[Paleopsych] BBS: Boden, Margaret A. (1994). Precis of The creative mind

Premise Checker checker at panix.com
Tue Dec 27 23:01:52 UTC 2005

BBS: Boden, Margaret A. (1994). Precis of The creative mind

    Below is the unedited preprint (not a quotable final draft) of:
    Boden, Margaret A. (1994). Precis of The creative mind: Myths and
    mechanisms. Behavioral and Brain Sciences 17 (3): 519-570.
    The final published draft of the target article, commentaries and
    Author's Response are currently available only in paper.

    EUR For information on becoming a commentator on this or other BBS
    target articles, write to: [1]bbs at soton.ac.uk
    For information about subscribing or purchasing offprints of the
    published version, with commentaries and author's response, write to:
    [2]journals_subscriptions at cup.org (North America) or
    [3]journals_marketing at cup.cam.ac.uk (All other countries). EUR

    Precis of "THE CREATIVE MIND: MYTHS AND MECHANISMS" London: Weidenfeld &
              Nicolson 1990 (Expanded edn., London: Abacus, 1991.)

     Margaret A. Boden
     School of Cognitive and Computing Sciences
     University of Sussex
     England FAX: 0273-671320
     [4]maggieb at syma.susx.ac.uk


    creativity, intuition, discovery, association, induction,
    representation, unpredictability, artificial intelligence, computer
    music, story-writing, computer art, Turing test


    What is creativity? One new idea may be creative, while another is
    merely new: what's the difference? And how is creativity possible? --
    These questions about human creativity can be answered, at least in
    outline, using computational concepts.

    There are two broad types of creativity, improbabilist and
    impossibilist. Improbabilist creativity involves (positively valued)
    novel combinations of familiar ideas. A deeper type involves METCS:
    the mapping, exploration, and transformation of conceptual spaces. It
    is impossibilist, in that ideas may be generated which -- with respect
    to the particular conceptual space concerned -- could not have been
    generated before. (They are made possible by some transformation of
    the space.) The more clearly conceptual spaces can be defined, the
    better we can identify creative ideas. Defining conceptual spaces is
    done by musicologists, literary critics, and historians of art and
    science. Humanist studies, rich in intuitive subtleties, can be
    complemented by the comparative rigour of a computational approach.

    Computational modelling can help to define a space, and to show how it
    may be mapped, explored, and transformed. Impossibilist creativity can
    be thought of in "classical" AI-terms, whereas connectionism
    illuminates improbabilist creativity. Most AI-models of creativity can
    only explore spaces, not transform them, because they have no
    self-reflexive maps enabling them to change their own rules. A few,
    however, can do so.

    A scientific understanding of creativity does not destroy our wonder
    at it, nor make creative ideas predictable. Demystification does not
    imply dehumanization.

    Chapter 1: The Mystery of Creativity

    Creativity surrounds us on all sides: from composers to chemists,
    cartoonists to choreographers. But creativity is a puzzle, a paradox,
    some say a mystery. Inventors, scientists, and artists rarely know how
    their original ideas arise. They mention intuition, but cannot say how
    it works. Most psychologists cannot tell us much about it, either.
    What's more, many people assume that there will never be a scientific
    theory of creativity -- for how could science possibly explain
    fundamental novelties? As if all this were not daunting enough, the
    apparent unpredictability of creativity seems (to many people) to
    outlaw any systematic explanation, whether scientific or historical.

    Why does creativity seem so mysterious? Artists and scientists
    typically have their creative ideas unexpectedly, with little if any
    conscious awareness of how they arose. But the same applies to much of
    our vision, language, and common-sense reasoning. Psychology includes
    many theories about unconscious processes. Creativity is mysterious
    for another reason: the very concept is seemingly paradoxical.

    If we take seriously the dictionary-definition of creation, "to bring
    into being or form out of nothing", creativity seems to be not only
    beyond any scientific understanding, but even impossible. It is hardly
    surprising, then, that some people have "explained" it in terms of
    divine inspiration, and many others in terms of some romantic
    intuition, or insight. From the psychologist's point of view, however,
    "intuition" is the name not of an answer, but of a question. How does
    intuition work?

    In this book, I argue that these matters can be better understood, and
    some of these questions answered, with the help of computational

    This claim in itself may strike some readers as absurd, since
    computers are usually assumed to have nothing to do with creativity.
    Ada Lovelace is often quoted in this regard: "The Analytical Engine
    has no pretensions whatever to originate anything. It can do [only]
    whatever we know how to order it to perform." If this is taken to mean
    that a computer can do only what its program enables it to do, it is
    of course correct. But it does not follow that there can be no
    interesting relations between creativity and computers.

    We must distinguish four different questions, which are often confused
    with each other. I call them Lovelace questions, and state them as

    (1) Can computational concepts help us to understand human creativity?

    (2) Could a computer, now or in the future, appear to be creative?

    (3) Could a computer, now or in the future, appear to recognize

    (4) Could a computer, however impressive its performance, really be

    The first three of these are empirical, scientific, questions. In
    Chapters 3-10, I argue that the answer to each of them is "Yes". (The
    first Lovelace question is discussed in each of those chapters; in
    chapters 7-8, the second and third are considered also.)

    The fourth Lovelace question is not a scientific enquiry, but a
    philosophical one. (More accurately, it is a mix of three complex, and
    highly controversial, philosophical problems.) I discuss it in Chapter
    11. However, one may answer "Yes" to the first three Lovelace
    questions without necessarily doing so for the fourth. Consequently,
    the fourth Lovelace question is ignored in the main body of the book,
    which is concerned rather with the first three Lovelace questions.

    Chapter 2: The Story so Far

    This chapter draws on some of the previous literature on creativity.
    But it is not a survey. Its aim is to introduce the main psychological
    questions, and some of the historical examples, addressed in detail
    later in the book. The main writers mentioned are Poincare (1982),
    Hadamard (1954) Koestler (1975), and Perkins (1981).

    Among the points of interest in Poincare's work are his views on
    associative memory. He described our ideas as "something like the
    hooked atoms of Epicurus," flashing in every direction like "a swarm
    of gnats, or the molecules of gas in the kinematic theory of gases".
    He was well aware that how the relevant ideas are aroused, and how
    they are joined together, are questions which he could not answer in
    detail. Another interesting aspect of Poincare's approach is his
    distinction between four "phases" of creativity, some conscious some

    These four phases were later named (by Hadamard) as preparation,
    incubation, inspiration and verification (evaluation). Hadamard,
    besides taking up Poincare's fourfold distinction, spoke of finding
    problem-solutions "quite different" from any he had previously tried.
    If (as Poincare had claimed) the gnat-like ideas were only "those from
    which we might reasonably expect the desired solution", then how could
    such a thing happen?

    Perkins has studied the four phases, and criticizes some of the
    assumptions made by Poincare and Hadamard. In addition, he criticizes
    the romantic notion that creativity is due to some special gift.
    Instead, he argues that "insight" involves everyday psychological
    capacities, such as noticing and remembering. (The "everyday" nature
    of creativity is discussed in Chapter 10.)

    Koestler's view that creativity involves "the bisociation of matrices"
    comes closest to my own approach. However, his notion is very vague.
    The body of my book is devoted to giving a more precise account of the
    structure of "matrices" (of various kinds), and of just how they can
    be "bisociated" so as to result in a novel idea -- sometimes (as in
    Hadamard's experience) one quite different from previous ideas.
    (Matrices appear in my terminology as conceptual spaces, and different
    forms of bisociation as association, analogy, exploration, or

    Among the examples introduced here are Kekule's discovery of the
    cyclical structure of the benzene molecule, Kepler's (and Copernicus')
    thoughts on elliptical orbits, and Coleridge's poetic imagery in Kubla
    Khan. Others mentioned in passing include Coleridge's announced
    intention to write a poem about an ancient mariner, Bach's
    harmonically systematic set of preludes and fugues, the
    jazz-musician's skill in improvising a melody to fit a chord sequence,
    and our everyday ability to recognize that two different apples fall
    into the same class. All these examples, and many others, are
    mentioned in later chapters.

    Chapter 3: Thinking the Impossible

    Given the seeming paradoxicality of the concept of creativity (noted
    in Chapter 1), we need to define it carefully before going further.
    This is not straightforward (over 60 definitions appear in the
    psychological literature (Taylor, 1988)). Part of the reason for this
    is that creativity is not a natural kind, such that a single
    scientific theory could explain every case. We need to distinguish
    "improbabilist" and "impossibilist" creativity, and also
    "psychological" and "historical" creativity.

    People of a scientific cast of mind, anxious to avoid romanticism and
    obscurantism, generally define creativity in terms of novel
    combinations of familiar ideas. Accordingly, the surprise caused by a
    creative idea is said to be due to the improbability of the
    combination. Many psychometric tests designed to measure creativity
    work on this principle.

    The novel combinations must be valuable in some way, because to call
    an idea creative is to say that it is not only new, but interesting.
    However, combination-theorists often omit value from their definition
    of creativity (although psychometricians may make implicit
    value-judgements when scoring the novel combinations produced by their
    experimental subjects). A psychological explanation of creativity
    focusses primarily on how creative ideas are generated, and only
    secondarily on how they are recognized as being valuable. As for what
    counts as valuable, and why, these are not purely psychological
    questions. They also involve history, sociology, and philosophy,
    because value-judgments are largely culture-relative (Brannigan, 1981;
    Schaffer, in press.) Even so, positive evaluation should be explicitly
    mentioned in definitions of creativity.

    Combination-theorists may think they are not only defining creativity,
    but explaining it, too. However, they typically fail to explain how it
    was possible for the novel combination to come about. They take it for
    granted, for instance, that we can associate similar ideas and
    recognize more distant analogies, without asking just how such feats
    are possible. A psychological theory of creativity needs to explain
    how associative and analogical thinking works (matters discussed in
    Chapters 6 and 7, respectively).

