The Evolution of Intelligent Systems: How Molecules became Minds

243 Pages·2010·1.584 MB·English

by  Ken Richardson (auth.)

The Evolution of Intelligent Systems
Also by Ken Richardson
COGNITIVE DEVELOPMENT TO ADOLESCENCE (co-edited)
UNDERSTANDING INTELLIGENCE
UNDERSTANDING PSYCHOLOGY
THE ORIGINS OF HUMAN POTENTIAL
MODELS OF COGNITIVE DEVELOPMENT
THE MAKING OF INTELLIGENCE
The Evolution of
Intelligent Systems
How Molecules Became Minds
Ken Richardson
© Ken Richardson 2010
Softcover reprint of the hardcover 1st edition 2010 978-0-230-25249-3
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First published 2010 by
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registered in England, company number 785998, of Houndmills, Basingstoke,
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ISBN 978-1-349-32211-4 ISBN 978-0-230-29924-5 (eBook)
DOI 10.1057/9780230299245
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managed and sustained forest sources. Logging, pulping and manufacturing
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country of origin.
A catalogue record for this book is available from the British Library.
Library of Congress Cataloging-in-Publication Data
Richardson, Ken.
The evolution of intelligent systems : how molecules became minds /
Ken Richardson.
p. cm.
1. Brain – Evolution. 2. Molecular evolution. 3. Evolutionary
psychology. I. Title.
QP376.R476 2010
612.8(cid:2)2—dc22 2010027546
10 9 8 7 6 5 4 3 2 1
19 18 17 16 15 14 13 12 11 10
Contents
List of Illustrations vi
Preface viii
1 Why So Complex? 1
2 Fit for What? 19
3 Intelligent Life 36
4 Bodily Intelligence 56
5 Evolution of Development 69
6 Intelligent Eye and Brain 90
7 From Neurons to Cognition 112
8 Cognitive Functions 135
9 Social Intelligence 160
10 Intelligent Humans 174
Notes 199
Index 229
v
Illustrations
Table
8.1 The XOR problem 156
Figures
1.1 Heads of some of the finches studied by Charles
Darwin showing the correlation between beak structure
and environmental structure (type of food): (1) a large
seedeater; (2) a small seedeater; (3) small insect eater;
(4) large insect eater. (Redrawn from Darwin) 9
1.2 Maturana and Varela’s notion of structural coupling
between organism and environment. (Redrawn from
Maturana and Varela) 16
2.1 Graph of interactive and non-linear relationships.
The non-linear associations between variables A and B
is conditioned by (varies with) values of variable C (C1-C3) 24
2.2 Phase diagrams showing movements of a frictionless
pendulum in a limit-cycle attractor (left) and a normal
pendulum settling into a steady-state (or point)
attractor (right) 29
2.3 The Lorenz attractor is a graphic description of patterns
of behaviour of complex systems like the weather 30
2.4 Bénard cells forming in a layer of liquid 33
3.1 Schematic diagram of E. coli to show receptors and flagella 48
3.2 Simplified diagram of regulations in circadian rhythm
of the fruitfly 53
5.1 Getting connected. Axon terminals, guided by molecular
signals, find target cells. There, development of dendrite
terminals is promoted by structured spike firing along
the axons 88
6.1 A sequence of stills from a ‘point light walker’. Volunteers
find the images unrecognizable when presented
individually, on a computer screen. But presentation
as a sequence at normal speed evokes almost immediate
recognition of a person walking 95
vi
Illustrations vii
6.2 Schematic diagram of retina and cell connections 97
6.3 Kanizsa triangle 98
7.1 Fragment of an artificial neural network. Stimuli
(usually some well-defined features, as conceived by
the experimenter) are input at the keyboard and received
by the input units. If the strength of signal exceeds a
certain threshold, the unit discharges along its connections
to other units. The connections can be excitatory or
inhibitory (e.g. a+/a–). After integration of a series of
signals in subsequent units, the outputs should reflect
statistical associations between the inputs 116
7.2 Extraction of covariation structure in early processing 121
7.3 Experiencing a ‘line’ 121
7.4 A new stimulus (arrows), moving as a wave of activation
containing ‘sample’ feature parameters, is drawn towards
basins of attraction containing corresponding sets of
parameters (‘contour’ lines, or isoclines, connect
parameters that have occurred with similar frequencies
in previous experience, those at deeper levels representing
