊ la cadena se enrolló en la rueda the chain wound itself around the wheel

a) ( hablar mucho):

◊ no te enrolles stop jabbering on (colloq);

b) ( tener relaciones amorosas):

◊ se enrollaron en la discoteca they made out (AmE colloq) o (BrE colloq) they got off together in a disco;

* * *

♦ vt

1. [arrollar] [papel, alfombra] to roll up;

enrolló el hilo en su bobina he wound the thread on to the bobbin

2. Fam [enredar, confundir] to bamboozle;

me enrollaron para que lo comprara they bamboozled me into buying it

3. Fam [gustar]

me enrolla mucho ir de camping I really get a kick out of going camping

♦ See also the pronominal verb enrollarse

* * *

1 roll up; cable coil; hilo wind

me enrolla fam I like it, I think it’s great fam

* * *

enrollar vt

: to roll up, to coil

♦ See also the reflexive verb enrollarse

* * *

enrollar vb

1. (enroscar papel) to roll up

2. (enroscar hilo) to wind up [pt. & pp. wound]

3. (convencer) to talk into

4. (molar) to be great

   1) Every Psychological Phenomenon Is a Cognitive Phenomenon

   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)

   2) Models of Cognition and Specific Architectures

   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)

    The Formal Structure of the Virtual Machine

   wo programs can be thought of as strongly equivalent or as different realizations of the same algorithm or the same cognitive process if they can be represented by the same program in some theoretically specified virtual machine. A simple way of stating this is to say that we individuate cognitive processes in terms of their expression in the canonical language of this virtual machine. The formal structure of the virtual machine-or what I call its functional architecture-thus represents the theoretical definition of, for example, the right level of specificity (or level of aggregation) at which to view mental processes, the sort of functional resources the brain makes available-what operations are primitive, how memory is organized and accessed, what sequences are allowed, what limitations exist on the passing of arguments and on the capacities of various buffers, and so on. (Pylyshyn, 1984, p. 92)

1) Aviation: steering system, steering unit, supervision system

2) Military: battlefield control system, command structure, command system, gun control system

3) Engineering: administrative system, control, controlling system, guidance, manager, operating system, pilot system

4) Construction: guidance system

5) Law: system of administration

6) Economy: governance system (в тексте речь шла о корпоративном управлении; англ. оборот взят из новостного сообщения агентства Bloomberg), managerial system, system of control

7) Accounting: (административного) managerial system

8) Information technology: executive system (исполнением программ), management console

9) Oil: control system, management system

10) Astronautics: brain, flight control, flying control, guidance, navigation and control system

11) Business: directional system, system of management

12) Management: (методы) management system

13) Network technologies: Management System (Объект, управляющий набором управляемых систем, которые могут представлять собой элементы сети, подсети или сети и т.п.), console

14) Programming: (!) control system (система, состоящая из управляющего объекта и объекта управления. См. Теория управления. Терминология. Вып. 107. М. Наука, 1988)

15) Automation: management system (производством)

16) Robots: (организационного) management system

17) Makarov: a system of government, system of government

1 function, interrelation, relationship, interrelationship.

2 show, performance.

3 function, duty, job.

4 function, feature.

5 function, mathematical function.

* * *

► nombre femenino

1 (gen) function

2 (cargo) duty

3 (espectáculo) performance, show

\

en función de according to

en funciones acting

■ el presidente en funciones cerró la reunión the acting president closed the meeting

entrar en función (persona) to take up one's post

estar en funciones to be in office

función de noche evening performance

función de tarde matinée

* * *

1) function

2) duty

3) performance

* * *

1) (=actividad) [física, de máquina] function

2) (=papel) function

3) pl funciones

a) (=deberes) duties

b)

4)

a) (=según) according to

b) (=basándose en) on the basis of

5) (=espectáculo) [de teatro, ópera] performance; [de títeres, variedades, musical] show

6)

7) (Mat) function

8) (Ling) function

* * *

1)

a) (cometido, propósito) function

b) funciones masculino plural duties (pl)

2) (Fisiol) function; (Mat) function; (Ling) function

- función gramatical

3) (de teatro, circo) performance; ( de cine) showing, performance

•

- función benéfica

- función continua

- función de medianoche

- función de noche

* * *

= capability, feature, function, function, purpose, role, hat, functionality, function.