    These two cavils aside, what is wrong with the combination-theory?
    Many ideas which we regard as creative are indeed based on unusual
    combinations. For instance, the appeal of Heath-Robinson machines lies
    in the unexpected uses of everyday objects; and poets often delight us
    by juxtaposing seemingly unrelated concepts. For creative ideas such
    as these, a combination-theory, supplemented by psychological
    explanations of association and analogy, might suffice.

    Many creative ideas, however, are surprising in a deeper way. They
    concern novel ideas that not only did not happen before, but which --
    we intuitively feel -- could not have happened before.

    Before considering just what this "could not" means, we must
    distinguish two further senses of creativity. One is psychological, or
    personal: I call it P-creativity. The other is historical:
    H-creativity. The distinction between P-creativity and H-creativity is
    independent of the improbabilist/impossibilist distinction made above:
    all four combinations occur. However, I use the P/H distinction
    primarily to compare cases of impossibilist creativity.

    Applied to impossibilist examples, a valuable idea is P-creative if
    the person in whose mind it arises could not (in the relevant sense of
    "could not") have had it before. It does not matter how many times
    other people have already had the same idea. By contrast, a valuable
    idea is H-creative if it is P-creative and no-one else, in all human
    history, has ever had it before.

    H-creativity is something about which we are often mistaken.
    Historians of science and art are constantly discovering cases where
    other people have had an idea popularly attributed to some national or
    international hero. Even assuming that the idea was valued at the time
    by the individual concerned, and by some relevant social group, our
    knowledge of it is largely accidental. Whether an idea survives, and
    whether historians at a given point in time happen to have evidence of
    it, depend on a wide variety of unrelated factors. These include
    flood, fashion, rivalries, illness, trade-patterns, and wars.

    It follows that there can be no systematic explanation of
    H-creativity, no theory that explains all and only H-creative ideas.
    For sure, there can be no psychological explanation of this historical
    category. But all H-creative ideas, by definition, are P-creative too.
    So a psychological explanation of P-creativity would include
    H-creative ideas as well.

    What does it mean to say that an idea "could not" have arisen before?
    Unless we know that, we cannot make sense of P-creativity (or
    H-creativity either), for we cannot distinguish radical novelties from
    mere "first-time" newness.

    An example of a novelty that clearly could have happened before is a
    newly-generated sentence, such as "The deckchairs are on the top of
    the mountain, three miles from the artificial flowers". I have never
    thought of that sentence before, and probably no-one else has, either.
    Chomsky remarked on this capacity of language-speakers to generate
    first-time novelties endlessly, and called language "creative"
    accordingly. But the word "creative" was ill-chosen. Novel though the
    sentence about deckchairs is, there is a clear sense in which it could
    have occurred before. For it can be generated by any competent speaker
    of English, following the same rules that can generate other English
    sentences. To come up with a new sentence, in general, is not to do
    something P-creative.

    The "coulds" in the previous paragraph are computational "coulds". In
    other words, they concern the set of structures (in this case, English
    sentences) described and/or produced by one and the same set of
    generative rules (in this case, English grammar). There are many sorts
    of generative system: English grammar is like a mathematical equation,
    a rhyming-schema for sonnets, the rules of chess or tonal harmony, or
    a computer program. Each of these can (timelessly) describe a certain
    set of possible structures. And each might be used, at one time or
    another, in actually producing those structures.

    Sometimes, we want to know whether a particular structure could, in
    principle, be described by a specific schema, or set of abstract
    rules. -- Is "49" a square number? Is 3,591,471 a prime? Is this a
    sonnet, and is that a sonata? Is that painting in the Impressionist
    style? Could that geometrical theorem be proved by Euclid's methods?
    Is that word-string a sentence? Is a benzene-ring a molecular
    structure describable by early nineteenth-century chemistry (before
    Kekule had his famous vision in 1865)? -- To ask whether an idea is
    creative or not (as opposed to how it came about) is to ask this sort
    of question.

    But whenever a structure is produced in practice, we can also ask what
    generative processes actually went on in its production. -- Did a
    particular geometer prove a particular theorem in this way, or in
    that? Was the sonata composed by following a textbook on sonata-form?
    Did Kekule rely on the then-familiar principles of chemistry to
    generate his seminal idea of the benzene-ring, and if not how did he
    come up with it? -- To ask how an idea (creative or otherwise)
    actually arose, is to ask this type of question.

    We can now distinguish first-time novelty from impossibilist
    originality. A merely novel idea is one which can be described and/or
    produced by the same set of generative rules as are other, familiar,
    ideas. A genuinely original, or radically creative, idea is one which
    cannot. It follows that the ascription of (impossibilist) creativity
    always involves tacit or explicit reference to some specific
    generative system.

    It follows, too, that constraints -- far from being opposed to
    creativity -- make creativity possible. To throw away all constraints
    would be to destroy the capacity for creative thinking. Random
    processes alone, if they happen to produce anything interesting at
    all, can result only in first-time curiosities, not radical surprises.
    (As explained in Chapter 9, randomness can sometimes contribute to
    creativity -- but only in the context of background constraints.)

    Chapter 4: Maps of the Mind

    The definition of (impossibilist) creativity given in Chapter 3
    implies that, with respect to the usual mental processing in the
    relevant domain (chemistry, poetry, music ...), a creative idea may be
    not just improbable, but impossible. How could it arise, then, if not
    by magic? And how can one impossible idea be more surprising, more
    creative, than another? If an act of creation is not mere combination,
    what is it? How can such creativity possibly happen?

    To understand this, we need to think of creativity in terms of the
    mapping, exploration, and transformation of conceptual spaces. (The
    notion of a conceptual space is used informally in this chapter;
    later, we see how conceptual spaces can be described more rigorously.)
    A conceptual space is a style of thinking. Its dimensions are the
    organizing principles which unify, and give structure to, the relevant
    domain. In other words, it is the generative system which underlies
    that domain and which defines a certain range of possibilities:
    chess-moves, or molecular structures, or jazz-melodies.

    The limits, contours, pathways, and structure of a conceptual space
    can be mapped by mental representations of it. Such mental maps can be
    used (not necessarily consciously) to explore -- and to change -- the
    spaces concerned.

    Evidence from developmental psychology supports this view. Children's
    skills are at first utterly inflexible. Later, imaginative flexibility
    results from "representational redescriptions" (RRs) of (fluent)
    lower-level skills (Clark & Karmiloff-Smith, in press;
    Karmiloff-Smith, 1993). These RRs provide many-levelled maps of the
    mind, which are used by the subject to do things he or she could not
    do before.

    For example, children need RRs of their lower-level drawing-skills in
    order to draw non-existent, or "funny", objects: a one-armed man, or
    seven-legged dog. Lacking such cognitive resources, a 4-year-old
    simply cannot spontaneously draw a one-armed man, and finds it very
    difficult even to copy a drawing of a two-headed man. But 10-year-olds
    can explore their own man-drawing skill, by using strategies such as
    distorting, repeating, omitting, or mixing parts. These imaginative
    strategies develop in a fixed order: children can change the size or
    shape of an arm before they can insert an extra one, and long before
    they can give the drawn man wings in place of arms.

    The development of RRs is a mapping-exercise, whereby people develop
    explicit mental representations of knowledge already possessed

    Few AI-models of creativity contain reflexive descriptions of their
    own procedures, and/or ways of varying them. Accordingly, most
    AI-models are limited to exploring their conceptual spaces, rather
    than transforming them (see Chapters 7 & 8).

    Conceptual spaces can be explored in various ways. Some exploration
    merely shows us something about the nature of the relevant conceptual
    space which we had not explicitly noticed before. When Dickens
    described Scrooge as "a squeezing, wrenching, grasping, scraping,
    clutching, covetous old sinner", he was exploring the space of English
    grammar. He was reminding the reader (and himself) that the rules of
    grammar allow us to use seven adjectives before a noun. That
    possibility already existed, although its existence may not have been
    realized by the reader.

    Some exploration, by contrast, shows us the limits of the space, and
    identifies specific points at which changes could be made in one
    dimension or another. To overcome a limitation in a conceptual space,
    one must change it in some way. One may also change it, of course,
    without yet having come up against its limits. A small change (a
    "tweak") in a relatively superficial dimension of a conceptual space
    is like opening a door to an unvisited room in an existing house. A
    large change (a "transformation"), especially in a relatively
    fundamental dimension, is more like the instantaneous construction of
    a new house, of a kind fundamentally different from (albeit related
    to) the first.

    A complex example of structural exploration and change can be found in
    the development of post-Renaissance Western music, based on the
    generative system known as tonal harmony. From its origins to the end
    of the nineteenth century, the harmonic dimensions of this space were
    continually tweaked to open up the possibilities (the rooms) implicit
    in it from the start. Finally, a major transformation generated the
    deeply unfamiliar (yet closely related) space of atonality.

    Each piece of tonal music has a "home-key", from which it starts, from
    which (at first) it did not stray, and in which it must finish.
    Reminders of the home-key were constantly provided, as fragments of
    scales, chords. or arpeggios. As time passed, the range of possible
    home-keys became increasingly well-defined (Bach's "Forty-Eight" was
    designed to explore, and clarify, the tonal range of the well-tempered

    Soon, travelling along the path of the home-key alone became
    insufficiently challenging. Modulations between keys were then
    allowed, within the body of the composition. At first, only a small
    number of modulations (perhaps only one, followed by its
    "cancellation") were tolerated, between strictly limited pairs of
    harmonically-related keys. Over the years, the modulations became more
    daring, and more frequent -- until in the late nineteenth century
    there might be many modulations within a single bar, not one of which
    would have appeared in early tonal music. The range of harmonic
    relations implicit in the system of tonality gradually became
    apparent. Harmonies that would have been unacceptable to the early
    musicians, who focussed on the most central or obvious dimensions of
    the conceptual space, became commonplace.

    Moreover, the notion of the home-key was undermined. With so many, and
    so daring, modulations within the piece, a "home-key" could be
    identified not from the body of the piece, but only from its beginning
    and end. Inevitably, someone (it happened to be Schoenberg) eventually
    suggested that the convention of the home-key be dropped altogether,
    since it no longer constrained the composition as a whole.
    (Significantly, Schoenberg suggested new musical constraints: using
    every note in the chromatic scale, for instance.)

