The Competitiveness of Nations
in a Global Knowledge-Based Economy
April 2004
J. J. Sparkes
Pattern Recognition
and Scientific Progress
Mind, New
Series,
Vol. 81, No. 321
Jan. 1972, 29-41.
Index
Objectivity and the Object of Science
This
paper falls into two quite distinct parts. In the first part I am concerned to show that there is nothing peculiarly
scientific about scientific theorising. In the second part I deal with the question
which necessarily arises out of the first, that if the logic of scientific
discovery is not especially scientific then what is it about science that
distinguishes it from other branches of knowledge and understanding?
I
shall take it as no longer a matter of dispute that science progresses by a
series of hypothetico-deductive steps at various
levels, from the deepest and most general, such as that which led to
Relativity, to the relatively trivial, such as that a T.V. picture is noisy
because of a neighbour’s electric motor. The question I want to discuss further is how
rival theories, each of which may be imperfect, are judged relative to each
other in the light of experimental evidence relating to them.
I
accept first, that as Popper [1] pointed
out, it is not possible to prove a theory true; second, as has been forcibly
argued by various authors recently (notably Peetz, [2] Lakatos, [3] Kuhn, [4]
Swinburne, [5] Agassi, [6] [7] Polanyi, [8] and
Braithwaite [9]) that, contrary to
Popper’s view, it is not in principle possible to prove a theory false either -
that indeed, to quote Peetz, “It looks as if the
process of refuting a scientific hypothesis is too complex a matter to allow a
purely formal criterion.” [10]
1. K. R. Popper, The Logic of Scientific Discovery, London,
1959.
2. D. W. Peetz, “Falsification in Science”, Aristotelian Society
Proceedings, 1969.
3. I. Lakatos, “Criticism and Methodology in Scientific Research Programmes ”, Aristotelian Society Proceedings, 1969.
4. T. S. Kuhn, The Structure of Scientific Revolutions, University
of Chicago Press, 1962.
5. R. G. Swinburne, “Falsifiability of
Scientific Theories ”, Mind, July 1964.
6. J~ Agassi, “Sensationalism”, Mind, N.S. 75, 1966.
7. J.
J. Sparkes, “Scientific Method”, Bulletin of the
Institute of Physics, November 1962.
8. M. Polanyi, Personal Knowledge, Routledge
& Kegan Paul, London, 1958.
9. R. B. Braithwaite, Scientific
Explanation, New York, 1960, p. 19.
10. D. W. Peetz, ibid. p.29.
29
The
various arguments in support of Peetz’s conclusion
may be summarised as follows.
Lakatos is impressed by the fact that experimental results
have to be interpreted by what he calls “touchstone theories” before they can
be seen as providing falsifying evidence for the hypothesis under examination. But how do we decide to cling to the
touchstone theories, for perhaps it is they that are at fault? Did Galileo observe the planets of Jupiter? He relied, according to Lakatos,
on a “virtually non-existent optical theory” to interpret his results, so he
might well have been mistaken. Actually Lakatos oversimplifies here. Galileo needed no theory to support him; he
merely had to turn his telescope on a nearby steeple to demonstrate beyond
reasonable doubt that he had nothing more than a magnifier.
Peetz himself begins from the total incompatibility of the
wave and corpuscular theories of light; (experiments can readily falsify
either, yet both survive) and moves to a general concern with the extreme
difficulty of finding criteria for choosing one imperfect hypothesis in
comparison with another.
Swinburne, Polanyi and Sparkes all give examples of falsifying experiments which
have not succeeded in falsifying theories which incorrectly predicted the
results of measurements. Sparkes goes on to list the various strategies the
scientist may adopt to reconcile his theory with new results. He may reject the evidence, or introduce new
concepts which, within the given theoretical framework, predict the new result.
He may introduce new ad hoc hypotheses,
quite apart from modifying the structure of the theory itself.
Kuhn,
I think, sees a staircase-like evolution of paradigms. He emphasises the
difference between “normal science”, when theories are more thoroughly articulated,
and “crisis science”, when paradigms compete and advances are made. But he says in his preface that this
distinction is “much too schematic”, and I suspect he would not quarrel with a
blurring of the edges of his crisis situations and would agree that
modifications occur to theories and hypotheses from time to time even during
near-normal science. For the moment,
however, his point is that the modifications bear no clear relationship to
experimental data.