the more coherent sets, e.g. the more ‘definite’ line image) 123
7.5 The Necker cube 127
8.1 Conservation of number over changes in appearance 149
Preface
Many people interested in the nature and origins of the human mind
complain about the lack of coherence and depth in existing scientific
accounts. They say that the area lacks a meaningful framework through
which they can relate the various models, theories and opinions being
expressed within it; and it lacks ‘roots’ in the way that evolutionary the-
ory gives ‘history’, coherence and grounding to all of biology. It is a
truism spoken by many that if we want to truly understand functions
of living things – including mental systems – then we really need to be
clear about why they evolved. Sadly, recent attempts to incorporate
psychology within the classical evolutionary framework, far from yield-
ing fresh insights, seem to have generated even more division and
controversy. In consequence, there are still crucial gaps in our under-
standing of human minds and human nature, and about their continu-
ity with the rest of life. This creates many problems for scientists, and
increasing public scepticism about scientific accounts of what we’ve
actually got in our heads and how it works.
The main reason for the gaps, I think, is the difficulty in envisaging
and describing the conditions under which something as brilliant, and
seemingly transcendental, as the human mind could have arisen from
far simpler material beginnings. To many people it seems to have
required something like a miracle, or the hand of an intelligent designer:
what else could account for the amazing complexity of human minds
and the gulf between it and all other living forms? Fortunately, that dif-
ficulty is now easing as new methodological and theoretical advances –
especially in the realm of dynamic systems – begin to make a coherent
picture of ‘mind from matter’ more feasible. It requires new concepts,
from a wide range of disciplines, and new ways of thinking about the
world, but the picture is transforming our views of science and evolu-
tion, as well as the nature and origins of mind. This book aims to bring
those new concepts together, hopefully in a style accessible to most gen-
eral readers, as well as serious students, and with widespread implica-
tions for our views of the origins and nature of mind.
This is, of course, a tall order, and a difficult trail to follow: a convin-
cing story calls for more than mere description of a sequence of forms
(a kind of ‘tree of intelligence’ to parallel Darwin’s tree of life). It also
requires an underlying ‘plot’, a causal rationale, or a description of the
viii
Preface ix
evolutionary ‘laws of motion’ wringing such a remarkable sequence
from ordinary natural materials and forces. The realisation that the
world is not the stable, orderly place that dogma once deemed it to be,
but one of continual interaction and change – a very dynamic place –
has also created new concepts of the complexity and structure of the
world, and, indeed, the great beauty, that lies within. These ideas have
brought new focus on environmental change and on the evolution of
intelligent systems for dealing with it. So the story, here, traces intelli-
gent systems from the origins of life in molecular ensembles, through
succeeding levels of complexity in cells, brains and cognitive systems,
to finally demystify the origins and nature of the human mind itself.
It has, of course, been a thrilling trail to follow and I hope readers too
will share some of that excitement. It would not have been possible
without the thoughts and inspiration of the many ideas-makers I have
had the pleasure of encountering along the way, either in person, or by
other means. They are too numerous to list, here, but I hope their pres-
ence in these pages and/or the nature of the product will reflect my
gratitude. The following, however, took time out from their own busy
schedules to read a draft of the present work and offer comment and
feedback: Eric Matthews, Hellen Matthews and Paul Prescott. I have not
been able to take all feedback on board, but most of it really helped.
Dan Richardson did most of the diagrams, and my partner Susan gave
me her usual unstinting support. I am grateful to all of them for helping
me turn a rough draft into a more readable and coherent product. If it
still isn’t, the responsibility is, of course, entirely my own.
KEN RICHARDSON