    However, exploring a conceptual space is one thing: transforming it is
    another. What is it to transform such a space?

    One example has just been mentioned: Schoenberg's dropping the
    home-key constraint to create the space of atonal music. Dropping a
    constraint is a general heuristic, or method, for transforming
    conceptual spaces. The deeper the generative role of the constraint in
    the system concerned, the greater the transformation of the space.
    Non-Euclidean geometry, for instance, resulted from dropping Euclid's
    fifth axiom.

    Another very general way of transforming conceptual spaces is to
    "consider the negative": that is, to negate a constraint. One
    well-known instance concerns Kekule's discovery of the benzene-ring.
    He described it like this:

    "I turned my chair to the fire and dozed. Again the atoms were
    gambolling before my eyes.... [My mental eye] could distinguish larger
    structures, of manifold conformation; long rows, sometimes more
    closely fitted together; all twining and twisting in snakelike motion.
    But look! What was that? One of the snakes had seized hold of its own
    tail, and the form whirled mockingly before my eyes. As if by a flash
    of lightning I awoke."

    This vision was the origin of his hunch that the benzene-molecule
    might be a ring, a hunch that turned out to be correct. Prior to this
    experience, Kekule had assumed that all organic molecules are based on
    strings of carbon atoms. But for benzene, the valencies of the
    constituent atoms did not fit.

    We can understand how it was possible for him to pass from strings to
    rings, as plausible chemical structures, if we assume three things
    (for each of which there is independent psychological evidence).
    First, that snakes and molecules were already associated in his
    thinking. Second, that the topological distinction between open and
    closed curves was present in his mind. And third, that the "consider
    the negative" heuristic was present also. Taken together, these three
    factors could transform "string" into "ring".

    A string-molecule is an open curve: one having at least one end-point
    (with a neighbour on only one side). If one considers the negative of
    an open curve, one gets a closed curve. Moreover, a snake biting its
    tail is a closed curve which one had expected to be an open one. For
    that reason, it is surprising, even arresting ("But look! What was
    that?"). Kekule might have had a similar reaction if he had been out
    on a country walk and happened to see a snake with its tail in its
    mouth. But there is no reason to think that he would have been stopped
    in his tracks by seeing a Victorian child's hoop. A hoop is a hoop, is
    a hoop: no topological surprises there. (No topological surprises in a
    snaky sine-wave, either: so two intertwined snakes would not have
    interested Kekule, though they might have stopped Francis Crick dead
    in his tracks, a century later.)

    Finally, the change from open curves to closed ones is a topological
    change, which by definition will alter neighbour-relations. And Kekule
    was an expert chemist, who knew very well that the behaviour of a
    molecule depends partly on how the constituent atoms are juxtaposed. A
    change in atomic neighbour-relations is very likely to have some
    chemical significance. So it is understandable that he had a hunch
    that this tail-biting snake-molecule might contain the answer to his

    Plausible though this talk of conceptual spaces may be, it is -- thus
    far -- largely metaphorical. I have claimed that in calling an idea
    creative one should specify the particular set of generative
    principles with respect to which it is impossible. But I have not said
    how the (largely tacit) knowledge of literary critics, musicologists,
    and historians of art and science might be explicitly expressed within
    a psychological theory of creativity. Nor have I said how we can be
    sure that the mental processes specified by the psychologist really
    are powerful enough to generate such-and-such ideas from such-and-such

    This is where computational psychology can help us. I noted above, for
    example, that representational redescription develops explicit mental
    representations of knowledge already possessed implicitly. In
    computational terms, one could -- and Karmiloff-Smith does -- put this
    by saying that knowledge embedded in procedures becomes available,
    after redescription, as part of the system's data-structures. Terms
    like procedures and data-structures are well understood, and help us
    to think clearly about the mapping and negotiation of conceptual
    spaces. In general, whatever computational psychology enables us to
    say, it enables us to say relatively clearly.

    Moreover, computational questions can be supplemented by computational
    models. A functioning computer program, in effect, enables the system
    to use its maps not just to contemplate the relevant conceptual
    territory, but to explore it actively. So as well as saying what a
    conceptual space is like (by mapping it), we can get some clear ideas
    about how it is possible to move around within it. In addition, those
    (currently, few) AI-models of creativity which contain reflexive
    descriptions of their own procedures, and ways of varying them, can
    transform their own conceptual spaces, as well as exploring them.

    The following chapters, therefore, employ a computational approach in
    discussing the account of creativity introduced in Chapters 1-4.

    Chapter 5: Concepts of Computation

    Computational concepts drawn from "classical" (as well as
    connectionist) AI can help us to think about the nature, form, and
    negotiation of conceptual spaces. Examples of such concepts, most of
    which were inspired by pre-existing psychological notions in the first
    place, include the following: generative system, heuristic (both
    introduced in previous chapters), effective procedure, search-space,
    search-tree, knowledge representation, semantic net, scripts, frames,
    what-ifs, and analogical representation.

    Each of these concepts is briefly explained in Chapter 5, for people
    who (unlike BBS-readers) may know nothing about AI or computational
    psychology. And they are related to a wide range of everyday and
    historical examples -- some of which will be mentioned again in later

    My main aim, here, is to encourage the reader to use these concepts in
    considering specific cases of human thought. A secondary aim is to
    blur the received distinction between "the two cultures". The
    differences between creativity in art and science lie less in how new
    ideas are generated than in how they are evaluated, once they have
    arisen. The uses of computational concepts in this chapter are
    informal, even largely metaphorical. But in bringing a computational
    vocabulary to bear on a variety of examples, the scene is set for more
    detailed consideration (in Chapters 6-8) of some computer models of

    In Chapter 5, I refer very briefly to a few AI-programs (such as
    chess-machines and Schankian question-answering programs). Only two
    are discussed at any length: Longuet-Higgins' (1987) work on the
    perception of tonal harmony, and Gelernter's (1963) geometry
    (theorem-proving) machine.

    Longuet-Higgins' work is not intended as a model of musical
    creativity. Rather, it provides (in my terminology) a map of a certain
    sort of musical space: the system of tonal harmony introduced in
    Chapter 4. In addition, it suggests some ways of negotiating that
    space, for it identifies musical heuristics that enable the listener
    to appreciate the structure of the composition. Just as speech
    perception is not the same as speech production, so appreciating music
    is different from composing it. Nevertheless, some of the musical
    constraints that face composers working in this particular genre have
    been identified in this work.

    I also mention Longuet-Higgins' recent work on musical expressiveness,
    but do not describe it here. In (Boden, in press), I say a little more
    about it. Without expression, music sounds "dead", even absurd. In
    playing the notes in a piano-score, for instance, pianists add such
    features as legato, staccato, piano, forte, sforzando, crescendo,
    diminuendo, rallentando, accelerando, ritenuto, and rubato. But how?
    Can we express this musical sensibility precisely? That is, can we
    specify the relevant conceptual space?

    Longuet-Higgins (in preparation), using a computational method, has
    tried to specify the musical skills involved in playing expressively.
    Working with two of Chopin's piano-compositions, he has discovered
    some counterintuitive facts. For example, a crescendo is not uniform,
    but exponential (a uniform crescendo does not sound like a crescendo
    at all, but like someone turning-up the volume-knob on a wireless);
    similarly, a rallentando must be exponentially graded (in relation to
    the number of bars in the relevant section) if it is to sound "right".
    Where sforzandi are concerned, the mind is highly sensitive: as little
    as a centisecond makes a difference between acceptable and clumsy

    This work is not a study of creativity. It does not model the
    exploration of a conceptual space, never mind its transformation. But
    it is relevant because creativity can be ascribed to an idea
    (including a musical performance) only by reference to a particular
    conceptual space. The more clearly we can map this space, the more
    confidently we can identify and ask questions about the creativity
    involved in negotiating it. A pianist whose playing-style sounds
    "original", or even "idiosyncratic", is exploring and transforming the
    space of expressive skills which Longuet-Higgins has studied.

    Gelernter's program, likewise, was not focussed on creativity as such.
    (It was not even intended as a model of human psychology.) Rather, it
    was an early exercise in automatic problem-solving, in the domain of
    Euclidean geometry. However, it is well known that the program was
    capable of generating a highly elegant proof (that the base-angles of
    an isosceles triangle are equal), whose H-creator was the
    fourth-century mathematician Pappus.

    Or rather, it is widely believed that Gelernter's program could do
    this. The ambiguity, not to say the mistake, arises because the
    program's proof is indeed the same as Pappus' proof, when both are
    written down on paper in the style of a geometry text-book. But the
    (creative) mental processes by which Pappus did this, and by which the
    modern geometer is able to appreciate the proof, were very different
    from those in Gelernter's program -- which were not creative at all.

    Consider (or draw) an isosceles triangle ABC, with A at the apex. You
    are required to prove that the base-angles are equal. The usual method
    of proving this, which the program was expected to employ, is to
    construct a line bisecting angle BAC, running from A to D (a point on
    the baseline, BC). Then, the proof goes as follows:

    Consider triangles ABD and ACD.

    AB = AC (given)

    AD = DA (common)

    Angle BAD = angle DAC (by construction)

    Therefore the two triangles are congruent (two sides and included
    angle equal)

    Therefore angle ABD = angle ACD.


    By contrast, the Gelernter proof involved no construction, and went as

    Consider triangles ABC and ACB.

    Angle BAC = angle CAB (common)

    AB = AC (given)

    AC = AB (given)

    Therefore the two triangles are congruent (two sides and included
    angle equal)

    Therefore angle ABC = angle ACB.