Polanyi deploys both the argument that evidence is not in practice always significant in rejecting or even modifying a theory, since it is often ignored in the hope that it will subsequently be shown to be mistaken, as well as the argument ascribed above to Lakatos. He says, for example, “within two different conceptual
30
frameworks
the same range of experience takes the shape of different evidence.”
Braithwaite
puts a similar argument in a different way. He notes that experimental results can be
explained in more than one way, using various “high level” hypotheses. In general the data can neither confirm nor
reject any particular approach.
These
arguments seem to me to be incontrovertible and I therefore take it as no
longer in doubt that the relationship between observational data and
hypothetical or theoretical explanations is complex; that clear-cut
confirmation or falsification of a theory by experiment or observation is not
possible. The question, then, of how we
do decide between rival theories and relate them to experimental data becomes a
much more difficult one to answer.
Lakatos addresses himself to this problem and clearly rejects
the view, which he unfairly attributes to Kuhn, that “scientific change - from
one paradigm to another - is a mystical conversion which is not and cannot be
governed by the rules of reason.” He
then proceeds to adduce rational criteria for planning and conducting research programmes. The
position I wish to argue in this paper is that, whilst it is clear that the
idea of mystical conversion must remain unacceptable if argument is to
continue, it does not follow that we must be able to delineate the rational
criteria we are to use in bringing about scientific change. Briefly, my view is that we use our somewhat
innate capabilities of pattern-recognition to find our way through the maze of
facts and hypotheses. Unfortunately,
although much research is being done on the problem of pattern recognition, the
rationale of the process is by no means clear yet. However, I agree with Suppes’
[1] view “(That) a convergence of effort
on the most difficult cognitive problems, those of perception and concept
formation, has been building up at least since 1960... Many scientists working
on these problems feel we are getting close to hitting on the one or two
fundamental ideas needed to move rapidly forward.” Indeed, we are moving towards a crisis
situation in this particular branch of science.
In
the first part of this paper I wish to show that the acceptance of a paradigm
in science is very similar to the recognition of “patterns”, an activity we
perform every day of our lives.
1. P.
Suppes, “Information Processing and Choice of Behaviour “, in Philosophy of Science, A. Musgrave
and I. Lakatos (Eds.). North Holland Publishing Co., Amsterdam, 1968, pp.
298-299.
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Here
I am using the word “pattern” to describe any distinguishable interrelationship
of data. The recognition of a face, a
chair, a printed or handwritten word, a locality, etc., are
examples of pattern recognition. So,
too, is the recognition of a tune, or a theme in a symphony or words in speech.
The remarkable thing is that we are able
to recognise a chair, for example, from any
direction, at any orientation, at any distance, whatever its colour and so on. The
concept “chair”, which seeing one stimulates, seems to bear no clear, or as
yet rational, relationship with the actual data entering our eyes. Furthermore we can perceive patterns even when
they are grossly distorted or severely affected by ‘noise’. (Here I use ‘noise’ in its technical sense. Random visual interference on a television
screen is noise, just as much as hissing and crackling on a telephone or the
clattering of cutlery in a restaurant.) It
is this capability which I believe we use in deciding upon and accepting a
particular hypothesis in science. Consider a simple example.
Suppose
we are presented with a printed character Ć
[HHC: figure intentionally distorted
in original] we might hypothesise that it is a new
type of character or that it is an imperfect H, or an imperfect A. How do we decide which? We might ask where it came from, and on being
told it was typed by a cheap English typewriter, we might assume a Roman font
and conclude it must be an A, since an H distorted thus would be most
improbable. But we could be wrong. Alternatively we could try to find examples of
the symbol in context. Thus if we found,
in an English text, ĆAT or TĆE we could conclude it was an H. But if we found HĆT or CĆR we would
probably conclude it was an A. Context
here, if we know we are dealing with English, is crucial.
Suppose now we play a slightly different game and try to decide whether a set of printed words made of strange characters such as ǚ, Ń, Ô, Đ [HHC: figures intentionally distorted in original] constitute a page of English. We might, for example, hypothesise that they are distorted letters intended to be A, N, O, D, or perhaps H. Y. C. G. If one set of interpretations of the distorted patterns leads to the build-up of an English-like sentence, but including a few mistakes, then anyone knowing English might settle for the conclusion that this indeed was the correct solution, especially if he had a reason to expect to find English. But a different set of interpretations could equally lead, say, to an imperfect Latin sentence. Thus it is quite possible for two sets of hypotheses about what the letters stand for to
32
lead
to two further hypotheses about what the sentences are, from which it should
fairly clearly follow what the language is. But the decisions are not clear cut and the
hypotheses are plainly hierarchical.