Ex. DOBIS/LIBIS contains a so-called 'help' capability.

Ex. Another way of doing this search is to use the lateral searching feature.

Ex. The function of a catalog is to inform a library user whether or not the library contains a particular book specified by author and title.

Ex. Select a function from the menu by pressing the key listed to the left.

Ex. Chapters 7 and 8 introduced the problems associated with author cataloguing and have surveyed the purpose of cataloguing codes.

Ex. The role of analytical entries in an online catalogue is less clear.

Ex. The city librarian has commonly been a general cultural consultant, often with more than one hat, with the curatorship of the museum and/or art gallery as additional offices.

Ex. This article describes the functionality of CARL software for this purpose, loads a brief rundown of data bases, and gives the criteria for selecting data bases.

Ex. The workshop consisted of an inaugural function, a series of lectures, a panel discussion and a valedictory function.

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* adaptarse a una función = step up to + role.

* adoptar una función = step up to + role.

* ambigüedad de funciones = role ambiguity.

* asumir una función = take upon + Reflexivo + role.

* claridad de funciones = role clarity.

* conferir una función = confer + function.

* conflicto de funciones = role conflict.

* con una función destacada = high profile.

* cumplir una función = fulfil + function.

* definir una función = formulate + role.

* descripción de las funciones = job description, job profile.

* desempeñando muchas funciones = in many capacities.

* desempeñar bien una función = perform + well.

* desempeñar + Posesivo + funciones = do + Posesivo + business.

* desempeñar una buena función = produce + the goods.

* desempeñar una función = fulfil + purpose, perform + function, play + role, discharge + function, discharge + duty, perform + role, articulate + role, fit into + the picture, play + Posesivo + part.

* distribución en función del género = gender distribution.

* ejecutar una función = run + function.

* en función de = according to, as a function of, depending on/upon.

* en funciones = acting + Cargo.

* estar en función de = be a function of.

* evaluación del impacto en función del género = gender-impact assessment.

* examinar la función de Algo = investigate + role.

* función bibliotecaria = library function.

* función cognitiva = cognitive function.

* función de autoedición = authoring aid, authoring tool.

* función de consulta = enquiry function.

* función de corrección = editing function.

* función de extracción y copiado = pull function.

* función de la biblioteca = library's function.

* función de la correlación de los cosenos = cosine correlation function.

* función de servicio = service function.

* función de solapamiento = overlap function.

* función dominante = alpha role.

* funciones de edición = editing facilities.

* funciones del personal = staff duties.

* funciones del puesto de trabajo = position + entail + duty.

* funciones de modificación = editing facilities.

* función esencial = vital role.

* función por tecla = keyboard function.

* función vital = vital role.

* indicador de función = operator, role indicator.

* inversión de funciones = reversal of roles, role reversal.

* operador de función = role.

* que se concede en función de las necesidades económicas = means-tested.

* rol establecido en función del sexo = gender role.

* seleccionar una función = invoke + function.

* tecla de función = function key.

* tecla de función del programa = programme function key.

* tener la doble función = double as, double up as.

* tener la función de = be in the business of.

* * *

1)

a) (cometido, propósito) function

b) funciones masculino plural duties (pl)

2) (Fisiol) function; (Mat) function; (Ling) function

- función gramatical

3) (de teatro, circo) performance; ( de cine) showing, performance

•

- función benéfica

- función continua

- función de medianoche

- función de noche

* * *

* * *

1 (cometido, propósito) function

un mueble que cumple distintas funciones a piece of furniture which serves more than one purpose o function

la función del árbitro en estas disputas the role o function of the mediator in these disputes

(tarea, deber): en el ejercicio de sus funciones in the performance of her duties, while carrying out her duties