    And, written down on paper, this is the outward form of Pappus' proof,

    The point, here, is that Pappus' own notes (as well as the reader's
    geometrical intuitions) show that in order to produce or understand
    this proof, a human being considers one and the same triangle rotated
    (as Pappus put it, lifted up and replaced in the trace left behind by
    itself). There were thus two creative aspects of this proof. First,
    when "congruence" is in question, the geometer normally thinks of two
    entirely separate triangles (or, sometimes, two distinct triangles
    having one side in common). Second, Euclidean geometry deals only with
    points, lines, and planes -- so one would expect any proof to be
    restricted to two spatial dimensions. But Pappus (and you, when you
    thought about this proof) imagined lifting and rotating the triangle
    in the third dimension. He was, if you like, cheating. However, to
    transform a rule (an aspect of some conceptual space) is to change it:
    in effect, to cheat. In that sense, transformational creativity always
    involves cheating.

    Gelernter's geometry-program did not cheat -- not merely because it
    was too rigid to cheat in any way, but also because it could not have
    cheated in this way. It knew nothing of the third dimension. Indeed,
    it had no visual, analogical, representation of triangles at all. It
    represented a triangle not as a two-dimensional spatial form, but as a
    list of three letters (e.g. ABC) naming points in an abstract
    coordinate space. Similarly, it represented an angle as a list of
    three letters naming the vertex and one of the points on each of the
    two rays. Being unable to inspect triangles visually, it even had to
    prove that every different letter-name for what we can see to be the
    same angle was equivalent. So it had to prove (for instance) that
    angle XYZ is the same as angle ZYX, and angle BAC the same as angle
    CAB. Consequently, this program was incapable not only of coming up
    with Pappus' proof in the way he did, but even of representing such a
    proof -- or of appreciating its elegance and originality. Its mental
    maps simply did not allow for the lifting and replacement of triangles
    in space (and it had no heuristics enabling it to transform those

    How did it come up with its pseudo-Pappus proof, then? Treating the
    "ABC's" as (spatially uninterpreted) abstract vectors, it did a
    massive brute-search to find the proof. Since this brute search
    succeeded, it did not bother to construct any extra lines.

    This example shows how careful one must be in ascribing creativity to
    a person, and in answering the second Lovelace question about a
    program. We have to consider not only the resulting idea, but also the
    mental processes which gave rise to it. Brute force search is even
    less creative than associative (improbabilist) thinking, and
    problem-dimensions which can be mapped by some systems may not be
    representable by others. (Analogously, a three-year old not showing
    flexible imagination in drawing a funny man: rather, she is showing
    incompetence in drawing an ordinary man.)

    It should not be assumed from the example of Pappus (or Kekule) that
    visual imagery is always useful in mapping and transforming one's
    ideas. An example is given of a problem for which a visual
    representation is almost always constructed, but which hinders
    solution. Where mental maps are concerned, visual maps are not always

    Chapter 6: Creative Connections

    This chapter deals with associative creativity: the spontaneous
    generation of new ideas, and/or novel combinations of familiar ideas,
    by means of unconscious processes of association. Examples include not
    only "mere associations" but also analogies, which may then be
    consciously developed for purposes of rhetorical exposition or
    problem-solving. In Chapter 6, I discuss the initial association of
    ideas. (The evaluation and use of analogy are addressed in Chapter 7.)

    One of the richest veins of associative creativity is poetic imagery.
    I consider some specific examples taken from Coleridge's poem The
    Ancient Mariner. For this poem (and also for his Kubla Khan), we have
    unusually detailed information about the literary sources of the
    imagery concerned. The literary scholar John Livingston Lowes (1951)
    studied Coleridge's Notebooks written while preparing for and writing
    the poem, and followed up every source mentioned there -- and every
    footnote given in each source. Despite the enormous quantity and range
    of Coleridge's reading, Lowes makes a subtle, and intuitively
    compelling, case in identifying specific sources for the many images
    in the poem.

    However, an intuitively compelling case is one thing, and an explicit
    justification or detailed explanation is another. Lowes took for
    granted that association can happen (he used Coleridge's term: the
    hooks and eyes of memory), without being able to say just how these
    hooks and eyes can come together. I argue that connectionism, and
    specifically PDP (parallel distributed processing), can help us to
    understand how such unexpected associations are possible.

    Among the relevant questions to which PDP-models offer preliminary
    answers are the following: How can ideas from very different sources
    (such as Captain Cook's diaries and Priestley's writings on optics) be
    spontaneously thought of together? How can two ideas be merged to
    produce a new structure, which shows the influence of both
    ancestor-ideas without being a mere "cut-and-paste" combination? How
    can the mind be "primed" (for instance, by the decision to write a
    poem about a seaman), so that one will more easily notice
    serendipitous ideas? Why may someone notice -- and remember --
    something fairly uninteresting (such as a word in a literary text), if
    it occurs in an interesting context? How can a brief phrase conjure up
    from memory an entire line or stanza, from this or some other poem?
    And how can we accept two ideas as similar (the words "love" and
    "prove" as rhyming, for instance) in respect of a feature not
    identical in both?

    The features of connectionist models which suggest answers to these
    questions are their powers of pattern-completion, graceful
    degradation, sensitization, multiple constraint-satisfaction, and
    "best-fit" equilibration. The computational processes underlying these
    features are described informally in Chapter 6 (I assume that it is
    not necessary to do so for BBS-readers).

    The message of this chapter is that the unconscious, "insightful",
    associative aspects of creativity can be explained -- in outline, at
    least -- in computational terms. Connectionism offers some specific
    suggestions about what sorts of processes may underlie the hooks and
    eyes of memory.

    This is not to say, however, that all aspects of poetry -- or even all
    poetic imagery -- can be explained in this way. Quite apart from the
    hierarchical structure of natural language itself, some features of a
    poem may require thinking of a type more suited (at present) to
    symbolic models. For example, Coleridge's use of "The Sun came up upon
    the left" and "The Sun now rose upon the right" as the opening-lines
    of two closely-situated stanzas enabled him to indicate to the reader
    that the ship was circumnavigating the globe, without having to detail
    all the uneventful miles of the voyage. (Compare Kubrick's use of the
    spinning thigh-bone turning into a space-ship, as a highly compressed
    history of technology, in his film 2001, A Space Odyssey.) But these
    expressions, too, were drawn from his reading -- in this case, of the
    diaries of the very early mariners, who recorded their amazement at
    first experiencing the sunrise in the "wrong" part of the sky.
    Associative memory was thus involved in this poetic conceit, but it is
    not the entire explanation.

    Chapter 7: Unromantic Artists

    This chapter and the next describe and criticize some existing
    computer models of creativity. The separation into "artists" (Chapter
    7) and "scientists" (Chapter 8) is to some extent an arbitrary
    rhetorical device. For example, analogy (discussed in Chapter 7) and
    induction and genetic algorithms (both outlined in Chapter 8) are all
    relevant to creativity in arts and sciences alike. In these two
    chapters, the second and third Lovelace-questions -- about apparent
    computer-creativity -- are addressed at length. However, the first
    Lovelace question, relating to human creativity, is still the
    over-riding concern.

    The computer models of creativity discussed in Chapter 7 include: a
    series of programs which produce line-drawings (McCorduck, 1991); a
    jazz-improviser (Johnson-Laird, 1991); a haiku-writer (Masterman &
    McKinnon Wood, 1968); two programs for writing stories (Klein et al.,
    1973; Meehan, 1981); and two analogy-programs (Chalmers, French, &
    Hofstadter, 1991; Holyoak & Thagard, 1989a, 1989b; Mitchell, 1993). In
    each case, the programmer has to try to define the dimensions of the
    relevant conceptual space, and to specify ways of exploring the space,
    so as to generate novel structures within it. Some evaluation, too,
    must be allowed for. In the systems described in this chapter, the
    evaluation is built into the generative procedures, rather than being
    done post hoc. (This is not entirely unrealistic: although humans can
    evaluate -- and modify -- their own ideas once they have produced
    them, they can also develop domain-expertise such that most of their
    ideas are acceptable without modification.)

    Sometimes, the results are comparable with non-trivial human
    achievements. Thus some of the computer's line-drawings are
    spontaneously admired, by people who are amazed when told their
    provenance. The haiku-program can produce acceptable poems, sometimes
    indistinguishable from human-generated examples (however, this is due
    to the fact that the minimalist haiku-style demands considerable
    projective interpretation by the reader). And the jazz-program can
    play -- composing its own chord-sequences, as well as improvising on
    them -- at about the level of a moderately competent human beginner.
    (Another jazz-improviser, not mentioned in the book, plays at the
    level of a mediocre professional musician; unlike the former example,
    it starts out with significant musical structure provided to it "for
    free" by the human user (Hodgson, 1990).)

    At other times, the results are clumsy and unconvincing, involving
    infelicities and absurdities of various kinds. This often happens when
    stories are computer-generated. Here, many rich conceptual spaces have
    to be negotiated simultaneously. Quite apart from the challenge of
    natural language generation, the model must produce sensible plots,
    taking account both of the motivation and action of the characters and
    of their common-sense knowledge. Where very simple plot-spaces, and
    very limited world-knowledge, are concerned, a program may be able
    (sometimes) to generate plausible stories.

    One, for example, produces Aesop-like tales, including a version of
    "The Fox and the Crow" (Meehan, 1981). A recent modification of this
    program (Turner, 1992), not covered in the book, is more subtle. It
    uses case-based reasoning and case-transforming heuristics to generate
    novel stories based on familiar ones; and because it distinguishes the
    author's goals from those of the characters, it can solve
    meta-problems about the story as well as problems posed within it. But
    even this model's story-telling powers are strictly limited, compared
    with ours.

    Models dealing with the interpretation of stories, and of concepts
    (such as betrayal) used in stories, are also relevant here.
    Computational definitions of interpersonal themes and scripts
    (Abelson, 1973), programs that can answer questions about (simple)
    stories and models which can -- up to a point -- interpret
    motivational and emotional structures within a story (Dyer, 1983) are
    all discussed.

    So, too, is a program that generates English text describing games of
    noughts-and-crosses (Davey, 1978). The complex syntax of the sentences
    is nicely appropriate to the structure of the particular game being
    described. Human writers, too, often use subtleties of syntax to
    convey certain aspects of their story-lines.