Now
this pseudo-problem is quite typical in character both of scientific problems
and of the pattern recognition problems which human beings, and many less
intelligent animals, encounter and solve every day of their lives. We are continually having
to recognise people, objects, handwriting, etc.,
using distorted or unfamiliar and inconclusive data. Often, possibly almost always, it is the
context of the events which enable us to solve the problem.
Similarly,
we have to recognise the speech of people we may not
have heard before, saying sentences we have never heard before; yet usually we
understand what we hear. Somehow we find
sufficient of a recognisable pattern in our perceptual
input for us to identify the incoming signal in terms of what we know and have
learnt, so that it can then impart some new knowledge or intelligence to us. In just the same way we interpret scientific
data using any relevant touchstone theories we can bring to bear on them, and
use them in order to confirm or deny or modify another scientific hypothesis. Thus my thesis is that the mental processes
which lead us to select theories and advance science are the same as those
which enable us to acquire knowledge, or form concepts at all times. The procedure adopted by human beings when
they recognize patterns, such as speech waveforms, is not yet known, but it is
possible to conclude from studies of human behaviour
some minimum essentials without which we could not do what we do do. I will consider
two such features of the procedure, and will discuss them in connection with
speech recognition. Since patterns of
sound are spread out in time the operations are necessarily sequential, and
seem easier to think about than are patterns distributed spatially.
First it appears to be a fact that it is just not possible in general to identify simple words spoken by an unknown speaker without knowing some of the characteristics of his speech. But, on the other hand, in order to discover the characteristics of his speech we have to know what he is trying to say. If we don’t know he is trying to say ‘make’ rather than ‘mike’ we cannot discover that he is a cockney. Where do we start? It is in fact like trying to solve two simultaneous equations. If you know the appropriate algebra it is easy otherwise you guess the value of one unknown and find a consistent solution for the other - -a simple iterative problem.
33
In a
practical situation, when we meet someone new, if he does not say something
conventional like “Hello” or “How do you do” which we can easily recognise and use to identify his speech characteristics,
we have to use this iterative technique. We attempt to recognise
his words and, as soon as possible, put up hypotheses both of what he is saying
and how he is saying it. We then analyse his speech signal using these hypotheses, improving
them continuously as more “experimental” data flow in. [1] [2]
Thus the putting up of hypotheses and clinging to them whilst
trying to force the data to fit is a typical perceptual strategy, just as it is
a typical scientific activity. It
is well known that we tend to see or hear what we expect, and it is also a
common experience that we can completely misinterpret sights and sounds when we
are prepared for something else. Only
context or a reprocessing of the signal can put things right again.
The
second point I wish to make about pattern recognition is concerned with the
part played by memory and learning in the recognition process. For the present purpose it is necessary to
distinguish two aspects of memory. First
the part which holds the information we can recall, a face, a poem, our
vocabulary, theorems, algebra etc. Second
the information which we have learnt and which presumably is stored somewhere
because we make use of it, but which we cannot retrieve; We cannot describe how
to recognise speech, how to balance when we walk, how
to turn an idea into sentences expressing that idea, how to drive a car, or any
other aspects of pattern processing, although the information must be stored in
our brains somewhere.
Thus,
in computer terms, one form of memory can be addressed, and its contents
recalled, the other form cannot.
The
present significance of this is that rational aspects of thought and knowledge
are in the addressable memory. We can
describe the steps in a logical or mathematical argument because we can recall
them from memory - they have been memorised. We cannot recall the steps of how we process a
speech signal in order to recognize a word or phrase, any more than we can
recall exactly how we came to formulate the theory that, for example,
comprehensive education is best. We can
all describe aspects of the evidence but our decisions are based on irrational
weightings of this evidence.
1. For
a lucid introductory exposition of speech processing see: P. Denes, and E. Pinson, The Speech Chain, Bell
Telephone Labs., 1963.
2. See J. J. Sparkes, “Pattern Recognition and a Model of the Brain “, International
Journal of Man-Machine Studies, 1, no. 3, July 1969.
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Thus
Lakatos’ determination to find rational criteria with
which to guide scientific progress, though laudable, since it would tend to
reduce arguments, is itself irrational, since many of our most important mental
processes, particularly those most akin to scientific theorising,
are not rational, or even, as yet, knowable.
In
summary then, let me be quite specific about this analogy between pattern
recognition and scientific progress.
In
speech recognition we receive an acoustic signal which our ears analyse into acoustic elements in a particular way. In science we have an environment from which
we extract data in a quite specific way (to be discussed in part II).
In
order to achieve the recognition of speech we have to have learnt how to split
up the coded sequence of acoustic elements which passes from our ears to our
brains into various phonetic or linguistic features. We have to interpret variations from what we
expect either as new information or else as peculiarities of the speaker,
whichever seems appropriate. In science
we have to arrange and select our data to articulate the touchstone hypotheses
we have read about or discovered, and thus augment or comment on the more
fundamental theories we are considering. Or, if it is more appropriate, we use the data
to revise our touchstone hypotheses, and thus obtain quite different
information regarding the fundamental theories.
In
speech if we recognise a sentence, say, but mishear a
word, we use the sentence to interpret the word. In science if a mass of data seems to bear
crucially upon a fundamental theory we may conclude that if some of our data do
not fit properly they must be wrong - we begin to look for a revised, low-level
hypothesis capable of interpreting this rogue data satisfactorily.
The whole of speech, whether its recognition or its synthesis, is
constrained by our rules of grammar, of phonetics, of semantics. It is extraordinarily difficult to invent a
new language other than a re-ordering of existing ones. In science it is extraordinarily difficult for
most of us to look at data from a new point of view. The Newtons and Einsteins are rightly distinguished for their achievements,
even though nowadays their viewpoint seems somehow inevitable. Similarly, if someone speaks only one language
it is difficult for him to imagine what it would be like to speak a second one. But if he speaks two it is difficult for him
to see what the difficulty is!
The only real difference between pattern recognition and scientific theorising is that speech or visual recognition takes place naturally on a time scale of milliseconds, whilst with scientific data we have to deliberate and it takes much longer.
35
Thus
my thesis is that the major problem at present confronting us in scientific
methodology, leaving aside creativity, namely how we choose between various
hypotheses and data and come up with an “improved” scientific edifice, is just
another aspect of a fundamental human capability, that of pattern recognition and
processing. Indeed I would go further
and maintain that, since pattern processing is so characteristic a human
activity, then if the logic of scientific discovery is
something different from it, we need to find an explanation for this
difference.
But
this thesis entails two further problems. Firstly, since we do not yet know how pattern
recognition is performed, my thesis does not solve the problem of how science
advances; it merely proposes a “progressive problem shift” to use Lakatos’ expression, away from the limited study of science
towards a general study of how brains deal with complex interrelated information.
Second, since I am now saying that there
is nothing peculiarly scientific about this part of the scientific method, I
must face the question of what it is about science which distinguishes it from
other areas of knowledge and understanding. For certainly the adjective ‘scientific’ means
something, even if we do not know quite what. This is one of the tasks of the second part of
this paper which now follows.
Objectivity
and the Object of Science
I
want now to discuss what it is that distinguishes scientific method from other
branches of learning so that we can understand its remarkable power, and so
that particularly we can see to what extent rationality is an essential part of
science.
If
we understood the purpose of science we might find it easier to isolate the
methods by which it reaches out for its objective. This is hardly a new problem to discuss, but
that it has a new implication and that various answers have been given to it
renders it non-trivial.
For Ziman, [1] for
instance, “the goal of science is a consensus of rational opinion over the
widest possible field.” For Hagstrom [2] the goal
is conceived in psychological terms; a scientist’s aim is to achieve social
recognition by providing to the community
1. J.M. Ziman, Public Knowledge, Cambridge University Press,
Cambridge, 1968. p. 9.
2. W. 0. Hagstrom, The Scientific
Community, Basic Books, London, 1965.
36
information
which it values. Neither viewpoint seems
adequate, for some scientists certainly defy the consensus of scientific
opinion, whilst others seem to have no desire to communicate their results and
ideas.