    The analogy programs described in Chapter 7 are ACME and ARCS (Holyoak
    & Thagard, 1989a, 1989b), and in the Preface to the paperback edition
    I add a discussion of Copycat (Chalmers et al., 1991; Mitchell, 1993),
    which I had originally intended to highlight in the main text.

    ACME and ARCS are an analogy-interpreter and an analogy-finder,
    respectively. Calling on a semantic net of over 30,000 items, to which
    items can be added by the user, these programs use structural,
    semantic, and pragmatic criteria to evaluate analogies between
    concepts (whose structure is pre-given by the programmers). Other
    analogy programs (e.g. Falkenhainer, Forbus, & Gentner, 1989) use
    structural and semantic similarity as criteria. But ARCS/ACME takes
    account also of the pragmatic context, the purpose for which the
    analogy is being sought. So a conceptual feature may be highlighted in
    one context, and downplayed in another. The context may be one of
    rhetoric or poetic imagery, or one of scientific problem-solving
    (ARCS/ACME forms part of an inductive program that compares the
    "explanatory coherence" of rival scientific theories (Thagard, 1992)).
    Examples of both types are discussed.

    The point of interest about Copycat is that it is a model of analogy
    in which the structure of the analogues is neither pre-assigned nor
    inflexible. The description of something can change as the system
    searches for an analogy to it, and its "perception" of an analogue may
    be permanently influenced by having seen it in a particular analogical
    relation to something else. Many analogies in the arts and sciences
    can be cited, to show that the same is true of the human mind.

    Among the points of general interest raised in this chapter is the
    inability of these programs (Copycat excepted) to reflect on what they
    have done, or to change their way of doing it.

    For instance, the line-drawing program that draws human acrobats in
    broadly realistic poses is unable to draw one-armed acrobats. It can
    generate acrobats with only one arm visible, if one arm is occluded by
    another acrobat in front. But that there might be a one-armed (or a
    six-armed) acrobat is strictly inconceivable. The reason is that the
    program's knowledge of human anatomy does not represent the fact that
    humans have two arms in a form which is separable from its
    drawing-procedures or modifiable by "imaginative" heuristics. It does
    not, for instance, contain anything of the form "Number of arms: 2",
    which might then be transformed by a "vary the variable" heuristic
    into "Number of arms: 1". Much as the four-year-old child cannot draw
    a "funny" one-armed man because she has not yet developed the
    necessary RR of her own man-drawing skill, so this program cannot vary
    what it does because -- in a clear sense -- it does not know what it
    is that it is doing.

    This failing is not shared by all current programs: some featured in
    the next chapter can evaluate their own ideas, and transform their own
    procedures, to some extent. Moreover, this failure is "bad news" only
    to those seeking a positive answer to the second and third Lovelace
    questions. It is useful to anyone asking the first Lovelace question,
    for it underlines the importance of the factors introduced in Chapter
    4: reflexive mapping of thought, evaluation of ideas, and
    transformation of conceptual spaces.

    Chapter 8: Computer-Scientists

    Like analogy, inductive thinking occurs across both arts and science.
    Chapter 8 begins with a discussion of the ID3 algorithm. This is used
    in many learning programs, including a world-beater -- better than the
    human expert who "taught" it -- at diagnosing soybean diseases
    (Michalski & Chilausky, 1980).

    ID3 learns from examples. It looks for the logical regularities which
    underlie the classification of the input examples, and uses them to
    classify new, unexamined, examples. Sometimes, it finds regularities
    of which the human experts were unaware, such as unknown strategies
    for chess endgames (Michie & Johnston, 1984). In short, ID3 can not
    only define familiar concepts in H-creative ways, but can also define
    H-creative concepts.

    However, all the domain-properties it considers have to be
    specifically mentioned in the input. (It does not have to be told just
    which input properties are relevant: in the chess end-game example,
    the chess-masters "instructing" the program did not know this.) That
    is, ID3-programs can restructure their conceptual space in P-creative
    -- and even H-creative -- ways. But they cannot change the dimensions
    of the space, so as to alter its fundamental nature.

    Another program capable of H-discovery is meta-DENDRAL, an early
    expert system devoted to the spectroscopic analysis of a certain group
    of organic molecules. The original program, DENDRAL, uses exhaustive
    search to describe all possible molecules made up of a given set of
    atoms, and heuristics to suggest which of these might be chemically
    interesting. DENDRAL uses only the chemical rules supplied to it, but
    meta-DENDRAL can find new rules about how these compounds decompose.
    It does this by identifying unfamiliar patterns in the spectrographs
    of familiar compounds, and suggesting plausible explanations for them.
    For instance, if it discovers a smaller structure located near the
    point at which a molecule breaks, it may suggest that other molecules
    containing that sub-structure may break at these points too.

    This program is H-creative, up to a point. It not only explores its
    conceptual space (using evaluative heuristics and exhaustive search)
    but enlarges it too, by adding new rules. It generates hunches, which
    have led to the synthesis of novel, chemically interesting, compounds.
    And it has discovered some previously unsuspected rules for analysing
    several families of organic molecules. However it relies on
    sophisticated theories built into it by expert chemists (which is why
    its novel hypotheses, though sometimes false, are always plausible).
    It casts no light on how those theories might have arisen in the first

    Some computational models of induction were developed with an eye to
    the history of science (and to psychology), rather than for practical
    scientific puzzle-solving. Their aim was not to come up with
    H-creative ideas, but to P-create in the same way as human H-creators.
    Examples include BACON, GLAUBER, STAHL, and DALTON (Langley, Simon,
    Bradshaw, & Zytkow, 1987), whose P-creative activities are modelled on
    H-creative episodes recorded in the notebooks of human scientists.

    BACON induces quantitative laws from empirical data. Its data are
    measurements of various properties at different times. It looks for
    simple mathematical functions defining invariant relations between
    numerical data-sets. For instance, it seeks direct or inverse
    proportionalities between measurements, or between their products or
    ratios. It can define higher-level theoretical terms, construct new
    units of measurement, and use mathematical symmetry to help find
    invariant patterns in the data. It can cope with noisy data, finding a
    best-fit function (within predefined limits). BACON has P-created many
    physical laws, including Archimedes' principle, Kepler's third law,
    Boyle's law, Ohm's law, and Black's law.

    GLAUBER discovers qualitative laws, summarizing the data by
    classifying things according to (non-measurable) observable
    properties. Thus it discovers relations between acids, alkalis, and
    bases (all identified in qualitative terms). STAHL analyses chemical
    compounds into their elements. Relying on the data-categories
    presented to it, it has modelled aspects of the historical progression
    from phlogiston-theory to oxygen-theory. DALTON reasons about atoms
    and molecular structure. Using early atomic theory, it generates
    plausible molecular structures for a given set of components (it could
    be extended to cover other componential theories, such as particle
    physics or Mendelian genetics).

    These four programs have rediscovered many scientific laws. However,
    their P-creativity is shallow. They are highly data-driven, their
    discoveries lying close to the evidence. They cannot identify
    relevance for themselves, but are "primed" with appropriate
    expectations. (BACON expects to find linear relationships, and
    rediscovered Archimedes' principle only after being told that things
    can be immersed in known volumes of liquid and the resulting volume
    measured.) They cannot model spontaneous associations or analogies,
    only deliberate reasoning. Some can suggest experiments, to test
    hypotheses they have P-created, but they have no sense of the
    practices involved. They can learn, constructing P-novel concepts used
    to make further P-discoveries. But their discoveries are exploratory
    rather than transformational: they cannot fundamentally alter their
    own conceptual spaces.

    Some AI-models of creativity can do this, to some extent. For
    instance, the Automatic Mathematician (AM) explores and transforms
    mathematical ideas (Lenat, 1983). It does not prove theorems, or do
    sums, but generates "interesting" mathematical ideas (including
    expressions that might be provable theorems). It starts with 100
    primitive concepts of set-theory (such as set, list, equality, and
    ordered pair), and 300 heuristics that can examine, combine,
    transform, and evaluate its concepts. One generates the inverse of a
    function (compare "consider the negative"). Others can compare,
    generalize, specialize, or find examples of concepts.
    Newly-constructed concepts are fed back into the pool.

    In effect, AM has hunches: its evaluation heuristics suggest which new
    structures it should concentrate on. For example, AM finds it
    interesting whenever the union of two sets has a simply expressible
    property which is not possessed by either of them (a set-theoretic
    version of the notion that emergent properties are interesting). Its
    value-judgments are often wrong. Nevertheless, it has constructed some
    powerful mathematical notions, including prime numbers, Goldbach's
    conjecture, and an H-novel theorem concerning maximally-divisible
    numbers (which the programmer had never heard of). In short, AM
    appears to be significantly P-creative, and slightly H-creative too.

    However, AM has been criticised (Haase, 1986; Lenat & Seely-Brown,
    1984; Ritchie & Hanna, 1984; Rowe & Partridge, 1993). Critics have
    argued that some heuristics were included to make certain discoveries,
    such as prime numbers, possible; that the use of LISP provided AM with
    mathematical relevance "for free", since any syntactic change in a
    LISP expression is likely to result in a mathematically-meaningful
    string; that the program's exploration was too often guided by the
    human user; and that AM had fixed criteria of interest, being unable
    to adapt its values. The precise extent of AM's creativity, then, is

    Because EURISKO has heuristics for changing heuristics, it can
    transform not only its stock of concepts but also its own
    processing-style. For example, one heuristic asks whether a rule has
    ever led to any interesting result. If it has not (but has been used
    several times), it will be less often used in future. If it has
    occasionally been helpful, though usually worthless, it may be
    specialized in one of several different ways. (Because it is sometimes
    useful and sometimes not, the specializing-heuristic can be applied to
    itself.) Other heuristics generalize rules, or create new rules by
    analogy with old ones. Using domain-specific heuristics to complement
    these general ones, EURISKO has generated H-novel ideas in genetic
    engineering and VLSI-design (one has been patented, so was not
    "obvious to a person skilled in the art").