That
science seeks out the truth is a very common formulation of its purpose. It suffers, as is well known, from the fact
that we have no way of knowing whether our theories are true or are merely
successful. Consider the conflict
between the scientific view of the world and that of common sense which, though
it is with us all the time, is merely a matter for mild amusement nowadays, for
our surrender to science as regards the way we describe our perceptions is
almost total. Most of us know about
optical illusions and agree that parallel lines when suitably cross-hatched
appear to converge, or that circles can appear as spirals or that we can
misjudge the lengths of lines. Furthermore, we would not normally quarrel
with the kind of description which refers to parallel lines in the above
illusion and to our impression being illusory. That these lines are parallel is
established by scientific measurement; that they are not parallel is an observation,
which enjoys a full consensus of rational observers, interpreted by “common
sense”. We believe science and ascribe
the error to ourselves; we call the phenomena illusions. Similarly we all know that time passes
slowly if we are bored and rapidly if we are absorbed, but this too is regarded
as an illusion; suitcases get heavier as we carry them, or so it seems, and so
on. Who knows the extent to which our
interpretations of what we observe have been influenced by different,
non-natural, orderings of our perceptual impressions. Thus qua scientists we are not in a
position to claim that we discover the truth; if we claim that we do, are we
stating a personal belief for which there can be no evidence, only argument. However, it is important to enquire why it is
that science does seem to be able to justify this special claim to truth and
objectivity, for by so doing the pattern of scientific achievement and progress
becomes clearer.
There
are two facts we should recall about scientific evidence and scientific
theories, which, although they are not peculiar to science, are characteristic
of it. These are:
First, scientific evidence is only admissible if all conceivable precautions have been taken to prevent the judgement and fallibility of the observer from affecting the results. We are all quite capable of estimating time or weight or height or colour or temperature, but our estimates are so variable that they are far too inaccurate to test quantitative theories. Instead, as
37
Eddington
[1] emphasised,
all measurements in the exact sciences are reduced to pointer readings or their
equivalent. The only pattern which an
observer is allowed to incorporate as evidence for his judgernent
is of the coincidence of a pointer with a scale or ruler calibration, or the
coincidence of two events in time, or of the matching of two frequencies or
hues. Even better, nowadays we use
instruments to do our observations for us. We are also “permitted” to count events, but
we have much more trouble here since we can argue about classifications of
events. The purpose of this, as Ziman emphasised, is to obtain a
consensus of rational opinion, for a “scientifically” acquired result of a
measurement can in principle be repeated by anyone and is therefore very
difficult to reject. In practice,
however, some measurements, such as the position of planets etc., are inevitably
unrepeatable!
In
the physical sciences an additional part of the design of a proper experiment
involves sufficient and diligent control of any unwanted influences, for example,
temperature variations, unwanted static electricity, etc. In the biological and social sciences control
of some of the influences is impossible, so statistics and “controls” are used
instead. Indeed, by the proper application
of statistics it is hoped that not only the unwanted external influences but
also the variability of the observer will be removed. In practice, however, few
physical scientists seem to believe that they are removed, for statistics and
controls are no substitute for a properly designed experiment unless the
influences which have to be eliminated, and their effects, are linear and the
ensemble is truly random. In practice
the quantitative effects of various influences are not normally known and it is
not possible to obtain random ensembles, so these techniques are not strictly
justifiable. But no better method has
yet been worked out.
The
first point then is that every effort is made in science to ensure that the
results around which theories revolve, albeit often very elliptically, are as
near as possible independent of the observer.
The
second point is that as far as possible general and fundamental theories,
especially those of the physical sciences, are normally presented in a time
independent manner, so that the actual period of time
occupied by a set of events does not affect the validity of theories applied to
them. The reason for this is of course
quite clear. One of the primary
functions of scientific theory is to predict future events, or explain past
ones, and,
1 A. S. Eddington, The Nature of the Physical World, Cambridge
University Press, Cambridge, 1928, pp. 251.
38
since
science is not clairvoyant, it can only achieve prediction by making statements
which can be expected to be valid in the past, the present and the future.
The
hypothesis that, for example, a bridge collapsed as a result of a sudden, localised increase in gravitational force is not acceptable
scientifically; indeed I doubt if even a non-scientist would have the temerity
to suggest it.
One
interesting exception to this general time-independence of theories is the
theory that the universe was created with a “big bang” 10,000 million years
ago. That this is not rejected out of
hand as unscientific is itself quite surprising. Why allow a vast quantity of hydrogen to be
created a long time ago, when all the universe might
have been created yesterday together with us all, all our memories, records and
undiscovered facts? Since both creations
are scientifically impossible - they violate the law of conservation of energy
- it is a moot point which is the more plausible. That we accept the former and not the latter
illustrates the strength of our conviction that science discovers the truth
about the world - it is too much to accept that it was all planted there
yesterday!
Thus
it is clear that the very properties which all of us would concede must characterise the ‘real’ world, namely independence of who
observes it and when or where he does so, are built into scientific results and
theories by its method. So we cannot
know if it tells us the truth about the world, or just seems to.
But
if we cannot be sure that science helps us make contact with reality, we are
simply left with the aim of gaining confidence, through consensus
of rational opinion, in its predictions and explanations. In the end the whole edifice of science seems
to serve our perhaps irrational desires for confidence, understanding and
predictive power. Its aim therefore is
not unlike that of many other human activities, it’s just that scientists
select their data in a peculiarly irrefutable manner.
Thus
there emerges a not unfamiliar picture of a science, whose objective is
prediction and explanation in terms of theories and concepts arrived at by hypothetico-deductive procedures. All the aspects of science are turned to this
end - controlled experiments, pointer readings, the creation of a consensus of
rational opinion, the use of mathematics and reason in the deductive part of
the process and so on. These, in my
view, and not the way scientists handle the interrelationships of hypotheses
and evidence, are the features of science which distinguish it from other types
of human thought.
Now the purpose of retracing these arguments is to arrive at a
39
decision
on how important is the use of rational criteria, or the rules of reason, in
giving precision to the logic of scientific discovery. Lakatos, is not alone in taking it for granted that the rules of
reason must be used. This is one of his
“touchstone theories”, even though it is evident that what is thought to be
regarded as rational varies as time passes. The question remains, then, how we decide
between these two views of science - that it is for prediction or that it must
be rational - when they come into conflict. Obviously it is irrational to insist on
rational criteria to decide this issue! Fortunately there are enough examples of
irrationality in science to convince me, in what I believe to be the usual kind
of way, that the rules of reason take second place.
Once
it was thought irrational to conceive of unaided movement of heavy bodies, or
to believe in heliocentric planetary motion. Nowadays both seem wholly reasonable. Today the idea that electromagnetic radiation
is both corpuscular and undulatory is the outstanding
monument to irrationality in physics, though the idea that massive bodies can
exert forces on each other through intervening empty space, not even containing
an aether, comes a close
second. Both ideas appear to contain
genuine internal conflicts, but few of us now worry about it. We put up with the irrationality, since both
ideas have great predictive and explanatory power, and perhaps hope that it all
will eventually be resolved rationally.
This
is precisely the point of view I am advocating with regard to the logic of
scientific discovery. It is important to
describe the way scientists actually proceed, in order to guide our future
scientific work, and this has been Kuhn’s major contribution. That some of our actions appear irrational, as
do at present some aspects of human pattern recognition, is relatively
unimportant. In due course we will find
a rationale, as we have of electromagnetic radiation, and the problem will have
been solved for the time being. But
resort to this kind of position is not mystical; it is, I hope, like all good
scientific hypotheses, the best platform from which to take the next forward
step.
The question raised in this paper is whether we should look for rational criteria, as at present understood, with which to assess the relative merits of rival imperfect hypotheses, or whether the assessment is a much more complex process. The
40
partial
answer proposed is that it is more useful to think of the process as an aspect
of pattern recognition. If the rationale
of pattern recognition, as performed daily by many living creatures, were
understood, this could be a full answer. But the process is not yet understood, so the
suggestion is, therefore, of a paradigm change or of a progressive
problem-shift towards a hypothesis of higher content. We know that simple rational criteria apply to
deductive arguments but we also know that they do not apply to many aspects of
induction, especially to hypothesis formation; we know that they do not apply
to the formulation of a number of important scientific theories. I am now suggesting that they do not apply to
the complex process by which we construct the scientific edifice. Pattern recognition is undoubtedly a deeply
ingrained human capability, and that it should be used for the kind of
information processing which goes on in science seems beyond reasonable doubt.
The Open
University
41
The Competitiveness of Nations
in a Global Knowledge-Based Economy
April 2004