    Other self-transforming systems described in this chapter are
    problem-solving programs based on genetic algorithms (GAs). GA-systems
    have two main features. They all use rule-changing algorithms
    (mutation and crossover) modelled on biological genetics. Mutation
    makes a random change in a single rule. Crossover mixes two rules, so
    that (for instance) the lefthand portion of one is combined with the
    righthand portion of the other; the break-points may be chosen
    randomly, or may reflect the system's sense of which rule-parts are
    the most useful. Most GA-systems also include algorithms for
    identifying the relatively successful rules, and rule-parts, and for
    increasing the probability that they will be selected for "breeding"
    future generations. Together, these algorithms generate a new system,
    better adapted to the task.

    An example cited in the book is an early GA-program which developed a
    set of rules to regulate the transmission of gas through a pipeline
    (Holland, Holyoak, Nesbitt, & Thagard, 1986). Its data were hourly
    measurements of inflow, outflow, inlet-pressure, outlet-pressure, rate
    of pressure-change, season, time, date, and temperature. It altered
    the inlet-pressure to allow for variations in demand, and inferred the
    existence of accidental leaks in the pipeline (adjusting the inflow

    Although the pipeline-program discovered the rules for itself, the
    potentially relevant data-types were given in its original list of
    concepts. How far that compromises its creativity is a matter of
    judgment. No system can work from a tabula rasa. Likewise, the
    selectional criteria were defined by the programmer, and do not alter.
    Humans may be taught evaluative criteria, too. But they can sometimes
    learn -- and adapt -- them for themselves.

    GAs, or randomizing thinking, are potentially relevant to art as well
    as to science -- especially if the evaluation is done interactively,
    not automatically. That is, at each generation the selection of items
    from which to breed for the next generation is done by a human being.
    This methodology is well-suited to art, where the evaluative criteria
    are not only controversial but also imprecise -- or even unknown. Two
    recent examples (not mentioned in the book, but described in: Boden,
    in press) concern graphics (Sims, 1991; Todd & Latham, 1993). Sims'
    aim is to provide an interactive environment for graphic artists,
    enabling them to generate otherwise unimaginable images. Latham's is
    to produce his own art-works, but he too uses the computer to generate
    images he could not have developed unaided.

    In a run of Sims' GA-system, the first image is generated at random.
    Then the program makes various independent random mutations in the
    image-generating rule, and displays the resulting images. The human
    now chooses one image to be mutated, or two to be "mated", and the
    process is repeated. The program can transform its image-generating
    code (simple LISP-functions) in many ways. It can alter parameters in
    pre-existing functions, combine or separate functions, or nest one
    function inside another (so many-levelled hierarchies can arise).

    Many of Sims' computer-generated images are highly attractive, even
    beautiful. Moreover, they often cause a deep surprise. The change(s)
    between parent and offspring are sometimes amazing. The one appears to
    be a radical transformation of the other -- or even something entirely
    different. In short, we seem to have an example of impossibilist

    Latham's interactive GA-program is much more predictable. Its mutation
    operators can change only the parameters within the image-generating
    code, not the body of the function. Consequently, it never comes up
    with radical novelties. All the offspring in a given generation are
    obviously siblings, and obviously related to their parents. So the
    results of Latham's system are less exciting than Sims'. But it is
    arguably even more relevant to artistic creativity.

    The interesting comparison is not between the aesthetic appeal of a
    typical Latham-image and Sims-image, but between the discipline -- or
    lack of it -- which guides the exploration and transformation of the
    relevant visual space. Sims is not aiming for particular types of
    result, so his images can be fundamentally transformed in random ways
    at every generation. But Latham (a professional artist) has a sense of
    what forms he hopes to achieve, and specific aesthetic criteria for
    evaluating intermediate steps. Random changes at the margins are
    exploratory, and may provide some useful ideas. But fundamental
    transformations -- especially, random ones -- would be
    counterproductive. (If they were allowed, Latham would want to pick
    one and then explore its possibilities in a disciplined way.)

    This fits the account of (impossibilist) creativity given in Chapters
    3 and 4. Creativity works within constraints, which define the
    conceptual spaces with respect to which it is identified. Maps or RRs
    (or LISP-functions) which describe the parameters and/or the major
    dimensions of the space can be altered in specific ways, to generate
    new, but related, spaces.

    Random changes are sometimes helpful, but only if they are integrated
    into the relevant style. Art, like science, involves discipline. Only
    after a space has been fairly thoroughly explored will the artist want
    to transform it in deeply surprising ways. A convincing
    computer-artist would therefore need not only randomizing operators,
    but also heuristics for constraining its transformations and
    selections in an aesthetically acceptable fashion. In addition, it
    would need to make its aesthetic selections (and perhaps guiding
    recommendations) for itself. And, to be true to human creativity, the
    evaluative rules should evolve also (Elton, 1993).

    Chapter 9: Chance, Chaos, Randomness, Unpredictability

    Unpredictability is often said to be the essence of creativity. And
    creativity is, by definition, surprising. But unpredictability is not
    enough. At the heart of creativity, as previous chapters have shown,
    lie constraints: the very opposite of unpredictability. Constraints
    and unpredictability, familiarity and surprise, are somehow combined
    in original thinking.

    In this chapter, I distinguish various senses of "chance", "chaos",
    "randomness", and "unpredictability". I also argue that a scientific
    explanation need not imply either determinism or predictability, and
    that even deterministic systems may be unpredictable. Below, it will
    suffice to mention a number of different ways in which
    unpredictability can enter into creativity.

    The first follows from the fact that creative constraints do not
    determine everything about the newly-generated idea. A style of
    thinking typically allows for many points at which two or more
    alternatives are possible. Several notes may be both melodious and
    harmonious; many words rhyme with moon; and perhaps there could be a
    ring-molecule with three, or five, atoms in the ring? At these points,
    some specific choice must be made. Likewise, many exploratory and
    transformational heuristics may be potentially available at a certain
    time, in dealing with a given conceptual space. But one or other must
    be chosen. Even if several heuristics can be applied at once (like
    parallel mutations in a GA-system), not all possibilities can be
    simultaneously explored. The choice has to be made, somehow.

    Occasionally, the choice is random, or as near to random as one can
    get. So it may be made by throwing a dice (as in playing Mozart's
    aleatory music); or by consulting a table of random numbers (as in the
    jazz-program); or even, possibly, as a result of some sudden
    quantum-jump inside the brain. There may even be psychological
    processes akin to GA-mechanisms, producing novel ideas in human minds.

    More often, the choice is fully determined, by something which bears
    no systematic relation to the conceptual space concerned. (Some
    examples are given below.) Relative to that style of thinking, the
    choice is made randomly. Certainly, nothing within the style itself
    could enable us to predict its occurrence.

    In either case, the choice must somehow be skilfully integrated into
    the relevant mental structure. Without such disciplined integration,
    it cannot lead to a positively valued, interesting, idea. With the
    help of this mental discipline, even flaws and accidents may be put to
    creative use. For instance, a jazz-drummer suffering from Tourette's
    syndrome is subject to sudden, uncontrollable, muscular tics, even
    when he is drumming. As a result, his drumsticks sometimes make
    unexpected sounds. But his musical skill is so great that he can work
    these supererogatory sounds into his music as he goes along. At worst,
    he "covers up" for them. At best, he makes them the seeds of unusual
    improvisations which he could not otherwise have thought of.

    One might even call the drummer's tics serendipitous. Serendipity is
    the unexpected finding of something one was not specifically looking
    for. But the "something" has to be something which was wanted, or at
    least which can now be used. Fleming's discovery of the dirty
    petri-dish, infected by Penicillium spores, excited him because he
    already knew how useful a bactericidal agent would be. Proust's
    madeleine did not answer any currently pressing question, but it
    aroused a flood of memories which he was able to use as the trigger of
    a life-long project. Events such as these could not have been
    foreseen. Both trigger and triggering were unpredictable. Who was to
    say that the dish would be left uncovered, and infected by that
    particular organism? And who could say that Proust would eat a
    madeleine on that occasion? Even if one could do this (perhaps the
    laboratory was always untidy, and perhaps Proust was addicted to
    madeleines), one could not predict the effect the trigger would have
    on these individual minds.

    This is so even if there are no absolutely random events going on in
    our brains. Chaos theory has taught us that fully deterministic
    systems can be, in practice, unpredictable. Our inescapable ignorance
    of the initial conditions means that we cannot forecast the weather,
    except in highly general (and short-term) ways. The inner dynamics of
    the mind are more complex than those of the weather, and the initial
    conditions -- each person's individual experiences, values, and
    beliefs -- are even more varied. Small wonder, then, if we cannot
    fully foresee the clouds of creativity in people's minds.

    To some extent, however, we can. Different thinkers have differing
    individual styles, which set a characteristic stamp on all their work
    in a given domain. Thus Dr. Johnson complained, "Who but Donne would
    have compared a good man to a telescope?". Authorial signatures are
    largely due to the fact that people can employ habitual ways of making
    "random" choices. There may be nothing to say, beforehand, how someone
    will choose to play the relevant game. But after several years of
    practice, their "random" choices may be as predictable as anything in
    the basic genre concerned.

    More mundane examples of creativity, which are P-creative but not
    H-creative, can sometimes be predicted -- and even deliberately
    brought about. Suppose your daughter is having difficulty mastering an
    unfamiliar principle in her physics homework. You might fetch a gadget
    that embodies the principle concerned, and leave it on the
    kitchen-table, hoping that she will play around with it and realise
    the connection for herself. Even if you have to drop a few hints, the
    likelihood is that she will create the central idea. Again, Socratic
    dialogue helps people to explore their conceptual spaces in (to them)
    unexpected ways. But Socrates himself, like those taking his role
    today, knew what P-creative ideas to expect from his pupils.

    We cannot predict creative ideas in detail, and we never shall be able
    to do so. Human experience is too richly idiosyncratic. But this does
    not mean that creativity is fundamentally mysterious, or beyond
    scientific understanding.

    Chapter 10: Elite or Everyman?

    Creativity is not a single capacity, and nor is it a special one. It
    is an aspect of intelligence in general, which involves many different
    capacities: noticing, remembering, seeing, speaking, classifying,
    associating, comparing, evaluating, introspecting, and the like.
    Chapter 10 offers evidence for this view, drawing on the work of
    Perkins (1981) and also on computational work of various kinds.

    For example, Kekule's description of "long rows, twining and twisting
    in snakelike motion", where "one of the snakes had seized hold of its
    own tail", assumes everyday powers of visual interpretation and
    analogy. These capacities are normally taken for granted in
    discussions of Kekule's H-creativity, but they require some
    psychological explanation. Relevant computational work on low-level
    vision suggests that Kekule's imagery was grounded in certain
    specific, and universal, visual capacities -- including the ability to
    identify lines and end-points. (His hunch, by contrast, required
    special expertise. As remarked in Chapter 4, only a chemist could have
    realized the potential significance of the change in
    neighbour-relations caused by the coalescence of end-points, or the
    "snake" which "seized hold of its tail".)

    Similarly, Mozart's renowned musical memory, and his reported capacity
    for hearing a whole symphony "all at once", can be related to
    computational accounts of powers of memory and comprehension common to
    us all. Certainly, his musical expertise was superior in many ways. He
    had a better grasp of the conceptual spaces concerned, and a better
    understanding -- better even than Salieri's -- of how to explore them
    so as to locate their farthest nooks and crannies. (Unlike Haydn, for
    example, he was not a composer who made adventurous transformations).
    But much of Mozart's genius may have lain in the better use, and the
    vastly more extended practice, of facilities we all share.

    Much -- but perhaps not all. Possibly, there was something special
    about Mozart's brain which predisposed him to musical genius (Gardner,
    1983). However, we have little notion, at present, of what this could
    be. It may have been some cerebral detail which had the emergent
    effect of giving him greater musical powers. For example, the
    jazz-improvisation program described in Chapter 7 employed only very
    simple rules to improvise, because its short-term memory was
    deliberately constrained to match the limited STM of people. Human
    jazz-musicians cannot improvise hierarchically nested chord-sequences
    "on the fly", but have to compose (or memorize) them beforetimes. A
    change in the range of STM might enable someone to improvise and
    appreciate musical structures of a complexity not otherwise
    intelligible. But this musically significant change might be due to an
    apparently "boring" feature of the brain.

    Many other examples of creativity (drawn, for instance, from poetry,
    painting, music, and choreography) are cited in this chapter. They all
    rely on familiar capacities for their effect, and arguably for their
    occurrence too. We appreciate them intuitively, and normally take
    their accessibility -- and their origins -- for granted. But
    psychological explanations in computational terms may be available, at
    least in outline.

    The role of motivation and emotion is briefly mentioned, but is not a
    prime theme. This is not because motivation and emotion are in
    principle outside the reach of a computational psychology. Some
    attempts have been made to bring these matters within a computational
    account of the mind (e.g. Boden, 1972; Sloman, 1987). But such
    attempts provide outline sketches rather than functioning models.
    Still less is it because motivation is irrelevant to creativity. But
    the main topic of the book is how (not why) novel ideas arise in human

    Chapter 11: Of Humans and Hoverflies

    The final chapter focusses on two questions. One is the fourth
    Lovelace question: could a computer really be creative? The other is
    whether any scientific explanation of creativity, whether
    computational or not, would be dehumanizing in the sense of destroying
    our wonder at it -- and at the human mind in general.

    With respect to the fourth Lovelace question, the answer "No" may be
    defended in at least four different ways. I call these the brain-stuff
    argument, the empty-program argument, the consciousness argument, and
    the non-human argument. Each of these applies to intelligence (and
    intentionality) in general, not just to creativity in particular.

    The brain-stuff argument (Searle, 1980) claims that whereas
    neuroprotein is a kind of stuff which can support intelligence, metal
    and silicon are not. This empirical claim is conceivably correct, but
    we have no specific reason to believe it. Moreover, the associated
    claim -- that it is intuitively obvious that neuroprotein can support
    intentionality and that metal and silicon cannot -- must be rejected.

    Intuitively speaking, that neuroprotein supports intelligence is
    utterly mysterious: how could that grey mushy stuff inside our skulls
    have anything to do with intentionality? Insofar as we understand
    this, we do so because of various functions that nervous tissue makes
    possible (as the sodium pump enables action potentials, or "messages",
    to pass along an axon). Any material substrate capable of supporting
    all the relevant functions could act as the embodiment of mind.
    Whether neurochemistry describes the only such substrate is an
    empirical question, not to be settled by intuitions.

    The empty-program argument is Searle's (1980) claim that a
    computational psychology cannot explain understanding, because
    programs are all syntax and no semantics: their symbols are utterly
    meaningless to the computer itself. I reply that a computer program,
    when running in a computer, has proto-semantic (causal) properties, in
    virtue of which the computer does things -- some of which are among
    the sorts of thing which enable understanding in humans and animals
    (Boden, 1988, ch. 8; Sloman, 1986). (This is not to say that any
    computer-artefact could possess understanding in the full sense, or
    what I have termed "intrinsic interests", grounded in evolutionary
    history (Boden, 1972).)

    The consciousness argument is that no computer could be conscious, and
    therefore -- since consciousness is needed for the evaluation phase,
    and even for much of the preparation phase -- no computer can be
    creative. I reply that it's not obvious that evaluation must be
    carried out consciously. A creative computer might recognize
    (evaluate) its creative ideas by using relevant reflexive criteria
    without also having consciousness. Moreover, some aspects of
    consciousness can be illuminated by a computational account, although
    admittedly "qualia" present an unsolved problem. The question must
    remain open -- not just because we do not know the answer, but because
    we do not clearly understand how to ask the question.

    According to the non-human argument, to regard computers as truly
    intelligent is not a mere factual mistake, but a moral absurdity: only
    members of the human, or animal, community should be granted moral and
    epistemological consideration (of their interests and opinions). If we
    ever agreed to remove all the scare-quotes around the psychological
    words we use in describing computers, so inviting them to join our
    human community, we would be committed to respecting their goals and
    judgments. This would not be a purely factual matter, but one of moral
    and political choice -- about which it is impossible to legislate now.

    In short, each of the four negative replies to the last Lovelace
    question is challengeable. But even someone who does accept a negative
    answer here can consistently accept positive answers to the first
    three Lovelace questions. The main argument of the book remains

    The second theme of this final chapter is the question whether, where
    creativity is in question, scientific explanation in general should be
    spurned. Many people, from Blake to Roszak, have seen the natural
    sciences as dehumanizing in various ways. Three are relevant here: the
    ignoring of mentalistic concepts, the denial of cherished beliefs, and
    the destructive demystification of some valued phenomena.

    The natural sciences have had nothing to say about psychological
    phenomena as such; and scientifically-minded psychologists have often
    conceptualized them in reductionist (e.g. behaviourist, or
    physiological) terms. To ignore something is not necessarily to deny
    it. But, given the high status of the natural sciences, the fact that
    they have not dealt with the mind has insidiously downplayed its
    importance, if not its very existence.

    This charge cannot be levelled at computational psychology, however.
    Intentional concepts, such as representation, lie at the heart of it,
    and of AI. Some philosophers claim that these sciences have no right
    to use such terms. Even so, they cannot be accused of deliberately
    ignoring intentional phenomena, or of rejecting intentionalist

    The second charge of dehumanization concerns what science explicitly
    denies. Some scientific theories have rejected comforting beliefs,
    such as geocentrism, special creation, or rational self-control. But a
    scientific psychology need not -- and a computational psychology does
    not -- deny creativity, as astronomy denies geocentrism. On the
    contrary, the preceding chapters have acknowledged creativity again
    and again. Even to say that it rests on universal features of human
    minds is not to deny that some ideas are surprising, and special,
    requiring explanation of how they could possibly arise.

    However, the humanist's worry concerns not only denial by rejection,
    but also denial by explanation. The crux of the third type of
    anti-scientific resistance is the feeling that scientific explanation
    of any kind must drive out wonder: that to explain something is to
    cease to marvel at it. Not only do we wonder at creativity, but
    positive evaluation is essential to the concept. So it may seem that
    to explain creativity is insidiously to downgrade it -- in effect, to
    deny it.

    Certainly, many examples can be given where understanding drives out
    wonder. For instance, we may marvel at the power of the hoverfly to
    fly to its mate hovering nearby (so as to mate in mid-air). Many
    people might be tempted to describe the hoverfly's activities in terms
    of its goals and beliefs, and perhaps even its determination in going
    straight to its mate without any coyness or prevarication. How
    wonderful is the mind of the humble hoverfly!

    In fact, the hoverfly's flight-path is determined by a simple and
    inflexible rule, hardwired into its brain. This rule transforms a
    specific visual signal into a specific muscular response. The fly's
    initial change of direction depends on the particular approach-angle
    subtended by the target-fly. The creature, in effect, always assumes
    that the size and velocity of the seen target (which may or may not be
    a fly) are those corresponding to hoverflies. When initiating a new
    flight-path, the fly's angle of turn is selected on this rigid, and
    fallible, basis. Moreover, the fly's path cannot be adjusted in
    midflight, there being no way in which it can be influenced by
    feedback from the movement of the target animal.

    This evidence must dampen the enthusiasm of anyone who had marvelled
    at the psychological subtlety of the hoverfly's behaviour. The
    insect's intelligence has been demystified with a vengeance, and it no
    longer seems worthy of much respect. One may see beauty in the
    evolutionary principles that enabled this simple computational
    mechanism to develop, or in the biochemistry that makes it function.
    But the fly itself cannot properly be described in anthropomorphic
    terms. Even if we wonder at evolution, and at insect-neurophysiology,
    we can no longer wonder at the subtle mind of the hoverfly.

    Many people fear that this disillusioned denial of intelligence in the
    hoverfly is a foretaste of what science will say about our minds too.
    A few "worrying" examples can indeed be given: for instance, think of
    how perceived sexual attractiveness turns out to relate to pupil-size.
    In general, however, this fear is mistaken. The mind of the hoverfly
    is much less marvellous than we had imagined, so our previous respect
    for the insect's intellectual prowess is shown up as mere ignorant
    sentimentality. But computational explanations of thinking can
    increase our respect for human minds, by showing them to be much more
    complex and subtle than we had previously recognized.

    Consider, for instance, the many different ways (some are sketched in
    Chapters 4 and 5) in which Kekule could have seen snakes as suggesting
    ring-molecules. Think of the rich analogy-mapping in Coleridge's mind,
    which drew on naval memoirs, travellers' tales, and scientific reports
    to generate the imagery of The Ancient Mariner (Chapter 6). Bear in
    mind the mental complexities (outlined in Chapter 7) of generating an
    elegant story-line, or improvising a jazz-melody. And remember the
    many ways in which random events (the mutations described in Chapter
    8, or the serendipities cited in Chapter 9) may be integrated into
    pre-existing conceptual spaces with creative effect.

    Writing about Coleridge's imagery, Livingston Lowes said: "I am not
    forgetting beauty. It is because the worth of beauty is transcendent
    that the subtle ways of the power that achieves it are transcendently
    worth searching out." His words apply not only to literary studies of
    creativity, but to scientific enquiry too. A scientific psychology,
    whether computational or not, allows us plenty of room to wonder at
    Mozart, or at our friends' jokes. Psychology leaves poetry in place.
    Indeed, it adds a new dimension to our awe on encountering creative
    ideas, for it helps us to see the richness, and yet the discipline, of
    the underlying mental processes.

    To understand, even to demystify, is not necessarily to denigrate. A
    scientific explanation of creativity shows how extraordinary is the
    ordinary person's mind. We are, after all, humans -- not hoverflies.


    Abelson, R. P. (1973) The structure of belief systems. In: Computer
    models of thought and language, eds. R. C. Schank & K. M. Colby (pp.

    Boden, M. A. (1972) Purposive explanation in psychology. Cambridge,
    Mass.: Harvard University Press.

    Boden, M. A. (1988) Computer models of mind: Computational approaches
    in theoretical psychology. Cambridge: Cambridge University Press.

    Boden, M. A. (1990) The creative mind: Myths and mechanisms. London:
    Weidenfeld & Nicolson. (Expanded edn., London: Abacus, 1991.)

    Boden, M. A. (in press) What is creativity? In: Dimensions of
    creativity, ed. M. A. Boden. Cambridge, Mass.: MIT Press.

    Brannigan, A. (1981) The social basis of scientific discoveries.
    Cambridge: Cambridge University Press.

    Chalmers, D. J., French, R. M., & Hofstadter, D. R. (1991) High-level
    perception, representation, and analogy: A critique of artificial
    intelligence methodology. CRCC Technical Report 49. Center for
    Research on Concepts and Cognition, Indiana University, Bloomington,

    Clark, A., & Karmiloff-Smith, A. (in press) The cognizer's innards.
    Mind and Language.

    Davey, A. (1978) Discourse production: A computer model of some
    aspects of a speaker. Edinburgh: Edinburgh University Press.

    Dyer, M. G. (1983) In-depth understanding: A computer model of
    integrated processing for narrative comprehension. Cambridge, Mass.:
    MIT Press.

    Elton, M. (1993) Towards artificial creativity. In: Proceedings of
    LUTCHI symposium on creativity and cognition, ed. E. Edmonds
    (un-numbered). Loughborough: University of Loughborough.

    Falkenhainer, B., Forbus, K. D., & Gentner, D. (1989) The
    structure-mapping engine: Algorithm and examples, AI Journal, 41,

    Gardner, H. (1983) Frames of mind: The theory of multiple
    intelligences. London: Heinemann.

    Gelernter, H. L. (1963) Realization of a geometry-theorem provi
    machine. In: Computers and thought, eds. E. A. Feigenbaum & J.
    Feldman, pp. 134-152. New Yo}McGraw-Hill.

    Haase, K. W. (1986) Discovery systems. Proc. European Conf. on AI, 1,

    Hadamard, J. (1954) An essay on the psychology of invention in the
    mathematical field. New York: Dover.

    Hodgson, P. (1990) Understanding computing, cognition, and creativity.
    MSc thesis, University of the West of England.

    Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R.
    (1986) Induction: Processes of inference, learning, and discovery.
    Cambridge, Mass.: MIT Press.

    Holyoak, K. J., & Thagard, P. R. (1989a) Analogical mapping by
    constraint satisfaction. Cognitive Science, 13, 295-356.

    Holyoak, K. J., & Thagard, P. R. (1989b) A computational model of
    analogical problem solving. In: S. Vosniadou & A. Ortony (Eds.),
    Similarity and analogical reasoning (pp. 242-266). Cambridge:
    Cambridge University Press.

    Johnson-Laird, P. N. (1991) Jazz improvisation: A theory at the
    computational level. In: Representing musical structure, eds. P.
    Howell, R. West, & I. Cross. (pp. 291-326). London: Academic Press.

    Karmiloff-Smith, A. (1993) Beyond modularity: A developmental
    perspective on cognitive science. Cambridge, Mass.: MIT Press.

    Klein, S., Aeschlimann, J. F., Balsiger, D. F., Converse, S. L.,
    Court, C., Foster, M., Lao, R., Oakley, J. D., & Smith, J. (1973)
    Automatic novel writing: A status report. Technical Report 186.
    Madison, Wis.: University of Wisconsin Computer Science Dept.

    Koestler, A. (1975) The act of creation. London: Picador.

    Langley, P., Simon, H. A., Bradshaw, G. L., & Zytkow, J. M. (1987)
    Scientific discovery: Computational explorations of the creative
    process. Cambridge, Mass.: MIT Press.

    Lenat, D. B. (1983) The role of heuristics in learning by discovery:
    Three case studies. In: Machine learning: An artificial intelligence
    approach, eds. R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (pp.
    243-306). Palo Alto, Calif.: Tioga.

    Lenat, D. B., & Seely-Brown, J. (1984) Why AM and EURISKO appear to
    work. AI Journal, 23, 269-94.

    Livingston Lowes, J. (1951) The road to Xanadu: A study in the ways of
    the imagination. London: Constable.

    Longuet-Higgins, H. C. (1987) Mental processes: Studies in cognitive
    science. Cambridge, Mass.: MIT Press.

    Longuet-Higgins, H. C. (in preparation) Musical aesthetics. In:
    Artificial intelligence and the mind: New breakthroughs or dead ends?,
    eds. M. A. Boden & A. Bundy. London: Royal Society & British Academy
    (to appear).

    McCorduck, P. (1991) Aaron's code. San Francisco: W. H. Freeman.

    Masterman, M., & McKinnon Wood, R. (1968) Computerized Japanese haiku.
    In: Cybernetic Serendipity, ed. J. Reichardt (pp. 54-5). London:
    Studio International.

    Meehan, J. (1981) TALE-SPIN. In: Inside computer understanding: Five
    programs plus miniatures, eds. R. C. Schank & C. J. Riesbeck (pp.
    197-226). Hillsdale, NJ: Erlbaum.

    Michie, D., & Johnston, R. (1984) The creative computer: Machine
    intelligence and human knowledge. London: Viking.

    Michalski, R. S., & Chilausky, R. L. (1980) Learning by being told and
    learning from examples: an experimental comparison of two methods of
    knowledge acquisition in the context of developing an expert system
    for soybean disease diagnosis. International Journal of Policy
    Analysis and Information Systems, 4, 125-61.

    Mitchell, M. (1993) Analogy-making as perception. Cambridge, Mass.:
    MIT Press.

    Perkins, D. N. (1981) The mind's best work. Cambridge, Mass.: Harvard
    University Press.

    Poincare, H. (1982) The foundations of science: Science and
    hypothesis, The value of science, Science and method. Washington:
    University Press of America.

    Ritchie, G. D., & Hanna, F. K. (1984) AM: A case study in AI
    methodology. AI Journal, 23, 249-63.

    Rowe, J., & Partridge, D. (1993) Creativity: A survey of AI
    approaches. Artificial Intelligence Review, 7, 43-70.

    Schaffer, S. (in press) Making up discovery. In: Dimensions of
    creativity, ed M. A. Boden. Cambridge, Mass.: MIT Press.

    Searle. J. R. (1980) Minds, brains, and programs. Behavioral and Brain
    Sciences 3, 473-497.

    Sims, K. (1991) Artificial evolution for computer graphics. Computer
    Graphics, 25 (no.4), July 1991, 319-328.

    Sloman, A. (1986) What sorts of machines can understand the symbols
    they use? Proceedings of the Aristotelian Society, Supplementary
    Volume, 60, 61-80.

    Sloman, A. (1987) Motives, mechanisms, and emotions. Cognition and
    Emotion, 1, 217-33. (Reprinted in: The philosophy of artificial
    intelligence, ed. M. A. Boden. Oxford: Oxford University Press, pp.

    Taylor, C. W. (1988) Various approaches to and definitions of
    creativity. In: The nature of creativity: Contemporary psychological
    perspectives, ed. R. J. Sternberg (pp. 99-121). Cambridge: Cambridge
    University Press.

    Thagard, P. R. (1992) Conceptual revolutions. Princeton, NJ: Princeton
    University Press.

    Todd, S., & Latham, W. (1992) Evolutionary art and computers. London:
    Academic Press.

    Turner, S. (1992) MINSTREL: A model of story-telling and creativity.
    Technical note UCLA-AI-17-92. Los Angeles: AI Laboratory, University
    of California at Los Angeles.


    1. mailto:bbs at cogsci.soton.ac.uk
    2. mailto:journals_subscriptions at cup.org
    3. mailto:journals_marketing at cup.cam.ac.uk
    4. mailto:maggieb at syma.susx.ac.uk

More information about the paleopsych mailing list